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IEEE Sensors Reviews 2025年 2卷 32-75页

作者： Bardia Safaei Mana Peiravian Mahdi Siamaki Department of Computer Engineering Sharif University of Technology Tehran Iran Reliable and Durable IoT Applications and Networks (RADIAN) Research Laboratory Sharif University of Technology Tehran Iran

Internet of Things (IoT) has revolutionized various industries with interconnected smart embedded devices featuring sensors and actuators. However, providing a sustainable power supply for these devices is a significant hurdle. The finite lifespan of traditional batteries necessitates frequent replacements, leading to increased maintenance costs, downtime, and environmental concerns associated with battery disposal. Energy harvesting offers an admirable alternative by converting ambient energy into usable electrical energy, enabling IoT devices to operate autonomously and sustainably, reducing maintenance needs, and enhancing reliability. Despite their diversity, energy harvesters face common limitations, such as variable energy availability, limited power output, and high initial costs. Recognizing these limitations is essential for informed decisions regarding their use in sustainable IoT applications and their communication performance. Due to the lack of a comprehensive and inclusive study in this context, this article gives an extensive survey on energy-harvesting technologies and their applications in IoT. By drawing insights from over 400 recent publications, this article first introduces a taxonomy by categorizing the existing technologies based on their energy sources into five general domains, i.e., photovoltaic, vibration and kinetic, radio frequency, thermoelectric, and chemical and biological. Then, it expands this categorization into 11 subdomains and delves into the working principles of every technology. Besides surveying potential IoT applications, this comprehensive study covers simulation-facilitating mathematical models, pros, cons, and commercial-off-the-shelf modules for each harvesting technology. It also includes evaluative simulations to analyze their output power behavior, focusing on their impact on wireless communication performance. Finally, by discussing the existing research trends, the survey aims to provide informed decision making a

关键词： Internet of Things Energy harvesting Batteries Reliability Costs Surveys Mathematical models Reviews Market research Maintenance

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In situ construction of PtSe_(2)/Ge Schottky junction array with interface passivation for broadband infrared photodetection and imaging

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InfoMat 2024年第4期6卷 37-46页

作者： Xue Li Shuo-En Wu Di Wu Tianxiang Zhao Pei Lin Zhifeng Shi Yongtao Tian Xinjian Li Longhui Zeng Xuechao Yu School of Physics and Microelectronics Key Laboratory of Material Physics Ministry of EducationZhengzhou UniversityZhengzhouHenanthe People's Republic of China Department of Electrical and Computer Engineering University of CaliforniaLa JollaCaliforniaUSA National Research Center for Optical Sensors/Communications Integrated Networks School of Electronic Science and EngineeringSoutheast UniversityNanjingJiangsuthe People's Republic of China Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhouJiangsuthe People's Republic of China

Infrared(IR)detection is vital for various military and civilian *** research has highlighted the potential of two-dimensional(2D)topological semimetals in IR detection due to their distinctive advantages,including van der Waals(vdW)stacking,gapless electronic structure,and Van Hove singularities in the electronic density of ***,challenges such as large-scale patterning,poor photoresponsivity,and high dark current of photodetectors based on 2D topological semimetals significantly impede their wider applications in low-energy photon ***,we demonstrate the in situ fabrication of PtSe_(2)/Ge Schottky junction by directly depositing 2D PtSe_(2) films with a vertical layer structure on a Ge substrate with an ultrathin AlOx *** to high quality junction,the photodetector features a broadband response of up to 4.6μm,along with a high specific detectivity of�1012 Jones,and operates with remarkable stability in ambient conditions as ***,the highly integrated device arrays based on PtSe_(2)/AlOx/Ge Schottky junction showcases excellent Mid-IR(MIR)imaging capability at room *** findings highlight the promising prospects of 2D topological semimetals for uncooled IR photodetection and imaging applications.

