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Goal-Oriented Communication, Estimation, and Control Over Bidirectional Wireless Links

IEEE Transactions on Communications 2025年第5期73卷 3031-3045页

作者： Cao, Jie Kurniawan, Ernest Boonkajay, Amnart Pappas, Nikolaos Sun, Sumei Popovski, Petar School of Electronic and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Institute of Infocomm Research Agency for Science Technology and Research Fusionopolis 138632 Singapore Linköping University Department of Computer and Information Science Linköping58183 Sweden Aalborg University Department of Electronic Systems Aalborg9220 Denmark

We consider a wireless networked control system (WNCS) with imperfect bidirectional links for real-time applications such as smart grids. To maintain the stability of WNCS, captured by the probability that plant state violates preset values, at minimal cost, heterogeneous physical processes are monitored by multiple sensors. This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control. However, scheduling multiple sensors and designing the controller with limited resources is challenging due to their coupling, delay, and transmission loss. We formulate a Constrained Markov Decision Problem (CMDP) to minimize violation probability with cost constraints. We reveal the relationship between the goal and different updating actions by analyzing the significance of information that incorporates goal-related usefulness and contextual importance. Subsequently, a goal-oriented deterministic scheduling policy is proposed. Two sensing-assisted control strategies and a control-aware estimation policy are proposed to improve the violation probability-cost tradeoff, integrated with the scheduling policy to form a goal-oriented co-design framework. Additionally, we explore retransmission in downlink transmission and qualitatively analyze its preference scenario. Simulation results demonstrate that the proposed goal-oriented co-design policy outperforms previous work in simultaneously reducing violation probability and cost. © 1972-2012 IEEE.

关键词： Markov processes

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An Architecture for Human Activity Recognition Using TCN-Bi-LSTM HAR based on Wearable Sensor

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Procedia computer science 2025年 260卷 805-813页

作者： Sadhna Bijrothiya Vaibhav Soni Department of Computer Science and Engineering Maulana Azad National Institute of Technology Bhopal MP India-462003

Human activity recognition (HAR) based on sensor data plays a vital role in various fields. Therefore, it is important to improve the recognition performance of different types of actions. In this work, we propose a Temporal Convolutional Network (TCN) combined with a Bidirectional Long Short-Term Memory (Bi-LSTM) architecture to address the issues of insufficient time-varying feature extraction and gradient explosion caused by too many network layers. This architecture effectively recognizes and emphasizes key feature information. The TCN enhances temporal feature extraction (TFE) with an appropriately sized receptive field, while the BiLSTM captures information from both past and future time steps, making it well-suited for tasks requiring bidirectional temporal context. This integration enables the architecture to learn and identify human activities more effectively. The performance of the proposed architecture is evaluated on three benchmark datasets: UCI-HAR, PAMAP2, and WISDM, achieving significant accuracies of 99.1%, 94.8%, and 98.3%, respectively, outperforming other state-of-the-art architectures.

关键词： Human Activity Recognition TCN Bi-LSTM TFE Wearable Sensors

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Effect of Ti3C2 MXene on Structural, Linear, Nonlinear Optical Properties of Polyvinyl Alcohol—Carboxymethyl Cellulose—Carbon Fiber Composites for Optoelectronics Applications

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Journal of Inorganic and Organometallic Polymers and Materials 2025年 1-14页

作者： Atta, M.M. Aldosari, Eman Zakaly, Hesham M. H. Zhang, Qinfang Egyptian Atomic Energy Authority Cairo Egypt Department of Chemistry College of Science King Saud University P. O. Box 145111 Riyadh Saudi Arabia Institute of Physics and Technology Ural Federal University Yekaterinburg620002 Russia Faculty of Engineering and Natural Sciences Computer Engineering Department Istinye University Istanbul34396 Turkey Department of Physics and Technical Sciences Western Caspian University Baku Azerbaijan School of Materials Science and Engineering Yancheng Institute of Technology Yancheng224051 China

