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

作者： Jinghui Guan Rui Zhou Wenqiang Pu Qingjiang Shi Tsung-Hui Chang School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Software Engineering Tongji University Shanghai China

Cooperative sensing, a technique employed in cognitive radio (CR) networks for spectrum sensing, exhibits promising potential in bolstering spectrum utilization and enhancing network performance. This approach leverages the information captured by distributed CR users, which is subsequently aggregated at a fusion center. However, the challenges arise when the data are transmitted with low-quality, resulting in the consequential issue of missing data. These factors introduce complexity in detecting primary signals and undermine the reliability of cooperative sensing. In this study, we present a significant advancement in cooperative sensing methodologies by introducing a novel approach: a generalized likelihood ratio test (GLRT) type detector specifically designed to be robust to missing data. More specifically, our proposed robust GLRT detector modifies the computation of the classical GLRT test statistic to accommodate the inherent incompleteness of the data and effectively estimates the desired unknown parameters. Through numerical experiments, we demonstrate the resilience and robustness of our proposed cooperative signal detection method.

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research on Question Matching of Operation System FAQ with Knowledge Graph and Image Information 3

Research on Question Matching of Operation System FAQ with K...

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2022 3rd International Conference on Computer Information and big data Applications, CIBDA 2022

作者： Zhao, Haomin Liu, Yu Gu, Jinguang College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Key Laboralory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Hubei Wuhan China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9783800758760

The FAQ system is to match the questions asked by the user with the questions in the FAQ library, and subsequently return the answers to the questions according to the smenatic similarity between questions. The existing matching methods of question pair are not specially designated for the OS domain and do not incorporate domain knowledge. The screenshots of Operating System provided by user usually contain important information, which can be uesed to improve the effectiveness of question matching method. In this paper, we evaluate the important of text in image with the highlighted evaluation, location evaluation, and text evaluation, and filter out important text for question matching. In order to use knowledge of Operation System, we introduce a pretraining model incorporating the domain knowledge that can be used for problem matching. In the experiment, the method with knowledge graph and image information can fully improve the accuracy rate, recall rate and F1 score of the matching reuslts compared to the mainstream method. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Domain Knowledge

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Generalizable Autonomous Driving System across Diverse Adverse Weather Conditions

arXiv

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arXiv 2024年

作者： Kou, Wei-Bin Zhu, Guangxu Ye, Rongguang Lin, Qingfeng Ren, Zeyi Tang, Ming Wu, Yik-Chung Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

Various adverse weather conditions pose a significant challenge to autonomous driving (AD) street scene semantic understanding (segmentation). A common strategy is to minimize the disparity between images captured in clear and adverse weather conditions. However, this technique typically relies on utilizing clear image as a reference, which is challenging to obtain in practice. Furthermore, this method typically targets a single adverse condition, and thus perform poorly when confronting a mixture of multiple adverse weather conditions. To address these issues, we introduce a reference-free and Adverse weatherImmune scheme (called AdvImmu) that leverages the invariance of weather conditions over short periods (seconds). Specifically, AdvImmu includes three components: Locally Sequential Mechanism (LSM), Globally Shuffled Mechanism (GSM), and Unfolded Regularizers (URs). LSM leverages temporal correlations between adjacent frames to enhance model performance. GSM is proposed to shuffle LSM segments to prevent overfitting of temporal patterns. URs are the deep unfolding implementation of two proposed regularizers to penalize the model complexity to enhance across-weather generalization. In addition, to overcome the over-reliance on consecutive frame-wise annotations in the training of AdvImmu (typically unavailable in AD scenarios), we incorporate a foundation model named Segment Anything Model (SAM) to assist to annotate frames, and additionally propose a cluster algorithm (denoted as SBICAC) to surmount SAM’s category-agnostic issue to generate pseudo-labels. Extensive experiments demonstrate that the proposed AdvImmu outperforms existing state-of-the-art methods by 88.56% in mean Intersection over Union (mIoU). © 2024, CC BY-NC-ND.

