326 skills found · Page 3 of 11
namanrajpal / FurnitureARBuilt to conduct a study on determining efficiency of AR in changing user perception. Users can rate furniture products by actually looking at them. Beta : Companies can upload there product models to create AR brochures instantly
sdsc / P3dfft.3P3DFFT++ (a.k.a. P3DFFT v. 3) is a new generation of P3DFFT library that aims to provide a comprehensive framework for simulating multiscale phenomena. It takes the essence of P3DFFT further by creating an extensible, modular structure uniquely adaptable to a greater range of use cases. The users can specify in detail what kind of data layout they would like to use, both in terms of local memory ordering and the processor layout. Just like P3DFFT, P3DFFT++ is a distributed software package, using MPI as the primary method for interprocessor commubnication. It supports 1D, 2D and 3D (to come soon) domain decomposition schemes. As P3DFFT, P3DFFT++ also relies on lower-level libraries, for example FFTW to perform optimized 1D FFTs. Unlike P3DFFT, which was written in Fortran90, P3DFFT++ is written in C++. Interfaces are provided for C and Fortran. To learn about using the code the user is encouraged to study example programs in C++, C and FORTRAN subdirectories. Please e-mail Dmitry Pekurovsky (dmitry@sdsc.edu) for any questions or suggestions. Software contributions are welcome, assuming they follow the main ideas of the framework.
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.
zengyh1900 / Handy Votinghandy tools for user study
anitalu724 / MutScapeＡ user-friendly Python toolkit, which provides a comprehensive pipeline to easily explore the cohort-based mutational characterization for studying cancer genomics.
Taintedy / Flock GptFlockGPT is a novel approach to drone flocking control using natural language and generative AI. It features an LLM-based interface for user interaction and a flocking technology system that ensures smooth movement of the drone swarm. Our user study confirmed its intuitive control and high performance.
pnfernandes / Python Code For Stress Constrained Topology Optimization In ABAQUSThis repository contains a Python code with five implementations of topology optimization approaches suitable for 2D and 3D problems, all considering bi-directional evolutionary structural optimization. The approaches implemented include both discrete and continuous methods, namely: - Optimality Criteria, for continuous or discrete variables; - Method of Moving Asymptotes; - Sequential Least Squares Programming (from SciPy module); - Trust-region (from SciPy module). The implementation of the Optimality Criteria method is suitable for compliance minimization problems with one mass or volume constraint. The implementation of the remaining methods is suitable for stress constrained compliance minimization and stress minimization problems, both with one mass or volume constraint. The code uses the commercial software ABAQUS to execute Finite Element Analysis (FEA) and automatically access most of the necessary information for the optimization process, such as initial design, material properties, and loading conditions from a model database file (.cae) while providing a simple graphic user interface. Although the code has been developed mainly for educational purposes, its modularity allows for easy editing and extension to other topology optimization problems, making it interesting for more experienced researchers. This code has been used in the article "Python code for 2D and 3D stress constrained topology optimization in ABAQUS: theory, implementation, and case studies" [1]. The folders included in this dataset contain the results obtained, as well as the information necessary to replicate them. In particular, the folder 'Validation' contains the data used to validate the functioning of the code provided. Notes: - Stress-dependent problems are only compatible with the following ABAQUS element types: CPE4, CPS4, 3DQ8, and S4. - The authorship of the functions 'mmasub' and 'subsolv' used in the Method of Moving Asymptotes are credited to Arjen Deetman. Source: https://github.com/arjendeetman/GCMMA-MMA-Python - Despite the validations performed, this program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
manyasrinivas2021 / AI BASED FACIAL EMOTION DETECTION USING DEEP LEARNING“AI Based Facial Emotion Detection”, developed using many machine learning algorithms including convolution neural networks (CNN) for a facial expression recognition task. The goal is to classify each facial image into one of the seven facial emotion categories considered in this study.Trained CNN models with different depth using gray-scale images from the Kaggle website.CNN models are developed in Pytorch and exploited Graphics Processing Unit (GPU) computation in order to expedite the training process. In addition to the networks performing based on raw pixel data,Hybrid feature strategy is employed by which trained a novel CNN model with the combination of raw pixel data and Histogram of Oriented Gradients (HOG) features. To reduce the over fitting of the models,different techniques are utilized including dropout and batch normalization in addition to L2 regularization. Cross validation is applied to determine the optimal hyper-parameters and evaluated the performance of the developed models by looking at their training histories. Visualization of different layers of a network is presented to show what features of a face can be learned by CNN models. Based on the emotion the program recommends the music for the user to up flit the mood.
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
dbgbench / Dbgbench.github.ioDBGBench Website:
ajk77 / SimpleEMRSystemThe Simple EMR System is a rapidly deployable and readily customizable electronic medical record (EMR) user interface for supporting laboratory-based research studies of EMR design and usability.
aaronstone1699 / Depression DetectionDepression is one of the most common mental disorders with millions of people suffering from it.It has been found to have an impact on the texts written by the affected masses.In this study our main aim was to utilise tweets to predict the possibility of a user at-risk of depression through the use of Natural Language Processing(NLP) tools and deep learning algorithms.LSTM has been used as a baseline model that resulted in an accuracy of 95.12% and an F1 score of 0.9436. We implemented a hybrid Bi-LSTM + CNN model which we trained on learned embeddings from the tweet dataset was able to improve upon previous works and produce precision and recall of 0.9943 and 0.9988 respectively,giving an F1 score of 0.9971.
