Historical Development:
– Signal processing principles trace back to classical numerical analysis techniques in the 17th century.
– Digital refinement of techniques emerged in the 1940s and 1950s with digital control systems.
– Claude Shannon’s 1948 paper laid the groundwork for information communication systems.
– Signal processing matured in the 1960s and 1970s.
– Digital signal processing became widely used with specialized chips in the 1980s.
Fundamentals of Signal Processing:
– A signal is represented by x(t) and can be deterministic or a realization of a stochastic process.
– Signal processing involves analyzing, modifying, and synthesizing various types of signals like sound, images, seismic signals, and scientific measurements.
– Techniques are used to optimize transmissions, digital storage, and signal correction.
Types of Signal Processing:
– Analog signal processing was prevalent in 20th-century radio, telephone, and TV systems.
– Continuous-time signal processing operates in the continuous domain.
– Discrete-time signal processing deals with sampled signals.
– Digital signal processing processes digitized discrete-time signals.
– Nonlinear signal processing analyzes signals from nonlinear systems.
Applications of Signal Processing:
– Seismic signal processing.
– Audio signal processing for sound representation.
– Image processing in digital cameras and computers.
– Video processing for interpreting moving pictures.
– Wireless communication for waveform generations and filtering.
Advanced Techniques and Specialized Applications:
– Optimization techniques like data-driven filterbank for automatic speaker verification.
– Genomic signal processing.
– Nonlinear system identification using NARMAX methods.
– Geophysics applications like time-frequency signal analysis.
– Machine learning for signal processing.
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Signal processing is an electrical engineering subfield that focuses on analyzing, modifying and synthesizing signals, such as sound, images, potential fields, seismic signals, altimetry processing, and scientific measurements. Signal processing techniques are used to optimize transmissions, digital storage efficiency, correcting distorted signals, subjective video quality, and to also detect or pinpoint components of interest in a measured signal.
