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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection 38

Textual Training for the Hassle-Free Removal of Unwanted Vis...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lee, Saehyung Mok, Jisoo Park, Sangha Shin, Yongho Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Qualcomm Korea YH Seoul Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT. © 2024 Neural information processing systems foundation. All rights reserved.

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Adaptive Strategies and Handover Optimization in 5G Networks to Address High Mobility Challenges

Adaptive Strategies and Handover Optimization in 5G Networks...

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2024 International Conference on electrical engineering and Informatics, ICELTICs 2024

作者： Murhaban Syahrial Arif, Teuku Yuliar Arnia, Fitri Doctoral Program School of Engineering Universitas Syiah Kuala Universitas Teuku Umar Departement Information Technology Engineering Banda Aceh Aceh Barat Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9798350366822

This study develops an adaptive handover strategy in 5G networks to tackle high mobility challenges and improve service continuity and quality. The research method uses a 5G network emulator with a topology of four base stations (gNBs) to test user equipment (UE) speeds of 20 km/h and 130 km/h. The adaptive algorithm developed utilizes data from the Received Signal Strength Indicator (RSSI) as well as the speed and direction of user mobility to make optimal handover decisions. The results show that at a speed of 130 km/h, the average handover latency reaches 12 ms, compared to 8 ms at 80 km/h. Additionally, the signal quality (RSSI) is lower at higher speeds, negatively affecting service quality. This adaptive algorithm successfully reduces handover latency and improves the stability and efficiency of the overall network. The testing also indicates that this adaptive algorithm can predict handover targets by considering UE speed and direction and uses hysteresis mechanisms to prevent overly frequent handovers. By employing technologies such as Multiple Input Multiple Output (MIMO) and beamforming, this strategy is expected to maintain high service quality even under high mobility conditions. © 2024 IEEE.

关键词： 5G mobile communication systems

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Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization 38

Unsupervised Homography Estimation on Multimodal Image Pair ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Song, Sanghyeob Lew, Jaihyun Jang, Hyemi Yoon, Sungroh Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Samsung Electro-Mechanics Korea Republic of

Estimating the homography between two images is crucial for mid- or high-level vision tasks, such as image stitching and fusion. However, using supervised learning methods is often challenging or costly due to the difficulty of collecting ground-truth data. In response, unsupervised learning approaches have emerged. Most early methods, though, assume that the given image pairs are from the same camera or have minor lighting differences. Consequently, while these methods perform effectively under such conditions, they generally fail when input image pairs come from different domains, referred to as multimodal image pairs. To address these limitations, we propose AltO, an unsupervised learning framework for estimating homography in multimodal image pairs. Our method employs a two-phase alternating optimization framework, similar to Expectation-Maximization (EM), where one phase reduces the geometry gap and the other addresses the modality gap. To handle these gaps, we use Barlow Twins loss for the modality gap and propose an extended version, Geometry Barlow Twins, for the geometry gap. As a result, we demonstrate that our method, AltO, can be trained on multimodal datasets without any ground-truth data. It not only outperforms other unsupervised methods but is also compatible with various architectures of homography estimators. The source code can be found at: https://***/songsang7/AltO. © 2024 Neural information processing systems foundation. All rights reserved.

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AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neural Activity 37

AMAG: Additive, Multiplicative and Adaptive Graph Neural Net...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Li, Jingyuan Scholl, Leo Le, Trung Rajeswaran, Pavithra Orsborn, Amy Shlizerman, Eli Department of Electrical & Computer Engineering University of Washington SeattleWA United States Department of Bioengineering University of Washington SeattleWA United States Department of Applied Mathematics University of Washington SeattleWA United States

