Application of remote control lift rod digital signal processing

Time：2020-11-05 View：

 

Digital signal processing technology is one of the technologies needed by remote control lift rod.

 

Application
Broadly speaking, digital signal processing is a technical subject that studies the analysis, transformation, filtering, detection, modulation, demodulator and fast algorithm of signals by digital methods. However, many people think that digital signal processing mainly focuses on digital filtering technology, discrete transformation fast algorithm and spectrum analysis method. With the development of digital circuit and system technology and computer technology, digital signal processing technology has been developed accordingly, and its application field is very wide.
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, CNC machine tools, etc.
Applications for low power consumption, handheld devices and wireless terminals mainly include: mobile phones, PDA, GPS, data transmission radio stations, 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, and can be realized by hardware and software. In the hardware implementation mode, it consists of adder, multiplier and other units, which is completely different from the analog filter composed of resistor, inductor and capacitor. The digital signal processing system is easy to be made of digital integrated circuit, showing the advantages of small volume, high stability, programmable and so on. Digital filters can also be implemented by software. The software implementation method is to use general purpose digital computer to compile a program according to the design algorithm of the filter for digital filtering calculation.

 

Fourier transform
In 1965, J.W. Cooley T.W. Tuji first proposed the fast algorithm of discrete Fourier transform, which is called fast Fourier transform for short, and is expressed by Fourier transform. With its own fast algorithm, the number of operations of discrete Fourier transform is greatly reduced, making the realization of digital signal processing possible. Fast Fourier transform can also be used to perform a series of related fast operations, such as correlation, convolution, power spectrum, etc. Fast Fourier transform can be made into special equipment or implemented by software. Similar to the fast Fourier transform, other forms of transformations, such as the Walsh transform and the number theory transform, can also have their own fast algorithms.

Spectral analysis
An analysis method describing signal characteristics in 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 given time function, and its value at any moment is determined; Random signals do not have such characteristics, its value at a certain moment is random. Therefore, random signal processing can only use statistical methods to analyze and process according to the random process theory, such as the mean value, mean square value, variance, correlation function, the power spectral density function and other statistics describe the characteristics of random processes or random signals.
In fact, the random processes that are often encountered are mostly stationary random processes and are experienced by various states. Therefore, 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 certain. When the mean value is zero, the Fourier transform or Z transform of its correlation function can just be expressed as the power spectral density function of random signals, which is generally referred to as power spectrum. This feature is very important, so that fast transformation algorithms can be used for calculation and processing.
In practice, the observed data is limited. This requires some estimation methods to estimate the power spectrum of the whole signal based on limited measured data. According to different requirements, such as reducing the deviation of spectral analysis, reducing the sensitivity to noise, improving spectral resolution, etc. Many different spectral estimation methods have been proposed. Among linear estimation methods, there are periodic graph method, correlation method and covariance method; Among nonlinear estimation methods, there are maximum likelihood method, maximum entropy method, autoregressive moving average signal model method, etc. Spectral analysis and spectral estimation are still in research and development.
The application field of digital signal processing is very wide. As for the source of the acquired signal, there are processing of communication signal, radar signal, remote sensing signal, control signal, biomedical signal, geophysical signal processing, vibration signal processing, etc. According to the characteristics of the processed signal, it can be divided into speech signal processing, image signal processing, one-dimensional signal processing and multi-dimensional signal processing, etc.

 

 

Speech Signal processing
Speech Signal processing is one of the important branches in signal processing. The main aspects include: Speech recognition, language understanding, speech synthesis, speech enhancement, speech data compression, etc. Various applications have their own special problems. Speech recognition is to extract the feature parameters of the speech signal to be recognized immediately and match with the known speech samples to determine the phoneme attributes of the speech signal to be recognized. As for the speech recognition methods, there are statistical pattern speech recognition, structure and sentence pattern speech recognition. These methods can be used to obtain important parameters such as resonance peak frequency, tone, voice, noise, etc, speech comprehension is the theoretical and technical basis of natural language dialogue between human and computer. The main purpose of speech synthesis is to enable computers to speak. Therefore, first of all, we need to study clearly the variation rule of voice feature parameters with time when pronouncing, and then use appropriate methods to simulate the process of pronunciation and synthesize it into a language. Other problems related to language processing also have their own characteristics. Speech Signal processing is the basis for the development of intelligent computers and robots, and the basis for the manufacture of vocoders. Speech Signal processing is a rapidly developing signal processing technology.