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International Conference on Dependable systems and Networks (DSN)

作者： Xuening Xu Chenglong Fu Xiaojiang Du E. Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems UNC Charlotte Charlotte NC USA Information Directorate Air Force Research Laboratory Rome NY USA

Smart speakers bring convenience to people's daily lives. However, various attacks can be launched against smart speakers to execute malicious commands, which may cause serious safety or security issues. The existing solutions against sophisticated attacks such as voice replay attacks and voice synthesis attacks require intrusive modifications of the smart speaker hardware and/or software, which are impractical for general users. In this work, we present a novel security scheme- VoiceGuard that can effectively detect and block unauthorized voice commands to smart speakers. VoiceGuard does not require any modification to smart speakers' hardware or software. We implement a prototype of VoiceGuard on two popular smart speakers: Amazon Echo Dot and Google Home Mini, and evaluate the scheme in three real-world testbeds, which include both single-user and multi-user scenarios. The experimental results show that VoiceGuard achieves an accuracy of 97% in blocking malicious voice commands issued by illegitimate sources while having a negligible impact on the user experience.

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Correction to: Michelson Interferometer for Global High-Resolution Thermospheric Imaging (MIGHTI) On-Orbit Wind Observations: Data Analysis and Instrument Performance

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Space Science Reviews 2023年第6期219卷 1-2页

作者： Englert, Christoph R. Harlander, John M. Marr, Kenneth D. Harding, Brian J. Makela, Jonathan J. Fae, Tori Brown, Charles M. Ratnam, M. Venkat Rao, S. Vijaya Bhaskara Immel, Thomas J. Space Science Division U.S. Naval Research Laboratory Washington USA Space Systems Research Corporation Alexandria USA Space Sciences Laboratory University of California Berkeley Berkeley USA Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana USA National Atmospheric Research Laboratory Pakala India Department of Physics Sri Venkateswara University Tirupati India

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Improved Particle Swarm Optimization Algorithm for EIT Image Reconstruction

Improved Particle Swarm Optimization Algorithm for EIT Image...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Oumayma Kahouli Mariem Hafsa Hiba Hellara Imed Eddine Bennour Najoua Essoukri Ben Amara Olfa Kanoun Department of Computer Science National Engineering School of Sousse University of Sousse Sousse Tunisia Laboratory of Advanced Technology and Intelligent Systems National Engineering School of Sousse Laboratory of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Center of Sfax University of Sfax Tunisia

electrical Impedance Tomography (EIT) is a clinical imaging technique that gained a lot of attention because it is a non-invasive and radiation-free method. To obtain an inner image of the domain under study, a specific number of electrodes is attached to the surface of the object, then through a pair of electrodes, a low alternative current is injected. Potentials produced by this injection are measured with the remaining electrodes. Based on these boundary voltages measurements, the inner conductivity can be inferred, thus the image is reconstructed. The focus of this paper is to overcome the major weakness of the EIT image reconstruction technique, which is the low spatial resolution of the reconstructed images caused by the non-linearity of EIT inverse problem. This paper considers this problem, an optimization problem that is solved using the Particle Swarm Optimization (PSO) meta-heuristic. A variant of the standard PSO is proposed and validated, first on a circular mesh composed of 686 triangles then on a lung mesh composed of 2707 triangles.

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Toward Scene Understanding with Depth and Object-Aware Clustering in Contested Environment

Toward Scene Understanding with Depth and Object-Aware Clust...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Manish Bhurtel Yuba R. Siwakoti Danda B. Rawat Brian M. Sadler John M. Fossaceca Daniel O. Rice Department of Electrical Engineering and Computer Science Howard University Washington DC USA U.S. Army Research Laboratory Adelphi MD USA FAST Labs BAE Systems Burlington MA USA

Scene understanding in a contested battlefield is one of the very difficult tasks for detecting and identifying threats. In a complex battlefield, multiple autonomous robots for multi-domain operations are likely to track the activities of the same threat/objects leading to inefficient and redundant tasks. To address this problem, we propose a novel and effective object clustering framework that takes into account the position and depth of objects scattered in the scene. This framework enables the robot to focus solely on the objects of interest. Our system model incorporates YOLOv5 object detection model (ODM) and a pre-trained depth estimation model (DEM), whose outputs are fed into the KMeans Clustering (KM C) model. To train YOLOv5, we create our own battlefield dataset entitled “Common Objects in Battlefield (COBA)” which is split into a training dataset and a validation dataset using our customized object-aware stratified 7-fold cross-validation technique. We present the creation of minimum optimal clusters assuming the minimum number of available battlefield robots. These clusters exhibit accurate relative distances among objects, further validating the effectiveness of our approach for depth and object-aware clustering for scene understanding. Overall, our proposed approach facilitates efficient work division among robots, enhances decision-making capabilities, and helps to improve situational awareness in battlefield scenarios.

