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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Beals, Garrett Zhao, Yun McNulty, Karl Dacha, Sai Kanth Lipson, Michal Gaeta, Alexander L. Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States Department of Electrical Engineering Columbia University New YorkNY10027 United States

We demonstrate strong thermal effects in the soliton state in a 16-GHz free-spectral range compact spiral microresonator by measuring a thermal-induced bistability and show ultralow-noise operation can be achieved via... 详细信息

ISBN: (纸本)9781957171395

关键词： Microresonators

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Anomaly detection using deep learning approach for IoT smart city applications

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Multimedia Tools and Applications 2025年第17期84卷 17929-17949页

作者： Shibu, S. Kirubakaran, S. Remamany, Krishna Priya Ahamed, Suhail Chitra, L. Kshirsagar, Pravin R. Tirth, Vineet Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad501401 India Engineering Department College of Engineereing and Technology University of Technology an Applied Sciences PC Musandam811 Oman Machine Learning Software Engineer Paragon Semvox GmbH Saarland Kirkel Germany Department of Electrical and Electronics Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai603104 India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Maharashtra Nagpur India Department of Mechanical Engineering College of Engineering King Khalid University Asir Abha61421 Saudi Arabia

With the advancements of IoT devices, many smart applications start to rule this era. In particular, smart cities has been adapted and realized by many countries around the world. In smart cities, vas amount of data is generated at every second. This vast data need a transmission medium which could be wireless standard. However, security is the main concern in such applications since the smart transmission always binds with anomalies. The existing anomaly detection systems need improvement in accuracy due to inefficient feature extraction and selection procedure. This paper proposes an accurate anomaly detection technique that built upon deep learning approach. We proposed a Combined Deep Q-Learning (CDQL) algorithm for anomaly detection. Priory, optimal features are selected by using Spider Monkey Optimizer (SMO). With the optimal features, CDQL detects anomalies accurately. In addition, the CDQL algorithm learns the environment in order to monitor the network data continuously. This continuous monitoring and optimum features helps in accuracy improvement up to 98%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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A Hardware-Aware Network for Real-World Single Image Super-Resolutions

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3482-3496页

作者： Ma, Rui Du, Xian Institute for Applied Life Sciences University of Massachusetts Center for Personalized Health Monitoring AmherstMA01003 United States University of Massachusetts Electrical and Computer Engineering Department AmherstMA01003 United States University of Massachusetts Mechanical and Industrial Engineering Department AmherstMA01003 United States

Most single image super-resolution (SISR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs, which are simulated by a predetermined degradation operation, such as bicubic downsampling. However, these methods only learn the inverse process of the predetermined operation, which fails to super resolve the real-world LR images, whose true formulation deviates from the predetermined operation. To address this, we propose a novel super-resolution (SR) framework named hardware-aware super-resolution (HASR) network that first extracts hardware information, particularly the camera degradation information. The LR images are then super resolved by integrating the extracted information. To evaluate the performance of HASR network, we build a dataset named Real-Micron from real-world micron-scale patterns. The paired LR and HR images are captured by changing the objectives and registered using a developed registration algorithm. Transfer learning is implemented during the training of Real-Micron dataset due to the lack of amount of data. Experiments demonstrate that by integrating the degradation information, our proposed network achieves state-of-the-art performance for the blind SR task on both synthetic and real-world datasets. © 2020 IEEE.

关键词： Optical resolving power

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Accurate and Fast Phase Compensation of Dynamic Samples in Digital Holographic Microscopy

Accurate and Fast Phase Compensation of Dynamic Samples in D...

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2023 Digital Holography and Three-Dimensional Imaging, DH 2023 in Optica Imaging Congress - Part of Imaging and applied Optics Congress 2023

作者： Obando-Vásquez, Sofía Doblas, Ana Trujillo, Carlos Applied Optics Group School of Applied Science and Engineering Universidad EAFIT Medellín Colombia Department of Electrical and Computer Engineering The University of Memphis TN United States

We present a fast algorithm for accurate phase compensation of holographic videos of dynamic phenomena recorded in a digital holographic microscope. The proposal is tested with human sperm and red blood cell samples. ... 详细信息

ISBN: (纸本)9781957171289

关键词： Holography

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Multiregion Light Control in Diffusive Media via Wavefront Shaping

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Physical Review Letters 2024年第14期133卷 146901页

作者： Liam Shaughnessy Rohin E. McIntosh Arthur Goetschy Chia Wei Hsu Nicholas Bender Hasan Yılmaz Alexey Yamilov Hui Cao Department of Applied Physics Ming Hsieh Department of Electrical and Computer Engineering School of Applied and Engineering Physics Institute of Materials Science and Nanotechnology Physics Department

Wavefront shaping allows focusing light through or inside strongly scattering media, but the background intensity also increases which reduces the target’s contrast. By combining transmission or deposition matrices for different regions, we construct joint operators to achieve spatially resolved control of light in diffusive systems. The eigenmode of a contrast operator can maximize the power contrast between a target and its surrounding. A difference operator enhances the power delivery to a target while avoiding the background increase. This work opens the door to coherent control of nonlocal effects in wave transport for practical applications.

