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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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Enhancing Micro Gesture Recognition for Emotion Understanding via Context-aware Visual-Text Contrastive Learning

arXiv

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arXiv 2024年

作者： Li, Deng Xing, Bohao Liu, Xin School of Electrical and Information Engineering Tianjin University Tianjin300072 China Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta53850 Finland

Psychological studies have shown that Micro Gestures (MG) are closely linked to human emotions. MG-based emotion understanding has attracted much attention because it allows for emotion understanding through nonverbal body gestures without relying on identity information (e.g., facial and electrocardiogram data). Therefore, it is essential to recognize MG effectively for advanced emotion understanding. However, existing Micro Gesture Recognition (MGR) methods utilize only a single modality (e.g., RGB or skeleton) while overlooking crucial textual information. In this letter, we propose a simple but effective visual-text contrastive learning solution that utilizes text information for MGR. In addition, instead of using handcrafted prompts for visual-text contrastive learning, we propose a novel module called Adaptive prompting to generate context-aware prompts. The experimental results show that the proposed method achieves state-of-the-art performance on two public datasets. Furthermore, based on an empirical study utilizing the results of MGR for emotion understanding, we demonstrate that using the textual results of MGR significantly improves performance by 6%+ compared to directly using video as input. Copyright © 2024, The Authors. All rights reserved.

关键词： Gesture recognition

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Tensor Based Sparse Estimation for RIS-Aided Massive MIMO 23

Tensor Based Sparse Estimation for RIS-Aided Massive MIMO

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Li, Weilai Wei, Li Huang, Chongwen Yang, Jun Yuen, Chau Zhang, Zhaoyang School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zte Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China

ISBN: (纸本)9798350325959

Reconfigurable intelligent surface (RIS) is intended as a potential technique for future 6-th Generation wireless communication system. However, acquiring accurate channel state information is challenging since the channel matrices are high-dimensional due to large number of reflective elements (REs) in RIS. In this paper, we investigate the problem of sparse channel estimation (CE) in massive multiple-input multiple-output (MIMO) system equipped with large RIS (IRIS), where there are only few REs that are active during CE phase. We propose a two-stage tensor based CE algorithm that exploits the merits of IRIS. Specifically, IRIS act as reflector in the first stage of CE. Then, we identify the condition for the IRIS to activate few specified REs in the second stage. Simulation results demonstrate that the proposed algorithm outperforms existing CE schemes. © 2023 IEEE.

关键词： massive MIMO RIS Sparse channel estimation tensor decomposition

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Single Block Encoder-Decoder Transformer Model for Multi-Step Traffic Flow Forecasting 6

Single Block Encoder-Decoder Transformer Model for Multi-Ste...

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6th IEEE Symposium on computers and Informatics, ISCI 2024

作者： Omar, Mas Yakub, Fitri Talip, Mohamad Sofian Abu Maslan, Mohd Nazmin Sinha, Vijay Kumar Muljono, Muljono Universiti Teknologi Malaysia Wee Laboratory Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology Department of Electronic System Engineering Kuala Lumpur Malaysia University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Universiti Teknikal Malaysia Faculty of Industrial and Manufacturing Technology and Engineering Melaka Malaysia Chitkara University School of Engineering & Technology Department of Computer Science & Engineering Himachal Pradesh India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350353853

Accurate traffic flow forecasting is crucial for managing and planning urban transportation systems. Despite the widespread use of sequence modelling models like Long Short-Term Memory (LSTM) for this purpose, the potential of Transformer models remains underexplored. This is particularly true for the simplest form of a single block encoder-decoder Transformer model, which can be finely tuned through optimised hyperparameters. This paper examines the performance of a singular horizon-step forecasting method for multi-step traffic flow forecasting using a proposed Single Block Encoder-Decoder Transformer model optimised with a Grid Search algorithm. Results demonstrate that this model can enhance forecasting accuracy compared to the state-of-the-art LSTM model typically used for multi-step forecasting. The model effectively captures long-range temporal dependencies within a single road traffic flow dataset. It was tested on hourly traffic flow data to forecast the next 24 hours for the I5-North freeway in California, sourced from the Caltrans Performance Measurement System. The optimal configuration included an embedding dimension of 32, a feed-forward dimension of 128, and 8 attention heads. Results show a significant improvement, with a 4.7% reduction in Root Mean Squared Error compared to an LSTM model with two hidden layers of 100 neurons each, showcasing the potential of Single Block Encoder-Decoder Transformer models for real-world traffic prediction applications. © 2024 IEEE.

