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Experimental study of radio-free communication in the constrained space of vessel using metal surface wave communication

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Experimental study of radio-free communication in the constr...

International Conference on engineering and Emerging Technologies (ICEET)

作者： JinWoo Kong Bong Hyun You Hak Sun Kim Bu Young Kim Woo Seong Shim Sunny Wave Tech co. Ltd Reserch Institute of Sunny Wave Tech co. Ltd Seoul Korea Division of Electrical and Computer Engineering Seoul National Uniersity Seoul Korea Division of Electrical Engineering lUlsan National Institute of Science and Technology Ulsan Korea Maritime Digital application Uni Korea Reserch Institute of Ships & Ocean Engineering Dae jeon Korea

ISBN: (纸本)9781665491075

In this study, Metal Surface Wave Communication (MSWC) using metal surfaces as a medium was applied to a vessel to check the possibility of implementation of the ‘RF free solution’ that overcomes difficulties of wireless communication over the constrained spaces on vessels. The experiment was conducted based on the Wi-fi method. Before the experiment on the ship, 17 dB higher data was confirmed under the same conditions as general WiFi communication on land through a Metal surface wave resonator. As a result of establishing a communication system in a constrained space of a ship using a Metal surface wave resonator, higher than 10 Mbps of up and download speed was confirmed compared to general RF communication. In addition, through noise analysis in engine rooms with many elements that interfere with Metal surface waves, it was confirmed that surface wave communication had no noise effect than general RF communication. Finally, after establishing a communication system in each area inside the ship, the transmission speed was superior when compared to general RF communication. The results of the experiment in this paper show that the transmission speed is lower than the average Wi-Fi of general wireless communication, but communication is possible in an environment where wireless communication is not possible on ships, and the transmission speed of 10Mbps or more is not problematic for voice, video, and data communication. Based on the results of the experiment, it is considered that when MSWC system is constructed on the metal structure of the vessel, Metal surface wave communication technology can replace RF solution and furthermore, MSWC can be applied in constrained space on the vessel and other maritime domain.

关键词： Radio frequency Wireless communication Surface waves Metals Land surface Data communication Marine vehicles

Integrative generalized convex clustering optimization and feature selection for mixed multi-view data

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2021年第1期22卷 2498-2571页

作者： Minjie Wang Genevera I. Allen Department of Statistics Rice University Houston TX Departments of Electrical and Computer Engineering Statistics and Computer Science Rice University Houston TX and Jan and Dan Duncan Neurological Research Institute Baylor College of Medicine Houston TX

In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group structure among the samples that may be hidden in individualistic cluster analyses of a single data view. While several techniques for such integrative clustering have been explored, we propose and develop a convex formalization that enjoys strong empirical performance and inherits the mathematical properties of increasingly popular convex clustering methods. Specifically, our Integrative Generalized Convex Clustering Optimization (iGecco) method employs different convex distances, losses, or divergences for each of the different data views with a joint convex fusion penalty that leads to common groups. Additionally, integrating mixed multi-view data is often challenging when each data source is high-dimensional. To perform feature selection in such scenarios, we develop an adaptive shifted group-lasso penalty that selects features by shrinking them towards their loss-specific centers. Our so-called iGecco+ approach selects features from each data view that are best for determining the groups, often leading to improved integrative clustering. To solve our problem, we develop a new type of generalized multi-block ADMM algorithm using subproblem approximations that more efficiently fits our model for big data sets. Through a series of numerical experiments and real data examples on text mining and genomics, we show that iGecco+ achieves superior empirical performance for high-dimensional mixed multi-view data.

关键词： integrative clustering convex clustering feature selection convex optimization sparse clustering GLM deviance Bregman divergences

A Hybrid Genetic Algorithm to Enhance the Laser Speckle Blood Image 12th

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A Hybrid Genetic Algorithm to Enhance the Laser Speckle Bloo...

