libXCam is a project for extended camera(not limited in camera) features and focus on image quality improvement and video analysis. There are lots features supported in image pre-processing, image post-processing and smart analysis. This library makes GPU/CPU/ISP working together to improve image quality. OpenCL is used to improve performance in different platforms.

MIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning

W3C Strategic Highlights September 2019 This report was prepared for the September 2019 W3C Advisory Committee Meeting (W3C Member link). See the accompanying W3C Fact Sheet — September 2019. For the previous edition, see the April 2019 W3C Strategic Highlights. For future editions of this report, please consult the latest version. A Chinese translation is available. ☰ Contents Introduction Future Web Standards Meeting Industry Needs Web Payments Digital Publishing Media and Entertainment Web & Telecommunications Real-Time Communications (WebRTC) Web & Networks Automotive Web of Things Strengthening the Core of the Web HTML CSS Fonts SVG Audio Performance Web Performance WebAssembly Testing Browser Testing and Tools WebPlatform Tests Web of Data Web for All Security, Privacy, Identity Internationalization (i18n) Web Accessibility Outreach to the world W3C Developer Relations W3C Training Translations W3C Liaisons Introduction This report highlights recent work of enhancement of the existing landscape of the Web platform and innovation for the growth and strength of the Web. 33 working groups and a dozen interest groups enable W3C to pursue its mission through the creation of Web standards, guidelines, and supporting materials. We track the tremendous work done across the Consortium through homogeneous work-spaces in Github which enables better monitoring and management. We are in the middle of a period where we are chartering numerous working groups which demonstrate the rapid degree of change for the Web platform: After 4 years, we are nearly ready to publish a Payment Request API Proposed Recommendation and we need to soon charter follow-on work. In the last year we chartered the Web Payment Security Interest Group. In the last year we chartered the Web Media Working Group with 7 specifications for next generation Media support on the Web. We have Accessibility Guidelines under W3C Member review which includes Silver, a new approach. We have just launched the Decentralized Identifier Working Group which has tremendous potential because Decentralized Identifier (DID) is an identifier that is globally unique, resolveable with high availability, and cryptographically verifiable. We have Privacy IG (PING) under W3C Member review which strengthens our focus on the tradeoff between privacy and function. We have a new CSS charter under W3C Member review which maps the group's work for the next three years. In this period, W3C and the WHATWG have succesfully completed the negotiation of a Memorandum of Understanding rooted in the mutual belief that that having two distinct specifications claiming to be normative is generally harmful for the Web community. The MOU, signed last May, describes how the two organizations are to collaborate on the development of a single authoritative version of the HTML and DOM specifications. W3C subsequently rechartered the HTML Working Group to assist the W3C community in raising issues and proposing solutions for the HTML and DOM specifications, and for the production of W3C Recommendations from WHATWG Review Drafts. As the Web evolves continuously, some groups are looking for ways for specifications to do so as well. So-called "evergreen recommendations" or "living standards" aim to track continuous development (and maintenance) of features, on a feature-by-feature basis, while getting review and patent commitments. We see the maturation and further development of an incredible number of new technologies coming to the Web. Continued progress in many areas demonstrates the vitality of the W3C and the Web community, as the rest of the report illustrates. Future Web Standards W3C has a variety of mechanisms for listening to what the community thinks could become good future Web standards. These include discussions with the Membership, discussions with other standards bodies, the activities of thousands of participants in over 300 community groups, and W3C Workshops. There are lots of good ideas. The W3C strategy team has been identifying promising topics and invites public participation. Future, recent and under consideration Workshops include: Inclusive XR (5-6 November 2019, Seattle, WA, USA) to explore existing and future approaches on making Virtual and Augmented Reality experiences more inclusive, including to people with disabilities; W3C Workshop on Data Models for Transportation (12-13 September 2019, Palo Alto, CA, USA) W3C Workshop on Web Games (27-28 June 2019, Redmond, WA, USA), view report Second W3C Workshop on the Web of Things (3-5 June 2019, Munich, Germany) W3C Workshop on Web Standardization for Graph Data; Creating Bridges: RDF, Property Graph and SQL (4-6 March 2019, Berlin, Germany), view report Web & Machine Learning. The Strategy Funnel documents the staff's exploration of potential new work at various phases: Exploration and Investigation, Incubation and Evaluation, and eventually to the chartering of a new standards group. The Funnel view is a GitHub Project where new area are issues represented by “cards” which move through the columns, usually from left to right. Most cards start in Exploration and move towards Chartering, or move out of the funnel. Public input is welcome at any stage but particularly once Incubation has begun. This helps W3C identify work that is sufficiently incubated to warrant standardization, to review the ecosystem around the work and indicate interest in participating in its standardization, and then to draft a charter that reflects an appropriate scope. Ongoing feedback can speed up the overall standardization process. Since the previous highlights document, W3C has chartered a number of groups, and started discussion on many more: Newly Chartered or Rechartered Web Application Security WG (03-Apr) Web Payment Security IG (17-Apr) Patent and Standards IG (24-Apr) Web Applications WG (14-May) Web & Networks IG (16-May) Media WG (23-May) Media and Entertainment IG (06-Jun) HTML WG (06-Jun) Decentralized Identifier WG (05-Sep) Extended Privacy IG (PING) (30-Sep) Verifiable Claims WG (30-Sep) Service Workers WG (31-Dec) Dataset Exchange WG (31-Dec) Web of Things Working Group (31-Dec) Web Audio Working Group (31-Dec) Proposed charters / Advance Notice Accessibility Guidelines WG Privacy IG (PING) RDF Literal Direction WG Timed Text WG CSS WG Web Authentication WG Closed Internationalization Tag Set IG Meeting Industry Needs Web Payments All Web Payments specifications W3C's payments standards enable a streamlined checkout experience, enabling a consistent user experience across the Web with lower front end development costs for merchants. Users can store and reuse information and more quickly and accurately complete online transactions. The Web Payments Working Group has republished Payment Request API as a Candidate Recommendation, aiming to publish a Proposed Recommendation in the Fall 2019, and is discussing use cases and features for Payment Request after publication of the 1.0 Recommendation. Browser vendors have been finalizing implementation of features added in the past year (view the implementation report). As work continues on the Payment Handler API and its implementation (currently in Chrome and Edge Canary), one focus in 2019 is to increase adoption in other browsers. Recently, Mastercard demonstrated the use of Payment Request API to carry out EMVCo's Secure Remote Commerce (SRC) protocol whose payment method definition is being developed with active participation by Visa, Mastercard, American Express, and Discover. Payment method availability is a key factor in merchant considerations about adopting Payment Request API. The ability to get uniform adoption of a new payment method such as Secure Remote Commerce (SRC) also depends on the availability of the Payment Handler API in browsers, or of proprietary alternatives. Web Monetization, which the Web Payments Working Group will discuss again at its face-to-face meeting in September, can be used to enable micropayments as an alternative revenue stream to advertising. Since the beginning of 2019, Amazon, Brave Software, JCB, Certus Cybersecurity Solutions and Netflix have joined the Web Payments Working Group. In April, W3C launched the Web Payment Security Group to enable W3C, EMVCo, and the FIDO Alliance to collaborate on a vision for Web payment security and interoperability. Participants will define areas of collaboration and identify gaps between existing technical specifications in order to increase compatibility among different technologies, such as: How do SRC, FIDO, and Payment Request relate? The Payment Services Directive 2 (PSD2) regulations in Europe are scheduled to take effect in September 2019. What is the role of EMVCo, W3C, and FIDO technologies, and what is the current state of readiness for the deadline? How can we improve privacy on the Web at the same time as we meet industry requirements regarding user identity? Digital Publishing All Digital Publishing specifications, Publication milestones The Web is the universal publishing platform. Publishing is increasingly impacted by the Web, and the Web increasingly impacts Publishing. Topic of particular interest to Publishing@W3C include typography and layout, accessibility, usability, portability, distribution, archiving, offline access, print on demand, and reliable cross referencing. And the diverse publishing community represented in the groups consist of the traditional "trade" publishers, ebook reading system manufacturers, but also publishers of audio book, scholarly journals or educational materials, library scientists or browser developers. The Publishing Working Group currently concentrates on Audiobooks which lack a comprehensive standard, thus incurring extra costs and time to publish in this booming market. Active development is ongoing on the future standard: Publication Manifest Audiobook profile for Web Publications Lightweight Packaging Format The BD Comics Manga Community Group, the Synchronized Multimedia for Publications Community Group, the Publishing Community Group and a future group on archival, are companions to the working group where specific work is developed and incubated. The Publishing Community Group is a recently launched incubation channel for Publishing@W3C. The goal of the group is to propose, document, and prototype features broadly related to: publications on the Web reading modes and systems and the user experience of publications The EPUB 3 Community Group has successfully completed the revision of EPUB 3.2. The Publishing Business Group fosters ongoing participation by members of the publishing industry and the overall ecosystem in the development of Web infrastructure to better support the needs of the industry. The Business Group serves as an additional conduit to the Publishing Working Group and several Community Groups for feedback between the publishing ecosystem and W3C. The Publishing BG has played a vital role in fostering and advancing the adoption and continued development of EPUB 3. In particular the BG provided critical support to the update of EPUBCheck to validate EPUB content to the new EPUB 3.2 specification. This resulted in the development, in conjunction with the EPUB3 Community Group, of a new generation of EPUBCheck, i.e., EPUBCheck 4.2 production-ready release. Media and Entertainment All Media specifications The Media and Entertainment vertical tracks media-related topics and features that create immersive experiences for end users. HTML5 brought standard audio and video elements to the Web. Standardization activities since then have aimed at turning the Web into a professional platform fully suitable for the delivery of media content and associated materials, enabling missing features to stream video content on the Web such as adaptive streaming and content protection. Together with Microsoft, Comcast, Netflix and Google, W3C received an Technology & Engineering Emmy Award in April 2019 for standardization of a full TV experience on the Web. Current goals are to: Reinforce core media technologies: Creation of the Media Working Group, to develop media-related specifications incubated in the WICG (e.g. Media Capabilities, Picture-in-picture, Media Session) and maintain maintain/evolve Media Source Extensions (MSE) and Encrypted Media Extensions (EME). Improve support for Media Timed Events: data cues incubation. Enhance color support (HDR, wide gamut), in scope of the CSS WG and in the Color on the Web CG. Reduce fragmentation: Continue annual releases of a common and testable baseline media devices, in scope of the Web Media APIs CG and in collaboration with the CTA WAVE Project. Maintain the Road-map of Media Technologies for the Web which highlights Web technologies that can be used to build media applications and services, as well as known gaps to enable additional use cases. Create the future: Discuss perspectives for Media and Entertainment for the Web. Bring the power of GPUs to the Web (graphics, machine learning, heavy processing), under incubation in the GPU for the Web CG. Transition to a Working Group is under discussion. Determine next steps after the successful W3C Workshop on Web Games of June 2019. View the report. Timed Text The Timed Text Working Group develops and maintains formats used for the representation of text synchronized with other timed media, like audio and video, and notably works on TTML, profiles of TTML, and WebVTT. Recent progress includes: A robust WebVTT implementation report poises the specification for publication as a proposed recommendation. Discussions around re-chartering, notably to add a TTML Profile for Audio Description deliverable to the scope of the group, and clarify that rendering of captions within XR content is also in scope. Immersive Web Hardware that enables Virtual Reality (VR) and Augmented Reality (AR) applications are now broadly available to consumers, offering an immersive computing platform with both new opportunities and challenges. The ability to interact directly with immersive hardware is critical to ensuring that the web is well equipped to operate as a first-class citizen in this environment. The Immersive Web Working Group has been stabilizing the WebXR Device API while the companion Immersive Web Community Group incubates the next series of features identified as key for the future of the Immersive Web. W3C plans a workshop focused on the needs and benefits at the intersection of VR & Accessibility (Inclusive XR), on 5-6 November 2019 in Seattle, WA, USA, to explore existing and future approaches on making Virtual and Augmented Reality experiences more inclusive. Web & Telecommunications The Web is the Open Platform for Mobile. Telecommunication service providers and network equipment providers have long been critical actors in the deployment of Web technologies. As the Web platform matures, it brings richer and richer capabilities to extend existing services to new users and devices, and propose new and innovative services. Real-Time Communications (WebRTC) All Real-Time Communications specifications WebRTC has reshaped the whole communication landscape by making any connected device a potential communication end-point, bringing audio and video communications anywhere, on any network, vastly expanding the ability of operators to reach their customers. WebRTC serves as the corner-stone of many online communication and collaboration services. The WebRTC Working Group aims to bringing WebRTC 1.0 (and companion specification Media Capture and Streams) to Recommendation by the end of 2019. Intense efforts are focused on testing (supported by a dedicated hackathon at IETF 104) and interoperability. The group is considering pushing features that have not gotten enough traction to separate modules or to a later minor revision of the spec. Beyond WebRTC 1.0, the WebRTC Working Group will focus its efforts on WebRTC NV which the group has started documenting by identifying use cases. Web & Networks Recently launched, in the wake of the May 2018 Web5G workshop, the Web & Networks Interest Group is chaired by representatives from AT&T, China Mobile and Intel, with a goal to explore solutions for web applications to achieve better performance and resource allocation, both on the device and network. The group's first efforts are around use cases, privacy & security requirements and liaisons. Automotive All Automotive specifications To create a rich application ecosystem for vehicles and other devices allowed to connect to the vehicle, the W3C Automotive Working Group is delivering a service specification to expose all common vehicle signals (engine temperature, fuel/charge level, range, tire pressure, speed, etc.) The Vehicle Information Service Specification (VISS), which is a Candidate Recommendation, is seeing more implementations across the industry. It provides the access method to a common data model for all the vehicle signals –presently encapsulating a thousand or so different data elements– and will be growing to accommodate the advances in automotive such as autonomous and driver assist technologies and electrification. The group is already working on a successor to VISS, leveraging the underlying data model and the VIWI submission from Volkswagen, for a more robust means of accessing vehicle signals information and the same paradigm for other automotive needs including location-based services, media, notifications and caching content. The Automotive and Web Platform Business Group acts as an incubator for prospective standards work. One of its task forces is using W3C VISS in performing data sampling and off-boarding the information to the cloud. Access to the wealth of information that W3C's auto signals standard exposes is of interest to regulators, urban planners, insurance companies, auto manufacturers, fleet managers and owners, service providers and others. In addition to components needed for data sampling and edge computing, capturing user and owner consent, information collection methods and handling of data are in scope. The upcoming W3C Workshop on Data Models for Transportation (September 2019) is expected to focus on the need of additional ontologies around transportation space. Web of Things All Web of Things specifications W3C's Web of Things work is designed to bridge disparate technology stacks to allow devices to work together and achieve scale, thus enabling the potential of the Internet of Things by eliminating fragmentation and fostering interoperability. Thing descriptions expressed in JSON-LD cover the behavior, interaction affordances, data schema, security configuration, and protocol bindings. The Web of Things complements existing IoT ecosystems to reduce the cost and risk for suppliers and consumers of applications that create value by combining multiple devices and information services. There are many sectors that will benefit, e.g. smart homes, smart cities, smart industry, smart agriculture, smart healthcare and many more. The Web of Things Working Group is finishing the initial Web of Things standards, with support from the Web of Things Interest Group: Web of Things Architecture Thing Descriptions Strengthening the Core of the Web HTML The HTML Working Group was chartered early June to assist the W3C community in raising issues and proposing solutions for the HTML and DOM specifications, and to produce W3C Recommendations from WHATWG Review Drafts. A few days before, W3C and the WHATWG signed a Memorandum of Understanding outlining the agreement to collaborate on the development of a single version of the HTML and DOM specifications. Issues and proposed solutions for HTML and DOM done via the newly rechartered HTML Working Group in the WHATWG repositories The HTML Working Group is targetting November 2019 to bring HTML and DOM to Candidate Recommendations. CSS All CSS specifications CSS is a critical part of the Open Web Platform. The CSS Working Group gathers requirements from two large groups of CSS users: the publishing industry and application developers. Within W3C, those groups are exemplified by the Publishing groups and the Web Platform Working Group. The former requires things like better pagination support and advanced font handling, the latter needs intelligent (and fast!) scrolling and animations. What we know as CSS is actually a collection of almost a hundred specifications, referred to as ‘modules’. The current state of CSS is defined by a snapshot, updated once a year. The group also publishes an index defining every term defined by CSS specifications. Fonts All Fonts specifications The Web Fonts Working Group develops specifications that allow the interoperable deployment of downloadable fonts on the Web, with a focus on Progressive Font Enrichment as well as maintenance of WOFF Recommendations. Recent and ongoing work includes: Early API experiments by Adobe and Monotype have demonstrated the feasibility of a font enrichment API, where a server delivers a font with minimal glyph repertoire and the client can query the full repertoire and request additional subsets on-the-fly. In other experiments, the Brotli compression used in WOFF 2 was extended to support shared dictionaries and patch update. Metrics to quantify improvement are a current hot discussion topic. The group will meet at ATypi 2019 in Japan, to gather requirements from the international typography community. The group will first produce a report summarizing the strengths and weaknesses of each prototype solution by Q2 2020. SVG All SVG specifications SVG is an important and widely-used part of the Open Web Platform. The SVG Working Group focuses on aligning the SVG 2.0 specification with browser implementations, having split the specification into a currently-implemented 2.0 and a forward-looking 2.1. Current activity is on stabilization, increased integration with the Open Web Platform, and test coverage analysis. The Working Group was rechartered in March 2019. A new work item concerns native (non-Web-browser) uses of SVG as a non-interactive, vector graphics format. Audio The Web Audio Working Group was extended to finish its work on the Web Audio API, expecting to publish it as a Recommendation by year end. The specification enables synthesizing audio in the browser. Audio operations are performed with audio nodes, which are linked together to form a modular audio routing graph. Multiple sources — with different types of channel layout — are supported. This modular design provides the flexibility to create complex audio functions with dynamic effects. The first version of Web Audio API is now feature complete and is implemented in all modern browsers. Work has started on the next version, and new features are being incubated in the Audio Community Group. Performance Web Performance All Web Performance specifications There are currently 18 specifications in development in the Web Performance Working Group aiming to provide methods to observe and improve aspects of application performance of user agent features and APIs. The W3C team is looking at related work incubated in the W3C GPU for the Web (WebGPU) Community Group which is poised to transition to a W3C Working Group. A preliminary draft charter is available. WebAssembly All WebAssembly specifications WebAssembly improves Web performance and power by being a virtual machine and execution environment enabling loaded pages to run native (compiled) code. It is deployed in Firefox, Edge, Safari and Chrome. The specification will soon reach Candidate Recommendation. WebAssembly enables near-native performance, optimized load time, and perhaps most importantly, a compilation target for existing code bases. While it has a small number of native types, much of the performance increase relative to Javascript derives from its use of consistent typing. WebAssembly leverages decades of optimization for compiled languages and the byte code is optimized for compactness and streaming (the web page starts executing while the rest of the code downloads). Network and API access all occurs through accompanying Javascript libraries -- the security model is identical to that of Javascript. Requirements gathering and language development occur in the Community Group while the Working Group manages test development, community review and progression of specifications on the Recommendation Track. Testing Browser testing plays a critical role in the growth of the Web by: Improving the reliability of Web technology definitions; Improving the quality of implementations of these technologies by helping vendors to detect bugs in their products; Improving the data available to Web developers on known bugs and deficiencies of Web technologies by publishing results of these tests. Browser Testing and Tools The Browser Testing and Tools Working Group is developing WebDriver version 2, having published last year the W3C Recommendation of WebDriver. WebDriver acts as a remote control interface that enables introspection and control of user agents, provides a platform- and language-neutral wire protocol as a way for out-of-process programs to remotely instruct the behavior of Web, and emulates the actions of a real person using the browser. WebPlatform Tests The WebPlatform Tests project now provides a mechanism which allows to fully automate tests that previously needed to be run manually: TestDriver. TestDriver enables sending trusted key and mouse events, sending complex series of trusted pointer and key interactions for things like in-content drag-and-drop or pinch zoom, and even file upload. Since 2014 W3C began work on this coordinated open-source effort to build a cross-browser test suite for the Web Platform, which WHATWG, and all major browsers adopted. Web of Data All Data specifications There have been several great success stories around the standardization of data on the web over the past year. Verifiable Claims seems to have significant uptake. It is also significant that the Distributed Identifier WG charter has received numerous favorable reviews, and was just recently launched. JSON-LD has been a major success with the large deployment on Web sites via schema.org. JSON-LD 1.1 completed technical work, about to transition to CR More than 25% of websites today include schema.org data in JSON-LD The Web of Things description is in CR since May, making use of JSON-LD Verifiable Credentials data model is in CR since July, also making use of JSON-LD Continued strong interest in decentralized identifiers Engagement from the TAG with reframing core documents, such as Ethical Web Principles, to include data on the web within their scope Data is increasingly important for all organizations, especially with the rise of IoT and Big Data. W3C has a mature and extensive suite of standards relating to data that were developed over two decades of experience, with plans for further work on making it easier for developers to work with graph data and knowledge graphs. Linked Data is about the use of URIs as names for things, the ability to dereference these URIs to get further information and to include links to other data. There are ever-increasing sources of open Linked Data on the Web, as well as data services that are restricted to the suppliers and consumers of those services. The digital transformation of industry is seeking to exploit advanced digital technologies. This will facilitate businesses to integrate horizontally along the supply and value chains, and vertically from the factory floor to the office floor. W3C is seeking to make it easier to support enterprise-wide data management and governance, reflecting the strategic importance of data to modern businesses. Traditional approaches to data have focused on tabular databases (SQL/RDBMS), Comma Separated Value (CSV) files, and data embedded in PDF documents and spreadsheets. We're now in midst of a major shift to graph data with nodes and labeled directed links between them. Graph data is: Faster than using SQL and associated JOIN operations More favorable to integrating data from heterogeneous sources Better suited to situations where the data model is evolving In the wake of the recent W3C Workshop on Graph Data we are in the process of launching a Graph Standardization Business Group to provide a business perspective with use cases and requirements, to coordinate technical standards work and liaisons with external organizations. Web for All Security, Privacy, Identity All Security specifications, all Privacy specifications Authentication on the Web As the WebAuthn Level 1 W3C Recommendation published last March is seeing wide implementation and adoption of strong cryptographic authentication, work is proceeding on Level 2. The open standard Web API gives native authentication technology built into native platforms, browsers, operating systems (including mobile) and hardware, offering protection against hacking, credential theft, phishing attacks, thus aiming to end the era of passwords as a security construct. You may read more in our March press release. Privacy An increasing number of W3C specifications are benefitting from Privacy and Security review; there are security and privacy aspects to every specification. Early review is essential. Working with the TAG, the Privacy Interest Group has updated the Self-Review Questionnaire: Security and Privacy. Other recent work of the group includes public blogging further to the exploration of anti-patterns in standards and permission prompts. Security The Web Application Security Working Group adopted Feature Policy, aiming to allow developers to selectively enable, disable, or modify the behavior of some of these browser features and APIs within their application; and Fetch Metadata, aiming to provide servers with enough information to make a priori decisions about whether or not to service a request based on the way it was made, and the context in which it will be used. The Web Payment Security Interest Group, launched last April, convenes members from W3C, EMVCo, and the FIDO Alliance to discuss cooperative work to enhance the security and interoperability of Web payments (read more about payments). Internationalization (i18n) All Internationalization specifications, educational articles related to Internationalization, spec developers checklist Only a quarter or so current Web users use English online and that proportion will continue to decrease as the Web reaches more and more communities of limited English proficiency. If the Web is to live up to the "World Wide" portion of its name, and for the Web to truly work for stakeholders all around the world engaging with content in various languages, it must support the needs of worldwide users as they engage with content in the various languages. The growth of epublishing also brings requirements for new features and improved typography on the Web. It is important to ensure the needs of local communities are captured. The W3C Internationalization Initiative was set up to increase in-house resources dedicated to accelerating progress in making the World Wide Web "worldwide" by gathering user requirements, supporting developers, and education & outreach. For an overview of current projects see the i18n radar. W3C's Internationalization efforts progressed on a number of fronts recently: Requirements: New African and European language groups will work on the gap analysis, errata and layout requirements. Gap analysis: Japanese, Devanagari, Bengali, Tamil, Lao, Khmer, Javanese, and Ethiopic updated in the gap-analysis documents. Layout requirements document: notable progress tracked in the Southeast Asian Task Force while work continues on Chinese layout requirements. Developer support: Spec reviews: the i18n WG continues active review of specifications of the WHATWG and other W3C Working Groups. Short review checklist: easy way to begin a self-review to help spec developers understand what aspects of their spec are likely to need attention for internationalization, and points them to more detailed checklists for the relevant topics. It also helps those reviewing specs for i18n issues. Strings on the Web: Language and Direction Metadata lays out issues and discusses potential solutions for passing information about language and direction with strings in JSON or other data formats. The document was rewritten for clarity, and expanded. The group is collaborating with the JSON-LD and Web Publishing groups to develop a plan for updating RDF, JSON-LD and related specifications to handle metadata for base direction of text (bidi). User-friendly test format: a new format was developed for Internationalization Test Suite tests, which displays helpful information about how the test works. This particularly useful because those tests are pointed to by educational materials and gap-analysis documents. Web Platform Tests: a large number of tests in the i18n test suite have been ported to the WPT repository, including: css-counter-styles, css-ruby, css-syntax, css-test, css-text-decor, css-writing-modes, and css-pseudo. Education & outreach: (for all educational materials, see the HTML & CSS Authoring Techniques) Web Accessibility All Accessibility specifications, WAI resources The Web Accessibility Initiative supports W3C's Web for All mission. Recent achievements include: Education and training: Inaccessibility of CAPTCHA updated to bring our analysis and recommendations up to date with CAPTCHA practice today, concluding two years of extensive work and invaluable input from the public (read more on the W3C Blog Learn why your web content and applications should be accessible. The Education and Outreach Working Group has completed revision and updating of the Business Case for Digital Accessibility. Accessibility guidelines: The Accessibility Guidelines Working Group has continued to update WCAG Techniques and Understanding WCAG 2.1; and published a Candidate Recommendation of Accessibility Conformance Testing Rules Format 1.0 to improve inter-rater reliability when evaluating conformance of web content to WCAG An updated charter is being developed to host work on "Silver", the next generation accessibility guidelines (WCAG 2.2) There are accessibility aspects to most specifications. Check your work with the FAST checklist. Outreach to the world W3C Developer Relations To foster the excellent feedback loop between Web Standards development and Web developers, and to grow participation from that diverse community, recent W3C Developer Relations activities include: @w3cdevs tracks the enormous amount of work happening across W3C W3C Track during the Web Conference 2019 in San Francisco Tech videos: W3C published the 2019 Web Games Workshop videos The 16 September 2019 Developer Meetup in Fukuoka, Japan, is open to all and will combine a set of technical demos prepared by W3C groups, and a series of talks on a selected set of W3C technologies and projects W3C is involved with Mozilla, Google, Samsung, Microsoft and Bocoup in the organization of ViewSource 2019 in Amsterdam (read more on the W3C Blog) W3C Training In partnership with EdX, W3C's MOOC training program, W3Cx offers a complete "Front-End Web Developer" (FEWD) professional certificate program that consists of a suite of five courses on the foundational languages that power the Web: HTML5, CSS and JavaScript. We count nearly 900K students from all over the world. Translations Many Web users rely on translations of documents developed at W3C whose official language is English. W3C is extremely grateful to the continuous efforts of its community in ensuring our various deliverables in general, and in our specifications in particular, are made available in other languages, for free, ensuring their exposure to a much more diverse set of readers. Last Spring we developed a more robust system, a new listing of translations of W3C specifications and updated the instructions on how to contribute to our translation efforts. W3C Liaisons Liaisons and coordination with numerous organizations and Standards Development Organizations (SDOs) is crucial for W3C to: make sure standards are interoperable coordinate our respective agenda in Internet governance: W3C participates in ICANN, GIPO, IGF, the I* organizations (ICANN, IETF, ISOC, IAB). ensure at the government liaison level that our standards work is officially recognized when important to our membership so that products based on them (often done by our members) are part of procurement orders. W3C has ARO/PAS status with ISO. W3C participates in the EU MSP and Rolling Plan on Standardization ensure the global set of Web and Internet standards form a compatible stack of technologies, at the technical and policy level (patent regime, fragmentation, use in policy making) promote Standards adoption equally by the industry, the public sector, and the public at large Coralie Mercier, Editor, W3C Marketing & Communications $Id: Overview.html,v 1.60 2019/10/15 12:05:52 coralie Exp $ Copyright © 2019 W3C ® (MIT, ERCIM, Keio, Beihang) Usage policies apply.

