Variational Bayesian algorithm for distributed compressive sensing

Distributed compressive sensing (DCS) concerns the reconstruction of multiple sensor signals with reduced numbers of measurements, which exploits both intra- and inter-signal correlations. In this paper, we propose a novel Bayesian DCS algorithm based on variational Bayesian inference. The proposed algorithm decouples the common component, that characterizes inter-signal correlation, from innovation components, that represent intra-signal correlation. Such an operation results in a computational complexity of reconstruction which is linear with the number of signals. The superior performance of the algorithm, in terms of the computing time and reconstruction quality, is demonstrated by numerical simulations in comparison with other existing reconstruction methods.This work is supported by EPSRC Research Grant (EP/K033700/1); the Natural Science Foundation of China (61401018, U1334202); the State Key Laboratory of Rail Traffic Control and Safety (RCS2014ZT08), Beijing Jiaotong University; the Fundamental Research Funds for the Central Universities (2014JBM149); the Key Grant Project of Chinese Ministry of Education (313006); the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education MinistryThis is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICC.2015.724909

Compressive sleeping wireless sensor networks with active node selection

In this paper, we propose an active node selection framework for compressive sleeping wireless sensor networks (WSNs) in order to improve the signal acquisition performance and network lifetime. The node selection can be seen as a specialized sensing matrix design problem where the sensing matrix consists of selected rows of an identity matrix. By capitalizing on a genie-aided reconstruction procedure, we formulate the active node selection problem into an optimization problem, which is then approximated by a constrained convex relaxation plus a rounding scheme. The proposed approach also exploits the partially known signal support, which can be obtained from the previous signal reconstruction. Simulation results show that our proposed active node selection approach leads to an improved reconstruction performance and network lifetime in comparison to various node selection schemes for compressive sleeping WSNs.This work is supported by EPSRC Research Grant EP/K033700/1; the Natural Science Foundation of China (61401018, U1334202); the Fundamental Research Funds for the Central Universities (2014JBM149); the State Key Laboratory of Rail Traffic Control and Safety (RCS2012ZT014) of Beijing Jiaotong University; the Key Grant Project of Chinese Ministry of Education (313006); the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/GLOCOM.2014.703677

A Decentralized Bayesian Algorithm for Distributed Compressive Sensing in Networked Sensing Systems

Compressive sensing (CS), as a new sensing/sampling paradigm, facilitates signal acquisition by reducing the number of samples required for reconstruction of the original signal, and thus appears to be a promising technique for applications where the sampling cost is high, e.g., the Nyquist rate exceeds the current capabilities of analog-to-digital converters (ADCs). Conventional CS, although effective for dealing with one signal, only leverages the intra-signal correlation for reconstruction. This paper develops a decentralized Bayesian reconstruction algorithm for networked sensing systems to jointly reconstruct multiple signals based on the distributed compressive sensing (DCS) model that exploits both intra- and inter-signal correlations. The proposed approach is able to address networked sensing system applications with privacy concerns and/or for a fusion-centre-free scenario, where centralized approaches fail. Simulation results demonstrate that the proposed decentralized approaches have good recovery performance and converge reasonably quicklyThis is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TWC.2015.248798

Compressive Sensing Reconstruction for Video: An Adaptive Approach Based on Motion Estimation

This paper focuses on the problem of causally reconstructing Compressive Sensing (CS) captured video. The state-of-art causal approaches usually assume the signal support is static or changing sufficiently slowly over time, where Magnetic Resonance Imaging (MRI) is widely used as a motivating example. However, such an assumption is too restrictive for many other video applications, where the signal support changes rapidly. In this paper, we propose a framework that combines Motion Estimation (ME), the Kalman Filter (KF) and CS to adapt the reconstruction process to motions in the video so that the slowly-changing assumption on the signal support is relaxed and consequently is more suitable for video reconstruction. Explicit and implicit ME are designed to provide motion aware predictions, upon which a modified KF procedure is applied. Furthermore, three CS algorithms with embedded ME and KF are developed, and theoretical analyses are conducted via reconstruction error upper bounds, to characterize the various factors that affect reconstruction accuracy. Extensive simulations utilizing actual videos are carried out and the superiority of our methods is demonstrated.This work is supported by EPSRC Research Grant EP/K033700/1; the Natural Science Foundation of China (61401018, U1334202).This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TCSVT.2016.254007