12th International Conference on Signal and Information Processing, Networking and computers, ICSINC 2024

作者： Gao, Xia Yuan, Mingbo Feng, Yuntong Li, Ran Yang, Hui School of Internet of Things and Intelligent Control Shandong College of Electronic Technology Jinan250200 China School of Integrated Circuits Shandong College of Electronic Technology Jinan250200 China Haide College Ocean University of China Shandong Qingdao266100 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Medical Instruments Shanghai University of Medicine and Health Science Shanghai201318 China

ISBN: (纸本)9789819649662

This study introduces a hybrid genetic algorithm to enhance blood flow laser speckle contrast images under low coherence laser conditions. The algorithm addresses the challenges of laser speckle imaging by improving speckle contrast analysis accuracy, thereby enhancing blood flow detection. By optimizing image quality, the proposed method provides a cost-effective solution that offers flexibility in light source selection for Laser Speckle Blood Imaging (LSBI) technology. The results highlight the algorithm’s effectiveness in optimizing speckle contrast and improving overall imaging performance, leading to more precise blood flow analysis and potential reductions in operational costs for medical imaging practices. By employing the hybrid genetic algorithm in Laser Speckle Blood Imaging (LSBI) technology, the method effectively mitigates errors in speckle contrast analysis caused by light source coherence. This results in higher-quality blood flow images, offering significant advancements in non-invasive human experiments. The enhanced accuracy and improved imaging quality underscore the algorithm's potential to elevate LSBI technology, making it a valuable tool for both clinical and research applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Coherence Hybrid genetic algorithm Laser speckle blood imaging

Towards Accurate Camouflaged Object Detection with Mixture Convolution and Interactive Fusion

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arXiv 2021年

作者： Chen, Geng Chen, Xinrui Dong, Bo Zhuge, Mingchen Wang, Yongxiong Bi, Hongbo Chen, Jian Wang, Peng Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University China Center for Brain Imaging Science and Technology Zhejiang University China School of Computer Science China University of Geo-sciences China Optical Electrical and Computer Engineering University of Shanghai for Science and Technology China School of Electrical Engineering & Information Department Northeast Petroleum University China School of Electronic Electrical Engineering and Physics Fujian University of Technology China

Camouflaged object detection (COD), which aims to identify the objects that conceal themselves into the surroundings, has recently drawn increasing research efforts in the field of computer vision. In practice, the success of deep learning based COD is mainly determined by two key factors, including (i) A significantly large receptive field, which provides rich context information, and (ii) An effective fusion strategy, which aggregates the rich multi-level features for accurate COD. Motivated by these observations, in this paper, we propose a novel deep learning based COD approach, which integrates the large receptive field and effective feature fusion into a unified framework. Specifically, we first extract multi-level features from a backbone network. The resulting features are then fed to the proposed dual-branch mixture convolution modules, each of which utilizes multiple asymmetric convolutional layers and two dilated convolutional layers to extract rich context features from a large receptive field. Finally, we fuse the features using specially-designed multilevel interactive fusion modules, each of which employs an attention mechanism along with feature interaction for effective feature fusion. Our method detects camouflaged objects with an effective fusion strategy, which aggregates the rich context information from a large receptive field. All of these designs meet the requirements of COD well, allowing the accurate detection of camouflaged objects. Extensive experiments on widely-used benchmark datasets demonstrate that our method is capable of accurately detecting camouflaged objects and outperforms the state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

The energy return on investment of whole-energy systems: application to Belgium.

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arXiv 2022年

作者： Dumas, Jonathan Dubois, Antoine Thiran, Paolo Jacques, Pierre Contino, Francesco Cornélusse, Bertrand Limpens, Gauthier Departments of Computer Science and Electrical Engineering Liege University Place du 20-Août 7 State LiegeB-4000 Belgium Institute of Mechanics Materials and Civil Engineering Catholic University of Louvain Place de l’Université 1 Ottignies-Louvain-la-Neuve1348 Belgium

Planning the defossilization of energy systems while maintaining access to abundant primary energy resources is a nontrivial multi-objective problem encompassing economic, technical, environmental, and social aspects. However, most long-term policies consider the cost of the system as the leading indicator in the energy system models to decrease the carbon footprint. This paper is the first to develop a novel approach by adding a surrogate indicator for the social and economic aspects, the energy return on investment (EROI), in a whole-energy system optimization model. In addition, we conducted a global sensitivity analysis to identify the main parameters driving the EROI uncertainty. This method is illustrated in the 2035 Belgian energy system for several greenhouse gas (GHG) emissions targets. Nevertheless, it can be applied to any worldwide or country energy system. The main results are threefold when the GHG emissions are reduced by 80%: (i) the EROI decreases from 8.9 to 3.9;(ii) the imported renewable gas (methane) represents 60 % of the system primary energy mix;(iii) the sensitivity analysis reveals this fuel drives 67% of the variation of the EROI. These results raise questions about meeting the climate targets without adverse socioeconomic impact, demonstrating the importance of considering the EROI in energy system models. © 2022, CC BY-NC-ND.

