This paper presents an optimized 3-D Discrete Wavelet Transform (3-DDWT) architecture. 1-DDWT employed for the design of 3-DDWT architecture uses reduced lifting scheme approach. Further the architecture is optimized ...This paper presents an optimized 3-D Discrete Wavelet Transform (3-DDWT) architecture. 1-DDWT employed for the design of 3-DDWT architecture uses reduced lifting scheme approach. Further the architecture is optimized by applying block enabling technique, scaling, and rounding of the filter coefficients. The proposed architecture uses biorthogonal (9/7) wavelet filter. The architecture is modeled using Verilog HDL, simulated using ModelSim, synthesized using Xilinx ISE and finally implemented on Virtex-5 FPGA. The proposed 3-DDWT architecture has slice register utilization of 5%, operating frequency of 396 MHz and a power consumption of 0.45 W.展开更多
In this paper,we address the frequency estimator for 2-dimensional(2-D)complex sinusoids in the presence of white Gaussian noise.With the use of the sinc function model of the discrete Fourier transform(DFT)coefficien...In this paper,we address the frequency estimator for 2-dimensional(2-D)complex sinusoids in the presence of white Gaussian noise.With the use of the sinc function model of the discrete Fourier transform(DFT)coefficients on the input data,a fast and accurate frequency estimator is devised,where only the DFT coefficient with the highest magnitude and its four neighbors are required.Variance analysis is also included to investigate the accuracy of the proposed algorithm.Simulation results are conducted to demonstrate the superiority of the developed scheme,in terms of the estimation performance and computational complexity.展开更多
In the traditional incremental analysis update(IAU)process,all analysis increments are treated as constant forcing in a model’s prognostic equations over a certain time window.This approach effectively reduces high-f...In the traditional incremental analysis update(IAU)process,all analysis increments are treated as constant forcing in a model’s prognostic equations over a certain time window.This approach effectively reduces high-frequency oscillations introduced by data assimilation.However,as different scales of increments have unique evolutionary speeds and life histories in a numerical model,the traditional IAU scheme cannot fully meet the requirements of short-term forecasting for the damping of high-frequency noise and may even cause systematic drifts.Therefore,a multi-scale IAU scheme is proposed in this paper.Analysis increments were divided into different scale parts using a spatial filtering technique.For each scale increment,the optimal relaxation time in the IAU scheme was determined by the skill of the forecasting results.Finally,different scales of analysis increments were added to the model integration during their optimal relaxation time.The multi-scale IAU scheme can effectively reduce the noise and further improve the balance between large-scale and small-scale increments in the model initialization stage.To evaluate its performance,several numerical experiments were conducted to simulate the path and intensity of Typhoon Mangkhut(2018)and showed that:(1)the multi-scale IAU scheme had an obvious effect on noise control at the initial stage of data assimilation;(2)the optimal relaxation time for large-scale and small-scale increments was estimated as 6 h and 3 h,respectively;(3)the forecast performance of the multi-scale IAU scheme in the prediction of Typhoon Mangkhut(2018)was better than that of the traditional IAU scheme.The results demonstrate the superiority of the multi-scale IAU scheme.展开更多
Sea ice surface roughness(SIR)affects the energy transfer between the atmosphere and the ocean,and it is also an important indicator for sea ice characteristics.To obtain a small-scale SIR with high spatial resolution...Sea ice surface roughness(SIR)affects the energy transfer between the atmosphere and the ocean,and it is also an important indicator for sea ice characteristics.To obtain a small-scale SIR with high spatial resolution,a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar(SAR)images,utilizing an ensemble learning method.Firstly,the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice,including the scale and direction of ice patterns.Secondly,a model is developed using the Adaboost Regression model to establish a relationship among SIR,radar backscatter and the spatial information of sea ice.The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper(ATM)in the summer Beaufort Sea.The determination of coefficient,mean absolute error,root-mean-square error and mean absolute percentage error of the testing data are 0.91,1.71 cm,2.82 cm,and 36.37%,respectively,which are reasonable.Moreover,K-fold cross-validation and learning curves are analyzed,which also demonstrate the method’s applicability in retrieving SIR from SAR images.展开更多
Among existing remote sensing applications, land-based X-band radar is an effective technique to monitor the wave fields, and spatial wave information could be obtained from the radar images. Two-dimensional Fourier T...Among existing remote sensing applications, land-based X-band radar is an effective technique to monitor the wave fields, and spatial wave information could be obtained from the radar images. Two-dimensional Fourier Transform （2-D FT） is the common algorithm to derive the spectra of radar images. However, the wave field in the nearshore area is highly non-homogeneous due to wave refraction, shoaling, and other coastal mechanisms. When applied in nearshore radar images, 2-D FT would lead to ambiguity of wave characteristics in wave number domain. In this article, we introduce two-dimensional Wavelet Transform （2-D WT） to capture the non-homogeneity of wave fields from nearshore radar images. The results show that wave number spectra by 2-D WT at six parallel space locations in the given image clearly present the shoaling of nearshore waves. Wave number of the peak wave energy is increasing along the inshore direction, and dominant direction of the spectra changes from South South West （SSW） to West South West （WSW）. To verify the results of 2-D WT, wave shoaling in radar images is calculated based on dispersion relation. The theoretical calculation results agree with the results of 2-D WT on the whole. The encouraging performance of 2-D WT indicates its strong capability of revealing the non-homogeneity of wave fields in nearshore X-band radar images.展开更多
