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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

ISBN: (纸本)9781665435413

The sixth generation (6G) mobile wireless networks are expected to provide a wide range of massive ultra-reliable and low-latency communications (mURLLC) for multimedia transmissions, which require extremely stringent delay and error-rate bounded quality of services (QoS). Massive multiple-input and multiple-output (MIMO) communication has been recognized as one of the promising techniques to support mURLLC thanks to its advantages in the beamforming gain and spatial multiplexing, etc. On the other hand, finite blocklength coding (FBC) based small packets communication technique has recently shown to be able to support the statistical delay and error-rate bounded QoS provisioning. However, how to achieve the statistical delay and error-rate bounded QoS provisioning through massive MIMO techniques has not been sufficiently studied. In this paper, we propose to apply the ∊-effective capacity into massive MIMO communications to achieve the statistical delay and error-rate bounded provisioning for mURLLC traffic transmissions. First, we develop the FBC based scheme over a Nakagami-m fading wireless channel. Then, using the developed FBC based system model, we derive a closed-form expression for ∊-effective capacity of massive MIMO communications, representing the maximum packet's arrival rate that a wireless channel can support under a given stringency of delay requirement and a constrained decoding error-rate. Finally, we use numerical analyses to validate and evaluate our proposed statistical delay and error-rate bounded QoS provisioning scheme over massive MIMO communication networks.

关键词： 6G mobile communication Fading channels Closed-form solutions Wireless networks Quality of service Massive MIMO Delays

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1 Lyapunov Stability Analysis for Controlling UAV Trajectory Over 6G Wireless Networks

1 Lyapunov Stability Analysis for Controlling UAV Trajectory...

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MILCOM, Military Communications Conference

作者： Xi Zhang Qixuan Zhu Laboratory Department of Electrical and Computer Engineering Networking and Information Systems Texas A&M University College Station TX USA

With the commercial realization of the fifth generation (5G) wireless networks, the development of the sixth generation (6G) wireless networks has attracted considerable research attention. To meet the requirement of providing ubiquitous coverage, unmanned aerial vehicle (UAV) plays the key role in extending the line-of-sight area, and thus, has been proposed as a promising candidate technique in 6G wireless networks However, how to design an optimal UAV trajectory when it is moving and communicating with a base station or a mobile user in the network and how to guarantee the stability of the UAV's trajectory remain open problems. To address these issues, in this paper we design an optimal UAV trajectory, obtain the expression of the UAV's trajectory error, and propose a stability analysis mechanism to show that the UAV trajectory error is globally and asymptotically stable. First, we design the UAV optimal trajectory to maximize the communication performance while minimizing the energy consumption. Then, we derive a closed-form expression for the UAV trajectory in the transient state, which results in major trajectory errors, including the critical damping, underdamping, and overdamping. Third, we employ the control Lyapunov function to show that our developed UAV trajectory error correction scheme is globally and asymptotically stable under the feedback control law. Finally, we use numerical analyses to validate and evaluate our proposed UAV trajectory error control scheme.

关键词： 6G mobile communication Wireless networks Autonomous aerial vehicles Stability analysis Trajectory Error correction Feedback control

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Artificial intelligence in cancer target identification and drug discovery

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Signal Transduction and Targeted Therapy 2022年第6期7卷 1951-1974页

作者： Yujie You Xin Lai Yi Pan Huiru Zheng Julio Vera Suran Liu Senyi Deng Le Zhang College of Computer Science Sichuan UniversityChengdu 610065China Laboratory of Systems Tumor Immunology Friedrich-Alexander-Universität Erlangen-Nürnberg(FAU)and Universitätsklinikum ErlangenErlangen 91052Germany Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesRoom D5131068 Xueyuan AvenueShenzhen University TownShenzhen 518055China School of Computing Ulster UniversityBelfast BT151EDUK Institute of Thoracic Oncology Department of Thoracic SurgeryWest China HospitalSichuan UniversityChengdu 610065China Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China Key Laboratory of Systems Health Science of Zhejiang Province Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as ***,we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover ***,we describe the scope of artificial intelligence biology analysis for novel anticancer target ***,we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence ***,we showcase the applications of artificial intelligence approaches in cancer target identification and drug *** together,the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer,thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

