Model recovery for multi-input signal-output nonlinear systems based on the compressed sensing recovery theory

This paper considers the parameter and order estimation for multiple-input single-output nonlinear systems. Since the orders of the system are unknown, a high-dimensional identification model and a sparse parameter vector are established to include all the valid inputs and basic parameters. Applying the data filtering technique, the input-output data are filtered and the original identification model with autoregressive noise is changed into the identification model with white noise. Based on the compressed sensing recovery theory, a data filtering-based orthogonal matching pursuit algorithm is presented for estimating the system parameters and the orders. The presented method can obtain highly accurate estimates from a small number of measurements by finding the highest absolute inner product. The simulation results confirm that the proposed algorithm is effective for recovering the model of the multiple-input single-output Hammerstein finite impulse response systems.     

压缩感知稀疏信号重构算法研究

压缩感知理论是利用信号的稀疏性,采用重构算法通过少量的观测值就可以实现对该信号的精确重构。SL0（Smoothed l0）算法是基于l0范数的稀疏信号重构算法,通过控制参数逐步逼近最优解。针对平滑函数的选取问题,文章提出一种新的平滑函数序列近似l0范数,实现稀疏信号的精确重构。仿真结果表明,在相同实验条件下文章算法较传统算法有着较高的重构概率。     

Exploiting prior knowledge in compressed sensing to design robust systems for endoscopy image recovery

In this work, we investigate compressed sensing (CS) techniques based on the exploitation of prior knowledge to support telemedicine. In particular, prior knowledge is obtained by computing the probability of appearance of non-zero elements in each row of a sparse matrix, which is then employed in sensing matrix design and recovery algorithms for CS systems. A robust sensing matrix is designed by jointly reducing the average mutual coherence and the projection of the sparse representation error. A Probability-Driven Normalized Iterative Hard Thresholding (PD-NIHT) algorithm is developed as the recovery method, which also exploits the prior knowledge of the probability of appearance of non-zero elements and can bring performance benefits. Simulations for synthetic data and different organs of endoscopy image are carried out, where the proposed sensing matrix and PD-NIHT algorithm achieve a better performance than previously reported algorithms.     

A new sensor selection scheme for Bayesian learning based sparse signal recovery in WSNs

In this paper, we address the issue of sparse signal recovery in wireless sensor networks (WSNs) based on Bayesian learning. We first formulate a compressed sensing (CS)-based signal recovery problem for the detection of sparse event in WSNs. Then, from the perspective of energy saving and communication overhead reduction of the WSNs, we develop an optimal sensor selection algorithm by employing a lower-bound of the mean square error (MSE) for the MMSE estimator. To tackle the nonconvex difficulty of the optimum sensor selection problem, a convex relaxation is introduced to achieve a suboptimal solution. Both uncorrelated and correlated noises are considered and a low-complexity realization of the sensor selection algorithm is also suggested. Based on the selected subset of sensors, the sparse Bayesian learning (SBL) is utilized to reconstruct the sparse signal. Simulation results illustrate that our proposed approaches lead to a superior performance over the reference methods in comparison.     

Multiple-prespecified-dictionary sparse representation for compressive sensing image reconstruction with nonconvex regularization

Multiple-prespecified-dictionary sparse representation (MSR) has shown powerful potential in compressive sensing (CS) image reconstruction, which can exploit more sparse structure and prior knowledge of images for minimization. Due to the popular L₁ regularization can only achieve the suboptimal solution of L₀ regularization, using the nonconvex regularization can often obtain better results in CS reconstruction. This paper proposes a nonconvex adaptive weighted L_p regularization CS framework via MSR strategy. We first proposed a nonconvex MSR based L_p regularization model, then we propose two algorithms for minimizing the resulting nonconvex L_p optimization problem. According to the fact that the sparsity levels of each regularizers are varying with these prespecified-dictionaries, an adaptive scheme is proposed to weight each regularizer for optimization by exploiting the difference of sparsity levels as prior knowledge. Simulated results show that the proposed nonconvex framework can make a significant improvement in CS reconstruction than convex L₁ regularization, and the proposed MSR strategy can also outperforms the traditional nonconvex L_p regularization methodology.     

