DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

基于QR迭代的量子奇异值分解

针对大型矩阵奇异值分解(singular value decomposition,SVD)时使用经典算法时间复杂度较高,以及已有的量子SVD算法要求待分解的矩阵必须具有非稀疏低秩的性质,并且在计算过程中构造任意大小酉矩阵对目前的量子计算机来说实现起来并不容易等问题,提出基于QR迭代的量子SVD。QR迭代使用的是Householder变换,通过量子矩阵乘法运算完成经典矩阵乘法运算过程。实验结果表明,该方法能够得到所求矩阵的奇异值及奇异矩阵,使大型矩阵的SVD具有可行性。

基于整数小波变换和SVD的视频水印算法

提出了一种以二值图像为水印的混合整数小波变换和奇异值分解的视频水印盲提取算法。对水印图像进行混沌加密和Arnold置乱处理,选择计算复杂度低的直方图算法将视频分割为若干场景;借助密钥随机选取某些场景的亮度分量进行l级整数小波变换,再对低频子带进行分块的奇异值分解;采用量化的方法,将预处理后的水印图像嵌入奇异值分解后的最大奇异值中。在嵌入了水印的视频场景中提取所有的水印版本之后,利用对提取的所有水印信号版本进行统计求和的方法得到最终提取的水印图像。实验表明,提出的算法具有较好的透明性,对常见的处理具有较好的鲁棒性。

基于SVD和TKEO的轴承振动信号特征提取

为了解决滚动轴承振动信号中微弱故障信息难以提取的问题,提出了一种基于奇异值分解(Singular Value Decomposition,SVD)和Teager-Kaiser能量算子(Teager-Kaiser Energy Operator,TKEO)的轴承振动信号特征提取方法。采用SVD将突变信息从背景噪声和光滑信号中分离,提取信号的突变信息;利用TKEO计算突变信息的瞬时能量,对该能量信号进行频谱分析,从而提取出轴承振动信号的能量频谱特征,用于故障检测。将该方法应用于轴承外圈、内圈局部故障状态下的振动信号特征提取,利用特征信息能够准确检测并识别出故障类型,表明了该方法的可行性和有效性。

高速列车万向轴动不平衡检测的EEMD-Hankel-SVD方法

针对聚合经验模式分解(Ensemble empirical model decomposition,EEMD)的等效滤波特性依然存在模式分量间频带重叠较大的根本缺陷,提出一种高速列车万向轴动不平衡动态检测的新方法。该方法的核心是对万向节安装机座的振动信号进行EEMD分解得到基本模式分量,应用基本模式分量信号来构造Hankel矩阵,对该矩阵进行正交化奇异值(Singular value decomposition,SVD)分解,以奇异值关键叠层作为奇异值的选择准则对信号进行重构,应用重构信号的傅里叶谱来检测高速列车万向轴的动不平衡,消除EEMD分解模式频带重叠对故障特征的淹没和混淆效应,提高了谱的清晰度,凸显了故障特征。应用万向轴动不平衡试验数据对该方法进行试验验证,结果表明,该方法能够有效检测万向轴动不平衡引起的故障特征和万向轴的固有振动特征,与纯EEMD方法相比,该方法在谱的清晰度和故障表征力上得到了显著提高。

洞庭湖区域夏季降水与全球大洋海温异常关系的SVD分析(英文)

By dint of the summer precipitation data from 21 stations in the Dongting Lake region during 1960-2008 and the sea surface temperature(SST) data from NOAA,the spatial and temporal distributions of summer precipitation and their correlations with SST are analyzed.The coupling relationship between the anomalous distribution in summer precipitation and the variation of SST has between studied with the Singular Value Decomposition(SVD) analysis.The increase or decrease of summer precipitation in the Dongting Lake region is closely associated with the SST anomalies in three key regions.The variation of SST in the three key regions has been proved to be a significant previous signal to anomaly of summer rainfall in Dongting region.

Analysis of heart rate variability based on singular value decomposition entropy

Assessing the dynamics of heart rate fluctuations can provide valuable information about heart status. In this study, regularity of heart rate variability （HRV） of heart failure patients and healthy persons using the concept of singular value decomposition entropy （SvdEn） is analyzed. SvdEn is calculated from the time series using normalized singular values. The advantage of this method is its simplicity and fast computation. It enables analysis of very short and non-stationary data sets. The results show that SvdEn of patients with congestive heart failure （CHF） shows a low value （SvdEn： 0.056±0.006, p 〈 0.01） which can be completely separated from healthy subjects. In addition, differences of SvdEn values between day and night are found for the healthy groups. SvdEn decreases with age. The lower the SvdEn values, the higher the risk of heart disease. Moreover, SvdEn is associated with the energy of heart rhythm. The results show that using SvdEn for discriminating HRV in different physiological states for clinical applications is feasible and simple.

