《电子工程师手册》学习资料（英文版）Signal Processing

Etter, D. Section II- Signal Processing The electrical Engineering Handbook Ed. Richard C. Dorf Boca raton crc Press llc. 2000

Etter, D. “Section II – Signal Processing” The Electrical Engineering Handbook Ed. Richard C. Dorf Boca Raton: CRC Press LLC, 2000

The world s most powerful digital signal processor, the TMS320C6x, performs at an unprec- dented 1600 million-instructions-per-second(MIPS). MIPS are the key measure of a chips capacity for executing signal processing tasks. This dSP delivers ten times the MIPS perf mance of any other DSP in history. The TMS320C6x is the first programmable DSp to ado an advanced Very Long Instruction Word(VLIW) architecture which increases the parallel execution of instructions by packing up to eight 32-bit instructions into a single cycle. The VLIW architecture, combined with the most efficient C compiler ever developed, dramatically proves performance and helps reduce the code development time The computing power of the TMS320C6x DSP will change the way new products are designed. The future generations of the TMS320C6x DSP will include devices fabricated with a new 0.18-micron, five-level metal process, operating at speeds beyond 250 MHz.(Photo courtesy of Texas Instruments c 2000 by CRC Press LLC

The world’s most powerful digital signal processor, the TMS320C6x, performs at an unprec￾edented 1600 million-instructions-per-second (MIPS). MIPS are the key measure of a chip’s capacity for executing signal processing tasks. This DSP delivers ten times the MIPS perfor￾mance of any other DSP in history. The TMS320C6x is the first programmable DSP to adopt an advanced Very Long Instruction Word (VLIW) architecture which increases the parallel execution of instructions by packing up to eight 32-bit instructions into a single cycle. The VLIW architecture, combined with the most efficient C compiler ever developed, dramatically improves performance and helps reduce the code development time. The computing power of the TMS320C6x DSP will change the way new products are designed. The future generations of the TMS320C6x DSP will include devices fabricated with a new 0.18-micron, five-level metal process, operating at speeds beyond 250 MHz. (Photo courtesy of Texas Instruments.) © 2000 by CRC Press LLC

Signal Processing 14 Digital Signal Processing W.K. Jenkins, A D. Poularikas, B W. Bomar, L.M. Smith, L.A. Cadzow Fourier Transforms. Fourier Transforms and the Fast Fourier Transform. Design and Implementation of Digital Filters. Signal Restoration 15 Speech Signal Processing S. McClellan, J.D. Gibson, Y. Ephraim, J.W. Fussell, D Wilcox, M.A. Bush, Y Gao, B Ramabhadran, M. Picheny Coding, Transmission, and Storage. Speech Enhancement and Noise Reduction. Analysis and Synthesis. Speech Recognition. Large Vocabulary Continuous Speech Recognition 16 Spectral Estimation and Modeling S.U. Pillai, T.I. Shim, S N. Batalama, D. Kazakos F Daum Spectral Analysis. Parameter Estimation. Kalman Filtering 17 Multidimensional Signal Processing E. Delp, J. Allebach, C A. Bouman, S.A. Rajala, NK.Bose, L.H. Sibul, W. Wolf, Y-Q Zhang Digital Image Processing. Video Signal Processing. Sensor Array Processing. Video Processing Architectures. MPEG-4 Based Multimedia Information System 18 VLSI for Signal Processing K.K. Parhi, R. Chassaing, B. Bitler Special Architectures. Signal Processing Chips and Applications 19 Acoustic Signal Processing J. Schroeter, S.K. Mehta, G.C. Carter Digital Signal Processing in Audio and Electroacoustics. Underwater Acoustical Signal Processing 20 Artificial Neural Networks J.C. Principe Definitions and Scope. Multilayer Perceptrons. Radial Basis Function Networks. Time Lagged Networks.HebbianLearningandPrincipalcOmponentAnalysisNetworks.competitive Learning and Kohonen Networks 21 Computing Environments for Digital Signal Processing D.M. Etter MATLAB Environment. Example 1: Signal Analysis. Example 2: Filter Design and Analysis Delores m. etter University of Colorado, Boulder ignal processing was defined at a meeting in 1991 of the National Science Foundations MIPS(Micro- electronics and Information Processing Systems) Advisory Committee as"the extraction of information bearing attributes from measured data, and any subsequent transformation of those attributes for the purposes of detection, estimation, classification, or waveform synthesis. If we expand this concise definition, we observe that the signals we typically use in signal processing are functions of time, such as temperature measurements, velocity measurements, voltages, blood pressures, earth motion, and speech signals. Most of these signals are initially continuous signals(also called analog signals) which are measured by sensors that convert energy to electricity. Some of the common types of sensors used for collecting data are microphones, which measure acoustic or sound data; seismometers, which measure earth motion; photocells, which measure ht intensity; thermistors, which measure temperature; and oscilloscopes, which measure voltage. When we c 2000 by CRC Press LLC

