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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin Poor, H. Vincent The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer and Information Technology Beijing Jiaotong University Beijing100044 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 Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Driven by advances in generative artificial intelligence (ai) techniques and algorithms, the widespread adoption of ai-generated content (aiGC) has emerged, allowing for the generation of diverse and high-quality content. Especially, the diffusion model-based aiGC technique has been widely used to generate content in a variety of modalities. However, the real-world implementation of aiGC models, particularly on resource-constrained devices such as mobile phones, introduces significant challenges related to energy consumption and privacy concerns. To further promote the realization of ubiquitous aiGC services, we propose a novel collaborative distributed diffusion-based aiGC framework. By capitalizing on collaboration among devices in wireless networks, the proposed framework facilitates the efficient execution of aiGC tasks, optimizing edge computation resource utilization. Furthermore, we examine the practical implementation of the denoising steps on mobile phones, the impact of the proposed approach on the wireless network-aided aiGC landscape, and the future opportunities associated with its real-world integration. The contributions of this paper not only offer a promising solution to the existing limitations of aiGC services but also pave the way for future research in device collaboration, resource optimization, and the seamless delivery of aiGC services across various devices. Our code is available at https://***/HongyangDu/DistributedDiffusion. © 2023, CC BY.

关键词： Wireless networks

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Deep Subwavelength Slotted Photonic Crystals Fabricated in a Monolithic Silicon Photonics Technology

Deep Subwavelength Slotted Photonic Crystals Fabricated in a...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Arnold, Kellen P. Allen, Joshua A. Halimi, Sami I. Ryder, Landen D. Afzal, Francis O. Bian, Yusheng Aboketaf, Abdelsalam Dezfulian, Kevin Rakowski, Michal Augur, Rod Hirokawa, Takako Nummy, Karen Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University 2201 WestEndAve. NashvilleTN37235 United States Department of Electrical and Computer Engineering Vanderbilt University 2201 WestEndAve. NashvilleTN37235 United States NASA GoddardSpace Flight Center Code 561 GreenbeltMD20771 United States GlobalFoundries 400 Stone Break Rd. Ext. MaltaNY12020 United States

ISBN: (纸本)9781957171258

关键词： Silicon photonics

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Enabling ai-Generated Content (aiGC) Services in Wireless Edge Networks

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu 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 Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (aiGC) refers to the use of ai to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of aiGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic aiGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the aiGC techniques and their applications in wireless networks. We then present the aiGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of aiGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

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Porous silicon on paper sensors: Factors affecting biosensing performance 17

Porous silicon on paper sensors: Factors affecting biosensin...

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Frontiers in Biological Detection: From Nanosensors to Systems XVII 2025

作者： An, Huijin Laibinis, Paul E. Locke, Andrea K. Weiss, Sharon M. Interdisciplinary Material Science Program Vanderbilt University NashvilleTN37235 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemical and Biomolecular Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemistry Vanderbilt University NashvilleTN37235 United States Department of Biomedical Engineering Vanderbilt University NashvilleTN37235 United States

ISBN: (纸本)9781510684249

A paper-based biosensor integrating a functionalized porous silicon (PSi) membrane as the active sensing element for quantifiable protein detection has been developed. For similar short-time exposures to an analyte, improved molecular transport in PSi membranes when on paper leads to larger signal changes compared to traditional PSi films that remain on a silicon substrate. In this work, we discuss controlling the incubation time of the analyte and the overall testing time of the sensor by incorporating different combinations of wicking and absorbent paper beneath the PSi membrane. With this control, the PSi-on-paper sensor platform has the potential to serve as an effective low-cost rapid diagnostic test with highly sensitive, quantitative readout for a wide range of analytes. © 2025 SPIE.