关键词： Mean square error

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Overview Paper Deep Learning for Face Super-Resolution: A Techniques Review

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APSIPA Transactions on Signal and Information Processing 2024年第1期13卷

作者： Zhu, Bolin Zhao, Kanghui Lu, Tao Jiang, Junjun Wang, Zhongyuan Jiang, Kui Xiong, Zixiang Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Computer Science Wuhan University Wuhan China Dept. Electrical and Computer Engineering Texas A&M University College StationTX United States

Face Super-Resolution (FSR) represents a significant branch of image super-resolution, aiming to reconstruct low-resolution face images into high-resolution counterparts. Recently, driven by rapid advancements in deep learning technology, FSR methods using deep learning have achieved notable subjective and objective reconstruction quality, attracting extensive industrial attention. However, detailed classifications of FSR methods remain limited. Therefore, this survey systematically and comprehensively reviews deep learning-based FSR methods. Initially, we introduce the background and technical framework of FSR. Subsequently, we detail the FSR problem definition, alongside commonly used datasets, evaluation metrics, and loss functions. We conduct comprehensive researches in deep learning FSR methods and classify them according to their solution strategies. Within each category, we begin with a general method description, and subsequently introduce representative approaches and discuss their respective pros and cons. Finally, we address current challenges in FSR methods and propose future research directions. © 2024 B. Zhu, K. Zhao, T. Lu, J. Jiang, Z. Wang, K. Jiang and Z. Xiong.

关键词： Contrastive Learning

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A Planar Substrate-Integrated Phased Array With an Over-One-Octave Bandwidth

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IEEE Transactions on Antennas and Propagation 2025年

作者： Sun, Liying Lu, Yunlong Wang, Kai Xu, Jun You, Yang Qu, Shi-Wei Ningbo University Faculty of Electrical Engineering and Computer Sciences Zhejiang Ningbo315211 China Pengcheng Laboratory Shenzhen518500 China Southeast University State Key Laboratory of Millimeter Waves School of Information Science and Engineering Nanjing210096 China School of Electronic Science and Engineering Chengdu611731 China

A new wideband planar substrate-integrated phased array is presented in this paper. The array element is based on an open slot excited by a coaxial probe. A T-shaped stripline with shorting pins is employed in each element to achieve a smooth mode conversion from quasi-coaxial to quasi-parallel plate waveguide in a compact manner. In this case, the element supports a full quasi-TEM mode for transmission and radiation. Additionally, the air-filled holes and metal pins are incorporated into the radiating and feeding structures to shift the undesirable resonance to a higher frequency. By combining these techniques, the impedance bandwidth can be extended to more than one octave. The scanning blindness caused by unexpected guided-wave mode during beam scanning along the H-plane is further analyzed in detail, and removed from the desired frequency range by adjusting the corresponding element period and manipulating the guided-wave characteristics. A 10×12-element phased array prototype covering 8-18 GHz (76.9%) is fabricated and tested. Experimental results demonstrate that the proposed phased array achieves a beam scanning of ± 60° in the E-, H- and D-planes, with no scanning blindness occurring across the operating band. © 1963-2012 IEEE.

关键词： Antenna phased arrays

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High-Fidelity Editable Portrait Synthesis with 3D GAN Inversion

High-Fidelity Editable Portrait Synthesis with 3D GAN Invers...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jindong Xie Jiachen Liu Yupei Lin Jinbao Wang Xianxu Hou Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Information Engineering Guangdong University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The 3D generative adversarial network (GAN) inversion converts an image into 3D representation to attain high-fidelity reconstruction and facilitate realistic image manipulation within the 3D latent space. However, previous approaches face challenges regarding the trade-off between the reconstruction ability and editability. That is, reversing a real-world image to a low-dimensional latent code would inevitably lead to information loss, and achieving a near-perfect reconstruction using high-rate triplane representation often limits the ability to manipulate the image freely in the latent space. To address these issues, we propose a novel latent conditioning encoder-based framework with the alignment between the low-dimensional latent and high-dimensional triplane. A non-semantic guided editing strategy bridges the intrinsic relation between the latent condition and triplane generation, making it possible to edit the high-dimensional representation by latent manipulation. As a result, our method can achieve high-fidelity reconstruction and editing simultaneously by directly controlling the latent code. Experimental results demonstrate that our approach excels in reconstruction and editing quality compared to previous 3D inversion methods. Furthermore, our method can also edit even real faces with large poses and out-of-domain cases.

