A Course in Digital Signal Processing with Examples
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Digital Signal Processing (DSP) is a field of acquiring, analyzing, transforming, filtering and enhancing signals, aiming at some application benefits. DSP expertise should learn linear algebra, Fourier analysis, Z-transform, wavelet-transform, filter design and filtering techniques, linear systems and system analysis. DSP expertise should also learn certain scientific and technical computing languages such as Python, Octave, R, Matlab, etc. to use the Fast Fourier Transform (FFT) algorithm and other open source DSP packages which are essential for solving and simulating DSP problems. Since mo...
Digital Signal Processing (DSP) is a field of acquiring, analyzing, transforming, filtering and enhancing signals, aiming at some application benefits. DSP expertise should learn linear algebra, Fourier analysis, Z-transform, wavelet-transform, filter design and filtering techniques, linear systems and system analysis. DSP expertise should also learn certain scientific and technical computing languages such as Python, Octave, R, Matlab, etc. to use the Fast Fourier Transform (FFT) algorithm and other open source DSP packages which are essential for solving and simulating DSP problems. Since most real-world signals and data are generated by random processes, then learning probability, statistics, statistical analysis and inference in order to process and handle these random signals is an essential subject. In this context, this book provides a first and basic DSP course for university students and those intending to become a DSP expertise. The next step to generate a course in statistical digital signal processing which will be in a separate book.
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