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Enzyme Activity Triggered Blocking of Plasmon Resonance Energy Transfer for Highly Selective Detection of Acid Phosphatase

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ANALYTICAL CHEMISTRY 2020年第2期92卷 2130-2135页

作者： Yan, Xin Xia, Chang Chen, Bin Li, Yuan Fang Gao, Peng Fei Huang, Cheng Zhi Southwest Univ Coll Chem & Chem Engn Key Lab Luminescent & Real Time Analyt Chem Minist Educ Chongqing 400715 Peoples R China Southwest Univ Coll Elect & Informat Engn Nonlinear Circuit & Intelligent Informat Proc Chongqing 400715 Peoples R China Southwest Univ Coll Pharmaceut Sci Chongqing Sci & Technol Commiss Chongqing Key Lab Biomed Anal Chongqing 400715 Peoples R China

Plasmon resonance energy transfer (PRET), as a new form of energy transfer first discovered in 2007, has been widely applied for the biomolecular recognition, detection of ions, cellular physiological status monitoring, and energy conversion. It occurs between noble metal nanoparticles (donor) and conjugated molecules or nanoparticles (acceptor). In this study, we used urchin-like gold nanoplasmonics (UGPs) and oxTMB as a new donor-acceptor pair to establish a novel PRET coupling system, avoiding trivial modification. PRET from UGPs to conjugated redox-active oxTMB leads to resonant quenching in the localized surface plasmon resonance (LSPR) spectra. However, when the acid phosphatase (ACP) was introduced, the hydrolyzate ascorbic acid (AA) converted from 2-phospho-L-ascorbic acid trisodium salt (AAP) could be capable of reducing oxTMB into TMB, thereby preventing the occurrence of PRET. The recovery of the scattering spectral intensity of UGPs was linearly related to the concentration of ACP in the range of 0.1 to 5.0 U/L, and the ACP with a detection limit of 0.076 U/L could be measured. In addition, this method also showed good selectivity attributed to the substrate specificity of enzyme.

关键词： Selectivity Surface plasmon resonance Acid phosphatase Plasmon Enzyme assay Surface plasmon

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A smoothing neural network for minimization l1-lp in sparse signal reconstruction with measurement noises

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NEURAL NETWORKS 2020年 122卷 40-53页

作者： Zhao, You He, Xing Huang, Tingwen Huang, Junjian Li, Peng Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Univ Educ Key Lab Machine Percept & Childrens Intelligence Chongqing 400067 Peoples R China Texas A&M Univ Qatar Doha 5825 Qatar Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

This paper investigates a smoothing neural network (SNN) to solve a robust sparse signal reconstruction in compressed sensing (CS), where the objective function is nonsmooth l(1)-norm and the feasible set satisfies an inequality of l(p)-norm (2 >= p >= 1) which is used for measuring residual errors. With a smoothing approximate technique, the non-smooth and non-Lipschitz continuous issues of the l(1)-norm and the gradient of l(p)-norm can be solved efficiently. We propose a SNN which is modeled by a differential equation and give its circuit implementation. In this case, we prove the proposed SNN converges to the optimal of considered problem. Simulation results are discussed to demonstrate the efficiency of the proposed algorithm. (c) 2019 Elsevier Ltd. All rights reserved.

关键词： Neural network l(1)-norm minimization l(p)-norm (2 >= p >= 1) Smoothing approximation

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Secure Resource Allocation for Polarization-Based Non-Linear Energy Harvesting Over 5G Cooperative Cognitive Radio Networks

Secure Resource Allocation for Polarization-Based Non-Linear...

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IEEE International Conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Wang, Fei Zhang, Xi Southwest Univ Chongqing Key Lab Nonlinear Circuit & Intelligent Coll Elect & Informat Engn Chongqing 400715 Peoples R China Texas A&M Univ Dept Elect & Comp Engn Networking & Informat Syst Lab College Stn TX 77843 USA

ISBN: (纸本)9781728150895

We address secure resource allocation for the energy harvesting (EH) based 5G cooperative cognitive radio networks (CRNs). To guarantee that the size-limited secondary users (SUs) can simultaneously send the primary user's and their own information, we assume that SUs are equipped with orthogonally dual-polarized antennas (ODPAs). In particular, we propose, develop, and analyze an efficient resource allocation scheme under a practical non-linear EH model, which can capture the non-linear characteristics of the end-to-end wireless power transfer (WPT) for radio frequency (RF) based EH circuits. Our obtained numerical results validate that a substantial performance gain can be obtained by employing the non-linear EH model.

