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Recurrent neural network-based volumetric fluorescence microscopy

Light(Science & Applications) 2021年第4期10卷 620-635页

作者： Luzhe Huang Hanlong Chen Yilin Luo Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California Nano Systems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA David Geffen School of Medicine University of CaliforniaLos AngelesCA 90095USA

Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical,medical and life *** we report a deep learning-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample *** a recurrent convolutional neural network,which we term as Recurrent-MZ,2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended *** experiments on *** and nanobead samples,Recurrent-MZ is demonstrated to significantly increase the depth-of-field of a 63×/1.4NA objective lens,also providing a 30-fold reduction in the number of axial scans required to image the same sample *** further illustrated the generalization of this recurrent network for 3D imaging by showing its resilience to varying imaging conditions,including e.g.,different sequences of input images,covering various axial permutations and unknown axial positioning *** also demonstrated wide-field to confocal cross-modality image transformations using Recurrent-MZ framework and performed 3D image reconstruction of a sample using a few wide-field 2D fluorescence images as input,matching confocal microscopy images of the same sample ***-MZ demonstrates the first application of recurrent neural networks in microscopic image reconstruction and provides a flexible and rapid volumetric imaging framework,overcoming the limitations of current 3D scanning microscopy tools.

关键词： neural network networks

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High-Performance Flexible Microwave Antennas with Ultra-high Visible Transparency 22

High-Performance Flexible Microwave Antennas with Ultra-high...

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22nd International Conference on Electromagnetics in Advanced Applications, ICEAA 2021

作者： Zhang, Cheng Zhu, Liang Ji, Chengang Guo, L. Jay Chen, Pai-Yen Huazhong University of Science and Technology School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics Wuhan430074 China University of Illinois Department of Electrical and Computer Engineering ChicagoIL60607 United States University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States

ISBN: (纸本)9781665413862

In recent years, with the rapid development of internet of things (IoTs) and smart city technologies, there is a growing demand for efficient use of space, power and communication bandwidth. Towards this goal, optically transparent flexible microwave antennas have attracted significant interest by their great integration capability with existing optoelectronic systems and modules, such as building windows, solar panels, and communication networks, etc. Current transparent materials employed for microwave antennas usually suffer from the tradeoff limitation between their optical transparency and electrical conductivity, which directly affects the device performance in the microwave regime [Fig. 1(a)]. Moreover, their limited mechanical flexibility and the associated complex fabrication process might further constrain the materials' wide applications in low-cost and efficient antenna systems. © 2021 IEEE.

关键词： Microwave antennas

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DDCM: Cracking Anonymized Facial Images Using Denoising Diffusion Cryptanalytic Model

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IEEE Transactions on Consumer Electronics 2025年

作者： Jiang, Donghua Ni, Jiangqun Alasbali, Nada Nafea, Ibtehal Liu, Qingliang Ahmad, Jawad Boulila, Wadii Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Peng Cheng Laboratory Department of New Networks Shenzhen518052 China King Khalid University College of Computer Science Department of Informatics and Computing Systems Abha61421 Saudi Arabia Taibah University College of Computer Science and Engineering Medina41477 Saudi Arabia Qingdao University of Science and Technology School of Mathematics and Physics Qingdao266061 China Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia Prince Sultan University Robotics and Internet-of-Things Laboratory Riyadh12435 Saudi Arabia

Currently, in consumer electronics industry, quite a number of E-healthcare products highly depend on consumers' facial biometrics for both disease diagnosis and identity authen-tication. In light of this, various facial privacy-preserving approa-ches oriented towards the internet of medical things have been proposed, including visual cryptography and desensitization tech-nique. Nevertheless, few attentions have been paid to investigating their countermeasure, i.e., facial privacy analysis, owning to significant distribution discrepancy between the original and anony-mized facial images. To this end, a general Denoising Diffusion Cryptanalytic Model (DDCM) is proposed in this paper to recon-struct the original facial images in a coarse-to-fine manner from their encrypted ones with diverse anonymous approaches. Spec-ifically, to accurately reconstruct the original facial image without any prior knowledge, a customized initial prediction module with an encoder-decoder structure is introduced by taking advantage of its leaked visual cues to generate the coarse face. Furthermore, the residual map between the original and coarse facial images is explicitly modeled with the output of the customized prediction module as a conditional prior, thereby obtaining a refined facial image. Finally, extensive experiments demonstrate that the propo-sed DDCM could not only effectively crack multiple handcrafted facial anonymization approaches, such as blurring, pixelation and thumbnail-preserving encryption, but also outperform the prev-ious works (e.g., Pix2Pix) in terms of image fidelity and attack success rate. © 1975-2011 IEEE.

