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Examining the Fourier Spectrum of Speech Signal from a Time-Frequency Perspective for Automatic Depression Level Prediction

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IEEE Transactions on Affective Computing 2025年

作者： Niu, Mingyue Tao, Jianhua He, Yongjun Zhang, Shiqing Li, Ming YanShan University School of Information Science and Engineering Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using convolutional neural networks or multilayer perceptrons. Therefore, they ignore the complex element composition and time-frequency attributes of Fourier spectra, which is not conducive to capturing the differences among individuals with different depression levels. For this reason, we construct a Time-Frequency Self-Embedding (TFSE) module, which not only stores the correlation relationship among real (imaginary) parts of Fourier spectra of different subjects from the time-frequency perspective, but also maintain the physical properties of data through the weight embedding process. Besides, Global Average Pooling (GAP) or linear layers are difficult to balance both temporal and frequency dimensions in the vectorization process. Therefore, we construct a Time-Frequency Tensor Vectorization (TFTV) module, which summarizes each channel along time and frequency dimensions, and then generates the vectorization result by integrating various channels. In this way, we combine TFSE and TFTV modules to form our SpectrumFormer model for predicting depression levels. Evaluation indicators on AVEC 2013 and AVEC 2014 depression databases imply the progressiveness of our model. © 2010-2012 IEEE.

关键词： Depression level prediction Real and imaginary parts Tensor vectorization Time-frequency attributes

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Active Power Control for Off-Shore Wind Turbines Against FDI Attack

Active Power Control for Off-Shore Wind Turbines Against FDI...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Xuguo Jiao Hao Luo Liuyang Jiang Lin Wang Xin Wang Zuqiang Huang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Information and Control Engineering Qingdao University of Technology Qingdao China Zhejiang Windey Co. Ltd. Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Civil Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Recently, offshore wind power has rapidly developed due to its low environmental interference and abundant wind resources. However, due to its remote geographical location and high demand for communication infrastructure, off-shore wind turbines (OWTs) are vulnerable to potential threats from false data injection (FDI) attack. FDI attack can interfere with active power control strategies by tampering with the rotor speed data measured by sensors and pose a threat to the stability of the power grid. To ensure that OWTs can accurately track the active power instructions set by wind farm's control center under FDI attack, this paper proposes a rotor speed control (RSC) strategy based on Kalman filtering technology (RSC- KF). This strategy can estimate the rotor speed of the wind turbine in real-time, and then a residual detection and compensation mechanism is designed to effectively detect and correct attacked rotor speed information caused by FDI attack. Further, based on the corrected rotor speed, the RSC method is developed, which can flexibly switch control strategies according to different rotor speed values, ensuring the efficient operation of OWTs under different wind speed conditions. The proposed approach's effectiveness is proved via simulations conducted on the OpenFAST platform.

关键词： Filtering Velocity control Power control Rotors Power system stability Stability analysis Real-time systems Wind turbines Kalman filters Cyberattack

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HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection

arXiv

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arXiv 2024年

作者： Xu, Shibiao Zheng, ShuChen Xu, Wenhao Xu, Rongtao Wang, Changwei Zhang, Jiguang Teng, Xiaoqiang Li, Ao Guo, Li Artificial Intelligence Beijing University of Posts and Telecommunications China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China School of Computer Science and Technology Harbin University of Science and Technology China

Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels. However, it encounters difficulties due to the diminutive size of the objects and the generally complex backgrounds in infrared images. In this paper, we propose a deep learning method, HCF-Net, that significantly improves infrared small object detection performance through multiple practical modules. Specifically, it includes the parallelized patch-aware attention (PPA) module, dimension-aware selective integration (DASI) module, and multi-dilated channel refiner (MDCR) module. The PPA module uses a multi-branch feature extraction strategy to capture feature information at different scales and levels. The DASI module enables adaptive channel selection and fusion. The MDCR module captures spatial features of different receptive field ranges through multiple depth-separable convolutional layers. Extensive experimental results on the SIRST infrared single-frame image dataset show that the proposed HCF-Net performs well, surpassing other traditional and deep learning models. Code is available at https://***/zhengshuchen/HCFNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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100 Gb/s NRZ OOK signal regeneration on a silicon chip

100 Gb/s NRZ OOK signal regeneration on a silicon chip

引用

CLEO: Science and Innovations, S and I 2022

作者： Wen, Hua Shun Cui, Jia Bin Zhou, Heng Chen, Yin Fang Jin, Ya Xu, Bo Rui Zhai, Kun Peng Sun, Jia Zheng Guo, Yuan Yuan Wu, Yan Ran Chen, Wen Chen, Wei Wang, Xin Zhu, Ning Hua Lu, Guo-Wei Ji, Gui Jun Zhou, Dennis Chi Cheng, Yiu-Kwok Yang, Da-Quan Li, Ming State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Key Laboratory of Optical Fiber Sensing and Communication Networks School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Division of Computer Engineering The University of Aizu Fukushima965-8580 Japan Ltd. Zhuhai519000 China

