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DSP A Khoa học máy tính quan điểm P2
We are now ready to commence our study of signals and signal processing systems, the former to be treated in Part I of this book and the latter in Part II. Part III extends the knowledge thus gained by presentation of specific algorithms and computational architectures, and Part IV applies all we will have learned to communications and speech signal processing. At times one wants to emphasize signals as basic entities, and to consider systems as devices to manipulate them or to measure their parameters....
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DSP A Khoa học máy tính quan điểm P3
The Spectrum of Periodic Signals Signals dwell both in the time and frequency domains; we can equally accurately think of them as values changing in time (time domain), or as blendings of fundamental frequencies (spectral domain). The method for determining these fundamental frequencies from the time variations is called Fourier or spectral analysis. Similar techniques allow returning to the time domain representation from the frequency domain description.
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DSP A Khoa học máy tính quan điểm P4
The concept of frequency is clearest for simple sinusoids, but we saw in the previous chapter that it can be useful for nonsinusoidal periodic signals as well. The Fourier series is a useful tool for description of arbitrary periodic signals, describing them in terms of a spectrum of sinusoids, the frequencies of which are multiples of a basic frequency. It is not immediately obvious that the concepts of spectrum and frequency can be generalized to nonperiodic signals.
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DSP A Khoa học máy tính quan điểm P5
Much of signal processing involves extracting signals of interest from noise. Without noise to combat, a radar receiver could detect an echo by simple energy thresholding. In a noiseless world an infinite amount of information could be transmitted through a communications channel every second. Were it not for noise, signal classification would be reduced to dictionary lookup. Yet signals in the real world are always noisy. Radar echoes are buried under noise, making their detection impossible without sophisticated processing. ...
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DSP A Khoa học máy tính quan điểm P6
The study of signals, their properties in time and frequency domains, their fundamental mathematical and physical limitations, the design of signals for specific purposes, and how to uncover a signal’s capabilities through observation belong to signal analysis. We now turn to signal processing, which requires adding a new concept, that of the signal processing system. A signal processing system is a device that processes input signals and/or produces output signals.
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DSP A Khoa học máy tính quan điểm P7
In everyday parlance a ‘filter’ is a device that removes some component from whatever is passed through it. A drinking-water filter removes salts and bacteria; a coffee filter removes coffee grinds; an air filter removes pollutants and dust. In electronics the word ‘filter’ evokes thoughts of a system that removes components of the input signal based on frequency. A notch filter may be employed to remove a narrow-band tone from a received transmission; a noise filter may remove high-frequency hiss or low-frequency hum from recordings; antialiasing filters are needed to remove frequencies above Nyquist before A/D conversion....
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DSP A Khoa học máy tính quan điểm P8
Filters have a lot going for them. In the previous chapter we have seen that they are simple to design, describe and implement. So why bother devoting an entire chapter to the subject of systems that are not filters? There are two good reasons to study nonfilters-systems that are either nonlinear, or not time-invariant, or both. First, no system in the real world is ever perfectly linear; all ‘linear’ analog systems are nonlinear if you look carefully enough, and digital signals become nonlinear due to round-off error and overflow....
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DSP A Khoa học máy tính quan điểm P9
Correlation Our study of signal processing systems has been dominated by the concept of ‘convolution’, and we have somewhat neglected its close relative the ‘correlation’. While formally similar (in fact convolution by a symmetric FIR filter can be considered a correlation as well), the way one should think about the two is different. Convolution is usually between a signal and a filter; we think of it as a system with a single input and stored coefficients.
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DSP A Khoa học máy tính quan điểm P10
Adaptation We have already learned about many different types of systems. We started with frequency selective filters and filters designed for their time-domain properties. Then we saw nonfilters that had capabilities that filters lack, such as PLLs that can lock onto desired frequency components. Next we saw how to match a filter to a prespecified signal in order to best detect that signal. We have even glimpsed higher-order signal processing systems that can differentiate between signals with identical power spectra....
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DSP A Khoa học máy tính quan điểm P11
Biological Signal Processing At first it may seem a bit unusual to find a chapter on biological signal processing in a book dedicated to digital signal processing; yet this is in reality no more peculiar than motivating DSP by starting with the analogous principles of analog signal processing. Indeed the biological motivation should be somewhat closer to our hearts (or eyes, ears and brains). In this book we have chosen to introduce analog and digital signal processing together, but have confined our discussion of biological signal processing to this chapter....
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DSP A Khoa học máy tính quan điểm P12
Digital signal processing means algorithmic processing, representing signals as streams of numbers that can be manipulated by a programmable computer. Since DSP algorithms are programmed, standard computer languages may be used in principle for their implementation. In particular, block diagrams, that are conventionally used to help one grasp the essential elements of complex conventional computer programs, may be useful as DSP description and specification tools as well.