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

Analysis of video quality datasets via design of minimalistic video quality models

We have come a long way since I was a child in the 1960s when all I wanted for Christmas was a slinky and some Rock’Em – Sock’Em Robots. Now imagine we have traveled ten years into the future, and it is Christmas 2031. Alexa has replaced kids’ parents and Santa Claus. Every toy is connected to the Internet and looks like a robot version of the animal it represents. Clean thermonuclear Christmas trees will be providing us with radiant, gamma-ray energy for all our holiday needs. Pogo sticks have also made a comeback, but they are solar-powered and can leap entire city blocks. And while I am busy pretending to be the Ghost of Christmas Future, I thought it would also be fun to ask the Office of the CTO team about their predictions for futuristic, technical toys. So, I posed these two questions: What cool TECHNICAL toy or gadget would you like Santa to bring you this year in 2021? As a participating member of the Office of the CTO, what cool TECHNICAL toy or gadget (that has not yet been invented) would you like Santa to bring you in 10 years from now in 2031? christmas wishlist for the octo team overlay You know what? We just might see I see a sneak preview of some of these magical tech toys of the future in just a few weeks at the CES 2022 conference. In the meantime, take a look at the wish list from all of our Extreme technical gurus: Marcus Burton – Wireless and Cloud Architect Christmas Wish 2021: Is a Tesla Cybertruck an option? I’ll even take a prototype. That will scratch several technology itches at the same time. Think about it…EV, autonomous driving, AI, 5G probably, cloud-connected, mobile-first, and all the best in materials sciences and mechanical engineering applied to trucks. What more could an outdoorsy tech guy want? Christmas Wish 2031: I’m kinda thinking that while everyone else has their brain slurped out in the metaverse (with VR!), I will prefer to go to the actual mountains. But you know, I have a wife and kids, so I have to think about safety. So here’s my wish: a smart personal device that has a full week of battery life (using ultra-thin silicon wafers) with rapid solar charging, LEO satellite connectivity (for sending “eat your heart out” 3D pics to my friends from the “there’s no 6G here” wilderness), and ultra-HD terrain feature maps for modern navigation. Carla Guzzetti – VP, Experience, Messaging & Enablement Christmas Wish 2021: I want this: Meeting Owl Pro – 360-Degree, 1080p HD Smart Video Conference Camera, Microphone, and Speaker Christmas Wish 2031: I want a gadget where we can have virtual meetings without the need for a wearable! Who wants to wear heavy goggles all day? Doug McDonald – Director of Product Management Christmas Wish 2021: As a technologist often looking for a balance between screen time and health and fitness I hope Santa brings me the Aura Strap. The Aura strap adds additional IoT sensory capabilities to compliment your Apple smartwatch. Bioelectrical impedance analysis is the cutting-edge science behind the AURA Strap. This innovation provides a way to truly see how your body changes over the course of a day. Their body composition analysis includes fat, muscle mass, minerals, and hydration; providing personalized insights that improve the results of your workouts, diet, and your lifestyle as a whole. Christmas Wish 2031: Hopefully, this innovation will be here sooner. Still, in the spirit of my first wish from Santa, I also hope to have a service engine warning light for me. The concept is utilizing advancements in biomedical sensory devices to pinpoint potential changes in your physical metrics that may help in seeking medical attention sooner than later if variances in health data occur. I spoke about this concept in the Digital Diagnosis episode of the Inflection Points podcast from the Office of the CTO. Ed Koehler – Principal Engineer Christmas Wish 2021: My answers are short and sweet. I want a nice drone with high-resolution pan, tilt, and zoom (PTZ) cameras. Christmas Wish 2031: In ten years, I want a drone that I can sit inside and fly away! Puneet Sehgal – Business Initiatives Program Manager Christmas Wish 2021: I have always wanted to enjoy the world from a bird’s eye view. Therefore, my wish is for Santa to bring me a good-quality drone camera this year. It is amazing how quickly drones have evolved from commercial /military use to becoming a personal gadget. Christmas Wish 2031: In 2031, I wish Santa could get me a virtual reality (VR) trainer to help me internalize physical motion by looking at a simulation video while sending an electrical impulse to mimic it. It will open endless possibilities, and I could become an ice skater, a karate expert, or a pianist – all in one. Maybe similar research is already being done, but we are far away from something like this maturing for practical use. So, who knows – it’s Santa after all and we are talking 2031! Tim Harrison – Director of Product Marketing, Service Provider Christmas Wish 2021: This year, I would love to extend my audio recording setup and move from a digital 24 channel mixer to a control surface that integrates with my DAW (digital audio workstation) and allows me to use my outboard microphone pre-amps. I’ve been looking at an ICON QCon Pro G2 plus one QCon EX G2 extender to give me direct control over 16 channels at once (I use 16 channels just for my drum kit). Christmas Wish 2031: Ten years from now, I sincerely hope to receive an anti-gravity platform. First, I’ll be old, and climbing stairs will have become more challenging for these creaky old bones. Secondly, who hasn’t hoped for a REAL hoverboard? Once we know what gravity is “made of,” we can start making it easier to manipulate objects on earth and make space more habitable for human physiology. Either that or a puppy. Puppy sitting Divya Balu Pazhayannur – Director of Business Initiatives Christmas Wish 2021: I’m upgrading parts of my house over the holidays and browsing online for kitchen and laundry appliances. If you had told me that I would be spending three hours reading blogs on choosing the right cooktop for me, I would not have believed you. Does it have the right power, is it reliable, is it Wi-Fi enabled, can you talk to it – I’m kidding on that last one. Having said that, I’d love to get the Bosch Benchmark Gas Stovetop. Although I can’t speak to my appliance, its minimalist look has me writing it down on my wish list for Santa. I’ll even offer him some crispy dosas in exchange. Christmas Wish 2031: Apart from flying cars and personal robot assistants, I’d love to get the gift of better connectivity. I miss my family and friends in India, and it would be amazing to engage with them through holographic technology. I imagine it would allow for a much higher level of communication than today’s ‘talking head’ approach. Although do I want my family sitting with me in my living room? Still – I’d like to think a holograph would be just fantastic. Yury Ostrovsky – Sr. Technology Manager Christmas Wish 2021: I believe 2022 will be the year of VR toys. Virtual Reality is already popular, but I believe more applications will be developed in this area. We might see radio waves coming from different sources (Wi-Fi, LTE, 5G, BT, etc.) and visualize propagation in real-time. Christmas Wish 2031: “Prediction is very difficult, especially if it’s about the future” – Niels Bohr Kurt Semba – Principal Architect Christmas Wish 2021: The Crown from Neurosity. It helps you get and stay in a deep focus to improve your work and gaming results. Christmas Wish 2031: A non-evasive health device that can quickly look deep into your body and cells and explain why you are not feeling well today. Jon Filson – Senior Producer, Content Christmas Wish 2021: I want a large rollable TV by LG. In part because I watch a lot of football. And while I have a Smart TV, I still can’t get it to connect to my Bluetooth speaker … so while I love it, I want it to work better, and isn’t that so often the way with tech? But more than that, I don’t like and have never liked that rooms have to be designed around TVs. They are big, which is fine, but they are often in the way, which is less so. They should disappear when not in use. It’s $100,000 so I don’t expect it any time soon. But it’s an idea whose time has come. Christmas Wish 2031: I cheated on this one and asked my 12-year-old son Jack what he would want. It’s the portal gun, from Rick and Morty, a show in which a crazed scientist named Rick takes his grandson Morty on wacky adventures in a multi-verse. That last part is important to me. Kids today are already well into multi-verses, while we adults are just struggling to make one decent Metaverse. The next generation is already way ahead of us digitally speaking, it’s clear. Alexey Reznik – Senior UX Designer Christmas Wish 2021: This awesome toy: DJI Mavic 2 Pro – Drone Quadcopter UAV with Hasselblad Camera 3-Axis Gimbal HDR 4K Video Adjustable Aperture 20MP 1″ CMOS Sensor, up to 48mph, Gray Christmas Wish 2031: Something along these lines: BMW Motorrad VISION NEXT 100 BMW Motorcycle Michael Rash – Distinguished Engineer – Security Christmas Wish 2021: Satechi USB-C Multiport MX Adapter – Dual 4K HDMI. Christmas Wish 2031: A virtual reality headset that actually works. Alena Amir – Senior Content and Communications Manager Christmas Wish 2021: With conversations around VR/AR and the metaverse taking the world by storm, Santa could help out with an Oculus Quest. Purely for research purposes of course! Christmas Wish 2031: The 1985 movie, Back to the Future, was a family favorite and sure we didn’t get it all exactly right by 2015 but hey, it’s almost 2022! About time we get those hoverboards! David Coleman – Director of Wireless Christmas Wish 2021: Well, it looks like drones are the #1 wish item for 2021, and I am no exception. My wife and I just bought a home in the mountains of Blue Ridge, Georgia, where there is an abundance of wildlife. I want a state-of-the-art drone for bear surveillance. Christmas Wish 2031: In ten years, I will be 71 years old, and I hope to be at least semi-retired and savoring the fruits of my long tech career. Even though we are looking to the future, I want a time machine to revisit the past. I would travel back to July 16th, 1969, and watch Apollo 11 liftoff from Cape Kennedy to the moon. I actually did that as a nine-year-old kid. Oh, and I would also travel back to 1966 and play with my Rock’Em – Sock’Em Robots. Rock'em Sock'em Robots To summarize, our peeps in the Office of the CTO all envision Christmas 2031, where the way we interact as a society will have progressed. In 2021, we already have unlimited access to information, so future tech toys might depend less on magical new technologies and more on the kinds of experiences these new technologies can create. And when those experiences can be shared across the globe in real-time, the world gains an opportunity to learn from each other and grow together in ways that would never have been possible.

With recent advances in both Artificial Intelligence (AI) and Internet of Things (IoT) capabilities, it is more possible than ever to implement surveillance systems that can automatically identify people who might represent a potential security threat to the public in real-time. Imagine a surveillance camera system that can detect various on-body weapons, suspicious objects, and traffic. This system could transform surveillance cameras from passive sentries into active observers, which would help prevent a possible mass shooting in a school, stadium, or mall. In this project, we tried to realize such systems by implementing Smart-Monitor, an AI-powered threat detector for intelligent surveillance cameras. The developed system can be deployed locally on the surveillance cameras at the network edge. Deploying AI-enabled surveillance applications at the edge enables the initial analysis of the captured images on-site, reducing the communication overheads and enabling swift security actions. We developed a mobile app that users can detect suspicious objects in an image and video captured by several cameras at the network edge. Also, the model can generate a high-quality segmentation mask for each object instance in the photo, along with the confidence percentage. The camera side used a Raspberry Pi 4 device, Neural Compute Stick 2 (NCS 2), Logitech C920 webcam, motion sensors, buzzers, pushbuttons, LED lights, Python Face recognition, and TensorFlow Custom Object Detection. When the system detects a motion in the surrounding environment, the motion sensors send a signal to the Raspberry Pi device notifying it to start capturing images for such physical activity. Using Python’s face recognition and TensorFlow 2 custom object detection Smart-Monitor can recognize eight classes, including a baseball bat, bird, cat, dog, gun, hammer, knife, and human faces. Finally, we evaluated our system using various performance metrics such as classification time and accuracy, scalability, etc.

