Pinned Repositories
Ball-and-Beam-Control-using-State-Feedback-Controller-Observer-and-descrete-Controller
Double-Inverted-Pendulums-Control-Using-State-Feedback
Function-Approximation-and-Adaptive-PID-Gain-Tuning-using-Neural-Networks-and-Reinforcement-Learning
System Identification and Self-Tuning PID Control using NN
Histogram-Oriented-Gradients-HOG-Supported-Vector-Machine-SVM-Face-Detection
Extract features from the Stanford Dataset using Histogram of Oriented Gradients (HOG) and use Supported Vector Machine (SVM) to map the features to the assigned labels. Next, use Non-Maximum Suppression and Heatmap method to find the best bounding box on the face.
Modeling-Simulation-Design-and-Fabrication-of-2-wheel-mobile-balanced-robot-2WMBR
Modeling a Mobile Robot using Advanced Dynamics Methods, Design & Simulation using Matlab and SolidWorks, Control using PID Controller, and Implemetation using Arduino UNO, Kalman Filter, and DC Motors..
PID-Tuning-using-Genetic-and-Particle-Swarm-Optimization
Offline Tuning PID gains in a given system using Heuristic Methods, including Genetic Algorithm andParticle Swarm Optimization
Quadcopter-Trajectory-Tracking-using-Adaptive-Nonlinear-Algorithms
PID, LQR, Feedback Linearization, Backstepping, Sliding Mode, and Model Reference Adaptive Control for 6-DoF Robot Control
Self-Tuning-PID-Control-using-Reinforcement-Learning-Based-Neural-Network
Novel adaptive tuning of the PID gains using an Actor-Critic-based Neural Network for Attitude Control of a 6-Dof, 4-motor Robot
Trajectory-Tracking-of-8DoF-Tiltrotor-via-Fuzzy-Sliding-Mode-Control-with-Fuzzy-Identification
Tiltrotor Control and System Identification using Fuzzy C-Means Clustering.
Video-Synopsis-Summarization-Car-Detection-Tracking
Detection, Counting, Tracking, and Summarization of the Vehicles using Computer Vision algorithms in a video camera recorded on a highway.
iman-sharifi-ghb's Repositories
iman-sharifi-ghb/Quadcopter-Trajectory-Tracking-using-Adaptive-Nonlinear-Algorithms
PID, LQR, Feedback Linearization, Backstepping, Sliding Mode, and Model Reference Adaptive Control for 6-DoF Robot Control
iman-sharifi-ghb/Function-Approximation-and-Adaptive-PID-Gain-Tuning-using-Neural-Networks-and-Reinforcement-Learning
System Identification and Self-Tuning PID Control using NN
iman-sharifi-ghb/Self-Tuning-PID-Control-using-Reinforcement-Learning-Based-Neural-Network
Novel adaptive tuning of the PID gains using an Actor-Critic-based Neural Network for Attitude Control of a 6-Dof, 4-motor Robot
iman-sharifi-ghb/Trajectory-Tracking-of-8DoF-Tiltrotor-via-Fuzzy-Sliding-Mode-Control-with-Fuzzy-Identification
Tiltrotor Control and System Identification using Fuzzy C-Means Clustering.
iman-sharifi-ghb/PID-Tuning-using-Genetic-and-Particle-Swarm-Optimization
Offline Tuning PID gains in a given system using Heuristic Methods, including Genetic Algorithm andParticle Swarm Optimization
iman-sharifi-ghb/Video-Synopsis-Summarization-Car-Detection-Tracking
Detection, Counting, Tracking, and Summarization of the Vehicles using Computer Vision algorithms in a video camera recorded on a highway.
iman-sharifi-ghb/Double-Inverted-Pendulums-Control-Using-State-Feedback
iman-sharifi-ghb/Modeling-Simulation-Design-and-Fabrication-of-2-wheel-mobile-balanced-robot-2WMBR
Modeling a Mobile Robot using Advanced Dynamics Methods, Design & Simulation using Matlab and SolidWorks, Control using PID Controller, and Implemetation using Arduino UNO, Kalman Filter, and DC Motors..
iman-sharifi-ghb/Ball-and-Beam-Control-using-State-Feedback-Controller-Observer-and-descrete-Controller
iman-sharifi-ghb/Histogram-Oriented-Gradients-HOG-Supported-Vector-Machine-SVM-Face-Detection
Extract features from the Stanford Dataset using Histogram of Oriented Gradients (HOG) and use Supported Vector Machine (SVM) to map the features to the assigned labels. Next, use Non-Maximum Suppression and Heatmap method to find the best bounding box on the face.
iman-sharifi-ghb/Inductive-Logic-Programming-ILP-using-Prolog-Programming-Aleph-Metagol-in-Maze
Extract Symbolic Rules using Background Knowledge and Environement Setting to find the State Transition rules in the Maze gridworld.
iman-sharifi-ghb/Inverted-Pendulum-Control-using-state-feedback-Controller-and-observer
iman-sharifi-ghb/Quadcopter-PID-Control-in-V-REP
Control Quadcopter using PID Controller in Vrep CoppeliaSim Software with Python
iman-sharifi-ghb/Camera-Operations-Homography-and-Fundamental-Matrix-Using-SIFT-KNN-RANSAC-Algorithms
SIFT, SURF, and ORB detector to extract the keypoints like corners. Fundumental Matrix and Homographical Transformation for Panorama Images.
iman-sharifi-ghb/CNN-Street-View-House-Number-Detection-SVHN
We use Convelutional Neural Networks (CNN), as a Deep Learning paradigm, to detect the house numbers using the MNIST dataset.
iman-sharifi-ghb/Retrospective-Cost-Adaptive-Controller-using-Recursive-Least-Square
iman-sharifi-ghb/Simulation-of-3DOF-Manipulator-on-6DoF-Satellite-using-Newtonian-Lagrangian-and-Quaternions-Methods
iman-sharifi-ghb/Symbolic-Reinforcement-Learning-Prolog-Programming
Safe Q-Learning via a Symbolic Logical Programming (Prolog) paradigm in Maze Gridworld.
iman-sharifi-ghb/Awesome-Fuzzy-Systems
Lets practice some fuzzy examples according to Wong's Book, including Gradient Descend Algorithm, Recursive Least Square, ...
iman-sharifi-ghb/Symbolic-Imitation-Learning
Extract geveral, symbolic rules in the environment to help Reinforcement Learning find the best, safe policy in the exploration phase. This method is interpretable, explainable, data-efficient, and enjoy symbolic reasoning.
iman-sharifi-ghb/iman-sharifi-ghb
Config files for my GitHub profile.
iman-sharifi-ghb/iman-sharifi-ghb.github.io
This is my personal website.
iman-sharifi-ghb/Safe-Reinforcement-Learning-via-Symbolic-Logical-Programming-for-Autonomous-Highway-Driving
Use Symbolic Logical Programming to find Safe actions in each state and help Reinforcment Learning to ensure safety in the exploration phase. We use it to make decision in Autonomous Highway Driving.