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MILCOM, Military Communications Conference

作者： Maryna Veksler David Langus Rodríguez Ahmet Aris Kemal Akkaya A. Selcuk Uluagac Dept. of Electrical and Computer Engineering Advanced Wireless and Security Lab. Florida International University Miami Florida USA Dept. of Electrical and Computer Engineering Cyber-Physical Systems Security Lab. Florida International University Miami Florida USA

The emerging proliferation of unmanned aerial vehicles (UAV) combined with their autonomous capabilities established the solid incorporation of UAVs for military applications. However, seamless deployment of drones into the adversarial environment and on the battlefield requires a robust and secure network stack, protected from adversarial intrusion. As LoRa became a low-cost solution for the long-distance control channel, it solved the challenge of long-range connectivity and prolonged lifespan present in UAV applications. However, the existing implementations lack protection mechanisms against unauthorized access. In this paper, we present LoFin, the first fingerprinting framework used to identify telemetry transceivers that communicate over the LoRa channel. LoFin exploits information leaked due to the differences in hardware structure, which results in processing time variations. Passively collecting network traffic, LoFin extracts unique signatures for UAV authentication while maintaining network integrity and data privacy. We evaluate the efficacy of our system on the extensive amount of network packets collected from the testbed of UAV transceivers in both isolated and adversarial settings. Our experiments demonstrate exceptional performance in distinguishing different LoRa-based devices with average precision and recall over 99 % and 96 % correspondinaly.

关键词： Performance evaluation Time series analysis Telecommunication traffic Fingerprint recognition Autonomous aerial vehicles Solids Transceivers

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Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor with MOSFET Structure

Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor wit...

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IEEE SENSORS

作者： Kamalesh Tripathy Dhanranjan Kumar Karthick Thiyagarajan Mitradip Bhattacharjee Department of Electrical Engineeringand Computer Science i-Lab Indian Institute of Science Education and Research Bhopal Bhopal Madhya Pradesh India Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith NSW Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

In this paper, we present a flexible capacitive tactile sensor based on a polydimethylsiloxane (PDMS) elastomer, which is well-suited for a wide range of applications. The sensor incorporates an n-channel depletion-mode MOSFET to achieve an extended gate architecture. To enhance the sensing performance, TiO 2 nanoparticles are incorporated as nanofillers, resulting in an improved dielectric material ideal for highly sensitive capacitive pressure and tactile sensors. The 99PDMS-01TiO 2 nanocomposite (comprising 99 wt % PDMS and 1 wt % TiO 2 ) is utilized for promising flexible tactile sensor applications due to the synergistic effects between the PDMS matrix and TiO 2 nanoparticles, which enhance both the mechanical and sensing properties of the composite. The fabricated sensor exhibits a sensitivity of $\mathbf{30} \boldsymbol{\mu}\mathbf{A}/\mathbf{kPa}$ within the range of 0-10.5 kPa and a sensitivity of $\mathbf{9} \boldsymbol{\mu}\mathbf{A}/\mathbf{kPa}$ in the range of 10.5-30 kPa.

关键词： Nanoparticles MOSFET Sensitivity Nanocomposites Capacitors Tactile sensors Logic gates Robustness Sensors Titanium dioxide

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Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis

Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yanzuo Lu Manlin Zhang Andy J Ma Xiaohua Xie Jianhuang Lai School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Pazhou Lab (HuangPu) Guangzhou China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Diffusion model is a promising approach to image generation and has been employed for Pose-Guided Person Image Synthesis (PGPIS) with competitive performance. While existing methods simply align the person appearance to the target pose, they are prone to overfitting due to the lack of a high-level semantic understanding on the source person image. In this paper, we propose a novel Coarse-to-Fine Latent Diffusion (CFLD) method for PGPIS. In the absence of image-caption pairs and textual prompts, we de-velop a novel training paradigm purely based on images to control the generation process of a pre-trained text-to-image diffusion model. A perception-refined decoder is designed to progressively refine a set of learnable queries and extract semantic understanding of person images as a coarse-grained prompt. This allows for the decoupling of fine-grained appearance and pose information controls at different stages, and thus circumventing the potential over-fitting problem. To generate more realistic texture details, a hybrid- granularity attention module is proposed to encode multi-scale fine-grained appearance features as bias terms to augment the coarse-grained prompt. Both quantitative and qualitative experimental results on the DeepFashion benchmark demonstrate the superiority of our method over the state of the arts for PGPIS. Code is available at https://***/YanzuoLu/CFLD.

关键词： Training Image synthesis Semantics Text to image Process control Diffusion models Generators

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Hybrid-Excited, Variable-Flux, and Inter-Modular Biased-Flux Motors: A Comparative Analysis

Hybrid-Excited, Variable-Flux, and Inter-Modular Biased-Flux...

