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Efficient and accurate numerical-projection of electromagnetic multipoles for scattering objects

Frontiers of Optoelectronics 2023年第4期16卷 215-226页

作者： Wenfei Guo Zizhe Cai Zhongfei Xiong Weijin Chen Yuntian Chen School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117576Singapore Wuhan National Laboratory of Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Optics Valley Laboratory Wuhan 430074China

In this paper,we develop an efcient and accurate procedure of electromagnetic multipole decomposition by using the Lebedev and Gaussian quadrature methods to perform the numerical ***,we briefy review the principles of multipole decomposition,highlighting two numerical projection methods including surface and volume ***,we discuss the Lebedev and Gaussian quadrature methods,provide a detailed recipe to select the quadrature points and the corresponding weighting factor,and illustrate the integration accuracy and numerical efciency(that is,with very few sampling points)using a unit sphere surface and regular *** the demonstrations of an isotropic dielectric nanosphere,a symmetric scatterer,and an anisotropic nanosphere,we perform multipole decomposition and validate our numerical projection *** obtained results from our procedure are all consistent with those from Mie theory,symmetry constraints,and fnite element simulations.

关键词： Multipole decomposition Numerical quadrature Light scattering

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AR-CNN: an attention ranking network for learning urban perception

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science China(Information sciences) 2022年第1期65卷 164-174页

作者： Zhetao LI Ziwen CHEN Wei-Shi ZHENG Sangyoon OH Kien NGUYEN Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Data and Computer Science Sun Yat-sen University Department of Computer and Information Engineering Ajou University Graduate School of Engineering Chiba University

An increasing number of deep learning methods is being applied to quantify the perception of urban environments, study the relationship between urban appearance and resident safety, and improve urban appearance. Most advanced methods extract image feature representations from street-level images through conventional visual computation algorithms or deep convolutional neural networks and then directly predict the results using features. Unfortunately, these methods take color and texture information together during processing. Color and texture are prime image features, and they affect human perception and judgment differently. We argue that color and texture should be operated differently; therefore, we formulate an end-to-end learning methodology to process input images according to color and texture information before inputting it into the neural network. The processed images and the original image constitute three input streams for the triad attention ranking convolutional neural network(AR-CNN) model proposed in this *** accordance with the aspects of color and texture, an improved attention mechanism in the convolution layer is proposed. Our objective is to obtain the scores of humans on urban appearance in accordance with the prediction results computed from pairwise comparisons generated by the AR-CNN model.

关键词： ranking network urban perception attribute learning attention network colour and texture

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Oxytocinergic Control of a Hypothalamic Social Fear Circuitry

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Neuroscience Bulletin 2024年第9期40卷 1399-1401页

作者： Junqiang Zheng Xingying Wu Han Xu Department of Neurobiology and Department of Psychiatry of the Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of MedicineHangzhou310058China Nanhu Brain-Computer Interface Institute Hangzhou311100China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-Machine IntegrationState Key Laboratory of Brain-Machine IntelligenceZhejiang UniversityHangzhou311121China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang UniversityHangzhou310058China Lingang Laboratory Shanghai200031China

Exhibiting appropriate social behavior toward conspecifics is crucial for survival within a social *** instance,after a conflict,it is essential for the loser to stay away from the victor to avoid further harm[1].Previous studies have indicated that the anterior ventrolateral part of the ventromedial hypothalamus(aVMHvl)plays a pivotal role in social fear and avoidance following a social ***,the precise mechanisms through which the aVMHvl effectively transforms social defeat to subsequent behavioral changes in social interaction remain largely unknown.

关键词： medial hypothalamus precise

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Neural Circuitry Involving Substance P in Male Sexual Behavior

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Neuroscience Bulletin 2024年第4期40卷 544-546页

作者： Xinrong Wang Junqiang Zheng Han Xu Department of Neurobiology and Department of Neurology of the Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of MedicineHangzhou310058China Nanhu Brain-Computer Interface Institute Hangzhou311100China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine IntegrationState Key Laboratory of Brain-Machine IntelligenceZhejiang UniversityHangzhou311121China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang UniversityHangzhou310058China Lingang Laboratory Shanghai200031China

Male sexual behavior is an innate social behavior crucial for reproduction[1].Previous studies have identified several brain regions crucial for the control of both sexual and aggressive behaviors in ***,molecularly specified neural circuits that specifically control male sexual behavior remain *** with the inherently rewarding nature of sexual behavior,previous research has implicated the role of the dopamine system in regulating sexual desire[2].However,the interaction between reward and copulation at the circuit level remains largely ***,it is unclear if the associated neural pathways are specific in modulating sexual reward.