This study presents a novel two-step approach to assess plate-like structural laminar damages,particularly for delamination damage detection of composite structures.Firstly,a 2-D continuous wavelet transform is employ...This study presents a novel two-step approach to assess plate-like structural laminar damages,particularly for delamination damage detection of composite structures.Firstly,a 2-D continuous wavelet transform is employed to identify the damage location and sizes from vibration curvature data.An inverse method is subsequently then used to determine the bending stiffness reduction ratio along a specified direction,enabling the quantification of the delamination severity.The method employed in this study is an extension of the one-dimensional inverse method developed in a previous work of the authors.The applicability of the two-step inverse approach is demonstrated in a simulation analysis and by an experimental study on a cantilever composite plate containing a single delamination.The inverse method is shown to have the capacity to reveal the detailed damage information of delamination within a constrained searching space and can be used to determine the effective flexural stiffness of composite plate structures,even in cases of complex delamination damage.展开更多
为了实现基于FPGA的CCSDS图像压缩算法,在提升小波变换结构的基础上,提出了一种改进的基于行的并行3级2-D整数9/7小波变换实现结构。结构充分利用流水线设计技术,对于每一级2-DDWT,结构包含2个行处理器同时处理2行数据,借助10个行缓存...为了实现基于FPGA的CCSDS图像压缩算法,在提升小波变换结构的基础上,提出了一种改进的基于行的并行3级2-D整数9/7小波变换实现结构。结构充分利用流水线设计技术,对于每一级2-DDWT,结构包含2个行处理器同时处理2行数据,借助10个行缓存存储变换的中间数据,实现了行、列变换的并行运算。同时对于3级小波变换,也采用了流水线结构,减少了存储器的使用量和对其访问造成的时间延迟,提高了变换速度。本结构完成分辨率为N×N灰度图像的3级小波分解所用的时钟周期约为O(N2/2)。采用Altera的Stratix II FPGA实验,结果表明,本整数小波变换结构具有较高的吞吐率和变换速度,可以工作在86.5MHz的频率下,实现1024×1024灰度图像100fps的图像实时变换。展开更多
文摘This paper presents an optimized 3-D Discrete Wavelet Transform (3-DDWT) architecture. 1-DDWT employed for the design of 3-DDWT architecture uses reduced lifting scheme approach. Further the architecture is optimized by applying block enabling technique, scaling, and rounding of the filter coefficients. The proposed architecture uses biorthogonal (9/7) wavelet filter. The architecture is modeled using Verilog HDL, simulated using ModelSim, synthesized using Xilinx ISE and finally implemented on Virtex-5 FPGA. The proposed 3-DDWT architecture has slice register utilization of 5%, operating frequency of 396 MHz and a power consumption of 0.45 W.
文摘In this paper,we address the frequency estimator for 2-dimensional(2-D)complex sinusoids in the presence of white Gaussian noise.With the use of the sinc function model of the discrete Fourier transform(DFT)coefficients on the input data,a fast and accurate frequency estimator is devised,where only the DFT coefficient with the highest magnitude and its four neighbors are required.Variance analysis is also included to investigate the accuracy of the proposed algorithm.Simulation results are conducted to demonstrate the superiority of the developed scheme,in terms of the estimation performance and computational complexity.
基金jointly sponsored by the Shenzhen Science and Technology Innovation Commission (Grant No. KCXFZ20201221173610028)the key program of the National Natural Science Foundation of China (Grant No. 42130605)
文摘In the traditional incremental analysis update(IAU)process,all analysis increments are treated as constant forcing in a model’s prognostic equations over a certain time window.This approach effectively reduces high-frequency oscillations introduced by data assimilation.However,as different scales of increments have unique evolutionary speeds and life histories in a numerical model,the traditional IAU scheme cannot fully meet the requirements of short-term forecasting for the damping of high-frequency noise and may even cause systematic drifts.Therefore,a multi-scale IAU scheme is proposed in this paper.Analysis increments were divided into different scale parts using a spatial filtering technique.For each scale increment,the optimal relaxation time in the IAU scheme was determined by the skill of the forecasting results.Finally,different scales of analysis increments were added to the model integration during their optimal relaxation time.The multi-scale IAU scheme can effectively reduce the noise and further improve the balance between large-scale and small-scale increments in the model initialization stage.To evaluate its performance,several numerical experiments were conducted to simulate the path and intensity of Typhoon Mangkhut(2018)and showed that:(1)the multi-scale IAU scheme had an obvious effect on noise control at the initial stage of data assimilation;(2)the optimal relaxation time for large-scale and small-scale increments was estimated as 6 h and 3 h,respectively;(3)the forecast performance of the multi-scale IAU scheme in the prediction of Typhoon Mangkhut(2018)was better than that of the traditional IAU scheme.The results demonstrate the superiority of the multi-scale IAU scheme.
