◆3.0 Introduction ◆3.1 z-Transform ◆3.2 Properties of the Region of Convergence for the z-transform ◆3.3 The inverse z-Transform ◆3.4 z-Transform Properties ◆3.5 z-Transform and LTI Systems ◆3.6 the Unilateral z-Transform
◆7.0 Introduction ◆7.1 Design of Discrete-Time IIR Filters From Continuous-Time Filters ◆7.2 Design of FIR Filters by Windowing ◆7.3 Examples of FIR Filters Design by the Kaiser Window Method ◆7.4 Optimum Approximations of FIR Filters ◆7.5 Examples of FIR Equiripple Approximation ◆7.6 Comments on IIR and FIR Discrete-Time Filters
◆8.0 Introduction ◆8.1 Representation of Periodic Sequence: the Discrete Fourier Series (DFS) ◆8.2 Properties of the DFS ◆8.3 The Fourier Transform of Periodic Signal ◆8.4 Sampling the Fourier Transform(DTFT) ◆8.5 Fourier Representation of Finite-Duration Sequence: the Discrete Fourier Transform(DFT) ◆8.6 Properties of the DFT ◆8.7 Linear Convolution using the DFT ◆8.8 the discrete cosine transform (DCT)
◆Signal processing is benefited from a close coupling between theory, application, and technologies for implementing signal processing systems. ◆Signal processing deals with the representation, transformation, and manipulation of signals and the information they contain
◆8.0 Introduction ◆8.1 Representation of Periodic Sequence: the Discrete Fourier Series ◆8.2 Properties of the Discrete Fourier Series ◆8.3 The Fourier Transform of Periodic Signal ◆8.4 Sampling the Fourier Transform ◆8.5 Fourier Representation of Finite-Duration Sequence: the Discrete Fourier Transform ◆8.6 Properties of the Discrete Fourier Transform ◆8.7 Linear Convolution using the Discrete Fourier Transform ◆8.8 the discrete cosine transform (DCT)