Towards understanding residual and dilated dense neural networks via convolutional sparse coding

Convolutional neural network (CNN) and its variants have led to many state-of-the-art results in various fields. However, a clear theoretical understanding of such networks is still lacking. Recently, a multilayer convolutional sparse coding (ML-CSC) model has been proposed and proved to equal such simply stacked networks (plain networks). Here, we consider the initialization, the dictionary design and the number of iterations to be factors in each layer that greatly affect the performance of the ML-CSC model. Inspired by these considerations, we propose two novel multilayer models: the residual convolutional sparse coding (Res-CSC) model and the mixed-scale dense convolutional sparse coding (MSD-CSC) model. They are closely related to the residual neural network (ResNet) and the mixed-scale (dilated) dense neural network (MSDNet), respectively. Mathematically, we derive the skip connection in the ResNet as a special case of a new forward propagation rule for the ML-CSC model. We also find a theoretical interpretation of dilated convolution and dense connection in the MSDNet by analyzing the MSD-CSC model, which gives a clear mathematical understanding of each. We implement the iterative soft thresholding algorithm and its fast version to solve the Res-CSC and MSD-CSC models. The unfolding operation can be employed for further improvement. Finally, extensive numerical experiments and comparison with competing methods demonstrate their effectiveness.     

Protein sensing by nanofluidic crystal and its signal enhancement

Nanofluidics has a unique property that ionic conductance across a nanometer-sized confined space is strongly affected by the space surface charge density, which can be utilized to construct electrical read-out biosensor. Based on this principle, this work demonstrated a novel protein sensor along with a sandwich signal enhancement approach. Nanoparticles with designed aptamer onside are assembled in a suspended micropore to form a 3-dimensional network of nanometer-sized interstices, named as nanofluidic crystal hereafter, as the basic sensing unit. Proteins captured by aptamers will change the surface charge density of nanoparticles and thereby can be detected by monitoring the ionic conductance across this nanofluidic crystal. Another aptamer can further enlarge the variations of the surface charge density by forming a sandwich structure (capturing aptamer/protein/signal enhancement aptamer) and the read-out conductance as well. The preliminary experimental results indicated that human α-thrombin was successfully detected by the corresponding aptamer modified nanofluidic crystal with the limit of detection of 5 nM (0.18 μg/ml) and the read-out signal was enhanced up to 3 folds by using another thrombin aptamer. Being easy to graft probe, facile and low-cost to prepare the nano-device, and having an electrical read-out, the present nanofluidic crystal scheme is a promising and universal strategy for protein sensing.     

An improved pipeline leak detection and localization method based on compressed sensing and event-triggered particle filter

This paper proposes an improved model based pipeline leak detection and localization method based on compressed sensing (CS) and event-triggered (ET) particle filter (ET-PF). First, the state space model of the pipeline system is established based on the characteristic line method. Then, the CS method is used to preprocess the sensor signals to recover the potentially lost leak information which is caused by the low sampling frequency of the industrial pipeline sensors, and an event based beetle antennae search (BAS) particle filter (BAS-PF) is proposed to improve the accuracy and efficiency of the pipeline state estimation. Finally, a pipeline leak detection and localization method is developed based on the proposed signal processing, and state estimation algorithms, as well as a pipeline partition strategy. Experiment results show that the proposed method can accurately detect and locate the leak of the pipeline system with a localization error of about 1.4%.     

