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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

Karnaugh maps solver is a web app that takes the truth table of a function as input, transposes it onto the respective Karnaugh map and finds the minimum forms SOP and POS according to the visual resolution method by Maurice Karnaugh, American physicist and mathematician.

:package: Qt C++ and Lepton Based K-Map solver for unlimited variables

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A C# Windows Desktop Application that solves many Boolean Algebra problems such as K-Map, QM, Multiple Output Minimization, hazard remover, Code Conversion and Hamming Code

The Karnaugh Map (KMap) Solver is a C++ application featuring a graphical interface for solving Karnaugh Maps. Users can interactively input values into a KMap grid and calculate corresponding minterms. The project aims to simplify Boolean expressions and visualize them using logic gates.

カルノー図作成ツール

4 by 4 Karnaugh map solver

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MapReduce is a programming model that involves two steps. The first, the map step, takes an input set I and groups it into N equivalence classes I0, I1, I2, ..., IN-1. I can be thought of as a set of tuples <key, data>, and the function map maps I into the equivalence classes based on the value of key. In the second reduce step, the equivalence classes are processed, and the set of tuples in an equivalence class Ij are reduced into a single value. MapReduce has become very popular in part because of its use by Google, but is an old parallel programming model. It is surprisingly general. To perform a parallel MapReduce, the input is spread across the available processors. Each processor runs one or more instances of map, followed by executing one or more instances of reduce. Each instance of map will potentially form equivalence classes I0, I1, I2, ..., IN-1. Consider the word counting problem, which can be solved in parallel using MapReduce. Given a list of words, the output should consist of how many times each word appeared in the list (or text). Viewing the input as tuples, the word is the key, and the data is the constant 1. A naive map function would collect all instances of a word into an equivalence class. Each equivalence class would then be assigned to a process pr, and process pr would determine the cardinality of the equivalence classes from all maps, which would be the word count. A more intelligent map function would form singleton equivalence classes Iword, where the only element is <word, count>. The process pr that reduces Iword would receive the Iword equivalence classes from all of the map functions, and would perform a reduction on the class. In Google terminology, the function that performs this optimization is called a combiner and executes on the same process as the map. This is important since its function is to combine many members of an equivalence class into a single member so as to decrease the volume of communicated data sent form the needed between the map and reduce stages. A second optimization that can be performed is to group multiple equivalence classes together to be sent together to the same reducer. Thus, the records for “cat”, “dog”, “test” and “homework” might be sent by different mappers to the same reducer. This enables all of the to be sent by a single communication operation, improving the efficiency of the communication. The question then becomes, how do we decide which equivalence classes to group together. This decision is done using a hash function H. Let’s say we will have R reducers. Then having a function 0 ≤ H(key) ≤ R-1 will group the equivalence classes into R groups to be sent to the R reducers. What we will program We will program a map reduce that executes on a distributed memory machine and uses OpenMP on each core to compute the map reduce. The project will be done in three steps: The OpenMP version and a wordcount map reduce (20% of the project grade) The MPI version that uses the OpenMP version to perform node-local computation with a wordcount map reduce (20% of the project grade) Final turn-in. (60% of the project grade) Details are given below. Note that even though I use OpenMP you can use Pthreads, Java or other code that supports multithreading to write the shared memory version. Note that if you use Java you will need to use Java isolates to communicate between nodes/processes. General information: The text for the map reduce will be distributed across FI input text files, where FI > Nmpi*C, where Nmpiis the number of nodes (machines and processes) used by MPI and C is the number of cores on each processor. OpenMP code (i.e. OpenMP code on a node). There will be four kinds of threads: Reader threads, which read files and put the data read (or created by self-initialization) into a work queue. For wordcount each work item will be a word. For the numerical problem, each entry can be a section of the array that a thread should work on; Mapper threads, which execute in parallel with Reader threads (at least until the Reader threads finish) and create combined records of words. I.e., if there are 2045 instances of “cat” in the files read by the program, the final output of the mapper threads will be a record that looks like <“cat”,2045>; Reducer threads that operate on work queue entries created by mapper threads and combine (reduce) them to a single record. Thus, for the word “cat”, there is potentially a <“cat”,counti> record sent by every mapper thread ti in the system and it will sum all of the counts and place it on a work queue. For each word there is exactly one Reducer thread in the system that handles it. Writer threads that take a sum from the work queue and write it to a file. Note that each process can write its results to a separate file. You may not need threads for each of these but only different work queue entries. Thus, Reader and Writer threads run at different times. Mapper and Reducer threads, within a node, can be made to run at different times. These threads can be made to do different tasks by pulling different work out of work queues. This is not mandatory, i.e., you can have different groups of threads to perform different tasks, thus you might have reader, mapper, reducer and writer threads. A work queue for each reducer thread. Mapper threads will put work items into this queue. For load balance purposes it is desirable that the range of function H that determines which reducer will get a work item be from 0 to R where R = k⋅numMappers, and k is some constant. You need to have mechanisms to ensure that Mapper threads wait until all Readers have finished before considering themselves complete, i.e. the work queue from which Mapper threads get their work may be empty at some point in time, but have data at a later point in time because an unfinished Reader thread put data in it. Mappers will need to put their data on a reducer’s work queue based on the key (word) for that data: As mentioned above, the reducer of a key should be determined by some sort of hash function g = H(key). All keys that map onto reducer g should be added to g’s work queue. Each process can assume it will be receiving data from every other node. This will simplify the communication structure of your program when you go to the MPI version. A node that sends no data should send an “empty” record letting the other process no it will get no data from it. As each process finishes its reduce work, it should write its results to an output file, close it, and notify the master thread that it is finished so that it can terminate the job, and then terminate itself. MPI version: The MPI version will use multiple nodes. Each node will run a copy of the OpenMP code above to perform local computations. A few changes need to be made to the OpenMP process on a node to communicate with the OpenMP processes running on other nodes. Instead of mappers putting their results onto a reducers work queue, they should put them onto a list to be sent to other nodes. A sender thread should be used to send the results of reducers in these lists to the appropriate node. Each node should have a receiver thread that obtains data sent to it by sender threads in other nodes The receiver thread for a node will place its received data onto work queues in the node for each reducer. Each node will read some portion of the FI > Nmpi*C input files. We could statically define the files each node will process, but this could lead to some nodes getting many big files and other nodes getting many small files. Instead, each node should request a file from a master node which will either send a filename back to the node or an “all done” that indicates that all files have been or are being processed. Performance data and tuning: You should collect performance data showing: What the bottlenecks are in the code. This might involve time Mapper threads are waiting for work from Reader threads, how long I/O takes vs. Mapping (not counting waiting for I/O on mapping) and data to support this other numbers below. How much load imbalance there is within a node. How much load imbalance there is across nodes (i.e. the difference in time between the first map node is ready to send its data and the latest/last map node is ready to send its data to be reduced. You should experiment with different numbers of Reader threads Step deliverables: For the OpenMP version: speedup numbers when using 1, 2, 4, . . . , #cores Mapper and Reader threads; For the MPI version: speedup numbers when using 1, 2, 4, …, #nodes to run the program, with Mapper and Reader threads for each core on a node (i.e. you don’t need to experiment with various numbers of nodes and cores For the final turn-in version: A paper not longer than ten pages that describes your overall strategy, performance bottlenecks, Performance numbers and implementation positives and negatives (what you are happy about, what you would like to change.) A full set of performance numbers either the word-count problem, and scaling by number of nodes, and dataset size, for the matrix multiply problem. Speedups and efficiencies for 2, 4, 8 and 16 processors. Do the Karp-Flatt analysis on 2, 4, 8 and 16 processors. Curves showing the number of Reader threads and performance, and the number of map and reduce threads and performance. Overall performance of the different parts of the map reduce, and the entire map reduce. For baseline “serial” numbers, use a system with one thread for each of the tasks above. Performance numbers for different numbers of nodes along with the various speedup metrics (speedup, efficiency and Karp-Flatt). An explanation of why you are getting the speedups you are getting. I may have a meeting with each group to have you demonstrate your code. This would likely happen during dead week. The point distribution will be 40% for a working parallel project with any speedup; 40% for the paper and presentation of your results and explanation of your results, 20% for acceptable speedups or non-trivial explanations of unacceptable speedups.

