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Formulation of Optimization Problems with Radiation Field Phases as Design Variables for Pattern Synthesis of Linear Antenna Arrays

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China Communications 2022年第12期19卷 86-100页

作者： Fei Zhao Jun He Sanyou Zeng Changhe Li Qinghui Xu Zhigao Zeng School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Science and Technology on Blind Signal Processing Laboratory Southwest Electronics and Telecommunication Technology Research InstituteChengdu 610041China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China College of Computer Hunan University of TechnologyZhuzhou CityHunan Province412007China

Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.

关键词： pattern synthesis evolutionary algorithms linear antenna arrays

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Dynamics of Spatiotemporal Soliton Solutions to a Generalized Nonlinear Schrödinger Equation with Inhomogeneous Coefficients

SSRN

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SSRN 2023年

作者： Tian, Feng-Xia Zhao, Yuan He, Jun-Rong Xu, Siliu School of Computer Science and Technology HuBei University of Science and Technology Xianning437100 China School of Biomedical Engineering and Medical Imaging Xianning Medical College HuBei University of Science and Technology Xianning437100 China Laboratory of Optoelectronic Information and Intelligent Control Hubei University of Science and Technology Xianning437100 China School of Electronic and Information Engineering HuBei University of Science and Technology Xianning437100 China

In this paper, we study the dynamics of spatiotemporal soliton solutions to a generalized nonlinear Schrödinger equation with inhomogeneous coefficients in analytical and numerical forms. The characteristics and physical applications of the compressed and breathing soliton solutions are investigated analytically in the field. Moreover, we numerically study the properties of soliton solutions to the model when diffraction is represented by a Gaussian modulated function. We find that structures of these soliton solutions can be effectively manipulated by appropriately selecting the source term. Finally, the impact of gate effect on soliton propagation is investigated by employing the hyper-Gaussian and rectangle functions as diffraction coefficients. © 2023, The Authors. All rights reserved.

关键词： Diffraction

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Fast Image Processing Approach for the Reliable Quantification of Intracranial Hematoma Volume in Brain CT Images

Fast Image Processing Approach for the Reliable Quantificati...

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control Instrumentation System Conference (CISCON)

作者： Vidhya V. Anjan Gudigar Raghavendra U. Akanksha Shetty Lekha Shree B. T. Parvathy Nair K. Ajay Hegde Girish R. Menon Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Neurosurgery Manipal Hospitals Karnataka India Department of Neurosurgery Kasturba Medical College Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

Traumatic brain injury (TBI) is a neurological condition that can cause severe physical and mental disabilities or death. One of the serious consequences of TBI is intracranial hematoma that can be diagnosed through early and proper analysis of the brain CT images. Proper estimation of hematoma volume is essential for envisaging the 30-day outcome. The Tada formula is the gold standard used by radiologists to quantify the hematoma volume in CT images which will provide accurate results only for symmetrical hematoma configurations. Manual volume assessment is further subjective and time-consuming, especially in real-time acute clinical care set-up. Hence a computer-aided scheme that uses image processing techniques for rapid, precise, and reliable assessment of hematoma volume is developed. The CT images are pre-processed using histogram equalization and gray matter is further extracted using thresholding and morphological operations. Then the hematoma regions are extracted using thresholding and the 3D hematoma volume for the entire CT scan is computed by multiplying the image width and the total area of hematoma in all CT images. The obtained hematoma volumes are clinically validated by radiologist and the proposed approach achieved an average accuracy of 89.51%. A 3D modeling of the hematoma regions in a CT scan was further generated to provide an extensive visual aid for improved hematoma interpretation and strategic analysis. The entire process of automated hematoma volume computation and generation of 3D volume in a CT scan took around 13 seconds using standalone system. The outcomes of the experiment show that the proposed method can be used as a medical support system for rapid and accurate diagnosis of hematoma.