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DSP A Khoa học máy tính quan điểm P13
It is easy enough to measure the frequency of a clean sinusoid, assuming that we have seen enough of the signal for its frequency to be determinable. For more complex signals the whole concept of frequency becomes more complex. We previously saw two distinct meanings, the spectrum and the instantaneous frequency. The concept of spectrum extends the single frequency of the sinusoid to a simultaneous combination of many frequencies for a general signal; as we saw in Section 4.5 the power spectral density (PSD) defines how much each frequency contributes to the overall signal. ...
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DSP A Khoa học máy tính quan điểm P14
The Fast Fourier Transform It is difficult to overstate the importance of the FFT algorithm for DSP. We have often seen the essential duality of signals in our studies so far; we know that exploiting both the time and the frequency aspects is critical for signal processing. We may safely say that were there not a fast algorithm for going back and forth between time and frequency domains, the field of DSP as we know it would never have developed. The discovery of the first FFT algorithm predated the availability of hardware capable of actually exploiting it. ...
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DSP A Khoa học máy tính quan điểm P15
Digital Filter Implementation In this chapter we will delve more deeply into the practical task of using digital filters. We will discuss how to accurately and efficiently implement FIR and IIR filters. You may be asking yourself why this chapter is important. We already know what a digital filter is, and we have (or can find) a program to find the coefficients that satisfy design specifications. We can inexpensively acquire a DSP processor that is so fast that computational efficiency isn’t a concern, and accuracy problems can be eliminated by using floating point processors...
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DSP A Khoa học máy tính quan điểm P16
Function Evaluation Algorithms Commercially available DSP processors are designed to efficiently implement FIR, IIR, and FFT computations, but most neglect to provide facilities for other desirable functions, such as square roots and trigonometric functions. The software libraries that come with such chips do include such functions, but one often finds these general-purpose functions to be unsuitable for the application at hand. Thus the DSP programmer is compelled to enter the field of numerical approximation of elementary functions....
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DSP A Khoa học máy tính quan điểm P17
Digital Signal Processors Until now we have assumed that all the computation necessary for DSP applications could be performed either using pencil and paper or by a generalpurpose computer. Obviously, those that can be handled by human calculation are either very simplistic or at least very low rate. It might surprise the uninitiated that general-purpose computers suffer from the same limitations. Being ‘general-purpose’, a conventional central processing unit (CPU) is not optimized for DSP-style ‘number crunching’, since much of its time is devoted to branching, disk access, string manipulation, etc....
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DSP A Khoa học máy tính quan điểm P18
In this chapter we will survey various topics in signal processing for communications. Communications, like signal processing itself, is commonly divided into analog and digital varieties. Analog communications consist of techniques for transmitting and receiving speech, music or images as analog signals, as in telephones, broadcast radio and television. Digital communications are methods of transferring digital information, usually in the form of bit streams.
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DSP A Khoa học máy tính quan điểm P19
Speech Signal Processing In this chapter we treat of one of the most intricate and fascinating signals ever to be studied, human. speech. The reader has already been exposed to the basic models of speech generation and perception in Chapter 11. In this chapter we apply our knowledge of these mechanisms to the practical problem of speech modeling. Speech synthesis is the artificial generation of understandable, and (hopefully) natural-sounding speech
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DSP phòng thí nghiệm thử nghiệm bằng cách sử dụng C và DSK TMS320C31 (P1)
Digital Signal Processing Development System Use of the TMS320C31 DSK Testing the software and hardware tools such as the debugger Programming examples in C and TMS320C3x code to test the tools Chapter 1 introduces several tooals available for digital signal processing (DSP). These tools include the TMS320C31-based DSP Starter Kit (DSK) with complete input and output support. Three examples are included to illustrate these development tools and, in particular, to test the DSK.
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DSP phòng thí nghiệm thử nghiệm bằng cách sử dụng C và DSK TMS320C31 (P2)
Architecture and Instruction Set of the TMS320C3x Processor Architecture and Instruction set of the TMS320C3x processor Memory addressing modes Assembler directives Programming examples using TMS320C3x assembly code, C code, and C-callable TMS320C3x assembly function. Several programming examples included in this chapter illustrate the architecture, the assembler directives, and the instruction set of the TMS320C3x processor and associated tools.
