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quantumkatas

Tutorials and programming exercises for learning Q# and quantum computing

quorumcomm

Wireless Sensor Network Simulator

radar-simulation

A simulation environment is created in MATLAB using Automated Driving Toolbox and then vehicle tracking is done using Kalman filter utilizing simulated RADAR data for measurement update

radar_target_detection

Detect Vehicle range and angle from radar noisy measurements.

radiology_optimization

Tomotherapy Dose Optimization according to Shepard's 1999 paper

random-forest-with-genetic-algorithm

Random Forest hyper-parameter fine tuning with a gentic algorithm

range-mcl

Range Image-based LiDAR Localization for Autonomous Vehicles Using Mesh Maps (chen2021icra)

rareeventdetection

Detection of rare-events from a time-series dataset using both LSTM autoencoders and also a normal LSTM network.

rc-gps

Code for "Causal inference in the context of an error prone exposure: Air pollution and mortality". A new approach for estimating causal effects when the exposure is measured with error.

react-google-maps

Driving directions with google maps integration

real-time-anomaly-prediction-in-distributed-systems

Using Hierarchical Temporal Memory for real time anomaly prediction in distributed systems

real-time-sentiment-tracking-on-twitter-for-brand-improvement-and-trend-recognition

A real-time interactive web app based on data pipelines using streaming Twitter data, automated sentiment analysis, and MySQL&PostgreSQL database (Deployed on Heroku)

recurrent-spatial-transformer-code

Code for Recurrent Spatial Transformer Networks

refined_rough_fuzzy_clustering

It is the code of my IEEE conference paper on refined rough fuzzy clustering.

reinforcement-learning

What is reinforcement learning? Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward. Although the designer sets the reward policy–that is, the rules of the game–he gives the model no hints or suggestions for how to solve the game. It’s up to the model to figure out how to perform the task to maximize the reward, starting from totally random trials and finishing with sophisticated tactics and superhuman skills. By leveraging the power of search and many trials, reinforcement learning is currently the most effective way to hint machine’s creativity. In contrast to human beings, artificial intelligence can gather experience from thousands of parallel gameplays if a reinforcement learning algorithm is run on a sufficiently powerful computer infrastructure.

reinforcement-learning-in-path-finding

Reinforce Learing, Q-Rounting, Shortest-Path

relational-nem

Code for the "Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions" paper.

relationprediction

ACL 2019: Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

reptile-pytorch

A PyTorch implementation of OpenAI's REPTILE algorithm

research

resume_projects

rf-cmoco

Handing Wang, Yaochu Jin, A Random Forest-Assisted Evolutionary Algorithm %for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems IEEE Transactions on Cybernetics, accept, 2018.

rf-coverage-optimization-using-machine-learning

• Data preprocessing and Data base management using MySQL. • Coverage Analysis using SVR (Support Vector Regression), ANN (Artificial Neural Network) with Keras, Tensor Flow and Outlier Detection through cluster-based approaches including K-Means clustering and DBSCAN • Network Health Estimation using Bayesian Kriging, Deep Learning and Graph Signal Processing by solving an optimization problem on graph using proximal splitting methods. • Developing a user-friendly GUI for the regularity authority for data visualization, analytics and store the results of the analysis

rf-localization

This package contains a collection of tools which implements different regression methods in order to localize and track an agent in an environment

rf_localization

Radio Frequency based localization (@433MHz) framework for micro underwater robots in confined tanks using software defined radio (SDR)

rl-agent-with-intrinsic-reward

Training agent with reinforcement learning and intrinsic reward to navigate through complex environment

rlcabdriver

RL- & Neural network enabled-agent to accept a request with optimum reward

rlcontinuousactionspace

Reinforcement Learning in continuous state and action spaces. DDPG: Deep Deterministic Policy Gradient and A3C: Asynchronous Actor-Critic Agents

rllibsumoutils

Python3 library able to connect the RLLIB framework with the SUMO simulator.

rnn-time-series-anomaly-detection

RNN based Time-series Anomaly detector model implemented in Pytorch.