聚天下英才而用之:将中国打造为充满竞争力的国际人才高地

纵观当今世界大势,科技决定国力、科技改变国运的历史趋势更加显著,科技创新能力已成为一个国家国际竞争力的核心部分。而科技创新最关键的因素无过于人才,因此能不能吸引、留住最优秀的科技人才,能不能保护、激发科技人才的创新热情,决定着国家创新能力的高度。在经济转型升级、对原创性科研成果高度渴求的时代背景下,中国社会对科技人才,尤其是高层次科技人才的需求不断增加。这种需求要求国家在高级人才选拔和引进方面视野更开阔一些,力度更大一些,动作更快一些,举措更可行一些。因此,文章建议放开国籍限制,在世界范围内以创新能力为准绳引进一流科技英才;改革具体政策,为外国专家来华工作、定居乃至入籍提供良好条件,让他们与中国出生的优秀人才一起推动国家的科技进步和经济增长。     

Simultaneous recovery model for aircraft and passengers

Usually some unforeseen events make airlines to reconstruct their schedules. A mathematical model for airlines schedule recovery which recovers aircrafts and disrupted passengers simultaneously is presented in this study. Aircraft recovery decisions affect on passengers but disrupted passengers and recovering them were not explicitly considered in the most previous aircraft recovery models so recovery of these two resources - aircrafts and passengers - concurrently is one of our contributions.The modeling is based on defining the recovery scope as well as employing aircraft rotations and passengers’ itineraries instead of flights. These are two of our other contributions.Our model examines possible flight re-timing, aircraft swapping, ferrying, utilization of reserve aircrafts, cancellation, and passenger reassignment to generate an efficient schedule recovery plan.Model parameters are user-specific therefore it helps airlines to apply their policies in the model. Defining the recovery scope reduces the problem size and ensures that the schedule returns to normal within a certain time. The objective is in the form of cost minimization which involves three kinds of cost—operational aircraft recovery, flight cancellation, and delay as well as disrupted passengers. A data set with two disruption scenarios is used to test the proposed model. The computational results show that it is capable of handling the simultaneous aircraft and passenger recovery problem successfully.     

Semantic understanding based on multi-feature kernel sparse representation and decision rules for mangrove growth

With the rapid development of remote sensing technology, using remote sensing technology is an important means to monitor the dynamic change of land cover and ecology. In view of the complexity of mangrove ecological monitoring in Dongzhaigang, Hainan Province of China, we propose a semantic understanding method of mangrove remote sensing image by combining a multi-feature kernel sparse classifier with a decision rule model in this paper. First, on the basis of multi-feature extraction, we take into account the spatial context relations of the samples and introduce the kernel function into the sparse representation classifier, a multi-feature kernel sparse representation classifier can be constructed to classify cover types of mangroves and their surrounding objects. Second, in view of growth conditions of mangrove area, we put forward a semantic understanding method of mangrove remote sensing image based on decision rules and divide mangrove and non-mangrove areas by combining classification results of the multi-feature kernel sparse representation classifier. We make a divisibility analysis based on the extracted features of spatial and spectral domains. Then select the best split attribute based on the maximum information gain criterion, to generate a semantic tree and extract semantic rules. Finally, we work on the semantic understanding of mangrove areas in line with decision rules and further divide mangrove areas into two categories: excellent growth and poor growth. Experimental results show that the proposed method can effectively identify mangrove areas and make decisions on mangrove growth.     

Generalized singular value thresholding operator based nonconvex low-rank and sparse decomposition for moving object detection

Moving object detection is one of the most challenging tasks in computer vision and many other fields, which is the basis for high-level processing. Low-rank and sparse decomposition (LRSD) is widely used in moving object detection. The existing methods primarily address the LRSD problem by exploiting the approximation of rank functions and sparse constraints. Conventional methods usually consider the nuclear norm as the approximation of the low-rank matrix. However, the actual results show that the nuclear norm is not the best approximation of the rank function since it simultaneously minimize all the singular values. In this paper, we exploit a novel nonconvex surrogate function to approximate the low-rank matrix and propose a generalized formulation for nonconvex low-rank and sparse decomposition based on the generalized singular value thresholding (GSVT) operator. And then, we solve the proposed nonconvex problem via the alternating direction method of multipliers (ADMM), and also analyze its convergence. Finally, we give numerical results to validate the proposed algorithm on both synthetic data and real-life image data. The results demonstrate that our model has superior performance. And we use the proposed nonconvex model for moving objects detection, and provide the experimental results. The results show that the proposed method is more effective than representative LRSD based moving objects detection algorithms.     