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DSP phòng thí nghiệm thử nghiệm bằng cách sử dụng C và DSK TMS320C31 (P3)
Input and Output with the DSK Input and output with the Analog Interface Circuit (AIC) chip Communication between the PC host and the C31 DSK Alternative memory using external and flash memory Alternative input and output with a 16-bit stereo codec Programming examples and experiments using C and TMS320C3x code
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DSP phòng thí nghiệm thử nghiệm bằng cách sử dụng C và DSK TMS320C31 (P4)
Finite Impulse Response Filters Introduction to the z-transform Design and implementation of finite impulse response (FIR) filters Programming examples using C and TMS320C3x code The z-transform is introduced in conjunction with discrete-time signals. Mapping from the s-plane, associated with the Laplace transform, to the z-plane, associated with the z-transform, is illustrated. FIR filters are designed with the Fourier series method and implemented by programming a discrete convolution equation. Effects of window functions on the characteristics of FIR filters are covered. ...
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DSP phòng thí nghiệm thử nghiệm bằng cách sử dụng C và DSK TMS320C31 (P5)
Infinite Impulse Response Filters Infinite impulse response filter structures: direct form I, direct form II, cascade, and parallel Bilinear transformation for filter design Sinusoidal waveform generation using difference equation Filter design and utility packages Programming examples using TMS320C3x and C code The finite impulse response (FIR) filter discussed in the previous chapter has no analog counterpart.
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DSP phòng thí nghiệm thử nghiệm bằng cách sử dụng C và DSK TMS320C31 (P6)
Fast Fourier Transform The fast Fourier transform using radix-2 and radix-4 Decimation or decomposition in frequency and in time Programming examples The fast Fourier transform (FFT) is an efficient algorithm that is used for converting a time-domain signal into an equivalent frequency-domain signal, based on the discrete Fourier transform (DFT). A real-time programming example is included with a main C program that calls an FFT assembly function.
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DSP phòng thí nghiệm thử nghiệm bằng cách sử dụng C và DSK TMS320C31 (P7)
Adaptive Filters Adaptive structures The least mean square (LMS) algorithm Programming examples using C and TMS320C3x code Adaptive filters are best used in cases where signal conditions or system parameters are slowly changing and the filter is to be adjusted to compensate for this change. The least mean square (LMS) criterion is a search algorithm that can be used to provide the strategy for adjusting the filter coefficients. Programming examples are included to give a basic intuitive understanding of adaptive filters. ...
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DSP phòng thí nghiệm thử nghiệm bằng cách sử dụng C và DSK TMS320C31 (P8)
DSP Applications and Projects This Chapter can be used as a source of experiments, projects, and applications. A wide range of projects have been implemented based on both the floatingpoint TMS320C30 digital signal processor [1–6], briefly described at the end of this chapter, and the fixed-point TMS320C25 [7]. They range in topics from communications and controls, to neural networks, and can be used as a source of ideas to implement other projects.
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Nguyên tắc cơ bản của thiết kế mạch RF với tiếng ồn thấp dao động P1
Equivalent circuit device models are critical for the accurate design and modelling of RF components including transistors, diodes, resistors, capacitors and inductors. This chapter will begin with the bipolar transistor starting with the basic T and then the π model at low frequencies and then show how this can be extended for use at high frequencies. These models should be as simple as possible to enable a clear understanding of the operation of the circuit and allow easy analysis. They should then be extendible to include the parasitic components to enable accurate optimisation....
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Nguyên tắc cơ bản của thiết kế mạch RF với tiếng ồn thấp dao động P2
This chapter will describe the important linear parameters which are currently used to characterise two port networks. These parameters enable manipulation and optimisation of RF circuits and lead to a number of figures of merit for devices and circuits. Commonly used figures of merit include hFE, the short circuit low frequency current gain, fT, the transition frequency at which the modulus of the short circuit current gain equals one, GUM (Maximum Unilateral Gain), the gain when the device is matched at the input and the output and the internal feedback has been assumed to be zero....
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Nguyên tắc cơ bản của thiết kế mạch RF với tiếng ồn thấp dao động P3
So far device models and the parameter sets have been presented. It is now important to develop the major building blocks of modern RF circuits and this chapter will cover amplifier design. The amplifier is usually required to provide low noise gain with low distortion at both small and large signal levels. It should also be stable, i.e. not generate unwanted spurious signals, and the performance should remain constant with time.
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Nguyên tắc cơ bản của thiết kế mạch RF với tiếng ồn thấp dao động P4
The oscillator in communication and measurement systems, be they radio, coaxial cable, microwave, satellite, radar or optical fibre, defines the reference signal onto which modulation is coded and later demodulated. The flicker and phase noise in such oscillators are central in setting the ultimate systems performance limits of modern communications, radar and timing systems. These oscillators are therefore required to be of the highest quality for the particular application as they provide the reference for data modulation and demodulation. ...
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