"Digital Signal Processing" issued by Machinery Industry Press in 2012, by Yang Yiming.
The course "Digital Signal Processing" introduces: transforming the movement of things into a series of numbers, and extracting useful information from them in a computational way to meet the needs of our actual application.
This definition comes from "Digital Signal Processing" by Yang Yiming and was issued by the Machinery Industry Press in 2012.
The initial form of most signals is the movement of things. In order to measure them and process them, sensors must be used to convert their characteristics into electrical signals. After these electrical signals are processed, they can be converted into something we can see and can Hearing or available form.
Before and after digital signal processing, some auxiliary circuits are required, and they form a system with the digital signal processor. Figure 1 is a typical digital signal processing system, which consists of 7 units.
The initial signal represents the motion transformation of something, and it can be changed into an electrical signal through the signal conversion unit. For example, sound waves, which become electrical signals after passing through a microphone. Another example is pressure, which becomes an electrical signal after passing through a pressure sensor. The electrical signal can be viewed as a combination of sine waves of many frequencies.
The low-pass filter unit filters out some high-frequency components of the signal to prevent the basic characteristics of the original signal from being lost during analog-to-digital conversion. The analog-to-digital conversion unit measures the analog signal every once in a while, and expresses the measurement result as a binary number.
The digital signal processing unit is actually a computer, which calculates binary digital signals according to instructions. For example, multiplying a sound wave signal with a high-frequency sine wave signal can achieve amplitude modulation. In fact, digital signals often have to be converted back to analog signals in order to play their role. For example, radio waves are emitted outward through an antenna, and the electromagnetic waves at this time can only be analog signals.
The digital-to-analog conversion unit converts the processed digital signal into a continuous-time signal. The characteristic of this signal is that it is connected by a straight line, and there are many places where the change is not smooth. For example, the modulated digital signal can only be sent to the antenna after it becomes an analog signal, and can be transmitted outward through the antenna. The low-pass filtering unit has an averaging effect, and the unsmoothed signal can become smoother after low-pass filtering.
After the smooth signal passes through the signal conversion unit, it becomes a movement change of a certain substance. For example, a speaker, it can turn radio waves into sound waves. Another example is an antenna, which can turn current into electromagnetic waves. Electromagnetic wave is a mutually changing electric field and magnetic field, which can move quickly in the form of waves in space.
If only the processing of electrical signals is considered, the digital signal processing system can be regarded as consisting of five units.
If low-pass filtering and analog-to-digital/digital-to-analog conversion are regarded as one unit, digital signal processing can also be regarded as consisting of three units.
Signal (signal) is the physical manifestation of information, or a function of transmitting information, and information is the specific content of the signal.
Analog signal: A signal with continuous time and continuous amplitude.
Digital signal: A signal that is discrete (quantized) in time and amplitude.
The digital signal can be expressed by a sequence of numbers, and each number can be expressed in the form of binary code, suitable for computer processing.
One-dimensional (1-D) signal: a function of independent variables.
Two-dimensional (2-D) signal: a function of two independent variables.
Multi-dimensional (M-D) signal: a function of multiple independent variables.
System: A physical device that processes signals. In other words, all kinds of equipment that can transform the signal to meet people's requirements. Analog system and digital system.
The content of signal processing: filtering, transformation, detection, spectrum analysis, estimation, compression, recognition and a series of processing.
Analog signals are encountered in most sciences and engineering. Previously, they all studied the theory and realization of analog signal processing.
Disadvantages of analog signal processing: it is difficult to achieve high precision, is greatly affected by the environment, has poor reliability, and is not flexible.
The advantages of digital systems: small size, low power consumption, high accuracy, high reliability, high flexibility, easy large-scale integration, and two-dimensional and multi-dimensional processing
With the rapid development of large-scale integrated circuits and digital computers, coupled with the maturity and perfection of digital signal processing theory and technology since the late 1960s, the use of digital methods to process signals, that is, digital signal processing, has gradually replaced analog signal processing.
With the advent of the information age and the digital world, digital signal processing has become an extremely important discipline and technical field.
