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.

Multiple Measurement Models of Articulated Arm Coordinate Measuring Machines

The existing articulated arm coordinate measuring machines（AACMM） with one measurement model are easy to cause low measurement accuracy because the whole sampling space is much bigger than the result in the unstable calibration parameters. To compensate for the deficiency of one measurement model, the multiple measurement models are built by the Denavit-Hartenberg＇s notation, the homemade standard rod components are used as a calibration tool and the Levenberg-Marquardt calibration algorithm is applied to solve the structural parameters in the measurement models. During the tests of multiple measurement models, the sample areas are selected in two situations. It is found that the measurement errors＇ sigma value（0.083 4 ram） dealt with one measurement model is nearly two times larger than that of the multiple measurement models（0.043 1 ram） in the same sample area. While in the different sample area, the measurement errors＇ sigma value（0.054 0 ram） dealt with the multiple measurement models is about 40% of one measurement model（0.137 3 mm）. The preliminary results suggest that the measurement accuracy of AACMM dealt with multiple measurement models is superior to the accuracy of the existing machine with one measurement model. This paper proposes the multiple measurement models to improve the measurement accuracy of AACMM without increasing any hardware cost.

Modified Iterative Method for Recovery of Sparse Multiple Measurement Problems

We consider the problem of constructing one sparse signal from a few measurements. This problem has been extensively addressed in the literature, providing many sub-optimal methods that assure convergence to a locally optimal solution under specific conditions. There are a few measurements associated with every signal, where the size of each measurement vector is less than the sparse signal＇s size. All of the sparse signals have the same unknown support. We generalize an existing algorithm for the recovery of one sparse signal from a single measurement to this problem and analyze its performances through simulations. We also compare the construction performance with other existing algorithms. Finally, the proposed method also shows advantages over the OMP （Orthogonal Matching Pursuit） algorithm in terms of the computational complexity.

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.

A Highly Sensitive Femtosecond Time-Resolved Sum Frequency Generation Vibrational Spectroscopy System with Simultaneous Measurement of Multiple Polarization Combinations

Characterization of real-time and ultrafast motions of the complex molecules at surface and interface is critical to understand how interracial molecules function. It requires to develop surface-sensitive, fast-identification, and time-resolved techniques. In this study, we employ several key technical procedures and successfully develop a highly sensitive femtosecond time-resolved sum frequency generation vibrational spectroscopy （SFG-VS） system. This system is able to measure the spectra with two polarization combinations （ssp and ppp, or psp and ssp） simultaneously. It takes less than several seconds to collect one spectrum. To the best of our knowledge, it is the fastest speed of collecting SFG spectra reported by now. Using the time-resolved measurement, ultrafast vibrational dynamics of the N-H mode of α-helical peptide at water interface is determined. It is found that the membrane environment does not affect the N-H vibrational relaxation dynamics. It is expected that the time-resolved SFG system will play a vital role in the deep understanding of the dynamics and interaction of the complex molecules at surface and interface. Our method may also provide an important technical proposal for the people who plan to develop time-resolved SFG systems with simultaneous measurement of multiple polarization combinations.

Extremal Entangled Four-Qubit Pure States with Respect to Multiple Entropy Measures

Four-qubit entanglement has been investigated using a recent proposed entanglement measure, multiple entropy measures （MEMS）. We have performed optimization for the nine different families of states of four-qubit system. Some extremal entangled states have been found.

Multiple Entropy Measures for Multi-particle Pure Quantum State

We propose to use a set of averaged entropies, the multiple entropy measures (MEMS), to partiallyquantify quantum entanglement of multipartite quantum state.The MEMS is vector-like with m = [N/2] components:[S_1, S_2,..., S_m], and the i-th component S_i is the geometric mean of i-qubits partial entropy of the system.The S_imeasures how strong an arbitrary i qubits from the system are correlated with the rest of the system.It satisfies theconditions for a good entanglement measure.We have analyzed the entanglement properties of the GHZ-state, theW-states, and cluster-states under MEMS.

