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Emotion Recognition by Learning the Manifold of Fused Multiscale Information of EEG Signals

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IEEE Transactions on Affective Computing 2025年

作者： Li, Cunbo Zhang, Shuhan Mu, Yufeng Yang, Lei Peng, Yueheng Li, Fali Zhang, Yangsong Liang, Zhen Cao, Zehong Wan, Feng Yao, Dezhong Li, Peiyang Xu, Peng University of Electronic Science and Technology of China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology Chengdu610054 China University of Macau Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation China Southwest University of Science and Technology School of Computer Science and Technology Mianyang621010 China Shenzhen University School of Biomedical Engineering Medical School Shenzhen518060 China Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen518060 China University of South Australia STEM Mawson Lakes Campus AdelaideSA5095 Australia School of Computer Science University of Technology Sydney Australia University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation China Chongqing University of Posts and Telecommunications School of Bioinfomatics Chongqing400065 China

Recent research has consistently indicated that the fusion of electroencephalography (EEG) features from multiple modalities can integrate cognitive state expressions across diverse dimensions, resulting in a substantial increase in emotion recognition accuracy. However, redundant information within the fused multimodal features could lead to the curse of dimensionality and overfitting of the learning model. In this work, we propose a multiscale EEG feature fusion and representation strategy for EEG emotion recognition named manifold of multiscale information fusion (MMIF), in which the optimal manifold of the multiscale fusion of local and global brain activation patterns can be automatically learned to realize an efficient representation of emotional EEG signals. To evaluate the performance, in this work, both off- and online EEG emotion recognition experiments were conducted, and the experimental results consistently verified the effectiveness and feasibility of the MMIF applied in real-time emotion decoding systems. Furthermore, the analytical experiments confirmed the discriminative capabilities and cognitive interpretability of the MMIF. In summary, the proposed MMIF model may provide an efficient avenue for exploring representations and enhancing the discrimination of multimodal fusion features, which may also provide a promising solution for designing online affective braincomputer interaction systems. © 2010-2012 IEEE.

关键词： Information fusion

Modularity Facilitates Classification Performance of Spiking Neural Networks for Decoding Cortical Spike Trains

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Modularity Facilitates Classification Performance of Spiking...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Tengjun Liu Yuxiao Ning Pengfu Liu Yiwei Zhang Yansong Chua Weidong Chen Shaomin Zhang Department of Biomedical Engineering Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China China Nanhu Academy of Electronics and Information Technology (CNAEIT) Jiaxing China Department of Computer Science and Technology Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China Department of Biomedical Engineering Qiushi Academy for Advanced Studies Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology Effectiveness Appraisal Zhejiang University Hangzhou China

After the introduction of recurrence, an important property of the biological brain, spiking neural networks (SNNs) have achieved unprecedented classification performance. But they still cannot outperform many artificial neural networks. Modularity is another crucial feature of the biological brain. It remains unclear if modularity can also improve the performance of SNNs. To investigate this idea, we proposed the modular SNN, and compared its performance with a uniform SNN without modularity by employing them to classify cortical spike trains. For the first time, a significant improvement was found in our modular SNN. Further, we probed into the factors influencing the performance of the modular SNN and found: (a). The modular SNN outperformed the uniform SNN more significantly when the number of neurons in the networks increased; (b). The performance of the modular SNNs increased as the number of modules dropped. These preliminary but novel findings suggest that modularity may help develop better artificial intelligence and brain-machine interfaces. Also, the modular SNN may serve as a model for the study of neuronal spike synchrony.

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Throughput Enhancement of Full-Duplex CSMA Networks Using Multiplayer Bandits

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Throughput Enhancement of Full-Duplex CSMA Networks Using Mu...

作者： Tong, Jingwen Fu, Liqun Han, Zhu School of Informatics Xiamen University Xiamen China School of Informatics The Key Laboratory of Underwater Acoustic Communication Marine Information Technology Ministry of Education Xiamen University Xiamen China Department of Electrical and Computer Engineering University of Houston HoustonTX United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

This article studies the network-level throughput of a full-duplex (FD)-enabled CSMA network, considering the link's transmit power (TP) control, carrier-sensing threshold (CST) adjustment, and logarithm access intensity (LAI) adaptation. With the FD technique, a transmitter-receiver pair can transmit and receive simultaneously in the same frequency band. We aim to find the best combination of TP, CST, and LAI for each link to maximize the FD-CSMA network throughput. However, adjusting each link's TP and CST will change the network's carrier-sensing relation or contention graph, consequently leading to a computationally intractable network optimization problem. On the other hand, it is difficult to jointly optimize these three parameters in a fully distributed network. To overcome these, we first decompose this network optimization problem into two subproblems: 1) a joint control and scheduling problem in the transport- and media access control (MAC)-layer and 2) a parameter selection problem in the PHY-layer. Then, the multiplayer multiarmed bandit (MPMAB) framework has been introduced to address this problem by solving the two subproblems alternately. We put forth a fully distributed algorithm, named the stochastic and adversarial optimal FD-CSMA (SAO-FD-CSMA) algorithm, to solve the MPMAB problem by taking advantage of the optimization tool and the bandit theory. The numerical results show that the proposed algorithm outperforms the state-of-the-art bandit algorithms and can improve the network throughput by 43% compared with the random selection method. © 2014 IEEE.