Polyvinyl alcohol (PVA)/Carboxymethylcellulose (CMC)/Carbon fiber (CF) is a well-known composite for various applications like automobile, EMI shielding, and industrial heating. In this study, the effect of Ti3C2 MXene on the structural and optical properties of PVA/CMC/CF fabricated by solution casting method was explored. FTIR validated the molecular interaction between PVA-CMC chains and MXene nanofiller. The growth of lattice imperfections and a reduction in crystalline quality of PVA-CMC–CF composite upon incorporating MXene were revealed by XRD. UV-Vis spectroscopy was employed to investigate the linear and nonlinear optical parameters. Some interesting results were demonstrated;for example, adding MXene to PVA-CMC-CF composites causes a halo to appear in the visible range, attributed to MXene’s significant surface plasmon resonance leading to an increase of extinction coefficient and refractive index. The optical band gap of PVA-CMC-CF composites lessened with MXene concentration augmentation because of establishing new energy levels between the optical band boundaries of the polymer. The dielectric constant and dielectric loss were increased after MXene doping due to variations in the density of localized energy states that arise amongst the HOMO and LUMO. Incorporating MXene into PVA-CMC-CF reduced VELF and SELF ascribed to increased free electrons in the host matrix. The optical conductivity was decreased with a rise in MXene concentration ascribed to increasing free charge carriers inside polymer composite. The optical features of PVA-CMC-CF-MXene nanocomposites nominate them for optoelectronics, such as fast optical switching, UV shielding, and highly reflecting filters. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Refractive index

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PEP: An Accurate and Efficient Pupil Detection Framework Based on Pupil Edge Points

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IEEE Sensors Journal 2025年

作者： Zhang, Wei Liu, Yu Lv, Linfeng Wang, Xiang Li, Ziqiang Liu, Lei Li, Bin Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230026 China Carnegie Mellon University Department of Electrical and Computer Engineering Pittsburgh United States Nanjing University of Information Science and Technology School of Computer Science Nanjing China

With the rapid development of headmounted devices, eye tracking as an emerging human-computer interaction technology, has gained increasing importance. However, pupil detection, the core algorithm in eye tracking, suffered low accuracy in complex scenarios and long runtime, which limits the frame rate. This paper proposes PEP (neural network based on Pupil Edge Points), an efficient pupil detection framework that not only achieves high detection accuracy but also significantly reduces computational costs, thereby addressing the performance bottlenecks of existing methods in challenging environments. The core innovation of the PEP framework lies in its focus on detecting edge-representative points (the points at the edge of the pupil) along the pupil edge, a method more precise than traditional full-region segmentation and better suited for accurate pupil ellipse fitting. Additionally, PEP enhances the accuracy of pupil edge point predictions through two key features: (1) a loss function that includes natural ellipse prior regularization term and (2) a data augmentation strategy that randomly masks edges. We evaluated PEP against state-of-the-art (SOTA) model-based and learning-based methods across multiple datasets. The evaluation results show that PEP achieved superior overall performance regarding pupil detection rate and accuracy. Moreover, PEP’s parameter number and computational cost are 80% lower than other learning-based methods, making it highly efficient. The proposed framework is practical, scalable, and demonstrates exceptional potential for pupil detection and gaze-tracking applications. © 2001-2012 IEEE.

关键词： Virtual reality

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PDET: Progressive Diversity Expansion Transformer for Cross-Modality Visible-Infrared Person Re-identification 27th

PDET: Progressive Diversity Expansion Transformer for Cross...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xiong, Mingfu Liang, Jingbang Guo, Yifei Lee, Ik Hyun Bakshi, Sambit Muhammad, Khan School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Wuhan430200 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China Department of Mechatronics Engineering Tech University of Korea and IKLAB Inc. Seoul Korea Republic of Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela India School of Convergence Sungkyunkwan University Seoul03063 Korea Republic of

ISBN: (纸本)9783031783401

Visible-Infrared Person Re-identification (VI-ReID) would effectively improve the recognition performance in weak-lighting and nighttime scenes, which is an important research direction in pattern recognition and computer vision. However, existing methods usually focus on reducing the image differences between modalities (visible and infrared) to extract more reliable features, while neglecting the ability to discriminate the different identities with similar appearances. To address this problem, we propose a framework called "Progressive Diversity Expansion Transformer (PDET)", which includes a Diversity Distinguishing Vision Transformer Module (DDViTM) and a Cross-Modality Similarity Matching (CMSM) module for VI-ReID in this study. The DDViTM is proposed to implement the multiple embedded output vectors for a single input, learning feature representations of individual pedestrians in different modalities. The second module (CMSM) is used to improve the feature similarity between visible and infrared images, and dynamically adjust the image sequence weights of the two modalities to complete the training and optimization efficiency for the entire network. We conducted extensive experiments on the SYSU-MM01 and RegDB datasets, widely recognized public datasets for VR-ReID. The results demonstrate that the algorithm presented in this work has achieved promising performance compared to state-of-the-art methods. The code is available at https://***/jxsiaj/***. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Infrared imaging

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Data-Driven Evolutionary Algorithms for Optimizing Pumping Stations in Water Distribution Networks: Classifier-Guided Search Space Reduction 27th

Data-Driven Evolutionary Algorithms for Optimizing Pumping S...