wgu-c950 Data Structures and Algorithms II - WGU Scenario The Western Governors University Parcel Service (WGUPS) needs to determine the best route and delivery distribution for their Daily Local Deliveries (DLD) because packages are not currently being consistently delivered by their promised deadline. The Salt Lake City DLD route has three trucks, two drivers, and an average of 40 packages to deliver each day; each package has specific criteria and delivery requirements. Your task is to determine the best algorithm, write code, and present a solution where all 40 packages, listed in the attached “WGUPS Package File,” will be delivered on time with the least number of miles added to the combined mileage total of all trucks. The specific delivery locations are shown on the attached “Salt Lake City Downtown Map” and distances to each location are given in the attached “WGUPS Distance Table.” While you work on this assessment, take into consideration the specific delivery time expected for each package and the possibility that the delivery requirements—including the expected delivery time—can be changed by management at any time and at any point along the chosen route. In addition, you should keep in mind that the supervisor should be able to see, at assigned points, the progress of each truck and its packages by any of the variables listed in the “WGUPS Package File,” including what has been delivered and what time the delivery occurred. The intent is to use this solution (program) for this specific location and to use the same program in many cities in each state where WGU has a presence. As such, you will need to include detailed comments, following the industry-standard Python style guide, to make your code easy to read and to justify the decisions you made while writing your program. Assumptions: Each truck can carry a maximum of 16 packages. Trucks travel at an average speed of 18 miles per hour. Trucks have a “infinite amount of gas” with no need to stop. Each driver stays with the same truck as long as that truck is in service. Drivers leave the hub at 8:00 a.m., with the truck loaded, and can return to the hub for packages if needed. The day ends when all 40 packages have been delivered. Delivery time is instantaneous, i.e., no time passes while at a delivery (that time is factored into the average speed of the trucks). There is up to one special note for each package. The wrong delivery address for package #9, Third District Juvenile Court, will be corrected at 10:20 a.m. The correct address is 410 S State St., Salt Lake City, UT 84111. The package ID is unique; there are no collisions. No further assumptions exist or are allowed. Requirements Your submission must be your original work. No more than a combined total of 30% of the submission and no more than a 10% match to any one individual source can be directly quoted or closely paraphrased from sources, even if cited correctly. An originality report is provided when you submit your task that can be used as a guide. You must use the rubric to direct the creation of your submission because it provides detailed criteria that will be used to evaluate your work. Each requirement below may be evaluated by more than one rubric aspect. The rubric aspect titles may contain hyperlinks to relevant portions of the course. Section 1: Programming/Coding A. Identify the algorithm that will be used to create a program to deliver the packages and meets all requirements specified in the scenario. B. Write a core algorithm overview, using the sample given, in which you do the following: Comment using pseudocode to show the logic of the algorithm applied to this software solution. Apply programming models to the scenario. Evaluate space-time complexity using Big O notation throughout the coding and for the entire program. Discuss the ability of your solution to adapt to a changing market and to scalability. Discuss the efficiency and maintainability of the software. Discuss the self-adjusting data structures chosen and their strengths and weaknesses based on the scenario. C. Write an original code to solve and to meet the requirements of lowest mileage usage and having all packages delivered on time. Create a comment within the first line of your code that includes your first name, last name, and student ID. Include comments at each block of code to explain the process and flow of the coding. D. Identify a data structure that can be used with your chosen algorithm to store the package data. Explain how your data structure includes the relationship between the data points you are storing. Note: Do NOT use any existing data structures. You must design, write, implement, and debug all code that you turn in for this assessment. Code downloaded from the internet or acquired from another student or any other source may not be submitted and will result in automatic failure of this assessment. E. Develop a hash table, without using any additional libraries or classes, with an insertion function that takes the following components as input and inserts the components into the hash table: package ID number delivery address delivery deadline delivery city delivery zip code package weight delivery status (e.g., delivered, in route) F. Develop a look-up function that takes the following components as input and returns the corresponding data elements: package ID number delivery address delivery deadline delivery city delivery zip code package weight delivery status (e.g., delivered, in route) G. Provide an interface for the insert and look-up functions to view the status of any package at any time. This function should return all information about each package, including delivery status. Provide screenshots to show package status of all packages at a time between 8:35 a.m. and 9:25 a.m. Provide screenshots to show package status of all packages at a time between 9:35 a.m. and 10:25 a.m. Provide screenshots to show package status of all packages at a time between 12:03 p.m. and 1:12 p.m. H. Run your code and provide screenshots to capture the complete execution of your code. Section 2: Annotations I. Justify your choice of algorithm by doing the following: Describe at least two strengths of the algorithm you chose. Verify that the algorithm you chose meets all the criteria and requirements given in the scenario. Identify two other algorithms that could be used and would have met the criteria and requirements given in the scenario. a. Describe how each algorithm identified in part I3 is different from the algorithm you chose to use in the solution. J. Describe what you would do differently if you did this project again. K. Justify your choice of data structure by doing the following: Verify that the data structure you chose meets all the criteria and requirements given in the scenario. a. Describe the efficiency of the data structure chosen. b. Explain the expected overhead when linking to the next data item. c. Describe the implications of when more package data is added to the system or other changes in scale occur. Identify two other data structures that can meet the same criteria and requirements given in the scenario. a. Describe how each data structure identified in part K2 is different from the data structure you chose to use in the solution. L. Acknowledge sources, using in-text citations and references, for content that is quoted, paraphrased, or summarized. M. Demonstrate professional communication in the content and presentation of your submission.

