Robust Joint Research Articles

Hyperspectral Unmixing with Robust Collaborative Sparse Regression

Recently, sparse unmixing (SU) of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM), which ignores the possible nonlinear effects (i.e., nonlinearity). In this paper, we propose a new method named robust collaborative sparse regression (RCSR) based on the robust LMM (rLMM) for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM) is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.

Two-sided friction stir riveting by extrusion: A process for joining dissimilar materials

Two-sided friction stir riveting (FSR) by extrusion is an innovative process developed to rapidly, efficiently, and securely join dissimilar materials. This process extends a previously developed one sided friction stir extrusion process to create a strong and robust joint by producing a continuous, rivet-like structure through a preformed hole in one of the materials with a simultaneous, two-sided friction stir spot weld. The two-sided FSR by extrusion process securely joins the dissimilar materials together and effectively locks them in place without the use of any separate materials or fasteners. In this paper we demonstrate the process by joining aluminum to steel and illustrate its potential application to automotive and aerospace manufacturing processes.

Robust joint nearest subspace for hyperspectral image classification

ABSTRACTThis letter presents a novel hyperspectral image (HSI) classification method based on robust joint nearest subspace and contextual prototype learning. First, we present a robust joint nearest subspace method to solve the HSI classification problem by exploiting a set-to-class distance with robust distance metric to consider both spectral and spatial characteristics effectively. Second, we develop an objective function to learn contextual prototypes robustly and present an iteration algorithm to solve it. Based on the learned contextual prototypes, the HSI classification performance can be further improved. Finally, we conduct numerous experiments to validate the effects of different parameters and components and to compare the proposed method with other algorithms on three popular data sets. The experimental results show that the proposed method achieves better performance than other competing algorithms.

Robust transmit design for secure AF relay networks with imperfect CSI

This paper studies robust transmit design for secure AF relay networks with imperfect channel state information (CSI). Two CSI error models, deterministically bounded error model and stochastic error model, are considered. In order to use the power at relays more efficiently, a joint cooperative relaying and jamming scheme is considered, where relays cooperate to forward the source signal and simultaneously they cooperate to send jamming signals to confuse the eavesdroppers. In this joint cooperative relaying and jamming framework, we address the robust joint optimization of the relay weights and the input covariance matrix of jamming signals for secrecy rate maximization (SRM), under both total and individual relay power constraints. Specifically, for the case of deterministically bounded error model, where the CSI error can be specified by an uncertainty set, we maximize the worst-case secrecy rate. While for the case of stochastic error model, where the probability distribution of the CSI error is Gaussian, we maximize the outage constrained secrecy rate. The challenge of these considered SRM-based optimizations lies in their non-convexity. This paper shows that such SRM-based optimization can be solved in a convex fashion and via solving a sequence of semidefinite programs (SDPs). Numerical results are presented to show the efficacy of the proposed scheme.

Robust Joint Feature Weights Learning Framework

Feature selection, selecting the most informative subset of features, is an important research direction in dimension reduction. The combinatorial search in feature selection is essentially a binary optimization problem, known as NP hard, which can be alleviated by learning feature weights. Traditional feature weights algorithms rely on heuristic search path. These approaches neglect the interaction and dependency between different features, and thus provide no guarantee for optimality. In this paper, we propose a novel joint feature weights learning framework, which imposes both nonnegative and $\ell _{2,1}$ -norm constraints on the feature weights matrix. The nonnegative property ensures the physical significance of learned feature weights. Meanwhile, $\ell _{2,1}$ -norm minimization achieves joint selection of the most relevant features by exploiting the whole feature space. More importantly, an efficient iterative algorithm with proved convergence is designed to optimize a convex objective function. Using this framework as a platform, we propose new supervised and unsupervised joint feature selection methods. Particularly, in the proposed unsupervised method, nonnegative graph embedding is developed to exploit intrinsic structure in the weighted space. Comparative experiments on seven real-world data sets indicate that our framework is both effective and efficient.

