Localize Tool & Localized Menu for Sublime Text 2/3/4 End User. Localization with 简体中文(Simplified Chinese)/繁体中文(Traditional Chinese)/Русский(Russian)/Español(Spanish)/Հայերեն(Armenian)/Svenska(Swedish)/Français(French)/Português(Portuguese)/Deutsch(German)/Magyar(Hungarian)/Any other language.

The code for my thesis project, Looks Good to Me (LGTM), Authentication for Augmented Reality. This is a full implementation of LGTM that localizes a wireless transmitter, and searches for a specific face at that location to authenticate two users trying to establish secure point to point communication.

Real-time Model-based Object Tracking in 3D

Localizations for my iOS projects. Email me if you'd like to contribute

LocalizeMe is a lightweight Android library for managing app localization. It allows you to easily switch languages at runtime, supports multiple locales, and works with XML-based and compose resources.

This library provides extra code to make it easier to support in different languages in your .NET application

Welcome to the repo for Localize Me, a mobile app that helps you learn new languages by translating your favorite songs. This project was created for my 2024 React Universe Conf talk called "Lost in Translation? Turning Babel into Brilliance" hosted by Callstack.

IOU Calculation for 2D Quadrilaterals The major functional components of autonomous vehicles are perception, control, planning, system management, and localization. Perception is a process that senses the surrounding environment using various sensors like Radars, LiDARs, Ultrasonic and Cameras sensors. Sensors are designed to extract information from the environment and hence, to perceive the surroundings. • Lidars are used to extract the information on the position and shape of surrounding obstacles within its range and field of view (FOV). • Camera sensor data provides information about the object class. • Radars are used to derive the position and velocity of the obstacles and so on. Multi-sensor fusion integrates the sequence of observations from a number of heterogeneous sensors into a single best estimate of the state of the environment. One of the Sensor Fusion outputs is the IOU (Intersection over Union) or Jaccard index during the object detection. When the object detection is performed through more than one source of sensors (such as Ultrasonic and Camera sensors), the IOU or Jaccard index is calculated to quantify the percent overlap from two different sources of sensors. The basic problem in multi-sensor fusion systems is to integrate a sequence of observations from a number of different sensors into a single best estimate of the state of the environment. In such a case, the IOU helps to identify the overlap area, which is captured from the multi-sensors. For example for the Autonomous Parking Functionality of ADAS (Autonomous Driving Assistance System), the Ultrasonic and Camera sensors are capturing the free space for Ego Vehicle Parking (as shown in the below figure). As per the capability and mounting position of different sensors, the available parking space is captured. The captured area from different sensors may or may not be the same. In that case, the IOU or Jaccard Index helps to quantify the overlap area detected by two different sensors. Figure 1: Practical use case of IOU The IOU or Jaccard Index is calculated as follows: Figure 2: IOU Calculation The IOU (Intersection over Union) value varies between 0 to 1. More the overlap region better the IOU value. Henceforth the confidence in the input data from the sensors increases. Lower the IOU, troubles in deciding the available space for the parking as different sensors are showing different spaces for parking. Figure 3: Confidence decision based on IOU Note: Decision of the High Confidence from calculated IOU value varies from application to application. For example, in some applications, High Confidence can be decided over 0.8 IOU value whereas, in some other applications, High Confidence can be decided over 0.9 IOU value. The IOU calculation can be done over the images or coordinates captured from the different sensors. In addition, the IOU calculation can also be performed considering the captured object as a 2D or 3D object. In this article, I have focused on the IOU calculation based on coordinates received from two different sensors. The captured coordinates would be of 2D Quadrilateral. Refer to the MATLAB Code for the Calculation of IOU using the X and Y coordinates captured from the two different sensors. The point of interest here is in finding the intersection points and identifying the quadrilateral vertices that lie inside another quadrilateral. A glimpse of the MATLAB code results: Figure 4: IOU calculation from MATLAB Code I have considered all the possible conditions for regular/irregular quadrilateral such as complete overlapping, no overlapping, vertices having negative and positive coordinates, and so on. Thank you for reading. I am open to discussion on this topic. Do reach out to me at chetan9chudhari@gmail.com. HAPPY LEARNING!!!

iMemScan for Trollstore Localized into English and German

A location based chat app built with firebase and framework7

A Windows Forms .Net 2003/C# customizable message box that supports custom buttons, icons, fonts ,"Don't ask me again" functionality, timeouts, localization and lots more.

Star me and I will localize your repositories

CCLoader mod to add locales

Controller Bot Localization (https://telegram.me/ControllerBot)

A method of converting machine codes to human-readable text in any language and phrasing.

Basic codes for Localized Collocation Methods (LCMs)

LocalizeMe is an open source library that is a simple and convenient localization tool for your projects.

Localizations for my iOS projects. Email me if you'd like to contribute: support@unlimapps.com

"🌐 Enhancing indoor navigation using Deep Learning. Predict accurate heading angles by fusing IMU sensor & GNSS data. Expanding architectures for precise direction estimation. Join me in advancing indoor localization. 🚀"

This project is done as a part of 1st semester ME curriculum. The main objective is to improve the speed and accuracy of detecting and localizing brain tumors based on MRI scans. In this project I have trained 2 AI models 1) RESNET 2) RESUNET. The main objective of RESNET model was to detect brain tumor by processing the MRI image. The RESUNET model was used for finding where exactly the brain tumor is present in the MRI image. After training our both models, we come to a single result of brain MRI with tumor mask on it and then compared that result with the already available result. We were available to achieve an accuracy of 98%.

My small Index of various software localized to Arabic by me.

App desenvolvido período ensino superior.

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Computer Vision ⬩ This is a short implementation of the YOLOv3 Framework to train a model to detect and localize the faces of me and 4 of my 'tropa' / friends (DJ, Gab, Nicolo, Nuy) using Pytorch backend and Roboflow for image labelling and annotation.

Grunt plugin for localization service lokalise.me

Creates localized branches of a metalsmith collection

Localizations for my iOS projects. Contact me if you would like to contribute.

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