关键词： Carbon footprint

Quantum error correction below the surface code threshold

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arXiv 2024年

作者： Acharya, Rajeev Aghababaie-Beni, Laleh Aleiner, Igor Andersen, Trond I. Ansmann, Markus Arute, Frank Arya, Kunal Asfaw, Abraham Astrakhantsev, Nikita Atalaya, Juan Babbush, Ryan Bacon, Dave Ballard, Brian Bardin, Joseph C. Bausch, Johannes Bengtsson, Andreas Bilmes, Alexander Blackwell, Sam Boixo, Sergio Bortoli, Gina Bourassa, Alexandre Bovaird, Jenna Brill, Leon Broughton, Michael Browne, David A. Buchea, Brett Buckley, Bob B. Buell, David A. Burger, Tim Burkett, Brian Bushnell, Nicholas Cabrera, Anthony Campero, Juan Chang, Hung-Shen Chen, Yu Chen, Zijun Chiaro, Ben Chik, Desmond Chou, Charina Claes, Jahan Cleland, Agnetta Y. Cogan, Josh Collins, Roberto Conner, Paul Courtney, William Crook, Alexander L. Curtin, Ben Das, Sayan Davies, Alex De Lorenzo, Laura Debroy, Dripto M. Demura, Sean Devoret, Michel Paolo, Agustin Di Donohoe, Paul Drozdov, Ilya Dunsworth, Andrew Earle, Clint Edlich, Thomas Eickbusch, Alec Elbag, Aviv Moshe Elzouka, Mahmoud Erickson, Catherine Faoro, Lara Farhi, Edward Ferreira, Vinicius S. Burgos, Leslie Flores Forati, Ebrahim Fowler, Austin G. Foxen, Brooks Ganjam, Suhas Garcia, Gonzalo Gasca, Robert Genois, Élie Giang, William Gidney, Craig Gilboa, Dar Gosula, Raja Dau, Alejandro Grajales Graumann, Dietrich Greene, Alex Gross, Jonathan A. Habegger, Steve Hall, John Hamilton, Michael C. Hansen, Monica Harrigan, Matthew P. Harrington, Sean D. Heras, Francisco J.H. Heslin, Stephen Heu, Paula Higgott, Oscar Hill, Gordon Hilton, Jeremy Holland, George Hong, Sabrina Huang, Hsin-Yuan Huff, Ashley Huggins, William J. Ioffe, Lev B. Isakov, Sergei V. Iveland, Justin Jeffrey, Evan Jiang, Zhang Jones, Cody Jordan, Stephen Joshi, Chaitali Juhas, Pavol Kafri, Dvir Kang, Hui Karamlou, Amir H. Kechedzhi, Kostyantyn Kelly, Julian Khaire, Trupti Khattar, Tanuj Khezri, Mostafa Kim, Seon Klimov, Paul V. Klots, Andrey R. Kobrin, Bryce Kohli, Pushmeet Korotkov, Alexander N. Kostritsa, Fedor Kothari, Robin Kozlovskii, Borislav Kreikebaum, John Mark Kurilovich, Vladislav D. Lacroix, Nathan Landhuis, David Lange Google Research United States Department of Electrical and Computer Engineering University of Massachusetts AmherstMA United States Google DeepMind United Kingdom Department of Physics University of California Santa BarbaraCA United States Department of Physics University of Connecticut StorrsCT United States Department of Electrical and Computer Engineering Auburn University AuburnAL United States Department of Physics ETH Zurich Switzerland Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Physics Massachusetts Institute of Technology CambridgeMA United States Department of Physics and Astronomy University of California RiversideCA United States Yale Quantum Institute Yale University New HavenCT United States Departments of Applied Physics and Physics Yale University New HavenCT United States

Quantum error correction [1–4] provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of Λ = 2.14 ± 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% ± 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit’s lifetime by a factor of 2.4 ± 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 µs at distance-5 up to a million cycles, with a cycle time of 1.1 µs. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3×109 cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms. © 2024, CC BY.