关键词： Green wireless networks and communications cooperative cognitive radio networks orthogonally dual-polarized antennas non-linear energy harvesting secure resource allocation

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Spatiotemporal Convolutional Neural Network with Convolutional Block Attention Module for Micro-Expression Recognition

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informatION 2020年第8期11卷 380-380页

作者： Chen, Boyu Zhang, Zhihao Liu, Nian Tan, Yang Liu, Xinyu Chen, Tong Southwest Univ Sch Elect & Informat Engn Chongqing 400715 Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuit & Intelligent Chongqing 400715 Peoples R China Chinese Acad Sci Inst Psychol Beijing 100101 Peoples R China

A micro-expression is defined as an uncontrollable muscular movement shown on the face of humans when one is trying to conceal or repress his true emotions. Many researchers have applied the deep learning framework to micro-expression recognition in recent years. However, few have introduced the human visual attention mechanism to micro-expression recognition. In this study, we propose a three-dimensional (3D) spatiotemporal convolutional neural network with the convolutional block attention module (CBAM) for micro-expression recognition. First image sequences were input to a medium-sized convolutional neural network (CNN) to extract visual features. Afterwards, it learned to allocate the feature weights in an adaptive manner with the help of a convolutional block attention module. The method was testified in spontaneous micro-expression databases (Chinese Academy of Sciences Micro-expression II (CASME II), Spontaneous Micro-expression Database (SMIC)). The experimental results show that the 3D CNN with convolutional block attention module outperformed other algorithms in micro-expression recognition.

关键词： micro-expression recognition 3D convolutional neural network (3D CNN) convolutional block attention module (CBAM) adaptive feature weights spatiotemporal features

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Robust constrained maximum total correntropy algorithm

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SIGNAL PROCESSING 2021年 181卷 107903-107903页

作者： Qian, Guobing He, Fuliang Wang, Shiyuan Herbert, H. C. Iu Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn 35 Stirling Hwy Crawley WA 6009 Australia

Constrained adaptive filtering has been paid more attentions recently. As a robust constrained adaptive filtering algorithm, constrained maximum correntropy criterion (CMCC) has shown its superiority for the output data contaminated by heavy-tail impulsive noises. However, when both input and output are contaminated by noises, the performance of CMCC will deteriorate dramatically. To address this issue, based on an errors-in-variables (EIV) model for noisy input and output, this paper proposes a novel constrained maximum total correntropy (CMTC) algorithm using the stochastic gradient. Then, the stability condition and the mean square deviation (MSD) of CMTC are obtained for theoretical analysis. Finally, simulation results validate the correctness of the theoretical MSD and the superiorities of CMTC for noisy input and output. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Constrained Total Robust Adaptive filter Stability MSD

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Data-Driven Optimal Synchronization for Complex Networks With Unknown Dynamics

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IEEE ACCESS 2020年 8卷 224083-224091页

作者： Hu, Wenjie Gao, Luli Dong, Tao Chongqing Technol & Business Univ Sch Business Adm Chongqing 400067 Peoples R China Southwest Univ Key Lab Nonlinear Circuits & Intelligent Informat Coll Elect & Informat Engn Chongqing 400715 Peoples R China

This paper studies the data-driven optimal synchronization problem for complex networks (CNs) with unknown dynamics. By using pre-compensation technology, a compensator and a controller are proposed. Then, an augmented error system is constructed, which can circumvent the requirement of system dynamics. It is revealed that the the optimal synchronization control of CNs works as the optimal regulation of the augmented system with a performance function. A novel policy iteration (PI) algorithm is given to ensure that the iterative performance function can converge to the optimal value which is the solution of the coupled Hamilton-Jacobi-Bellman equation (HJB), which means that the optimal regulation of the augmented system can be solved and the synchronization can be achieved. Based on this, a novel data-driven control scheme is proposed, which is composed of parts: compensator, controller and critic network. The iterative performance is generated by critic network. The compensator is used to construct the control parameter by using performance and the controller is used to construct control input by using control parameter. Both compensator and critic network are implemented by neural networks (NNs) and only depend on the process sampling data. Finally, we use robot network as an example to verify the effectiveness of proposed control scheme.

关键词： Data-driven optimal synchronization control compensator complex networks

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Facial Expression Recognition Method Based on a Part-Based Temporal Convolutional Network with a Graph-Structured Representation 29th

Facial Expression Recognition Method Based on a Part-Based T...

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29th International Conference on Artificial Neural Networks (ICANN)

作者： Zhong, Lei Bai, Changmin Li, Jianfeng Chen, Tong Li, Shigang Southwest Univ Sch Elect & Informat Engn Chongqing Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuit & Intelligent Chongqing Peoples R China Hiroshima City Univ Hiroshima Japan

ISBN: (纸本)9783030616090;9783030616083

Facial expressions are controlled by facial muscles and can be regarded as appearance and shape variations in key parts. A key challenge in facial expression recognition is capturing effective information from a facial image. In this paper, we propose a basic graph contour that is based on key parts for facial expression recognition. Each node on the graph contour represents a landmark, and each edge represents the connection between the two selected nodes. To further investigate the graph representation and to make the graphs more distinctive, we use a Gabor filter to extract appearance variations around the graph nodes while applying an affine transformation to capture the shape variations from graphs without expression in graphs with expression. Then, to serve as an efficient network for processing in which the graph extracts the appearance and shape representations, we introduce the temporal convolutional network (TCN). Finally, we propose a part-based temporal convolutional network (PTCN) that emphasizes the key facial parts. The experimental results demonstrate that this method realizes significant improvements over state-of-the-art methods utilizing three widely used facial databases: Oulu-CASIA, CK+, and MMI.