关键词： drugs cancer drug

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DSDFormer: An Innovative Transformer-Mamba Framework for Robust High-Precision Driver Distraction Identification

arXiv

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arXiv 2024年

作者： Chen, Junzhou Zhang, Zirui Yu, Jing Huang, Heqiang Zhang, Ronghui Xu, Xuemiao Sheng, Bin Yan, Hong Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou510275 China The School of Computer Science and Engineering South China University of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University China The Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong

Driver distraction remains a leading cause of traffic accidents, posing a critical threat to road safety globally. As intelligent transportation systems evolve, accurate and real-time identification of driver distraction has become essential. However, existing methods struggle to capture both global contextual and fine-grained local features while contending with noisy labels in training datasets. To address these challenges, we propose DSDFormer, a novel framework that integrates the strengths of Transformer and Mamba architectures through a Dual State Domain Attention (DSDA) mechanism, enabling a balance between long-range dependencies and detailed feature extraction for robust driver behavior recognition. Additionally, we introduce Temporal Reasoning Confident Learning (TRCL), an unsupervised approach that refines noisy labels by leveraging spatiotemporal correlations in video sequences. Our model achieves state-of-the-art performance on the AUC-V1, AUC-V2, and 100-Driver datasets and demonstrates real-time processing efficiency on the NVIDIA Jetson AGX Orin platform. Extensive experimental results confirm that DSDFormer and TRCL significantly improve both the accuracy and robustness of driver distraction detection, offering a scalable solution to enhance road safety. Copyright © 2024, The Authors. All rights reserved.

关键词： Highway accidents

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Augmenting Short Enrollment Speech via Synthesis for Target Speaker Extraction

Augmenting Short Enrollment Speech via Synthesis for Target ...

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

作者： Zikang Huang Jingru Lin Meng Ge Yu Jiang Xiaobao Wang Longbiao Wang Jianwu Dang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Department of Electrical and Computer and Engineering National University of Singapore Huiyan Technology (Tianjin) Co. Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

A high-quality enrollment speech is crucial to target speaker extraction (TSE), since it provides essential cues for identifying the target speaker in the mixture. However, real applications usually only permit a short enrollment speech, e.g. a wakeup word for a mobile device, that provides limited cues. To address this issue, we propose an enrollment augmentation strategy that allows us to enrich the limited enrollment speech with massive text data through speech synthesis. By doing so, the extended enrollment speech contains enhanced speaker timbre and phonetic content which leads to better extraction quality. Furthermore, we propose a training data augmentation strategy to improve the model’s robustness and generalization in short enrollment speech scenarios. Experiments on Libri2Mix demonstrate that our proposed strategies bring a significant improvement in extreme scenarios where only 0.5s and 1-word enrollment speech is provided. We also release our code at https://***/HuangZikang-TJU/Aug4TSE.

关键词： Training data Speech enhancement Phonetics Signal processing Speech Robustness Mobile handsets Data models Timbre Speech synthesis

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Joint Beamforming and Antenna Design for Near-Field Fluid Antenna System

arXiv

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arXiv 2024年

作者： Chen, Yixuan Chen, Mingzhe Xu, Hao Yang, Zhaohui Wong, Kai-Kit Zhang, Zhaoyang Hangzhou310027 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom Yonsei Frontier Laboratory Yonsei University Seoul03722 Korea Republic of

In this letter, we study the energy efficiency maximization problem for a fluid antenna system (FAS) in near field communications. Specifically, we consider a point-to-point near-field system where the base station (BS) transmitter has multiple fixed-position antennas and the user receives the signals with multiple fluid antennas. Our objective is to jointly optimize the transmit beamforming of the BS and the fluid antenna positions at the user for maximizing the energy efficiency. Our scheme is based on an alternating optimization algorithm that iteratively solves the beamforming and antenna position subproblems. Our simulation results validate the performance improvement of the proposed algorithm and confirm the effectiveness of FAS. Copyright © 2024, The Authors. All rights reserved.

关键词： Beam forming networks

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Wi-Wheat+: Contact-free wheat moisture sensing with commodity WiFi based on entropy

Wi-Wheat+: Contact-free wheat moisture sensing with commodit...

