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Conference on Lasers and Electro-Optics (CLEO)

作者： Kellen P. Arnold Joshua A. Allen Sami I. Halimi Landen D. Ryder Francis O. Afzal Yusheng Bian Abdelsalam Aboketaf Kevin Dezfulian Michal Rakowski Rod Augur Takako Hirokawa Karen Nummy Sharon M. Weiss Interdisciplinary Materials Science Program Vanderbilt University Nashville TN USA Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA NASA Goddard Space Flight Center Greenbelt MD US GlobalFoundries Malta NY USA

We report deep subwavelength-engineered slotted photonic crystal nanobeams with minimum features < 70 nm fabricated using a 90 nm monolithic silicon photonics technology at GlobalFoundries. This work enables potent... 详细信息

ISBN: (纸本)9781957171258

关键词： Deep ultraviolet Integrated photonics Photonic crystals Photonics applications Scanning electron microscopy Silicon photonics

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Reducing information bottleneck for weakly supervised semantic segmentation 21

Reducing information bottleneck for weakly supervised semant...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Jungbeom Lee Jooyoung Choi Jisoo Mok Sungroh Yoon Department of Electrical and Computer Engineering Seoul National University Department of Electrical and Computer Engineering Seoul National University and Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (纸本)9781713845393

Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object. We interpret this phenomenon using the information bottleneck principle: the final layer of a deep neural network, activated by the sigmoid or softmax activation functions, causes an information bottleneck, and as a result, only a subset of the task-relevant information is passed on to the output. We first support this argument through a simulated toy experiment and then propose a method to reduce the information bottleneck by removing the last activation function. In addition, we introduce a new pooling method that further encourages the transmission of information from non-discriminative regions to the classification. Our experimental evaluations demonstrate that this simple modification significantly improves the quality of localization maps on both the PASCAL VOC 2012 and MS COCO 2014 datasets, exhibiting a new state-of-the-art performance for weakly supervised semantic segmentation.

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Meta-optic accelerators for object classifier

Meta-optic accelerators for object classifier

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Conference on Lasers and Electro-Optics (CLEO)

作者： Hanyu Zheng Quan Liu You Zhou Ivan I. Kravchenko Yuankai Huo Jason Valentine Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA Department of Computer Science Vanderbilt University Nashville TN USA Interdisciplinary Materials Science Program Vanderbilt University Nashville TN USA Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN USA Department of Mechanical Engineering Vanderbilt University Nashville TN USA

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in a high-speed and robust classifier with 93.1% accurac... 详细信息

ISBN: (纸本)9781957171258

关键词： Image analysis Image resolution Machine vision Neural networks Optical systems Signal processing

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Accelerating neural architecture search via proxy data

arXiv

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arXiv 2021年

作者： Na, Byunggook Mok, Jisoo Choe, Hyeokjun Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of AIIS ASRI INMC Interdisciplinary Program in AI Seoul National University Seoul Korea Republic of

Despite the increasing interest in neural architecture search (NAS), the significant computational cost of NAS is a hindrance to researchers. Hence, we propose to reduce the cost of NAS using proxy data, i.e., a representative subset of the target data, without sacrificing search performance. Even though data selection has been used across various fields, our evaluation of existing selection methods for NAS algorithms offered by NAS-Bench-1shot1 reveals that they are not always appropriate for NAS and a new selection method is necessary. By analyzing proxy data constructed using various selection methods through data entropy, we propose a novel proxy data selection method tailored for NAS. To empirically demonstrate the effectiveness, we conduct thorough experiments across diverse datasets, search spaces, and NAS algorithms. Consequently, NAS algorithms with the proposed selection discover architectures that are competitive with those obtained using the entire dataset. It significantly reduces the search cost: executing DARTS with the proposed selection requires only 40 minutes on CIFAR-10 and 7.5 hours on ImageNet with a single GPU. Additionally, when the architecture searched on ImageNet using the proposed selection is inversely transferred to CIFAR-10, a state-of-the-art test error of 2.4% is yielded. Our code is available at https://***/nabk89/NAS-with-Proxy-data. © 2021, CC BY-NC-ND.

关键词： Data reduction

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Comprehensive Performance Evaluation of Topcon, Hit, and Perc Tandem Solar Cells: Analysing Performance and Stability

SSRN

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SSRN 2024年

作者： Yi, Junsin Rahman, Rafi Ur Khokhar, Muhammad Quddamah Madara, Polgampola Chamani aida, Maha Nur Jony, Jaljalalul Abedin Alamgeer Yousuf, Hasnain Chu, Mengmeng Park, Sangheon Department of Electrical and Computer Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Department of Future Energy Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Research Institute for Clean Energy College of Information and Communication Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of

The evolution of photovoltaic technologies has led to significant advancements in tandem solar cells, notably TOPCon, HIT, and PERC cells. Historically, these technologies have demonstrated substantial improvements in efficiency and stability. This study conducts a comprehensive performance evaluation of TOPCon, HIT, and PERC tandem solar cells by measuring light current-voltage (LIV) characteristics in both series and parallel connections under varying sunlight intensities (0 to 0.5 suns). The maximum efficiency recorded for the TOPCon/TOPCon cells in series connection was 6.63%, while in parallel, it was 25.78%. For HIT/HIT cells, series and parallel connections yielded maximum efficiencies of 27.00% and 6.58%, respectively. PERC/PERC cells achieved efficiencies of 17.81% in series and 21.38% in parallel. These results were obtained by systematically varying the sunlight intensity from 0 to 0.5 suns, revealing that TOPCon technology excels under low-light conditions. Future work will optimize material properties and interconnection methods to enhance performance and commercial viability. © 2024, The Authors. All rights reserved.

