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An Overview of LoRa Localization Technologies

computers, Materials & Continua 2025年第2期82卷 1645-1680页

作者： Huajiang Ruan Panjun Sun Yuanyuan Dong Hamid Tahaei Zhaoxi Fang Institute of Artificial Intelligence Shaoxing UniversityShaoxing312000China Department of Computer Science and Engineering School of Mechanical and Electrical EngineeringShaoxing UniversityShaoxing312000China

Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)*** paper explores LoRa as an alternative localization technology,leveraging its low power consumption,robust indoor penetration,and extensive coverage area,which render it highly suitable for diverse IoT *** comprehensively review several LoRa-based localization techniques,including time of arrival(ToA),time difference of arrival(TDoA),round trip time(RTT),received signal strength indicator(RSSI),and fingerprinting *** this review,we evaluate the strengths and limitations of each technique and investigate hybrid models to potentially improve positioning *** studies in smart cities,agriculture,and logistics exemplify the versatility of LoRa for indoor and outdoor *** findings demonstrate that LoRa technology not only overcomes the limitations of GPS regarding power consumption and coverage but also enhances the scalability and efficiency of IoT deployments in complex environments.

关键词： LoRa localization IoT TDoA RSSI application hybrid models

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Thin film ferroelectric photonic-electronic memory

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Light(Science & Applications) 2024年第10期13卷 2251-2262页

作者： Gong Zhang Yue Chen Zijie Zheng Rui Shao Jiuren Zhou Zuopu Zhou Leming Jiao Jishen Zhang Haibo Wang Qiwen Kong Chen Sun Kai Ni Jixuan Wu Jiezhi Chen Xiao Gong Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore Department of Microelectronic Engineering Rochester Institute of TechnologyRochesterNY 14623USA School of Information Science and Engineering Shandong UniversityJinan 250100China

To reduce system complexity and bridge the interface between electronic and photonic circuits,there is a high demand for a non-volatile memory that can be accessed both electrically and ***,practical solutions are still lacking when considering the potential for large-scale complementary metal-oxide semiconductor compatible ***,we present an experimental demonstration of a non-volatile photonic-electronic memory based on a 3-dimensional monolithic integrated ferroelectric-silicon ring *** successfully demonstrate programming and erasing the memory using both electrical and optical methods,assisted by optical-to-electrical-to-optical *** memory cell exhibits a high optical extinction ratio of 6.6 dB at a low working voltage of 5 V and an endurance of 4×10^(4) ***,the multi-level storage capability is analyzed in detail,revealing stable performance with a raw bit-error-rate smaller than 5.9×10^(−2).This ground-breaking work could be a key technology enabler for future hybrid electronic-photonic systems,targeting a wide range of applications such as photonic interconnect,high-speed data communication,and neuromorphic computing.

关键词： resonator electronic ferroelectric

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Resolution-aware beam scanning for joint detection and communication in ISAC systems

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Science China(Information Sciences) 2025年第5期68卷 250-266页

作者： Yiming XU Dongfang XU Zhanyuan XIE Zheng JIANG Shenghui SONG Derrick Wing Kwan NG Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Telecom Research Institute School of Electrical Engineering and Telecommunications University of New South Wales

Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.

关键词： integrated sensing and communication (ISAC) detection beam scanning resolution beamwidth

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Free-form acoustic topological waveguides enabled by topological lattice defects

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Physical Review B 2024年第18期109卷 L180101-L180101页

作者： Yin Wang Yong Ge Yu-Jing Lu Shuai Gu Yi-Jun Guan Qiao-Rui Si Shou-Qi Yuan Qiang Wang Hong-Xiang Sun Haoran Xue Research Center of Fluid Machinery Engineering and Technology School of Physics and Electrical Engineering School of Computer Science and Communication Engineering School of Physics Collaborative Innovation Center of Advanced Microstructures State Key Laboratory of Acoustics Department of Physics The

The emerging field of topological acoustics provides exciting possibilities for controlling sound propagation with extraordinary robustness. Conventional topological acoustic waveguides built from topological edge states, which arise solely from the nontrivial topology in the momentum space, usually have restricted shapes due to their crystalline structures. Here, we show that, using an acoustic topological system with both nontrivial topologies in the real and momentum spaces, free-form acoustic topological waveguides can be constructed. These topological waveguides support disclination-localized states, caused by the interplay between topological lattice defects and the valley-Hall topology. As demonstrated in our simulations and experiments, such disclination waveguides can take arbitrary shapes and form open arcs within the lattice, which are not possible for previous crystalline acoustic topological waveguides. Furthermore, by increasing the number of topological lattice defects, we can realize more than one topological waveguide in one lattice. They join at the topological lattice defect and form a topological waveguide network, enabling more complicated functionalities such as beam splitting. Our results point to a promising direction for free-form acoustic topological devices with the advantages of embedded forms, easy cascading, and high robustness.

