Some Results for Exact Support Recovery of Block Joint Sparse Matrix via Block Multiple Measurement Vectors Algorithm

Block multiple measurement vectors (BMMV) is a reconstruction algorithm that can be used to recover the support of block K-joint sparse matrix X from Y = ΨX + V. In this paper, we propose a sufficient condition for accurate support recovery of the block K-joint sparse matrix via the BMMV algorithm in the noisy case. Furthermore, we show the optimality of the condition we proposed in the absence of noise when the problem reduces to single measurement vector case.

基于降维变换的低复杂度双基地EMVS-MIMO雷达高分辨多维参数估计

针对当前算法在实现双基地电磁矢量传感器多输入多输出(electromagnetic vector sensors multiple input multiple output,EMVS-MIMO)雷达的多维参数估计时计算代价较高的问题,通过利用降维变换技术来实现低复杂度的角度参数和极化参数求解。针对阵列接收数据维度较大问题,通过设计相应的波束空间变换矩阵来实现对阵列接收数据的降维处理。针对算法本身的较高计算复杂度问题,采用低计算复杂度的平行因子分解算法。所提算法能够精确地实现对发射因子矩阵和接收因子矩阵的求解。同时,通过新的旋转不变关系构建新的估计信号参数,可以实现对发射/接收俯仰角的求解。进一步,发射/接收方位角、发射/接收极化角和发射/接收极化相位差的估计可以通过发射/接收空间响应矩阵的重构来实现。仿真实验结果表明,所提算法在降低计算复杂度的同时能够保持优越的多维参数估计性能。

Endpoint Prediction of EAF Based on Multiple Support Vector Machines

The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines （MSVM）. In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub- models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.

Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

Comparison of Five Expression Vectors for the Ha Gene in Constructing a DNA Vaccine for H6N2 Influenza Virus in Chickens

A number of eukaryotic expression vectors have been developed for use as DNA vaccines. They showed varying abilities to initiate immune responses;however, there is little data to indicate which of these vectors will be the most useful and practical for DNA vaccines in different species. This report examines the use of five expression vectors with different promoters and Kozak sequence to express the same hemagglutinin (HA) protein of an H6N2 avian influenza virus for DNA vaccination in chickens. Although intramuscular vaccination with seven DNA constructs elicited no or limited measurable H6 HA antibody responses in Hy-Line chickens, variable reduction in virus shedding for either oropharyngeal or cloacal swabs post-virus challenge were observed. This indicated that all DNA constructs generated some levels of protective immunity against homologous virus challenge. Interestingly, lower dose (50 or 100 μg) of plasmid DNAs consistently induced better immune response than higher dose (300 or 500 μg). In the transfection experiments there appeared to be a hierarchy in the in vitro expression efficiency in the order of pCAG-optiHAk/ pCAG-HAk > pCI-HAk > VR-HA > pCI-HA > pCI-neo-HA > pVAX-HA. Since the level of in vitro expression correlates with the level of immune response in vivo, in vitro expression levels of the DNA constructs can be used as an indicator for pre-selection of plasmid vaccines prior to in vivo assessment. Moreover, our results suggested that the Kozak sequence could be used as an effective tool for DNA vaccine design.

Multi-Dimension Support Vector Machine Based Crowd Detection and Localisation Framework for Varying Video Sequences

