Code acquisition is the kernel operation for signal synchronization in the spread-spectrum receiver.To reduce the computational complexity and latency of code acquisition,this paper proposes an efficient scheme employ...Code acquisition is the kernel operation for signal synchronization in the spread-spectrum receiver.To reduce the computational complexity and latency of code acquisition,this paper proposes an efficient scheme employing sparse Fourier transform(SFT)and the relevant hardware architecture for field programmable gate array(FPGA)and application-specific integrated circuit(ASIC)implementation.Efforts are made at both the algorithmic level and the implementation level to enable merged searching of code phase and Doppler frequency without incurring massive hardware expenditure.Compared with the existing code acquisition approaches,it is shown from theoretical analysis and experimental results that the proposed design can shorten processing latency and reduce hardware complexity without degrading the acquisition probability.展开更多
现有图像修复技术通常很难为缺失区域生成视觉上连贯的内容,其原因是高频内容质量下降导致频谱结构的偏差,以及有限的感受野无法有效建模输入特征之间的非局部关系。为解决上述问题,提出一种融合双向感知Transformer与频率分析策略的图...现有图像修复技术通常很难为缺失区域生成视觉上连贯的内容,其原因是高频内容质量下降导致频谱结构的偏差,以及有限的感受野无法有效建模输入特征之间的非局部关系。为解决上述问题,提出一种融合双向感知Transformer与频率分析策略的图像修复网络(bidirect-aware Transformer and frequency analysis,BAT-Freq)。具体内容包括,设计了双向感知Transformer,用自注意力和n-gram的组合从更大的窗口捕获上下文信息,以全局视角聚合高级图像上下文;同时,提出了频率分析指导网络,利用频率分量来提高图像修复质量,并设计了混合域特征自适应对齐模块,有效地对齐并融合破损区域的混合域特征,提高了模型的细节重建能力。该网络实现空间域与频率域相结合的图像修复。在CelebA-HQ、Place2、Paris StreetView三个数据集上进行了大量的实验,结果表明,PSNR和SSIM分别平均提高了2.804 dB和8.13%,MAE和LPIPS分别平均降低了0.0158和0.0962。实验证明,该方法能够同时考虑语义结构的完善和纹理细节的增强,生成具有逼真感的修复结果。展开更多
In order to improve the acquisition probability of satellite navigation signals, this paper proposes a novel code acquisition method based on wavelet transform filtering. Firstly, the signal vector based on the signal...In order to improve the acquisition probability of satellite navigation signals, this paper proposes a novel code acquisition method based on wavelet transform filtering. Firstly, the signal vector based on the signal passing through a set of partial matched filters （PMFs） is built. Then, wavelet domain filtering is performed on the signal vector value. Since the correlation signal is low in frequency and narrow in bandwidth, the noise out-of-band can be filtered out and the most of the useful signal energy is retained. Thus this process greatly improves the signal to noise ratio （SNR）. Finally, the detection variable when the filtered signal goes through the combination process is constructed and the detection based on signal energy is made. Moreover, for the better retaining useful signal energy, the rule of selection of wavelet function has been made. Simulation results show the proposed method has a better detection performance than the normal code acquisition methods under the same false alarm probability.展开更多
In this paper, we propose a new shape-coding algorithm called wavelet-based shape coding (WBSC). Performing wavelet transform on the orientation of original planar curve gives the corners called corner-1 points and en...In this paper, we propose a new shape-coding algorithm called wavelet-based shape coding (WBSC). Performing wavelet transform on the orientation of original planar curve gives the corners called corner-1 points and end of arcs that belong to the original curve. Each arc is represented by a broken line and the corners called corner-2 points of the broken line are extracted. A polygonal approximation of a contour is an ordered list of corner-1 points, ends of arcs and corner-2 points which are extracted by using the above algorithm. All of the points are called polygonal vertices which will be compressed by our adaptive arithmetic encoding. Experimental results show that our method reduces code bits by about 26% compared with the context-based arithmetic encoding (CAE) of MPEG-4, and the subjective quality of the reconstructed shape is better than that of CAE at the same Dn.展开更多
Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enorm...Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.展开更多
In order to overcome the phenomenon of image blur and edge loss in the process of collecting and transmitting medical image,a denoising method of medical image based on discrete wavelet transform(DWT)and modified medi...In order to overcome the phenomenon of image blur and edge loss in the process of collecting and transmitting medical image,a denoising method of medical image based on discrete wavelet transform(DWT)and modified median filter for medical image coupling denoising is proposed.The method is composed of four modules:image acquisition,image storage,image processing and image reconstruction.Image acquisition gets the medical image that contains Gaussian noise and impulse noise.Image storage includes the preservation of data and parameters of the original image and processed image.In the third module,the medical image is decomposed as four sub bands(LL,HL,LH,HH)by