AI RF Signal Classification Using MATLAB Deep Learning

This MATLAB project implements machine learning to classify radio frequency signals automatically. Using convolutional neural networks (CNNs), the system identifies modulation types like QAM, PSK, and FSK from raw I/Q data. The workflow includes signal preprocessing, spectrogram generation, and model training with Deep Learning Toolbox. Ideal for spectrum monitoring and cognitive radio systems, this solution achieves 95% accuracy in real-world tests. Demonstrates MATLAB's capabilities in handling complex RF datasets and AI integration for wireless communications. Keywords: RF signal classification, AI wireless, MATLAB deep learning, spectrum analysis, cognitive radio, modulation recognition.

Classify Object Behavior for Vehicle Safety

Enhance autonomous vehicle safety through object behavior classification using MATLAB's Deep Learning Toolbox. This project implements CNN-LSTM hybrid networks to predict pedestrian and vehicle trajectories from camera/LiDAR data. The tutorial covers anomaly detection in traf.....

Autonomous Driving vehicle safety MATLAB AI

Deep Image Prior for Inverse Problems in Imaging

Explore cutting-edge deep image prior techniques for solving inverse problems in computational imaging using MATLAB. This project implements untrained convolutional neural networks as natural image priors for tasks like denoising and super-resolution. The tutorial covers medical i.....

Computational Imagin inverse problems image restoration

Quadruped Robot with a Manipulator

Design advanced locomotion controllers for quadruped robots with manipulators using MATLAB's SimMechanics. This project implements dynamic gait generation and payload compensation algorithms for stable mobile manipulation. The tutorial covers footstep planning, whole-body con.....

Robotics quadruped robot MATLAB robotics