The Singular Value Decomposition as a Tool of Investigating Central MHD Instabilities in the HL-1M Tokamak

A variety of strong MHD instabilities are always resulted from MHD activity of Tokamak plasmas. Central MHD instabilities can be observed with pinhole cameras to record soft x-ray (SXR) emission from the plasma along many chords with a high temporal resolution. The investigation of MHD instabilities often necessitates an analysis on spatial-temporal signals. The method of Singular Value Decomposition (SVD) can split such signals into orthogonal spatial and temporal vectors. By this means, the repetition time and the characteristic radius of various MHD phenomena such as sawteeth and snake-like perturbation can be obtained. Moreover, the (1,1) MHD mode is analyzed in great detail by SVD and used to determine the radius of the q = 1 surface.

Effects of surface heating on precipitation over the Tibetan Plateau and its eastern margin

The high terrain of the Tibetan Plateau(TP)has a very important impact on the weather and climate of China,East Asia,South Asia,and even the Northern Hemisphere.However,in recent years,the reasons for the decrease in precipitation in the southeastern edge of the plateau have resulted in cutting-edge research regarding the impact of the TP and its surrounding areas on downstream weather and climate.In this study,the spatial and temporal distribution of surface heat flux and precipitation were analyzed from 1998 to 2022,and the possible mechanism of the decrease of precipitation in the eastern edge of the plateau is explored.The main conclusions are as follows:The annual average sensible heat flux in the TP and its east side is positive,with an average of 33.73 W/m^(2).The annual average latent heat flux is positive,with an average of 42.71 W/m^(2).Precipitation has a similar annual average and seasonal distribution,with modest amounts in the northwest and substantial amounts in the southeast.The average annual accumulated precipitation is 670.69 mm.The first mode of the Empirical Orthogonal Function(EOF)shows that sensible heat flux decreases first,then increases,and then finally decreases during 1998–2022.The modes show the opposite trend in middle part of the plateau.The latent heat flux initially decreases,then increases,and finally decreases in the western plateau and near Sichuan Basin.The mode,however,displays the opposite tendency throughout the rest of the region.The precipitation in the north and south sides of the plateau has decreased since 2013,which is consistent with the changing trend of sensible heat flux.In the rest of the region,the change trend is not obvious.The sensible heat of the main body of the plateau and its east side and Sichuan Basin is negatively correlated with precipitation,that is,when sensible heat flux of the main body of the plateau and its east side and Sichuan Basin is more(less),local precipitation is less(more).The latent heat of the main body of the plateau and its east side,Sichuan Basin is positively correlated with precipitation,indicating that when latent heat flux of the main body of the plateau and its east side,Sichuan Basin is more(less),local precipitation is more(less).

Effective and Efficient Video Compression by the Deep Learning Techniques

Deep learning has reached many successes in Video Processing.Video has become a growing important part of our daily digital interactions.The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving,distributing,compressing and revealing highquality video content.In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask,which creatively combines the Deep Learning Techniques on Convolutional Neural Networks(CNN)and Generative Adversarial Networks(GAN).The video compression method involves the layers are divided into different groups for data processing,using CNN to remove the duplicate frames,repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory(LSTM).Instead of the complete image,the small changes generated using GAN are substituted,which helps with frame-level compression.Pixel wise comparison is performed using K-nearest Neighbours(KNN)over the frame,clustered with K-means and Singular Value Decomposition(SVD)is applied for every frame in the video for all three colour channels[Red,Green,Blue]to decrease the dimension of the utility matrix[R,G,B]by extracting its latent factors.Video frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original video.Repeated experiments on several videos with different sizes,duration,Frames per second(FPS),and quality results demonstrated a significant resampling rate.On normal,the outcome delivered had around a 10%deviation in quality and over half in size when contrasted,and the original video.

A Hybrid Air Quality Prediction Model Based on Empirical Mode Decomposition

Air pollution is a severe environmental problem in urban areas.Accurate air quality prediction can help governments and individuals make proper decisions to cope with potential air pollution.As a classic time series forecasting model,the AutoRegressive Integrated Moving Average(ARIMA)has been widely adopted in air quality prediction.However,because of the volatility of air quality and the lack of additional context information,i.e.,the spatial relationships among monitor stations,traditional ARIMA models suffer from unstable prediction performance.Though some deep networks can achieve higher accuracy,a mass of training data,heavy computing,and time cost are required.In this paper,we propose a hybrid model to simultaneously predict seven air pollution indicators from multiple monitoring stations.The proposed model consists of three components:(1)an extended ARIMA to predict matrix series of multiple air quality indicators from several adjacent monitoring stations;(2)the Empirical Mode Decomposition(EMD)to decompose the air quality time series data into multiple smooth sub-series;and(3)the truncated Singular Value Decomposition(SvD)to compress and denoise the expanded matrix.Experimental results on the public dataset show that our proposed model outperforms the state-of-art air quality forecasting models in both accuracy and time cost.