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Multi-modal Long-Short Distance Attention-based Transformer-GAN for PET Reconstruction with Auxiliary MRI

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Zeng, Pinxian Zeng, Xinyi Wang, Yan Zhou, Luping Zu, Chen Wu, Xi Zhou, Jiliu Shen, Dinggang Sichuan University School of Computer Science China University of Sydney School of Electrical and Information Engineering Australia Department of Risk Controlling Research *** China Chengdu University of Information Technology School of Computer Science China ShanghaiTech University School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd Shanghai200232 China Shanghai Clinical Research and Trial Center Shanghai201210 China

To obtain high-quality PET scans while minimizing potential radiation hazards for patients, various GAN-based methods have been developed to reconstruct high-quality standard-count PET (SPET) images from low-count PET (LPET) ones. While recent efforts try to integrate MRI or CT to enhance reconstruction in a multi-modal way, current architectures mainly face two limitations: (1) CNN backbones or simple Transformer bottleneck layers are insufficient for robust semantic understanding, and (2) the identical strategies for multi-modal feature extraction and fusion overlook each modality's respective importance for the reconstruction task. In this work, we propose the Multi-modal Long-Short Distance Attention-based Transformer-GAN (MLSDA-GAN), a novel network combining 3D transformer and CNN architecture for PET image reconstruction. Specifically, to extract fine-grained features with a small number of parameters, our MLSDA-GAN integrates multi-scale convolution into the embedding part of the transformer. As for our multi-modal design, given the strong correlation between LPET and SPET in structural characteristics, we treat MRI as an auxiliary modality to LPET and achieve effective multi-modal extraction and fusion strategies. These strategies include (1) a PET-specific Self-attention Extraction (PSE) block for comprehensive feature extraction of the primary LPET and (2) a Multi-modality Cross-attention Fusion (MCF) block for effective multi-modal interaction and fusion, enabling us to more efficiently model both long- and short-range relationships in the corresponding feature extraction and fusion processes. Experiments demonstrate superiority of our method quantitatively and qualitatively. Code is available at https://***/Aru321/MLSDA-GAN. © 1991-2012 IEEE.

关键词： Radiation hazards

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Joint Optimization Air Base Stations Deployment and User Association in Heterogeneous Space-Air-Ground Integrated Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Ao, Shaoyou Niu, Yong Han, Zhu Xiong, Lei Wang, Ning Ai, Bo Beijing Jiaotong University Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications School of electronic and Information Engineering Beijing100044 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Zhengzhou University School of Information Engineering Zhengzhou450001 China

With the development of 6 G and beyond, space-air-ground integrated networks (SAGINs) have evolved into key elements to promote high-speed and seamless connections for users. This paper studies a heterogeneous SAGIN with satellite, multiple unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) serve as aerial base stations (ABSs) in a scenario where ground base stations (GBSs) are overloaded. To optimize the number of ABSs within the system's power limit and maximize the system's downlink throughput, we optimize the 3D deployment of ABSs and user association in the millimeter wave (mmWave) frequency band. We formulate an optimization problem to satisfy the access quality of service (QoS) requirements. This problem is a mixed- integer nonlinear programming problem. Therefore, we divide it into two subproblems: the heterogeneous network ABS deployment problem and the user association problem. We propose a Broyden-Fletcher-Goldfarb-Shanno (BFGS) formula based sequential quadratic programming (SQP) algorithm for the ABS deployment problem and a coalition game algorithm for the user association problem. Finally, we employ a block coordinate descent (BCD) algorithm to iteratively solve the two subproblems until convergence. Extensive simulations with various parameters and user deployments demonstrate that our algorithm outperforms other selected schemes. © 1967-2012 IEEE.

关键词： Quadratic programming

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MIM and MIIM-Based Optical Rectennas for Infrared Energy Harvesting at 10.6 μm

MIM and MIIM-Based Optical Rectennas for Infrared Energy Har...