资源再生产业的运行机制与政策体系

本文基于循环经济发展模式的理论构架，对作为循环经济试点重点领域之一，连接生产和消费的新兴领域——资源再生产业的运行机制和政策体系进行了初步的理论探讨和政策分析。     

Context-aware style learning and content recovery networks for neural style transfer

Neural text transfer aims to change the style of a text sequence while keeping its original content. Due to the lack of parallel data, unsupervised learning-based approaches have gained considerable development. However, there are still several problems in these approaches: (1) The generated transferred sequences sometimes have inconsistencies between the transferred style and content, and (2) It is difficult to ensure sufficient preservation of the core semantics of original sequences in the transferred sequences. To address these defects, we propose Context-aware Style Learning and Content Recovery networks (CSLCR) for neural text transfer. Specifically, to improve the consistency between the transferred style and content, the designed context-aware style learning layer (CSL) retrieves target style samples with similar semantics to the original sequence, and promotes deep interactive fusion with the original sequence, so as to generate transferred sequence with context-aware style. To tackle the second problem, we explore content constraint recovery layer (CCR) from an indirect perspective, which decodes and recovers the core content semantics of the original sequence and the transferred sequence by both recovery decoding layers, respectively, and intensifies the preservation of the core semantics of both the sequences by a multi-level constraint mechanism. Experiments on two public datasets demonstrate the superiority of our proposed method.     

Biofunctionalized nanoslits for wash-free and spatially resolved real-time sensing with full target capture

We propose biofunctionalized nanofluidic slits (nanoslits) as an effective platform for real-time fluorescence-based biosensing in a reaction-limited regime with optimized target capture efficiency. This is achieved by the drastic reduction of the diffusion length, thereby a boosted collision frequency between the target analytes and the sensor, and the size reduction of the sensing element down to the channel height comparable to the depletion layer caused by the reaction. Hybridization experiments conducted in DNA-functionalized nanoslits demonstrate the analyte depletion and the wash-free detection ∼10 times faster compared to the best microfluidic sensing platforms. The signal to background fluorescence ratio is drastically increased at lower target concentrations, in favor of low-copy number analyte analysis. Experimental and simulation results further show that biofunctionalized nanoslits provide a simple means to study reaction kinetics at the single-pixel level using conventional fluorescence microscopy with reduced optical depth.     

基于特征矢量稀疏分解的非圆信号DOA估计

文中提出当信源为非圆信号时,基于特征矢量稀疏分解进行DOA估计;并在稀疏恢复过程中,比较空间范数变化对误差的影响.该方法对协方差矩阵进行了扩展,在利用L曲线方法自适应得到正则化参数的同时,对空间范数应用进行了推广.不仅提高信息利用率,能够处理相干信号源,而且不需要已知信号源数目,性能优于平滑处理过后的NC-MUSIC算法.     

The geometry of linearly and quadratically constrained optimization problems for signal processing and communications

Constrained minimization problems considered in this paper arise in the design of beamformers for radar, sonar, and wireless communications, and in the design of precoders and equalizers for digital communications. The problem is to minimize a quadratic form under a set of linear or quadratic constraints. We present solutions to these problems and establish a connection between them. A majorization result for matrix trace and Poincare's separation theorem play key roles in establishing the connection. We show that our solutions can be formulated as generalized sidelobe cancellers (GSCs), which tie our constrained minimizations to linear minimum mean-squared error (LMMSE) estimations. We then express our solutions in terms of oblique projection matrices and establish the geometry of our constrained minimizations.