Simple scripts to generate a Karnaugh map from a truth table and solve the expression for it.

SimpliMap is a Flutter app for creating, visualizing and minimizing Boolean expressions using Karnaugh maps (K-maps). It provides a solver UI, step-by-step minimization, and components to explore K-map grouping and simplified expressions.

This is a python code which solves the karnaugh map of 2 to 4 variables for you. It uses quine mccluskey algorithm to do so.

This is my Java OOP course project.

4-Variable K-Map Solver is an online tool/web app that lets you work with a Karnaugh map (K-map) for four variables directly in your browser. This type of solver is typically used in digital logic design and Boolean algebra to simplify logic expressions by organizing truth values into a visual grid, helping you find minimal logic expressions.

A c++ program that solves 2x2, 2x4, and 4x4 k-map for simplifying Boolean expressions

K-Map Solver Using Quine McCluskey Method

A k-map solver for 2 to 3 variables.

Python Script to solve 4x4 K-Maps

An interactive Karnaugh Map solver with automatic Boolean minimization and circuit synthesis.

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No description available

No description available

No description available

A simple program in C++ to calculate the simplest form of a binary expression through Karnaugh Maps (K-Maps), created for the CSCE2301 (Digital Design I) course, in Spring 2022.

Karnaugh-Map-Solver

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