关键词： broadband photodetection imaging platinum diselenide Schottky junction van der Waals integration

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A Dynamic Selective Parameter Sharing Mechanism Embedded with Multi-Level Reasoning Abstractions 26

A Dynamic Selective Parameter Sharing Mechanism Embedded wit...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liu, Yan He, Ying Ming, Zhong Yu, F. Richard Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China State Key Laboratory of Integrated Services Networks Xidian University China Shenzhen China

ISBN: (纸本)9781643684369

Cooperative multi-agent reinforcement learning (Co-MARL) commonly employs different parameter sharing mechanisms, such as full and partial sharing. However, imprudent application of these mechanisms can potentially constrain policy diversity and limit cooperation flexibility. Recent methods that group agents into distinct sharing categories often exhibit poor performance due to challenges in precisely differentiating agents and neglecting the issue of promoting cooperation among these categories. To address these issues, we introduce a dynamic selective parameter sharing mechanism embedded with multi-level reasoning abstractions (DSPS-MA). Our approach uses self-comparison sequences to infer agents' abstract concepts, defining the differences between agents and allowing them to dynamically select partners to share parameters based on these abstract concepts. We also design an intrinsic reward to offer comprehensive collaboration guidance for agents, and introduce a policy cosine similarity regularization term to ensure sufficient policy diversity. Empirical evaluations demonstrate that our approach yields higher returns and faster convergence than state-of-the-art methods. © 2023 The Authors.

关键词： Dynamics

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Analysis and performance optimization of LoRa network using the CE & SC hybrid approach

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International Journal of Hybrid Intelligent Systems 2022年第1-2期18卷 53-68页

作者： Amzil, Abdellah Bellouch, Abdessamad Boujnoui, Ahmed Hanini, Mohamed Zaaloul, Abdellah Computer Networks Mobility and Modeling Laboratory Faculty of Sciences and Techniques Hassan First University of Settat IR2M Settat Morocco Engineering Mathematics and Computer Science Laboratory (IMI) Faculty of Sciences Ibn Zohr University of Agadir Agadir Morocco

In this research, we assess the impact of collisions produced by simultaneous transmission using the same Spreading Factor (SF) and over the same channel in LoRa networks, demonstrating that such collisions significantly impair LoRa network performance. We quantify the network performance advantages by combining the primary characteristics of the Capture Effect (CE) and Signature Code (SC) approaches. The system is analyzed using a Markov chain model, which allows us to construct the mathematical formulation for the performance measures. Our numerical findings reveal that the proposed approach surpasses the standard LoRa in terms of network throughput and transmitted packet latency. © 2022 - IOS Press. All rights reserved.

关键词： capture effect IoT LoRa LoRaWAN performance evaluation service quality signature codes

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Interference Cancellation at the Source for Passive Wireless Sensing Applications 14

Interference Cancellation at the Source for Passive Wireless...

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IEEE International Conference on RFID-Technology and Applications (RFID-TA)

作者： Ali Abedi Electrical and Computer Engineering Dept Wireless Sensor Networks Laboratory (WiSe-Net) University of Maine Orono ME USA

ISBN: (数字)9798331540227

ISBN: (纸本)9798331540234

Wireless sensor networks operating on a battery source have limited lifetime and can not be used in extreme environments. Passive sensors (battery-free) on the other hand may operate for a long time even in harsh environmental conditions. Passive sensors are powered using radio frequency waves and reflect a modified signal transporting the measured parameter that is embedded in the reflected wave. There are three dimensions where we can embed information and operate in an orthogonal fashion to support multi sensor operations: time domain, frequency domain, and code domain. One of the major challenges in such networks is the interference management to support scalability as the number of sensors grow to support a variety of applications. There has been several methods in time, frequency, and code domain to minimize the interference after it happens. In this paper, a novel interference mitigation method is proposed, which limits interfering signals at each source preventing their aggregation beyond repair at the receiver.

关键词： Wireless communication Wireless sensor networks Time-frequency analysis Codes Prevention and mitigation Scalability Interference Sensors Reliability Time-domain analysis

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Optimizing Cost and Transmission Power of Near-Ground Wireless Sensor networks

Optimizing Cost and Transmission Power of Near-Ground Wirele...