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Mediterrannean Microwave Symposium (MMS)

作者： Ali Yahyaoui Ahmad Elsharabasy Jawad Yousaf Hatem Rmili Taoufik Aguili University of Tunis El Manar (UTM) National Engineering School of Tunis (ENIT) Communications Systems Laboratory (SysCom) Tunis Tunisia Electrical and Computer Engineering Department McMaster University Hamilton ON Canada Department of Electrical Computer and Biomedical Engineering Abu Dhabi University United Arab Emirates Electrical and Computer Engineering Department Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

This work presents a comparative study between metal-insulator-metal (MIM) and metal-insulator-insulator-metal (MIIM) based optical log spiral rectennas for infrared IR energy harvesting at 10.6 μm. We have considered the spiral antenna terminals as the electrodes of the rectifying diode and we have integrated the insulators between the two arms (electrodes) to enhance the harvested energy with the proposed nano-antennas. The study presents a comparison between the performance (E-field, I/V, responsivity, and resistivity) of the two proposed rectennas.

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National laboratory, Lemont, USA." Argonne National laboratory was left off the original publication. © The Author(s) 2025.

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Track Coalescence and Repulsion in Multitarget Tracking: An Analysis of MHT, JPDA, and Belief Propagation Methods

arXiv

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arXiv 2023年

作者： Kropfreiter, Thomas Meyer, Florian Crouse, David F. Coraluppi, Stefano Hlawatsch, Franz Willett, Peter The Scripps Institution of Oceanography The Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA United States The Naval Research Laboratory WashingtonDC United States Systems & Technology Research WoburnMA United States The Institute of Telecommunications TU Wien Vienna Austria The Department of Electrical and Computer Engineering University of Connecticut StorrsCT United States

Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT). However, they are known to exhibit undesirable behavior in tracking scenarios with targets in close proximity: JPDA filter methods suffer from the track coalescence effect, i.e., the estimated tracks of targets in close proximity tend to merge and can become indistinguishable, while MHT methods suffer from an opposite effect known as track repulsion, i.e., the estimated tracks of targets in close proximity tend to repel each other in the sense that their separation is larger than the actual distance between the targets. In this paper, we review the JPDA filter and MHT methods and discuss the track coalescence and track repulsion effects. We also consider a more recent methodology for MTT that is based on the belief propagation (BP) algorithm. We argue that BP-based MTT does not exhibit track repulsion because it is not based on maximum a posteriori estimation, and that it exhibits significantly reduced track coalescence because certain properties of the BP messages related to data association encourage separation of target state estimates. Our theoretical arguments are confirmed by numerical results for four representative simulation scenarios. © 2023, CC BY.

关键词： Coalescence

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Loss-induced phase transition in mid-infrared plasmonic metamaterials for ultrasensitive vibrational spectroscopy

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InfoMat 2022年第12期4卷 85-102页

作者： Hong Zhou Dongxiao Li Zhihao Ren Xiaojing Mu Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Key Laboratory of Optoelectronic Technology&Systems of Ministry of Education International R&D center of Micro-nano Systems and New Materials TechnologyChongqing UniversityChongqingPeople's Republic of China Center for Intelligent Sensors and MEMS(CISM) National University of SingaporeSingaporeSingapore National University of Singapore Suzhou Research Institute(NUSRI) SuzhouPeople's Republic of China NUS Graduate School—Integrative Sciences and Engineering Programme(ISEP) National University of SingaporeSingaporeSingapore

Metamaterials have proven their ability to possess extraordinary physical properties distinct from naturally available materials,leading to exciting sensing functionalities and ***,metamaterial-based sensing applications suffer from severe performance limitations due to noise interference and design ***,we propose a dual-phase strategy that leverages loss-induced different Fano-resonant phases to access both destructive and constructive signals of molecular *** the two reverse signals are innovatively combined,the noise in the detection system is effectively suppressed,thereby breaking through the noise-related ***,by utilizing loss optimization of the plasmon-molecule coupling system,our dual-phase strategy enhances the efficiency of infrared energy transfer into the molecule without any additional fabrication complex,thereby overcoming the trade-off dilemma between performance and fabrication *** to the pioneering breakthroughs in the limitations,our dual-phase strategy possesses an overwhelming competitive advantage in ultrasensitive vibrational spectroscopy over traditional metamaterial technology,including strong signal strength(×4),high sensitivity(×4.2),effective noise suppression(30%),low detection limit(13 ppm),and excellent selectivity among CO_(2),NH_(3),and CH_(4) *** work not only opens the door to various emerging ultrasensitive detection applications,including ultrasensitive in-breath diagnostics and high-information analysis of molecular information in dynamic reactions,but also gains new insights into the plasmon-molecule interactions in advanced metamaterials.

关键词： Fano resonance IR vibrational spectroscopy metamaterials phase transition ultrasensitive sensing

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