AI-powered YouTube video analysis toolkit using MCP. Extract transcripts, generate knowledge graphs, generate high-quality detailed notes, perform sentiment analysis, and topic modeling through an intuitive Streamlit dashboard.

**Professional video analysis API based on FFmpeg/FFprobe with comprehensive Quality Control (QC) features** Complete media analysis solution with 19 major professional QC categories and AI-powered insights.

a Video Quality Analysis Toolkit

Repository for the Evaluation of ffmpeg Video Codecs on Quality Metrics - MS-SSIM, PSNR, and VMAF. Currently supports H264. H.265, VP9 and AV1. Extensible to other codecs, useful for RD plot analysis, studying the impact of encoding configuration on compression, and comparing Video Codecs.

ReTrace AI analyzes images and videos to detect AI-generated content, deepfakes, edits, watermarks, and NSFW material. It verifies authenticity, checks metadata, identifies manipulations, provides quality suggestions, and generates a complete trust score, ensuring safe and reliable media analysis.

------------------ System Information ------------------ Time of this report: 9/2/2015, 19:28:59 Machine name: MANO-PC Operating System: Windows 7 Professional 32-bit (6.1, Build 7601) Service Pack 1 (7601.win7sp1_gdr.150722-0600) Language: Lithuanian (Regional Setting: Lithuanian) System Manufacturer: Acer, inc. System Model: Aspire 5920 BIOS: ZD1 v1.3811 3H11 Processor: Intel(R) Core(TM)2 Duo CPU T5550 @ 1.83GHz (2 CPUs), ~1.8GHz Memory: 2048MB RAM Available OS Memory: 2038MB RAM Page File: 2100MB used, 2995MB available Windows Dir: C:\Windows DirectX Version: DirectX 11 DX Setup Parameters: Not found User DPI Setting: Using System DPI System DPI Setting: 96 DPI (100 percent) DWM DPI Scaling: Disabled DxDiag Version: 6.01.7601.17514 32bit Unicode ------------ DxDiag Notes ------------ Display Tab 1: No problems found. Sound Tab 1: No problems found. Sound Tab 2: No problems found. Sound Tab 3: No problems found. Input Tab: No problems found. -------------------- DirectX Debug Levels -------------------- Direct3D: 0/4 (retail) DirectDraw: 0/4 (retail) DirectInput: 0/5 (retail) DirectMusic: 0/5 (retail) DirectPlay: 0/9 (retail) DirectSound: 0/5 (retail) DirectShow: 0/6 (retail) --------------- Display Devices --------------- Card name: Mobile Intel(R) 965 Express Chipset Family Manufacturer: Intel Corporation Chip type: Mobile Intel(R) 965 Express Chipset Family DAC type: Internal Device Key: Enum\PCI\VEN_8086&DEV_2A02&SUBSYS_01211025&REV_03 Display Memory: 358 MB Dedicated Memory: 0 MB Shared Memory: 358 MB Current Mode: 1280 x 800 (32 bit) (60Hz) Monitor Name: Generic PnP Monitor Monitor Model: unknown Monitor Id: SEC3945 Native Mode: 1280 x 800(p) (60.004Hz) Output Type: Internal Driver Name: igdumdx32.dll,igd10umd32.dll Driver File Version: 8.14.0010.1930 (English) Driver Version: 8.15.10.1930 DDI Version: 10 Driver Model: WDDM 1.1 Driver Attributes: Final Retail Driver Date/Size: 9/23/2009 19:14:54, 536576 bytes WHQL Logo'd: Yes WHQL Date Stamp: Device Identifier: {D7B78E66-6942-11CF-1F74-2B21A2C2C535} Vendor ID: 0x8086 Device ID: 0x2A02 SubSys ID: 0x01211025 Revision ID: 0x0003 Driver Strong Name: oem2.inf:Intel.Mfg:i965GM0:8.15.10.1930:pci\ven_8086&dev_2a02 Rank Of Driver: 00EC2001 Video Accel: ModeMPEG2_A ModeMPEG2_C ModeWMV9_B ModeVC1_B Deinterlace Caps: {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(YUY2,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(YUY2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(YUY2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(UYVY,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(UYVY,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(UYVY,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(YV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(YV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(YV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(NV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(NV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(NV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC1,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC1,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC1,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend D3D9 Overlay: Not Supported DXVA-HD: Not Supported DDraw Status: Enabled D3D Status: Enabled AGP Status: Enabled ------------- Sound Devices ------------- Description: Speakers (Realtek High Definition Audio) Default Sound Playback: Yes Default Voice Playback: Yes Hardware ID: HDAUDIO\FUNC_01&VEN_10EC&DEV_0888&SUBSYS_10250121&REV_1001 Manufacturer ID: 1 Product ID: 100 Type: WDM Driver Name: RTKVHDA.sys Driver Version: 6.00.0001.5901 (English) Driver Attributes: Final Retail WHQL Logo'd: Yes Date and Size: 7/23/2009 17:56:12, 2737248 bytes Other Files: Driver Provider: Realtek Semiconductor Corp. HW Accel Level: Basic Cap Flags: 0xF1F Min/Max Sample Rate: 100, 200000 Static/Strm HW Mix Bufs: 1, 0 Static/Strm HW 3D Bufs: 0, 0 HW Memory: 0 Voice Management: No EAX(tm) 2.0 Listen/Src: No, No I3DL2(tm) Listen/Src: No, No Sensaura(tm) ZoomFX(tm): No Description: Realtek HDMI Output (Realtek High Definition Audio) Default Sound Playback: No Default Voice Playback: No Hardware ID: HDAUDIO\FUNC_01&VEN_10EC&DEV_0888&SUBSYS_10250121&REV_1001 Manufacturer ID: 1 Product ID: 100 Type: WDM Driver Name: RTKVHDA.sys Driver Version: 6.00.0001.5901 (English) Driver Attributes: Final Retail WHQL Logo'd: Yes Date and Size: 7/23/2009 17:56:12, 2737248 bytes Other Files: Driver Provider: Realtek Semiconductor Corp. HW Accel Level: Basic Cap Flags: 0xF1F Min/Max Sample Rate: 100, 200000 Static/Strm HW Mix Bufs: 1, 0 Static/Strm HW 3D Bufs: 0, 0 HW Memory: 0 Voice Management: No EAX(tm) 2.0 Listen/Src: No, No I3DL2(tm) Listen/Src: No, No Sensaura(tm) ZoomFX(tm): No Description: Realtek Digital Output (Realtek High Definition Audio) Default Sound Playback: No Default Voice Playback: No Hardware ID: HDAUDIO\FUNC_01&VEN_10EC&DEV_0888&SUBSYS_10250121&REV_1001 Manufacturer ID: 1 Product ID: 100 Type: WDM Driver Name: RTKVHDA.sys Driver Version: 6.00.0001.5901 (English) Driver Attributes: Final Retail WHQL Logo'd: Yes Date and Size: 7/23/2009 17:56:12, 2737248 bytes Other Files: Driver Provider: Realtek Semiconductor Corp. HW Accel Level: Basic Cap Flags: 0xF1F Min/Max Sample Rate: 100, 200000 Static/Strm HW Mix Bufs: 1, 0 Static/Strm HW 3D Bufs: 0, 0 HW Memory: 0 Voice Management: No EAX(tm) 2.0 Listen/Src: No, No I3DL2(tm) Listen/Src: No, No Sensaura(tm) ZoomFX(tm): No --------------------- Sound Capture Devices --------------------- Description: Microphone (Realtek High Definition Audio) Default Sound Capture: Yes Default Voice Capture: Yes Driver Name: RTKVHDA.sys Driver Version: 6.00.0001.5901 (English) Driver Attributes: Final Retail Date and Size: 7/23/2009 17:56:12, 2737248 bytes Cap Flags: 0x1 Format Flags: 0xFFFFF ------------------- DirectInput Devices ------------------- Device Name: Mouse Attached: 1 Controller ID: n/a Vendor/Product ID: n/a FF Driver: n/a Device Name: Keyboard Attached: 1 Controller ID: n/a Vendor/Product ID: n/a FF Driver: n/a Device Name: Microsoft eHome Infrared Transceiver Attached: 1 Controller ID: 0x0 Vendor/Product ID: 0x045E, 0x006D FF Driver: n/a Device Name: Microsoft eHome Infrared Transceiver Attached: 1 Controller ID: 0x0 Vendor/Product ID: 0x045E, 0x006D FF Driver: n/a Device Name: Microsoft eHome Infrared Transceiver Attached: 1 Controller ID: 0x0 Vendor/Product ID: 0x045E, 0x006D FF Driver: n/a Device Name: Microsoft eHome Infrared Transceiver Attached: 1 Controller ID: 0x0 Vendor/Product ID: 0x045E, 0x006D FF Driver: n/a Device Name: Generic USB Joystick Attached: 1 Controller ID: 0x0 Vendor/Product ID: 0x1345, 0x1000 FF Driver: n/a Poll w/ Interrupt: No ----------- USB Devices ----------- + USB Root Hub | Vendor/Product ID: 0x8086, 0x2832 | Matching Device ID: usb\root_hub | Service: usbhub | Driver: usbhub.sys, 8/18/2015 00:51:18, 258560 bytes | Driver: usbd.sys, 8/18/2015 00:51:18, 6016 bytes ---------------- Gameport Devices ---------------- ------------ PS/2 Devices ------------ + Standard PS/2 Keyboard | Matching Device ID: *pnp0303 | Service: i8042prt | Driver: i8042prt.sys, 7/14/2009 02:11:24, 80896 bytes | Driver: kbdclass.sys, 7/14/2009 04:20:36, 42576 bytes | + Microsoft eHome Remote Control Keyboard keys | Matching Device ID: hid\irdevicev2&col05 | Service: kbdhid | Driver: kbdhid.sys, 11/21/2010 00:29:03, 28160 bytes | Driver: kbdclass.sys, 7/14/2009 04:20:36, 42576 bytes | + Microsoft eHome MCIR Keyboard | Matching Device ID: hid\irdevicev2&col06 | Service: kbdhid | Driver: kbdhid.sys, 11/21/2010 00:29:03, 28160 bytes | Driver: kbdclass.sys, 7/14/2009 04:20:36, 42576 bytes | + Microsoft eHome MCIR 109 Keyboard | Matching Device ID: hid\irdevicev2&col07 | Service: kbdhid | Driver: kbdhid.sys, 11/21/2010 00:29:03, 28160 bytes | Driver: kbdclass.sys, 7/14/2009 04:20:36, 42576 bytes | + Terminal Server Keyboard Driver | Matching Device ID: root\rdp_kbd | Upper Filters: kbdclass | Service: TermDD | Driver: i8042prt.sys, 7/14/2009 02:11:24, 80896 bytes | Driver: kbdclass.sys, 7/14/2009 04:20:36, 42576 bytes | + Synaptics PS/2 Port TouchPad | Matching Device ID: *syn1b03 | Upper Filters: SynTP | Service: i8042prt | + HID-compliant mouse | Matching Device ID: hid_device_system_mouse | Service: mouhid | Driver: mouhid.sys, 7/14/2009 02:45:08, 26112 bytes | Driver: mouclass.sys, 7/14/2009 04:20:44, 41552 bytes | + HID-compliant mouse | Vendor/Product ID: 0x04F3, 0x0235 | Matching Device ID: hid_device_system_mouse | Service: mouhid | Driver: mouhid.sys, 7/14/2009 02:45:08, 26112 bytes | Driver: mouclass.sys, 7/14/2009 04:20:44, 41552 bytes | + Terminal Server Mouse Driver | Matching Device ID: root\rdp_mou | Upper Filters: mouclass | Service: TermDD | Driver: termdd.sys, 11/21/2010 00:29:03, 53120 bytes | Driver: sermouse.sys, 7/14/2009 02:45:08, 19968 bytes | Driver: mouclass.sys, 7/14/2009 04:20:44, 41552 bytes ------------------------ Disk & DVD/CD-ROM Drives ------------------------ Drive: C: Free Space: 14.2 GB Total Space: 114.5 GB File System: NTFS Model: Hitachi HTS542512K9SA00 Drive: D: Model: TSSTcorp CDDVDW TS-L632H ATA Device Driver: c:\windows\system32\drivers\cdrom.sys, 6.01.7601.17514 (Lithuanian), 11/21/2010 00:29:03, 108544 bytes -------------- System Devices -------------- Name: Mobile Intel(R) PM965/GM965/GL960/GS965 Express Processor to DRAM Controller - 2A00 Device ID: PCI\VEN_8086&DEV_2A00&SUBSYS_01211025&REV_03\3&21436425&0&00 Driver: n/a Name: Intel(R) ICH8 Family USB2 Enhanced Host Controller - 2836 Device ID: PCI\VEN_8086&DEV_2836&SUBSYS_01211025&REV_03\3&21436425&0&EF Driver: C:\Windows\system32\drivers\usbehci.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 43520 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 284672 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 258560 bytes Name: Intel(R) 82801 PCI Bridge - 2448 Device ID: PCI\VEN_8086&DEV_2448&SUBSYS_00000000&REV_F3\3&21436425&0&F0 Driver: C:\Windows\system32\DRIVERS\pci.sys, 6.01.7601.17514 (English), 11/21/2010 00:29:03, 153984 bytes Name: Intel(R) ICH8M Ultra ATA Storage Controllers - 2850 Device ID: PCI\VEN_8086&DEV_2850&SUBSYS_01211025&REV_03\3&21436425&0&F9 Driver: C:\Windows\system32\DRIVERS\intelide.sys, 6.01.7600.16385 (English), 7/14/2009 04:20:36, 15424 bytes Driver: C:\Windows\system32\DRIVERS\pciidex.sys, 6.01.7600.16385 (Lithuanian), 7/14/2009 04:19:03, 42560 bytes Driver: C:\Windows\system32\DRIVERS\atapi.sys, 6.01.7600.16385 (English), 7/14/2009 04:26:15, 21584 bytes Driver: C:\Windows\system32\DRIVERS\ataport.sys, 6.01.7601.18231 (Lithuanian), 8/18/2015 00:51:05, 133056 bytes Name: Intel(R) ICH8 Family USB Universal Host Controller - 2835 Device ID: PCI\VEN_8086&DEV_2835&SUBSYS_01211025&REV_03\3&21436425&0&D1 Driver: C:\Windows\system32\drivers\usbuhci.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 24064 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 284672 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 258560 bytes Name: Broadcom NetLink (TM) Gigabit Ethernet Device ID: PCI\VEN_14E4&DEV_1693&SUBSYS_01211025&REV_02\4&1D1097F2&0&00E5 Driver: n/a Name: High Definition Audio Controller Device ID: PCI\VEN_8086&DEV_284B&SUBSYS_01211025&REV_03\3&21436425&0&D8 Driver: C:\Windows\system32\DRIVERS\hdaudbus.sys, 6.01.7601.17514 (English), 11/21/2010 00:29:03, 108544 bytes Name: Intel(R) ICH8 Family USB Universal Host Controller - 2834 Device ID: PCI\VEN_8086&DEV_2834&SUBSYS_01211025&REV_03\3&21436425&0&D0 Driver: C:\Windows\system32\drivers\usbuhci.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 24064 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 284672 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 258560 bytes Name: Ricoh xD-Picture Card Controller Device ID: PCI\VEN_1180&DEV_0852&SUBSYS_01211025&REV_12\4&6AD4B7A&0&4CF0 Driver: C:\Windows\system32\DRIVERS\rixdptsk.sys, 6.00.0001.0000 (Japanese), 11/14/2006 17:35:20, 37376 bytes Driver: C:\Windows\system32\rixdicon.dll, 5/6/2005 19:06:00, 16480 bytes Name: Intel(R) ICH8 Family PCI Express Root Port 6 - 2849 Device ID: PCI\VEN_8086&DEV_2849&SUBSYS_01211025&REV_03\3&21436425&0&E5 Driver: C:\Windows\system32\DRIVERS\pci.sys, 6.01.7601.17514 (English), 11/21/2010 00:29:03, 153984 bytes Name: Intel(R) ICH8 Family USB Universal Host Controller - 2832 Device ID: PCI\VEN_8086&DEV_2832&SUBSYS_01211025&REV_03\3&21436425&0&EA Driver: C:\Windows\system32\drivers\usbuhci.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 24064 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 284672 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 258560 bytes Name: Ricoh SD/MMC Host Controller Device ID: PCI\VEN_1180&DEV_0843&SUBSYS_01211025&REV_12\4&6AD4B7A&0&4AF0 Driver: C:\Windows\system32\DRIVERS\rimmptsk.sys, 6.00.0002.0003 (Japanese), 2/24/2007 15:42:22, 39936 bytes Name: Intel(R) ICH8 Family PCI Express Root Port 4 - 2845 Device ID: PCI\VEN_8086&DEV_2845&SUBSYS_01211025&REV_03\3&21436425&0&E3 Driver: C:\Windows\system32\DRIVERS\pci.sys, 6.01.7601.17514 (English), 11/21/2010 00:29:03, 153984 bytes Name: Intel(R) ICH8 Family USB Universal Host Controller - 2831 Device ID: PCI\VEN_8086&DEV_2831&SUBSYS_01211025&REV_03\3&21436425&0&E9 Driver: C:\Windows\system32\drivers\usbuhci.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 24064 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 284672 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 258560 bytes Name: Ricoh 1394 OHCI Compliant Host Controller Device ID: PCI\VEN_1180&DEV_0832&SUBSYS_01211025&REV_05\4&6AD4B7A&0&48F0 Driver: C:\Windows\system32\DRIVERS\1394ohci.sys, 6.01.7601.17514 (English), 11/21/2010 00:29:03, 164864 bytes Name: Intel(R) PRO/Wireless 3945ABG Network Connection Device ID: PCI\VEN_8086&DEV_4222&SUBSYS_10018086&REV_02\4&10F04939&0&00E3 Driver: n/a Name: Intel(R) ICH8 Family PCI Express Root Port 1 - 283F Device ID: PCI\VEN_8086&DEV_283F&SUBSYS_01211025&REV_03\3&21436425&0&E0 Driver: C:\Windows\system32\DRIVERS\pci.sys, 6.01.7601.17514 (English), 11/21/2010 00:29:03, 153984 bytes Name: Intel(R) ICH8 Family USB Universal Host Controller - 2830 Device ID: PCI\VEN_8086&DEV_2830&SUBSYS_01211025&REV_03\3&21436425&0&E8 Driver: C:\Windows\system32\drivers\usbuhci.