wavelet decomposition,where LL is low frequency,LH,HL,HH are respective for horizontal,vertical and in the diagonal line high frequency component.Using improved wavelet threshold to process high frequency coefficients and retain low frequency coefficients,the modified median filtering is performed on three high frequency sub bands after wavelet threshold processing.The last module is image reconstruction,which means getting the image after denoising by wavelet reconstruction.The advantage of this method is combining the advantages of median filter and wavelet to make the denoising effect better,not a simple combination of the two previous methods.With DWT and improved median filter coefficients coupling denoising,it is highly practical for high-precision medical images containing complex noises.The experimental results of proposed algorithm are compared with the results of median filter,wavelet transform,contourlet and DT-CWT,etc.According to visual evaluation index PSNR and SNR and Canny edge detection,in low noise images,PSNR and SNR increase by 10%–15%;in high noise images,PSNR and SNR increase by 2%–6%.The experimental results of the proposed algorithm achieved better acceptable results compared with other methods,which provides an important method for the diagnosis of medical condition.展开更多
A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of avail...A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.展开更多
Fault detection of an induction motor was carried out using the information of the stator current. After synchronizing the actual data, Fourier and wavelet transformations were adopted in order to obtain the sideband ...Fault detection of an induction motor was carried out using the information of the stator current. After synchronizing the actual data, Fourier and wavelet transformations were adopted in order to obtain the sideband or detail value characteristics under healthy and various faulty operating conditions. The most reliable phase current among the three phase currents was selected using an approach that employs the fuzzy entropy measure. Data were trained with a neural network system, and the fault detection algorithm was verified using the unknown data. Results of the proposed approach based on Fourier and wavelet transformations indicate that the faults can be properly classified into six categories. The training error is 5.3×10-7, and the average test error is 0.103.展开更多
基金supported by the National Natural Science Foundation of China(61801503).
文摘Code acquisition is the kernel operation for signal synchronization in the spread-spectrum receiver.To reduce the computational complexity and latency of code acquisition,this paper proposes an efficient scheme employing sparse Fourier transform(SFT)and the relevant hardware architecture for field programmable gate array(FPGA)and application-specific integrated circuit(ASIC)implementation.Efforts are made at both the algorithmic level and the implementation level to enable merged searching of code phase and Doppler frequency without incurring massive hardware expenditure.Compared with the existing code acquisition approaches,it is shown from theoretical analysis and experimental results that the proposed design can shorten processing latency and reduce hardware complexity without degrading the acquisition probability.
文摘现有图像修复技术通常很难为缺失区域生成视觉上连贯的内容,其原因是高频内容质量下降导致频谱结构的偏差,以及有限的感受野无法有效建模输入特征之间的非局部关系。为解决上述问题,提出一种融合双向感知Transformer与频率分析策略的图像修复网络(bidirect-aware Transformer and frequency analysis,BAT-Freq)。具体内容包括,设计了双向感知Transformer,用自注意力和n-gram的组合从更大的窗口捕获上下文信息,以全局视角聚合高级图像上下文;同时,提出了频率分析指导网络,利用频率分量来提高图像修复质量,并设计了混合域特征自适应对齐模块,有效地对齐并融合破损区域的混合域特征,提高了模型的细节重建能力。该网络实现空间域与频率域相结合的图像修复。在CelebA-HQ、Place2、Paris StreetView三个数据集上进行了大量的实验,结果表明,PSNR和SSIM分别平均提高了2.804 dB和8.13%,MAE和LPIPS分别平均降低了0.0158和0.0962。实验证明,该方法能够同时考虑语义结构的完善和纹理细节的增强,生成具有逼真感的修复结果。
基金supported by the National Natural Science Foundation of China(6117213861401340)the Fundamental Research Funds for the Central Universities(K5051302015)
文摘In order to improve the acquisition probability of satellite navigation signals, this paper proposes a novel code acquisition method based on wavelet transform filtering. Firstly, the signal vector based on the signal passing through a set of partial matched filters （PMFs） is built. Then, wavelet domain filtering is performed on the signal vector value. Since the correlation signal is low in frequency and narrow in bandwidth, the noise out-of-band can be filtered out and the most of the useful signal energy is retained. Thus this process greatly improves the signal to noise ratio （SNR）. Finally, the detection variable when the filtered signal goes through the combination process is constructed and the detection based on signal energy is made. Moreover, for the better retaining useful signal energy, the rule of selection of wavelet function has been made. Simulation results show the proposed method has a better detection performance than the normal code acquisition methods under the same false alarm probability.