关键词： Semantic Segmentation

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Symptom Dimensions and Cognitive Impairments in Individuals at Clinical High Risk for Psychosis

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第6期10卷 646-655页

作者： Zhang, TianHong Xu, LiHua Wei, YanYan Cui, HuiRu Tang, XiaoChen Hu, YeGang Liu, HaiChun Wang, ZiXuan Chen, Tao Yi, ZhengHui Li, ChunBo Wang, JiJun Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Department of Psychology Shanghai Xinlianxin Psychological Counseling Center Shanghai China Big Data Research Laboratory University of Waterloo Waterloo ON Canada Labor and Worklife Program Harvard University Cambridge Massachusetts United States Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University Shanghai China

Background: Understanding the intricate relationships between symptom dimensions, clusters, and cognitive impairments is crucial for early detection and intervention in individuals at clinical high risk for psychosis. This study delves into this complex interplay in a clinical high risk sample with the aim of predicting the conversion to psychosis. Methods: A comprehensive cognitive assessment was performed in 744 clinical high risk individuals. The study included a 3-year follow-up period to allow assessment of conversion to psychosis. Symptom profiles were determined using the Structured Interview for Prodromal Syndromes. By applying factor analysis, symptom dimensions were categorized as dominant negative symptoms (NS), positive symptoms-stressful, and positive symptoms-odd. The factor scores were used to define 3 dominant symptom groups. Latent class analysis (LCA) and the factor mixture model (FMM) were employed to identify discrete clusters based on symptom patterns. The 3-class solution was chosen for the LCA and FMM analysis. Results: Individuals in the dominant NS group exhibited significantly higher conversion rates to psychosis than those in the other groups. Specific cognitive variables, including performance on the Brief Visuospatial Memory Test–Revised (odds ratio = 0.702, p = .001) and Neuropsychological Assessment Battery Mazes Test (odds ratio = 0.776, p = .024), significantly predicted conversion to psychosis. Notably, cognitive impairments associated with NS and positive symptoms-stressful groups affected different cognitive domains. LCA and FMM cluster 1, which was characterized by severe NS and positive symptoms-odd, exhibited more impairments in cognitive domains than other clusters. No significant difference in the conversion rate was observed among the LCA and FMM clusters. Conclusions: These findings highlight the importance of NS in the development of psychosis and suggest specific cognitive domains that are affected by symptom dimensions.

关键词： Negative symptoms Neurocognition Subtype Transition Ultra high risk

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A Q-learning-based Multi-timescale Resilience Enhancement Approach for Power Grids with High Renewables

A Q-learning-based Multi-timescale Resilience Enhancement Ap...

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Power science and Technology (ICPST), IEEE International Conference on

作者： Yanting Huang Qing Zhong Akang Wang Shunjiang Lin Chaoyi Peng Shunbo Lei School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen China School of Electric Power Engineering South China University of Technology Guangzhou China Shenzhen Research Institute of Big Data Shenzhen China Power Dispatch and Control Center China Southern Power Grid Guangzhou China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Power grid resilience is critically undermined by extreme weather events, especially when coupled with uncertain renewable energy generation, which can lead to a severe power outage. When facing such outages, it is essential for system operators to restore damaged lines in a sequence that maximizes the overall power supply. Identifying the optimal restoration sequence can be cast as a multi-stage problem where at each stage a single line is first restored in an hourly basis and then numerous power flow problems within the timescale of minutes are optimized under the resulting network topology. Evaluating the economic effect of restoring a specific line entails the optimization of power flow problems with time-varying renewables, lacking a closed-form expression. To address this, we employ the Q-learning algorithm, utilizing a well-designed Q-function to approximate the effect of restoring each line and embedding such a function into a training process. Simulations on the IEEE 33-Bus and IEEE 39-Bus Systems confirm the efficacy of the proposed approach, showcasing significant enhancements in resilience and recovery capabilities.

关键词： Renewable energy sources Q-learning Heuristic algorithms Power system dynamics Approximation algorithms Power grids Optimization

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Collision-Aware MPCC Integrated with Neural Augmentation for Quadrotor Trajectory Tracking

Collision-Aware MPCC Integrated with Neural Augmentation for...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Hu, Chuanqi Wang, Jin Chen, Yikai Chen, Yonghang Zheng, Zhi Meng, Jun Lu, Guodong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310058 China Zhejiang University Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China National Elite Institute of Engineering Chongqing University Chongqing401135 China School of Mechanical Engineering Zhejiang University Hangzhou310058 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331533892

This paper presents a trajectory tracking algorithm designed for autonomous quadrotors to navigate complex dy-namic environments that is resilient to collision disturbances. Since it combines Model Predictive Contouring Control (MPCC) with the capabilities of neural networks, our method is named MPCC Integrated with Neural Augmentation (MINA). By equip-ping the quadrotor with PVDF piezoelectric sensors, we achieve real-time perception of collision forces and utilize S4D-Conv Adaptive Net Architecture (SANA) for state computation, a network that integrates the advantages of S4D and convolutional kernels to enhance the accuracy of state predictions. We incorporate the predicted results into the MPCC framework to optimize the thrust output of the quadrotor for the next time step, ensuring precise trajectory tracking. Experimental validation demonstrates the reliability and effectiveness of our algorithm, providing an innovative solution for autonomous flight and trajectory tracking of quadrotors in complex dynamic environments. © 2025 IEEE.

