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International Conference on Electronic Engineering and Informatics (EEI)

作者： Jing Pang Ye Su Yiyuan Xie College of Electronic and Information Engineering Southwest University Chongqing People's Republic of China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing People's Republic of China Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing Chongqing People's Republic of China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Integrated photonic neural networks (IPNNs), with their ability to process large-scale data and parallelize multiple computational tasks, have shown impressive promise for applications in the field of artificial intelligence. However, due to some hard-to-ignore non-ideal characteristics of the underlying silicon photonic MZI devices constituting the IPNN, these non-ideal characteristics can cause the input signal to be affected by crosstalk noise and optical loss during transmission, thus affecting the training and inference accuracy of the IPNN. In order to solve these problems, this paper establishes an analytical model of IPNN affected by crosstalk noise and loss during the training process, covering component level, layer level and system level. Simulations using this model show that the inference accuracy of IPNN decreases by about 65% when the crosstalk noise in the training phase of IPNN reaches -20 dB, while the inference accuracy of IPNN decreases by about 55% when the optical loss reaches -0.5 dB.

关键词： Training Optical losses Optical interferometry Accuracy Neural networks Crosstalk Optical computing Optical crosstalk Optical noise Optical devices

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Design of Integrated Energy Monitoring System Based on Cloud Computing

Design of Integrated Energy Monitoring System Based on Cloud...

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Electronic Materials and information Engineering (EMIE), International Conference on

作者： Longlong Zhang Maoqiu Pu Peizhi Yang Zhiyuan Zhu Key Laboratory of Advanced Technique & Preparation for Renewable Energy Materials Ministry of Education Yunnan Normal University Kunming China Guangxi key laboratory of Automobile Components and Vehicle technology Guangxi University of Science and Technology Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China

ISBN: (数字)9798350390223

ISBN: (纸本)9798350390230

To fully utilize limited energy resources and address the inefficiencies in existing energy management, the authors proposed a comprehensive energy monitoring system design based on cloud computing, starting from the perspective of system network architecture. This system leverages existing resources effectively and incorporates overall scheduling, real-time monitoring, and predictive functionalities. The system operates on a cloud computing platform, processing data from three types of energy metering devices generated by sensors (electricity meter, water meter, gas meter) to provide users with real-time energy consumption data, represented as (0.50, 0.23, 0.10). These values are very close to the actual power consumption values (0.47, 0.21, 0.08), within an acceptable energy consumption range, achieving an average accuracy of 98%. This proves the high accuracy of cloud computing in remote real-time data transmission. Furthermore, the system utilizes the high reliability and scalability of cloud computing to offer integrated resource management, customized application, and virtualization platform switching services to customers, enabling information-based management of energy reserves, consumption, and equipment operation.

关键词： Meters Cloud computing Energy consumption Accuracy Switches Real-time systems Virtualization

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On outage of dual-hop relay SWIPT system with antenna selection and GSC over Nakagami-m fading channels

On outage of dual-hop relay SWIPT system with antenna select...

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International Conference on Communication Systems

作者： Danyang Wang Han Yu Jiliang Zhang Gaofeng Pan Hongjiang Lei Tingting Li Yunfei Chen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing SWU Chongqing China Chongqing Key Lab of Mobile Communications Technology CQUPT Chongqing China School of Mathematics and Statistics Southwest University Chongqing China School of Engineering University of Warwick Coventry UK

ISBN: (纸本)9781509034246

In this paper, a wireless powered dual-hop relay system, which consists of a source, a destination and a relay equipped with N R (N R > 1) receiving antennas, is considered. Antenna selection scheme is employed for the relay to harvest energy from the received signals, under which the L R (0 R ≤ N R ) strongest received signals are combined for information processing and the others L Z = N R - L R copies of signals are used for energy harvesting. Considering Nakagami-m fading scenarios, we first derive the closed-form expressions for the probability density function and the cumulative distribution function of the signal-to-noise ratio with ascending generalized selection combining, and then the closed-form analysis expression for the outage probability is derived. Finally, simulation results are presented to verify our proposed analysis model.