关键词： Porous silicon

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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Spectral Extremal Connectivity of Two-State Seizure Brain Waves

arXiv

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arXiv 2025年

作者： Talento, Mara Sherlin D. Richards, Jordan Pinto-Orellana, Marco Huser, Raphael Ombao, Hernando C. Statistics Program Computer Electrical and Mathematical Science & Engineering Division King Abdullah University of Science and Technology 23955 - 6900 Saudi Arabia Neuro-AI Laboratory Computer Electrical and Mathematical Science & Engineering Division King Abdullah University of Science and Technology 23955 - 6900 Saudi Arabia School of Mathematics Maxwell Institute for Mathematical Sciences University of Edinburgh EdinburghEH9 3FD United Kingdom Department of Biomedical Engineering University of California Irvine92697 United States

Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading findings when ignoring variations of connectivity in the tails of the distribution. This limitation becomes important when investigating extreme neural events that are characterized by large signal amplitudes. The focus of this paper is to examine connectivity in the tails of the distribution, as this reveals salient information that may be overlooked by standard methods. We develop a novel notion of spectral tail association of periodograms to study connectivity in the network of electroencephalogram (EEG) signals of seizure-prone neonates. We further develop a novel non-stationary extremal dependence model for multivariate time series that captures differences in extremal dependence during different brain phases, namely burst-suppression and non-burst-suppression. One advantage of our proposed approach is its ability to identify tail connectivity at key frequency bands that could be associated with outbursts of energy which may lead to seizures. We discuss these novel scientific findings alongside a comparison of the extremal behavior of brain signals for epileptic and non-epileptic patients. © 2025, CC BY.

关键词： Electroencephalography

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Boosting the Performance of Image Restration Models Through Training with Deep-Feature Auxiliary Guidance

SSRN

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SSRN 2024年

作者： Jang, Cheolhun Ji, Deahyun Cho, Nam Ik Interdisciplinary Program in Artificial Intelligence INMC Seoul National University Seoul08826 Korea Republic of Samsung Advanced Institute of Technology Suwon-si Gyenggi-do16678 Korea Republic of Department of Electrical and Computer Engineering INMC Seoul National University Seoul08826 Korea Republic of

Many neural network architectures have been suggested for image restoration models to enhance the accuracy of restored images while keeping the computational time reasonable. However, most previous studies have primarily focused on introducing new network architectures, with relatively less emphasis on efficient training techniques. This paper introduces a novel training strategy that improves the accuracy of image restoration models without increasing inference times by preserving their structures. To achieve this, we add auxiliary guidance at a feature map of each multi-scale level during the training process. All modules added to the model for the application of auxiliary guidance are only used during training time and can be removed at the inference phase, thereby eliminating the need for additional computation. Our proposed training method, named deep-feature auxiliary guidance (DFAG), can be easily applied to existing encoder-decoder architectures. By applying DFAG to train existing image restoration models, we have obtained enhanced performance in real-image denoising, Gaussian denoising, motion deblurring, JPEG artifact reduction, and super-resolution for all tested models. The source code and pre-train models will be released. © 2024, The Authors. All rights reserved.

关键词： Image denoising

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Instance Segmentation of Damaged Insulators for Power System Inspection and Maintenance

Instance Segmentation of Damaged Insulators for Power System...

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Decision aid Sciences and Application (DASA), International Conference on

作者： Jovencio Merin Alvin Sarraga Alon Joel Pinkihan Ricky C. Sandil Michael Angelo Delos Santos Ligayo Ryan Flores Arago Rovenson Sevilla Christopher Cunanan Dept. of Electrical Engineering Technological University of the Philippines Manila Philippines Department of Trade and Industry Center for AI Research Makati City Philippines College of Computing Sciences Ifugao State University Ifugao Philippines College Preparatory Program Kingdom of Saudi Arabia Saudi Arabia College of Engineering Quezon City University Quezon City Philippines Department of Electrical Engineering Technological University of the Philippines Manila Philippines Christopher Franco Cunanan Computer Engineering Department Bulacan State University Bulacan Philippines

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This paper introduces a novel approach for power system inspection using Mask-RCNN, an advanced model for instance segmentation. The accuracy of our model in identifying and diagnosing damaged low- to medium-voltage insulators is exceptional, as evidenced by its Mean Average Precision (mAP) score of 98.7 % • Throughout the training process, the model exhibited minimal loss metrics, indicating its proficiency in predicting bounding boxes precisely, segmenting, classifying, and aligning masks. The model exhibited consistent reliability in distinguishing between undamaged and damaged components during the inference process, as seen by its consistently high detection rates for both “normal intact” and “damaged broken” insulators. The results demonstrate the practical usefulness of the model and offer promising prospects for enhancing power system maintenance and inspection protocols. The model's consistent and accurate identification of damaged insulators has the potential to significantly enhance the safety and efficiency of power systems, thereby revolutionizing the industry.