关键词： Flight paths

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DDoS Detection and Defense Based on FLAD and SDN

DDoS Detection and Defense Based on FLAD and SDN

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Jie Dong Wenyu Fang Wanling Zheng Jinkun Liu Yanhua Liu College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm.

关键词： Training data privacy Network topology Denial-of-service attack Feature extraction Real-time systems Topology Labeling Computer crime Long short term memory

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Semantics-Guided Diffusion for Deep Joint Source-Channel Coding in Wireless Image Transmission

arXiv

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arXiv 2025年

作者： Zhang, Maojun Wu, Haotian Zhu, Guangxu Jin, Richeng Chen, Xiaoming Gündüz, Deniz The College of Information Science and Electronic Engineering Zhejiang University Hangzhou China The Department of Electrical and Electronic Engineering Imperial College London United Kingdom Shenzhen Research Institute of Big Data Shenzhen China

Joint source-channel coding (JSCC) offers a promising avenue for enhancing transmission efficiency by jointly incorporating source and channel statistics into the system design. A key advancement in this area is the deep joint source and channel coding (DeepJSCC) technique that designs a direct mapping of input signals to channel symbols parameterized by a neural network, which can be trained for arbitrary channel models and semantic quality metrics. This paper advances the DeepJSCC framework toward a semantics-aligned, high-fidelity transmission approach, called semantics-guided diffusion DeepJSCC (SGD-JSCC). Existing schemes that integrate diffusion models (DMs) with JSCC face challenges in transforming random generation into accurate reconstruction and adapting to varying channel conditions. SGD-JSCC incorporates two key innovations: (1) utilizing some inherent information that contributes to the semantics of an image, such as text description or edge map, to guide the diffusion denoising process;and (2) enabling seamless adaptability to varying channel conditions with the help of a semantics-guided DM for channel denoising. The DM is guided by diverse semantic information and integrates seamlessly with DeepJSCC. In a slow fading channel, SGD-JSCC dynamically adapts to the instantaneous signal-to-noise ratio (SNR) directly estimated from the channel output, thereby eliminating the need for additional pilot transmissions for channel estimation. In a fast fading channel, we introduce a training-free denoising strategy, allowing SGD-JSCC to effectively adjust to fluctuations in channel gains. Numerical results demonstrate that, guided by semantic information and leveraging the powerful DM, our method outperforms existing DeepJSCC schemes, delivering satisfactory reconstruction performance even at extremely poor channel conditions. The proposed scheme highlights the potential of incorporating diffusion models in future communication systems. The code and pretrained c

关键词： Channel coding

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Networked ISAC Beamforming Design with Capacity-Limited Fronthaul Links

Networked ISAC Beamforming Design with Capacity-Limited Fron...

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

作者： Kexin Zhang Yanqing Xu Tsung-Hui Chang School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Science & Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This study investigates a networked integrated sensing and communication (ISAC) system, where a central processor coordinates multiple ISAC transmitters and a sensing receiver through capacity-limited fronthaul links. The system aims to serve multiple mobile users and simultaneously sense a point target. The primary objective is to minimize the total transmit power while meeting the minimum signal-to-interference-plus-noise ratio (SINR) requirements for both communication and sensing, as well as adhering to the fronthaul capacity constraints. This leads to a complicated joint fronthaul compression and beamforming design (J-FCBD) problem. Although the J-FCBD problem is challenging to solve, we demonstrate that the optimal fronthaul compression variables can be determined in closed form alongside the beamformers. This novel finding has not been previously reported in the literature. Leveraging this insight, we prove that the remaining problem can be globally solved using the semidefinite relaxation (SDR) technique. Simulation results verify the effectiveness of the proposed design.

关键词： Array signal processing Transmitters Simulation System performance Signal processing algorithms Interference Integrated sensing and communication Solids Speech processing Signal to noise ratio

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kun Liu Min Li Chu He Xu Tian Junbin Mao Min Zeng Jin Liu Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications.

关键词： Performance evaluation Sensitivity Spaceborne radar Millimeter wave radar Depression data models Reliability Monitoring Signal resolution Diseases

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