Digital signal processing is an important basic course for communication engineering and electronics majors. Its main task is to study the basic concepts and basic analysis methods of digital signal processing theory, and demonstrate the practical application of these theories and methods by establishing mathematical models and appropriate mathematical analysis and processing. Digital signal processing technology has developed rapidly. It is not only an independent discipline, but also affects and permeates other disciplines in different forms: it is closely related to the national economy and national defense construction; It affects or changes our production and lifestyle, so it has aroused widespread concern. Information science is a science that studies the acquisition, transmission, processing and utilization of information. Information can only be transmitted, processed, stored, displayed and utilized if it is represented by some form of signal. It can be said that the signal is the manifestation of information, and information is the specific content contained in the signal.
We deeply understand the properties and characteristics of time-domain discrete signals and time-domain discrete systems in a unit course; Time domain analysis methods for time domain discrete signals and time domain discrete systems; Digital processing method of analog signal.
In the second unit, we learned about Fourier transform of time-domain discrete signals (sequences), Z transform of time-domain discrete signals and frequency domain analysis of time-domain discrete systems.
In the course of three units, we learned the definition and properties of discrete Fourier transform, which uses fast convolution and spectrum analysis.
In the four-unit course, we focused on the time domain extraction method, frequency domain extraction method, FFT programming method and split-base FFT algorithm.
In the five-unit course, we learned the representation method of network structure, signal flow diagram, basic network structure of infinite impulse response, basic network structure of finite impulse response, and state variable analysis method of time-domain discrete system.
In the six-unit course, we learned the basic concept of digital filter, the design of analog filter, Butterworth filter and Chebyshev filter, the design of infinite impulse response word digital filter with impulse response invariance method, the design of infinite impulse response word digital filter with bilinear transformation method, and digital Qualcomm, bandpass and bandstop filters.
In Unit 7, we studied the linear phase finite impulse response (FIR) digital filter, designed the FIR digital filter with the window function method, and designed the FIR digital filter with the frequency sampling method. Communication engineering is an engineering discipline, which mainly deals with some practical problems in communication by using various engineering methods on the basis of mastering the basic theory of communication. Through the study of this major, we can master the principles of various communication systems such as telephone network, radio and television network and Internet, study the technology of improving information transmission speed, design new communication systems according to actual needs, and develop communication tools that can transmit all kinds of information quickly and accurately.
For our communication major, I think it is a very good major. This major is very popular now, and it has many employment directions and a wide range of employment. After graduation, you can work in equipment manufacturers, operators, proprietary service providers and banks. Of course, the employment situation changes every year, and the key is to look at yourself. Can be engaged in hardware, such as PCB, don't underestimate this technology. Usually, we will make it simple during the experiment. The technical difficulty is that the more layers of the board, the more difficult it is to stabilize it. This is very difficult. If you study well, it is easy to find a job. We can also engage in software, which actually requires us to have a good basic knowledge of analog and digital electricity. I chose this major, and I have been studying communication knowledge here for three years. I still hope to be able to work in this field after graduation. Now I have learned these useful professional courses, such as communication principle and digital signal processing. So in the future study, I will study this knowledge solidly, and I am also prepared to work hard on technology. I'm still young, and it's okay to suffer more when I'm young, for my bright future.
Digital Signal Processing is a professional basic course, which mainly includes the basic concepts and description methods of discrete-time signals and systems, discrete Fourier transform and fast Fourier transform, and the structure and design of digital filters. For students majoring in electrical information, these contents are an important basis for studying subsequent professional courses, and are also essential professional basic knowledge in practical work. At present, almost all colleges and universities offer this course to undergraduates majoring in electronic engineering, information engineering, communication engineering, electronic technology, automatic control, electrical engineering, electromechanical engineering and computer science. With the development of computer technology, microelectronics technology and digital signal processing theory and methods, the methods and applications of digital signal processing have developed by leaps and bounds in the past half century, especially in the last thirty years, and the position and role of digital signal processing are becoming more and more important. Therefore, it is of great significance to strengthen the construction of this course.
Our digital signal processing course is taught by Mr. Luo. Teacher Luo has practical work experience and is very familiar with the practical application of this course. Teacher Luo uses a variety of teaching methods to enrich the teaching content and attract students' attention to the course. Using the experimental class to let students program in person, experience the fun of signal processing course, stimulate students' interest and improve the teaching effect. Therefore, the students in our class have performed well in this semester's courses.