In this paper, we propose a novel method for anomalous crowd behaviour detection and localization with divergent centers in intelligent video sequence through multiple SVM (support vector machines) based appearance model. In multi-dimension SVM crowd detection, many features are available to track the object robustly with three main features which include 1) identification of an object by gray scale value, 2) histogram of oriented gradients (HOG) and 3) local binary pattern (LBP). We propose two more powerful features namely gray level co-occurrence matrix (GLCM) and Gaber feature for more accurate and authenticate tracking result. To combine and process the corresponding SVMs obtained from each features, a new collaborative strategy is developed on the basis of the confidence distribution of the video samples which are weighted by entropy method. We have adopted subspace evolution strategy for reconstructing the image of the object by constructing an update model. Also, we determine reconstruction error from the samples and again automatically build an update model for the target which is tracked in the video sequences. Considering the movement of the targeted object, occlusion problem is considered and overcome by constructing a collaborative model from that of appearance model and update model. Also if update model is of discriminative model type, binary classification problem is taken into account and overcome by collaborative model. We run the multi-view SVM tracking method in real time with subspace evolution strategy to track and detect the moving objects in the crowded scene accurately. As shown in the result part, our method also overcomes the occlusion problem that occurs frequently while objects under rotation and illumination change due to different environmental conditions.

Rank-defective millimeter-wave channel estimation based on subspace-compressive sensing

Millimeter-wave communication （mmWC） is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station （BS） and mobile sets （MS）. Unlike the conventional MIMO systems, Millimeter-wave （mmW） systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding （combining） architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector （MMV） greedy sparse framework and subspace method of Multiple Signal Classification （MUSIC） which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.

Iterative subspace matching pursuit for joint sparse recovery

Joint sparse recovery(JSR)in compressed sensing(CS)is to simultaneously recover multiple jointly sparse vectors from their incomplete measurements that are conducted based on a common sensing matrix.In this study,the focus is placed on the rank defective case where the number of measurements is limited or the signals are significantly correlated with each other.First,an iterative atom refinement process is adopted to estimate part of the atoms of the support set.Subsequently,the above atoms along with the measurements are used to estimate the remaining atoms.The estimation criteria for atoms are based on the principle of minimum subspace distance.Extensive numerical experiments were performed in noiseless and noisy scenarios,and results reveal that iterative subspace matching pursuit(ISMP)outperforms other existing algorithms for JSR.

ACCURATE AND EFFICIENT IMAGE RECONSTRUCTION FROM MULTIPLE MEASUREMENTS OF FOURIER SAMPLES

Several problems in imaging acquire multiple measurement vectors(MMVs)of Fourier samples for the same underlying scene.Image recovery techniques from MMVs aim to exploit the joint sparsity across the measurements in the sparse domain.This is typically accomplished by extending the use of`1 regularization of the sparse domain in the single measurement vector(SMV)case to using`2,1 regularization so that the“jointness”can be accounted for.Although effective,the approach is inherently coupled and therefore computationally inefficient.The method also does not consider current approaches in the SMV case that use spatially varying weighted`1 regularization term.The recently introduced variance based joint sparsity(VBJS)recovery method uses the variance across the measurements in the sparse domain to produce a weighted MMV method that is more accurate and more efficient than the standard`2,1 approach.The efficiency is due to the decoupling of the measurement vectors,with the increased accuracy resulting from the spatially varying weight.Motivated by these results,this paper introduces a new technique to even further reduce computational cost by eliminating the requirement to first approximate the underlying image in order to construct the weights.Eliminating this preprocessing step moreover reduces the amount of information lost from the data,so that our method is more accurate.Numerical examples provided in the paper verify these benefits.

Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds

In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection （SPA） strategy, is used as the descriptor selection and model development method. Then, the support vector machine （SVM） and multiple linear regression （MLR） model are utilized to construct the non-linear and linear quantitative structure-property relationship models. The results obtained using the SVM model are compared with those obtained using MLR reveal that the SVM model is of much better predictive value than the MLR one. The root-mean-square errors for the training set and the test set for the SVM model were 0.1911 and 0.2569, respectively, while by the MLR model, they were 0.4908 and 0.6494, respectively. The results show that the SVM model drastically enhances the ability of prediction in QSPR studies and is superior to the MLR model.

PARAMETRIC AND NON-PARAMETRIC COMBINATION MODEL TO ENHANCE OVERALL PERFORMANCE ON DEFAULT PREDICTION

The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.