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 24064 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 284672 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 258560 bytes Name: SDA Standard Compliant SD Host Controller Device ID: PCI\VEN_1180&DEV_0822&SUBSYS_01211025&REV_22\4&6AD4B7A&0&49F0 Driver: C:\Windows\system32\DRIVERS\sdbus.sys, 6.01.7601.17514 (English), 11/21/2010 00:29:03, 84992 bytes Name: Mobile Intel(R) 965 Express Chipset Family Device ID: PCI\VEN_8086&DEV_2A03&SUBSYS_01211025&REV_03\3&21436425&0&11 Driver: n/a Name: Intel(R) ICH8 Family SMBus Controller - 283E Device ID: PCI\VEN_8086&DEV_283E&SUBSYS_01211025&REV_03\3&21436425&0&FB Driver: n/a Name: Intel(R) 82801HEM/HBM SATA AHCI Controller Device ID: PCI\VEN_8086&DEV_2829&SUBSYS_01211025&REV_03\3&21436425&0&FA Driver: C:\Windows\system32\DRIVERS\iaStor.sys, 7.00.0001.1001 (English), 10/30/2007 15:05:00, 277784 bytes Name: Ricoh Memory Stick Controller Device ID: PCI\VEN_1180&DEV_0592&SUBSYS_01211025&REV_12\4&6AD4B7A&0&4BF0 Driver: C:\Windows\system32\snymsico.dll, 1.00.0000.9120 (English), 9/4/2004 04:00:00, 90112 bytes Driver: C:\Windows\system32\DRIVERS\rimsptsk.sys, 6.00.0001.0010 (Japanese), 1/23/2007 17:40:20, 42496 bytes Name: Mobile Intel(R) 965 Express Chipset Family Device ID: PCI\VEN_8086&DEV_2A02&SUBSYS_01211025&REV_03\3&21436425&0&10 Driver: C:\Windows\system32\DRIVERS\igdkmd32.sys, 8.14.0010.1930 (English), 9/23/2009 19:18:14, 4808192 bytes Driver: C:\Windows\system32\igdumd32.dll, 8.14.0010.1930 (English), 9/23/2009 19:18:08, 3829760 bytes Driver: C:\Windows\system32\igkrng400.bin, 9/23/2009 19:16:08, 2050952 bytes Driver: C:\Windows\system32\iglhxs32.vp, 9/23/2009 19:45:20, 39440 bytes Driver: C:\Windows\system32\iglhxo32.vp, 9/23/2009 18:45:12, 60015 bytes Driver: C:\Windows\system32\iglhxc32.vp, 9/23/2009 18:45:12, 60226 bytes Driver: C:\Windows\system32\iglhxg32.vp, 9/23/2009 18:45:12, 60254 bytes Driver: C:\Windows\system32\iglhxa32.vp, 9/23/2009 18:45:12, 1090 bytes Driver: C:\Windows\system32\iglhxa32.cpa, 9/23/2009 18:45:12, 1921265 bytes Driver: C:\Windows\system32\hccutils.dll, 8.14.0010.1930 (English), 9/23/2009 18:49:04, 94208 bytes Driver: C:\Windows\system32\igfxsrvc.dll, 8.14.0010.1930 (English), 9/23/2009 18:49:24, 51712 bytes Driver: C:\Windows\system32\igfxsrvc.exe, 8.14.0010.1930 (English), 9/23/2009 12:30:48, 252952 bytes Driver: C:\Windows\system32\igfxpph.dll, 8.14.0010.1930 (English), 9/23/2009 18:49:42, 199680 bytes Driver: C:\Windows\system32\igfxcpl.cpl, 8.14.0010.1930 (English), 9/23/2009 18:49:34, 119296 bytes Driver: C:\Windows\system32\igfxcfg.exe, 8.14.0010.1930 (English), 9/23/2009 12:30:50, 672792 bytes Driver: C:\Windows\system32\igfxdev.dll, 8.14.0010.1930 (English), 9/23/2009 18:49:00, 218112 bytes Driver: C:\Windows\system32\igfxdo.dll, 8.14.0010.1930 (English), 9/23/2009 18:49:10, 130048 bytes Driver: C:\Windows\system32\igfxtray.exe, 8.14.0010.1930 (English), 9/23/2009 12:30:48, 141848 bytes Driver: C:\Windows\system32\hkcmd.exe, 8.14.0010.1930 (English), 9/23/2009 12:30:48, 173592 bytes Driver: C:\Windows\system32\igfxress.dll, 8.14.0010.1930 (English), 9/23/2009 18:48:52, 5702656 bytes Driver: C:\Windows\system32\igfxpers.exe, 8.14.0010.1930 (English), 9/23/2009 12:30:48, 150552 bytes Driver: C:\Windows\system32\igfxTMM.dll, 8.14.0010.1930 (English), 9/23/2009 18:49:42, 257536 bytes Driver: C:\Windows\system32\TVWSetup.exe, 1.00.0001.0000 (English), 9/23/2009 12:30:50, 8198680 bytes Driver: C:\Windows\system32\igfxext.exe, 8.14.0010.1930 (English), 9/23/2009 12:30:48, 173080 bytes Driver: C:\Windows\system32\igfxexps.dll, 8.14.0010.1930 (English), 9/23/2009 18:49:36, 23552 bytes Driver: C:\Windows\system32\oemdspif.dll, 8.14.0010.1930 (English), 9/23/2009 18:49:38, 59392 bytes Driver: C:\Windows\system32\igfxrara.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:08, 252416 bytes Driver: C:\Windows\system32\igfxrchs.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:10, 178176 bytes Driver: C:\Windows\system32\igfxrcht.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:10, 179712 bytes Driver: C:\Windows\system32\igfxrdan.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:10, 280576 bytes Driver: C:\Windows\system32\igfxrdeu.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:10, 303616 bytes Driver: C:\Windows\system32\igfxrenu.lrc, 8.14.0010.1930 (English), 9/23/2009 18:48:52, 275968 bytes Driver: C:\Windows\system32\igfxresp.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:12, 303104 bytes Driver: C:\Windows\system32\igfxrfin.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:12, 281088 bytes Driver: C:\Windows\system32\igfxrfra.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:12, 303616 bytes Driver: C:\Windows\system32\igfxrheb.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:14, 249856 bytes Driver: C:\Windows\system32\igfxrita.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:14, 304640 bytes Driver: C:\Windows\system32\igfxrjpn.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:14, 206848 bytes Driver: C:\Windows\system32\igfxrkor.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:14, 205312 bytes Driver: C:\Windows\system32\igfxrnld.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:16, 299520 bytes Driver: C:\Windows\system32\igfxrnor.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:16, 280064 bytes Driver: C:\Windows\system32\igfxrplk.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:16, 287744 bytes Driver: C:\Windows\system32\igfxrptb.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:16, 289280 bytes Driver: C:\Windows\system32\igfxrptg.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:18, 294912 bytes Driver: C:\Windows\system32\igfxrrus.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:18, 291328 bytes Driver: C:\Windows\system32\igfxrsky.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:18, 282624 bytes Driver: C:\Windows\system32\igfxrslv.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:18, 277504 bytes Driver: C:\Windows\system32\igfxrsve.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:18, 282624 bytes Driver: C:\Windows\system32\igfxrtha.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:20, 262656 bytes Driver: C:\Windows\system32\igfxrcsy.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:10, 282624 bytes Driver: C:\Windows\system32\igfxrell.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:12, 310784 bytes Driver: C:\Windows\system32\igfxrhun.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:14, 288256 bytes Driver: C:\Windows\system32\igfxrtrk.lrc, 8.14.0010.1930 (English), 9/23/2009 18:52:20, 279040 bytes Driver: C:\Windows\system32\ig4icd32.dll, 8.14.0010.1930 (English), 9/23/2009 18:58:12, 4104192 bytes Driver: C:\Windows\system32\ig4dev32.dll, 8.14.0010.1930 (English), 9/23/2009 18:58:38, 2686976 bytes Driver: C:\Windows\system32\igd10umd32.dll, 8.14.0010.1930 (English), 9/23/2009 19:09:58, 2551808 bytes Driver: C:\Windows\system32\igdumdx32.dll, 8.14.0010.1930 (English), 9/23/2009 19:14:54, 536576 bytes Driver: C:\Windows\system32\igfxCoIn_v1930.dll, 1.01.0017.0000 (English), 9/23/2009 19:27:44, 155648 bytes Name: Intel(R) ICH8 Family USB2 Enhanced Host Controller - 283A Device ID: PCI\VEN_8086&DEV_283A&SUBSYS_01211025&REV_03\3&21436425&0&D7 Driver: C:\Windows\system32\drivers\usbehci.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 43520 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 284672 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7601.18328 (English), 8/18/2015 00:51:18, 258560 bytes Name: Intel(R) ICH8M LPC Interface Controller - 2815 Device ID: PCI\VEN_8086&DEV_2815&SUBSYS_01211025&REV_03\3&21436425&0&F8 Driver: C:\Windows\system32\DRIVERS\msisadrv.sys, 6.01.7600.16385 (English), 7/14/2009 04:20:43, 13888 bytes ------------------ DirectShow Filters ------------------ DirectShow Filters: WMAudio Decoder DMO,0x00800800,1,1,WMADMOD.DLL,6.01.7601.17514 WMAPro over S/PDIF DMO,0x00600800,1,1,WMADMOD.DLL,6.01.7601.17514 WMSpeech Decoder DMO,0x00600800,1,1,WMSPDMOD.DLL,6.01.7601.17514 MP3 Decoder DMO,0x00600800,1,1,mp3dmod.dll,6.01.7600.16385 Mpeg4s Decoder DMO,0x00800001,1,1,mp4sdecd.dll,6.01.7600.16385 WMV Screen decoder DMO,0x00600800,1,1,wmvsdecd.dll,6.01.7601.17514 WMVideo Decoder DMO,0x00800001,1,1,wmvdecod.dll,6.01.7601.18221 Mpeg43 Decoder DMO,0x00800001,1,1,mp43decd.dll,6.01.7600.16385 Mpeg4 Decoder DMO,0x00800001,1,1,mpg4decd.dll,6.01.7600.16385 ffdshow Video Decoder,0xff800001,2,1,ffdshow.ax,1.03.4534.0000 ffdshow raw video filter,0x00200000,2,1,ffdshow.ax,1.03.4534.0000 ffdshow Audio Decoder,0xff800001,1,1,ffdshow.ax,1.03.4534.0000 DV Muxer,0x00400000,0,0,qdv.dll,6.06.7601.17514 Color Space Converter,0x00400001,1,1,quartz.dll,6.06.7601.18741 LAV Splitter,0x00400001,1,1,LAVSplitter.ax,0.65.0000.0047 WM ASF Reader,0x00400000,0,0,qasf.dll,12.00.7601.17514 Screen Capture filter,0x00200000,0,1,wmpsrcwp.dll,12.00.7601.17514 AVI Splitter,0x00600000,1,1,quartz.dll,6.06.7601.18741 VGA 16 Color Ditherer,0x00400000,1,1,quartz.dll,6.06.7601.18741 SBE2MediaTypeProfile,0x00200000,0,0,sbe.dll,6.06.7601.17528 Microsoft DTV-DVD Video Decoder,0x005fffff,2,4,msmpeg2vdec.dll,12.00.9200.17037 AC3 Parser Filter,0x00600000,1,1,mpg2splt.ax,6.06.7601.17528 StreamBufferSink,0x00200000,0,0,sbe.dll,6.06.7601.17528 Microsoft TV Captions Decoder,0x00200001,1,0,MSTVCapn.dll,6.01.7601.17715 MJPEG Decompressor,0x00600000,1,1,quartz.dll,6.06.7601.18741 CBVA DMO wrapper filter,0x00200000,1,1,cbva.dll,6.01.7601.17514 MPEG-I Stream Splitter,0x00600000,1,2,quartz.dll,6.06.7601.18741 SAMI (CC) Parser,0x00400000,1,1,quartz.dll,6.06.7601.18741 VBI Codec,0x00600000,1,4,VBICodec.ax,6.06.7601.17514 MPEG-2 Splitter,0x005fffff,1,0,mpg2splt.ax,6.06.7601.17528 Closed Captions Analysis Filter,0x00200000,2,5,cca.dll,6.06.7601.17514 SBE2FileScan,0x00200000,0,0,sbe.dll,6.06.7601.17528 Microsoft MPEG-2 Video Encoder,0x00200000,1,1,msmpeg2enc.dll,6.01.7601.17514 Internal Script Command Renderer,0x00800001,1,0,quartz.dll,6.06.7601.18741 MPEG Audio Decoder,0x03680001,1,1,quartz.dll,6.06.7601.18741 DV Splitter,0x00600000,1,2,qdv.dll,6.06.7601.17514 Video Mixing Renderer 9,0x00200000,1,0,quartz.dll,6.06.7601.18741 Microsoft MPEG-2 Encoder,0x00200000,2,1,msmpeg2enc.dll,6.01.7601.17514 ACM Wrapper,0x00600000,1,1,quartz.dll,6.06.7601.18741 Video Renderer,0x00800001,1,0,quartz.dll,6.06.7601.18741 MPEG-2 Video Stream Analyzer,0x00200000,0,0,sbe.dll,6.06.7601.17528 Line 21 Decoder,0x00600000,1,1,qdvd.dll,6.06.7601.18741 Video Port Manager,0x00600000,2,1,quartz.dll,6.06.7601.18741 Video Renderer,0x00400000,1,0,quartz.dll,6.06.7601.18741 VPS Decoder,0x00200000,0,0,WSTPager.ax,6.06.7601.17514 WM ASF Writer,0x00400000,0,0,qasf.dll,12.00.7601.17514 VBI Surface Allocator,0x00600000,1,1,vbisurf.ax,6.01.7601.17514 File writer,0x00200000,1,0,qcap.dll,6.06.7601.17514 iTV Data Sink,0x00600000,1,0,itvdata.dll,6.06.7601.17514 Bandisoft MPEG-1 Video Decoder,0xff800001,1,1,bdfilters.dll,1.00.0005.0016 iTV Data Capture filter,0x00600000,1,1,itvdata.dll,6.06.7601.17514 VSFilter,0x00200000,2,1,vsfilter.dll,1.07.0009.0145 VSFilter (auto-loading version),0x00800002,2,1,vsfilter.dll,1.07.0009.0145 DVD Navigator,0x00200000,0,3,qdvd.dll,6.06.7601.18741 Microsoft TV Subtitles Decoder,0x00200001,1,0,MSTVCapn.dll,6.01.7601.17715 Overlay Mixer2,0x00200000,1,1,qdvd.dll,6.06.7601.18741 AVI Draw,0x00600064,9,1,quartz.dll,6.06.7601.18741 RDP DShow Redirection Filter,0xffffffff,1,0,DShowRdpFilter.dll, DC-Bass Source,0x00400000,0,1,DCBassSourceMod.ax,1.05.0002.0000 Microsoft MPEG-2 Audio Encoder,0x00200000,1,1,msmpeg2enc.dll,6.01.7601.17514 WST Pager,0x00200000,1,1,WSTPager.ax,6.06.7601.17514 MPEG-2 Demultiplexer,0x00600000,1,1,mpg2splt.ax,6.06.7601.17528 DV Video Decoder,0x00800000,1,1,qdv.dll,6.06.7601.17514 ffdshow Audio Processor,0x00200000,1,1,ffdshow.ax,1.03.4534.0000 LAV Splitter Source,0x00400001,0,1,LAVSplitter.ax,0.65.0000.0047 SampleGrabber,0x00200000,1,1,qedit.dll,6.06.7601.18501 Null Renderer,0x00200000,1,0,qedit.dll,6.06.7601.18501 MPEG-2 Sections and Tables,0x005fffff,1,0,Mpeg2Data.ax,6.06.7601.17514 Microsoft AC3 Encoder,0x00200000,1,1,msac3enc.dll,6.01.7601.17514 StreamBufferSource,0x00200000,0,0,sbe.dll,6.06.7601.17528 Smart Tee,0x00200000,1,2,qcap.dll,6.06.7601.17514 Overlay Mixer,0x00200000,0,0,qdvd.dll,6.06.7601.18741 AVI Decompressor,0x00600000,1,1,quartz.dll,6.06.7601.18741 NetBridge,0x00200000,2,0,netbridge.dll,6.01.7601.17514 AVI/WAV File Source,0x00400000,0,2,quartz.dll,6.06.7601.18741 Wave Parser,0x00400000,1,1,quartz.dll,6.06.7601.18741 MIDI Parser,0x00400000,1,1,quartz.dll,6.06.7601.18741 Multi-file Parser,0x00400000,1,1,quartz.dll,6.06.7601.18741 File stream renderer,0x00400000,1,1,quartz.dll,6.06.7601.18741 ffdshow subtitles filter,0x00200000,2,1,ffdshow.ax,1.03.4534.0000 Microsoft DTV-DVD Audio Decoder,0x005fffff,1,1,msmpeg2adec.dll,6.01.7140.0000 StreamBufferSink2,0x00200000,0,0,sbe.dll,6.06.7601.17528 AVI Mux,0x00200000,1,0,qcap.dll,6.06.7601.17514 Bandisoft MPEG-1 Audio Decoder,0xff800001,1,1,bdfilters.dll,1.00.0005.0016 Line 21 Decoder 2,0x00600002,1,1,quartz.dll,6.06.7601.18741 File Source (Async.),0x00400000,0,1,quartz.dll,6.06.7601.18741 File Source (URL),0x00400000,0,1,quartz.dll,6.06.7601.18741 Media Center Extender Encryption Filter,0x00200000,2,2,Mcx2Filter.dll,6.01.7601.17514 AudioRecorder WAV Dest,0x00200000,0,0,WavDest.dll, AudioRecorder Wave Form,0x00200000,0,0,WavDest.dll, SoundRecorder Null Renderer,0x00200000,0,0,WavDest.dll, LAV Audio Decoder,0x00800003,1,1,LAVAudio.ax,0.65.0000.0047 LAV Video Decoder,0xff800000,1,1,LAVVideo.ax,0.65.0000.0047 Infinite Pin Tee Filter,0x00200000,1,1,qcap.dll,6.06.7601.17514 Enhanced Video Renderer,0x00200000,1,0,evr.dll,6.01.7601.18741 BDA MPEG2 Transport Information Filter,0x00200000,2,0,psisrndr.ax,6.06.7601.17669 MPEG Video Decoder,0x40000001,1,1,quartz.dll,6.06.7601.18741 WDM Streaming Tee/Splitter Devices: Tee/Sink-to-Sink Converter,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 Video Compressors: WMVideo8 Encoder DMO,0x00600800,1,1,wmvxencd.dll,6.01.7600.16385 WMVideo9 Encoder DMO,0x00600800,1,1,wmvencod.dll,6.01.7600.16385 MSScreen 9 encoder DMO,0x00600800,1,1,wmvsencd.dll,6.01.7600.16385 DV Video Encoder,0x00200000,0,0,qdv.dll,6.06.7601.17514 ffdshow video encoder,0x00100000,1,1,ffdshow.ax,1.03.4534.0000 MJPEG Compressor,0x00200000,0,0,quartz.dll,6.06.7601.18741 Cinepak Codec by Radius,0x00200000,1,1,qcap.dll,6.06.7601.17514 Intel IYUV codec,0x00200000,1,1,qcap.dll,6.06.7601.17514 Intel IYUV codec,0x00200000,1,1,qcap.dll,6.06.7601.17514 Bandi MJPEG Video Decoder,0x00200000,1,1,qcap.dll,6.06.7601.17514 Bandi MPEG-1 Video Decoder,0x00200000,1,1,qcap.dll,6.06.7601.17514 Microsoft RLE,0x00200000,1,1,qcap.dll,6.06.7601.17514 Microsoft Video 1,0x00200000,1,1,qcap.dll,6.06.7601.17514 Audio Compressors: WM Speech Encoder DMO,0x00600800,1,1,WMSPDMOE.DLL,6.01.7600.16385 WMAudio Encoder DMO,0x00600800,1,1,WMADMOE.DLL,6.01.7600.16385 IMA ADPCM,0x00200000,1,1,quartz.dll,6.06.7601.18741 PCM,0x00200000,1,1,quartz.dll,6.06.7601.18741 Microsoft ADPCM,0x00200000,1,1,quartz.dll,6.06.7601.18741 GSM 6.10,0x00200000,1,1,quartz.dll,6.06.7601.18741 CCITT A-Law,0x00200000,1,1,quartz.dll,6.06.7601.18741 CCITT u-Law,0x00200000,1,1,quartz.dll,6.06.7601.18741 MP2,0x00200000,1,1,quartz.dll,6.06.7601.18741 MPEG Layer-3,0x00200000,1,1,quartz.dll,6.06.7601.18741 Audio Capture Sources: Microphone (Realtek High Defini,0x00200000,0,0,qcap.dll,6.06.7601.17514 PBDA CP Filters: PBDA DTFilter,0x00600000,1,1,CPFilters.dll,6.06.7601.17528 PBDA ETFilter,0x00200000,0,0,CPFilters.dll,6.06.7601.17528 PBDA PTFilter,0x00200000,0,0,CPFilters.dll,6.06.7601.17528 Midi Renderers: Default MidiOut Device,0x00800000,1,0,quartz.dll,6.06.7601.18741 Microsoft GS Wavetable Synth,0x00200000,1,0,quartz.dll,6.06.7601.18741 WDM Streaming Capture Devices: ,0x00000000,0,0,, ,0x00000000,0,0,, ,0x00000000,0,0,, Acer CrystalEye webcam,0x00200000,1,2,ksproxy.ax,6.01.7601.17514 WDM Streaming Rendering Devices: Realtek HD Audio output,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 Realtek HDA HDMI Out,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 Realtek HDA SPDIF Out,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 BDA Network Providers: Microsoft ATSC Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7601.17514 Microsoft DVBC Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7601.17514 Microsoft DVBS Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7601.17514 Microsoft DVBT Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7601.17514 Microsoft Network Provider,0x00200000,0,1,MSNP.ax,6.06.7601.17514 Video Capture Sources: Acer CrystalEye webcam,0x00200000,1,2,ksproxy.ax,6.01.7601.17514 Multi-Instance Capable VBI Codecs: VBI Codec,0x00600000,1,4,VBICodec.ax,6.06.7601.17514 BDA Transport Information Renderers: BDA MPEG2 Transport Information Filter,0x00600000,2,0,psisrndr.ax,6.06.7601.17669 MPEG-2 Sections and Tables,0x00600000,1,0,Mpeg2Data.ax,6.06.7601.17514 BDA CP/CA Filters: Decrypt/Tag,0x00600000,1,1,EncDec.dll,6.06.7601.17708 Encrypt/Tag,0x00200000,0,0,EncDec.dll,6.06.7601.17708 PTFilter,0x00200000,0,0,EncDec.dll,6.06.7601.17708 XDS Codec,0x00200000,0,0,EncDec.dll,6.06.7601.17708 WDM Streaming Communication Transforms: Tee/Sink-to-Sink Converter,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 Audio Renderers: Speakers (Realtek High Definiti,0x00200000,1,0,quartz.dll,6.06.7601.18741 Default DirectSound Device,0x00800000,1,0,quartz.dll,6.06.7601.18741 Default WaveOut Device,0x00200000,1,0,quartz.dll,6.06.7601.18741 DirectSound: Realtek Digital Output (Realtek High Definition Audio),0x00200000,1,0,quartz.dll,6.06.7601.18741 DirectSound: Realtek HDMI Output (Realtek High Definition Audio),0x00200000,1,0,quartz.dll,6.06.7601.18741 DirectSound: Speakers (Realtek High Definition Audio),0x00200000,1,0,quartz.dll,6.06.7601.18741 Realtek Digital Output (Realtek,0x00200000,1,0,quartz.dll,6.06.7601.18741 Realtek HDMI Output (Realtek Hi,0x00200000,1,0,quartz.dll,6.06.7601.18741 --------------- EVR Power Information --------------- Current Setting: {5C67A112-A4C9-483F-B4A7-1D473BECAFDC} (Quality) Quality Flags: 2576 Enabled: Force throttling Allow half deinterlace Allow scaling Decode Power Usage: 100 Balanced Flags: 1424 Enabled: Force throttling Allow batching Force half deinterlace Force scaling Decode Power Usage: 50 PowerFlags: 1424 Enabled: Force throttling Allow batching Force half deinterlace Force scaling Decode Power Usage: 0