Digital signal processing course is characterized by strong theory, many formulas and abstract concepts, and students often find it boring and difficult to learn. In recent years, some schools at home and abroad mainly emphasize applied learning in the teaching of this course of general electrical specialty, mainly introducing the use and usage of digital signal processing, but only giving a general introduction to its profound theoretical derivation, and providing students with opportunities to carry out experiments to stimulate students' interest and initiative in this course.
The reform idea of this course is that the course content should adapt to the development status of digital signal processing technology, dilute boring mathematical derivation, and set up corresponding experimental courses with modern teaching methods. Combining with the professional status quo, part of classroom teaching is converted into multimedia teaching, and some theoretical analysis is displayed by graphic means as far as possible to enhance students' perceptual knowledge. The experimental class is mainly based on MATLAB, which makes full use of various functions provided by MATLAB's digital signal processing toolbox to let students simulate what they have learned in class. In the experimental class, the function of digital signal processing can also be demonstrated to students by using the DSP test box.
Experience of Digital Signal Processing Training
First,? Digital signal processing? The new subject orientation of the course
Traditional digital signal processing attaches importance to the explanation of concepts and principles. Nowadays, in addition to the teaching of basic concepts and theories, we also pay attention to engineering application. Therefore, the content of Matlab programming experiment is still to add DSP experiment. Students can intuitively verify the effectiveness of some algorithms by doing experiments, and can easily use some algorithms to solve practical problems, such as fft and wavelet transform. Basic experiments should be innovative, open up thinking, strengthen understanding and flexible application. This cultivates students' ability to solve practical engineering problems through signal processing, which is conducive to improving students' hands-on ability and independent thinking ability. Therefore, digital signal processing is not only a theoretical course, but also an applied course. This is a comprehensive understanding, and this overall goal should be achieved in the teaching process.
Second, the importance of teaching team
From Professor Peng's report, we can see the importance of an excellent teaching team to the construction of excellent courses. In every report, Professor Peng almost emphasized that this achievement is the result of the joint efforts of all teachers in the teaching team. I feel the same way about it. Building a good course can't be completed by one person, and it needs many people to make unremitting efforts and unite and cooperate for many years. Therefore, we need to find like-minded people and form a teaching group. Communicate with each other on subject construction and teaching methods. A good teaching echelon is the prerequisite for the success of the construction of excellent courses. At the same time, a good teaching team should also pay equal attention to teaching and scientific research.
Third, teachers need to have a broader vision.
Speak well? Digital signal processing? The class demands a lot of teachers. This requires our teachers to keep up with the development of the times and understand the cutting-edge technologies and trends while teaching basic theories. Only in this way can new ideas be passed on to students during lectures. Stimulating their innovative thinking is also beneficial for them to face the society. Students can better understand the latest development trend of technology and adapt to the jobs they will choose.
I think teachers should refer to some original English textbooks in the teaching process. In this way, teachers can have an international perspective and convey the progress of international order to students in the teaching process. Students can also refer to the relevant English literature, strengthen the study of professional English while learning new knowledge, and lay a good foundation for reading English materials in the future. Therefore, this is a learning method that kills two birds with one stone.
Although there is only a short three-day training time, I have gained a lot. Especially as a young teacher who has just worked for two years, I have learned a lot in this process. In the process of communicating with experts and peers, I have increased my knowledge and learned many good teaching methods. Of course, in the process of communicating with you, I also found some shortcomings. The new problems found this time and the new conclusions discussed need to be further explored and practiced in the future work. In short, it is three days of full harvest and three days of happiness!
Learning experience of digital signal processing
Digital signal processing is the Ministry of Education? Quality engineering? Project? Network training system for college teachers? One of the digital online training courses launched by the project features autonomous learning, expert guidance, experience sharing, interactive communication and full service. The training object is the in-service teachers who undertake the teaching tasks of digital signal processing courses or similar courses in colleges and universities.
The teacher is Peng Qiyan, who won the prize in 20XX? The first college teacher award? University of Electronic Science and Technology of China? Digital signal processing? The course was graded? 20XX National Fine Crystal Course? .