All-optical regeneration of 100 Gb/s NRZ OOK signal with 3.6 dB extinction ratio and 0.5 dB quality factor improvements is experimentally demonstrated using a silicon waveguide with reverse-biased p-i-n junction for t... 详细信息

ISBN: (纸本)9781557528209

关键词： Amplitude shift keying

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QoE-Aware Latency Optimization in Semantic Transmission Empowered Edge Assisted AR

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IEEE Transactions on Vehicular Technology 2025年

作者： Li, Yang Wu, Yuan Xing, Huanlai Feng, Li Wang, Tian Jia, Weijia Southwest Jiaotong University School of Computer and Artificial Intelligent Chengdu611756 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Macau China Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519087 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

Augmented reality (AR) enhances human perception by overlaying digital information in the real environment and can be applied to various fields such as education, healthcare, and industry. To relieve the computing pressure of resourcelimited AR devices, edge assisted AR has attracted significant attention in recent years. It enables AR devices to offload the computation-intensive environment analysis tasks to the edge server and to download the point-based 3D virtual objects for rendering. Although this method can effectively reduce the processing latency, it may introduce a long transmission latency. To address this issue, this work proposes a semantic communication empowered edge assisted AR to reduce the quality of experience (QoE)-aware latency. To this end, the AR devices first utilize an image encoder to extract semantic features of source images. Due to the importance difference of extracted features and limited communication resources, the feature selection and bandwidth allocation are explored to improve transmission efficiency. In addition, when the edge server completes the generation of pointbased 3D virtual objects, it employs a transformer-based encoder to mask a proportion of points and learn a high-level representation of unmasked points. In this way, low-complexity semantic information of 3D objects is sent to the AR devices, which can reduce the transmission overhead of 3D objects. Moreover, the mask ratio of the point encoder is further optimized to balance the reconstructed quality and transmission latency of 3D objects. Therefore, this work aims to minimize the overall QoE-aware latency of edge assisted AR by jointly optimizing the semantic feature selection, mask ratio, and bandwidth allocation. A convexbased improved Harris Hawks optimization algorithm is designed to solve the formulated problem. Experimental results demonstrate that our semantic communication empowered edge assisted AR can effectively reduce the overall latency and outperform

关键词： Augmented reality

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LEGNN: A Label Ensemble Perspective for Training a Label-Noise-Resistant GNN with Reduced Complexity

arXiv

引用

arXiv 2024年

作者： Zhao, Rui Shi, Bin Liang, Zhiming Ruan, Jianfei Dong, Bo Lin, Lu School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China School of Distance Education Xi'an Jiaotong University China College of Information Sciences and Technology Penn State University United States

Graph Neural networks (GNNs) have been widely employed for semi-supervised node classification tasks on graphs. However, the performance of GNNs is significantly affected by label noise, that is, a small amount of incorrectly labeled nodes can substantially misguide model training. Mainstream solutions define node classification with label noise (NCLN) as a reliable labeling task, often introducing node similarity with quadratic computational complexity to more accurately assess label reliability. To this end, in this paper, we introduce the Label Ensemble Graph Neural Network (LEGNN), a lower complexity method for robust GNNs training against label noise. LEGNN reframes NCLN as a label ensemble task, gathering informative multiple labels instead of constructing a single reliable label, avoiding high-complexity computations for reliability assessment. Specifically, LEGNN conducts a two-step process: bootstrapping neighboring contexts and robust learning with gathered multiple labels. In the former step, we apply random neighbor masks for each node and gather the predicted labels as a high-probability label set. This mitigates the impact of inaccurately labeled neighbors and diversifies the label set. In the latter step, we utilize a partial label learning based strategy to aggregate the high-probability label information for model training. Additionally, we symmetrically gather a low-probability label set to counteract potential noise from the bootstrapped high-probability label set. Extensive experiments on six datasets demonstrate that LEGNN achieves outstanding performance while ensuring efficiency. Moreover, it exhibits good scalability on dataset with over one hundred thousand nodes and one million edges. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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GrayINT – Detection and Localization of Gray Failures via Hybrid In-band Network Telemetry

GrayINT – Detection and Localization of Gray Failures via H...

引用

Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Kuichao Zhang Wei Su Huiling Shi Kai Zhang Wei Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China National Engineering Laboratory for Next Generation Internet Interconnection Devices (NGIID) School of Electronics and Information Engineering Beijing Jiaotong University Beijing China

As the underlying infrastructure, data center networks (DCNs) provide an important platform for cloud computing and next-generation networks. However, due to the influence of many factors, such as network, hardware, and software, various failures may occur during the operation of the DCN, which brings great challenges to its reliability. Particularly, a more insidious type of network failure, known as gray failure, presents a significant threat to system stability. In response to this challenge, in this work, we propose GrayINT, a gray failure detection and localization system that leverages In-band Network Telemetry (INT) technology to monitor all paths within the data center network in real-time. To verify the feasibility of our design, we build a fat-tree data center network on a virtual network testbed. A large number of experimental results demonstrate that our system can monitor device and link status in real-time, achieving gray failure detection and localization.

