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International Conference on Electronics Packaging (ICEP)

作者： Qinghua Su Cadmus Yuan K.N. Chiang Taiwan Dept. of Power Mechanical Engineering Advanced Microsystem Packaging and Nano-Mechanics Research Lab National Tsing Hua University Hsinchu Taiwan R.O.C. Dept. of Mechanical and Computer-Aided Engineering Feng Chia University Taichung Taiwan R.O.C.

ISBN: (数字)9784991191176

ISBN: (纸本)9798350385908

Applying AI-assisted design on simulation (AI-DoS) requires sufficient training database that is generated by a validated, parametric nonlinear finite element model. This study focuses to investigate a training database design method to achieve the balance between the AI model training accuracy and the computational efforts of the FE model execution. Considering the importance of training data, we will use the K-Medoids to select data points for training set. Due to small data, ensemble learning is introduced to enhance the prediction performance of the ANN model.

关键词： Training Semiconductor device modeling Databases Computational modeling Training data Artificial neural networks Predictive models

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OpenSlot: Mixed Open-Set Recognition with Object-Centric Learning

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IEEE Transactions on Multimedia 2025年

作者： Yin, Xu Pan, Fei An, Guoyuan Huo, Yuchi Xie, Zixuan Yoon, Sung-Eui Korea Advanced Institute of Science and Technology School of Computing Daejeon34141 Korea Republic of University of Michigan School of Computer Science Engineering United States Zhejiang University State Key Lab of CAD and CG China Zhejiang Lab 310058 China University of Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China Korea Advanced Institute of Science and Technology Faculty of School of Computing Deajeon34141 Korea Republic of

Existing open-set recognition (OSR) studies typically assume that each image contains only one class label, with the unknown test set (negative) having a disjoint label space from the known test set (positive), a scenario referred to as full-label shift. This paper introduces the mixed OSR problem, where test images contain multiple class semantics, with both known and unknown classes co-occurring in the negatives, leading to a more complex super-label shift that better reflects real-world scenarios. To tackle this challenge, we propose the OpenSlot framework, based on object-centric learning, which uses slot features to represent diverse class semantics and generate class predictions. The proposed anti-noise slot (ANS) technique helps mitigate the impact of noise (invalid or background) slots during classification training, addressing the semantic misalignment between class predictions and ground truth. We evaluate OpenSlot on both mixed and conventional OSR benchmarks. Without elaborate designs, our method not only excels existing approaches in detecting super-label shifts across OSR tasks, but also achieves state-of-the-art performance on conventional benchmarks. Meanwhile, OpenSlot can localize class objects without using bounding boxes during training, demonstrating competitive performance in open-set object detection and potential for generalization. © 1999-2012 IEEE.

关键词： Object detection

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LoFin: LoRa-based UAV Fingerprinting Framework

LoFin: LoRa-based UAV Fingerprinting Framework

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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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Scalable Electrophysiology of Millimeter-Scale Animals with Electrode Devices

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Biomedical engineering Frontiers 2023年第1期4卷 3-20页

作者： Kairu Dong Wen-Che Liu Yuyan Su Yidan Lyu Hao Huang Nenggan Zheng John A.Rogers Kewang Nan College of Pharmaceutical Sciences Zhejiang UniversityHangzhou 310058China National Key Laboratory of Advanced Drug Delivery and Release Systems Zhejiang UniversityHangzhou 310058China College of Biomedical Engineering&Instrument Science Zhejiang UniversityHangzhou310027China Department of Gastroenterology Brigham and Women’s HospitalHarvard Medical SchoolBostonMA 02115USA College of Chemical and Biological Engineering Zhejiang UniversityHangzhou 310058China Qiushi Academy for Advanced Studies Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhou 310058China CCAI by MOE and Zhejiang Provincial Government(ZJU) Hangzhou 310027China Querrey Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Department of Biomedical Engineering Northwestern UniversityEvanstonIL 60208USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL 60208USA Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA Jinhua Institute of Zhejiang University Jinhua 321299China

Millimeter-scale animals such as Caenorhabditis elegans,Drosophila larvae,zebrafish,and bees serve as powerful model organisms in the fields of neurobiology and *** methods exist for recording large-scale electrophysiological signals from these *** approaches often lack,however,real-time,uninterrupted investigations due to their rigid constructs,geometric constraints,and mechanical mismatch in integration with soft *** recent research establishes the foundations for 3-dimensional flexible bioelectronic interfaces that incorporate microfabricated components and nanoelectronic function with adjustable mechanical properties and multidimensional variability,offering unique capabilities for chronic,stable interrogation and stimulation of millimeter-scale animals and miniature tissue *** review summarizes the most advanced technologies for electrophysiological studies,based on methods of 3-dimensional flexible bioelectronics.A concluding section addresses the challenges of these devices in achieving freestanding,robust,and multifunctional biointerfaces.

关键词： offering recording millimeter

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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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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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