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IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)

作者： Mersedeh Najishabahang Ali Abedi Department of Electrical and Computer Engineering Wireless Sensor Networks (WiSe-Net) Laboratory University of Maine Orono ME USA

ISBN: (数字)9798350351118

ISBN: (纸本)9798350351125

Environmental monitoring systems that utilize wireless sensors have become increasingly crucial in light of climate change and severe weather events. These systems deploy numerous sensors across large regions, often in extreme environments, such as forests, oceans, or space. As the number of sensors increases (network size), minimizing power consumption and implementation costs becomes essential. This study focuses on developing a new method to determine the optimal transmitted power and cell size using realistic propagation models for near-ground sensors. The optimum amplification gain and range for each sensor node are calculated based on an iterative optimization algorithm to reach the minimum cost of deployment. A data-driven adjustment was applied to conventional empirical models by estimating the distance through the foliage and the probability of the electromagnetic wave traveling through the various canopy layers in the area under coverage.

关键词： Wireless communication Wireless sensor networks Analytical models Adaptation models Costs Forests Propagation losses Sensor systems Sensors Optimization

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Multimodal Aspect-Based Sentiment Analysis under Conditional Relation 31

Multimodal Aspect-Based Sentiment Analysis under Conditional...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Xinjing Li, Ruifan Ye, Shuqin Zhang, Guangwei Wang, Xiaojie School of Artificial Intelligence Beijing University of Posts and Telecommunications China School of Computer Science Beijing University of Posts and Telecommunications China Engineering Research Center of Information Networks Ministry of Education China Key Laboratory of Interactive Technology and Experience System Ministry of Culture and Tourism China

ISBN: (纸本)9798891761964

Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to extract aspect terms from text-image pairs and identify their sentiments. Previous methods are based on the premise that the image contains the objects referred by the aspects within the text. However, this condition cannot always be met, resulting in a suboptimal performance. In this paper, we propose COnditional Relation based Sentiment Analysis framework (CORSA). Specifically, we design a conditional relation detector (CRD) to mitigate the impact of the unmet conditional image. Moreover, we design a visual object localizer (VOL) to locate the exact condition-related visual regions associated with the aspects. With CRD and VOL, our CORSA framework takes a multi-task form. In addition, to effectively learn CORSA we conduct two types of annotations. One is the conditional relation using a pretrained referring expression comprehension model;the other is the bounding boxes of visual objects by a pretrained object detection model. Experiments on our built C-MABSA dataset show that CORSA consistently outperforms existing methods. The code and data are available at https://***/Liuxj-Anya/CORSA. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Visible Light Positioning Method for Indoor Line-of-Sight Scene Based on Time Series Coding 9

Visible Light Positioning Method for Indoor Line-of-Sight Sc...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Yu, Yonghao Guo, Yongde Zhao, Dawei Fu, Kexue Tang, Yongwei Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong University School of Mechanical Engineering Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Jinan China

ISBN: (纸本)9798350391367

Existing indoor visible light positioning methods, such as those based on fingerprint database algorithms, are complex to construct and consume significant resources. In response to this issue, the text proposes an end-to-end indoor visible light positioning method that samples continuous signals at a small number of random coordinate positions in indoor environments. Through a deep learning method based on attention mechanism, a nonlinear mapping between the received signal and spatial position coordinates is constructed, and the time series properties in the received signal are encoded as a feature in the data. The method is further improved by combining residual networks to improve accuracy. Through simulation experiments, it has been verified that the deep learning based localization method described in this article can achieve sub meter level accuracy on small sample datasets, and it has been proven that time series features have a significant impact on localization accuracy in indoor visible light positioning algorithms based on deep learning. © 2024 IEEE.

