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It is characterized by the use of digital signals to represent these signals as discrete time, discrete frequency, or other discrete domain signals in the form of a sequence of numbers or symbols to permit the digital processing of these signals. Theoretical analyses and derivations are typically performed on discrete-time signal models, created by the abstract process of sampling.

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Numerical methods require a digital signal, such as those produced by an analog-to-digital converter ADC. The processed result might be a frequency spectrum or a set of statistics.


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MATLAB: An Introduction with Applications

It also treats important applications including signal detection in noise, radar range estimation for airborne targets, binary communication systems, channel estimation, banking and financial applications, and audio effects production. Part II considers selected signal processing systems and techniques. Core topics covered are the Hilbert transformer, binary signal transmission, phase-locked loops, sigma-delta modulation, noise shaping, quantization, adaptive filters, and non-stationary signal analysis.


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    Treats important applications including signal detection in noise, radar range estimation for airborne targets, binary communication systems, channel estimation, banking and financial applications, and audio effects production Covers advanced core topics Features extensive Matlab implementations Includes many examples see more benefits.

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