ISBN: (纸本)9781713899921

Latent Variable Models (LVMs) propose to model the dynamics of neural populations by capturing low-dimensional structures that represent features involved in neural activity. Recent LVMs are based on deep learning methodology where a deep neural network is trained to reconstruct the same neural activity given as input and as a result to build the latent representation. Without taking past or future activity into account such a task is non-causal. In contrast, the task of forecasting neural activity based on given input extends the reconstruction task. LVMs that are trained on such a task could potentially capture temporal causality constraints within its latent representation. Forecasting has received less attention than reconstruction due to recording challenges such as limited neural measurements and trials. In this work, we address modeling neural population dynamics via the forecasting task and improve forecasting performance by including a prior, which consists of pairwise neural unit interaction as a multivariate dynamic system. Our proposed model-Additive, Multiplicative, and Adaptive Graph Neural Network (AMAG)-leverages additive and multiplicative message-passing operations analogous to the interactions in neuronal systems and adaptively learns the interaction among neural units to forecast their future activity. We demonstrate the advantage of AMAG compared to non-GNN based methods on synthetic data and multiple modalities of neural recordings (field potentials from penetrating electrodes or surface-level micro-electrocorticography) from four rhesus macaques. Our results show the ability of AMAG to recover ground truth spatial interactions and yield estimation for future dynamics of the neural population. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Forecasting

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A Dual-Track Framework for Enhancing Age-Related Macular Degeneration Diagnosis Through Embedding and Vision Classification 22

A Dual-Track Framework for Enhancing Age-Related Macular Deg...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Balaha, Hossam Magdy Aboudessouki, Ahmed Mahmoud, Ali Ghazal, Mohammed El-Baz, Ayman J.B. Speed School of Engineering University of Louisville Bioengineering Department LouisvilleKY40292 United States Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331520526

Age-related macular degeneration (AMD) is a leading cause of vision loss in older adults, making timely and accurate diagnosis crucial for effective treatment. This study suggests a new framework for AMD diagnosis that integrates embedding extraction and direct vision classification to improve diagnostic performance. We employ a dual-track methodology, utilizing advanced pretrained models for embedding extraction (utilizing Virchow2) alongside a vision-based approach (utilizing Nomic) to classify fundus images into distinct AMD categories: (1) normal, (2) intermediate, (3) geographic atrophy, and (4) wet AMD. Our experiments achieved the best results, with an accuracy of 98.03%, an F1 score of 98.05%, and a precision of 98.08%. By experimentally investigating the applicability of pretrained embedding models originally designed for histopathological patches to fundus images, we bridge a gap in multimodal image analysis, potentially paving the way for improved diagnostic tools in ophthalmology. The experimental results demonstrate the potential of combining embedding extraction and direct vision classification to improve the accuracy of AMD detection, addressing the urgent need for automated diagnostic systems in the clinical environment. © 2025 IEEE.

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Application of 3D Printing and Design in Healthcare: A Focus on Undergraduate Research to Attract Students to bioengineering and Related Disciplines 54

Application of 3D Printing and Design in Healthcare: A Focus...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Zelada-Quiroz, Cindy M. Reyes-Santiago, Brian L. Ortiz-Rivera, Eduardo I. University of Puerto Rico at Mayagüez Bio-Engineering Graduate Program Mayagüez Puerto Rico University of Puerto Rico at Mayagüez Department of Computer Science and Engineering Mayagüez Puerto Rico University of Puerto Rico at Mayagüez Department of Electrical and Computer Engineering Mayagüez Puerto Rico

ISBN: (纸本)9798350351507

This research-to-practice paper describes developing and analyzing state-of-the-art smart boots created by combining CAD technology and advanced 3D printing techniques to attract students in bio-engineering and related fields. The primary objective of this innovative immobilization boot is to expedite fracture recovery phases through an ergonomic design to ensure optimal patient comfort during its use. Technological solutions are crucial in aiding the rehabilitation process for fractures caused by falls, heavy lifting, or rotational trauma. However, cost and comfort-related issues persist, underscoring the need for alternative approaches. This research addresses these challenges and delves into the broader implications of fracture treatment, catalyzing future projects and investigations in bioengineering. Additionally, this study serves as an educational tool that sparks the interest of high school and engineering students, promoting multidisciplinary collaboration in innovation. By involving students in specialized courses covering 3D design, human bone anatomy, biology, and materials science, this initiative empowers them to deepen their knowledge and develop new technologies to address bone injury problems. Material analyses include evaluating the type of material depending on the fracture site, such as PLA for printing and cotton and silicone gel for the midsection between the splint and the body. This research aims to advance our understanding of the type of fracture, the methods associated with their treatment, and tissue repair processes during bone callus formation. To summarize, this multidisciplinary approach drives advancements in bio-engineering and related fields, aiming to enhance patient outcomes and inspire students to pursue further research in bio-engineering and related fields. As part of this endeavor, a list of university-level courses based on the experience of the University of Puerto Rico at Mayaguez (UPRM), such as biology, bio-materials, 3D