伪谱计算的增广块Householder Arnoldi算法（英文）

伪谱是解释非正规矩阵或算子行为的一个有用工具.矩阵伪谱计算的一个常用方法是grid-SVD算法,实现这个算法需要在每一个网格点处作奇异值分解(SVD);另外一个计算方法是基于Schur分解的逆Lanczos算法.由于上述方法的计算量比较大,通常只适用于中小型矩阵.近些年,有些学者探讨了大规模矩阵伪谱计算的Krylov子空间投影方法.在探讨了Householder Arnoldi(HA)算法块情形的计算行为和实用性能的基础上,提出了计算大规模矩阵伪谱的增广块HA(ABHA)算法,并对一些典型测试矩阵进行了一系列的数值试验.数值结果表明,增广块HA(ABHA)算法比HA算法,块隐式重启Arnoldi(BLIRA)算法和逆Lanczos算法的计算效率更高,更具优越性.

Impact of Planetary Wave Reflection on Tropospheric Blocking over the Urals–Siberia Region in January 2008

Planetary wave reflection from the stratosphere played a significant role in changing the tropospheric circulation pattern over Eurasia in mid-January 2008. We studied the 2008 event and compared with composite analysis （winters of 2002/2003, 200412005, 200612007, 200712008, 201012011 and 2011/2012）, when the downward coupling was stronger, by employing time-lagged singular value decomposition analysis on the geopotential height field. In the Northern Hemisphere, the geopo- tential fields were decomposed into zonal mean and wave components to compare the relative covariance patterns. It was found that the wavenumber 1 （WN1） component was dominant compared with the wavenumber 2 （WN2） component and zonal mean process. For the WNI field, the covariance was much higher （lower） for the negative （positive） lag, with a prominent peak around ＋15 days when the leading stratosphere coupled strongly with the troposphere. It contributed to the downward coupling due to reflection, when the stratosphere exhibited a partially reflective background state. We also analyzed the evolution of the WNI anomaly and heat flux anomaly, both in the troposphere and stratosphere, during January- March 2008. The amplitude of the tropospheric WN 1 pattern reached a maximum and was consistent with a downward wave coupling event influenced by the stratospheric WN1 anomaly at 10 hPa. This was consistent with the reflection of the WN1 component over Eurasia, which triggered an anomalous blocking high in the Urals-Siberia region. We further clarified the impact of reflection on the tropospheric WNI field and hence the tropospheric circulation pattern by changing the propagation direction during and after the event.

Teleconnections:Summer Monsoon over Korea and India

This study investigates the relationship between the summer monsoon rainfall over Korea and India, by using correlation analysis and Singular Value Decomposition (SVD).Results reveal that summer monsoon rainfall over Korea is negatively (significant at the 99% level) cor-related with the rainfall over the northwest and central parts of India. In addition, coupled spatial modes be-tween the rainfall over Korea and India have been identified by the SVD analysis. The squared covariance fraction explained by the first mode is 70% and the correlation coefficient between the time coefficients of the two fields is significant at the 99% level, indicating that the coupled mode reflects a large part of the interaction between the summer monsoon rainfall over Korea and India. The first mode clearly demon-strates the existence of a significant negative correlation between the rainfall over the northwest and central parts of India and the rainfall over Korea.Possible mechanisms of this correlation are investigated by analyzing the variation of upper-level at-mospheric circulation associated with the Tibetan high using NCEP/NCAR Reanalysis data.

Application of Singular Value Decomposition(SVD)to the Extraction of Gravity Anomalies Associated with Ag-Pb-Zn-W Polymetallic Mineralization in the Bozhushan Ore Field,Southwestern China

The Bozhushan Ore Field,located at the western margin of the South China Block,is an important area for Ag-Pb-Zn-W polymetallic mineralization which may be associated with the Late Cretaceous granitic magmaism.In this paper,the singular value decomposition(SVD)was effectively applied to decompose gravity data at scale of 1:50000 within the Bozhushan Ore Field to extract deep ore-finding information.Two gravity anomaly images displaying different scales of the ore-controlling factors were obtained.(1)The low-pass filtered image may reflect the deeply buried geological structures,hidden intrusions and concealed ore bodies.The negative gravity anomaly may reflect the overall distribution of granite bodies in the Bozhushan Ore Field.One negative gravity anomaly area may correspond to the exposed part of the Baozhushan granitic intrusion and the other corresponds to the concealed part of the granitic intrusion.The granitic intrusions are the main ore-controlling factors in this ore district.(2)The band-pass filtered image depicts the shallow concealed geological structures and geological bodies within this study area.There are two obvious negative gravity anomalies,which may be created by the hidden granites at different depths at both northwestern and southeastern sides of the exposed granitic intrusion.Thus the two negative gravity anomalies are favorable prospecting areas for various type of polymetallic ore deposits at depth.The gravity anomalies extracted by using the SVD exactly reflect the distribution of the ore deposits,structures and intrusions,which will give new insights for further mineral exploration in the study area.