关键词： Relays Fading channels Diversity reception Receiving antennas Transmitting antennas IP networks Wireless communication

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Efficient Finite-Difference Time-Domain Method for Solving Electromagnetic Scattering in Layered Anisotropies

Efficient Finite-Difference Time-Domain Method for Solving E...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yilin Kang Yuxian Zhang Xiaoli Feng Lixia Yang Zhixiang Huang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education and with the Information Materials and Intelligent Sensing Laboratory of Anhui Province Industry-Education-Research Institute of Advanced Materials and Technology for Integrated Circuits and with School of Electronic and Information Engineering Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

In this article, we propose an efficient numerical method for layered models based on the conventional finite difference time-domain (FDTD) method. The three-dimensional (3-D) layered model is compressed into one-dimensional (1-D) calculation, which improves the problem of large space memory and long running time for 3-D modeling while ensuring the accuracy and feasibility of the algorithm. On the one hand, by employing the transformation relationship between the transverse wave vector and the field in space, the expanded Maxwell's rotation equations are simplified, so that the equation system only retains the spatial transformation in the propagation direction. This processing allows for the calculation of the propagation characteristics of layered media without requiring more computational resources. On the other hand, to ensure the universality of the algorithm, a fully tensor anisotropic medium is selected for detecting the propagation data. At the same time, we design the layered structure and conduct the consistent 3-D modeling in the simulation software COMSOL. The results showed that our proposed efficient numerical algorithm greatly reduced the loss of computing resources while ensuring accuracy.

关键词： Solid modeling Three-dimensional displays Accuracy Tensors Software algorithms Vectors Mathematical models Numerical models Time-domain analysis Finite difference methods

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Stress recognition based on graph structure representation of facial StO2

Stress recognition based on graph structure representation o...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiaxiu Wang Xinyu Liu Yingjuan Jia Tong Chen Jingru Huang Zheyuan Zhang College of Electronic and Information Engineering Southwest University Chongqing China Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing China Chongqing Key Laboratory of Non-Linear Circuits and Intelligent Information Processing Southwest University Chongqing China

Stress is an integral state that affects physical and mental health of individuals. Tissue Oxygen Saturation (StO2) is an emerging physiological signal and can reflect different stress states. Previous studies have simply extracted features of StO2 from specific regions of the face without exploring potential associations between them. In this paper, we analyze the facial Region of Interest (ROI) based on StO2, so as to explore the deep relationship between ROIs. Building upon this, we construct, for the first time, a StO2-based facial graph structure for stress recognition by using ROIs as nodes. In addition, we propose a new graph pooling method called FTPool which allows to measure node significance in terms of both node characteristics and graph topology. Finally, we further propose a dual-stream network (GCNet) combining GNN and CNN for the baseline-independent stress recognition. The experimental results show that the proposed GCNet can achieve state-of-the-art (SOTA) results with an accuracy of 78.57% on the original unbalanced database.

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Adversarial Domain Adaptation Guided by Farthest Distance for Open Set Electronic Nose Drift Compensation

SSRN

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SSRN 2024年

作者： Pan, Yong Li, Chuandong Xiong, Jiang Hou, Ziye Yao, Youbin College of Computer Science and Engineering Chongqing Three Gorges University Chongqing404020 China College of Electronic and Information Engineering Southwest University Chongqing400715 China College of Computer Science Chongqing University Chongqing440044 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing China

With advancements in modern science and technology, electronic noses (ENs) have gained significant attention for their applications in environmental monitoring, food quality inspection, and medical equipment. ENs mimic biological olfactory systems to classify gases using arrays of sensors and pattern recognition models. However, gas sensor drift poses a major challenge, leading to performance degradation in EN systems. To address this, Domain Adaptation (DA) methods have been introduced, aligning source domain data with target domain drift data. While traditional DA methods assume identical class compositions in both domains, this is often unrealistic in practice, leading to suboptimal results. Open Set Domain Adaptation (OSDA) methods have been developed to handle unknown categories in the target domain, but they often become overly focused on distinguishing unknown classes, neglecting the accurate recognition of known classes. To overcome these limitations, we propose an Adversarial Domain Adaptation Guided by Farthest Distance (ADA-FDG) approach. ADA-FDG combines a Farthest Distance Guide Set Selection algorithm and Confidence Normalized Adaptive Factor, enhancing EN performance by efficiently using labeled data, robustly aligning domain distributions, and improving drift compensation. Our approach demonstrates superior performance on two datasets, effectively handling both known and unknown gases, and significantly enhancing the robustness of EN systems in dynamic environments. © 2024, The Authors. All rights reserved.

关键词： Electronic medical equipment

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Secrecy performance analysis of SIMO underlay cognitive radio systems with outdated CSI

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IET Communications 2017年第12期11卷 1961-1969页

作者： Lei, Hongjiang Zhang, Jianming Park, Ki-Hong Ansari, Imran Shafique Pan, Gaofeng Alouini, Mohamed-Slim Chongqing Key Lab of Mobile Communications Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Electrical and Computer Engineering (ECEN) Texas A and M University at Qatar (TAMUQ) Education City Doha Qatar Chongqing Key Laboratory of Non-linear Circuits and Intelligent Information Processing Southwest University Chongqing 400715 China

This study investigates the secrecy outage performance of a single-input multiple-output underlay cognitive radio network (CRN) with outdated channel state information (CSI). The confidential messages are transmitted from transmitter to the destination, while a multi-antenna eavesdropper exists. The maximal ratio combining and selection combining schemes are utilised at the receivers to improve the quality of the received signal-to-noise ratio. The exact and asymptotic closed-form expressions of secrecy outage probability are derived, and simulation results are provided to verify the authors' proposed analytical results. The results reveal that imperfect CSI of main channels deteriorates the secrecy outage performance while that of eavesdropping and interfering channels has contrary effect, and only a unity diversity order can be obtained in underlay CRNs with imperfect CSI. © The Institution of Engineering and Technology.