关键词： Students

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Freestanding region-responsive bilayer for functional packaging of ingestible devices

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Microsystems & Nanoengineering 2023年第3期9卷 285-295页

作者： Michael A.Straker Joshua A.Levy Justin M.Stine Vivian Borbash Luke A.Beardslee Reza Ghodssi Fischell Department of Bioengineering University of MarylandCollege ParkMD 20742USA Institute for Systems Research University of MarylandCollege ParkMD 20740USA Robert E.Fischell Institute for Biomedical Devices University of MarylandCollege ParkMD 20850USA Department of Material Science and Engineering University of MarylandCollege ParkMD 20740USA Department of Electrical and Computer Engineering University of MarylandCollege ParkMD 20742USA

Ingestible capsules have the potential to become an attractive alternative to traditional means of treating and detecting gastrointestinal(GI)*** device complexity increases,so too does the demand for more effective capsule packaging technologies to elegantly target specific GI *** pH-responsive coatings have been traditionally used for the passive targeting of specific GI regions,their application is limited due to the geometric restrictions imposed by standard coating ***,pan,and spray coating methods only enable the protection of microscale unsupported openings against the harsh GI ***,some emerging technologies have millimeter-scale components for performing functions such as sensing and drug *** this end,we present the freestanding region-responsive bilayer(FRRB),a packaging technology for ingestible capsules that can be readily applied for various functional ingestible capsule *** bilayer is composed of rigid polyethylene glycol(PEG)under a flexible pH-responsive Eudragit®FL 30 D 55,which protects the contents of the capsule until it arrives in the targeted intestinal *** FRRB can be fabricated in a multitude of shapes that facilitate various functional packaging mechanisms,some of which are demonstrated *** this paper,we characterize and validate the use of this technology in a simulated intestinal environment,confirming that the FRRB can be tuned for small intestinal *** also show a case example where the FRRB is used to protect and expose a thermomechanical actuator for targeted drug delivery.

关键词： coating packaging attractive

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Offsets Reconstruction Method for Clock Skew Measurement Over High-Jitter Communication

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SN computer Science 2025年第5期6卷 1-14页

作者： Sastra, Nyoman Putra Saputra, Komang Oka Teng, Wei-Chung Study Program of Electrical Engineering Faculty of Engineering Udayana University Jalan Raya Kampus Bukit Jimbaran Bali Badung 80361 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Taipei 106 China

Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the clock skew sought. When some outliers separate from the thick line due to higher delay jitter, current advanced approaches use the lower bound or the majority of offsets to derive accurate clock skew. However, outliers in offsets that shape parallel dotted lines, reduce the availability of offsets in the lower region, and scatter widely the offsets from their majority zone. These conditions drive the current skew estimation approaches to lose their accuracy. This paper then developed a new method, which reconstructs back the outliers into their origin positions, in order to provide ideal offsets for clock skew measurement. The experimental evaluations have demonstrated that the clock skew accuracy of dotted-lines offsets tampered by outliers was improved from 2.1 to 0.9 ppm by the proposed reconstruction method. For a broader purpose, the reconstruction method has been adjusted to be implemented also on non-dotted-lines offsets. The robustness of the proposed method for this case has also been exhibited by the very ideal reconstructed offsets that shape like a thick line. © The Author(s) 2025.

关键词： Clock skew Delay jitter Dotted lines Hough transform

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Image denoising using diffractive optical processors

Image denoising using diffractive optical processors

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Isil, Çagatay Gan, Tianyi Ardic, F. Onuralp Mentesoglu, Koray Digani, Jagrit Karaca, Huseyin Chen, Hanlong Li, Jingxi Mengu, Deniz Jarrahi, Mona Aksit, Kaan Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States University College London Department of Computer Science London United Kingdom

We report an image denoising analog processor composed of passive diffractive layers engineered through deep learning to filter out various types of noise from input images, instantly projecting denoised images at the... 详细信息

ISBN: (纸本)9781957171395

关键词： Image denoising

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