关键词： Anonymity

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UAV-based Maritime Communications: Relaying to Enhance the Link Quality

arXiv

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arXiv 2023年

作者： Çağan, Abdullah Taha Koç, Görkem Berkay Yakın, Handan Çiloğlu, Berk Ashgar, Muhammad Zeeshan Ersoy, Özgün Hämäläinen, Jyri Öztürk, Metin Ankara Yıldırım Beyazıt University Ankara Turkey School of Electrical Engineering Aalto University Espoo Finland Non-Terrestrial Networks Lab Systems and Computer Engineering Carleton University Ottawa Canada

Providing stable connectivity in maritime communications is of utmost importance to unleash the full potential of smart ports. Nonetheless, due to the crowded nature of harbor environments, it is likely that some ships are shadowed by others, resulting in reduced received power that subsequently diminishes their data rates-even threatens basic connectivity requirements. Given that uncrewed aerial vehicles (UAVs) have been regarded as an integral part of future generations of wireless communication networks, they can be employed in maritime communications as well. In this paper, we investigate the use of UAV-mounted relays in order to help mitigate the reduced data rates of blocked links in maritime communications. Various communication architectures are considered based on the positioning mechanism of the UAV;in this regard, fixed, k-means algorithm-based, and landing spot-based positioning approaches are examined. Additionally, since UAVs are predominantly battery-operated, the energy consumption performances of these approaches are also measured. Results reveal that the landing spot-based UAV relay positioning approach finds the best trade-off between the data rate and energy consumption. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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GPUSecBench: Evaluating the Cache Side-Channel Resilience of a GPU Security Execution Pipeline

GPUSecBench: Evaluating the Cache Side-Channel Resilience of...

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Intelligent Cyber Physical systems and Internet of Things (ICoICI), International Conference on

作者： Nelson Lungu Bibhuti Bhusan Dash Manoj Ranjan Mishra Lalbihari Barik Abinash Tripathy Sudhansu Shekhar Patra Electrical and Electronic Engineering University of Zambia Lusaka Zambia School of Computer Applications KIIT Deemed to be University Bhubaneswar India Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Kingdom of Saudi Arabia

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Graphics processing units (GPUs) are increasingly used for security-critical workloads but remain vulnerable to cache side-channel attacks that can leak sensitive data like cryptographic keys and model parameters. Hardware-based defences incorporated into the GPU architecture, such as partitioning and randomisation, can help mitigate these risks by increasing isolation and unpredictability. However, rigorous benchmarks are imperative to evaluate the security-performance tradeoffs of potential defences. We developed GPUSecBench, a comprehensive benchmarking suite comprising timed workloads, real-world attack implementations, and rigorous analysis tools to quantify cache side-channel leakage and overhead of defences empirically. Our experiments demonstrated measurable information leakage across diverse workloads, including cryptography, machine learning, and graphics rendering. The modelled cache timing attacks succeeded in recovering secrets with over 90% success rates. At the same time, defences incurred less than 20% performance slowdown. GPUSecBench provides security insights to enable GPU hardware architects to make data-driven design decisions, guiding the development of micro architectures resilient to timing side-channel attacks with acceptable performance tradeoffs. The benchmark can also benefit system developers in building robust GPU -accelerated applications.

关键词： Protocols Pipelines Graphics processing units Side-channel attacks Machine learning Benchmark testing Rendering (computer graphics) Timing Internet of Things Resilience

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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

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IEEE Transactions on Image Processing 2025年 34卷 3139-3153页

作者： Chen, Jun Yang, Liling Yu, Wei Gong, Wenping Cai, Zhanchuan Ma, Jiayi China University of Geosciences School of Automation Wuhan 430074 China Automation for Complex Systems Hubei Key Laboratory of Advanced Control and Intelligent Wuhan 430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan 430074 China China University of Geosciences Faculty of Engineering Wuhan 430074 China Macau University of Science and Technology School of Computer Science and Engineering Macau Macao Wuhan University Electronic Information School Wuhan 430072 China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. © 1992-2012 IEEE.

关键词： adversarial generative network image fusion Multi-modal fusion semantic awareness

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Diffusion Model-based Metaverse Rendering in UAV-Enabled Edge Networks With Dual Connectivity

Diffusion Model-based Metaverse Rendering in UAV-Enabled Edg...

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IEEE Conference on Wireless Communications and Networking

作者： Guoquan Wu Jiangtian Nie Jianhang Tang Yuling Chen Yang Zhang Luchao Han Zehui Xiong School of Information Science and Engineering Yanshan University China School of Computer Science and Engineering Nanyang Technological University Singapore State Key Laboratory of Public Big Data Guizhou University China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China National Natural Science Foundation of China China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Metaverse is an immersive, seamless, interactive, comprehensive virtual world, as well as a replication, extension, and transcendence of the real world. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is becoming a key technology for ubiquitous Metaverse services. To enhance network resource utilization, we introduce dual connectivity (DC) technologies in UAV-enabled MEC, which increases the time complexity associated with resource management. Considering the specific features of DC communication channels, we propose a UAV-assisted Metaverse rendering problem to enhance the Metaverse service experience and reduce the energy cost of edge devices. To solve the rendering problem with low complexity, we propose a diffusion model-based Metaverse rendering algorithm, where a novel diffusion model is used to generate integer rendering decisions with the aid of the gradient provided by the model-based Metaverse rendering problem. Moreover, with the given rendering decisions, the communication and computation resource allocation results are derived by the model-based optimization method. Finally, we conduct extensive simulation experiments based on real-world datasets. Comprehensive simulation results demonstrate that the diffusion model-based Metaverse rendering algorithm can reduce the Metaverse frame rendering time and improve user experience.

