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Pervasive Indoor User Identification Leveraging Mobile Single Station Localization

IEEE Internet of Things Journal 2025年第11期12卷 15224-15237页

作者： Nie, Wendi Liu, Zexing Wang, Xiaoyang Duan, Yaoxin Lam, Kam-Yiu Liu, Kai Ng, Joseph K. Y. Xue, Chun Jason Gui, Guan Ministry of Education Key Laboratory of Industrial Internet of Things and Networked Control China Chongqing University of Posts and Telecommunications School of Automation/School of Industrial Internet Chongqing40065 China Institute of Industrial Internet Chongqing401120 China City University of Hong Kong Computer Science Department Hong Kong Chongqing University College of Computer Science Chongqing400030 China Hong Kong Baptist University Computer Science Department Hong Kong Mohamed Bin Zayed University of Artificial Intelligence Department of Computer Science Abu Dhabi United Arab Emirates Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China

The utilization of Wi-Fi-based technology for pervasive indoor user identification has gained prominence due to its cost-effective nature and compatibility with user devices. Previous works proposed capturing the media access control (MAC) address emitted from a user's device and using information Elements (IE)-based MAC de-randomization methods to mitigate the impairment caused by random MAC. However, IE types of different Wi-Fi devices are not consistently differentiated, leading to identification errors in IE-based methods. Additionally, typical Wi-Fi fingerprinting approaches require densely pre-deployed Wi-Fi stations, contradicting the principle of pervasive localization. To address these challenges, we propose the mobile single station-based user identification (***) technique, which leverages Wi-Fi mobile single stations for pervasive indoor user identification. *** includes mobile single station localization (MSL) and MAC de-randomization based on users' spatio-temporal location and IE information (***). MSL can be implemented on a standard mobile Wi-Fi station without extensive pre-deployment. *** performs MAC de-randomization using the LIC algorithm to identify users with random MAC addresses. Experimental results demonstrate that *** outperforms previous IE-based user identification methods and multi-station localization techniques. MSL achieves a localization error of 1.15 meters which is better than multi-station with 12 APs of 1.40 meters. *** demonstrates an identification accuracy of 95.24% which is better than AIMAC of 85.48%. © 2014 IEEE.

关键词： Medium access control

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Low Chirp Dual-Racetrack Modulator for PAM-4 Modulation with Low Driving Voltage

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Journal of Lightwave Technology 2025年第10期43卷 4840-4847页

作者： Yu, Fuhao Zeng, Zhaobang Tang, Kaifei Yang, Shichong Ji, Xiang Xin, Yu Gu, Tingyi Jiang, Wei Nanjing University Key Laboratory of Intelligent Optical Sensing and Manipulation Engineering Research Center for Precision Photonics Integration & System Applications Ministry of Education Nanjing210023 China Nanjing University National Laboratory of Solid-State Microstructures College of Engineering and Applied Sciences Institute of Optical Communication Engineering Nanjing210023 China Nanjing University of Information Science & Technology School of Physics and Optoelectronic Engineering Nanjing210044 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

Micro-ring modulators with a small footprint and energy consumption have been explored for densely integrated high throughput optical interconnects. However, their strong dispersive response results in frequency chirp and pulse broadening, which limits their signal transmission distance. Here we explore the low chirp operation of a dual-racetrack modulator symmetrically coupled to a bus waveguide. With opposite index changes in the two resonators, the coherent couplings of two resonators' optical fields with the bus waveguide and their interference can cancel out the output phase variation due to a single resonator. This allows the push-pull operation to achieve a lower chirp compared to the single-drive operation. The advantage of lower chirp operation is experimentally verified through open eye diagram with 100 Gbps PAM-4 transmitted over 2 km single-mode fiber (SMF), with a peak-to-peak driving voltage of 1.6 V. © 1983-2012 IEEE.