关键词： Current voltage characteristics

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Generative ai for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Van Huynh, Nguyen Du, Hongyang Hoang, Dinh Thai Niyato, Dusit Zhu, Kun Kang, Jiawen Xiong, Zehui Jamalipour, Abbas In Kim, Dong The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Department of Electrical Engineering and Electronics University of Liverpool Liverpool L69 3GJ United Kingdom The School of Electrical and Data Engineering University of Technology Sydney Australia The School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney Australia The College of Information and Communication Engineering Sungkyunkwan University Korea Republic of

With recent advances in artificial intelligence (ai) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional ai methods. Generative ai (Gai), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of Gai in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various Gai techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of Gai in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of Gai in unmanned vehicle swarms and discuss potential research directions. © 2024, CC BY.

关键词： Metadata

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Developing an Explainable Deep Neural Network for Stress Detection Using Biosignals and Human-Engineered Features

SSRN

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SSRN 2024年

作者： Lee, Hyeokjong Chang, Jooyoung Kim, Jaewon Han, Bohyung Park, Sang Min Department of Biomedical Sciences Seoul National University College of Medicine Seoul03080 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of XAIMED Co. Ltd. Seoul02738 Korea Republic of

Detecting stress is crucial for preventing long-term health issues, and recent research, particularly using the WESAD (WEarable Stress and Affect Detection) dataset, has made significant advances in this area. Deep Neural Networks (DNNs) demonstrate a key advantage by efficiently processing raw signals, unlike traditional machine learning methods that predominantly rely on human-engineered *** our study, we enhance DNN capabilities by integrating both raw signals and human-engineered features altogether. Our model, augmented with attention mechanisms, significantly improves its performance. This approach yields state-of-the-art results, demonstrating high accuracies across various modalities in the stress classification task: 92.89% in Electrodermal Activity (EDA), 92.16% in Photoplethysmography (PPG), and 96.58% in all *** model decisions in biosignals is challenging compared to the image domain, where images are inherently more intuitive. Many Explainable ai (Xai) techniques, optimized for images, face difficulties when applied to biosignals. To address this, we employ human-engineered features for biosignals, grounded in the ‘Human Comprehensible Space’ concept, making explanations intuitively understandable. Furthermore, a survey conducted to statistically validate the comprehensibility of our Xai methods confirms their effectiveness in enhancing the accessibility of explanations. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Harnessing Outer Space for Improved Electrocaloric Cooling

SSRN

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SSRN 2024年

作者： Seo, Dong Hyun Kim, Hyung Rae Yun, Joo Ho Kim, Do Hyeon Heo, Se-Yeon Ma, Rujun Lee, Gil Ju Song, Young Min School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of School of Materials Science and Engineering National Institute for Advanced Materials Smart Sensing Interdisciplinary Science Center Nankai University Tianjin300350 China Department of Electronics Engineering Pusan National University Busan46241 Korea Republic of AI Graduate School Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of

Solid-state refrigeration, exploiting the electrocaloric (EC) effect, has sparked substantial interest due to its minimal energy consumption and zero-carbon emissions. However, one challenge to solid-state refrigeration technology resides in the limitation of cooling performance by the adiabatic temperature change (ΔT) inherent to caloric materials. To fully replace the traditional cooling technology, an augmentation of the innate heat dissipation capability beyond the intrinsic properties of the material is essential. In this study, we pioneer a method to transcend the native heat dissipation threshold of EC, by implementing a novel radiative heat sink/source-integrated electrocaloric (R-iEC) thermal regulation system. This system ingeniously leverages the outer cold space as a heat sink without energy. Moreover, a non-metallic thermally conductive radiative cooler (TCRC) is implemented with both a high thermal conductivity of 1.1 W/mK and exceptional spectral features (i.e., solar reflectivity ~96% and emissivity ~95% in atmospheric window region). The R-iEC system fortified with the TCRC demonstrates superior heat dissipation relative to conventional EC and radiative cooling, with an extra cooling heat flux of 87 W/m2. This improvement yields a temperature decrease of the heat source by 7.3 K relative to ambient temperature, showcasing the potential for a novel and zero-carbon emission cooling system. © 2024, The Authors. All rights reserved.

关键词： Heat flux

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Recent Advancements in Nanophotonics for Optofluidics

arXiv

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arXiv 2024年

作者： Yang, Sen Hong, Chuchuan Zhu, Guodong Anyika, Theodore Hong, Ikjun Ndukaife, Justus C. Institute of Physics Chinese Academy of Sciences Beijing National Laboratory for Condensed Matter Physics Beijing100190 China Department of Chemistry Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37235 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37240 United States

Optofluidics is dedicated to achieving integrated control of particle and fluid motion, particularly on the micrometer scale, by utilizing light to direct fluid flow and particle motion. The field has seen significant growth recently, driven by the concerted efforts of researchers across various scientific disciplines, notably for its successful applications in biomedical science. In this review, we explore a range of optofluidic architectures developed over the past decade, with a primary focus on mechanisms for precise control of micro and nanoscale biological objects and their applications in sensing. Regarding nanoparticle manipulation, we delve into mechanisms based on optical nanotweezers using nanolocalized light fields and light-based hybrid effects with dramatically improved performance and capabilities. In the context of sensing, we emphasize those works that used optofluidics to aggregate molecules or particles to promote sensing and detection. Additionally, we highlight emerging research directions, encompassing both fundamental principles and practical applications in the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Flow of fluids

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Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

Vision-based Semantic Communications for Metaverse Services:...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangyuan Liu Hongyang Du Dusit Niyato Jiawen Kang Zehui Xiong Boon Hee Soong Energy Research Institute@NTU Interdisciplinary Graduate Program School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

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