关键词： Costs Metaverse Computational modeling System performance Rendering (computer graphics) Autonomous aerial vehicles User experience

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Parsing Objects at a Finer Granularity: A Survey

arXiv

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arXiv 2022年

作者： Zhao, Yifan Li, Jia Tian, Yonghong School of Computer Science Peking University Beijing100871 China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Peking University Shenzhen518055 China

Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms, while the inherent relations between these tasks are neglected. Moreover, given most of the research remains fragmented, we conduct an in-depth study of the advanced work from a new perspective of learning the part relationship. In this perspective, we first consolidate recent research and benchmark syntheses with new taxonomies. Based on this consolidation, we revisit the universal challenges in fine-grained part segmentation and recognition tasks and propose new solutions by part relationship learning for these important challenges. Furthermore, we conclude several promising lines of research in fine-grained visual parsing for future research. Copyright © 2022, The Authors. All rights reserved.

关键词： Object recognition

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Contributions of Industry 4.0 to resilience achievement in the context of COVID-19 pandemic

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IFAC-PapersOnLine 2022年第10期55卷 3226-3231页

作者： Saloua Said Hafida Bouloiz Maryam Gallab Systems engineering and decision support laboratory Ibn Zohr University ENSA Agadir Morocco. MIS-LISTD Laboratory Computer Science Department Mines-Rabat School (ENSMR) Rabat Morocco.

In the totally unprecedented context of the COVID-19 health crisis, the widespread adoption of Industry 4.0 technologies, and the great interest in resilience, have been stronger than ever. Within this framework, the present paper outlines the involvement of technologies emerging from the fourth industrial revolution in the fight against the epidemic expansion, and the results of this implication in terms of strengthening and achieving resilience in diverse fields. In order to gain a fuller understanding of these points, fourteen resilience domains related to the COVID-19 pandemic are defined. On the other hand, the third section of this paper digs into the literature to expose a variety of Industry 4.0 solutions developed to cope with the sanitary crisis. Afterwards, a fuzzy cognitive map is elaborated, using mental modeler, in order to emphasize the causal links between Industry 4.0 technologies and resilience domains. Subsequently, a simulation of this model is performed to evaluate the contribution of an optimized joint use of Industry 4.0 core technologies in the achievement of resilience in its different dimensions during the COVID-19 pandemic, and to discuss how the identified gaps or weaknesses can be addressed.

关键词： COVID-19 Fuzzy Cognitive Mapping Industry 4.0 Modeling Resilience Simulation

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Nash social distancing games with equity constraints: How inequality aversion affects the spread of epidemics

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Applied Mathematics and Computation 2022年 434卷

作者： Kordonis, Ioannis Lagos, Athanasios-Rafail Papavassilopoulos, George P. School of Electrical and Computer Engineering National Technical University of Athens 9 Iroon Polytechniou str. Athens157 80 Greece Department of Electrical Engineering-Systems University of Southern California Los Angeles United States

In this paper, we present a game-theoretic model describing voluntary social distancing during the spread of an epidemic. The payoffs of the agents depend on the social distancing they practice and on the probability of getting infected. We consider two types of agents: the non-vulnerable agents with a small cost if they get infected and the vulnerable agents with a higher cost. For the modeling of the epidemic outbreak, we consider a variant of the SIR (Susceptible-Infected-Removed) model involving populations of susceptible, infected, and removed persons of vulnerable and non-vulnerable types. The Nash equilibria of this social distancing game are studied. The main contribution of this work is the analysis of the case where the players, desiring to achieve a low social inequality, pose a bound on the variance of the payoffs. In this case, we introduce and characterize a notion of Generalized Nash Equilibrium (GNE) for games with a continuum of players and provide characterizations for this type of GNE. It turns out that often there is a continuum of GNE. However, among the GNE, for a given value of the variance bound, there is one that Pareto dominates the others. We also provide conditions under which a more restrictive variance constraint benefits all the game participants. Furthermore, we describe a bargaining-based algorithm for choosing the variance constraint. Through numerical studies, we show that inequality constraints result in a slower spread of the epidemic and an improved cost for the vulnerable players. Furthermore, we present some examples where inequality constraints are also beneficial for non-vulnerable players. © 2022 Elsevier Inc.

关键词： COVID-19

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