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2012 national conference on information technology and computer science, CITCS 2012

ISBN: (纸本)9789491216381

The proceedings contain 271 papers. The topics discussed include: application of ant colony algorithm in fingerprint identification system based on Internet of things;research on lash-up maintenance technology of engineering equipment;the research of future-market-like supervision of the exchange of cultural products and artworks;applicability evaluation to capital asset pricing model;the innovative study of Jilin province's urban integration development;the research of online transaction risk in service provider to consumer;innovation of emergency management establishment of new mode for holographic command and collaborative work;the market competition, law enforcement, and banking system's efficiency in China;mutual authentication scheme for multi-server environment without using smart cards;and research on data acquisition and signal processing of fault diagnosis system for electromechanical equipment.

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Proceedings of 2015 2nd national Foundation for science and technology Development conference on information and computer science, NICS 2015

Proceedings of 2015 2nd National Foundation for Science and ...

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2nd national Foundation for science and technology Development conference on information and computer science, NICS 2015

ISBN: (纸本)9781467366397

The proceedings contain 57 papers. The topics discussed include: checking topological integrity constraints imposed on objects in real-time databases;reversible image watermarking on prediction errors by balance the capacity of negative and positive histogram;an analysis of persuasive text passwords;hand gesture recognition based on depth image using kinect sensor;multi-tenant web application framework architecture pattern;integrating and checking access permissions in object oriented models;obstacle detection and warning for visually impaired people based on electrode matrix and mobile kinect;EduICR - an intelligent call routing system;a robust and effective algorithmic framework for incomplete educational data clustering;stochastic gradient descent support vector clustering;adaptable linear support vector machine;using feature of nose and HAT-MARS descriptor for face recognition with pose variance;and design of a loop patch antenna system with improved bandwidth for 2.4-GHz WLAN applications.

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CTIC 2012 - Proceedings of the 1st national conference on computer science and information and Communication Technologies

CTIC 2012 - Proceedings of the 1st National Conference on Co...

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1st national conference on computer science and information and Communication Technologies, CTIC 2012

The proceedings contain 13 papers. The topics discussed include: ontology and automatic code generation on modeling and simulation;a graphical tool for automatic generation of OWL ontologies;support vector machine based fingerprint identification;multimodal biometrics system's resistance to noise (fingerprint and voice);global and local sensor clustering using genetic algorithms and fuzzy logic;mediator IP telephony;fractal image compression using rational numbers;and parallel model by graph transformation applied to AGG tool.

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NICS 2016 - Proceedings of 2016 3rd national Foundation for science and technology Development conference on information and computer science

NICS 2016 - Proceedings of 2016 3rd National Foundation for ...

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3rd national Foundation for science and technology Development conference on information and computer science, NICS 2016

ISBN: (纸本)9781509020980

The proceedings contain 45 papers. The topics discussed include: a method for automated unit testing of C programs;study on a two-loop control architecture to balance an inertia wheel pendulum;transmit antenna selection protocols in random cognitive networks under impact of hardware impairments;measurements on indoor channel characteristics using wideband MIMO antennas;optimal precoder designs for energy-efficiency maximization in secure MIMO systems;computational method for a fourth order nonlinear elliptic boundary value problem;POCAD: a novel payload-based one-class classifier for anomaly detection;an effective and efficient hash-based algorithm for time series discord discovery;approximation of triangular b-spline surfaces by local geometric fitting algorithm;secure distributed switch-and-stay combining networks: secure outage probability analysis;secrecy performance analysis of energy harvesting wireless networks with multiple power transfer stations and destinations in the presence of multiple eavesdroppers;impact of hardware impairments on secrecy performance of multi-hop relay networks in presence of multiple eavesdroppers;AF relay-assisted MIMO/FSOIQAM systems in gamma-gamma fading channels;and adaptive methods for job recommendation based on user clustering.