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Iranian Conference on Electrical engineering (ICEE)

作者： Mohammad Amirkhani Ehsan Farmahini Farahani Alireza Eikani Mojtaba Mirsalim Javad Shokrollahi Moghani Department of Electrical Engineering Electrical Machines and Transformers Research Laboratory Amirkabir University of Technology Tehran Iran Electrical Power Research Group School of Engineering Newcastle University Newcastle upon Tyne U.K Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper analyses three prominent biased-flux permanent magnet (BFPM) motors and compares them with each other. The structures of these BFPMs are different from each other, but they share the same operation principle. These structures are, namely, hybrid-excited multi-tooth slot-PM (HETSPM), multi-tooth variable flux reluctance (TVFR), and inter-modular PM (IMPM). The reason for this selection of structure is because of complementary features that each one possesses. For example, the HETSPM motor places in between the TVFR and IMPM motors in terms of PM consumption and demagnetization. However, the IMPM structure produces high amount torque at high efficiency. The demagnetization of the HETSPM and IMPM structures is also investigated and is shown that, in the similar situation, the HETSPM provides a safer operation environment for the PMs. Two-dimensional finite element method is used to extract the important profiles of the mentioned structures.

关键词： Electrical engineering Magnetic flux Torque Motors Permanent magnets Finite element analysis Topology

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StressBal: Personalized Just-in-time Stress Intervention with Wearable and Phone Sensing

StressBal: Personalized Just-in-time Stress Intervention wit...

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Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable computers

作者： Yunjo Han Hyemin Lee Kobiljon E. Toshnazarov Youngtae Noh Uichin Lee Korea Advanced Institute of Science & Technology (KAIST) Korea Republic of Hanyang University Korea Republic of Electrical and Computer Engineering / Engineering school / Networked Systems Lab Inha University Korea Republic of KENTECH Korea Republic of School of Computing KAIST Korea Republic of

ISBN: (纸本)9781450394239

This paper proposes StressBal, a mobile system that offers personalized just-in-time stress intervention with mobile and wearable sensing. The system runs entirely on-device, detecting stress in real-time and providing respiratory exercise-based interventions. It also updates the stress detection algorithm continuously, based on user feedback. We envision an open mobile platform that helps researchers to design and evaluate automatic, seamless, and unobtrusive stress interventions during everyday activities.

关键词： Mobile and Wearable Computing Stress Intervention Just-in-time Intervention Stress Detection

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Adaptive Incentivize for Federated Learning with Cloud-Edge Collaboration under Multi-level Information Sharing

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IEEE Transactions on computers 2025年

作者： Yuan, Shijing Dong, Beiyu Li, Jie Guo, Song Chen, Hongyang Wu, Chentao Wu, Jie Zhao, Wei China Telecom Research Institute Department of Fundamental Network Operations Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Hong Kong University of Science and Technology Department of Computing Hong Kong Zhejiang Lab Hangzhou311121 China Temple University Department of Computer and Information Sciences United States CAS Shenzhen Institute of Advanced Technology Shenzhen518055 China

Federated Learning with Cloud-Edge Collaboration (FL-CEC) has emerged as a cutting-edge paradigm in distributed learning. Efficient resource investment incentive mechanisms are crucial to encouraging clients in FL-CEC to contribute the necessary data and computational resources for training. However, existing studies are inadequate in meeting the incentive design requirements under multi-level information-sharing scenarios. Moreover, current works often rely on specific functional relationships between resource investment and global model accuracy. To bridge these gaps, this paper investigates the incentive problem for data and computational resource investment under multi-level information-sharing levels. We design a resource investment incentive mechanism based on a weighted potential game without depending on any specific functional relationship between data investment and model accuracy. Furthermore, we propose four algorithms to solve resource investment strategies for different levels of information sharing. The complexity and convergence rates of the proposed algorithms are thoroughly analyzed. Finally, we construct a simulation incentive platform on the Aliyun. Extensive evaluations demonstrate that the proposed scheme effectively enhances social welfare, and improves collaborative training accuracy and efficiency. © 1968-2012 IEEE.

关键词： Open access

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MAD-TI: Meta-Path Aggregated-Graph Attention Network for Drug Target Interaction Prediction

MAD-TI: Meta-Path Aggregated-Graph Attention Network for Dru...

