Signal Processing

Signal processing represents a critical discipline in numerous modern applications, focusing on the manipulation and analysis of information derived from various signal sources such as audio, video, biomedical signals, radar data, and more. Its implementation typically involves key algorithms like Fast Fourier Transform (FFT) for frequency analysis, digital filtering techniques (FIR/IIR filters) for noise reduction, and wavelet transforms for multi-resolution analysis. The applications span diverse domains including medical diagnostics (ECG signal analysis), audio processing (spectral editing), image processing (edge detection), and even financial analytics (time-series forecasting). Mastering core digital signal processing concepts enables professionals to implement efficient algorithms using programming frameworks like MATLAB (with Signal Processing Toolbox) or Python (utilizing libraries such as SciPy and NumPy) for real-time signal manipulation and feature extraction.