Multimodal deep quality embedding network (MMDQEN) for affective video content analysis. (MM'19, TAFFC'20)

------------------ System Information ------------------ Time of this report: 8/20/2013, 21:40:50 Machine name: SONY-PC Operating System: Windows 7 Professional 64-bit (6.1, Build 7601) Service Pack 1 (7601.win7sp1_rtm.101119-1850) Language: Portuguese (Regional Setting: Portuguese) System Manufacturer: Sony Corporation System Model: VGN-FZ420E BIOS: Ver 1.00PARTTBL Processor: Intel(R) Core(TM)2 Duo CPU T5550 @ 1.83GHz (2 CPUs), ~1.8GHz Memory: 3072MB RAM Available OS Memory: 3062MB RAM Page File: 1806MB used, 4316MB available Windows Dir: C:\Windows DirectX Version: DirectX 11 DX Setup Parameters: Not found User DPI Setting: Using System DPI System DPI Setting: 96 DPI (100 percent) DWM DPI Scaling: Disabled DxDiag Version: 6.01.7601.17514 32bit Unicode ------------ DxDiag Notes ------------ Display Tab 1: No problems found. Sound Tab 1: No problems found. Input Tab: No problems found. -------------------- DirectX Debug Levels -------------------- Direct3D: 0/4 (retail) DirectDraw: 0/4 (retail) DirectInput: 0/5 (retail) DirectMusic: 0/5 (retail) DirectPlay: 0/9 (retail) DirectSound: 0/5 (retail) DirectShow: 0/6 (retail) --------------- Display Devices --------------- Card name: Mobile Intel(R) 965 Express Chipset Family (Microsoft Corporation - WDDM 1.1) Manufacturer: Intel Corporation Chip type: Mobile Intel(R) 965 Express Chipset Family DAC type: Internal Device Key: Enum\PCI\VEN_8086&DEV_2A02&SUBSYS_9005104D&REV_0C Display Memory: 358 MB Dedicated Memory: 0 MB Shared Memory: 358 MB Current Mode: 1280 x 800 (32 bit) (59Hz) Monitor Name: Monitor Genérico PnP Monitor Model: unknown Monitor Id: MS_0040 Native Mode: 1280 x 800(p) (59.940Hz) Output Type: Internal Driver Name: igdumd64.dll,igd10umd64.dll Driver File Version: 8.15.0010.1749 (English) Driver Version: 8.15.10.1749 DDI Version: 10 Driver Model: WDDM 1.1 Driver Attributes: Final Retail Driver Date/Size: 7/13/2009 22:41:07, 5437952 bytes WHQL Logo'd: Yes WHQL Date Stamp: Device Identifier: {D7B78E66-6942-11CF-2875-0FB0ACC2C535} Vendor ID: 0x8086 Device ID: 0x2A02 SubSys ID: 0x9005104D Revision ID: 0x000C Driver Strong Name: igdlh.inf:Intel.Mfg.NTamd64...1:i965GM0:8.15.10.1749:pci\ven_8086&dev_2a02 Rank Of Driver: 00EC2001 Video Accel: ModeMPEG2_A ModeMPEG2_C ModeWMV9_B ModeVC1_B Deinterlace Caps: {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(YUY2,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(YUY2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(YUY2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(UYVY,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(UYVY,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(UYVY,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(YV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(YV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(YV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(NV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(NV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(NV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC1,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC1,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC1,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend D3D9 Overlay: Supported DXVA-HD: Not Supported DDraw Status: Enabled D3D Status: Enabled AGP Status: Enabled ------------- Sound Devices ------------- Description: Alto-falantes (Dispositivo de High Definition Audio) Default Sound Playback: Yes Default Voice Playback: Yes Hardware ID: HDAUDIO\FUNC_01&VEN_8384&DEV_7662&SUBSYS_104D2300&REV_1002 Manufacturer ID: 1 Product ID: 65535 Type: WDM Driver Name: HdAudio.sys Driver Version: 6.01.7601.17514 (Portuguese) Driver Attributes: Final Retail WHQL Logo'd: Yes Date and Size: 11/20/2010 02:44:24, 350208 bytes Other Files: Driver Provider: Microsoft HW Accel Level: Basic Cap Flags: 0xF1F Min/Max Sample Rate: 100, 200000 Static/Strm HW Mix Bufs: 1, 0 Static/Strm HW 3D Bufs: 0, 0 HW Memory: 0 Voice Management: No EAX(tm) 2.0 Listen/Src: No, No I3DL2(tm) Listen/Src: No, No Sensaura(tm) ZoomFX(tm): No --------------------- Sound Capture Devices --------------------- Description: Microfone (Dispositivo de High Definition Audio) Default Sound Capture: Yes Default Voice Capture: Yes Driver Name: HdAudio.sys Driver Version: 6.01.7601.17514 (Portuguese) Driver Attributes: Final Retail Date and Size: 11/20/2010 02:44:24, 350208 bytes Cap Flags: 0x1 Format Flags: 0xFFFFF Description: Microfone (Dispositivo de High Definition Audio) Default Sound Capture: No Default Voice Capture: No Driver Name: HdAudio.sys Driver Version: 6.01.7601.17514 (Portuguese) Driver Attributes: Final Retail Date and Size: 11/20/2010 02:44:24, 350208 bytes Cap Flags: 0x1 Format Flags: 0xFFFFF ------------------- DirectInput Devices ------------------- Device Name: Mouse Attached: 1 Controller ID: n/a Vendor/Product ID: n/a FF Driver: n/a Device Name: Teclado Attached: 1 Controller ID: n/a Vendor/Product ID: n/a FF Driver: n/a Device Name: USB Receiver Attached: 1 Controller ID: 0x0 Vendor/Product ID: 0x046D, 0xC51B FF Driver: n/a Poll w/ Interrupt: No ----------- USB Devices ----------- + USB Root Hub | Vendor/Product ID: 0x8086, 0x2830 | Matching Device ID: usb\root_hub | Service: usbhub ---------------- Gameport Devices ---------------- ------------ PS/2 Devices ------------ + Teclado Padrão PS/2 | Matching Device ID: *pnp0303 | Service: i8042prt | + Terminal Server Keyboard Driver | Matching Device ID: root\rdp_kbd | Upper Filters: kbdclass | Service: TermDD | + Mouse compatível com PS/2 | Matching Device ID: *pnp0f13 | Service: i8042prt | + Mouse compatível com HID | Vendor/Product ID: 0x046D, 0xC51B | Matching Device ID: hid_device_system_mouse | Service: mouhid | + Terminal Server Mouse Driver | Matching Device ID: root\rdp_mou | Upper Filters: mouclass | Service: TermDD ------------------------ Disk & DVD/CD-ROM Drives ------------------------ Drive: C: Free Space: 386.5 GB Total Space: 476.8 GB File System: NTFS Model: SAMSUNG HM500JI ATA Device Drive: D: Model: Optiarc DVD RW AD-7560A ATA Device Driver: c:\windows\system32\drivers\cdrom.sys, 6.01.7601.17514 (Portuguese), , 0 bytes -------------- System Devices -------------- Name: Mobile Intel(R) 965 Express Chipset Family (Microsoft Corporation - WDDM 1.1) Device ID: PCI\VEN_8086&DEV_2A03&SUBSYS_9005104D&REV_0C\3&33FD14CA&0&11 Driver: n/a Name: Intel(R) ICH8 Family PCI Express Root Port 1 - 283F Device ID: PCI\VEN_8086&DEV_283F&SUBSYS_9005104D&REV_03\3&33FD14CA&0&E0 Driver: n/a Name: Intel(R) ICH8 Family USB Universal Host Controller - 2830 Device ID: PCI\VEN_8086&DEV_2830&SUBSYS_9005104D&REV_03\3&33FD14CA&0&E8 Driver: n/a Name: Mobile Intel(R) 965 Express Chipset Family (Microsoft Corporation - WDDM 1.1) Device ID: PCI\VEN_8086&DEV_2A02&SUBSYS_9005104D&REV_0C\3&33FD14CA&0&10 Driver: n/a Name: Intel(R) ICH8 Family SMBus Controller - 283E Device ID: PCI\VEN_8086&DEV_283E&SUBSYS_9005104D&REV_03\3&33FD14CA&0&FB Driver: n/a Name: Intel(R) ICH8M SATA AHCI Controller - 2829 Device ID: PCI\VEN_8086&DEV_2829&SUBSYS_9005104D&REV_03\3&33FD14CA&0&FA Driver: n/a Name: Mobile Intel(R) PM965/GM965/GL960/GS965 Express Processor to DRAM Controller - 2A00 Device ID: PCI\VEN_8086&DEV_2A00&SUBSYS_9005104D&REV_0C\3&33FD14CA&0&00 Driver: n/a Name: Intel(R) ICH8 Family USB2 Enhanced Host Controller - 283A Device ID: PCI\VEN_8086&DEV_283A&SUBSYS_9005104D&REV_03\3&33FD14CA&0&D7 Driver: n/a Name: Intel(R) ICH8M LPC Interface Controller - 2815 Device ID: PCI\VEN_8086&DEV_2815&SUBSYS_9005104D&REV_03\3&33FD14CA&0&F8 Driver: n/a Name: Intel(R) ICH8M Ultra ATA Storage Controllers - 2850 Device ID: PCI\VEN_8086&DEV_2850&SUBSYS_9005104D&REV_03\3&33FD14CA&0&F9 Driver: n/a Name: Intel(R) ICH8 Family USB2 Enhanced Host Controller - 2836 Device ID: PCI\VEN_8086&DEV_2836&SUBSYS_9005104D&REV_03\3&33FD14CA&0&EF Driver: n/a Name: Intel(R) 82801 PCI Bridge - 2448 Device ID: PCI\VEN_8086&DEV_2448&SUBSYS_9005104D&REV_F3\3&33FD14CA&0&F0 Driver: n/a Name: Controlador de High Definition Audio Device ID: PCI\VEN_8086&DEV_284B&SUBSYS_9005104D&REV_03\3&33FD14CA&0&D8 Driver: n/a Name: Intel(R) ICH8 Family USB Universal Host Controller - 2835 Device ID: PCI\VEN_8086&DEV_2835&SUBSYS_9005104D&REV_03\3&33FD14CA&0&D1 Driver: n/a Name: Marvell Yukon 88E8036 PCI-E Fast Ethernet Controller Device ID: PCI\VEN_11AB&DEV_4351&SUBSYS_9005104D&REV_16\4&16BEB6B9&0&00E4 Driver: n/a Name: Intel(R) ICH8 Family PCI Express Root Port 5 - 2847 Device ID: PCI\VEN_8086&DEV_2847&SUBSYS_9005104D&REV_03\3&33FD14CA&0&E4 Driver: n/a Name: Intel(R) ICH8 Family USB Universal Host Controller - 2834 Device ID: PCI\VEN_8086&DEV_2834&SUBSYS_9005104D&REV_03\3&33FD14CA&0&D0 Driver: n/a Name: Controlador de armazenamento em massa Device ID: PCI\VEN_104C&DEV_803B&SUBSYS_9005104D&REV_00\4&3A867C58&0&1AF0 Driver: n/a Name: Intel(R) ICH8 Family PCI Express Root Port 3 - 2843 Device ID: PCI\VEN_8086&DEV_2843&SUBSYS_9005104D&REV_03\3&33FD14CA&0&E2 Driver: n/a Name: Intel(R) ICH8 Family USB Universal Host Controller - 2832 Device ID: PCI\VEN_8086&DEV_2832&SUBSYS_9005104D&REV_03\3&33FD14CA&0&EA Driver: n/a Name: Texas Instruments 1394 OHCI Compliant Host Controller Device ID: PCI\VEN_104C&DEV_803A&SUBSYS_9005104D&REV_00\4&3A867C58&0&19F0 Driver: n/a Name: Intel(R) Wireless WiFi Link 4965AGN Device ID: PCI\VEN_8086&DEV_4229&SUBSYS_11008086&REV_61\4&17422A72&0&00E2 Driver: n/a Name: Intel(R) ICH8 Family PCI Express Root Port 2 - 2841 Device ID: PCI\VEN_8086&DEV_2841&SUBSYS_9005104D&REV_03\3&33FD14CA&0&E1 Driver: n/a Name: Intel(R) ICH8 Family USB Universal Host Controller - 2831 Device ID: PCI\VEN_8086&DEV_2831&SUBSYS_9005104D&REV_03\3&33FD14CA&0&E9 Driver: n/a Name: Texas Instruments PCI-8x12/7x12/6x12 CardBus Controller Device ID: PCI\VEN_104C&DEV_8039&SUBSYS_9005104D&REV_00\4&3A867C58&0&18F0 Driver: n/a ------------------ DirectShow Filters ------------------ DirectShow Filters: WMAudio Decoder DMO,0x00800800,1,1,WMADMOD.DLL,6.01.7601.17514 WMAPro over S/PDIF DMO,0x00600800,1,1,WMADMOD.DLL,6.01.7601.17514 WMSpeech Decoder DMO,0x00600800,1,1,WMSPDMOD.DLL,6.01.7601.17514 MP3 Decoder DMO,0x00600800,1,1,mp3dmod.dll,6.01.7600.16385 Mpeg4s Decoder DMO,0x00800001,1,1,mp4sdecd.dll,6.01.7600.16385 WMV Screen decoder DMO,0x00600800,1,1,wmvsdecd.dll,6.01.7601.17514 WMVideo Decoder DMO,0x00800001,1,1,wmvdecod.dll,6.01.7601.17514 Mpeg43 Decoder DMO,0x00800001,1,1,mp43decd.dll,6.01.7600.16385 Mpeg4 Decoder DMO,0x00800001,1,1,mpg4decd.dll,6.01.7600.16385 Nero Audible Decoder,0x00200000,1,1,NeAudible.ax,4.11.0003.0007 Nero Subpicture Decoder,0x00400000,1,1,NeSubpicture.ax,4.11.0003.0007 ArcSoft TimeShift2.0 Client Filter,0x00400000,0,1,TimeShift2.ax,1.00.0000.0015 Nero Scene Detector 2,0x00200000,2,0,NeSceneDetector.ax,4.11.0003.0007 Nero Stream Buffer Sink,0x00200000,0,0,NeSBE.ax,4.11.0003.0007 Nero Subtitle,0x00200000,1,1,NeSubtitle.ax,4.11.0003.0007 DV Muxer,0x00400000,0,0,qdv.dll,6.06.7601.17514 DV Scenes,0x00200000,1,1,NVDV.dll,3.00.0004.0000 Color Space Converter,0x00400001,1,1,quartz.dll,6.06.7601.17514 WM ASF Reader,0x00400000,0,0,qasf.dll,12.00.7601.17514 Screen Capture filter,0x00200000,0,1,wmpsrcwp.dll,12.00.7601.17514 AVI Splitter,0x00600000,1,1,quartz.dll,6.06.7601.17514 VGA 16 Color Ditherer,0x00400000,1,1,quartz.dll,6.06.7601.17514 SBE2MediaTypeProfile,0x00200000,0,0,sbe.dll,6.06.7601.17514 Arcsoft PutDataSample Filter 1.0,0x00200000,1,1,ArcPutDataSample.ax,1.00.0000.0005 CyberLink AudioCD Filter (PDVD7),0x00600000,0,1,CLAudioCD.ax,5.00.0000.4417 Nero FTC,0x00200000,1,1,NeFTC.ax,1.00.0000.0000 Microsoft DTV-DVD Video Decoder,0x005fffff,2,4,msmpeg2vdec.dll,6.01.7140.0000 AC3 Parser Filter,0x00600000,1,1,mpg2splt.ax,6.06.7601.17514 CyberLink Audio Decoder (PDVD7),0x00201000,1,1,CLAud.ax,6.01.0000.4227 StreamBufferSink,0x00200000,0,0,sbe.dll,6.06.7601.17514 Nero Resize,0x00400000,1,1,NeResize.ax,4.11.0003.0007 MJPEG Decompressor,0x00600000,1,1,quartz.dll,6.06.7601.17514 CyberLink Audio Effect (PDVD7),0x00200000,1,1,CLAudFx.ax,6.00.0000.4111 MPEG-I Stream Splitter,0x00600000,1,2,quartz.dll,6.06.7601.17514 ArcSoft Mpeg Encoder Filter,0x00200000,2,0,ArcMpegCodec.ax,2.05.0000.0013 MPEG-2 PSI Reader Filter,0x00200000,0,0,Mpeg2PsiReader.ax,1.00.0000.0004 SAMI (CC) Parser,0x00400000,1,1,quartz.dll,6.06.7601.17514 Nero AV Synchronizer,0x00200000,1,1,NeAVSync.ax,4.11.0003.0007 VBI Codec,0x00600000,1,4,VBICodec.ax,6.06.7601.17514 Nero Audio Stream Renderer,0x00200000,1,0,NeRender.ax,4.11.0003.0007 MPEG-2 Splitter,0x005fffff,1,0,mpg2splt.ax,6.06.7601.17514 Closed Captions Analysis Filter,0x00200000,2,5,cca.dll,6.06.7601.17514 SBE2FileScan,0x00200000,0,0,sbe.dll,6.06.7601.17514 Microsoft MPEG-2 Video Encoder,0x00200000,1,1,msmpeg2enc.dll,6.01.7601.17514 Nero Digital AVC Audio Encoder,0x00200000,1,2,NeNDAud.ax,4.11.0003.0007 Nero Digital AVC File Writer,0x00200000,1,0,NeNDMux.ax,4.11.0003.0007 Nero Digital AVC Video Enc,0x00200000,1,2,NeNDVid.ax,4.11.0003.0007 Nero Digital AVC Null Renderer,0x00200000,1,0,NeNDMux.ax,4.11.0003.0007 Nero Digital AVC