基金The National Key Research and Development Program of China under contract No.2021YFC2803301the National Natural Science Foundation of China under contract No.41977302+2 种基金the National Natural Science Youth Foundation of China under contract No.41506199the Natural Science Youth Foundation of Jiangsu Province under contrant No.BK20150905the Science and Technology Project of China Huaneng Group Co.,Ltd.under contract No.HNKJ20-H66.
文摘Sea ice surface roughness(SIR)affects the energy transfer between the atmosphere and the ocean,and it is also an important indicator for sea ice characteristics.To obtain a small-scale SIR with high spatial resolution,a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar(SAR)images,utilizing an ensemble learning method.Firstly,the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice,including the scale and direction of ice patterns.Secondly,a model is developed using the Adaboost Regression model to establish a relationship among SIR,radar backscatter and the spatial information of sea ice.The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper(ATM)in the summer Beaufort Sea.The determination of coefficient,mean absolute error,root-mean-square error and mean absolute percentage error of the testing data are 0.91,1.71 cm,2.82 cm,and 36.37%,respectively,which are reasonable.Moreover,K-fold cross-validation and learning curves are analyzed,which also demonstrate the method’s applicability in retrieving SIR from SAR images.
基金Project supported by the Open Research Fund of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University (Grant No. 2008491011)the Special Fund of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University (Grant Nos. 2009585812, 2009586712)+1 种基金the Key Project of Chinese Ministry of Education (Grant No. 20100094120008)supported by the Funds for the Central Universities, Hohai University (Grant No. 2009B00214)
文摘Among existing remote sensing applications, land-based X-band radar is an effective technique to monitor the wave fields, and spatial wave information could be obtained from the radar images. Two-dimensional Fourier Transform （2-D FT） is the common algorithm to derive the spectra of radar images. However, the wave field in the nearshore area is highly non-homogeneous due to wave refraction, shoaling, and other coastal mechanisms. When applied in nearshore radar images, 2-D FT would lead to ambiguity of wave characteristics in wave number domain. In this article, we introduce two-dimensional Wavelet Transform （2-D WT） to capture the non-homogeneity of wave fields from nearshore radar images. The results show that wave number spectra by 2-D WT at six parallel space locations in the given image clearly present the shoaling of nearshore waves. Wave number of the peak wave energy is increasing along the inshore direction, and dominant direction of the spectra changes from South South West （SSW） to West South West （WSW）. To verify the results of 2-D WT, wave shoaling in radar images is calculated based on dispersion relation. The theoretical calculation results agree with the results of 2-D WT on the whole. The encouraging performance of 2-D WT indicates its strong capability of revealing the non-homogeneity of wave fields in nearshore X-band radar images.
文摘This study presents a novel two-step approach to assess plate-like structural laminar damages,particularly for delamination damage detection of composite structures.Firstly,a 2-D continuous wavelet transform is employed to identify the damage location and sizes from vibration curvature data.An inverse method is subsequently then used to determine the bending stiffness reduction ratio along a specified direction,enabling the quantification of the delamination severity.The method employed in this study is an extension of the one-dimensional inverse method developed in a previous work of the authors.The applicability of the two-step inverse approach is demonstrated in a simulation analysis and by an experimental study on a cantilever composite plate containing a single delamination.The inverse method is shown to have the capacity to reveal the detailed damage information of delamination within a constrained searching space and can be used to determine the effective flexural stiffness of composite plate structures,even in cases of complex delamination damage.
文摘为了实现基于FPGA的CCSDS图像压缩算法,在提升小波变换结构的基础上,提出了一种改进的基于行的并行3级2-D整数9/7小波变换实现结构。结构充分利用流水线设计技术,对于每一级2-DDWT,结构包含2个行处理器同时处理2行数据,借助10个行缓存存储变换的中间数据,实现了行、列变换的并行运算。同时对于3级小波变换,也采用了流水线结构,减少了存储器的使用量和对其访问造成的时间延迟,提高了变换速度。本结构完成分辨率为N×N灰度图像的3级小波分解所用的时钟周期约为O(N2/2)。采用Altera的Stratix II FPGA实验,结果表明,本整数小波变换结构具有较高的吞吐率和变换速度,可以工作在86.5MHz的频率下,实现1024×1024灰度图像100fps的图像实时变换。