关键词：

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A Blockchain-Enabled Secure and Decentralized ADS-B System for Intelligent Vehicles With Robust Authentication

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IEEE Transactions on Vehicular Technology 2025年

作者： Yao, Chong Zhang, Xuejun Liu, Yizhong Zhao, Boyu Tiwari, Prayag Kumar, Neeraj Beihang University State Key Laboratory of CNS/ATM the School of Electronic Information Engineering China Beihang University School of Cyber Science and Technology Beijing China Halmstad University School of Information Technology Sweden Thapar Institute of Engineering and Technology CSED Punjab Patiala India UEHS Poland Imam Abdulrahman Bin Faisal University Department of Networks and Communications the College of Computer Science and Information Technology Dammam Saudi Arabia

As the next-generation air traffic surveillance technology, the Automatic Dependent Surveillance-Broadcast (ADS-B) system plays a critical role in broadcast communication for intelligent vehicles such as unmanned aerial vehiclesand aircraft. However, the unauthenticated channels expose the ADS-B system to a high risk of attacks. Besides, the centralization of intelligent vehicles management center is susceptible to single point failures and performance bottlenecks. In this paper, we propose a robust and distributed intelligent vehicles management architecture leveraging blockchain technology to resolve security and performance issues lying in the registration, update, and revocation of intelligent vehicles' identity public keys. Then, we leverage certificate-less short signature (CLSS) to achieve the dynamic identity verification of ADS-B system participants while ensuring message integrity, non-repudiation, and compatibility with low-data-bit message packets. Furthermore, to enhance intelligent vehicles transaction process efficiency within the proposed architecture, we develop an Verifiable Random Function-based Byzantine Fault Tolerance protocol (VRBFT) combining threshold signatures. Finally, through a comprehensive security analysis and feasibility assessment, the results indicate that authentication requires less than 30 ms, block generation occurs within 200 ms, and the system exhibits high throughput and low latency. These findings demonstrate that our solution significantly enhances the security of the ADS-B system and offers a practical and promising approach for real-time, large-scale intelligent vehicle management. © 2025 IEEE.

关键词： Blockchain

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A Survey on Extreme Multi-label Learning

arXiv

引用

arXiv 2022年

作者： Wei, Tong Mao, Zhen Shi, Jiang-Xin Li, Yu-Feng Zhang, Min-Ling The School of Computer Science and Engineering Southeast University Nanjing210096 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The National Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210023 China

Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good performance in various tasks, they implicitly assume the size of target label space is not huge, which can be restrictive for real-world scenarios. Moreover, it is infeasible to directly adapt them to extremely large label space because of the compute and memory overhead. Therefore, eXtreme Multi-label Learning (XML) is becoming an important task and many effective approaches are proposed. To fully understand XML, we conduct a survey study in this paper. We first clarify a formal definition for XML from the perspective of supervised learning. Then, based on different model architectures and challenges of the problem, we provide a thorough discussion of the advantages and disadvantages of each category of methods. For the benefit of conducting empirical studies, we collect abundant resources regarding XML, including code implementations, and useful tools. Lastly, we propose possible research directions in XML, such as new evaluation metrics, the tail label problem, and weakly supervised XML. © 2022, CC BY.

关键词： XML

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Demonstration of Orbital-Angular-Momentum-Based Optical Switching Using Dual-Area Mirrors

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Optics and Photonics Journal 2021年第8期11卷 351-359页

作者： Shengtao Chen Xizheng Ke Xi’an University of Technology School of Automation and Information Engineering Xi’an China Shaanxi Civil-Military Integration Key Laboratory of Intelligence Collaborative Networks Xi’an China

We have designed an optical separation device called dual-area mirror for the data link of orbital angular momentum (OAM) multiplexing. Experiments show that the OAM multiplexed beams can be switched by using dual-area mirrors, using OOK to modulate four channels separately to form two inputs, channel A and channel B. There are two OAM beams that are multiplexed in each of channel A and channel B, using a spatial light modulator (SLM) to convert the OAM multiplexed beams in each channel. One of the beams is converted into a Gaussian beam, and then separated by a dual-area mirror, so as to realize the switch of a beam carrying different data in the two channels. Then these channels are detected. The waveform indicates that the switch is successful, and the measured optical power indicates that the dual-area mirror can reduce the bit error rate in the communication links. In addition, the device reduces the experimental cost, is easy to implement, is easy to integrate, and increases the angle between the separated beams.

关键词： Optical Communication Dual-Area Mirror Switching Optical Network

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