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27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, computer Vision, and Applications, CIARP 2024

作者： Faúndez-Lizama, Thalía Gajardo-Sepúlveda, Nicolás Gutiérrez-Bahamondes, Jimmy H. Mora-Melia, Daniel Astudillo, César A. Master’s in Operations Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Computer Science Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Engineering and Construction Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile

ISBN: (纸本)9783031766060

In the context of increasing water scarcity and the need to enhance water distribution network efficiency, this study focuses on optimizing the design of pumping stations using data-driven evolutionary algorithms guided by classification models. These facilities are critical components in water networks due to their high energy consumption and role in maintaining necessary water pressure. The proposed approach integrates a machine learning classifier model within the evolutionary process to pre-label solutions as feasible or infeasible, thereby reducing search space and computational costs associated with hydraulic simulations. Classification models include support vector classifier, artificial neural networks, and random forest. These models are trained on data generated from initial evolutionary optimization, enabling the classifier to predict the viability of new solutions. Results show that all tested classification models reduced the number of objective function evaluations and consequently, required hydraulic simulations. In some cases, the optimal solution defined by the original method was improved, demonstrating dual benefits by reducing both cost and computational effort. This work highlights that data-driven evolutionary optimization can effectively tackle highly complex problems, offering potential future applications in other areas of civil engineering and water resource management. Implementing this methodology promises significant time and resource savings in large-scale optimization processes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hydraulic models

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O2SC: Realizing Channel-Adaptive Semantic Communication With One-Shot Online-Learning

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IEEE Transactions on Communications 2025年第5期73卷 3268-3282页

作者： Zhang, Guangyi Kang, Kai Cai, Yunlong Hu, Qiyu Eldar, Yonina C. Swindlehurst, A. Lee Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Peng Cheng Laboratory Shenzhen518071 China Zhejiang Provincial Government Hangzhou310007 China Weizmann Institute of Science Department of Mathematics and Computer Science Rehovot7610001 Israel University of California at Irvine Center for Pervasive Communications and Computing IrvineCA92697 United States

Motivated by progress in data-driven supervised learning, semantic communication has witnessed remarkable advancements in improving the efficiency of data transmission under various channel conditions. These advancements typically require a substantial amount of training data for offline training, which is challenging in practical systems. Therefore, in this work, we propose O2SC, a one-shot online-learning framework for semantic communication to achieve adaptive transmission under different channel conditions. Since semantic communication relies on acquired channel state information (CSI), we jointly design the channel estimation and semantic communication processes. Specifically, we introduce a denoising module based on one-shot self-supervised learning, allowing semantic communication systems to adapt to new channel conditions without the need to collect extensive training data. The denoising module is utilized to eliminate noise in the received data samples, using only the data samples themselves. Following this, we further exploit meta-learning to allow the system to quickly adapt to diverse channel conditions, by finding an appropriate initialization for each data sample in a timely way. Simulation results demonstrate that the proposed method achieves performance close to that of supervised learning-based approaches while also providing improved generalizability across different channel conditions. © 1972-2012 IEEE.