DSP chip, also known as digital signal processor, is a kind of microprocessor especially suitable for digital signal processing operation. Its main application is to realize various digital signal processing algorithms in real time. According to the requirements of digital signal processing, DSP chips generally have the following main features:
(1) One multiplication and one addition can be completed in one instruction cycle;
(2) Separate program and data space, you can access instructions and data at the same time;
(3) There is fast RAM in the chip, which can usually be accessed simultaneously in two blocks through an independent data bus;
(4) Hardware support with low or no overhead loop and jump;
(5) Fast interrupt handling and hardware I/O support;
(6) With multiple hardware address generators operating in a single cycle;
(7) Multiple operations can be performed in parallel;
(8) Support pipeline operation, so that operations such as instruction fetching, decoding and execution can be overlapped.
Of course, compared with general-purpose microprocessors, other general-purpose functions of DSP chips are relatively weak.
Broadly speaking, digital signal processing is a technical discipline that studies the analysis, transformation, filtering, detection, modulation, demodulation, and fast algorithms of signals using digital methods. But many people think that: digital signal processing is mainly to study about digital filtering technology, fast algorithm of discrete transform and spectral analysis method. With the development of digital circuit and system technology and computer technology, digital signal processing technology has also been developed accordingly, and its application fields are very extensive.
The applications of digital control and motion control mainly include disk drive control, engine control, laser printer control, inkjet printer control, motor control, power system control, robot control, high-precision servo system control, and CNC machine tools.
The applications for low power consumption, handheld devices, and wireless terminals are: mobile phones, PDA, GPS, digital radio, etc.
Digital filter
There are many practical types of digital filters, which can be roughly divided into two types: finite impulse response type and infinite impulse response type, which can be implemented in two ways: hardware and software. In the hardware implementation, it is composed of adders, multipliers and other units, which is completely different from analog filters composed of resistors, inductors and capacitors. The digital signal processing system is easy to make with digital integrated circuits, showing the advantages of small size, high stability, and programmable control. Digital filters can also be implemented in software. The software implementation method is to use a general digital computer to compile a program according to the filter design algorithm to perform digital filter calculation.
Fourier transform
In 1965, J.W. Curry and T.W. Tukey first proposed the fast algorithm of discrete Fourier transform, referred to as fast Fourier transform, expressed in FFT. Since the fast algorithm, the number of discrete Fourier transform operations has been greatly reduced, making digital signal processing possible. The fast Fourier transform can also be used to perform a series of related fast operations, such as correlation, convolution, power spectrum and other operations. The fast Fourier transform can be made as a dedicated device, or it can be realized by software. Similar to the fast Fourier transform, other forms of transformation, such as Walsh transform, number theory transformation, etc., can also have their fast algorithms.
Spectrum Analysis
An analysis method for describing signal characteristics in the frequency domain can be used not only for deterministic signals but also for random signals. The so-called deterministic signal can be expressed by a predetermined time function, its value at any time is determined; the random signal does not have such characteristics, its value at a certain time is random. Therefore, random signal processing can only be analyzed and processed using statistical methods according to the theory of random processes. For example, statistics such as mean, mean square, variance, correlation function, and power spectral density function are often used to describe the characteristics of random processes or random The characteristics of the signal.
In fact, the random processes that are often encountered are mostly stationary random processes and ergodic, so the average of its sample function set can be determined according to the time average of a certain sample function. Although the stationary random signal itself is still uncertain, its correlation function is determined. When the mean is zero, the Fourier transform or Z transform of its correlation function can be expressed as the power spectrum density function of the random signal, generally referred to as the power spectrum. This feature is very important, so that you can use the fast transformation algorithm for calculation and processing.
The data observed in practice is limited. This requires the use of some estimation methods to estimate the power spectrum of the entire signal based on limited measured data. According to different requirements, such as reducing the deviation of spectral analysis, reducing the sensitivity to noise, and improving the spectral resolution. Many different spectral estimation methods have been proposed. Among the linear estimation methods, there are periodic graph method, correlation method and covariance method; among the nonlinear estimation methods, there are maximum likelihood method, maximum entropy method, autoregressive moving average signal model method and so on. Spectral analysis and spectral estimation are still under research and development.
The application fields of digital signal processing are very extensive. As for the source of the acquired signals, there are communication signal processing, radar signal processing, remote sensing signal processing, control signal processing, biomedical signal processing, geophysical signal processing, vibration signal processing, etc. In terms of the characteristics of the processed signal, it can be divided into voice signal processing, image signal processing, one-dimensional signal processing and multi-dimensional signal processing.