Spatiotemporal Changes of Vegetation Cover in Response to Climate Change on the Tibetan Plateau

The Tibetan Plateau is one of the most important ecological barriers in China.Resolving the internal relations and dynamics ruling the association between regional vegetation and climate change is important to understand and protect the regional ecosystems.Based on vegetation,temperature and precipitation data of the Tibetan Plateau from 2001 to 2010,we analyze the spatial and temporal variations of vegetation cover over the past 10 years and discuss the vegetation response to climate change using empirical orthogonal function and singular value decomposition.Our results reveal the following：（1） vegetation cover gradually decreases from the southeast to the northwest of the Tibetan Plateau; （2） vegetation cover has increased on the Tibetan Plateau over the past 10 years,mainly in the central and eastern zones; and （3） a significant positive relationship was suggested between vegetation cover during growing season and the temperature in the entire region and with precipitation in the central and southern zones.

Properties of the total least squares estimation

Through theoretical derivation, some properties of the total least squares estimation are found. The total least squares estimation is the linear transformation of the least squares estimation, and the total least squares estimation is unbiased. The condition number of the total least squares estimation is greater than the least squares estimation, so the total least squares estimation is easier to be affected by the data error than the least squares estimation. Then through the further derivation, the relationships of solutions, residuals and unit weight variance estimations between the total least squares and the least squares are given.

Effect of Facet Displacement on Radiation Field and Its Application for Panel Adjustment of Large Reflector Antenna

Large reflector antennas are widely used in radars, satellite communication, radio astronomy, and so on. The rapid developments in these fields have created demands for development of better performance and higher surface accuracy. However, low accuracy and low effi- ciency are the common disadvantages for traditional panel alignment and adjustment. In order to improve the surface accuracy of large reflector antenna, a new method is pre- sented to determinate panel adjustment values from far field pattern. Based on the method of Physical Optics （PO）, the effect of panel facet displacement on radiation field value is derived. Then the linear system is constructed between panel adjustment vector and far field pattern. Using the method of Singular Value Decomposition （SVD）, the adjustment value for all panel adjustors are obtained by solving the linear equations. An experiment is conducted on a 3.7 m reflector antenna with 12 segmented panels. The results of simulation and test are similar, which shows that the presented method is feasible. Moreover, thediscussion about validation shows that the method can be used for many cases of reflector shape. The proposed research provides the instruction to adjust surface panels efficiently and accurately.

HIGH-ACCURACY BAND TO BAND REGISTRATION METHOD FOR MULTI-SPECTRAL IMAGES OF HJ-1A/B

Band-to-band registration accuracy is an important parameter of multispectral data. A novel band-to-band registration approach with high precision is proposed for the multi-spectral images of HJ-1A/B. Firstly, the main causes resulted in misregistration are analyzed, and a high-order polynomial model is proposed. Secondly, a phase fringe filtering technique is employed to Phase Correlation Method based on Singular Value Decomposition (SVD-PCM) for reducing the noise in phase difference matrix. Then, experiments are carried out to build nonlinear registration models, and images of green band and red band are aligned to blue band with an accuracy of 0.1 pixels, while near infrared band with an accuracy of 0.2 pixels.

Tropical Convective Activities Related to Summer Rainfall Anomaly in China

The paper presents the SVD-revealed relation of the tropical convection anomaly patterns to the summer rainfall counterparts of China, indicating that a) the ENSO-associated tropical con-vection anomaly is highly advantageous but the corresponding rainfall anomaly can only account for 10.3% of total variance, the rainfall anomaly related to tropical monsoon variation with the northern South China Sea as the center of convection abnormality for 18.8% and to the variation inside the tropical monsoon for 11.2%; b) the ENSO-related summer precipitation anomaly dis-plays a pattern of excessive rainfall in the south and deficit in the north, the anomaly relative to the tropical monsoon variation a pattern of more precipitation in the Yangtze River valleys and less in North, Northeast and South China, and that in relation to the variation within the tropical mon-soon a pattern of two rainbands, one in the Yangtze River valleys and the other in North China. Key words Tropical convection - Singular value decomposition (SVD) - Rainfall pattern (1)This work is supported by the National Natural Sciences Foundation of China under Grant No. 49775265.