关键词： Single mode fibers

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Low-profile metamaterial with ultra-wide band ratio based on active frequency selective surfaces

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Optics Express 2025年第7期33卷 15901-15916页

作者： Xu, Ruize Ren, Peng Li, Yi Gu, Chao Xu, Baoyi Xue, Wei Wang, Minrui Xiang, Zheng State Key Laboratory of Integrated Services Networks Xidian University No. 2 South Taibai Road Xi’an710071 China Centre for Wireless Innovation School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast BelfastBT3 9DT United Kingdom School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China

This paper presents a low-profile metamaterial with ultra-wide band ratio (BR) based on active frequency selective surfaces (AFSSs) to overcome the limited tuning range and suboptimal profile of current designs. The proposed design consists of a periodic pattern printed on the top of a thin substrate and a bias network on the bottom, with varactor diodes symmetrically integrated into the top metal layer. Based on the equivalent circuit model, the mechanism of wideband tuning is analyzed theoretically. The corresponding simulation results indicate that the designed AFSS can achieve a large tuning range for the first operating band from 1.12 to 7.86 GHz while maintaining stability in the second operating band. This allows for a continuous variation from a small BR to a large BR, with a tuning range reaching 601.79%. To the best of the authors’ knowledge, this is the first time that the concept of band ratio range (BRR) has been proposed to characterize the level of BR variation. Considering the feasibility of fabrication and limitations due to the tuning range of a single commercially available varactor diode, measurements were performed based on two fabricated prototypes. The simulation results are consistent with the experimental results. Compared with existing literature, the proposed AFSS can provide significant advantages such as good angular stability, polarization insensitivity, miniaturization, and ultra-wideband tunability. Our design is applicable to controlling the propagation of electromagnetic waves in various communication systems and is well-suited for electromagnetic shielding in communication devices or optical devices across different frequency bands. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Electromagnetic shielding

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Collaborative Marine Targets Search Algorithm for USVs and AAVs Under Energy Constraint

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IEEE Internet of Things Journal 2025年第9期12卷 12137-12152页

作者： Xue, Mingyao Huang, Haibin Zhuang, Yufei Si, Jinshuai Lü, Tengping Sharma, Sanjay Zhang, Lei School of Information Science and Engineering Harbin Institute of Technology at Weihai Weihai264209 China Weihai Skysailing Technology Company Ltd. Research and Development Department Weihai264200 China Weihai Key Laboratory of Autonomous Control and Cooperation Technology for Unmanned Marine Systems Weihai264200 China Wuhan 2nd Ship Design and Research Institute Information Research Office Wuhan430205 China

The collaborative search of unmanned surface vessel (USV) and autonomous aerial vehicle (AAV) for tasks with high timeliness or a large number of target points is a current research focus in the field of Marine Internet of Things technology, such as counter-mine search and data collection of ocean sensor nodes. However, the slower navigation speed of USVs cannot meet the timeliness requirements of search tasks. Moreover, AAVs have a short endurance, making it unsuitable for large-area target search missions. Combining the characteristics of long endurance of USVs and the high speed of AAVs, this article propose a heterogeneous collaborative search algorithm based on time sequence constraints (TimeSeries). Initially, a time-sliding window is designed to control the trigger time for each agent to select the next search target point. Only when the current search time of the agent is in the time-sliding window, the conditions for the next search will be satisfied, ensuring the fairness of the search. Second, considering the constraint of limited energy for USVs and AAVs, a mathematical model of energy consumption under the constraint of sea wind and ocean current environment is established, then an objective function based on energy constraint and target weight is designed. Finally, the discretized isomorphic agent collision avoidance constraint strategy and the communication radius constraint strategy among agents based on network topology are added to enhance the robustness of the cooperative system. Finally, several simulation experiments are designed and compared with other algorithms to show the effectiveness of the proposed algorithm. © 2014 IEEE.

关键词： Motion planning

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Computational imaging based on single-photon detection: a survey

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Artificial Intelligence Review 2025年第8期58卷 1-54页

作者： Pu, Yanyun Zhu, Chengyuan Yao, Gongxin Li, Chao Pan, Yu Yang, Kaixiang Yang, Qinmin State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

The rapid advancements in single-photon detectors with picosecond timing resolution over the past decade have significantly driven the development of time-correlated single-photon counting (TCSPC) for computational imaging applications, including bioimaging and remote sensing. In this review, we utilize the CiteSpace tool to create knowledge maps and perform a bibliometric analysis of this research area. Furthermore, we provide a comprehensive overview of the key challenges associated with computational imaging using temporal single-photon counting. We also highlight how these challenges have been addressed under extreme conditions to establish a reference model for future imaging solutions. We examine the performance evaluation parameters of single-photon detectors to enhance the understanding of detector array scaling and their application in constructing efficient computational imaging systems. Lastly, we aim to elucidate the current technical challenges in single-photon detector-based computational imaging and explore their potential future developments.