Achieving Optimality in Robust Joint Optimization of Linear Transceiver Design

This paper presents new results on linear transceiver designs in a multiple-input–multiple-output (MIMO) link. By considering the minimal total mean-square error (MSE) criterion, we prove that the robust optimal linear transceiver design has a channel-diagonalizing structure, which verifies the conjecture in the previous work. Based on this property, the original design problem can be transformed into a scalar problem, whose global optimal solution is first obtained in this work. Simulation results show the performance advantages of our solution over the existing schemes.

Robust beamforming and cooperative jamming for secure transmission in DF relay systems

In this paper, we study robust joint beamforming and cooperative jamming (CJ) in a secure decode-and-forward (DF) relay system in the presence of multiple eavesdroppers, in which a multi-antenna DF relay employs transmit beamforming to help the source deliver information to the destination and simultaneously generates Gaussian artificial noise to confuse these eavesdroppers. We assume that all the channel state information (CSI) is imperfectly known at the relay subject to norm-bounded CSI errors. Our objective is to maximize the achievable secrecy rate at the destination by jointly optimizing the transmit beamforming vector of information signals and the covariance matrix of jamming signals at the relay subject to its total transmit power constraint. Although this problem is non-convex and in general difficult to be solved, we obtain its optimal solution via the technique of semi-definite relaxation (SDR) together with a one-dimensional (1-D) search. Specifically, we prove that the SDR is tight for the problem of our interest. Finally, numerical results are provided to validate our proposed scheme.

Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection.

In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution. Furthermore, we extend JGSC to a robust JGSC (RJGSC) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. Finally, experimental results on real data sets showed that both JGSC and RJGSC outperformed the state-of-the-art algorithms in terms of k -nearest neighbor classification performance.

On Visualizing Mixed-Type Data

Survey data are usually of mixed type (quantitative, multistate categorical, and/or binary variables). Multidimensional scaling (MDS) is one of the most extended methodologies to visualize the profile structure of the data. Since the past 60s, MDS methods have been introduced in the literature, initially in publications in the psychometrics area. Nevertheless, sensitivity and robustness of MDS configurations have been topics scarcely addressed in the specialized literature. In this work, we are interested in the construction of robust profiles for mixed-type data using a proper MDS configuration. To this end, we propose to compare different MDS configurations (coming from different metrics) through a combination of sensitivity and robust analysis. In particular, as an alternative to classical Gower’s metric, we propose a robust joint metric combining different distance matrices, avoiding redundant information, via related metric scaling. The search for robustness and identification of outliers is done through a distance-based procedure related to geometric variability notions. In this sense, we propose a statistic for detecting multivariate outliers in the context of mixed-type data and evaluate its performance through a simulation study. Finally, we apply these techniques to a real data set provided by the largest humanitarian organization involved in social programs in Spain, where we are able to find in a robust way the most relevant factors defining the profiles of people that were under risk of being socially excluded in the beginning of the 2008 economic crisis.

Robust Joint Calibration of Mutual Coupling and Channel Gain/Phase Inconsistency for Uniform Circular Array

A new robust method is designed to estimate mutual coupling and channel gain/phase inconsistency parameters for a uniform circular array (UCA) jointly. It only needs a single signal source and two trials with different impinging directions to complete the calibration. The directions of the trials do not need to be calibrated beforehand. First, the two kinds of parameters are uncoupled into a linear equation in discrete Fourier transform (DFT) beam space by a novel decoupling technique. Then, a new object function is set according to the symmetry of the mutual coupling parameters. Finally, with some prior directional information, all the parameters can be estimated through a limited angular search. Simulations verify the effectiveness and robustness of the proposed method.

Optimizing Radar Waveform and Doppler Filter Bank via Generalized Fractional Programming

Assuming unknown target Doppler shift, we focus on robust joint design of the transmit radar waveform and receive Doppler filter bank in the presence of signal-dependent interference. We consider the worst case signal-to-interference-plus-noise-ratio (SINR) at the output of the filter bank as the figure of merit to optimize under both a similarity and an energy constraint on the transmit signal. Based on a suitable reformulation of the original non-convex max-min optimization problem, we develop an optimization procedure which monotonically improves the worst-case SINR and converges to a stationary point. Each iteration of the algorithm, involves both a convex and a generalized fractional programming problem which can be globally solved via the generalized Dinkelbach’s procedure with a polynomial computational complexity. Finally, at the analysis stage, we assess the performance of the new technique versus some counterparts which are available in open literature.