SPKavati / Threat Intelligence Machine Learning Approach To ICS Security The growing network connectivity witnessed in Supervisory Control and Data Acquisition (SCADA) systems raises cyber security concerns for Industrial Control System (ICS) facilities. To sustain critical infrastructure objective principles such as confidentiality, integrity, and availability from security breaches or devastating cyberattacks, compelling, proactive, and continuous security monitoring is needed. In this study, we propose a process to build an intelligent backend and visual system to handle real time data analytics. For that we demonstrate the use of the Security Information and Event Management (SIEM) tool, Splunk, to aggregate operational intelligence including network, system, and user behavior data. Also, to transform collected raw data into Indicators of Compromise (IOC) added intelligence data, we demonstrate the use of open source threat intelligence platforms. Real time analytics is then applied to prepared intelligence test data using MATLAB. With the proof of concept tool, Tableau, we present ICS system visual solutions, which can support security personnel to make decisions, understand concepts, or foresee the network problems.
SandhyaRani18 / ChatbotChat bots in health care services have the potential to provide patients access to immediate medical information. Health care chat bots could help patients better manage own health related issues. An AI based chat bot can provide an adequate and instant solution for human health related queries, so this proposed methodology provides a reliable solution that resolves users’ health care issues through a user-friendly environment. Chat bots could be useful for preliminary information; however, it’s vital that patients don’t try to use them to replace human doctors. They can answer fundamental questions, but patients should always verify the information with a medical professional before taking the prescribed prescription. Hence, the chat bot reaches each and every individual for not only answering his/her questions but also provides reliable suggestions. In the study AWS has been implemented for a trouble-free output, which provides sequences of services to provide effective and efficient usage of bot. The Simulations have been enhanced in the study and automated the bot to do the simulations by itself.
gomoku / RenLibThe most popular Renju software in the world. The function of Renlib is to store analyses and games in the special "lib" format. It also has the possibility to write comments on analyses. It has a good VCF (victory by continuous fours) search function. Almost every serious renju player is using renlib as part of their everyday renju study. Besides Renlib software, you can get some samples of lib files with opening theories and analyses. Lots of players exchange lib files and publish the games and analyses collections in lib format in their homepages. In addition to that, Renlib can help you to create the Java-based renju diagrams for your website, together with the space for comments. Windows XP users should know that their Internet Explorer does not have Java support and in that case if you want to create web pages with renju diagrams then you could just visit www.java.com and install Java support first. Renlib is programmed by Frank Arkbo, Sweden. Location: http://www.renju.se/renlib/
snousias / AvatreeThis paper presents AVATREE, a computational modelling framework that generates Anatomically Valid Airway tree conformations and provides capabilities for simulation of broncho-constriction apparent in obstructive pulmonary conditions. Such conformations are obtained from the personalized 3D geometry generated from computed tomography (CT) data through image segmentation. The patient-specific representation of the bronchial tree structure is extended beyond the visible airway generation depth using a knowledge-based technique built from morphometric studies. Additional functionalities of AVATREE include visualization of spatial probability maps for the airway generations projected on the CT imaging data, and visualization of the airway tree based on local structure properties. Furthermore, the proposed toolbox supports the simulation of broncho-constriction apparent in pulmonary diseases, such as chronic obstructive pulmonary disease (COPD) and asthma. AVATREE is provided as an open-source toolbox in C++ and is supported by a graphical user interface integrating the modelling functionalities. It can be exploited in studies of gas flow, gas mixing, ventilation patterns and particle deposition in the pulmonary system, with the aim to improve clinical decision making.
Chaloemphisit / KMITL Study Table Chrome ExtensionChrome extension to re-render user interface for KMITL Study Table page. It is designed base on old design adding some features
eugenbobrov / Adaptive Regularized Zero Forcing Beamforming In Massive MIMO With Multi Antenna UsersModern wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This technology involves operations with an antenna array at a base station that simultaneously serves multiple mobile devices which also use multiple antennas on their side. For this, various precoding and detection techniques are used, allowing each user to receive the signal intended for him from the base station. There is an important class of linear precoding called Regularized Zero-Forcing (RZF). In this work, we propose Adaptive RZF (ARZF) with a special kind of regularization matrix with different coefficients for each layer of multi-antenna users. These regularization coefficients are defined by explicit formulas based on SVD decompositions of user channel matrices. We study the optimization problem, which is solved by the proposed algorithm, with the connection to other possible problem statements. We also compare the proposed algorithm with state-of-the-art linear precoding algorithms on simulations with the Quadriga channel model. The proposed approach provides a significant increase in quality with the same computation time as in the reference methods.
jddeguia / Compare Forecast ModelsEnergy production of photovoltaic (PV) system is heavily influenced by solar irradiance. Accurate prediction of solar irradiance leads to optimal dispatching of available energy resources and anticipating end-user demand. However, it is difficult to do due to fluctuating nature of weather patterns. In the study, neural network models were defined to predict solar irradiance values based on weather patterns. Models included in the study are artificial neural network, convolutional neural network, bidirectional long-short term memory (LSTM) and stacked LSTM. Preprocessing methods such as data normalization and principal component analysis were applied before model training. Regression metrics such as mean squared error (MSE), maximum residual error (max error), mean absolute error (MAE), explained variance score (EVS), and regression score function (R2 score), were used to evaluate the performance of model prediction. Plots such as prediction curves, learning curves, and histogram of error distribution were also considered as well for further analysis of model performance. All models showed that it is capable of learning unforeseen values, however, stacked LSTM has the best results with the max error, R2, MAE, MSE, and EVS values of 651.536, 0.953, 41.738, 5124.686, and 0.946, respectively.
akanakia / Microsoft Academic Paper Recommender User StudyThe raw data and analysis code for the Microsoft Academic paper recommender system user study conducted in 2018.