关键词： antenna arrays cognitive radio diversity reception probability radio networks telecommunication security

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Outlier Detection for Spotting Micro-expressions

Outlier Detection for Spotting Micro-expressions

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ranlei Cao Xinyu Liu Ju Zhou Dong Chen Dairong Peng Tong Chen College of Electronic and Information Engineering Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing Southwest University Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China Institute of Psychology CAS Beijing China

ISBN: (纸本)9781665429825

Facial expression, as a basic communication method, is an important way of emotion expression and cognition. Facial emotional expression impairment seriously affects interpersonal communication and social life. Micro-expressions (MEs) are involuntary and instant facial dynamics that occurs when the subject failed to suppress their genuine emotions, especially in high-stake situations. Psychological research has shown that MEs can reflect people’s true emotions, which is of great help to the treatment of Facial emotional expression impairment. ME spotting aims to locate the apex frame positions of MEs from long videos, which is the first step in ME analysis. Unlike previous researches that used binary classification or maximum feature difference for analysis, in this paper, we apply the idea of outlier detection to spot ME for the first time. MEs are unusual facial dynamics whose movement patterns diverge from others, so they can be regarded as outliers in the feature space of long videos. Our proposed method uses Gaussian model to estimate the probability density function and locates outliers by analyzing the statistical features of long videos to achieve ME spotting. This method was evaluated on CASME I, CASME II and SAMM datasets that only include spontaneous MEs in long videos. The results show that this method can efficiently locate apex frames of ME efficiently in long videos and also provide a new perspective for ME spotting.

关键词： Geometry Databases Psychology Manuals Probability density function Feature extraction Labeling

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Dynamics-Based Algorithm-Level Privacy Preservation for Push-Sum Average Consensus

arXiv

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arXiv 2023年

作者： Cheng, Huqiang Xie, Mengying Yang, Xiaowei Lü, Qingguo Li, Huaqing Key Laboratory of Dependable Services Computing in Cyber Physical Society-Ministry of Education College of Computer Science Chongqing University Chongqing401331 China School of Software Engineering South China University of Technology Guangzhou510006 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China

In the intricate dance of multi-agent systems, achieving average consensus is not just vital–it is the backbone of their functionality. In conventional average consensus algorithms, all agents reach an agreement by individual calculations and sharing information with their respective neighbors. Nevertheless, the information interactions that occur in the communication network may make sensitive information be revealed. In this paper, we develop a new privacy-preserving average consensus method on unbalanced directed networks. Specifically, we ensure privacy preservation by carefully embedding randomness in mixing weights to confuse communications and introducing an extra auxiliary parameter to mask the state-updated rule in initial several iterations. In parallel, we exploit the intrinsic robustness of consensus dynamics to guarantee that the average consensus is precisely achieved. Theoretical results demonstrate that the designed algorithms can converge linearly to the exact average consensus value and can guarantee privacy preservation of agents against both honest-but-curious and eavesdropping attacks. © 2023, CC BY.

关键词： Consensus algorithm

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The predominant functional connections of recognizing fear and surprise expression: a MEG study

The predominant functional connections of recognizing fear a...

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International Conference on intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Yang Tan Ke Zhao Tong Chen Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing Southwest University Chongqing China School of Electronic and Information Engineering Southwest University Chongqing China Institute of Psychology China Academy of Sciences Beijing China

ISBN: (数字)9781728180229

ISBN: (纸本)9781728180236

Facial expression, powerful non-verbal signals, contains abundant personal information and social communication information. Accurately identifying these signals is critical to the success of interpersonal communication. Studies have shown that both children and adults tend to confuse surprise with fear rather than sadness, anger, or disgust. However, the studies of fear and surprise expression recognition using network pattern based on MEG is only a few. In this study, we monitored the brain activity of 6 subjects as they perform a recognition task of fear and surprise, and subsequently constructed a network of brain functional connections. By using rank sum test and random forest, the most discriminative and representative 6 FCs from 2278 FCs were selected. The group of these 6 FCs can give a best prediction performance of 78.56%. Additionally, we also found that people tend to refer to surprise as fear when distinguishing between fear and surprise.

关键词： Face recognition Task analysis Occipital Lobe Informatics Biomedical monitoring Random forests Monitoring

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