关键词： Bridges Three-dimensional displays Codes Signal processing Generative adversarial networks Vectors Acoustics Speech processing Image reconstruction Faces

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Enhancing Long-and Short-Term Representations for Next POI Recommendations via Frequency and Hierarchical Contrastive Learning 39

Enhancing Long-and Short-Term Representations for Next POI R...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Jiajie Sang, Yu Zhang, Peng-Fei Wang, Jiaan Qu, Jianfeng Li, Zhixu School of Computer Science and Technology Soochow University Suzhou China School of Artificial Intelligence and Computer Science Jiangnan University China School of Electrical Engineering and Computer Science University of Queensland Australia School of Information Renmin University of China Beijing China Renmin University of China Suzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

ISBN: (纸本)157735897X

Next POI recommendation aids users in predicting their destinations of interest and plays an increasingly vital role in location-based social services. Recent works focus on analyzing both long-term and short-term interests in POI recommendation to gain a deeper understanding of user profiles. However, these methods for modeling long-term user's sequences primarily rely on the Transformer model, which functions as a low-pass filter, often leading to the loss of high-frequency information. Additionally, long-term and short-term sequences are typically modeled independently, with short-term sequences often defined solely by the most recent check-ins, overlooking their interactions and dependencies. Therefore, we propose Enhancing Long-and Short-Term Representations for Next POI Recommendations via Frequency and Hierarchical Contrastive Learning (FHCRec). FHCRec captures both high-frequency and low-frequency information in long-term sequences to model richer long-term user's preference representations. Moreover, it harnesses the characteristics of the short-term subsequences embedded within long-term sequences to enhance short-term preference characterization via local and global hierarchical contrastive learning, resulting in more personalized short-term preferences. The enhanced long-term and short-term preferences are integrated to improve model recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Photonic-assisted multi-functional system for simultaneous distance, velocity and frequency measurement 14

Photonic-assisted multi-functional system for simultaneous d...

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14th International Photonics and Optoelectronics Meetings, POEM 2022

作者： Huang, Zequan Wo, Jianghai Yao, Jianping Institute of Photonics Technology Jinan University GD Guangzhou511442 China Microwave Photonics Research Laboratory School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada

ISBN: (纸本)9781510664050

A photonic-assisted multi-functional system that can implement simultaneous distance, velocity and frequency measurement is proposed and experimentally demonstrated. The distance and the radial velocity of a moving target are measured by transmitting a complementarily (V-shaped) linear-frequency-modulated microwave waveform, and by mixing the reflected microwave waveform with the transmitted microwave waveform, two de-chirped waveforms with different carrier frequencies are generated. The distance and the radial velocity are measured by analyzing the de-chirped microwave waveforms. Frequency measurement can also be performed by the system which is done by modulating a received unknown signal on an optical carrier which is mixed with a frequency-sweeping light. By passing the mixed signal through an electrical bandpass filter, short electrical pulses are generated. By measuring the time delay difference between adjacent pulses, the frequency of the unknown signal is measured. The operation of the proposed system is evaluated experimentally. The results show that the distance and velocity can be measured with mean absolute errors of 2.95 cm and 6.09 cm/s, respectively, with a range resolution better than 5.5 cm. The frequency measurement function with the capability of implementing single-tone, multi-tone and broadband signals is verified for an input signal with a frequency ranging from 1 to 20 GHz. The mean absolute error of the frequency measurement is 34.22 MHz. © 2023 SPIE.

关键词： Microwave photonics

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A Networked Competitive Multi-Virus SIR Model: Analysis and Observability

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IFAC-PapersOnLine 2022年第13期55卷 13-18页

作者： Ciyuan Zhang Sebin Gracy Tamer Başar Philip E. Paré Elmore Family School of Electrical and Computer Engineering at Purdue University School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Coordinated Science Laboratory University of Illinois Urbana-Champaign

This paper proposes a novel discrete-time multi-virus SIR (susceptible-infected-recovered) model that captures the spread of competing SIR epidemics over a population network. First, we provide a sufficient condition for the infection level of all the viruses over the networked model to converge to zero in exponential time. Second, we propose an observation model which captures the summation of all the viruses’ infection levels in each node, which represents the individuals who are infected by different viruses but share similar symptoms. We present a sufficient condition for the model to be locally observable. We propose a Luenberger observer for the system state estimation and show via simulations that the estimation error of the Luenberger observer converges to zero before the viruses die out.

关键词： Biological network epidemics dynamics

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