Signal Processing Enhancements with Machine Learning

This project focuses on integrating machine learning techniques into digital signal processing (DSP) to improve signal accuracy, noise reduction, and pattern recognition. By leveraging AI-driven algorithms, I provide intelligent solutions for various signal processing applications in communications, biomedical, audio, and radar systems.

✅ AI-Powered Noise Reduction – Implement adaptive filtering and deep learning techniques for enhanced signal clarity. ✅ Time-Series Analysis & Forecasting – Utilize ML models like LSTMs and CNNs to analyze and predict signal trends. ✅ Feature Extraction & Classification – Automate signal classification using ML-based feature extraction methods. ✅ Real-Time DSP Implementations – Optimize DSP algorithms on FPGA, microcontrollers, and embedded systems. ✅ Speech & Audio Processing – Improve speech recognition, sound enhancement, and audio signal classification.

🔹 Python (TensorFlow, PyTorch, Scikit-learn) 🔹 MATLAB & Simulink 🔹 FPGA (Verilog, VHDL) 🔹 Embedded Systems (Microcontrollers, DSP Processors)

With my extensive background in AI/ML and DSP, I deliver high-performance solutions tailored to industry-specific challenges. Let’s discuss how I can enhance your signal processing applications! 🚀