© 2000 by CRC Press LLC II Signal Processing 14 Digital Signal Processing W.K. Jenkins, A.D. Poularikas, B.W. Bomar, L.M. Smith, J.A. Cadzow Fourier Transforms • Fourier Transforms and the Fast Fourier Transform • Design and Implementation of Digital Filters • Signal Restoration 15 Speech Signal Processing S. McClellan, J.D. Gibson, Y. Ephraim, J.W. Fussell, L.D. Wilcox, M.A. Bush, Y. Gao, B. Ramabhadran, M. Picheny Coding, Transmission, and Storage • Speech Enhancement and Noise Reduction • Analysis and Synthesis • Speech Recognition • Large Vocabulary Continuous Speech Recognition 16 Spectral Estimation and Modeling S.U. Pillai, T.I. Shim, S.N. Batalama, D. Kazakos, F. Daum Spectral Analysis • Parameter Estimation • Kalman Filtering 17 Multidimensional Signal Processing E.J. Delp, J. Allebach, C.A. Bouman, S.A. Rajala, N.K. Bose, L.H. Sibul, W. Wolf, Y-Q Zhang Digital Image Processing • Video Signal Processing • Sensor Array Processing • Video Processing Architectures • MPEG-4 Based Multimedia Information System 18 VLSI for Signal Processing K.K. Parhi, R. Chassaing, B. Bitler Special Architectures • Signal Processing Chips and Applications 19 Acoustic Signal Processing J. Schroeter, S.K. Mehta, G.C. Carter Digital Signal Processing in Audio and Electroacoustics • Underwater Acoustical Signal Processing 20 Artificial Neural Networks J.C. Principe Definitions and Scope • Multilayer Perceptrons • Radial Basis Function Networks • Time Lagged Networks • Hebbian Learning and Principal Component Analysis Networks • Competitive Learning and Kohonen Networks 21 Computing Environments for Digital Signal Processing D.M. Etter MATLAB Environment • Example 1: Signal Analysis • Example 2: Filter Design and Analysis • Example 3: Multirate Signal Processing Delores M. Etter University of Colorado, Boulder ignal processing was defined at a meeting in 1991 of the National Science Foundation’s MIPS (Micro￾electronics and Information Processing Systems) Advisory Committee as “the extraction of information￾bearing attributes from measured data, and any subsequent transformation of those attributes for the purposes of detection, estimation, classification, or waveform synthesis.” If we expand this concise definition, we observe that the signals we typically use in signal processing are functions of time, such as temperature measurements, velocity measurements, voltages, blood pressures, earth motion, and speech signals. Most of these signals are initially continuous signals (also called analog signals) which are measured by sensors that convert energy to electricity. Some of the common types of sensors used for collecting data are microphones, which measure acoustic or sound data; seismometers, which measure earth motion; photocells, which measure light intensity; thermistors, which measure temperature; and oscilloscopes, which measure voltage. When we S