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Exploring Impacts of Age of information on Data Accuracy for Wireless Sensing Systems: An information Entropy Perspective

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4907-4924页

作者： Yang, Yaoqi Du, Hongyang Xiong, Zehui Xu, Renhui Niyato, Dusit Han, Zhu Army Engineering University of PLA College of Communications Engineering Nanjing210000 China University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore Singapore NTU College of Computing and Data Science Singapore University of Houston Department of Electrical and Computer Engineering Houston77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Wireless sensing systems have been employed in the field of healthcare, environment monitoring, and smart agriculture, etc. Since the freshness and accuracy indicators of the sensing data are critical to wireless sensing systems, it is of great significance to ensure their performances simultaneously, i.e., the Age of information (AoI) and information entropy of the sensing data should be jointly optimized. In this regard, we first establish the wireless sensing system models, including AoI and information entropy expressions. Next, from the information entropy viewpoint, we theoretically analyze an impact of the AoI on data accuracy. Then, we formulate the joint optimization problem of AoI, information entropy, and sensing energy consumption. Furthermore, we propose two numerical algorithms to solve the formulated problem in the known or unknown transmission environment, respectively. Finally, we evaluate the correctness and effectiveness of our proposals under various parameter settings, where the proposed scheme can obtain a better sum-weighted performance on AoI, information entropy, and sensing energy consumption than baselines in the literature. © 2002-2012 IEEE.

关键词： Data accuracy

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Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 1053-1100页

作者： Le, Mai Huynh-The, Thien Do-Duy, Tan Vu, Thai-Hoc Hwang, Won-Joo Pham, Quoc-Viet Pusan National University Department of Information Convergence Engineering Busan46241 Korea Republic of Trinity College Dublin School of Computer Science and Statistics DublinD02 PN40 Ireland Ho Chi Minh City University of Technology and Education Department of Computer and Communications Engineering Ho Chi Minh City71307 Viet Nam University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan44610 Korea Republic of Pusan National University School of Computer Science and Engineering Center for Artificial Intelligence Research Busan46241 Korea Republic of

The emergence of new services and applications in emerging wireless networks (e.g., beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) in the Internet of Things (IoTs). However, the proliferation of massive IoT connections and the availability of computing resources distributed across future IoT systems have strongly demanded the development of distributed AI for better IoT services and applications. Therefore, existing AI-enabled IoT systems can be enhanced by implementing distributed machine learning (aka distributed learning) approaches. This work aims to provide a comprehensive survey on distributed learning for IoT services and applications in emerging networks. In particular, we first provide a background of machine learning and present a preliminary to typical distributed learning approaches, such as federated learning, multi-agent reinforcement learning, and distributed inference. Then, we provide an extensive review of distributed learning for critical IoT services (e.g., data sharing and computation offloading, localization, mobile crowdsensing, and security and privacy) and IoT applications (e.g., smart healthcare, smart grid, autonomous vehicle, aerial IoT networks, and smart industry). From the reviewed literature, we also present critical challenges of distributed learning for IoT and propose several promising solutions and research directions in this emerging area. © 1998-2012 IEEE.

关键词： Reinforcement learning

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Online Energy and Interference Management for Dynamic Target Tracking with Cellular-Connected UAV

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5496-5510页

作者： Zhan, Cheng Yan, Huan Fan, Rongfei Hu, Han Xu, Shubin Yang, Jian Southwest University School of Computer and Information Science Chongqing400715 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Information and Electronics Beijing China CETC Research Institute of Cyberspace Security Beijing100846 China University of Science and Technology of China School of Information Science and Technology Hefei China

Cellular-connected Unmanned Aerial Vehicles (UAVs) have significant potential for target tracking in future cellular networks due to their broad coverage and operational flexibility. In this paper, we consider a multi-cell cellular network with a cellular-connected UAV for target tracking, which encounters challenges such as unpredictable flight energy consumption from the stochastic movements of the tracking target and severe uplink interference from ground devices (GDs). To tackle these challenges, we propose a multi-stage stochastic optimization framework focused on energy-efficient target tracking with interference coordination. Our objective is to optimize the long-Term average uplink throughput of both aerial users and GDs by jointly optimizing the UAV's trajectory, power allocation, and cell association across multiple orthogonal communication resource blocks (RBs). The formulated stochastic non-convex problem is first transformed into a deterministic problem for each time slot by using the Lyapunov optimization framework. An online optimization strategy is proposed, utilizing the optimal structure, alternative optimization, and successive convex approximation (SCA) techniques. Simulation results show that the proposed approach significantly enhances network throughput and UAV energy queue stability compared to existing baseline schemes. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Multi-Receiver Transmission Analysis for Mobile SLIPT Using Resonant Beam

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IEEE Communications Letters 2025年第5期29卷 1112-1116页

作者： Han, Shun Fang, Wen Xu, Mengyuan Xiong, Mingliang Deng, Hao Liu, Qingwen Tongji University School of Computer Science and Technology Shanghai201804 China Tongji University College of Electronic and Information Engineering Shanghai201804 China