Among them, the difficult teaching design part is analyzed emphatically. Digital signal processing? The development of the course, its important position and wide application in science and technology, and the engineering realization of digital signal processing method? DSP technology, how to do a good job in the teaching design of experimental courses, etc.
Broadly speaking, digital signal processing is a technical subject that uses digital methods to study signal analysis, transformation, filtering, detection, modulation, demodulation and fast algorithm. Widely used in all walks of life.
I have been engaged in acoustic signal detection of power plant boilers for a long time, and this course is helpful to my scientific research level. The collected acoustic signals are filtered, and then the temperature information in the furnace is obtained by using the correlation algorithm. At the same time, it also has some inspiration for my future teaching process. I plan to have the opportunity to open a postgraduate course on signal measurement and processing, including analog signals such as pressure signals and temperature signals. It will be very practical to extract the feature quantity and analyze the algorithm to get useful information after converting it into digital signal.
Finally, I thank the school for organizing the online course of teacher-student training. These courses are very rich and can be selected in a targeted way, which will greatly promote teachers' own scientific research and teaching.
4 "Random Digital Signal Processing" learning experience
Random digital signal processing is formed by the cross-infiltration of various disciplines, and it is inseparable from random digital signal processing in communication, radar, voice processing, image processing, acoustics, earthquake, geological exploration, meteorology, remote sensing, biomedical engineering, nuclear engineering, aerospace engineering and other fields. With the progress of computer technology, random digital signal processing technology has developed rapidly. This course mainly studies two main problems of random digital signal processing: filter design and spectrum analysis.
In digital signal processing, filtering technology plays an extremely important role. Digital filtering is a basic processing algorithm in speech and image processing, pattern recognition, spectrum analysis and other applications. However, in many applications, it is often necessary to deal with some unpredictable signals, noise or time-varying signals. If a digital filter with fixed filter coefficients is used, optimal filtering cannot be achieved. In this case, an adaptive filter must be designed to make the dynamic characteristics of the filter change with the change of signal and noise, so as to achieve the best filtering effect.
Adaptive filter is a kind of filter about signal processing method and technology developed in recent decades, and its design method has great influence on the performance of the filter. Adaptive filter is a special Wiener filter. Compared with fixed filter, it can automatically adjust its own parameters. The research of adaptive filtering algorithm is one of the most active research topics in adaptive signal processing. Among them, the two most basic linear filtering algorithms are Least Mean Square Error (LMS) algorithm and Least Squares (RLS) algorithm. Because LMS algorithm has the shortcomings of slow initial convergence and poor execution stability, this course focuses on RLS algorithm. The initial convergence speed of RLS algorithm is one order of magnitude faster than that of LMS algorithm, and its execution stability is good.
Spectrum analysis is another important content of random digital signal processing, which studies the distribution of some characteristics of signals such as amplitude, energy or power with frequency in frequency domain. The spectrum analysis of ordinary non-time-limited signals can only be calculated by intercepting samples with finite length, and the result is the approximation of its real spectrum, that is, spectrum estimation. In addition to the model parameter method, modern spectrum estimation algorithms also put forward other methods, such as Capon maximum likelihood spectrum estimation algorithm, Pisarenk harmonic decomposition method, MUSIC algorithm, ESPRIT algorithm and so on. In the actual spectrum estimation process, whether starting from the sample data (direct method) or starting from the autocorrelation function of the sample (indirect method), the introduction of window function is inevitable, because the simple interception of the data sample itself means passing through a rectangular window. The influence of window effect in spectrum analysis or spectrum estimation is manifested in reducing the frequency resolution of spectrum and producing energy leakage. This course introduces the short-time Fourier transform and a series of spectrum analysis methods derived from it, which have been verified and achieved good results.
To sum up, for my understanding and cognition of this course. Through the study of this course, I have a further understanding of the techniques and methods of random digital signal processing, and deepened my understanding of basic theories and concepts. Many algorithms and ideas involved in this course have great inspiration for my personal research direction. I will continue to study relevant theories and algorithms, and strive to combine them with scientific research practice as soon as possible to realize the usefulness of learning. Finally, I would like to thank the teacher for his tireless explanation, introducing new ideas for us and helping us grow faster.
I recommend it carefully.