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Based on the study of traditional matching methods, this paper implements a low-frequency image matching system based on wavelet transform, which is composed of wavelet preprocessing, low-frequency image extraction, and image matching. The low-frequency image after wavelet decomposition is used for matching, which can reduce the calculation time of matching. The low-frequency image still contains most of the visual information of the original image, making the matching result stable and reliable.In this system, image wavelet decomposition and matching use mature and fast algorithms. The matching is performed on low-frequency images, which makes the amount of calculation for matching very small. Using the low-frequency components of the image to match also greatly removes the interference of noise on the image matching. Since the highest proportion of high-frequency noise in the noise has been removed before the algorithm is matched, all the matching algorithms have good anti-noise ability.The matching system in this paper adopts a matching method based on low-frequency components after wavelet transform, discusses and realizes the use of low-frequency images after image wavelet decomposition to perform image matching. The experimental results show that the matching algorithm used in the article has fast calculation speed, less matching time, and certain practicability.

Fingerprint characteristics will not change due to growth or aging, and are unique and suitable for the field of identification. The fingerprint image is scanned from left to right and from top to bottom in order to scan the points on the entire image, and then the points on the image are judged to achieve fingerprint feature point extraction. Then, the fingerprint image is preprocessed and feature extracted, and the center point of the fingerprint, the direction field of the fingerprint image and the coordinates of the feature point are obtained. The reference direction of the fingerprint is extracted. After the reference point and the reference direction of the fingerprint are determined, the characteristic information of the fingerprint is modified and the characteristic information is expressed in polar coordinates. The template and the feature information of the input image are sorted in the direction according to the direction of increasing polar angle, and the feature information is connected in series. If the matching degree of feature points is greater than the preset value, it is considered that the two fingerprints are matched successfully. It can be seen from the experimental results that the two fingerprint images are loaded, the feature point map and the smooth processing map are extracted after preprocessing, and the feature point matching degree is calculated. It can be judged that the two fingerprint images are from the same fingerprint.

This article discusses the face two-dimensional code recognition technology, using the PCA algorithm to study the dimensionality reduction processing process of the face image. Based on the analysis of QR encoding and decoding methods, the generation of face two-dimensional codes is realized. Choose 140 pictures in ORL face database as the experimental data set, study the face two-dimensional code recognition system, and realize a process of face recognition. The experimental results show that the face image data has a large dimension, and the principal component analysis method can be used to obtain the key data. Through the experiment, PCA can be used to reduce the dimensionality to obtain the effective information of the face, and to encode and decode the two-dimensional code. The ZXing two-dimensional code open source library called by the experiment has high experimental efficiency, which is beneficial to improve the operating efficiency of face two-dimensional code recognition and facilitate the machine to complete the simulation.

In view of the disadvantages of large image space, high dimension of feature representation and large storage, this paper uses principal component analysis to compress the data, which can effectively reduce the loss of information, reduce the dimension of data and extract features in all aspects of image compression. In this paper, based on the analysis of PCA algorithm dimension reduction, reconstruction, feature extraction principle, the PCA algorithm is applied to image compression and reconstruction. Through the principal component analysis of sample variables, the feature vector of sample variance is calculated, and its principal component is extracted, and the contribution rate is calculated to realize image PCA compression. By extracting different eigenvalues from the original image and calculating its compression ratio and contribution rate, we can see that the clarity of the image is positively correlated with the value of the eigenvalue, and the larger the value of the eigenvalue, the higher the contribution rate. PCA based image processing technology is simple to use, high compression rate and high quality of image reconstruction, which is unmatched by many other methods.

In this paper, wavelet analysis is used to remove noise superimposed on the speech signal to achieve denoising of the speech signal. The prepared speech signal is superimposed with Gaussian white noise with different signal-to-noise ratios, and is removed by using a forced noise removal processing method and a given threshold denoising processing method; The wavelet soft and hard threshold denoising method is used to separately superimpose different Gaussian white noise speech signals, and the hard threshold and soft threshold denoising are respectively performed, and different wavelets are used for processing; Four threshold selection methods are used to denoise the noisy signal using different wavelets. The denoising effects of these methods are simulated in MATLAB software, and combined with the signal-to-noise ratio of the signal after denoising, the denoising effects of various methods are judged. Experimental results show that soft threshold denoising is slightly better than hard threshold denoising. Among the four threshold selection methods, Rigrsure rule denoising is the best. Although other methods are effective, they are not obvious.

Image compression based on wavelet transform is to apply wavelet algorithm to multi-resolution decomposition of the image, and to achieve image compression by encoding the obtained wavelet coefficients. First, multi-level wavelet decomposition is performed on the image to obtain the corresponding wavelet coefficients. Then, each layer of wavelet coefficients is quantized to obtain quantized coefficient objects. Finally, the quantized coefficient objects are encoded to obtain compression results. This paper uses Haar wavelet as the wavelet base, selects the 2-level decomposition scale, performs wavelet transform, compresses the reconstructed image by setting a threshold, and calculates the file size ratio and PSNR value before and after compression. The wavelet reconstruction function uses the coefficient matrix, dimensionality information and wavelet base type obtained by wavelet decomposition to perform wavelet reconstruction. The global threshold setting method is selected, and the wavelet high-frequency coefficients are threshold filtered to achieve compression before the reconstruction operation, and then the compressed image is obtained through wavelet reconstruction. After the image has undergone wavelet transformation with a specified scale, most of the energy is concentrated in the fractional part of the wavelet decomposition coefficients. The coefficients of other parts are set to constants by setting a threshold, and only a few decomposition coefficients are retained to represent the entire image.The experimental results show that the storage space of the compressed image is greatly saved and the compressed image does not change significantly from the original image visually.