Exploiting hidden block sparsity: Interdependent matching pursuit for cyclic feature detection

In this paper, we propose a novel Compressive Sensing (CS)-enhanced spectrum sensing approach for Cognitive Radio (CR) systems. The new framework enables cyclic feature detection with a significantly reduced sampling rate. We associate the new framework with a novel model-based greedy reconstruction algorithm: interdependent matching pursuit (IMP). For IMP, the hidden block sparsity owing to the symmetry present in the cyclic spectrum is exploited which effectively reduces the degree of freedom of problem. Compared with conventional CS with independent support selection, a remarkable spectrum reconstruction improvement is achieved by IMP.The work of Wei Chen is supported by the State Key Laboratory of Rail Traffic Control and Safety (No. RCS2012ZT014), Beijing Jiaotong University, and the Key grant Project of Chinese Ministry of Education (No.313006).This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/GLOCOM.2013.683122

Dictionary design for distributed compressive sensing

Conventional dictionary learning frameworks attempt to find a set of atoms that promote both signal representation and signal sparsity for a class of signals. In distributed compressive sensing (DCS), in addition to intra-signal correlation, inter-signal correlation is also exploited in the joint signal reconstruction, which goes beyond the aim of the conventional dictionary learning framework. In this letter, we propose a new dictionary learning framework in order to improve signal reconstruction performance in DCS applications. By capitalizing on the sparse common component and innovations (SCCI) model [1], which captures both intra- and inter-signal correlation, the proposed method iteratively finds a dictionary design that promotes various goals: i) signal representation; ii) intra-signal correlation; and iii) inter-signal correlation. Simulation results show that our dictionary design leads to an improved DCS reconstruction performance in comparison to other designs.This work is supported by EPSRC Research Grant EP/K033700/1 and EP/K033166/1, the Fundamental Research Funds for the Central Universities (No. 2014JBM149), the State Key Laboratory of Rail Traffic Control and Safety (RCS2012ZT014) of Beijing Jiaotong University, the Natural Science Foundation of China (U1334202), the Key Grant Project of Chinese Ministry of Education (313006).This is the published manuscript. It is freely available online from the IEEE website here: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6880772. © 2014 IEE

Alternative antibody for the detection of CA125 antigen: a European multicenter study for the evaluation of the analytical and clinical performance of the Access (R) OV Monitor assay on the UniCel (R) Dxl 800 Immunoassay System

Background: Cancer antigen CA125 is known as a valuable marker for the management of ovarian cancer. Methods: The analytical and clinical performance of the Access OV Monitor Immunoassay System (Beckman Coulter) was evaluated at five different European sites and compared with a reference system, defined as CA125 on the Elecsys System (Roche Diagnostics). Results: Total imprecision (%CV) of the OV Monitor ranged between 3.1% and 8.8%, and inter-laboratory reproducibility between 4.7% and 5.0%. Linearity upon dilution showed a mean recovery of 100% (SD+8.1%). Endogenous interferents had no influence on OV Monitor levels (mean recoveries: hemoglobin 107%, bilirubin 103%, triglycericles 103%). There was no high-dose hook effect up to 27,193 kU/L. Clinical performance investigated in sera from 1811 individuals showed a good correlation between the Access OV Monitor and Elecsys CA125 (R = 0.982, slope = 0.921, intercept = + 1.951). OV Monitor serum levels were low in healthy individuals (n = 267, median = 9.7 kU/L, 95th percentile = 30.8 kU/L), higher in individuals with various benign diseases (n = 549, medians = 10.9-16.4 kU/L, 95th percentiles = 44.2-355 kU/L) and even higher in individuals suffering from various cancers (n = 995, medians= 12.4-445 kU/L; 95th percentiles = 53.4-4664 kU/L). Optimal diagnostic accuracy for cancer detection against the relevant benign control group by the OV Monitor was found for ovarian cancer {[}area under the curve (AUC) 0.898]. Results for the reference CA125 assay were comparable (AUC 0.899). Conclusions: The Access OV Monitor provides very good methodological characteristics and demonstrates an excellent analytical and clinical correlation with Elecsys CA125. The best diagnostic accuracy for the OV Monitor was found in ovarian cancer. Our results also suggest a clinical value of the OV Monitor in other cancers