Robust Transceiver Optimization for Power-Splitting Based Downlink MISO SWIPT Systems

This letter considers a downlink multi-input single-out (MISO) system where each user performs simultaneous wireless information and power transfer (SWIPT) based on a power splitting receiver architecture. Assuming imperfect channel state information (CSI) at the base station, we develop two robust joint beamforming and power splitting (BFPS) designs that minimize the transmission power under both the signal-to-interference-plus-noise ratio (SINR) and energy harvesting (EH) constraints per user. In the first design, we consider the worst-case (WC) SINR and EH constraints, and show that the WC-BFPS problem can be relaxed as a semidefinite program (SDP) through a linear matrix inequality representation for (infinitely many) robust quadratic matrix inequality constraints. In the second design, we consider the chance constraints (CCs) for SINR and EH, and resort to both semidefinite relaxation and Bernstein-type inequality restriction to transform the CC-BFPS problem into another convex SDP. Based on these convex reformulations, the (near-)optimal robust BFPS designs can be efficiently solved. Numerical results are provided to demonstrate the merit of the proposed robust designs.

Robust Joint Beamforming and Jamming for Secure AF Networks: Low-Complexity Design

This paper investigates the physical-layer security issue of an amplify-and-forward (AF) relay network. We propose a joint cooperative beamforming (CB) and cooperative jamming (CJ) design that is robust to the imperfect channel state information (CSI) of the multiple multiantenna eavesdroppers. The objective is the worst-case secrecy rate maximization (WCSRM). Based on the inner convex approximation and semidefinite relaxation (SDR) technique, the original nonconvex problem is transformed into a sequence of approximate convex problems, which can be solved conveniently. The proposed robust design guarantees to converge to a Karush–Kuhn–Tucker (KKT) solution and is efficient in computational complexity. Furthermore, we prove that SDR always has a rank-1 solution, which identifies that SDR is tight. Simulations demonstrate the validity of the proposed strategy.

Robust Joint Transmit Beamforming With QoS Guarantees in Time-Asynchronous DAS

This paper studies the joint transmit beamforming optimization in the downlink of a single-cell multiuser distributed antenna system. Due to the distributed nature of transmit antennas, the signals received from different distributed antenna units (DAUs) will experience various transmission delays, and it is difficult to realize accurate symbol-level time synchronization at the user terminal. Thus, we turn to finding a robust joint transmit beamforming algorithm by replacing the received signal-to-interference-plus-noise ratio (SINR) with its lower-bound value. Moreover, the joint transmission incurs huge amount of data sharing among DAUs. Under the per-DAU maximum transmit power constraint, we aim to minimize the system backhaul overhead while satisfying the quality-of-service (QoS) requirement of each user. Since the backhaul overhead minimization requires the transmit beam vectors to have a group sparse structure and is NP-hard, we first make a convex relaxation and then apply the reweighted method to improve the sparsity of the beam vectors. Furthermore, an admission control scheme is applied to guarantee the feasibility of the problem, which iteratively removes the unsatisfied user with the worst quality of experience (QoE) away from the system. Simulation results show that our proposed robust algorithm can achieve a much better performance than the traditional nonrobust algorithm when time asynchronism is less than 30% of the symbol duration and can significantly reduce the system backhaul overhead by designing group sparse transmit beam vectors.

Better informing decision making with multiple outcomes cost-effectiveness analysis under uncertainty in cost-disutility space.