关键词： Acoustics Defects Topological phase transition Topological phases of matter

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Resilient generation planning considering long periods of low-RES output

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Environment Systems and Decisions 2025年第2期45卷 1-13页

作者： Stasinos, Ektor-Ioannis Panteli, Mathaios Hatziargyriou, Nikos School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

The growing concerns over mitigating climate change effects resulted in power system planning and generation expansion strategies that aim in increasing penetration of intermittent renewable energy sources (RES) to fulfill the national energy and climate plans (NECPs) of countries worldwide. However, the variable and intermittent nature of RES output, compounded by uncertainties arising from climatic and weather changes, poses significant challenges. These challenges necessitate careful consideration in generation planning and in determining the operational reserves of RES-rich power systems to ensure resilience. This paper employs a generation capacity operational planning method based on the Screening Curve Method (SCM), assuming various levels of large RES penetration and diverse spinning generation reserve policies, necessary to cope with various levels of RES generation deficits, to guarantee power system resilience. The proposed approach can be used to assess the overall reduction of capacity and decommissioning of thermal units, while ensuring resilient operation against various levels of annual RES generation deficits. Furthermore, an approximation of the total annual system operation cost is also provided, considering the impact of the employed spinning reserve policies. © The Author(s) 2025.

关键词： Decommissioning of thermal units Low-renewable energy sources generation Power system resilient generation planning Screening curve method

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A Comprehensive Analysis of Fetal Electrocardiogram Signals 5

A Comprehensive Analysis of Fetal Electrocardiogram Signals

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Camacho, Brandon Ullah, Muhammad Sana Florida Polytechnic University Electrical and Computer Engineering Department Lakeland United States

ISBN: (纸本)9798331519957

This paper reviews different feature extraction and analysis methods for FECG signals. The FECG signals are very critical in monitoring the health of the fetuses;hence, most of the time, they are masked by maternal ECG and noise and introducing significant challenges during the analysis of the same signal. In this paper, several feature-extraction techniques are reviewed and compared. Techniques included, but were not limited to, time-domain, frequency-domain, and time-frequency domain methods such as PCA and ICA. We also review some newer approaches for machine learning, including deep learning methods that could be applied further to enhance the performance of signal separation and classification performance. Then, each method is tested on synthetic and realworld datasets, emphasizing computational efficiency and clinical applicability. By elaborating on the strengths and weaknesses of each approach, this paper summarize the results that give insight into optimum strategies to improve FECG signal processing for better diagnosis regarding fetal health. © 2025 IEEE.

关键词： Frequency domain analysis

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Xf-Calc: Towards a Fair Comparison between Approaches for Fuzzy Numbers Arithmetic

Xf-Calc: Towards a Fair Comparison between Approaches for Fu...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Pisco-Jordan, Juan Ramos-Pozo, Luis Townsend-Hinostroza, Danae Tapia-Rosero, Ana ESPOL Polytechnic University Department of Electrical and Computer Engineering Guayaquil Ecuador

ISBN: (纸本)9798350374575

Since arithmetic operations that involve uncertain or imprecise linguistic expressions are often mentally solved, multiple approaches have been developed to emulate them with fuzzy numbers. Each approach may provide different results, and some may depend on a specific context. It is common to compare and analyze these approaches to determine which one is the best in different contexts. These comparisons can be laborious and prone to errors. In this paper a context-free calculator for fuzzy arithmetic is proposed. To facilitate the analysis of comparisons, this calculator is capable of solving an arithmetic problem using multiple approaches while providing reports. The first report shows the solution of each approach while the second report shows their respective graphical representation. The calculator has nine different approaches for addition, subtraction and multiplication with the flexibility to incorporate new approaches for further study. © 2024 IEEE.