关键词： Computational Imaging

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Decoupling While Coupling: Towards More Accurate Stereo Image Sand Removal Beyond Certainty

Decoupling While Coupling: Towards More Accurate Stereo Imag...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wei, Bingcai Liu, Hui Qian, Chuang Jia, Yi Wu, Wangyu Li, Zhishan School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool United Kingdom School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns. © 2025 IEEE.

关键词： Cross-view Inter-view Sand removal Stereo image Uncertainty estimation

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Joint Task Partitioning and Resource Allocation in UAV-Enabled Vehicular Edge Computing Based on Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 15453-15466页

作者： Liang, Hongbin Zhang, Han Ale, Laha Hong, Xintao Wang, Lei Jia, Qiong Zhao, Dongmei Southwest Jiaotong University School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Chinese Academy of Sciences Laboratory of Noise and Vibration Research Institute of Acoustics Beijing100190 China McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada

Vehicle Edge Computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs' restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. Comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods. © 2014 IEEE.

关键词： Convex optimization

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II-NVM: Enhancing Map Accuracy and Consistency With Normal Vector-Assisted Mapping

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IEEE Robotics and Automation Letters 2025年第6期10卷 5465-5472页

作者： Zhao, Chengwei Li, Yixuan Jian, Yina Xu, Jie Wang, Linji Ma, Yongxin Jin, Xinglai Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Hangzhou Qisheng Intelligent Techology Company Ltd Hangzhou311217 China Columbia University in the City of New York Department of Computer Science New YorkNY10027 United States Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin150001 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

SLAM technology plays a crucial role in indoor mapping and localization. A common challenge in indoor environments is the "double-sided mapping issue", where closely positioned walls, doors, and other surfaces are mistakenly identified as a single plane, significantly hindering map accuracy and consistency. To addressing this issue this letter introduces a SLAM approach that ensures accurate mapping using normal vector consistency. We enhance the voxel map structure to store both point cloud data and normal vector information, enabling the system to evaluate consistency during nearest neighbor searches and map updates. This process distinguishes between the front and back sides of surfaces, preventing incorrect point-to-plane constraints. Moreover, we implement an adaptive radius KD-tree search method that dynamically adjusts the search radius based on the local density of the point cloud, thereby enhancing the accuracy of normal vector calculations. To further improve real-time performance and storage efficiency, we incorporate a Least Recently Used (LRU) cache strategy, which facilitates efficient incremental updates of the voxel map. The code is released as open-source and validated in both simulated environments and real indoor scenarios. Experimental results demonstrate that this approach effectively resolves the "double-sided mapping issue" and significantly improves mapping precision. Additionally, we have developed and open-sourced the first simulation and real-world dataset specifically tailored for the "double-sided mapping issue". © 2016 IEEE.

关键词： Mapping

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On Finite Word Length Computing Error of Fixed-Point SAR Imaging Processing*

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Chinese Journal of Electronics 2025年第3期23卷 645-648页

作者： Yizhuang Xie Bocheng Zhu Wen Yan Weijie Zhang School of Information and Electronics Beijing Institute of Technology Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Science and Technology on Electronic Information Control Laboratory Chengdu China

Synthetic aperture radar (SAR) imaging system is generally realized by fixed-point with the purpose of reducing system implementation scale and enhancing real-time performance. Finite word length computing error of fixed-point SAR imaging system is studied. The characteristic of computing error in SAR imaging system is analyzed. A finite word length computing error model of SAR imaging system is built, by means of which the empirical formula of system's output noise-to-signal ratio is derived. Based on the empirical formula, SAR imaging system processing word length is presented according to different processing granularities. The validity of this proposed finite word length computing error model of SAR imaging system is verified by system level fixed-point simulation.

关键词： Computational modeling Imaging Transforms Radar polarimetry Real-time systems Synthetic aperture radar System implementation

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