关键词： Decoding

SSCU-Net: Spatial-Spectral Collaborative Unmixing Network for Hyperspectral Images

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arXiv 2022年

作者： Qi, Lin Gao, Feng Dong, Junyu Gao, Xinbo Du, Qian The School of Computer Science and Technology Ocean University of China Qingdao266100 China The Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China The Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States

Linear spectral unmixing is an essential technique in hyperspectral image processing and interpretation. In recent years, deep learning-based approaches have shown great promise in hyperspectral unmixing, in particular, unsupervised unmixing methods based on autoencoder networks are a recent trend. The autoencoder model, which automatically learns low-dimensional representations (abundances) and reconstructs data with their corresponding bases (endmembers), has achieved superior performance in hyperspectral unmixing. In this article, we explore the effective utilization of spatial and spectral information in autoencoder-based unmixing networks. Important findings on the use of spatial and spectral information in the autoencoder framework are discussed. Inspired by these findings, we propose a spatial-spectral collaborative unmixing network, called SSCU-Net, which learns a spatial autoencoder network and a spectral autoencoder network in an end-to-end manner to more effectively improve the unmixing performance. SSCU-Net is a two-stream deep network and shares an alternating architecture, where the two autoencoder networks are efficiently trained in a collaborative way for estimation of endmembers and abundances. Meanwhile, we propose a new spatial autoencoder network by introducing a superpixel segmentation method based on abundance information, which greatly facilitates the employment of spatial information and improves the accuracy of unmixing network. Moreover, extensive ablation studies are carried out to investigate the performance gain of SSCU-Net. Experimental results on both synthetic and real hyperspectral data sets illustrate the effectiveness and competitiveness of the proposed SSCU-Net compared with several state-of-the-art hyperspectral unmixing methods. © 2022, CC BY.

关键词： Deep learning

Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation

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arXiv 2025年

作者： Gao, Feng Fu, Miao Cao, Jingchao Dong, Junyu Du, Qian School of Computer Science and Technology Ocean University of China Qingdao266100 China Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States

Semantic segmentation of high-resolution remote sensing images plays a crucial role in land-use monitoring and urban planning. Recent remarkable progress in deep learning-based methods makes it possible to generate satisfactory segmentation results. However, existing methods still face challenges in adapting network parameters to various land cover distributions and enhancing the interaction between spatial and frequency domain features. To address these challenges, we propose the Adaptive Frequency Enhancement Network (AFENet), which integrates two key components: the Adaptive Frequency and Spatial feature Interaction Module (AFSIM) and the Selective feature Fusion Module (SFM). AFSIM dynamically separates and modulates high- and low-frequency features according to the content of the input image. It adaptively generates two masks to separate high- and low-frequency components, therefore providing optimal details and contextual supplementary information for ground object feature representation. SFM selectively fuses global context and local detailed features to enhance the network’s representation capability. Hence, the interactions between frequency and spatial features are further enhanced. Extensive experiments on three publicly available datasets demonstrate that the proposed AFENet outperforms state-of-the-art methods. In addition, we also validate the effectiveness of AFSIM and SFM in managing diverse land cover types and complex scenarios. Our codes are available at https://***/oucailab/AFENet. © 2025, CC BY.

关键词： Semantic Segmentation

Microstructure of Si Films Deposited on Si(100) Surfaces by Remote Plasma-Enhanced Chemicalvapor Deposition, Rpecvd: Dependence on Process Pressure and Substrate Temperature

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MRS Online Proceedings Library 2020年第1期311卷 379-384页

作者： S. Habermehl S. S. He Y. L. Chen G. Lucovsky Departments of Physics Materials Science and Engineering and Electrical and Computer Science North Carolina State University Raleigh USA

The microstructure of Si thin films, deposited on in-situ cleaned Si(100) surfaces by remote plasma-enhanced chemical-vapor deposition (RPECVD), is dependent on the process pressure, substrate temperature and H2 flow rate. Surface characterization by on-line low energy electron diffraction, LEED, has been used to detect changes in the character of the deposited films which can either be amorphous, microcrystalline or crystalline, hereafter designated as a-Si, µc-Si, and c-Si, respectively. We have used these results to generate phase diagrams for the Si microstructure as a function of the process pressure and substrate temperature, including the flow rate of H2 as an additional deposition parameter.

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