关键词： Self-supervised learning

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Spatial Analysis and Morphological Clustering of Barchan Dunes Using Advanced Computational Techniques

Spatial Analysis and Morphological Clustering of Barchan Dun...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Shobha P Sujay B R Swarna Girish Bennur Nalini N Raghunandan K R Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Benagaluru India Department of Computer Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Nitte India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Barchan dunes, crescent-shaped aeolian landforms shaped by unidirectional wind regimes, are integral to understanding sediment transport and landscape evolution in arid regions. This study conducts a geomorphometric analysis and clustering of barchan dunes, focusing on their spatial distribution, morphological characteristics, and environmental controls. Leveraging high-resolution digital elevation models (DEMs) and advanced clustering algorithms such as K-Means, DBSCAN, and Gaussian Mixture Models, the research identifies patterns in dune morphology and examines their relationships with local topography and climatic drivers. The methodology integrates preprocessing steps, including feature extraction and scaling, with unsupervised clustering to classify dunes based on key attributes such as width, length, height, and geographic location. Morphometric analyses reveal spatial and structural variations, while clustering results highlight patterns linked to wind regimes and sediment availability. Among the clustering techniques, DBSCAN emerges as the most effective for identifying irregularly shaped clusters, achieving higher evaluation metrics such as Silhouette Scores and Davies-Bouldin Index values. This study investigates the morphology and spatial distribution of barchan dunes using advanced clustering techniques such as DBSCAN and K-Means. The primary issue addressed is the challenge of accurately classifying and analyzing irregularly shaped dune formations under varying environmental conditions. Utilizing high-resolution digital elevation models (DEMs), the research identifies distinct morphological patterns and their correlation with wind regimes and sediment availability. The study finds that DBSCAN outperforms other clustering algorithms in handling the irregular geometries of dunes, achieving superior evaluation metrics like Silhouette Scores and Davies-Bouldin Index. These findings provide valuable insights for environmental monitoring and desertification m

关键词： Measurement Graphical models Clustering algorithms Morphology Surfaces Classification algorithms Sediments Indexes Digital elevation models Distribution functions

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Optimal Frame Extraction for Event Detection in Surveillance Videos

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Procedia computer science 2025年 256卷 1192-1199页

作者： Venkataraman Ranganath S Padmavathi R Aarthi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore 641112 India

This paper addresses the efficient identification of candidate keyframes in long and untrimmed surveillance videos. Existing fixed- length segmentation methods for violence detection suffer from computational inefficiency and fragmented short-term event repre- sentation. To overcome these limitations, we propose an Optimal Keyframe Extraction framework utilizing shot boundary detection to segment videos based on significant visual changes. Candidate Keyframe segments are extracted from the shots, focusing on potential anomalous segments. Keyframes are selected based on the highest motion changes, accurately representing short term dynamic events. This dynamic approach aims to minimize the processing time of long and untrimmed surveillance videos by reducing the number of segments required for analysis of the event. Results show that the complexity of analyzing the event ef- fectively can be done by extracting an optimal set of keyframes within 2-3% of total number frames for each video. This research improves violence detection by efficiently analyzing large video datasets and accurately identifying short-term dynamic events, thereby enhancing public safety and security applications.

关键词： Surveillance Videos Deep Learning Shot Boundary Detection Optimal Keyframe Extraction

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EEWS: energy-efficient multi-objective workflow scheduling in IaaS cloud environments with CP-FPA optimization

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International Journal of Information Technology (Singapore) 2025年 1-16页

作者： Jaiprakash, Sahani Pooja Badal, Tapas Kumar, Naween School of Computer Science Engineering and Technology (SCSET) Bennett University U.P. Greater Noida India Department of CSE ITER Siksha ’O’ Anusandhan (Deemed to be University) Jagamara Odisha Bhubaneswar India

With the rapid growth of cloud data centers, concerns about energy consumption and its impact on natural resources have become increasingly critical. In response, organizations and global communities are actively pursuing strategies to enhance energy efficiency, particularly in areas such as server operations, cooling systems, and heating technologies. This paper introduces a novel approach to workflow scheduling in cloud environments, leveraging the MaxUtil model. At its core is the Critical Path-based Flower Pollination Algorithm (CP-FPA), a robust metaheuristic designed to minimize energy consumption while simultaneously optimizing makespan. The proposed methodology operates in two key phases: (i) allocating tasks to active virtual machines and (ii) scheduling these tasks using optimal or near-optimal heuristics. The model incorporates a dynamic multi-objective fitness function and an efficient pollen representation mechanism. Extensive computational experiments were conducted across five scientific applications, demonstrating the algorithm’s rapid convergence and the effectiveness of the proposed solution. Comparative analysis against established scheduling algorithms such as PSO, GSA, GA, BAT, and ACO reveals that the CP-FPA approach consistently outperforms these methods in various scenarios. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Critical path Energy efficient Flower-pollination Makespan Optimizing scheduling Workflow

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