文摘In this paper, we propose a new shape-coding algorithm called wavelet-based shape coding (WBSC). Performing wavelet transform on the orientation of original planar curve gives the corners called corner-1 points and end of arcs that belong to the original curve. Each arc is represented by a broken line and the corners called corner-2 points of the broken line are extracted. A polygonal approximation of a contour is an ordered list of corner-1 points, ends of arcs and corner-2 points which are extracted by using the above algorithm. All of the points are called polygonal vertices which will be compressed by our adaptive arithmetic encoding. Experimental results show that our method reduces code bits by about 26% compared with the context-based arithmetic encoding (CAE) of MPEG-4, and the subjective quality of the reconstructed shape is better than that of CAE at the same Dn.
基金supported by the National Natural Science Foundation of China(61072120)
文摘Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.
基金Project(2016JJ4074)supported by the Natural Science Foundation of Hunan Province,ChinaProject(14C0920)supported by the Hunan Provincial Education Department,ChinaProject(61771191)supported by the National Natural Science Foundation of China
文摘In order to overcome the phenomenon of image blur and edge loss in the process of collecting and transmitting medical image,a denoising method of medical image based on discrete wavelet transform(DWT)and modified median filter for medical image coupling denoising is proposed.The method is composed of four modules:image acquisition,image storage,image processing and image reconstruction.Image acquisition gets the medical image that contains Gaussian noise and impulse noise.Image storage includes the preservation of data and parameters of the original image and processed image.In the third module,the medical image is decomposed as four sub bands(LL,HL,LH,HH)by wavelet decomposition,where LL is low frequency,LH,HL,HH are respective for horizontal,vertical and in the diagonal line high frequency component.Using improved wavelet threshold to process high frequency coefficients and retain low frequency coefficients,the modified median filtering is performed on three high frequency sub bands after wavelet threshold processing.The last module is image reconstruction,which means getting the image after denoising by wavelet reconstruction.The advantage of this method is combining the advantages of median filter and wavelet to make the denoising effect better,not a simple combination of the two previous methods.With DWT and improved median filter coefficients coupling denoising,it is highly practical for high-precision medical images containing complex noises.The experimental results of proposed algorithm are compared with the results of median filter,wavelet transform,contourlet and DT-CWT,etc.According to visual evaluation index PSNR and SNR and Canny edge detection,in low noise images,PSNR and SNR increase by 10%–15%;in high noise images,PSNR and SNR increase by 2%–6%.The experimental results of the proposed algorithm achieved better acceptable results compared with other methods,which provides an important method for the diagnosis of medical condition.
基金Project(51561135003)supported by the International Cooperation and Exchange of the National Natural Science Foundation of ChinaProject(51338003)supported by the Key Project of National Natural Science Foundation of China
文摘A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.
基金Project supported by the Second Stage of Brain Korea 21 Projects
文摘Fault detection of an induction motor was carried out using the information of the stator current. After synchronizing the actual data, Fourier and wavelet transformations were adopted in order to obtain the sideband or detail value characteristics under healthy and various faulty operating conditions. The most reliable phase current among the three phase currents was selected using an approach that employs the fuzzy entropy measure. Data were trained with a neural network system, and the fault detection algorithm was verified using the unknown data. Results of the proposed approach based on Fourier and wavelet transformations indicate that the faults can be properly classified into six categories. The training error is 5.3×10-7, and the average test error is 0.103.
