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2023 International Conference on System Science and engineering, ICSSE 2023

作者： Wong, Shang-Wen Chiu, Yu-Chen Tsai, Chi-Yi Tamkang University Doctoral Program in Robotics College of Engineering New Taipei City Taiwan Tamkang University Department of Electrical and Computer Engineering New Taipei City Taiwan

ISBN: (纸本)9798350322941

This paper proposes a pose estimation system for robot grasping based on a novel Object Affordance Detection and Segmentation (OADS) network. The proposed system consists of four modules: (1) OADS network;(2) point cloud extraction;(3) object pose estimation;(4) grasp pose estimation. Based on the OADS network, the proposed system achieves affordance-based object pose estimation results. The proposed grasp pose estimation system is evaluated on a laboratory-made dual-arm robot. Experimental results show that the proposed system achieves high detection rate and high accuracy in affordance detection and segmentation tasks, leading to a high success rate in object grasping tasks with lab-made dual-arm robot. © 2023 IEEE.

关键词： Object detection

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Automated classification of renal tumors on ultrasound images using deep neural network 9

Automated classification of renal tumors on ultrasound image...

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9th International Conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Wu, Wei-Chieh Chiang, Te-Mei Tsai, Zhi-Ren Asia University Program in Artificial Intelligence College of Information and Electrical Engineering Taichung City Taiwan Asia University Hospital Department of Radiation Oncology Taichung City Taiwan Asia University Department of Computer Science & Information Engineering Taiwan

ISBN: (纸本)9798350363043

Kidney cancer is one of the most common cancers worldwide. The major types of malignant renal tumors are renal cell carcinoma and urothelial carcinoma. Although the majority of renal tumors are malignant, up to 20% are benign, most commonly renal cyst and angiomyolipomas. Ultrasound is the most accessible imaging tool in medical practice, but it highly depends on operator skill, which may lead to high false-negative rate in diagnosis. The purpose of this study was to develop a predictive model for the automated classification of renal tumors on ultrasound images using deep neural network. A total of 880 kidney ultrasound images were used for training and testing. Transfer learning was used to the ten Convolution neural network models. The kidney ultrasound images were classified as benign or malignant tumors. The classification performance of the model was evaluated by sensitivity and specificity. The research results show that VGG18 yielded the best performance, with a sensitivity of 79%, and a specificity of 86%. © 2024 IEEE.

关键词： Ultrasonic imaging

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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

IEEE Open Journal of Nanotechnology

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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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FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior

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IEEE Access 2025年 13卷 87753-87776页

作者： Huang, Xiang-Rui Chen, Liang-Bi National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu880011 Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan

In the past decade, studies on illegal fishing have neglected to consider illegal underwater fishing. Traditionally, supervisor-based methods have been used to manually interpret underwater behavior;however, existing artificial intelligence (AI) and Internet of Things (IoT) methods are not applicable to illegal underwater fishing. Therefore, in this study, we used a computer vision AI method to perform the challenging task of illegal underwater fishing image recognition. Illegal underwater fishing images have unclear features and high feature similarity. In this study, we define normal underwater travel as wearing diving clothing. Abnormal underwater behavior includes underwater personnel with illegal fishing equipment. Herein, we propose a feature attention front-end network (FA-FENet), a novel end-to-end convolutional neural network (CNN) architecture that differs from previous methods in that it allows flexible integration with various backbone networks. The front end of the backbone network can be directly replaced to improve the learning efficiency and performance of the proposed network. The convolutional layers in our proposed FA-FENet include dilated convolutions, group convolutions, padded convolutions, pooling layers, and activation functions. We also comprehensively review the performance of the proposed FA-FENet. A series of ablation studies demonstrate that the proposed FA-FENet can be applied in shallow backbone networks. In addition, FA-FENet has potential in emerging networks with good performance in illegal underwater fishing identification tasks. Finally, we conducted experiments with an underwater fishing behavior dataset. The experimental results demonstrate that our proposed method outperforms recently reported methods with attention mechanisms. Specifically, FA-FENet-RegNet achieves an accuracy of 88.81%, FA-FENet-ResNet18 reaches 78.32%, and FA-FENet-ShuffleNetV2 attains 80.41%. © 2013 IEEE.

关键词： Convolutional neural networks

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Fast non-iterative algorithm for 3D point-cloud holography

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Optics Express 2023年第22期31卷 36468-36485页

作者： Ersaro, Nathan Tessema Yalcin, Cem Murray, Liz Kabuli, Leyla Waller, Laura Muller, Rikky Graduate Program in Bioengineering University of California Berkeley and University of California San Francisco BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States

Recently developed iterative and deep learning-based approaches to computer-generated holography (CGH) have been shown to achieve high-quality photorealistic 3D images with spatial light modulators. However, such approaches remain overly cumbersome for patterning sparse collections of target points across a photoresponsive volume in applications including biological microscopy and material processing. Specifically, in addition to requiring heavy computation that cannot accommodate real-time operation in mobile or hardware-light settings, existing sampling-dependent 3D CGH methods preclude the ability to place target points with arbitrary precision, limiting accessible depths to a handful of planes. Accordingly, we present a non-iterative point cloud holography algorithm that employs fast deterministic calculations in order to efficiently allocate patches of SLM pixels to different target points in the 3D volume and spread the patterning of all points across multiple time frames. Compared to a matched-performance implementation of the iterative Gerchberg-Saxton algorithm, our algorithm’s relative computation speed advantage was found to increase with SLM pixel count, reaching >100,000x at 512 × 512 array format. © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Iterative methods

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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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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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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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