A Context Model of Wavelet Coefficients for Image Compression

Through the combination of the idea of the linear prediction used in the GlicBawls coding scheme and the idea of the quantization taken in the CALIC coding scheme to reduce the number of contexts, a new context model of wavelet coefficients for image compression is proposed. Wavelet coefficients are encoded by the arithmetic encoder, with the contexts formed by quantizing linear prediction values. Experimental results show that the model achieves higher lossless compression rate of image than lossless SPIHT and lossless EBCOT used in JPEG2000. In addition, by exploiting the multiresolution property of wavelet, the model can compress the transformed image for progressive resolution and earn higher compression rate for each scale of the image than EBCOT.     

Embedded Image Coding on Wavelet Coefficients

The modeling of wavelet coefficient for still image compression is studied.Firstly,through simulation experiments on three classical wavelet coefficient models(i.e.,EZW model,SPIHT model/and EBCOT model),it is shown that SPIHT is superior to EZW in terms of compression performance(0.6 dB higher)and the compression performance of EBCOT is significantly better than that of SPIHT on average. Then the cause for such performance differences is explained through the analysis of models.Finally a new wavelet coefficients model is proposed which combines the advantages of the three classical models.Experimental results show that our model is better than the above mentioned three models with respect to compression ratio.     

VBR Coding Algorithm Based on Wavelet Transform

The introduction of the asynchronous transfer mode(ATM) concept has significantly influenced the coding theory.According to the characteristics of the ATM network,using the advantages based on the wavelet transform,how to apply the wavelet transform to plane separation and how to apply VBR coding to the layered data are discussed.The results of experiment are described respectively.     

Video Compression of Vector Coding based on Wavelet Transform

It is possible to get high compression rate for the interrelationship between neighboring frames of moving pictures. The kernel of research on video compression is that the efficient image compression algorithm is adopted while the interrelationship between neighboring frames and time consumption is concerned to improve the compression efficiency. It is significant that motion compensation is applied to video compression, nevertheless it is too time consuming. A vector coding method based on wavelet transform is presented to avoid searching procedure of motion compensation. Simulation shows that high compression rate is achieved with less time consumption.     

A Vector Quantification Image Coding Scheme Based on Wavelet Transform

The paper proposes an image compression and coding scheme based on discrete wavelet transform (DWT), which makes the best of the relativities among original image and coefficients of wavelet, the relativities among pixels of subimages, the relativities among directions of subimages. Human visual specialities were considered, so the compression scheme leads up to make full use of the statistical redundancy and visual redundancy of image. To get high compression ratio and good quality of image, the coefficients of image working on the quality of image greatly were reserved accurately, but the coefficients of image working on the quality of image slightly were quantified roughly. Also, the regrouping and coding of the coefficients could be used universally. The experimental results show that the compression method is provided with simply calculating, little time for coding and decoding and satisfying quality companied with high compression ratio.     

Choosing Method of Wavelet Neuron Based on ECG Data Compression

The paper discusses a wavelet network for the ECG data compression and proposes the method for choosing its wavelet neuron.According to the spectrum range of the ECG data,we decide the time-frequency field of ECG.And the time-frequency field of wavelet is also determined by the spectrum range of it.The wavelet neuron is fixed preliminarily by the first two steps.Then the preliminary wavelet neuron is screened by using OLS algorithm.We choose Morlet as the mother wavelet,and use the ECG signal to validate by the method.The result demonstrates that the number of Morlet whose spectrums locate at the ECG's is up to 152.But after screening by the OLS algorithm,it reduces sharply.This method can make the size of the wavelet network driving to optimum and also reduce the training time of the wavelet network sharply.     

Application of Wavelet Transform and HMM Model in Recognition of Speech Signals

Some parameters based on wavelet transform and ARMA Model are presented. Wavelet transform provides a high frequency resolution, and the ARMA model is more powerful due to its including of the zeros pole in the model. The experimental result of alphabet of A to N from National Institute of Standard Technology (NIST) database is given. The error rate has been improved, especially C.     

Systems Analysis Based on Continuous Wavelet Transform

The linear time invariant vibration system is analyzed by Continuous Wavelet Transform(CWT),the relationship of wavelet transform of output signal with the pulse response of system and input signal is put forward.As an exapmle,the wavelet transform of the output signal of system with single degree of freedom is calculated and compared with the direct wavelet transform result of the actual output signal,it shows that the two results are in complete agreement.     