关键词： Measurement Training Accuracy Medium voltage Inspection Predictive models Insulators Maintenance Power system reliability Safety

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Wireless,multimodal sensors for continuous measurement of pressure,temperature,and hydration of patients in wheelchair

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npj Flexible Electronics 2023年第1期7卷 479-493页

作者： Seokjoo Cho Hyeonseok Han Hyunwoo Park Sung-Uk Lee Jae-Hwan Kim Sung Woo Jeon Mengqiu Wang Raudel Avila Zhaoqian Xi Kabseok Ko Minsu Park Jungyup Lee Myungwoo Choi Je-Sang Lee Weon Gi Min Byeong-Ju Lee Soyeong Lee Jungrak Choi Jimin Gu Jaeho Park Min Seong Kim Junseong Ahn Osman Gul Chankyu Han Gihun Lee Seunghwan Kim Kyuyoung Kim Jeonghyun Kim Chang-Mo Kang Jahyun Koo Sung Soo Kwak Sungbong Kim Dong Yun Choi Seokwoo Jeon Hyung Jin Sung Yong Bae Park Minkyu Je Young Tae Cho Yong Suk Oh Inkyu Park Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Advanced 3D Printing Technology Development Division Korea Atomic Energy Research InstituteDaejeon 34057Republic of Korea Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian 116023P.R.China Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA DUT-BSU Joint Institute Dalian University of TechnologyDalian 116023China Ningbo Institute of Dalian University of Technology Ningbo 315016China Department of Electronics Engineering Kangwon National UniversityChuncheon 24341Republic of Korea Querrey-Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Sibel Health Inc NilesIL 60714USA Department of Materials Science and Engineering KAIST Institute for The Nanocentury(KINC)Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of planning and development Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of rehabilitation medicine Pusan national university hospitalBusan 49241Republic of Korea Department of AI Convergence Network Ajou UniversitySuwon 16499Republic of Korea Department of Electronic Convergence Engineering Kwangwoon UniversitySeoul 01897Republic of Korea Korea Photonics Technology Institute Gwangju 61007Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02841Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02841Republic of Korea Center

Individuals who are unable to walk independently spend most of the day in a *** population is at high risk for developing pressure injuries caused by ***,early diagnosis and prevention of these injuries still remain ***,we introduce battery-free,wireless,multimodal sensors and a movable system for continuous measurement of pressure,temperature,and hydration at skin *** device design includes a crack-activated pressure sensor with nanoscale encapsulations for enhanced sensitivity,a temperature sensor for measuring skin temperature,and a galvanic skin response sensor for measuring skin hydration *** movable system enables power harvesting,and data communication to multiple wireless devices mounted at skin-cushion interfaces of wheelchair users over full body *** evaluations and numerical simulations of the devices,together with clinical trials for wheelchair patients,demonstrate the feasibility and stability of the sensor system for preventing pressure injuries caused by sitting.

关键词： injuries modal mounted

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Comprehensive Study on Cherenkov Radiation for Ultrafast PET Detectors, Considering Refractive Index and Wavelength

Comprehensive Study on Cherenkov Radiation for Ultrafast PET...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： H.S. Shim H. Kang J. Kim J.S. Lee Y. Jeong Interdisciplinary Program of Bioengineering Seoul National University Seoul Republic of Korea Integrated Major in Innovative Medical Science Seoul National University Seoul Republic of Korea Department of Electrical and Computer Engineering Seoul National University Seoul Republic of Korea

This study aims to investigate the characteristics of Cherenkov radiation with respect to wavelength and refractive index. Refractive index is a function of wavelength, and when it changes, not only does the gain of Cherenkov radiation increase, but also the photon loss in the medium increases. Therefore, we examine how photons of different wavelengths behave under changing refractive index conditions. We adjust the refractive index to maintain a constant emission angle of Cherenkov radiation based on the deceleration rate of electrons moving in the medium. When electrons generating Cherenkov radiation move a total of 200 μm, both long-wavelength and short-wavelength photons are emitted at the initial stage; however, only short-wavelength photons are emitted at the late stage. Although the shorter-wavelength photons arrive first among the photons that reach the detector, the longer-wavelength photons are detected more, forming a significant peak, which is due to the fact that the former experience more absorption-based photon loss in the medium than the latter.

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