关键词： Open source software

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Smart Pressure E-Mat for Human Sleeping Posture and Dynamic Activity Recognition

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 9-20页

作者： Yuan, Liangqi Wei, Yuan Li, Jia Purdue University School of Electrical and Computer Engineering West LafayetteIN47907 United States Oakland University Department of Electrical and Computer Engineering RochesterMI48309 United States

With the emphasis on healthcare, early childhood education, and fitness, noninvasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied because of its advantages of simple structure, easy access, visualization application, and harmlessness. This article introduces a Smart Pressure e-Mat (SPeM) system based on piezoresistive material, Velostat, for human monitoring applications, including recognition of sleeping postures, sports, and yoga. After a subsystem scans the e-mat readings and processes the signal, it generates a pressure image stream. Deep neural networks are used to fit and train the pressure image stream and recognize the corresponding human behavior. Four sleeping postures and 13 dynamic activities inspired by Nintendo Switch Ring Fit Adventure are used as a preliminary validation of the proposed SPeM system. The SPeM system achieves high accuracies in both applications, demonstrating the high accuracy and generalizability of the models. Compared with other pressure sensor-based systems, SPeM possesses more flexible applications and commercial application prospects, with reliable, robust, and repeatable properties. © 2024 The Authors.

关键词： Deep neural networks

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Deep multi-scale convolutional neural networks for automated classification of multi-class leaf diseases in tomatoes

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Neural Computing and Applications 2024年第2期36卷 803-822页

作者： Elfatimi, Elhoucine Eryiğit, Recep Elfatimi, Lahcen Department of Computer Engineering Ankara University Ankara06830 Turkey Computer Engineering Department Polytechnic School of Montreal 2500 MontrealH3T 1J Canada

Deep learning techniques have gained immense popularity recently because of their remarkable capacity to learn complex patterns and features from large datasets. These techniques have revolutionized many fields by achieving advanced performance in various tasks. The availability of large datasets and the advancement of computing resources have enabled deep learning models to perform well in solving challenging problems. As a result, they have become an essential tool in many industries, including agriculture. The application of deep learning in agriculture has great potential for increasing productivity, reducing costs, and improving sustainability by aiding in the early identification and prevention of plant leaf diseases, optimizing crop yields, and facilitating precision agriculture. This paper suggests using a novel approach to automatically classify multi-class leaf diseases in tomatoes using a deep multi-scale convolutional neural network (DMCNN). The proposed DMCNN architecture consists of parallel streams of convolutional neural networks at different scales, which get merged at the end to form a single output. The images of tomato leaves are preprocessed using data augmentation techniques and fed into the DMCNN model to classify disease. The proposed approach is evaluated on a dataset of tomato plant images containing 10 distinct classes of diseases and compared with different existing models. The research results reveal that the suggested DMCNN model performs better than other models in terms of accuracy, precision, recall, and F1 score. Furthermore, the proposed model reported an overall accuracy of 99.1%, which is higher than the accuracy of existing models tested on the same dataset. The study demonstrates the potential of deep learning techniques for automated disease classification in agriculture, which can aid in early disease detection and prevent crop loss. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Spring

关键词： Convolutional neural networks

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Effects of Image Normalization on CNN-Based EEG-EMG Fusion

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IEEE Sensors Journal 2025年第11期25卷 20894-20906页

作者： Tryon, Jacob Guillermo Colli Alfaro, J. Trejos, Ana Luisa Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada Western University Department of Electrical and Computer Engineering School of Biomedical Engineering LondonONN6A 5B9 Canada

The rise in popularity of wearable robotic devices has brought many opportunities in the area of assistive devices for rehabilitation. However, despite their numerous advantages, these wearable mechatronic devices are not widely adopted due to a poor interaction between the device and the wearer. To solve this issue, several studies have proposed the use of sensor fusion of electroencephalography (EEG) and electromyography (EMG) signals using Convolutional Neural Networks (CNN) to detect the motion intention of the user and control the wearable device. Although normalization techniques can be applied during data pre-processing in order to improve performance, the correct normalization methods to apply for combined EEG and EMG data are unknown, hindering the potential improvement of the resulting CNN models. Therefore, in this study, no normalization, subject-wise, speed-wise, image-wise, and channel-wise normalization methods were compared. The effectiveness of these methods was tested on the overall and speed specific accuracies of CNN models trained using a database of combined EEG-EMG data collected during elbow flexion-extension motions at different speeds. The results of this study showed that no normalization improved the performance of the overall accuracy of CNN models, with grouped spectrogram-based models achieving an accuracy of 81.57 ± 7.11%. A similar trend was found for speed specific accuracies, in which no normalization showed to be a slightly better option during slow motions. Overall, these results provide information on the different tools that can be used to improve the performance of CNN-based models used for the control of wearable robotic devices. © 2001-2012 IEEE.

关键词： Electromyography

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