## Digital Signal Processing: DSP and ApplicationsThis book is a uniquely practical DSP text which places the emphasis on understanding the principles and applications of DSP with a minimum of mathematics. In one volume, it covers a broad area of digital signal processing systems such as A/D and D/A converters, adaptive filters, spectral estimation, neural networks, Kalman filters, fuzzy logic, data compression, error correction and DSP programming. Many courses will find that this book will replace several texts currently in use. The level is ideal for introductory university modules, and similar courses such as HNC/D. As DSP has come to be studied at a lower academic level over recent years this text meets a genuine need. It is also suitable for use on industrial training courses and ideal as a reference text for professionals. A readable introduction to the practical application of DSP Broad coverage of the subject means this will cover a typical undergraduate module in just one book Practical focus with maths treated as a practical tool - not an advanced maths text |

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### Contents

1 | |

Chapter 2 The analogdigital interface | 20 |

Chapter 3 Adaptive digital systems | 48 |

Chapter 4 Nonlinear applications | 69 |

Chapter 5 Spectral analysis and modulation | 100 |

Chapter 6 Introduction to Kalman filters | 119 |

Chapter 7 Data compression | 136 |

### Common terms and phrases

algorithm amplitude analog input antenna applications artificial neural network assume binary bit errors BRAKE calculated channel circuit code word common commonly complex constant converters convolution corresponding data compression decoder delay line delta modulation digital signal processing DSP chips encoder equation estimate example feedback Figure filter coefficients finite impulse response FIR filter frequency fuzzy gradient Hamming distance hardware Hence implemented implies impulse response input data input signal input vector instance instructions integrator Kalman filter layer linear loop matrix maximum median filter membership functions memory method multiplication needed node noise non-linear obtain operation output signal oversampling parameters performance pointer polynomial predictor probability problems processor pulse quantization recursive sampling rate sequence signal x(n spectral speech signal speed step symbols tion transfer function transform transmitted Uref voltage wavelet weight vector word length xˆ(N zero