关键词： behavior neural specified

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Analysis of Partition Methods for Dominated Solution Removal from Large Solution Sets

Analysis of Partition Methods for Dominated Solution Removal...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Shu, Tianye Nan, Yang Shang, Ke Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9781665430654

In evolutionary multi-objective optimization (EMO), one important issue is to efficiently remove dominated solutions from a large number of solutions examined by an EMO algorithm. An efficient approach to remove dominated solutions from a large solution set is to partition it into small subsets. Dominated solutions are removed from each subset independently. This partition method is fast but cannot guarantee to remove all dominated solutions. To further remove remaining dominated solutions, a simple idea is to iteratively perform this approach. In this paper, we first examine three partition methods (random, objective value-based and cosine similarity-based methods) and their iterative versions through computational experiments on artificial test problems (DTLZ and WFG) and real-world problems. Our results show that the choice of an appropriate partition method is problem dependent. This observation motivates us to use a hybrid approach where different partition methods are used in an iterative manner. The results show that all dominated solutions are removed by the hybrid approach in most cases. Then, we examine the effects of the following factors on the computation time and the removal performance: the number of objectives, the shape of the Pareto front, and the number of subsets in each partition method. © 2023 IEEE.

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OPTIMIZING FOREST FIRE PREVENTION: INTELLIGENT SCHEDULING ALGORITHMS FOR DRONE-BASED SURVEILLANCE SYSTEM

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arXiv 2023年 225卷 1562-1571页

作者： Jemmali, Mahdi Melhim, Loai Kayed B. Boulila, Wadii Amdouni, Hajer Alharbi, Mafawez T. Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah11952 Saudi Arabia MARS Laboratory University of Sousse Sousse Tunisia Department of Computer Science Higher Institute of Computer Science and Mathematics Monastir University Monastir5000 Tunisia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Higher School of Economic and Commercial Sciences Tunis University of Tunis Tunisia Unit of Scientific Research Applied College Qassim University Buraydah51452 Saudi Arabia

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones’ capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones’ assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm RID achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds. © 2023, CC BY.

关键词： Fires

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A Data-Driven Platform for the Coordination of Independent Visual Analytics Tools

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computers and graphics (Pergamon) 2022年 106卷 152-160页

作者： Nonnemann, Lars Hogräfer, Marius Röhlig, Martin Schumann, Heidrun Urban, Bodo Schulz, Hans-Jörg University of Rostock Institute for Visual and Analytic Computing Albert-Einstein-Strasse 22 Rostock18059 Germany University of Aarhus Department of Computer Science Abogade 34 Aarhus8200 Denmark Fraunhofer Institute for Computer Graphics Research Joachim-Jungius-Strasse 11 Rostock18059 Germany

Visual analysis of unknown data requires the combined use of various functions that are often part of standalone visual analytics (VA) tools. Performing cross-tool visual analysis with standalone VA tools, however, is a challenging and cumbersome endeavor. Some dedicated frameworks address this issue, yet in order to utilize any of them, a visual analytics tool needs to support their required API or architecture. Contrary to most existing frameworks, we present an approach that does not rely on a single predefined interchange mechanism for the entire ensemble of VA tools. Instead, we propose using any available channel for data exchange between two consecutive VA tools. This allows mixing and matching of different data exchange strategies over the course of a cross-tool analysis. In this paper, we identify the challenges associated with establishing such tool chaining platform for data-driven coordination. We further describe the structure and capabilities of data exchange and explain various functionalities of our platform in detail. Based on a demonstrating example, we discuss the limitations of our approach and elaborate new insight for the coordination of the visual output of multiple VA tools. © 2022 Elsevier Ltd

关键词： Electronic data interchange

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Topological magnonic properties of an antiferromagnetic chain

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Physical Review B 2024年第14期110卷 144448页