Muxer,0x00200000,2,1,NeNDMux.ax,4.11.0003.0007 CyberLink Video/SP Decoder(PDVD7 HomeNetwork),0x00200000,2,3,CLVSD.ax,6.00.0000.3313 Arcsoft GetDataSample Filter 1.0,0x00200000,1,1,ArcGetDataSample.ax,1.00.0000.0012 ArcSoft MPEG Audio Decoder,0x00600000,1,1,mpgaudio.ax,2.04.0002.0016 Nero QuickTime(tm) Video Decoder,0x00400000,1,1,NeQTDec.ax,4.11.0003.0007 Internal Script Command Renderer,0x00800001,1,0,quartz.dll,6.06.7601.17514 MPEG Audio Decoder,0x03680001,1,1,quartz.dll,6.06.7601.17514 Nero Digital AVC Subpicture Enc,0x00200000,1,0,NeNDMux.ax,4.11.0003.0007 Nero Format Converter,0x00200000,1,1,NeroFormatConv.ax,4.11.0003.0007 Nero Overlay Mixer,0x00200000,1,1,NeOverlayMixer.ax,4.11.0003.0007 Nero MP4 Splitter,0x00600000,1,1,NeMP4Splitter.ax,4.11.0003.0007 DV Splitter,0x00600000,1,2,qdv.dll,6.06.7601.17514 HighMAT and MPV Navigator Filter,0x00200000,0,3,HMNavigator.ax,4.11.0003.0007 Video Mixing Renderer 9,0x00200000,1,0,quartz.dll,6.06.7601.17514 Nero Photo Source,0x00200000,0,1,NePhotoSource.ax,4.11.0003.0007 CyberLink Demux (PDVD7),0x00602000,1,0,CLDemuxer.ax,1.00.0000.4528 CyberLink MPEG Splitter,0x00200000,1,2,CLSplter.ax,3.01.0000.3022 ArcSoft TimeShift2.0 Server Filter,0x00200000,1,0,TimeShift2.ax,1.00.0000.0015 Nero Video Analyzer,0x00200000,2,0,NeVideoAnalyzer.ax,4.11.0003.0007 Nero ES Video Reader,0x00600000,0,1,NDParser.ax,4.11.0003.0007 CyberLink Line21 Decoder (PDVD7),0x00200000,0,2,CLLine21.ax,4.00.0000.7602 Microsoft MPEG-2 Encoder,0x00200000,2,1,msmpeg2enc.dll,6.01.7601.17514 DV Source Filter,0x00400000,0,1,NVDV.dll,3.00.0004.0000 MPEG-2 Stream Reader Filter,0x00200000,0,0,Mpeg2StreamReader.ax,1.04.0000.0000 Nero Audio CD Filter,0x00200000,0,1,NeAudCD.ax,4.11.0003.0007 Nero Video Renderer,0x00200000,1,0,NeVideoRenderer.ax,4.11.0003.0007 Nero PresentationGraphics Decoder,0x00600000,2,1,NeBDGraphic.ax,4.11.0003.0007 ACM Wrapper,0x00600000,1,1,quartz.dll,6.06.7601.17514 Video Renderer,0x00800001,1,0,quartz.dll,6.06.7601.17514 ArcSoft File Dump,0x00200000,1,0,FileDump.ax,2.00.0000.0008 MPEG-2 Video Stream Analyzer,0x00200000,0,0,sbe.dll,6.06.7601.17514 Line 21 Decoder,0x00600000,1,1,qdvd.dll,6.06.7601.17514 Nero InteractiveGraphics Decoder,0x00600000,1,1,NeBDGraphic.ax,4.11.0003.0007 Video Port Manager,0x00600000,2,1,quartz.dll,6.06.7601.17514 CyberLink Push-Mode CLStream (PDVD7),0x00200000,0,1,CLStream(PushMode).ax,1.00.0000.1627 CyberLink Audio Decoder (PDVD7 UPnP),0x00200000,1,1,CLAud.ax,6.01.0000.3816 Video Renderer,0x00400000,1,0,quartz.dll,6.06.7601.17514 Nero Sound Processor,0x00200000,1,1,NeSoundProc.ax,4.11.0003.0007 Nero Audio Sample Renderer,0x00200000,1,0,NeRender.ax,4.11.0003.0007 CyberLink Audio Spectrum Analyzer (PDVD7),0x00200000,1,1,CLAudSpa.ax,1.00.0000.0924 Nero Vcd Navigator,0x00600000,0,2,NeVCD.ax,4.11.0003.0007 ArcSoft VideoEffect Filter,0x00200000,1,1,ArcVideoEffect.ax,1.00.0000.0010 VPS Decoder,0x00200000,0,0,WSTPager.ax,6.06.7601.17514 WM ASF Writer,0x00400000,0,0,qasf.dll,12.00.7601.17514 Nero Mpeg2 Encoder,0x00200000,2,1,NeVCR.ax,4.11.0003.0007 VBI Surface Allocator,0x00600000,1,1,vbisurf.ax,6.01.7601.17514 ArcSoft Null Render,0x00200000,1,0,ArcNullRender.ax,1.00.0000.0001 Nero Video Stream Renderer,0x00200000,1,0,NeRender.ax,4.11.0003.0007 File writer,0x00200000,1,0,qcap.dll,6.06.7601.17514 iTV Data Sink,0x00600000,1,0,itvdata.dll,6.06.7601.17514 Nero FLV Splitter,0x00600000,1,1,NeFLVSplitter.ax,4.11.0003.0007 iTV Data Capture filter,0x00600000,1,1,itvdata.dll,6.06.7601.17514 CyberLink Video/SP Decoder (PDVD7),0x00602000,2,3,CLVsd.ax,8.00.0000.1918 Nero Stream Buffer Source,0x00200000,0,0,NeSBE.ax,4.11.0003.0007 Nero PS Muxer,0x00200000,1,1,NePSMuxer.ax,4.11.0003.0007 CyberLink Audio Wizard,0x00201010,1,1,CLAudWizard.ax,1.00.0000.1730 DVD Navigator,0x00200000,0,3,qdvd.dll,6.06.7601.17514 CyberLink DVD Navigator (PDVD7),0x00600000,0,3,CLNavX.ax,7.00.0000.3112 CyberLink TimeStretch Filter (PDVD7),0x00200000,1,1,clauts.ax,1.00.0000.5423 Overlay Mixer2,0x00200000,1,1,qdvd.dll,6.06.7601.17514 Cyberlink SubTitle Importor (PDVD7),0x00200000,1,1,CLSubTitle.ax,1.00.0000.1604 Nero Splitter,0x00600000,1,3,NeSplitter.ax,4.11.0003.0007 Nero Deinterlace,0x00200000,1,1,NeDeinterlace.ax,4.11.0003.0007 AVI Draw,0x00600064,9,1,quartz.dll,6.06.7601.17514 RDP DShow Redirection Filter,0xffffffff,1,0,DShowRdpFilter.dll, Nero File Source / Splitter,0x00600000,0,3,NeFSource.ax,4.11.0003.0007 Microsoft MPEG-2 Audio Encoder,0x00200000,1,1,msmpeg2enc.dll,6.01.7601.17514 WST Pager,0x00200000,1,1,WSTPager.ax,6.06.7601.17514 MPEG-2 Demultiplexer,0x00600000,1,1,mpg2splt.ax,6.06.7601.17514 DV Video Decoder,0x00800000,1,1,qdv.dll,6.06.7601.17514 CyberLink MPEG-4 Splitter (PDVD7),0x00600000,1,2,clm4splt.ax,1.00.0000.3229 ArcSoft Realtime Capture Encoder Filter,0x00200000,2,0,ArcCaptureEncoder.ax,2.05.0000.0032 Nero Video Processor,0x00200000,1,1,NeroVideoProc.ax,4.11.0003.0007 SampleGrabber,0x00200000,1,1,qedit.dll,6.06.7601.17514 Null Renderer,0x00200000,1,0,qedit.dll,6.06.7601.17514 Nero Sound Switcher,0x00200000,1,1,NeSoundSwitch.ax,4.11.0003.0007 Arcsoft WMV/ASF Splitter,0x00200000,1,0,ArcWmvSpl.ax,1.00.0000.0012 MPEG-2 Sections and Tables,0x005fffff,1,0,Mpeg2Data.ax,6.06.7601.17514 Microsoft AC3 Encoder,0x00200000,1,1,msac3enc.dll,6.01.7601.17514 Nero Audio CD Navigator,0x00200000,0,1,NeAudCD.ax,4.11.0003.0007 StreamBufferSource,0x00200000,0,0,sbe.dll,6.06.7601.17514 Video MotionDetect,0x00200000,1,1,motiondetect.ax,1.00.0000.0005 Smart Tee,0x00200000,1,2,qcap.dll,6.06.7601.17514 Nero Thumbnail Decoder,0x00600000,1,1,NeBDThumbnail.ax,4.11.0003.0007 Overlay Mixer,0x00200000,0,0,qdvd.dll,6.06.7601.17514 Nero Scene Detector,0x00200000,1,0,NeSceneDetector.ax,4.11.0003.0007 Nero Stream Control,0x00200000,1,1,NeStreamControl.ax,1.00.0000.0000 AVI Decompressor,0x00600000,1,1,quartz.dll,6.06.7601.17514 Nero Sample Queue,0x00200000,1,1,NeSampleQueue.ax,1.00.0000.0000 AVI/WAV File Source,0x00400000,0,2,quartz.dll,6.06.7601.17514 Arcsoft Snapshot Filter 1.0,0x00200000,1,1,ArcSnap.ax,1.00.0000.0024 Wave Parser,0x00400000,1,1,quartz.dll,6.06.7601.17514 MIDI Parser,0x00400000,1,1,quartz.dll,6.06.7601.17514 Multi-file Parser,0x00400000,1,1,quartz.dll,6.06.7601.17514 File stream renderer,0x00400000,1,1,quartz.dll,6.06.7601.17514 ArcSoft MPEG Splitter,0x00400000,1,2,ArcSpl.ax,2.04.0002.0045 Nero File Source,0x00200000,0,1,NeFileSrc.ax,4.11.0003.0007 Nero QuickTime(tm) Audio Decoder,0x00400000,1,1,NeQTDec.ax,4.11.0003.0007 Nero File Source (Async.),0x00400000,0,1,NeFileSourceAsync.ax,4.11.0003.0007 Nero Ogg Splitter,0x00400000,1,1,NeOggSplitter.ax,4.11.0003.0007 Microsoft DTV-DVD Audio Decoder,0x005fffff,1,1,msmpeg2adec.dll,6.01.7140.0000 Nero Digital Parser,0x00600000,0,3,NDParser.ax,4.11.0003.0007 StreamBufferSink2,0x00200000,0,0,sbe.dll,6.06.7601.17514 AVI Mux,0x00200000,1,0,qcap.dll,6.06.7601.17514 Line 21 Decoder 2,0x00600002,1,1,quartz.dll,6.06.7601.17514 File Source (Async.),0x00400000,0,1,quartz.dll,6.06.7601.17514 File Source (URL),0x00400000,0,1,quartz.dll,6.06.7601.17514 Nero MP3 Encoder,0x00200000,1,1,NeMp3Encoder.ax,4.11.0003.0007 ArcSoft Time Stamp,0x00200000,1,1,ArcTimeStamp.ax,1.00.0000.0003 CyberLink Demux (PDVD7 UPnP),0x00200000,1,0,CLDemuxer.ax,1.00.0000.4513 Nero Frame Capture,0x00200000,1,1,NeCapture.ax,4.11.0003.0007 Nero Video Sample Renderer,0x00200000,1,0,NeRender.ax,4.11.0003.0007 ArcSoft MPEG Video Decoder,0x00600000,1,1,mpgvideo.ax,2.04.0000.0048 HighMAT/MPV Navigator Client Filter,0x00200000,0,0,HMNavigator.ax,4.11.0003.0007 Infinite Pin Tee Filter,0x00200000,1,1,qcap.dll,6.06.7601.17514 Nero DV Splitter,0x00200000,1,2,NeDVSplitter.ax,4.11.0003.0007 Enhanced Video Renderer,0x00200000,1,0,evr.dll,6.01.7601.17514 CyberLink Streamming Filter (PDVD7),0x00200000,0,1,CLStream.ax,1.01.0000.2902 BDA MPEG2 Transport Information Filter,0x00200000,2,0,psisrndr.ax,6.06.7601.17514 MPEG Video Decoder,0x40000001,1,1,quartz.dll,6.06.7601.17514 WDM Streaming Tee/Splitter Devices: Conversor em T entre Coletores,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 Video Compressors: WMVideo8 Encoder DMO,0x00600800,1,1,wmvxencd.dll,6.01.7600.16385 WMVideo9 Encoder DMO,0x00600800,1,1,wmvencod.dll,6.01.7600.16385 MSScreen 9 encoder DMO,0x00600800,1,1,wmvsencd.dll,6.01.7600.16385 ArcSoft Mpeg Encode Filter,0x00200000,0,0,ArcMpegCodec.ax,2.05.0000.0013 ArcSoft Realtime Capture Encoder Filter,0x00200000,0,0,ArcCaptureEncoder.ax,2.05.0000.0032 DV Video Encoder,0x00200000,0,0,qdv.dll,6.06.7601.17514 MJPEG Compressor,0x00200000,0,0,quartz.dll,6.06.7601.17514 Cinepak Codec by Radius,0x00200000,1,1,qcap.dll,6.06.7601.17514 Codec IYUV Intel,0x00200000,1,1,qcap.dll,6.06.7601.17514 Codec IYUV Intel,0x00200000,1,1,qcap.dll,6.06.7601.17514 Microsoft RLE,0x00200000,1,1,qcap.dll,6.06.7601.17514 Microsoft Video 1,0x00200000,1,1,qcap.dll,6.06.7601.17514 Audio Compressors: WM Speech Encoder DMO,0x00600800,1,1,WMSPDMOE.DLL,6.01.7600.16385 WMAudio Encoder DMO,0x00600800,1,1,WMADMOE.DLL,6.01.7600.16385 IMA ADPCM,0x00200000,1,1,quartz.dll,6.06.7601.17514 PCM,0x00200000,1,1,quartz.dll,6.06.7601.17514 Microsoft ADPCM,0x00200000,1,1,quartz.dll,6.06.7601.17514 GSM 6.10,0x00200000,1,1,quartz.dll,6.06.7601.17514 CCITT A-Law,0x00200000,1,1,quartz.dll,6.06.7601.17514 CCITT u-Law,0x00200000,1,1,quartz.dll,6.06.7601.17514 MPEG Layer-3,0x00200000,1,1,quartz.dll,6.06.7601.17514 Audio Capture Sources: Microfone (Dispositivo de High ,0x00200000,0,0,qcap.dll,6.06.7601.17514 PBDA CP Filters: PBDA DTFilter,0x00600000,1,1,CPFilters.dll,6.06.7601.17514 PBDA ETFilter,0x00200000,0,0,CPFilters.dll,6.06.7601.17514 PBDA PTFilter,0x00200000,0,0,CPFilters.dll,6.06.7601.17514 Midi Renderers: Default MidiOut Device,0x00800000,1,0,quartz.dll,6.06.7601.17514 Microsoft GS Wavetable Synth,0x00200000,1,0,quartz.dll,6.06.7601.17514 WDM Streaming Capture Devices: Captura Mista de HD Audio,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 Dispositivo de vídeo USB,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 WDM Streaming Rendering Devices: Alto-falante de HD Audio,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 BDA Network Providers: Microsoft ATSC Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7601.17514 Microsoft DVBC Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7601.17514 Microsoft DVBS Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7601.17514 Microsoft DVBT Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7601.17514 Microsoft Network Provider,0x00200000,0,1,MSNP.ax,6.06.7601.17514 Video Capture Sources: Dispositivo de vídeo USB,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 Multi-Instance Capable VBI Codecs: VBI Codec,0x00600000,1,4,VBICodec.ax,6.06.7601.17514 BDA Transport Information Renderers: BDA MPEG2 Transport Information Filter,0x00600000,2,0,psisrndr.ax,6.06.7601.17514 MPEG-2 Sections and Tables,0x00600000,1,0,Mpeg2Data.ax,6.06.7601.17514 BDA CP/CA Filters: Decrypt/Tag,0x00600000,1,1,EncDec.dll,6.06.7601.17514 Encrypt/Tag,0x00200000,0,0,EncDec.dll,6.06.7601.17514 PTFilter,0x00200000,0,0,EncDec.dll,6.06.7601.17514 XDS Codec,0x00200000,0,0,EncDec.dll,6.06.7601.17514 WDM Streaming Communication Transforms: Conversor em T entre Coletores,0x00200000,1,1,ksproxy.ax,6.01.7601.17514 Audio Renderers: Alto-falantes (Dispositivo de H,0x00200000,1,0,quartz.dll,6.06.7601.17514 CyberLink Audio Renderer (PDVD7),0x00200000,1,0,cladr.ax,6.00.0000.3916 Default DirectSound Device,0x00800000,1,0,quartz.dll,6.06.7601.17514 Default WaveOut Device,0x00200000,1,0,quartz.dll,6.06.7601.17514 DirectSound: Alto-falantes (Dispositivo de High Definition Audio),0x00200000,1,0,quartz.dll,6.06.7601.17514 --------------- EVR Power Information --------------- Current Setting: {5C67A112-A4C9-483F-B4A7-1D473BECAFDC} (Quality) Quality Flags: 2576 Enabled: Force throttling Allow half deinterlace Allow scaling Decode Power Usage: 100 Balanced Flags: 1424 Enabled: Force throttling Allow batching Force half deinterlace Force scaling Decode Power Usage: 50 PowerFlags: 1424 Enabled: Force throttling Allow batching Force half deinterlace Force scaling Decode Power Usage: 0

The Sleep Health Analysis system combines computer vision, NLP, and machine learning to monitor sleep quality non-invasively. It analyzes video and sleep diaries to detect movements, posture, and disturbances, estimates key sleep metrics, and generates easy-to-understand reports with personalized insights.

A Model Context Protocol (MCP) server for video quality analysis, providing objective metrics such as PSNR, SSIM, and artifact detection via OpenCV and FFmpeg. Designed to integrate with Claude Desktop to enable automated video quality inspection, diagnostics, and decision-making workflows.

A real-time analysis tool that compares H.264 and H.265 encoded videos against a reference (original) video, enabling users to assess video quality metrics like PSNR, SSIM, and VMAF in a graphical interface.

Enhanced ExoPlayer wrapper with intelligent error handling, automatic recovery, adaptive quality switching, and AI-powered video analysis including scene detection, motion tracking, audio analysis, face detection, automatic highlight generation, and chapter creation.