关键词： Time series

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EEG based Parkinson Detection through Supervised Information Enhanced Contrastive Learning

EEG based Parkinson Detection through Supervised Information...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Song, Jian Li, Xiang Jiang, Wenjing Wang, Chunxiao Zhao, Zhigang Lv, Jialiang Hu, Bin Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Qilu Hospital of Shandong University Department of Geriatric Neurology Key Laboratory of Cardiovascular Proteomics of Shandong Province Jinan China Beijing Institute of Technology Institute of Engineering Medicine Beijing China

ISBN: (纸本)9798350337488

This study presents a novel Supervised Information Enhanced Contrastive Learning Algorithm for Parkinson's Disease Detection (SI-CLAPD) based on Electroencephalography (EEG). SI-CLAPD performs contrastive learning in a multi-granularity manner on enhanced contextual views to achieve robust contextual representations for EEG, thus could contribute to improving the effectiveness of PD detection. Unlike existing methods for constructing PD detection models guided solely by classification loss, we propose a joint learning model that combines self-supervised contrastive learning with supervised classification learning. This model is optimized using both contrastive loss and classification loss, allowing it to capture subtle differences between EEG signals and representations that are specific to PD detection. Through extensive experimental evaluations, we demonstrate that SI-CLAPD achieves robust and high accuracy in PD detection tasks on three benchmark datasets. To the best of our knowledge, this study represents the first effort in validating the effectiveness of contrastive learning for the detection of PD. Besides, within the realm of contrastive learning research in EEG, it also represents the first endeavor to fuse supervised learning with self-supervised contrastive learning for EEG classification. This investigation unveils an universally applicable approach to EEG signal processing, with the potential to confer advantages to a multitude of EEG classification tasks. © 2023 IEEE.

关键词： Contrastive learning Deep Learning Electroencephalogram Joint training Parkinson's disease

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Uncertainty-Guided Discriminative Priors Mining for Flexible Unsupervised Spectral Reconstruction

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IEEE Transactions on Neural networks and Learning Systems 2025年第6期36卷 10592-10605页

作者： Yihong Leng Jiaojiao Li Rui Song Yunsong Li Qian Du State Key Laboratory of Integrated Service Networks School of Telecommunications Engineering Xidian University Xi’an China CAS Key Laboratory of Spectral Imaging Technology Xi’an China Department of Electrical and Computer Engineering Mississippi State University Starkville MS USA

Existing supervised spectral reconstruction (SR) methods adopt paired RGB images and hyperspectral images (HSIs) to drive the overall paradigms. Nonetheless, in practice, “paired” requires higher device requirements such as specific well-calibrated dual cameras or more complex and exact registration processes among images with different time phases, widths, and spatial resolution. To tackle the above challenges, we propose a flexible uncertainty-aware unsupervised SR paradigm, which dynamically establishes the forceful and potent constraints with RGBs for driving unsupervised learning. As a specific plug-and-play tail in our paradigm, the uncertainty-aware saliency alignment module (USAM) calculates pixel- and spectralwise information entropy for uncertainty estimation, which attempts to represent the corresponding reflectivity or radiance to the light among different objects in various scenes, forcing the paradigm to adaptively explore the scene-agnostic prominent features. Furthermore, a progressively parallel network under our unsupervised paradigm is conducted to excavate discriminate structural and semantic priors of RGBs to assist in recovering dependable HSIs: 1) a learnable rank-guided structural representation (LRSR) flow is leveraged to characterize the latent structural priors via excavating nonzero elements in the full-rank matrix and further preserve evident boundaries in HSIs; and 2) a coarse-to-fine bandwise semantic perception (CBSP) flow is conducted to propagate perceptual bandwise affinity for aggregating and strengthening intrinsic interband dependencies, and further extract delicate semantic priors, which can recover plentiful contiguous spectral information in HSIs. Comprehensive quantitative and qualitative experimental results on three visual and two remote sensing benchmarks have shown the superiority and robustness of our method. We also conducted nine existing SR methods in our unsupervised paradigm to recover HSIs without any manual inter

关键词： Image reconstruction Semantics Spatial resolution Robustness Hyperspectral imaging Feature extraction Excavation Visualization Uncertainty Narrowband

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