Radio Frequency Machine Learning with PyTorch

Motor Imagery EEG Signal Classification Using Random Subspace Ensemble Network

Code for "Complex-Valued Depthwise Separable Convolutional Neural Network for Automatic Modulation Classification"

Detection of human emotions from eeg signals using the amigos dataset

This repository contains the implementation of the modified convolutional neural network (MCNN) for classification of Frequency-Hopping Spread Spectrum Signals using the time-frequency representations (TFRs) as an images.

Production-grade ensemble framework combining XGBoost, PyTorch & Sklearn - 70%+ test coverage with Optuna optimization for time-series prediction

Real-time RF signal intelligence pipeline for drone RF classification, live SDR replay/receive, ONNX export, TensorRT deployment on NVIDIA Jetson, and Nsight-profiled edge inference.

A transform with at most 25 code lines proved so far a convenient substitute for more sophisticated transforms (e.g. MFCCs) in various signal recognition problems. Particularly useful in TinyML context

Classification of biosignals, including EEG and MEG, through feature extraction in the time and frequency domains using machine learning methods.

AtrialFibrillation-Classification-SMoLK is a deep learning project designed to classify arrhythmias, specifically focusing on atrial fibrillation (Afib), using the MIT-BIH Arrhythmia Database. The project leverages a neural network model with learned filters to achieve high classification accuracy across multiple classes.

AI-Trading Agent that processes trading data, detects price-action patterns, classifies market movements as bullish, bearish, etc and explains those findings in clear human language.

📈 Análise e processamento de sinais com SVD

Real-time robot collision detection using supervised learning methods

ML-based trading signal system: XGBoost classifier on financial time-series, 51+ features, SMC filter, live multi-asset inference with Telegram alerts

VBS Power Line Fault Detection (Kaggle challenge)

Deep Leaning for Classification of drone signals.

An ML system for automated medical signal analysis, evolving from deep learning research to a containerized MLOps pipeline.

"Machine learning project for classifying music into genres using audio feature extraction and classification models."

One of the firsts dataset level explanability libraries for 1d signal using GRAD-CAM++