Exploiting the convex-concave penalty for tracking: A novel dynamic reweighted sparse Bayesian learning algorithm

We propose a novel dynamic reweighted ℓ2 (DRℓ2) algorithm in the regime of dynamic compressive sensing. Our analysis shows that aiming to solve a Type II optimization problem, DRℓ2 is effectively minimizing a `convex-concave' penalty in the coefficients that transitions from a convex region to a concave function using knowledge of past estimations. DRℓ2 thus provides superior reconstruction performance compared with state-of-the-art dynamic CS algorithms.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICASSP.2014.685422

Vertical distributions of non-methane hydrocarbons and halocarbons in the lower troposphere over northeast China

Vertical distributions of air pollutants are crucial for understanding the key processes of atmospheric transport and for evaluating chemical transport models. In this paper, we present measurements of non-methane hydrocarbons (NMHCs) and halocarbons obtained from an intensive aircraft study over northeast (NE) China in summer 2007. Most compounds exhibited a typical negative profile of decreasing mixing ratios with increasing altitude, although the gradients differed with different species. Three regional plumes with enhanced VOC mixing ratios were discerned and characterized. An aged plume transported from the northern part of the densely populated North China Plain (NCP; i.e. Beijing-Tianjin area) showed relatively higher levels of HCFC-22, 1,2-dichloroethane (1,2-DCE) and toluene. In comparison, the plume originating from Korea had higher abundances of CFC-12, tetrachloroethene (C2Cl4) and methyl chloride (CH3Cl), while regional air masses from NE China contained more abundant light alkanes. By comparing these results with the earlier PEM-West B (1994) and TRACE-P (2001) aircraft measurements, continuing declining trends were derived for methyl chloroform (CH3CCl3), tetrachloromethane (CCl4) and C2Cl4 over the greater China-northwestern Pacific region, indicating the accomplishment of China in reducing these compounds under the Montreal protocol. However, the study also provided evidence for the continuing emissions of several halocarbons in China in 2007, such as CFCs (mainly from materials in stock) and HCFCs. © 2011 Elsevier Ltd

Personality stability from age 14 to age 77 years.

There is evidence for differential stability in personality trait differences, even over decades. The authors used data from a sample of the Scottish Mental Survey, 1947 to study personality stability from childhood to older age. The 6-Day Sample (N = 1,208) were rated on six personality characteristics by their teachers at around age 14. In 2012, the authors traced as many of these participants as possible and invited them to take part in a follow-up study. Those who agreed (N = 174) completed a questionnaire booklet at age 77 years, which included rating themselves and asking someone who knew them well to rate them on the same 6 characteristics on which they were rated in adolescence. Each set of 6 ratings was reduced to the same single underlying factor, denoted dependability, a trait comparable to conscientiousness. Participants' and others' older-age personality characteristic ratings were moderately correlated with each other, and with other measures of personality and wellbeing, but correlations suggested no significant stability of any of the 6 characteristics or their underlying factor, dependability, over the 63-year interval. However, a more complex model, controlling rater effects, indicated significant 63-year stability of 1 personality characteristic, Stability of Moods, and near-significant stability of another, Conscientiousness. Results suggest that lifelong differential stability of personality is generally quite low, but that some aspects of personality in older age may relate to personality in childhood. (PsycINFO Database Recor