Simultaneous lightwave information and power transfer (SLIPT) technology offers a promising solution to address the communication and power demands of Internet of Things (IoT) devices. The resonant beam (RB) system, based on the self-aligning properties of a spatially separated laser resonator (SSLR), enables energy transmission without precise alignment. However, existing RB-based SLIPT systems are limited to single-receiver configurations. This work introduces a multi-receiver RB-SLIPT system capable of simultaneously transmitting energy and information to multiple receivers. An analytical model based on multi-mode rate equations and diffraction transmission theory is developed to evaluate the energy transfer and communication performance in multi-receiver scenarios. Numerical simulation results demonstrate that in a dual-receiver scenario, the proposed system is capable of transmitting 2 W of optical power to each receiver while achieving a communication capacity of 5.8 bit/s/Hz. © 1997-2012 IEEE.

关键词： Laser resonators

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Bridging Domain Gap of Point Cloud Representations via Self-Supervised Geometric Augmentation

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IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 4846-4856页

作者： Yu, Li Zhong, Hongchao Zou, Longkun Chen, Ke Gao, Pan Nanjing University of Information Science & Technology School of Computer Science Nanjing210044 China Nanjing China South China University of Technology School of Electronic and Information Engineering Guangzhou510641 China Peng Cheng Laboratory Shenzhen China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

Recent progress of semantic point clouds analysis is largely driven by synthetic data (e.g., the ModelNet and the ShapeNet), which are typically complete, well-aligned and noisy-free. Therefore, representations of those ideal synthetic point clouds have limited variations in the geometric perspective and can gain good performance on a number of 3D vision tasks such as point cloud classification. In the context of unsupervised domain adaptation (UDA), representation learning designed for synthetic point clouds can hardly capture domain invariant geometric patterns from incomplete and noisy point clouds. To address such a problem, we introduce a novel scheme for induced geometric invariance of point cloud representations across domains, via regularizing representation learning with two self-supervised geometric augmentation tasks. On one hand, a novel pretext task of predicting translation distances of augmented samples is proposed to alleviate centroid shift of point clouds due to occlusion and noises. On the other hand, we pioneer an integration of the self-supervised relational learning on geometrically-augmented point clouds in a cascade manner, utilizing the intrinsic relationship of augmented variants and other samples as extra constraints of cross-domain geometric features. Experiments on the PointDA-10 dataset demonstrate the effectiveness of the proposed method, achieving the state-of-the-art performance. © 1991-2012 IEEE.

关键词： Self-supervised learning

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LLMs Encounter Critical Elements Prompts: Semantically Guided Partial Supervision Skeleton-based Action Recognition

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IEEE Sensors Journal 2025年第10期25卷 17350-17363页

作者： Xin, Wentian Liu, Yi Fu, Xianping Wang, Huibing Miao, Qiguang Liu, Ruyi Li, Long Pun, Chi-Man Dalian Maritime University School of Information Science and Technology Liaoning Dalian116026 China Xidian University School of Computer Science and Technology Shanxi Xi’an710071 China Guilin University of Electronic Technology Guilin Guangxi Key Laboratory of Trusted Software Guangxi541004 China University of Macau Faculty of Science and Technology Department of Computer and Information Science China

Semantically guided skeleton-based action recognition is a crucial but challenging task in the data processing with RGB and depth sensors. The generation of action descriptions is facilitated by the pretrained language model, thereby imbuing the skeleton with essential contextual semantics. However, it also presents two challenges that require careful consideration: (1) efficiently and automatically generating the core semantics of actions, and (2) extending the applicability of supervised learning when only partial labels are available. Therefore, we propose Critical Elements Prompts for Partial Supervision in Skeleton-based Action Contrastive Learning (CEPCLR) as a significant attempt to break through the existing limitations of skeleton and semantic multimodal fusion. Firstly, we design action-specific prompts that enable large language models (LLMs) to generate general semantic descriptions both efficiently and accurately, followed by iterative prompting that mines discriminative descriptions through controlled, rule-based emergence. Secondly, we adopt Language-Skeleton Contrastive Learning for multimodal fusion, which significantly enhances the integration of positive and negative action pairs. Notably, we introduce a novel partially supervised learning task for skeleton data, where labels are used selectively for semantic generation but are not involved in the classification tasks. The proposed CEPCLR achieves strong results on standard datasets like NTU RGB+D, NTU RGB+D 120, and PKU-MMD, effectively addressing missing contextual information in human skeleton and action expressions. © 2001-2012 IEEE.

关键词： Musculoskeletal system

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