Lifetime prediction for tantalum capacitors with multiple degradation measures and particle swarm optimization based grey model

A lifetime prediction method for high-reliability tantalum （Ta） capacitors was proposed, based on multiple degradation measures and grey model （GM）. For analyzing performance degradation data, a two-parameter model based on GM was developed. In order to improve the prediction accuracy of the two-parameter model, parameter selection based on particle swarm optimization （PSO） was used. Then, the new PSO-GM（1, 2, co） optimization model was constructed, which was validated experimentally by conducting an accelerated testing on the Ta capacitors. The experiments were conducted at three different stress levels of 85, 120, and 145℃. The results of two experiments were used in estimating the parameters. And the reliability of the Ta capacitors was estimated at the same stress conditions of the third experiment. The results indicate that the proposed method is valid and accurate.

The Method on Deducing Multiplicity Measurement Equations of Neutron/γ

Based on some important domestic and international references,the third γ multiplicity measurement equation is derived,but it is different from the results given in current researches.The neutron multiplicity equation is deduced in this paper,especially the fourth fast-neutron multiplicity equation based on the liquid scintillation detectors,which is more complex than the other multiplicity equations up to the fourth order.The equations given in this paper can be used to verify the validity and availability of principles for the multiplicity measurement up to the fourth order,and extend the application scopes of the neutron multiplicity measurement,such as correcting the additional measurement value to eliminate influences for dead times.It will be the foundation of nuclear researches,if the higher order multiplicity measurement is important for nuclear materials’control and accountability.

APPROXIMATION OF SPHERICAL MEANS OF MULTIPLE FOURIER INTEGRALS AND FOURIER SERIES ON SET OF FULL MEASURE

In this paper we consider the approximation for functions in some subspaces of L^2 by spherical means of their Fourier integrals and Fourier series on set of full measure. Two main theorems are obtained.

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.

A novel multiple-outlier-robust Kalman filter

This paper presents a novel multiple-outlier-robust Kalman filter(MORKF)for linear stochastic discretetime systems.A new multiple statistical similarity measure is first proposed to evaluate the similarity between two random vectors from dimension to dimension.Then,the proposed MORKF is derived via maximizing a multiple statistical similarity measure based cost function.The MORKF guarantees the convergence of iterations in mild conditions,and the boundedness of the approximation errors is analyzed theoretically.The selection strategy for the similarity function and comparisons with existing robust methods are presented.Simulation results show the advantages of the proposed filter.

CLINICAL SIGNIFICANCE OF MEASURING SERUM IL-6 ACTIVITIES IN MULTIPLE MYELOMA AN ANALYSIS OF 81 CASES

Interleukin 6(IL-6) is known as hybridoma cell growth factor,B-cell differentiating factor and so on One of its important biological functions in to induce

Learning multiple metrics for ranking

Directly optimizing an information retrieval （IR） metric has become a hot topic in the field of learning to rank. Conventional wisdom believes that it is better to train for the loss function on which will be used for evaluation. But we often observe different results in reality. For example, directly optimizing averaged preci- sion achieves higher performance than directly optimizing precision@3 when the ranking results are evaluated in terms of precision@3. This motivates us to combine multiple metrics in the process of optimizing IR metrics. For simplicity we study learning with two metrics. Since we usually conduct the learning process in a restricted hypothesis space, e.g., linear hypothesis space, it is usually difficult to maximize both metrics at the same time. To tackle this problem, we propose a relaxed approach in this paper. Specifically, we incorporate one metric within the constraint while maximizing the other one. By restricting the feasible hypothesis space, we can get a more robust ranking model. Empirical results on the benchmark data set LETOR show that the relaxed approach is superior to the direct linear combination approach, and also outperforms other baselines.

Stock Efficiency Evaluation Based on Multiple Risk Measures:A DEA-Like Envelopment Approach

This paper proposes a new approach for stock efficiency evaluation based on multiple risk measures. A derived programming model with quadratic constraints is developed based on the envelopment form of data envelopment analysis(DEA). The derived model serves as an input-oriented DEA model by minimizing inputs such as multiple risk measures. In addition, the Russell input measure is introduced and the corresponding efficiency results are evaluated. The findings show that stock efficiency evaluation under the new framework is also effective. The efficiency values indicate that the portfolio frontier under the new framework is more externally enveloped than the DEA efficient surface under the standard DEA framework.

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.