york with the continuous electrical signals collected by sensors, we often convert the continuous signal to a digital signal (a sequence of values)with a piece of hardware called an analog-to-digital(A/D)converter. Once we have collected the digital signal, ady to use the computer to apply digital signal processing(DSP) techniques to it. These DSP techniques can be designed to perform a number of operations such as Removing noise that is distorting the signal, such as static on a communication line. Extracting information from the signal, such as the average value and the power in a signal. Separating components of the signal, such as the separation of a band of frequencies that represent the television signal for a specific channel. Encoding the information in a more efficient way for transmission, such as the encoding of speech signals Ito digital signals for transmitting across telephone lines. Detecting information in a signal, such as the detection of a surface ship in a sonar signal. These are just a few of the types of operations that can be performed by signal processing techniques. For some applications, an analog or continuous output signal is needed, and thus a digital-to-analog(D/A)converter is sed to convert the modified digital signal to a continuous signal. another device called a transducer can be used to convert the continuous electrical signal to another form; for example, a speaker converts a continuous electrical signal to an acoustical signal In this section the variety and diversity of signal processing is presented from a theoretical point of view, from an implementation point of view, and from an applications point of view. The theoretical point of view includes the development of mathematical models and the development of software algorithms and computer simulations to evaluate and analyze the models both with simulated data and with real data. High-level software tools are important in both the development of new theoretical results and in establishing the validity of the results when applied to real data. The applications determine the way in which the theory is implemented; a key element in the implementation of a signal processing technique relates to whether the technique is applied in real-time(or close to real-time)or whether the processing can be handled off-line Real-time implementation can use VLSI(very large scale integration)techniques, with commercial DSP chips, or it can involve custom design of chips, MCMs(multichip modules), or ASICs(application-specific integrated circuits). The selection of topics in this section covers the three points of view(theoretical, application, implementation) but should not be assumed to include a complete summary of these topic Nomenclature Unit A(k) sampled amplitude spectrum ctrum Genu) spectral gain function DET discrete Fourier transform H entropy passband ripple Hen transfer function of discrete 8(r) dirac or impulse function h(n) impulse response Ao transition bandwidth eer) Fourier transform of error L(x) modified Bessel function of order n L f(n) sequence u,(r) mble average f(r continuous sign N number of sample fast Fourier transform digital frequency frequency c 2000 by CRC Press LLC

© 2000 by CRC Press LLC work with the continuous electrical signals collected by sensors, we often convert the continuous signal to a digital signal (a sequence of values) with a piece of hardware called an analog-to-digital (A/D) converter. Once we have collected the digital signal, we are ready to use the computer to apply digital signal processing (DSP) techniques to it. These DSP techniques can be designed to perform a number of operations such as: • Removing noise that is distorting the signal, such as static on a communication line. • Extracting information from the signal, such as the average value and the power in a signal. • Separating components of the signal, such as the separation of a band of frequencies that represent the television signal for a specific channel. • Encoding the information in a more efficient way for transmission, such as the encoding of speech signals into digital signals for transmitting across telephone lines. • Detecting information in a signal, such as the detection of a surface ship in a sonar signal. These are just a few of the types of operations that can be performed by signal processing techniques. For some applications, an analog or continuous output signal is needed, and thus a digital-to-analog (D/A) converter is used to convert the modified digital signal to a continuous signal. Another device called a transducer can be used to convert the continuous electrical signal to another form; for example, a speaker converts a continuous electrical signal to an acoustical signal. In this section the variety and diversity of signal processing is presented from a theoretical point of view, from an implementation point of view, and from an applications point of view. The theoretical point of view includes the development of mathematical models and the development of software algorithms and computer simulations to evaluate and analyze the models both with simulated data and with real data. High-level software tools are important in both the development of new theoretical results and in establishing the validity of the results when applied to real data. The applications determine the way in which the theory is implemented; a key element in the implementation of a signal processing technique relates to whether the technique is applied in real-time (or close to real-time) or whether the processing can be handled off-line. Real-time implementation can use VLSI (very large scale integration) techniques, with commercial DSP chips, or it can involve custom design of chips, MCMs (multichip modules), or ASICs (application-specific integrated circuits). The selection of topics in this section covers the three points of view (theoretical, application, implementation) but should not be assumed to include a complete summary of these topics. Nomenclature Symbol Quantity Unit AG array gain dB A(k) sampled amplitude spectrum C compression rate DFT discrete Fourier transform δp passband ripple δs stopband attenuation δ(t) dirac or impulse function ∆ω transition bandwidth Hz E(ejω) Fourier transform of error sequence f analog frequency Hz f(n) sequence f (t) continuous signal FFT fast Fourier transform φ azimuthal angle Symbol Quantity Unit φ(K) sampled degree phase spectrum G(ejω) spectral gain function H entropy H(ejω) transfer function of discrete time system h(n) impulse response η learning rate parameter In(x) modified Bessel function of order n L length of continuous function s µx (t) ensemble average N number of sample values ω digital frequency rad/s Ω angular frequency rad