TubeLearns is a Python script designed for extracting and cleaning YouTube video transcripts for preprocessing in machine learning. This versatile tool streamlines the process of acquiring high-quality text data from YouTube videos, making it ideal for various natural language processing tasks, sentiment analysis, speech recognition, and more.

DIGITAL MARKETING BY SARA ATIQ INTRODUCTION “It is a marketing technique that involves usage of digital mediums such as internet & wireless for creating awareness, consideration, purchase & loyalty for a brand product or a service". It is the term used to describe any marketing efforts that place on the internet or a digital device. It has different channels that enable the business to entice their customer into buying their product & services. Philip Kotler is considered the father of digital marketing who is the author of 60 marketing books and provides us important lessons that can be applied to our digital strategy. Before digital marketing, we have Traditional marketing, which is a conventional mode of marketing that helps to reach out to the semi-targeted audience with various offline advertising & promotion modes. CONSTITUENT OF DIGITAL MARKETING TRAFFIC ACQUISITION CHANNELS SEARCH ENGINE MARKETING(SEM): It is a form of internet marketing that involves the promotion of websites by increasing their visibility in search engine result pages (SERP) primarily through paid advertising. SEM may incorporate search engine optimization (SEO), which adjusts or rewrites website content and site architecture to achieve a higher ranking in search engine result pages to enhance pay per click. SOCIAL MEDIA MARKETING: Social media marketing involves the use of social media platforms to connect with the audience to build your brand, increase sales & drive website traffic. It also allows to publish great content on social media platforms & run social media advertisements. Major social media platforms are Facebook, Instagram, Twitter, LinkedIn, Pinterest, YouTube, and Snapchat. EMAIL MARKETING: It is an act of sending a commercial message particularly to a group of people, using email. It involves using emails to send advertisements, request business, or sales, or donations. It usually refers to sending email to enhance a merchant's relationship with a current or previous customer, encouraging customer loyalty, acquiring new customers, or convincing new customers to purchase something immediately. DISPLAY ADVERTISING: It is an online form of advertising in which a company's Ads appear on third-party sites or appear on the search engine result page such as publishers or social networks. This advertisement can increase the website page view of a company from most types of customers except the non-unauthenticated visitor who visits the site before. The main purpose of display advertising is to support brand awareness and it also helps to increase the purchase, intention of the consumers. AFFILIATE MARKETING: It is a type of performance-based marketing in which a business reward one or more affiliates for each visitor or customer brought by the affiliate's marketing efforts. The internet has increased the prominence of affiliate marketing. Amazon popularized the practice by creating the affiliate marketing program whereby the website and bloggers put the link to the Amazon page for a reviewed product to receive an advertising fee when a purchase is made. So, it is essentially a pay-for-performance marketing program where the act of selling is outsourced across a vast network. SUPPORTING CHANNELS MOBILE MARKETING: Mobile marketing is a multi-channel, digital marketing strategy aimed at reaching a target audience on their smartphones, tablets, or other mobile devices via websites, email, social media, and Apps. Mobile marketing is an important piece of the puzzle when it comes to building out any short-term or long-term marketing plan. From email to pay per click (PPC), search engine optimization (SEO)content marketing, and social media marketing, there is a mobile marketing channel to reach every part of your audience where they are most comfortable. mobile marketing can do wonders to drive brand value. WEBSITE: Website is the must-have tool for your business as it provides you with a dedicated platform where you can educate your audience about your brands, products, and services. This requires a solid understanding of your target audience and an effective content marketing strategy. Your website is an ideal channel for your content marketing campaigns. Through blogs, posts, and announcements you can provide existing and potential customers with valuable and relevant content to help them solve their pain points. Because websites have multimedia capabilities you can easily distribute different types of content in the form of articles, infographics, and even videos. If your website will have high-quality relevant and insightful content then your website will have increased organic traffic. WEB ANALYTICS: Web analytics is the measurement, collection, analysis, and reporting of internet data for understanding and optimizing web usage. The focus of web analytics is to understand the users of a site, their behavior, and their activities. The study of online user behavior and activities generate valuable marketing intelligence and provide - Performance measures of the website against the target. Insight on user behaviors and needs, and how the sight meets those needs. Optimization ability to make modifications to improve the website based on the result. Web analytics tools offer hundreds of metrics. all of them are interesting but only a few would be useful for measuring website performance. PROCESS FRAMEWORK OF DIGITAL MARKETING The framework of digital marketing is based on the 4 main objectives of digital marketing. 1.awareness 3. purchase 2.consideration 4. loyalty Loyalty Buyer -> loyal customer Purchase Interested -> buyer Awareness Unaware -> aware Consideration Aware -> interested

Real-Time Video Quality Analysis

FrameDeployer is an innovative tool designed to automate the creation of engaging video content using trending topics. With features like trend analysis, linguistic summarization, automated image search, sentiment analysis, text-to-speech, and subtitle generation, you can effortlessly create professional-quality videos. 🚀📹✨

VideoWise is a video transcription and AI-powered analysis tool that helps users easily upload, transcribe, and interact with video content. Using WhisperX for high-quality transcriptions and Ollama for AI-driven insights, VideoWise makes it easy to search, analyze, and export video data.

iCompressor is an android application developed and submitted as university project for "System analysis and design" course. It has two compressing options. First one is image compression and second one is video compression. The application is very user friendly and light. User can choose the quality of the compression of the selected files and also may choose to delete the original files. For storing the compressed files it has a default location but it is also changeable. Lastly It was developed while i was still learning android so it may not be good enough for people to use but it was enough well-developed to get me A+ . :D Happy Coding .

Source Code for 'Machine Learning For Network Traffic and Video Quality Analysis' by Tulsi Pawan Fowdur and Lavesh Babooram

Rates quality of YouTube videos through sentiment analysis on comments | John Wu Award Winning Project @ StormHacks 2023 🏆

Project aims at improving the quality of E-education over a local network, through one-to-many Video communication. We are making it more analysis-oriented by using in it technologies like Facial Detection using Deep Learning, real-time Feedback using Charts and Alert systems, etc. At the end, based on the data received by the ML Model, we can show the general attentiveness of all the students in the class to the teacher, highlighting the durations of the lecture where the students felt the most boredom or where most students had doubts, giving the lecturer an analysis on his/her performance and helping him in furtehr improving the course. Besides these features, the students and teachers, both would have a dashboard - where their past performance would be stored and also, they can add a to-do list for them to finish for a particular day.

Computer Forensics Investigations Clarity and precision in an inexact world Data Acquisition Forensic Examination Data Analysis Forensic Reporting Request Help Call: 888.521.1551 data breach services video What makes us different Computer Forensic Analysis Done Right We are specializing in the following computer forensics services: Deleted Data Recovery Database Forensics Internet Artifact Analysis On-Site Acquisition Electronic Risk Control Expert Witness Testimony Get Help with just a few clicks Response within 10 minutes Send Request We answer your questions Did someone backdate an electronic document? Did an employee e-mail company trade secret documents? Did a security camera capture footage of an accident? Was electronic evidence produced in a reasonably usable format? Did an employer send harassing text messages to an employee? Did a company spoliate electronic evidence? Was an e-mail manufactured after the fact? Was a computer intentionally used to download illegal content? Contact us for analysis and quote We offer immediate, 24/7 assistance from our team of digital investigators. Request Help Call: 888.521.1551 We are ready to help you Unlock Your Digital Evidence Expert Consulting and Best Practices Cost efficient handling of digital evidence Quality acquisition of data and processing Fast turn-around times Professional forensic reports What sets us apart Digital Forensics Corp has proven success working with Fortune 500 companies across industries to handle data breach incidents. Experience across the USA and Canada With locations across North America, our digital forensics experts are near and ready to help. We are able to work on your case remotely, in-lab and onsite. Contact us or submit a case today to learn more about how we can help you. Leading Experts The DFC team is comprised of forensic investigators, certified fraud examiners, former law enforcement officials, certified digital forensic examiners , data analysts, and system and network domain experts.