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作者： Yang, Weidong Shen, Erbo Wang, Xuyu Mao, Shiwen Gong, Yuehong Hu, Pengming College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China College of International Education Kaifeng University Henan Kaifeng475004 China Knight Foundation School of Computing & Information Sciences Florida International University MiamiFL33199 United States Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

In this paper, we propose a contact-free wheat moisture monitoring system, termed Wi-Wheat+, to address the several limitations of the existing grain moisture detection technologies, such as time-consuming process, expensive equipment, low accuracy, and difficulty in real-time monitoring. The proposed system is based on Commodity WiFi and is easy to deploy. Leveraging WiFi CSI data, this paper proposes a feature extraction method based on multi-scale and multi-channel entropy. The feasibility and stability of the system are validated through experiments in both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) scenarios, where ten types of wheat moisture content are tested using multi-class Support Vector Machine (SVM). Compared with the Wi-Wheat system proposed in our prior work, Wi-Wheat+ has higher efficiency, requiring only a simple training process, and can sense more wheat moisture content levels. © 2022 Chongqing University of Posts and Telecommunications

关键词： Channel state information (CSI) Multi-class support vector machine (SVM) Multi-scale entropy Radio frequency (RF) sensing WiFi

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Correction to: A comprehensive performance evaluation of container migration strategies

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computing 2025年第4期107卷 1-2页

作者： Feitosa, Leonel Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Fé, Iure Silva, Luis Guilherme Callou, Gustavo Carvalho, Juliana Leão, Erico Nguyen, Tuan Anh Rego, Paulo Silva, Francisco Airton Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piauí (UFPI) Picos Brazil Department of Computing Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Software Engineering Faculty of Information Technology University of Industry and Trade (HUIT) Ho Chi Minh City Vietnam Department of Computer Science Federal University of Ceará (UFC) Fortaleza Brazil

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Exploring Data Geometry for Continual Learning

arXiv

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arXiv 2023年

作者： Gao, Zhi Xu, Chen Li, Feng Jia, Yunde Harandi, Mehrtash Wu, Yuwei Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Electrical and Computer Systems Eng. Monash University Data61 Australia

Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used Euclidean space cannot gracefully capture non-Euclidean geometric structures of data, leading to inferior results. In this paper, we study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and prevents forgetting by keeping geometric structures of old data into account. In doing so, making use of the mixed curvature space, we propose an incremental search scheme, through which the growing geometric structures are encoded. Then, we introduce an angular-regularization loss and a neighbor-robustness loss to train the model, capable of penalizing the change of global geometric structures and local geometric structures. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space. Copyright © 2023, The Authors. All rights reserved.

关键词： Geometry

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Enhanced Consumer Healthcare Data Protection through AI-Driven TinyML and Privacy-Preserving Techniques

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IEEE Access 2025年 13卷 97428-97440页

作者： Aanjankumar, S. Muchahari, Monoj Kumar Urooj, Shabana Kaur, Ishmeet Dhanaraj, Rajesh Kumar Mengash, Hanan Abdullah Poonkuntran, S. Kaveri, Parag Ravikant VIT Bhopal University School of Computing Science and Engineering Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Institute of Engineering and Management University of Engineering and Management Department of Computer Application and Science Kolkata700091 India Princess Nourah bint Abdulrahman University Department of Electrical Engineering College of Engineering P.O. Box 84428 Riyadh11671 Saudi Arabia VIT Bhopal University School of Computing Science and Artificial Intelligence Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia

In the contemporary digital landscape, securing healthcare data stored on mobile devices has become imperative due to the increasing prevalence of cyberattacks. Healthcare data, often vulnerable to breaches, requires robust privacy-preserving mechanisms to ensure secure storage and sharing across various platforms. This study proposes a novel approach integrating TinyML (machine learning) with federated learning (FL) and differential privacy (DP) to enhance the security and privacy of healthcare data on resource-constrained mobile devices. The proposed TinyML model processes patient data, including ECG readings and cardiac arrhythmia diagnoses, directly on handheld devices, enabling real-time analysis with minimal resource consumption. Federated learning facilitates decentralized training of models on local devices, ensuring that sensitive data remains on user devices while model parameters are aggregated centrally. Differential privacy further strengthens security by introducing controlled noise into the data, safeguarding against malicious attacks without compromising data utility. The system demonstrates a high accuracy of 99%, significantly outperforming traditional models such as Decision Tree (76.0%), Random Forest (98.1%), and Federated Learning with Deep Q (95%). The proposed framework offers a scalable and efficient solution for real-time healthcare data processing, ensuring data privacy and security across distributed environments. Experimental results validate the model’s effectiveness in safeguarding healthcare data, with implications for broader applications in secure, privacy-preserving medical data analysis. © 2013 IEEE.

关键词： Electrocardiography

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