关键词： Carrier sense multiple access

UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation

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arXiv 2024年

作者： Zhang, Pengfei Li, Chang Zhang, Yongjun Qin, Rongjun Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province College of Urban and Environmental Science Central China Normal University Wuhan China School of Remote Sensing and Information Engineering Wuhan University China Department of Civil Environmental and Geodetic Engineering Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio State University ColumbusOH United States

Remotely sensed imagery interpretation (RSII), including semantic segmentation and change detection, faces the three major problems: (1) objective representation of spatial distribution patterns with coexistence of spatial stationary and non-stationary;(2) edge uncertainty problem caused by downsampling encoder and intrinsic edge noises (e.g., mixed pixel and edge occlusion etc.);and (3) false detection problem caused by geometric registration error in change detection. To solve the aforementioned problems, uncertainty-diffusion-model-based high-Frequency TransFormer network (UDHF2-Net) is the first to be proposed, whose superiorities are as follows: (1) a spatially-stationary-and-non-stationary high-frequency connection paradigm (SHCP) is proposed to enhance the interaction of spatially frequency-wise stationary and non-stationary features to yield high-fidelity edge extraction result. Inspired by HRFormer, SHCP proposes high-frequency-wise stream to replace high-resolution-wise stream in HRFormer through the whole encoder-decoder process with parallel frequency-wise high-to-low streams, so it improves the edge extraction accuracy by continuously remaining high-frequency information;(2) a mask-and-geo-knowledge-based uncertainty diffusion module (MUDM), which is a self-supervised learning strategy, is proposed to improve the edge accuracy of extraction and change detection by gradually removing the simulated spectrum noises based on geo-knowledge and the generated diffused spectrum noises;(3) a frequency-wise semi-pseudo-Siamese UDHF2-Net is the first to be proposed to balance accuracy and complexity for change detection. Besides the aforementioned spectrum noises in semantic segmentation, MUDM is also a self-supervised learning strategy to effectively reduce the edge false change detection from the generated imagery with geometric registration error. In semantic segmentation experiments, UDHF2-Net achieved the best mIoU values of 92.00%, 87.31% and 55.27% respecti

关键词： Semantic Segmentation

FedVQCS: Federated Learning via Vector Quantized Compressed Sensing

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arXiv 2022年

作者： Oh, Yongjeong Jeon, Yo-Seb Chen, Mingzhe Saad, Walid The Department of Electrical Engineering POSTECH Gyeongbuk Pohang37673 Korea Republic of The Institute for Convergence Research and Education in Advanced Technology Yonsei University Incheon21983 Korea Republic of The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

In this paper, a new communication-efficient federated learning (FL) framework is proposed, inspired by vector quantized compressed sensing. The basic strategy of the proposed framework is to compress the local model update at each device by applying dimensionality reduction followed by vector quantization. Subsequently, the global model update is reconstructed at a parameter server by applying a sparse signal recovery algorithm to the aggregation of the compressed local model updates. By harnessing the benefits of both dimensionality reduction and vector quantization, the proposed framework effectively reduces the communication overhead of local update transmissions. Both the design of the vector quantizer and the key parameters for the compression are optimized so as to minimize the reconstruction error of the global model update under the constraint of wireless link capacity. By considering the reconstruction error, the convergence rate of the proposed framework is also analyzed for a non-convex loss function. Simulation results on the MNIST and FEMNIST datasets demonstrate that the proposed framework provides more than a 2.4% increase in classification accuracy compared to state-of-the-art FL frameworks when the communication overhead of the local model update transmission is 0.1 bit per local model entry. Copyright © 2022, The Authors. All rights reserved.

关键词： Vector quantization

Quantitative Modeling of Point Defects in β-Ga2O3 Combining Hybrid Functional Energetics with Semiconductor and Processes Thermodynamics

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arXiv 2025年

作者： Arnab, Khandakar Aaditta Stephens, Megan Maxfield, Isaac Lee, Channyung Ertekin, Elif Frodason, Ymir K. Varley, Joel B. Scarpulla, Michael A. Department of Materials Science and Engineering University of Utah Salt Lake CityUT84112 United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Materials Research Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Centre for Materials Science and Nanotechnology Physics University of Oslo Oslo Norway Lawrence Livermore National Laboratory LivermoreCA93106 United States Department of Electrical and Computer Engineering University of Utah Salt Lake CityUT84112 United States