------------------ System Information ------------------ Time of this report: 9/14/2013, 22:53:12 Machine name: THAY-PC Operating System: Windows 7 Professional 32-bit (6.1, Build 7600) (7600.win7_gdr.100226-1909) Language: English (Regional Setting: English) System Manufacturer: Acer System Model: Extensa 4620 BIOS: Ver 1.00PARTTBL Processor: Intel(R) Pentium(R) Dual CPU T2310 @ 1.46GHz (2 CPUs), ~1.5GHz Memory: 2048MB RAM Available OS Memory: 2038MB RAM Page File: 1423MB used, 2652MB available Windows Dir: C:\Windows DirectX Version: DirectX 11 DX Setup Parameters: Not found User DPI Setting: Using System DPI System DPI Setting: 96 DPI (100 percent) DWM DPI Scaling: Disabled DxDiag Version: 6.01.7600.16385 32bit Unicode ------------ DxDiag Notes ------------ Display Tab 1: No problems found. Sound Tab 1: No problems found. Input Tab: No problems found. -------------------- DirectX Debug Levels -------------------- Direct3D: 0/4 (retail) DirectDraw: 0/4 (retail) DirectInput: 0/5 (retail) DirectMusic: 0/5 (retail) DirectPlay: 0/9 (retail) DirectSound: 0/5 (retail) DirectShow: 0/6 (retail) --------------- Display Devices --------------- Card name: Mobile Intel(R) 965 Express Chipset Family (Microsoft Corporation - WDDM 1.1) Manufacturer: Intel Corporation Chip type: Mobile Intel(R) 965 Express Chipset Family DAC type: Internal Device Key: Enum\PCI\VEN_8086&DEV_2A02&SUBSYS_011C1025&REV_03 Display Memory: 358 MB Dedicated Memory: 0 MB Shared Memory: 358 MB Current Mode: 1280 x 800 (32 bit) (60Hz) Monitor Name: Generic PnP Monitor Monitor Model: unknown Monitor Id: AUO4444 Native Mode: 1280 x 800(p) (60.004Hz) Output Type: Internal Driver Name: igdumd32.dll,igd10umd32.dll Driver File Version: 8.15.0010.1749 (English) Driver Version: 8.15.10.1749 DDI Version: 10 Driver Model: WDDM 1.1 Driver Attributes: Final Retail Driver Date/Size: 7/13/2009 21:15:31, 3805184 bytes WHQL Logo'd: Yes WHQL Date Stamp: Device Identifier: {D7B78E66-6942-11CF-4075-1621A3C2C535} Vendor ID: 0x8086 Device ID: 0x2A02 SubSys ID: 0x011C1025 Revision ID: 0x0003 Driver Strong Name: igdlh.inf:Intel.Mfg.NTx86...1:i965GM0:8.15.10.1749:pci\ven_8086&dev_2a02 Rank Of Driver: 00EC2001 Video Accel: ModeMPEG2_A ModeMPEG2_C ModeWMV9_B ModeVC1_B Deinterlace Caps: {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(YUY2,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(YUY2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(YUY2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(UYVY,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(UYVY,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(UYVY,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(YV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(YV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(YV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(NV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(NV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(NV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC1,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC1,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC1,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend D3D9 Overlay: Supported DXVA-HD: Not Supported DDraw Status: Enabled D3D Status: Enabled AGP Status: Enabled ------------- Sound Devices ------------- Description: Speakers (High Definition Audio Device) Default Sound Playback: Yes Default Voice Playback: Yes Hardware ID: HDAUDIO\FUNC_01&VEN_10EC&DEV_0268&SUBSYS_1025011C&REV_1000 Manufacturer ID: 1 Product ID: 65535 Type: WDM Driver Name: HdAudio.sys Driver Version: 6.01.7600.16385 (English) Driver Attributes: Final Retail WHQL Logo'd: Yes Date and Size: 7/13/2009 19:51:47, 304128 bytes Other Files: Driver Provider: Microsoft HW Accel Level: Basic Cap Flags: 0xF1F Min/Max Sample Rate: 100, 200000 Static/Strm HW Mix Bufs: 1, 0 Static/Strm HW 3D Bufs: 0, 0 HW Memory: 0 Voice Management: No EAX(tm) 2.0 Listen/Src: No, No I3DL2(tm) Listen/Src: No, No Sensaura(tm) ZoomFX(tm): No --------------------- Sound Capture Devices --------------------- Description: Microphone (High Definition Audio Device) Default Sound Capture: Yes Default Voice Capture: Yes Driver Name: HdAudio.sys Driver Version: 6.01.7600.16385 (English) Driver Attributes: Final Retail Date and Size: 7/13/2009 19:51:47, 304128 bytes Cap Flags: 0x1 Format Flags: 0xFFFFF Description: Microphone (High Definition Audio Device) Default Sound Capture: No Default Voice Capture: No Driver Name: HdAudio.sys Driver Version: 6.01.7600.16385 (English) Driver Attributes: Final Retail Date and Size: 7/13/2009 19:51:47, 304128 bytes Cap Flags: 0x1 Format Flags: 0xFFFFF Description: Line In (High Definition Audio Device) Default Sound Capture: No Default Voice Capture: No Driver Name: HdAudio.sys Driver Version: 6.01.7600.16385 (English) Driver Attributes: Final Retail Date and Size: 7/13/2009 19:51:47, 304128 bytes Cap Flags: 0x1 Format Flags: 0xFFFFF ------------------- DirectInput Devices ------------------- Device Name: Mouse Attached: 1 Controller ID: n/a Vendor/Product ID: n/a FF Driver: n/a Device Name: Keyboard Attached: 1 Controller ID: n/a Vendor/Product ID: n/a FF Driver: n/a Poll w/ Interrupt: No ----------- USB Devices ----------- + USB Root Hub | Vendor/Product ID: 0x8086, 0x2832 | Matching Device ID: usb\root_hub | Service: usbhub | Driver: usbhub.sys, 7/13/2009 19:52:09, 258560 bytes | Driver: usbd.sys, 7/13/2009 19:51:05, 5888 bytes ---------------- Gameport Devices ---------------- ------------ PS/2 Devices ------------ + Launch Manager | Matching Device ID: *pnp0303 | Upper Filters: DKbFltr | Service: i8042prt | Driver: DKbFltr.sys, 3/26/2009 11:14:34, 21000 bytes | Driver: i8042prt.sys, 7/13/2009 19:11:24, 80896 bytes | Driver: kbdclass.sys, 7/13/2009 21:20:36, 42576 bytes | + Terminal Server Keyboard Driver | Matching Device ID: root\rdp_kbd | Upper Filters: kbdclass | Service: TermDD | Driver: i8042prt.sys, 7/13/2009 19:11:24, 80896 bytes | Driver: kbdclass.sys, 7/13/2009 21:20:36, 42576 bytes | + PS/2 Compatible Mouse | Matching Device ID: *pnp0f13 | Service: i8042prt | Driver: i8042prt.sys, 7/13/2009 19:11:24, 80896 bytes | Driver: mouclass.sys, 7/13/2009 21:20:44, 41552 bytes | + Terminal Server Mouse Driver | Matching Device ID: root\rdp_mou | Upper Filters: mouclass | Service: TermDD | Driver: termdd.sys, 7/13/2009 21:19:10, 51776 bytes | Driver: sermouse.sys, 7/13/2009 19:45:08, 19968 bytes | Driver: mouclass.sys, 7/13/2009 21:20:44, 41552 bytes ------------------------ Disk & DVD/CD-ROM Drives ------------------------ Drive: C: Free Space: 116.9 GB Total Space: 142.5 GB File System: NTFS Model: Hitachi HTS541616J9SA00 ATA Device -------------- System Devices -------------- Name: Mobile Intel(R) 965 Express Chipset Family (Microsoft Corporation - WDDM 1.1) Device ID: PCI\VEN_8086&DEV_2A03&SUBSYS_011C1025&REV_03\3&33FD14CA&0&11 Driver: n/a Name: Intel(R) ICH8 Family SMBus Controller - 283E Device ID: PCI\VEN_8086&DEV_283E&SUBSYS_011C1025&REV_03\3&33FD14CA&0&FB Driver: n/a Name: Standard AHCI 1.0 Serial ATA Controller Device ID: PCI\VEN_8086&DEV_2829&SUBSYS_011C1025&REV_03\3&33FD14CA&0&FA Driver: C:\Windows\system32\DRIVERS\msahci.sys, 6.01.7600.16385 (English), 7/13/2009 21:20:44, 27712 bytes Driver: C:\Windows\system32\DRIVERS\pciidex.sys, 6.01.7600.16385 (English), 7/13/2009 21:19:03, 42560 bytes Driver: C:\Windows\system32\DRIVERS\atapi.sys, 6.01.7600.16385 (English), 7/13/2009 21:26:15, 21584 bytes Driver: C:\Windows\system32\DRIVERS\ataport.sys, 6.01.7600.16385 (English), 7/13/2009 21:26:15, 133200 bytes Name: Texas Instruments PCI-8x12/7x12/6x12 CardBus Controller Device ID: PCI\VEN_104C&DEV_8039&SUBSYS_011C1025&REV_00\4&1549EFE7&0&30F0 Driver: C:\Windows\system32\DRIVERS\pcmcia.sys, 6.01.7600.16385 (English), 7/13/2009 21:19:03, 180288 bytes Name: Mobile Intel(R) 965 Express Chipset Family (Microsoft Corporation - WDDM 1.1) Device ID: PCI\VEN_8086&DEV_2A02&SUBSYS_011C1025&REV_03\3&33FD14CA&0&10 Driver: C:\Windows\system32\DRIVERS\igdkmd32.sys, 8.15.0010.1749 (English), 6/10/2009 17:19:30, 4756480 bytes Driver: C:\Windows\system32\igdumd32.dll, 8.15.0010.1749 (English), 7/13/2009 21:15:31, 3805184 bytes Driver: C:\Windows\system32\igkrng400.bin, 6/10/2009 17:19:31, 1498564 bytes Driver: C:\Windows\system32\iglhxs32.vp, 6/10/2009 17:19:31, 39292 bytes Driver: C:\Windows\system32\iglhxo32.vp, 6/10/2009 17:19:31, 59105 bytes Driver: C:\Windows\system32\iglhxc32.vp, 6/10/2009 17:19:31, 58952 bytes Driver: C:\Windows\system32\iglhxg32.vp, 6/10/2009 17:19:31, 60072 bytes Driver: C:\Windows\system32\iglhxa32.vp, 6/10/2009 17:19:31, 1073 bytes Driver: C:\Windows\system32\iglhxa32.cpa, 6/10/2009 17:19:31, 2584543 bytes Driver: C:\Windows\system32\igd10umd32.dll, 8.15.0010.1749 (English), 7/13/2009 21:15:29, 2531328 bytes Name: Intel(R) ICH8 Family USB2 Enhanced Host Controller - 283A Device ID: PCI\VEN_8086&DEV_283A&SUBSYS_011C1025&REV_03\3&33FD14CA&0&D7 Driver: C:\Windows\system32\drivers\usbehci.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:14, 41472 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:15, 284160 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7600.16385 (English), 7/13/2009 19:52:09, 258560 bytes Name: Intel(R) ICH8M LPC Interface Controller - 2815 Device ID: PCI\VEN_8086&DEV_2815&SUBSYS_011C1025&REV_03\3&33FD14CA&0&F8 Driver: C:\Windows\system32\DRIVERS\msisadrv.sys, 6.01.7600.16385 (English), 7/13/2009 21:20:43, 13888 bytes Name: Mobile Intel(R) PM965/GM965/GL960/GS965 Express Processor to DRAM Controller - 2A00 Device ID: PCI\VEN_8086&DEV_2A00&SUBSYS_011C1025&REV_03\3&33FD14CA&0&00 Driver: n/a Name: Intel(R) ICH8 Family USB2 Enhanced Host Controller - 2836 Device ID: PCI\VEN_8086&DEV_2836&SUBSYS_011C1025&REV_03\3&33FD14CA&0&EF Driver: C:\Windows\system32\drivers\usbehci.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:14, 41472 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:15, 284160 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7600.16385 (English), 7/13/2009 19:52:09, 258560 bytes Name: Intel(R) 82801 PCI Bridge - 2448 Device ID: PCI\VEN_8086&DEV_2448&SUBSYS_00000000&REV_F3\3&33FD14CA&0&F0 Driver: C:\Windows\system32\DRIVERS\pci.sys, 6.01.7600.16385 (English), 7/13/2009 21:20:45, 153680 bytes Name: Intel(R) ICH8M Ultra ATA Storage Controllers - 2850 Device ID: PCI\VEN_8086&DEV_2850&SUBSYS_011C1025&REV_03\3&33FD14CA&0&F9 Driver: C:\Windows\system32\DRIVERS\intelide.sys, 6.01.7600.16385 (English), 7/13/2009 21:20:36, 15424 bytes Driver: C:\Windows\system32\DRIVERS\pciidex.sys, 6.01.7600.16385 (English), 7/13/2009 21:19:03, 42560 bytes Driver: C:\Windows\system32\DRIVERS\atapi.sys, 6.01.7600.16385 (English), 7/13/2009 21:26:15, 21584 bytes Driver: C:\Windows\system32\DRIVERS\ataport.sys, 6.01.7600.16385 (English), 7/13/2009 21:26:15, 133200 bytes Name: Intel(R) ICH8 Family USB Universal Host Controller - 2835 Device ID: PCI\VEN_8086&DEV_2835&SUBSYS_011C1025&REV_03\3&33FD14CA&0&D1 Driver: C:\Windows\system32\drivers\usbuhci.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:10, 24064 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:15, 284160 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7600.16385 (English), 7/13/2009 19:52:09, 258560 bytes Name: Broadcom 802.11g Network Adapter Device ID: PCI\VEN_14E4&DEV_4311&SUBSYS_04221468&REV_01\4&20886337&0&00E1 Driver: C:\Windows\system32\DRIVERS\BCMWL6.SYS, 4.176.0075.0018 (English), 7/13/2009 18:02:48, 1131008 bytes Driver: C:\Windows\system32\drivers\vwifibus.sys, 6.01.7600.16385 (English), 7/13/2009 19:52:02, 19968 bytes Name: High Definition Audio Controller Device ID: PCI\VEN_8086&DEV_284B&SUBSYS_011C1025&REV_03\3&33FD14CA&0&D8 Driver: C:\Windows\system32\DRIVERS\hdaudbus.sys, 6.01.7600.16385 (English), 7/13/2009 19:50:56, 108544 bytes Name: Intel(R) ICH8 Family USB Universal Host Controller - 2834 Device ID: PCI\VEN_8086&DEV_2834&SUBSYS_011C1025&REV_03\3&33FD14CA&0&D0 Driver: C:\Windows\system32\drivers\usbuhci.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:10, 24064 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:15, 284160 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7600.16385 (English), 7/13/2009 19:52:09, 258560 bytes Name: Broadcom NetLink (TM) Gigabit Ethernet Device ID: PCI\VEN_14E4&DEV_1693&SUBSYS_011C1025&REV_02\4&8FA1E14&0&00E0 Driver: n/a Name: Intel(R) ICH8 Family PCI Express Root Port 3 - 2843 Device ID: PCI\VEN_8086&DEV_2843&SUBSYS_011C1025&REV_03\3&33FD14CA&0&E2 Driver: C:\Windows\system32\DRIVERS\pci.sys, 6.01.7600.16385 (English), 7/13/2009 21:20:45, 153680 bytes Name: Intel(R) ICH8 Family USB Universal Host Controller - 2832 Device ID: PCI\VEN_8086&DEV_2832&SUBSYS_011C1025&REV_03\3&33FD14CA&0&EA Driver: C:\Windows\system32\drivers\usbuhci.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:10, 24064 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:15, 284160 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7600.16385 (English), 7/13/2009 19:52:09, 258560 bytes Name: SDA Standard Compliant SD Host Controller Device ID: PCI\VEN_104C&DEV_803C&SUBSYS_011C1025&REV_00\4&1549EFE7&0&33F0 Driver: C:\Windows\system32\DRIVERS\sdbus.sys, 6.01.7600.16385 (English), 7/13/2009 19:19:26, 84992 bytes Name: Intel(R) ICH8 Family PCI Express Root Port 2 - 2841 Device ID: PCI\VEN_8086&DEV_2841&SUBSYS_011C1025&REV_03\3&33FD14CA&0&E1 Driver: C:\Windows\system32\DRIVERS\pci.sys, 6.01.7600.16385 (English), 7/13/2009 21:20:45, 153680 bytes Name: Intel(R) ICH8 Family USB Universal Host Controller - 2831 Device ID: PCI\VEN_8086&DEV_2831&SUBSYS_011C1025&REV_03\3&33FD14CA&0&E9 Driver: C:\Windows\system32\drivers\usbuhci.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:10, 24064 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:15, 284160 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7600.16385 (English), 7/13/2009 19:52:09, 258560 bytes Name: Texas Instruments PCIxx12 Integrated FlashMedia Controller Device ID: PCI\VEN_104C&DEV_803B&SUBSYS_011C1025&REV_00\4&1549EFE7&0&32F0 Driver: C:\Windows\system32\DRIVERS\tifm21.sys, 2.00.0000.0008 (English), 5/2/2007 03:52:00, 290816 bytes Name: Intel(R) ICH8 Family PCI Express Root Port 1 - 283F Device ID: PCI\VEN_8086&DEV_283F&SUBSYS_011C1025&REV_03\3&33FD14CA&0&E0 Driver: C:\Windows\system32\DRIVERS\pci.sys, 6.01.7600.16385 (English), 7/13/2009 21:20:45, 153680 bytes Name: Intel(R) ICH8 Family USB Universal Host Controller - 2830 Device ID: PCI\VEN_8086&DEV_2830&SUBSYS_011C1025&REV_03\3&33FD14CA&0&E8 Driver: C:\Windows\system32\drivers\usbuhci.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:10, 24064 bytes Driver: C:\Windows\system32\drivers\usbport.sys, 6.01.7600.16385 (English), 7/13/2009 19:51:15, 284160 bytes Driver: C:\Windows\system32\drivers\usbhub.sys, 6.01.7600.16385 (English), 7/13/2009 19:52:09, 258560 bytes Name: Texas Instruments 1394 OHCI Compliant Host Controller Device ID: PCI\VEN_104C&DEV_803A&SUBSYS_011C1025&REV_00\4&1549EFE7&0&31F0 Driver: C:\Windows\system32\DRIVERS\1394ohci.sys, 6.01.7600.16385 (English), 7/13/2009 19:52:00, 163328 bytes ------------------ DirectShow Filters ------------------ DirectShow Filters: WMAudio Decoder DMO,0x00800800,1,1,WMADMOD.DLL,6.01.7600.16385 WMAPro over S/PDIF DMO,0x00600800,1,1,WMADMOD.DLL,6.01.7600.16385 WMSpeech Decoder DMO,0x00600800,1,1,WMSPDMOD.DLL,6.01.7600.16385 MP3 Decoder DMO,0x00600800,1,1,mp3dmod.dll,6.01.7600.16385 Mpeg4s Decoder DMO,0x00800001,1,1,mp4sdecd.dll,6.01.7600.16385 WMV Screen decoder DMO,0x00600800,1,1,wmvsdecd.dll,6.01.7600.16385 WMVideo Decoder DMO,0x00800001,1,1,wmvdecod.dll,6.01.7600.16385 Mpeg43 Decoder DMO,0x00800001,1,1,mp43decd.dll,6.01.7600.16385 Mpeg4 Decoder DMO,0x00800001,1,1,mpg4decd.dll,6.01.7600.16385 DV Muxer,0x00400000,0,0,qdv.dll,6.06.7600.16385 Color Space Converter,0x00400001,1,1,quartz.dll,6.06.7600.16490 WM ASF Reader,0x00400000,0,0,qasf.dll,12.00.7600.16385 Screen Capture filter,0x00200000,0,1,wmpsrcwp.dll,12.00.7600.16385 AVI Splitter,0x00600000,1,1,quartz.dll,6.06.7600.16490 VGA 16 Color Ditherer,0x00400000,1,1,quartz.dll,6.06.7600.16490 SBE2MediaTypeProfile,0x00200000,0,0,sbe.dll,6.06.7600.16385 Microsoft DTV-DVD Video Decoder,0x005fffff,2,4,msmpeg2vdec.dll,6.01.7140.0000 AC3 Parser Filter,0x00600000,1,1,mpg2splt.ax,6.06.7600.16590 StreamBufferSink,0x00200000,0,0,sbe.dll,6.06.7600.16385 Microsoft TV Captions Decoder,0x00200001,1,0,MSTVCapn.dll,6.01.7600.16385 MJPEG Decompressor,0x00600000,1,1,quartz.dll,6.06.7600.16490 CBVA DMO wrapper filter,0x00200000,1,1,cbva.dll,6.01.7600.16385 MPEG-I Stream Splitter,0x00600000,1,2,quartz.dll,6.06.7600.16490 SAMI (CC) Parser,0x00400000,1,1,quartz.dll,6.06.7600.16490 VBI Codec,0x00600000,1,4,VBICodec.ax,6.06.7600.16385 MPEG-2 Splitter,0x005fffff,1,0,mpg2splt.ax,6.06.7600.16590 Closed Captions Analysis Filter,0x00200000,2,5,cca.dll,6.06.7600.16385 SBE2FileScan,0x00200000,0,0,sbe.dll,6.06.7600.16385 Microsoft MPEG-2 Video Encoder,0x00200000,1,1,msmpeg2enc.dll,6.01.7600.16385 Internal Script Command Renderer,0x00800001,1,0,quartz.dll,6.06.7600.16490 MPEG Audio Decoder,0x03680001,1,1,quartz.dll,6.06.7600.16490 DV Splitter,0x00600000,1,2,qdv.dll,6.06.7600.16385 Video Mixing Renderer 9,0x00200000,1,0,quartz.dll,6.06.7600.16490 Microsoft MPEG-2 Encoder,0x00200000,2,1,msmpeg2enc.dll,6.01.7600.16385 ACM Wrapper,0x00600000,1,1,quartz.dll,6.06.7600.16490 Video Renderer,0x00800001,1,0,quartz.dll,6.06.7600.16490 MPEG-2 Video Stream Analyzer,0x00200000,0,0,sbe.dll,6.06.7600.16385 Line 21 Decoder,0x00600000,1,1,qdvd.dll,6.06.7600.16385 Video Port Manager,0x00600000,2,1,quartz.dll,6.06.7600.16490 Video Renderer,0x00400000,1,0,quartz.dll,6.06.7600.16490 VPS Decoder,0x00200000,0,0,WSTPager.ax,6.06.7600.16385 WM ASF Writer,0x00400000,0,0,qasf.dll,12.00.7600.16385 VBI Surface Allocator,0x00600000,1,1,vbisurf.ax,6.01.7600.16385 File writer,0x00200000,1,0,qcap.dll,6.06.7600.16385 iTV Data Sink,0x00600000,1,0,itvdata.dll,6.06.7600.16385 iTV Data Capture filter,0x00600000,1,1,itvdata.dll,6.06.7600.16385 DVD Navigator,0x00200000,0,3,qdvd.dll,6.06.7600.16385 Microsoft TV Subtitles Decoder,0x00200001,1,0,MSTVCapn.dll,6.01.7600.16385 Overlay Mixer2,0x00200000,1,1,qdvd.dll,6.06.7600.16385 AVI Draw,0x00600064,9,1,quartz.dll,6.06.7600.16490 RDP DShow Redirection Filter,0xffffffff,1,0,DShowRdpFilter.dll, Microsoft MPEG-2 Audio Encoder,0x00200000,1,1,msmpeg2enc.dll,6.01.7600.16385 WST Pager,0x00200000,1,1,WSTPager.ax,6.06.7600.16385 MPEG-2 Demultiplexer,0x00600000,1,1,mpg2splt.ax,6.06.7600.16590 DV Video Decoder,0x00800000,1,1,qdv.dll,6.06.7600.16385 SampleGrabber,0x00200000,1,1,qedit.dll,6.06.7600.16385 Null Renderer,0x00200000,1,0,qedit.dll,6.06.7600.16385 MPEG-2 Sections and Tables,0x005fffff,1,0,Mpeg2Data.ax,6.06.7600.16385 Microsoft AC3 Encoder,0x00200000,1,1,msac3enc.dll,6.01.7600.16385 StreamBufferSource,0x00200000,0,0,sbe.dll,6.06.7600.16385 Smart Tee,0x00200000,1,2,qcap.dll,6.06.7600.16385 Overlay Mixer,0x00200000,0,0,qdvd.dll,6.06.7600.16385 AVI Decompressor,0x00600000,1,1,quartz.dll,6.06.7600.16490 NetBridge,0x00200000,2,0,netbridge.dll,6.01.7600.16385 AVI/WAV File Source,0x00400000,0,2,quartz.dll,6.06.7600.16490 Wave Parser,0x00400000,1,1,quartz.dll,6.06.7600.16490 MIDI Parser,0x00400000,1,1,quartz.dll,6.06.7600.16490 Multi-file Parser,0x00400000,1,1,quartz.dll,6.06.7600.16490 File stream renderer,0x00400000,1,1,quartz.dll,6.06.7600.16490 Microsoft DTV-DVD Audio Decoder,0x005fffff,1,1,msmpeg2adec.dll,6.01.7140.0000 StreamBufferSink2,0x00200000,0,0,sbe.dll,6.06.7600.16385 AVI Mux,0x00200000,1,0,qcap.dll,6.06.7600.16385 Line 21 Decoder 2,0x00600002,1,1,quartz.dll,6.06.7600.16490 File Source (Async.),0x00400000,0,1,quartz.dll,6.06.7600.16490 File Source (URL),0x00400000,0,1,quartz.dll,6.06.7600.16490 Media Center Extender Encryption Filter,0x00200000,2,2,Mcx2Filter.dll,6.01.7600.16385 AudioRecorder WAV Dest,0x00200000,0,0,WavDest.dll, AudioRecorder Wave Form,0x00200000,0,0,WavDest.dll, SoundRecorder Null Renderer,0x00200000,0,0,WavDest.dll, Infinite Pin Tee Filter,0x00200000,1,1,qcap.dll,6.06.7600.16385 Enhanced Video Renderer,0x00200000,1,0,evr.dll,6.01.7600.16385 BDA MPEG2 Transport Information Filter,0x00200000,2,0,psisrndr.ax,6.06.7600.16385 MPEG Video Decoder,0x40000001,1,1,quartz.dll,6.06.7600.16490 WDM Streaming Tee/Splitter Devices: Tee/Sink-to-Sink Converter,0x00200000,1,1,ksproxy.ax,6.01.7600.16385 Video Compressors: WMVideo8 Encoder DMO,0x00600800,1,1,wmvxencd.dll,6.01.7600.16385 WMVideo9 Encoder DMO,0x00600800,1,1,wmvencod.dll,6.01.7600.16385 MSScreen 9 encoder DMO,0x00600800,1,1,wmvsencd.dll,6.01.7600.16385 DV Video Encoder,0x00200000,0,0,qdv.dll,6.06.7600.16385 MJPEG Compressor,0x00200000,0,0,quartz.dll,6.06.7600.16490 Cinepak Codec by Radius,0x00200000,1,1,qcap.dll,6.06.7600.16385 Intel IYUV codec,0x00200000,1,1,qcap.dll,6.06.7600.16385 Intel IYUV codec,0x00200000,1,1,qcap.dll,6.06.7600.16385 Microsoft RLE,0x00200000,1,1,qcap.dll,6.06.7600.16385 Microsoft Video 1,0x00200000,1,1,qcap.dll,6.06.7600.16385 Audio Compressors: WM Speech Encoder DMO,0x00600800,1,1,WMSPDMOE.DLL,6.01.7600.16385 WMAudio Encoder DMO,0x00600800,1,1,WMADMOE.DLL,6.01.7600.16385 IMA ADPCM,0x00200000,1,1,quartz.dll,6.06.7600.16490 PCM,0x00200000,1,1,quartz.dll,6.06.7600.16490 Microsoft ADPCM,0x00200000,1,1,quartz.dll,6.06.7600.16490 GSM 6.10,0x00200000,1,1,quartz.dll,6.06.7600.16490 Messenger Audio Codec,0x00200000,1,1,quartz.dll,6.06.7600.16490 CCITT A-Law,0x00200000,1,1,quartz.dll,6.06.7600.16490 CCITT u-Law,0x00200000,1,1,quartz.dll,6.06.7600.16490 MPEG Layer-3,0x00200000,1,1,quartz.dll,6.06.7600.16490 Audio Capture Sources: Microphone (High Definition Aud,0x00200000,0,0,qcap.dll,6.06.7600.16385 Line In (High Definition Audio ,0x00200000,0,0,qcap.dll,6.06.7600.16385 PBDA CP Filters: PBDA DTFilter,0x00600000,1,1,CPFilters.dll,6.06.7600.16590 PBDA ETFilter,0x00200000,0,0,CPFilters.dll,6.06.7600.16590 PBDA PTFilter,0x00200000,0,0,CPFilters.dll,6.06.7600.16590 Midi Renderers: Default MidiOut Device,0x00800000,1,0,quartz.dll,6.06.7600.16490 Microsoft GS Wavetable Synth,0x00200000,1,0,quartz.dll,6.06.7600.16490 WDM Streaming Capture Devices: HD Audio Muxed capture,0x00200000,1,1,ksproxy.ax,6.01.7600.16385 Acer CrystalEye webcam,0x00200000,1,2,ksproxy.ax,6.01.7600.16385 WDM Streaming Rendering Devices: HD Audio Speaker,0x00200000,1,1,ksproxy.ax,6.01.7600.16385 BDA Network Providers: Microsoft ATSC Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7600.16385 Microsoft DVBC Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7600.16385 Microsoft DVBS Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7600.16385 Microsoft DVBT Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.7600.16385 Microsoft Network Provider,0x00200000,0,1,MSNP.ax,6.06.7600.16590 Video Capture Sources: Acer CrystalEye webcam,0x00200000,1,2,ksproxy.ax,6.01.7600.16385 Multi-Instance Capable VBI Codecs: VBI Codec,0x00600000,1,4,VBICodec.ax,6.06.7600.16385 BDA Transport Information Renderers: BDA MPEG2 Transport Information Filter,0x00600000,2,0,psisrndr.ax,6.06.7600.16385 MPEG-2 Sections and Tables,0x00600000,1,0,Mpeg2Data.ax,6.06.7600.16385 BDA CP/CA Filters: Decrypt/Tag,0x00600000,1,1,EncDec.dll,6.06.7600.16385 Encrypt/Tag,0x00200000,0,0,EncDec.dll,6.06.7600.16385 PTFilter,0x00200000,0,0,EncDec.dll,6.06.7600.16385 XDS Codec,0x00200000,0,0,EncDec.dll,6.06.7600.16385 WDM Streaming Communication Transforms: Tee/Sink-to-Sink Converter,0x00200000,1,1,ksproxy.ax,6.01.7600.16385 Audio Renderers: Speakers (High Definition Audio,0x00200000,1,0,quartz.dll,6.06.7600.16490 Default DirectSound Device,0x00800000,1,0,quartz.dll,6.06.7600.16490 Default WaveOut Device,0x00200000,1,0,quartz.dll,6.06.7600.16490 DirectSound: Speakers (High Definition Audio Device),0x00200000,1,0,quartz.dll,6.06.7600.16490 --------------- EVR Power Information --------------- Current Setting: {5C67A112-A4C9-483F-B4A7-1D473BECAFDC} (Quality) Quality Flags: 2576 Enabled: Force throttling Allow half deinterlace Allow scaling Decode Power Usage: 100 Balanced Flags: 1424 Enabled: Force throttling Allow batching Force half deinterlace Force scaling Decode Power Usage: 50 PowerFlags: 1424 Enabled: Force throttling Allow batching Force half deinterlace Force scaling Decode Power Usage: 0