作者： Topojit Debnath Shri Hari Soundararaj Sohee Kwon Alexander A. Balandin Roger K. Lake Laboratory for Terahertz & Terascale Electronics (LATTE) Department of Electrical and Computer Engineering Materials Science and Engineering Department of Materials Science and Engineering

The magnonic excitations of a dimerized, one-dimensional, antiferromagnetic chain can be trivial or topological depending on the signs and magnitudes of the alternating exchange couplings and the anisotropy. The topological phase that occurs when the signs of the two different exchange couplings alternate is qualitatively different from that of the Su-Schrieffer-Heeger model. A material that may exhibit these properties is the quasi-one-dimensional material MoI3 that consists of dimerized chains weakly coupled to adjacent chains. The magnetic ground state and its excitations are analyzed both analytically and numerically using exchange and anisotropy parameters extracted from density functional theory calculations.

关键词： First-principles calculations Magnetic anisotropy Magnetic coupling Magnetic interactions Magnetic order Magnetic phase transitions Magnetic texture Magnetism Magnons Topological phase transition 1-dimensional spin chains Antiferromagnets Classical spin chains Density functional calculations Heisenberg model Holstein-Primakoff method Linear spin wave theory Quantum spin chains

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Pathological Image Segmentation of Gastric Cancer Based on Deep Learning 15

Pathological Image Segmentation of Gastric Cancer Based on D...

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15th International Conference on graphics and Image Processing, ICGIP 2023

作者： Zhou, Hehu Pan, Jingshan Na, Li Jing, Ge Zhou, Chengjun Du, Wantong Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Pathology the Second Hospital of Shandong University China

ISBN: (纸本)9781510675018

Gastric cancer is a serious health threat and pathological images are an important criterion in its diagnosis. These images can help doctors accurately identify cancerous regions and provide important evidence for clinical decision-making. Thanks to the remarkable achievements of deep learning technology in the field of image processing, an increasing number of superior image segmentation models have emerged. The Swin-Unet model has achieved great success in the field of image segmentation. However, when applied to the image segmentation of gastric cancer pathological section data, the segmentation boundary appears jagged. We have put forth two potential solutions. Initially, we devised an attention connection module to supplant the skip connections within the model, thereby enhancing the model’s predictive precision. Subsequently, we engineered a prognostic processing unit that inputs the model’s predictive outcomes and employs a Conditional Random Field (CRF) for further predictive computations. The enhanced model increases the DSC by 2% and decreases the HD by 17%. Additionally, the issue of jagged boundaries in prediction results has been better optimized. We conducted comparative and ablation experiments, and the results showed that our improved method increased the accuracy of the model’s predictions and reduced the jaggedness of the results at the boundary. © 2024 SPIE. All rights reserved.

关键词： Image segmentation

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Epileptic Seizure Detection Using the EEG Signal Empirical Mode Decomposition and Machine Learning

Epileptic Seizure Detection Using the EEG Signal Empirical M...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Nassir, Jana Alasabi, Majeda Qaisar, Saeed Mian Khan, Muzammil Effat University Computer Science Department Jeddah22332 Saudi Arabia Effat University Electrical and Computer Engineering Department Jeddah22332 Saudi Arabia Cesi Engineering School Lineact Laboratory Villeurbanne69100 France University of Swat Department of Computer and Software Technology Swat Pakistan

ISBN: (纸本)9798350347050

Epileptic seizures affect millions of people worldwide. Medical treatments exist to help lessen the severity of the damage caused by these seizures. However, people with epilepsy still struggle with unexpected seizures. People who experience epileptic seizures have Electroencephalogram (EEG) signals that show different features in comparison to a healthy brain. In this study, EEG signals are studied to detect the seizures. The incoming signals are denoised by using linear phase filters. In next step these are divided in fix-length segments. Then, each segment is broken down using the Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs). For an automatic identification of EEG signals, features are extracted from the collected IMFs and then processed using machine learning techniques. A dataset on epilepsy that is available to the public is used for evaluation. To determine which is the best predictor for the under-consideration dataset, four different classification methods are performed and the results are examined. The system achieves a classification accuracy of 96.70%. © 2023 IEEE.

关键词： Empirical mode decomposition

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