438 skills found · Page 6 of 15
dp770 / Aws Deepracer WorksheetWorksheet and Utilities for AWS DeepRacer – one of the most exciting ways of building strong skills in reinforcement learning and through a hands-on approach. This repository offers: 1) Functionally-rich and flexible reward function 2) Utilities with Jupiter notes for Racing Line calculation and visualisation of track 3) Scripts to parse RoboMaker training and evaluation logs to CSV file 4) Sample Excel file for car behaviour analysis as well as designing and planning new reward curves 5) Coordinates and AWS DeepRacer tracks and images.
abdallahkhairy / GP Data Analysis And MLHuman locomotion affects our daily living activities. Losing limbs or having neurological disorders with motor deficits could affect the quality of life. Gait analysis is a systematic study of human locomotion, which is defined as body movements through aerial, aquatic, or terrestrial space. This analysis has been used to study people ambulation, registration, and reconstruction of physical location and orientation of individual limbs used to quantify and characterize human locomotion using different gait parameters including gait activities such as walking, stairs ascending/descending, … etc., phases, and spatiotemporal parameters of human gait. Additionally, gait analysis parameters can be used to evaluate the functionality of patients and wearable system users. The evaluation is based on patient's stability, energy consumption, gait symmetry, ability to recover from perturbations, and ability to perform activities of daily living. Many companies develop assistive, wearable, and rehabilitation devices for patients with lower limb neurological disorders. These devices are tested and evaluated inside controlled lab environments. However, they don’t have enough data on the patient's performance in real world and harsh environments. Collecting large datasets of device users and their gait performance data in real environment are notoriously difficult. Additionally, collecting data on less prevalent or on gait activities other than level walking, stair ascending/descending, sitting, standing, …etc. on hard surfaces is rarely attempted. However, the scope for collecting gait data from alternative sources other than traditional gait labs could be attained with the help of IoT data collection embedded on the wearable and assistive devices and well-established cloud platforms equipped with big-data analytics and data visualization capabilities. This project aims to develop a cloud platform capable of collect data from wearable and assistive devices such as prostheses, exoskeleton, gait analysis wearable sensors, …etc. using IoT technologies. This platform is capable of automatically use data mining and visualization tools. Additionally, it uses statistical and machine learning techniques to estimate gait events, gait symmetry, gait speed, gait activities, stability, energy consumption, …etc. Also, it is capable of predicting patient's progress over time. The project will be composed of two major components, hardware component and software component. In hardware component, the students will design and implement the IoT that collects the different readings for gait analysis and send them to the cloud. Meanwhile, in the software component, the students will design and implement a set of algorithms to visualize the collected data, then design and implement data analytics to automatically analyze the collected data, so that we can estimate gait events, gait symmetry, gait speed, classify gait activities, stability, energy consumption, …etc. and predicting patient's progress over time. By analyzing the collected data, the patient's progress can be predicted over time. Additionally, these data can be used through manufacturers of prostheses legs to improve their products, as well as through health-care centers to assess the patient's performance. The following figures describe the main modules of our graduation project.
DCAN-Labs / Functional Random ForestFunctional Analysis with Random Forests
mibur1 / CometA toolbox for dynamic functional connectivity and multiverse analysis
NanditaRao / Sentiment Analysis On Finance DataThe application is a cloud service that provides the functionality of performing sentiment analysis on stock market and financial data. The application can be hosted on Google App Engine and makes use of many of the GAE services like Search Service, MemCache, DataStore etc. Given the name of a company, data from various sources like Twitter, Facebook Graph, Google News, Google Finance etc is aggregated. For each source, different models have been pretrained using some prior data. Using different models provided us with a chance to utilize different Machine Learning methodologies based on the type of data from each source. The various techniques that we have built and tested on are :Naive Bayes, Multinomial and Bernoulli text representations, KNN.
waldronlab / EnrichOmicsFunctional enrichment analysis of high-throughput omics data
nikolamilosevic86 / TableDisentanglerFunctional and structural analysis of tables in research papers (Table disentangling)
CBICA / NiCHARTThe neuro-imaging brain aging chart [niCHART] is a comprehensive solution to analyze standard structural and functional brain MRI data across studies. [niCHART] and the associated pre-processing tools implement computational morphometry, functional signal analysis, quality control, statistical harmonization, data standardization, interactive visual
COS301-SE-2023 / ABC Service Request SystemThis is a project management and ticketing system built by LUNA. It allows efficient task allocation and tracking across management, technical, and functional teams. Key features include user management, ticket dashboard, time tracking, and AI-based data analysis. Built with Angular, Node.js, Express and MongoDB.
intel / Reference PE And Measurements DB For WiFi Time Based Scalable LocationThe repository includes a reference Matlab code for a mobile WiFi client receiver, Kalman-Filter-based positioning engine (PE) and a database of time-delay measurements of an indoor environment. The database includes both real-measured data, and simulated time-delay data, as well as ground-truth client position information, which can be used for validation and performance analysis. The PE code is designed to process the database data format and produce estimates of the mobile WiFi receiver position within the indoor venue. The database may be used for the development, validation and performance analysis of additional positioning engines. This kind of WiFi client PE enables an unlimited number of clients to estimate the position and navigate within the Wi-Fi network coverage area. Hence, it enables so-called "Wi-Fi Scalable Location". A *.pdf whitepaper is included in the repository and explains the functionality and usage of the code and database.
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yjingj / BFDAMatlab toolbox for Bayesian functional data analysis
decodebiology / GeneSCF InactiveGeneSCF moved to a dedicated GitHub page, https://github.com/genescf/GeneSCF
SchrodingerZhu / Reussir LangThe Reussir Programming Language. Reuse Analysis in MLIR and Rust. Functional programming meets performance.
JayGLXR / PhantomKeystrokeAn advanced attribution deception tool that adds subtle, region-specific fingerprints to command-line operations to mislead forensic analysis. It seamlessly integrates with C2 frameworks and provides realistic keyboard patterns from different regions while maintaining full command functionality.
ilitzkyd / Stock Market AnalysisThis Python script provides two main functionalities: stock and economic indicators analysis. It utilizes the Yahoo Finance to fetch historical stock price data for multiple tickers and the fredapi library to fetch economic indicator data from the FRED API.
mubdi / Pyastrolibpyastrolib is a python library providing professional astronomers a robust set of tools for astronomical data analysis. This project will provide all the similar functionality as NASA's IDL Astronomy User's Library in addition to other planned features.
Arsha89 / AGC AnalysisExtracting neuronal functional network dynamics via adaptive Granger causality analysis
bow / Pytest PipelinePytest plugin for functional testing of data analysis pipelines
XMMGOF / PysaspySAS is a python wrapper for the Science Analysis Software (SAS) used for analyzing XMM-Newton data. This repository is maintained by the XMM-Newton Guest Observer Facility (GOF) at Goddard Space Flight Center for testing new pySAS functionality.