EEG Signal Denoising Based on Wavelet Transform

The article focuses on the method of noise cancellation for EEG signal. The method of notch filter is discussed. According to the frequency of noise and the principle of notch filter, the design result of the notch filter and the denoised signal are presented. Then, the analysis of EEG signal are proposed based on wavelet transform (WT) and noise cancellation using WT. Wavelet transform is a multi-resolution time-frequency analysis method. It can decompose mixed signal into signals at different frequency bands. The EEG signal is analyzed and denoised using WT, then the results are presented respectively. Comparing the experiment results shows that WT can detect and process noise in the EEG signal effectively.     

Optimal Design of Plane Cascades Based on Wavelet Theory

Based on the optimal control theory and Lagrange interpolation an optimal design model of plane cascades of 3D blade in an arbitrary surface of revolution is established. Using wavelet theory to solve the model, and lots of numerical results are obtained. The results show that the wavelet method is more effective than the conventional methods.     

The Signals Analysis System Based on Fast Wavelet Transform

The fast wavelet transform (FWT) algorithm in wavelet analysis was introduced in the paper. With quadrature mirror filters (QMF) associated with popular wavelet bases, the fast wavelet decomposition and reconstruction for signals were implemented. Combined with virtual instrument technique, the FWT analysis system for signals was successfully developed. The system can break up signal not only into approximations, which are the high-scale and low-frequency components of the signal, but also into details which are the low-scale and high-frequency components. Especially it can identify singularity signal, which contain some important message of equipment condition and fault, and refine signal from noisy signal, which is corrupted by noise.     

Speedy Recognition of Noise in Real Time Signal Based on Wavelet Transform

It's necessary to process position and gesture of rocket on real time within the flight of rocket. This work should base exact data. But much noise has been found in the data and the noise recognize is regarded as a focus. A speedy online arithmetic recognizing noise is proposed based on wavelet transform. The computing complexity measured by time of this arithmetic is a constant which is greatly reduces the works of calculation of wavelet transform. It can recognize the noise fast when the signal is gathered. The applications in these problems show that the effective arithmetic satisfies the needs of real time and can handle the real time data measured in other yields.     

Processing Fault Data Method in Load Forecasting Based on Wavelet Analysis

The reliability and reality of load historical data is the foundation of load forecasting.But,the impact load in running power system,and the disturb data in collecting load data through the SCADA may cause much fault data in load historical data. Focusing on solving this problem, a method through adjusting amplitade of its wavele modulus maxima and processing the wavelet decomposed detail signal by soft threshold based on wavelet analysis and singularity theory, then fault date can be eliminated,so that,the real historical imformation and regulation data can be gained by load forecasting.     

Realizing 3 Layers Transport Model in the Communication Between Embedded System and PC

This article puts forward a 3 Layers Model for the communication between embedded system and PC. It is based on the speciality of them and the TCP/IP model. The model defines the communicate process of embedded system to be three layers: Transport Layer, Control Layer and Application Layer. Each layer serves for its higher-up. It can keep the efficiency and credibility of the communicate process and the authors prove and analyse that in the article. the concept of the model is also suitable for Bluetooth, IrDA and other kinds of communication about embedded system.     

Wavelet Packet Decomposition Based on Entropy Threshold for White-noise Rejection in PD Signal

This paper analyzes the different characteristics of white-noise interference in the signals of partial discharge (PD) after wavelet transform. There is high value in lots of scales for PD and white-noise interference is to zero with increasing scale. The threshold is set for wavelet coefficient in all scales. If the coefficient of signal is higher than the threshold, it is PD signal. Otherwise it is noise interference. A threshold-based wavelet packet transform (WPT) algorithm is put forward to suppress white noise interference in PD signals. The results testifies that it has a favorable adaptability to extract PD signals using WPT.     

中国小麦进口贸易格局及其影响因素研究——基于贸易引力模型

中国是小麦的净进口国,对国际市场的依赖性高,研究中国小麦进口的来源结构以及影响因素,对中国粮食安全问题至关重要。在1992—2017年小麦贸易的形势分析基础之上,利用引力模型对小麦的进口贸易的影响因素进行研究。结果表明:自2013年以来,中国小麦贸易量处于平稳状态,逐步形成了以澳大利亚为主,美国加拿大和哈萨克斯坦为辅的进口格局;中国小麦进口结构趋于优化,与哈萨克斯坦、以色列等一带一路沿线国家的小麦贸易增速较快;地理距离、是否沿海、是否加入APEC、出口国当年GDP和上年粮食产量对中国小麦进口有显著影响。最后,还提出了相应的对策建议。