关键词： Facial expression recognition TCN Graph-structured representation

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SWU-NIRPV: a near-infrared pose variation face database and pose-invariant face recognition

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JOURNAL OF ELECTRONIC IMAGING 2021年第2期30卷 023018-023018页

作者： Yu, Yeda Liu, Xinyu Liu, Nian Chen, Boyu Chen, Tong Southwest Univ Coll Elect Informat Engn Chongqing Peoples R China Chongqing Key Lab Nonlinear Circuit & Intelligent Chongqing Peoples R China Chinese Acad Sci Inst Psychol Beijing Peoples R China

Academy Sciences, Psychology, Beijing, Abstract. Near-infrared (NIR) face recognition (FR) has demonstrated robustness against changes in ambient illumination, which makes it suitable for surveillance even under weak illumination conditions. However, the existing database for NIR FR only contains frontal face images, and the impact of pose variation on the robustness of NIR FR remains unascertained. We developed an NIR face database with 57 pose variations in a dark environment, which can be used in pose-invariant FR research. Convolutional neural networks (CNNs) were designed and tested in comparison to the traditional method in the database. The experimental results showed that a difference of even 10 deg between the gallery and testing sets can dramatically reduce the recognition performance. Additionally, an average accuracy of 90.58% was obtained for pose invariant recognition by employing more pose variations in the gallery set using the CNN-based method. (c) 2021 SPIE and IS&T

关键词： near-infrared face database pose invariant convolutional neural network face recognition

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Integrated plasmonic full adder based on cascaded rectangular ring resonators for optical computing

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OPTICS AND LASER TECHNOLOGY 2022年 156卷

作者： Ye, Yichen Xie, Yiyuan Song, Tingting Guan, Nan Lv, Mingsong Li, Chuandong Southwest Univ Sch Elect & Informat Engn Chongqing 400715 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Normal Univ Sch Comp & Informat Sci Chongqing 401331 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

A novel and integrated plasmonic full adder based on cascaded rectangular ring resonators for optical computing is presented. This full adder consists of cascaded rectangular ring resonators covered by graphene layer and resonator-enhanced waveguide crossings. Owing to the nonlinear optical properties of graphene, the plasmonic resonators can work as all-optical switches with low power consumption and ultrashort response time to change the propagation path of surface plasmon polaritons (SPPs). Finite-difference time-domain (FDTD) simulation results numerically verify the realization of full-adder operations. This proposed plasmonic device can realize the optical full-adder operations with small feature size, fast response time, and lower power consumption than when using other traditional nonlinear optical Kerr materials. It can provide new concepts to design novel optical computing devices.

关键词： All-optical full adder Graphene Metal-insulator-metal (MIM) waveguide Optical computing Surface plasmon polaritons

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DBAN: Adversarial Network With Multi-Scale Features for Cardiac MRI Segmentation

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH informatICS 2021年第6期25卷 2018-2028页

作者： Yang, Xinyu Zhang, Yuan Lo, Benny Wu, Dongrui Liao, Hongen Zhang, Yuan-Ting Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Imperial Coll London Hamlyn Ctr London SW7 2AZ England Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Tsinghua Univ Sch Med Dept Biomed Engn Beijing 100084 Peoples R China City Univ Hong Kong Dept Mech & Biomed Engn Hong Kong Peoples R China

With the development of medical artificial intelligence, automatic magnetic resonance image (MRI) segmentation method is quite desirable. Inspired by the power of deep neural networks, a novel deep adversarial network, dilated block adversarial network (DBAN), is proposed to perform left ventricle, right ventricle, and myocardium segmentation in short-axis cardiac MRI. DBAN contains a segmentor along with a discriminator. In the segmentor, the dilated block (DB) is proposed to capture, and aggregate multi-scale features. The segmentor can produce segmentation probability maps while the discriminator can differentiate the segmentation probability map, and the ground truth at the pixel level. In addition, confidence probability maps generated by the discriminator can guide the segmentor to modify segmentation probability maps. Extensive experiments demonstrate that DBAN has achieved the state-of-the-art performance on the ACDC dataset. Quantitative analyses indicate that cardiac function indices from DBAN are similar to those from clinical experts. Therefore, DBAN can be a potential candidate for short-axis cardiac MRI segmentation in clinical applications.

关键词： Image segmentation Magnetic resonance imaging Convolution Training informatics Kernel Biomedical imaging Cardiac MRI Medical Image Processing Automatic Segmentation Method Adversarial Network

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