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Iranian Conference on Electrical engineering (ICEE)

作者： Reza Shami Tanha Maryam Sadighian Arash Zabihian Mohsen Hooshmand Mohsen Afsharchi dept. Computer Science and IT Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran lab. Bioinformatics and Drug Design (LBD) Institute of Biochemistry and Biophysics University of Tehran Tehran Iran dept. Electrical and Computer Engineering University of Zanjan Zanjan Iran

Computational identification of unknown drug-target interactions (DTI) is crucial in locating new drug treat-ments for proteins, viruses, and diseases. This work proposes MAD-TI a meta-path-based, GAT-oriented method to predict DTIs. Our proposed method uses a heterogeneous graph of drugs, targets, diseases, and side effects as the input graph. Then, it applies two graph attention networks to generate the embeddings of drugs and targets. Using the embeddings, it predicts the unknown DTIs. The results show that MAD-TI outperforms the state-of-the-art methods.

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Interactive Activities Initiation through Retrieving Hidden Social Information Networks

Interactive Activities Initiation through Retrieving Hidden ...

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IEEE International Conference on Data Mining (ICDM)

作者： Yulong Song Bin Fu Jianxiong Guo Xiaofeng Gao Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China Department of Computer Science University of Texas Rio Grande Valley Texas USA Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China

The rise of social platforms based on online social networks has greatly enriched people’s lives, resulting in various applications. Traditional research mainly focuses on users but pays less attention to the edges between users, and they all assume the topology of social networks is known in advance. Indeed, obtaining the network topology is challenging because of privacy protection and business competition. In this paper, we propose an activity initiation problem inspired by real business applications, such as Pinduoduo and Tencent, where each edge can be abstracted as an activity in which both ends (users) of the edge participate together, and the edge can be initiated by one of them. At this time, we hope to select as few users as possible to initiate activities and make the users of the whole network participate together. This problem can be reduced to the classic vertex cover problem, but the network information is hidden by social platforms as much as possible. To address this challenge, we put forward a solver-detector model. In each round of interaction, the solver uses a detector to obtain a small amount of edge information and achieves vertex coverage. This is a model in which a solver has limited access to input, but still gives a 2-approximation that is as good as the conventional model. Finally, we can cover the whole network with very few edge samples, which is a brand-new research perspective.

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Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance

arXiv

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arXiv 2023年

作者： Ju, Ce Kobler, Reinmar Josef Guan, Cuntai S-Lab and School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore Singapore RIKEN Artificial Intelligence Project Tokyo Japan Advanced Telecommunications Research Institute International Kyoto Japan

The efficacy of Electroencephalogram (EEG) classifiers can be augmented by increasing the quantity of available data. In the case of geometric deep learning classifiers, the input consists of spatial covariance matrices derived from EEGs. In order to synthesize these spatial covariance matrices and facilitate future improvements of geometric deep learning classifiers, we propose a generative modeling technique based on state-of-the-art score-based models. The quality of generated samples is evaluated through visual and quantitative assessments using a left/right-hand-movement motor imagery dataset. The exceptional pixel-level resolution of these generative samples highlights the formidable capacity of score-based generative modeling. Additionally, the center (Frechet mean) of the generated samples aligns with neurophysiological evidence that event-related desynchronization and synchronization occur on electrodes C3 and C4 within the Mu and Beta frequency bands during motor imagery processing. The quantitative evaluation revealed that 84.3% of the generated samples could be accurately predicted by a pre-trained classifier and an improvement of up to 8.7% in the average accuracy over ten runs for a specific test subject in a holdout experiment. Copyright © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

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A Distributed Deep Learning Approach for A Team of Unmanned Aerial Vehicles for Wildfire Tracking and Coverage

A Distributed Deep Learning Approach for A Team of Unmanned ...

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IEEE International Conference on Robotic Computing (IRC)

作者： Kripash Shrestha Hung Manh La Hyung-Jin Yoon Amazon Robotics Research Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno USA Department of Mechanical Engineering University of Nevada Reno USA

ISBN: (纸本)9781665472616

Recent large wildfires in the United States and the subsequent damage that they have caused have increased the importance of wildfire monitoring and tracking. However, human monitoring on the ground or in the air may be too dangerous and therefore, there need to be alternatives to monitoring wildfires. Unmanned Aerial Vehicles (UAVs) have been previously used in this problem domain to track and monitor wildfires with approaches such as artificial potential fields and reinforcement learning. Our work aims to look at a team of UAVs, in a distributed approach, over an area to maximize the sensor coverage in dynamic wildfire environments. We proposed and implemented the Deep Q-Network (DQN) with a state estimator (auto-encoder), then compared it to existing methods including a Q-learning, a Q-learning with experience replay, and a DQN. The proposed DQN with a state estimator outperformed existing deep learning methods in terms of reward maximization and convergence.

关键词： Deep learning Q-learning Computational modeling Fires Autonomous aerial vehicles Vehicle dynamics Task analysis

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