β-gallium oxide (β-Ga2O3) is of high interest for power electronics because of its unique combination of melt growth, epitaxial growth, n-type dopability, ultrawide bandgap, and high critical field. Optimization of crystal growth processes to promote beneficial defects and suppress harmful ones requires accurate quantitative modelling of both native and impurity defects. Herein we quantitatively model defect concentrations as a function of bulk crystal growth conditions and demonstrate the necessity of including effects such as bandgap temperature dependence, chemical potentials from thermochemistry, and defect vibrational entropy in modelling based on defect formation energies computed by density functional theory (DFT) with hybrid functionals. Without these contributions, grossly-erroneous and misleading predictions arise, e.g. that n-type doping attempts would be fully compensated by Ga vacancies. Including these effects reproduces the experimental facts that melt-grown Sn-doped β-Ga2O3 crystals are conductive with small compensation while annealing the same crystals in O2 at intermediate temperatures renders them insulating. To accomplish this modeling, we developed a comprehensive modelling framework (KROGER) based on calculated defect formation energies and flexible thermodynamic conditions. These capabilities allow KROGER to capture full and partial defect equilibria amongst native defects and impurities occurring during specific semiconductor growth or fabrication processes. We use KROGER to model 873 charge-states of 259 defects involving 19 elements in conditions representing bulk crystal growth by edge-fed growth (EFG) and annealing in oxygen. Our methodology is transferrable to a wide range of materials beyond β-Ga2O3. The integration of thermodynamic and first-principles modelling of point defects provides insight into optimization of point defect populations in growth and processing. Copyright © 2025, The Authors. All rights reserved.

关键词： Point defects

Characteristic Mode Design of Dual-Band Multistate Tunable Absorber Based on Programmable Flexible Metasurface

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TechRxiv

TechRxiv 2022年

作者： Li, Mengmeng Ning, Zihao Ding, Dazhi Ai, Xia Liu, Jiaqi Wang, Chao-Fu The Department of Communication Engineering Nanjing University of Science and Technology Nanjing China The National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics Beijing100076 China The Department of Electrical and Computer Engineering National University of Singapore Singapore

Theory of characteristic modes (TCM) is applied to the design of a programmable flexible metasurface (PFMS) to achieve its multistate tunable microwave absorption. The designed metasurface consists of two layers. The first layer is a very thin polyimide (PI), on which unit cells consisting of two trapezoidal patches symmetrical along the central bias microstrip are printed. The second layer is grounded polyvinyl chloride (PVC) substrate to maintain the flexibility of the designed structure. The TCM is used to analyze and understand the modal behavior of the unit cell, which provides clear underlying electromagnetic (EM) physics to guide us how to load positive-intrinsic-negative (PIN) diode as a tunable resistance to achieve variable absorption states. The designed PFMS absorber have been fabricated and experimentally verified on the flat and cylindrical foams, respectively. Good agreements can be found for both planar and cylindrical metasurface structures between full-wave simulations and measurements. Less than -10 dB reflectivity is achieved from 3.6 to 10.7 GHz (99%). The mimicry of time-varying scattering cross section (SCS) is further realized through real-time coding PFMS absorber, which can be precisely tuned from -30 dB to 0 dB with a step of 1.5 dB at 5.5 GHz and 10.4 GHz, respectively. © 2022, CC BY.

关键词： Polyvinyl chlorides

Microcomb-driven photonic convolutional engine

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Microcomb-driven photonic convolutional engine

Conference on Lasers and Electro-Optics (CLEO)

作者： Bowen Bai Qipeng Yang Haowen Shu Lin Chang Yichen Wu Fenghe Yang Bitao Shen Zihan Tao Weiqiang Xie Weiwei Hu John E. Bowers Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China Zhangjiang Laboratory Shanghai China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

We demonstrate a microcomb-driven chip-based photonic convolutional engine utilizing time-wavelength plane stretching approach. A preeminent photonic-core compute density of 1.04 TOPS/mm 2 and a record high weight co... 详细信息

ISBN: (纸本)9781957171258

关键词： Calibration Distributed feedback lasers Edge detection Optical delay lines Parallel processing Silicon modulators

Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study

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arXiv 2025年

作者： Liu, Tianhao Liu, Jiqiang Zhang, Tao Wang, Jian Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100045 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Ensuring end-to-end cross-layer communication security in military networks by selecting covert schemes between nodes is a key solution for military communication security. With the development of communication technology, covert communication has expanded from the physical layer to the network and application layers, utilizing methods such as artificial noise, private networks, and semantic coding to transmit secret messages. However, as adversaries continuously eavesdrop on specific communication channels, the accumulation of sufficient data may reveal underlying patterns that influence concealment, and establishing a cross-layer covert communication mechanism emerges as an effective strategy to mitigate these regulatory challenges. In this article, we first survey the communication security solution based on covert communication, specifically targeting three typical scenarios: device-to-device, private network communication, and public network communication, and analyze their application scopes. Furthermore, we propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI), highlighting challenges and their solutions. Additionally, a case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication. © 2025, CC BY.

关键词： Generative adversarial networks