关键词： Optics & lasers Random & disordered media Coherent control

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Guest Editorial: Special Issue of Selected Extended Papers From the IEEE International Conference on Flexible, Printable Sensors and Systems (FLEPS) 2024

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第1期4卷 2-3页

作者： Mantysalo, Matti Marrocco, Gaetano Agarwale, Shweta Grau, Gerd Gao, Wei Tampere University Faculty of Information Technology and Communication Sciences Tampere33720 Finland University of Roma Tor Vergata Dipartimento di Ingegneria Civile ed ed Ingegneria Informatica Rome00133 Italy Aarhus University Department of Electrical and Computer Engineering Aarhus8200 Denmark York University Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada Caltech Department of Engineering and Applied Science PasadenaCA91125 United States

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Invertibility of Discrete-Time Linear Systems with Sparse Inputs 63

Invertibility of Discrete-Time Linear Systems with Sparse In...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Poe, Kyle Mallada, Enrique Vidal, Rene University of Pennsylvania Applied Mathematics and Computational Science Group PA19104 United States Johns Hopkins University Mallada Is with the Department of Electrical and Computer Engineering MD21218 United States University of Pennsylvania Radiology Computer and Information Science Statistics and Data Science Departments of Electrical and Systems Engineering PA19104 United States

ISBN: (纸本)9798350316339

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control. © 2024 IEEE.

关键词： State space methods

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Enhancing Quality Control: Defect State Classification of Taralli Biscuits with MobileNet-v2 and DenseNet-201 12

Enhancing Quality Control: Defect State Classification of Ta...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Tutuncu, Kemal Yasin, Elham Tahsin Koklu, Murat Selcuk University Department of Electrical & Electronic Engineering Konya Turkey Graduate School of Natural and Applied Sciences Computer Engineering Selcuk University Konya Turkey Selcuk University Department of Computer Engineering Konya Turkey

ISBN: (纸本)9798350358056

Industrial production and packaging face significant challenges, such as product damage, color changes, and the presence of foreign bodies. These issues greatly impact product quality, profitability, and marketability, leading to increased consumer complaints. To address these concerns, this study presents a novel method for classifying Taralli biscuits using image processing techniques. The research encompasses a dataset of 4,900 images, featuring four types of defects: no defect, defect-shape, defect-object, and defect-color. Leveraging advanced deep learning architectures, including MobileNet-v2 and DenseNet-201, the classification process achieves impressive accuracy rates of 98.71% and 99.39% respectively. By automating the detection of biscuit damage, the proposed method enhances quality control and inspection processes within the food industry. The combination of state-of-the-art image processing and deep learning techniques in this research provides an effective solution for automatically detecting and categorizing biscuit defects. © 2023 IEEE.

关键词： biscuits classification deep learning defect states DenseNet-201 MobileNet-v2

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Scalable global state synchronization of discrete-time double integrator multi-agent systems with input saturation via linear protocol 41

Scalable global state synchronization of discrete-time doubl...

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第41届中国控制会议

作者： Zhenwei Liu Ali Saberi Anton A.Stoorvogel College of Information Science and Engineering Northeastern University School of Electrical Engineering and Computer Science Washington State University Department of Electrical Engineering Mathematics and Computer ScienceUniversity of Twente

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper studies scalable global state synchronization of discrete-time double integrator multi-agent systems in presence of input saturation based on localized information exchange.A scale-free collaborative linear dynamic protocols design methodology is developed for discrete-time multi-agent systems with both full and partialstate *** the protocol design methodology does not need any knowledge of the directed network topology and the spectrum of the associated Laplacian ***,the protocols are parametric based on a parameter set in which the designed protocols can guarantee the global synchronization ***,the proposed protocol is scalable and achieves synchronization for any arbitrary number of agents.

关键词： Discrete-time double integrator multi-agent systems Global state synchronization Scale-free linear protocol

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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