IntroductionComparing multiple, diverse outcomes with cost-effectiveness analysis (CEA) is important, yet challenging in areas like palliative care where domains are unamenable to integration with survival. Generic multi-attribute utility values exclude important domains and non-health outcomes, while partial analyses—where outcomes are considered separately, with their joint relationship under uncertainty ignored—lead to incorrect inference regarding preferred strategies. ObjectiveThe objective of this paper is to consider whether such decision making can be better informed with alternative presentation and summary measures, extending methods previously shown to have advantages in multiple strategy comparison.MethodsMultiple outcomes CEA of a home-based palliative care model (PEACH) relative to usual care is undertaken in cost disutility (CDU) space and compared with analysis on the cost-effectiveness plane. Summary measures developed for comparing strategies across potential threshold values for multiple outcomes include: expected net loss (ENL) planes quantifying differences in expected net benefit; the ENL contour identifying preferred strategies minimising ENL and their expected value of perfect information; and cost-effectiveness acceptability planes showing probability of strategies minimising ENL.ResultsConventional analysis suggests PEACH is cost-effective when the threshold value per additional day at home ( 1) exceeds $1,068 or dominated by usual care when only the proportion of home deaths is considered. In contrast, neither alternative dominate in CDU space where cost and outcomes are jointly considered, with the optimal strategy depending on threshold values. For example, PEACH minimises ENL when 1=$2,000 and 2=$2,000 (threshold value for dying at home), with a 51.6% chance of PEACH being cost-effective.ConclusionComparison in CDU space and associated summary measures have distinct advantages to multiple domain comparisons, aiding transparent and robust joint comparison of costs and multiple effects under uncertainty across potential threshold values for effect, better informing net benefit assessment and related reimbursement and research decisions.

Advancing Self-Pierce Rivet Technology Through Alloy Development

Abstract Mechanical spot-joining methods such as self-pierce riveting (SPR) enable the use of different material combinations in vehicle design. The SPR technique provides a robust joint that often outperforms clinching and spot welding under both static and fatigue conditions, while providing fast cycle times and zero waste.

Safe Approximations for Distributionally Robust Joint Chance Constrained Program

In this paper, we present a bilinear second-order cone programming safe approximation for the distributionally robust chance constrained program (DRCCP), assuming that the support of the uncertain parameters, and the first and second marginal moments of the probability with respect to the interval constraint imposed on the sum of the uncertain parameters are given. If we further know the covariance matrix, we can obtain a bilinear semi-definite programming safe approximation. Preliminary numerical tests indicate that the proposed models are competitive.

Robust Joint Non-linear Mixed-Effects Models and Diagnostics for Censored HIV Viral Loads with CD4 Measurement Error

Despite technological advances in efficiency enhancement of quantification assays, biomedical studies on HIV RNA collect viral load responses that are often subject to detection limits. Moreover, some related covariates such as CD4 cell count may be often measured with errors. Censored non-linear mixed-effects models are routinely used to analyze this type of data and are based on normality assumptions for the between-subject and within-subject random terms. However, derived inference may not be robust when the underlying normality assumptions are questionable, especially in presence of skewness and heavy tails. In this article, we address these issues simultaneously under a Bayesian paradigm through joint modeling of the response and covariate processes using an attractive class of skew-normal independent densities. The methodology is illustrated using a case study on longitudinal HIV viral loads. Diagnostics for outlier detection is immediate from the MCMC output. Both simulation and real data analysis reveal the advantage of the proposed models in providing robust inference under non-normality situations commonly encountered in HIV/AIDS or other clinical studies. Supplementary materials accompanying this paper appear on-line.

M410 熱負荷を受けるボルト締結体へのチタンボルトの適用(GS1,3 材料力学(6),修士研究発表セッション)

M410 熱負荷を受けるボルト締結体へのチタンボルトの適用(GS1,3 材料力学(6),修士研究発表セッション)

Matrix-based robust joint fingerprinting and decryption method for multicast distribution of multimedia

This paper addresses the problem of unauthorized redistribution of multimedia content by malicious users (pirates). The solution proposed here is a new joint fingerprinting and decryption method which meets the requirements for both imperceptibility and robustness of fingerprints and scalability in terms of design and distribution of fingerprinted multimedia content. The proposed method uses a simple block cipher based on matrix multiplication to encrypt images which are then sent to all users via multicast transmission. Individual decryption keys are designed depending on the users’ fingerprints so that a different fingerprint will be introduced into the image during decryption for each unique decryption key. Encryption and fingerprinting are performed on DCT coefficients, which results in high robustness against compression, low-pass filtering, scaling, etc. The experimental results show that the proposed method is robust against collusion attacks and compression.