Voice signal processing
Speech signal processing is one of the important branches in signal processing. It includes the main aspects: speech recognition, language understanding, speech synthesis, speech enhancement, speech data compression, etc. Each application has its own special problems. Voice recognition is to extract the characteristic parameters of the voice signal to be recognized in real time and match it with known voice samples to determine the phoneme attributes of the voice signal to be recognized. Regarding speech recognition methods, there are statistical pattern speech recognition, structure and sentence pattern speech recognition. With these methods, important parameters such as formant frequency, pitch, voice, noise, etc. can be obtained. Speech understanding is the theory and technology of natural language dialogue between humans and computers basis. The main purpose of speech synthesis is to enable the computer to speak. To this end, it is necessary to study clearly the changes of speech feature parameters with time during pronunciation, and then use appropriate methods to simulate the pronunciation process and synthesize it into language. Other issues related to language processing also have their own characteristics. Speech signal processing is the basis for the development of intelligent computers and intelligent robots, and the basis for manufacturing vocoders. Speech signal processing is a rapidly developing signal processing technology.
Image signal processing
The application of image signal processing has penetrated into various fields of science and technology. For example, image processing technology can be used to study the trajectory of particles, the structure of biological cells, the state of landforms, the analysis of meteorological clouds, and the composition of cosmic stars. In the practical application of image processing, remote sensing image processing technology, tomographic imaging technology, computer vision technology and scene analysis technology have achieved great results. According to the application characteristics of image signal processing, the processing technology can be roughly divided into several aspects such as image enhancement, restoration, segmentation, recognition, coding and reconstruction. These processing technologies have their own characteristics and are rapidly developing.
Vibration signal processing
The analysis and processing technology of mechanical vibration signals has been applied to the research and production of automobiles, airplanes, ships, mechanical equipment, house construction, dam design, etc. The basic principle of vibration signal processing is to add an excitation force to the test body as an input signal. Monitor the output signal at the measurement point. The ratio of the output signal to the input signal is called the transfer function (or transfer function) of the system composed of the test body. According to the obtained transfer function, the so-called modal parameter identification is carried out to calculate the main parameters of the system, such as the modal stiffness and modal damping. In this way, a mathematical model of the system is established. Furthermore, the dynamic optimization design of the structure can be made. These tasks can be carried out using digital processors. This analysis and processing method is generally called modal analysis. In essence, it is a special method used by signal processing in vibration engineering.
Geophysical processing
In order to explore oil and gas and other mineral deposits stored deep underground, seismic exploration methods are usually used to detect stratum structure and lithology. The basic principle of this method is to apply artificial shock at a selected location. For example, a vibration wave generated by the explosion method propagates underground, and a reflection wave is generated when it encounters the stratum interface. A column is placed at a certain distance from the vibration source. The sensor receives the reflected wave reaching the ground. Determine the depth and structure of the formation from the delay time and intensity of the reflected wave. The seismic records received by the susceptor are relatively complex and require processing to perform geological interpretation. There are many methods to deal with, including deconvolution method, homomorphic filtering method, etc. This is a problem that is still being studied.
Biomedical treatment
Signal processing in biomedicine is mainly used to assist in the research of basic theories of biomedicine and for diagnostic examination and monitoring. For example, it is used for basic theoretical research in cytology, brain neurology, cardiovascular science, genetics and so on. The human brain nervous system is composed of about 10 billion nerve cells and is a very complex and huge information processing system. In this processing system, the transmission and processing of information are carried out side by side and have special functions. Even if one part of the system fails, the other part can still work, which is beyond the reach of a computer. Therefore, the research on the information processing model of the human brain has become an important subject of basic theoretical research. In addition, the study of nerve cell models, the study of chromosome function, etc., can be carried out by means of the principles and techniques of signal processing.
Examples of signal processing that are more successful in diagnostic tests include automatic analysis systems for EEG or ECG, and tomography techniques. Tomography is a major invention in the field of diagnostics. The basic principle of X-ray tomography is that X-rays pass through the object under observation to form a two-dimensional projection of the object. After the receiver receives it, and then recovers or reconstructs it, it can calculate the two-dimensional projection in a series of different orientations, and obtain the tomographic information of the entity after the arithmetic processing, so as to obtain the tomographic image on the large screen. The application of signal processing in biomedicine is in a stage of rapid development.
Digital signal processing has many uses in other areas, such as radar signal processing and geoscience signal processing. Although they have their own special requirements, the basic technologies used are basically the same. In these respects, digital signal processing technology plays a major role.
This lends power savings to High-end Communication equipment and speed to battery operated devices.
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