In order to improve the robustness of digital image watermarking algorithms, the digital image blind watermarking algorithm proposed in this paper embeds watermark information in a reasonable position. First, scramble the watermark information and embed the random signal into the low-frequency domain of the image. On the one hand, it can effectively prevent attacks on the watermark system and enhance the security of the system, disperse as much as possible. In the watermark embedding process, a method based on the wavelet transform domain is used. The original image is not required to participate in the watermark extraction process, and the blind watermark function is implemented. In order to verify the effectiveness of the algorithm, the watermarked image is subjected to filtering, scaling, noise, cropping and rotation attacks, and the peak signal-to-noise ratio and normalized correlation coefficient are used to quantitatively evaluate the watermarked image. Experimental results show that the algorithm can resist a variety of attacks, and has good imperceptibility and robustness.

A traffic signal control system based on parallel simulation technology is designed in this article. Based on the second-level acquisition of intersection data, this system builds a control algorithm matching rule base based on offline algorithm adaptive analysis. In addition, the system can select the signal control algorithm that matches the real-time traffic status online, and realize the online evaluation of the control algorithm through the online parallel simulation and control algorithm evaluation platform, and provide a decision basis for further implementation of the algorithm. This system improves some existing deficiencies in signal control systems, and also provides decision support for the application of complex control algorithms. The system is applied to the control of actual signalized intersections. Practice shows that the system can better adapt to the actual situation of signalized intersections in District, improve the capacity of intersections and the efficiency of the entire road network.

In this paper, the wavelet decomposition of multi-focus images is used to fuse the low-frequency components and high-frequency components obtained after decomposition. In order to make more information of the original image in the merged image, the method of maximizing the coefficient and the method of maximizing the variance of the region mean when the high frequency coefficient is selected, and combining the average and the selection in the selection of the low frequency coefficient. The simulation experiment results and the objective evaluation index values show that the modification method can make up for the defects of the two original images and obtain a relatively complete and clear image.

Absrtact. Image compression technology is the basis of all kinds of media compression and transmission, and its compression effect is directly related to the compression effect of media. In this paper, four commonly used image compression coding methods, namely, undistorted Huffman coding, run-length coding, finite distortion predictive coding and transform coding, are used to compress still images, and the efficiency of each coding method is analyzed. The experimental results show that the two lossless compression coding methods have remarkable compression effect, but the compression efficiency is low. Although the compression ratio of lossy compression coding method is high, the decompressed image has certain distortion.

The motors of new energy vehicles adopt the built-in permanent magnet synchronous motor (PMSM) with high system efficiency. The mainstream control method of electric drive system is space vector PWM (SVPWM) algorithm. The SVPWM algorithm has higher low-order harmonic content in the low-speed segment voltage waveform, large asynchronous motor torque, and large motor iron loss, which makes the electric drive system less efficient in the low-speed segment system. This paper creatively proposes to change the modulation ratio M of the SVPWM waveform by adjusting the bus voltage, switching frequency and different control angles to reduce the harmonic content of the system to improve the overall efficiency of the electric drive system. In this paper, a theoretical model is established for the motor phase voltage harmonics, and various factors and components affecting the harmonic content are analyzed. Combined with the digital signal processor (DSP) based SVPWM vector control algorithm, the motor control SIMULINK simulation system is built. The voltage harmonic analysis of the simulation system is carried out by using the FFT tool in POWERGUI. The electric drive system bench is established for testing, the electric drive system simulation and the test bench test data are analyzed, and a clear method and means are proposed to reduce the harmonic content.

In order to quickly and accurately obtain useful information needed by people from massive video information, a structured, hierarchical video retrieval platform is constructed from the perspectives of reliability, efficiency, universality, and ease of use. In the monitoring of pedestrian flow information, multi-scale analysis and fractal texture analysis methods are used. First, two-level wavelet decomposition is performed on the crowd image, and the original image is decomposed into multiple sub-graphs. Then the box model is used to represent the texture model. Then, the support vector machine is used to classify the fractal dimension. In vehicle image detection, a hidden Markov model is used to identify vehicle models. First, observation sequence o and model λ = (A, B, π) are used to calculate the observation sequence P(O | π) produced by the model. Then, the Viterbi algorithm is used to find the best state sequence. Finally, the Baum-Welch algorithm is used to train the model. Judging the abnormal situation of the vehicle through the output of the model, the video surveillance system can monitor the people and vehicles.

Traffic control and route guidance are two subsystems of the Intelligence Traffic system. It’s of great value to cooperate the management of them. Meanwhile, different cooperative strategies should be established aimed at different traffic situations. This paper proposes a cooperative strategy to solve the problem of traffic congestion in a section by guiding the traffic flow to some other sections as well as transferring the control method in downstream intersections. A simulation on individual traffic control strategy and cooperative strategy in a typical traffic network was developed with Paramics software, respectively. The output of the simulation showed that the total mean delay of the network was reduced when the cooperative strategy is applied. The result demonstrated that the cooperative strategy is practical and useful. U