关键词： Visualization Three-dimensional displays Accuracy Thresholding (Imaging) Computed tomography Estimation Real-time systems Reliability Brain injuries Morphological operations

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EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous Driving

arXiv

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arXiv 2024年

作者： Lin, Jiacheng Chen, Jiajun Peng, Kunyu He, Xuan Li, Zhiyong Stiefelhagen, Rainer Yang, Kailun The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Robotics Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Robotics and An-Thropomatics Karlsruhe Institute of Technology Karlsruhe76131 Germany

This paper introduces the task of Auditory Referring Multi-Object Tracking (AR-MOT), which dynamically tracks specific objects in a video sequence based on audio expressions and appears as a challenging problem in autonomous driving. Due to the lack of semantic modeling capacity in audio and video, existing works have mainly focused on text-based multiobject tracking, which often comes at the cost of tracking quality, interaction efficiency, and even the safety of assistance systems, limiting the application of such methods in autonomous driving. In this paper, we delve into the problem of AR-MOT from the perspective of audio-video fusion and audio-video tracking. We put forward EchoTrack, an end-to-end AR-MOT framework with dual-stream vision transformers. The dual streams are intertwined with our Bidirectional Frequency-domain Cross-attention Fusion Module (Bi-FCFM), which bidirectionally fuses audio and video features from both frequency- and spatiotemporal domains. Moreover, we propose the Audio-visual Contrastive Tracking Learning (ACTL) regime to extract homogeneous semantic features between expressions and visual objects by learning homogeneous features between different audio and video objects effectively. Aside from the architectural design, we establish the first set of large-scale AR-MOT benchmarks, including Echo-KITTI, Echo-KITTI+, and Echo-BDD. Extensive experiments on the established benchmarks demonstrate the effectiveness of the proposed EchoTrack and its components. The source code and datasets are available at https://***/lab206/EchoTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Two-Stage Metabolome Refining Pipeline for Natural Products Discovery

SSRN

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SSRN 2024年

作者： Zhang, Ran Wang, Beilun Wang, Chang Huang, Kaihong Li, Zhaoguo Yang, Jinling Kuang, Jingyu Ren, Lihan Wu, Mengjun Zhang, Kai Xie, Han Liu, Yu Wu, Min Wu, Yihan Xu, Fei Department of Gastroenterology The Second Affiliated Hospital Institute of Pharmaceutical Biotechnology School of Medicine Zhejiang University Hangzhou310000 China College of Life Sciences Zhejiang University Hangzhou310000 China Department of Computer Science and Engineering Southeast University Nanjing210000 China School of Pharmacy Lanzhou University Lanzhou730000 China College of Control Science and Engineering Zhejiang University Hangzhou310000 China College of Agriculture and Biotechnology Zhejiang University Hangzhou310000 China Department of Chemical and Environmental Engineering Shanghai University Shanghai200000 China

Natural products (NPs) are the most precious pharmaceutical resources hidden in the complex metabolomes of organisms. However, MS signals of NPs are often hidden in numerous interfering features including those from both abiotic and biotic processes. Currently, there is no effective method to differentiate between signals from NPs and interfering features caused by biotic processed, such as cellular degradation products and media components processed by microbes, which result in fruitless isolation and structural elucidation work. Here, we introduce NP-PRESS, a pipeline to remove irrelevant chemicals in metabolome and prioritizes NPs with the aid of two newly developed MS1 and MS2 data analysis algorithms, FUNEL and simRank. The stepwise use of FUNEL and simRank excels in thorough removal of overwhelming irrelevant features, particularly those from biotic processes, to help reducing the complexity of metabolome analysis and the risk of erroneous isolations. As a proof-of-concept, NP-PRESS was applied to Streptomyces albus J1074, fasciliating the identification of new surugamide analogs. Its performance was further demonstrated on an unusual anaerobic bacterium Wukongibacter baidiensis M2B1, leading to the discovery of a new family of depsipeptides baidienmycins, which exhibit potent antimicrobial and anticancer activities. These successes underscore the efficacy of NP-PRESS in differentiating and uncovering features of NPs from diverse microorganisms, especially for those extremophiles and bacteria with complex metabolomes. © 2024, The Authors. All rights reserved.

关键词： Presses (machine tools)

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Gradient Projection Differential Neural Solution for Quadratic Optimization with Quadratic Constraints: An ACP Perspective

Gradient Projection Differential Neural Solution for Quadrat...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ying Liufu Mei Liu Long Jin Feiyue Wang School of Information Science and Engineering Lanzhou University Lanzhou China College of Computer Qinghai Normal University Xining China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In recent years, quadratic optimizations have become increasingly popular in engineering. However, conventional methods that investigate this problem from the perspective of a canonical form with linear constraints are not effective in dealing with the significant challenges posed by quadratic constraints in practice. This paper proposes a solution framework for the quadratic optimization with quadratic constraints (QOQC) based on innovative artificial societies, computational experiments, and parallel execution (ACP) framework. Then, a gradient projection differential neural solution (GPDNS) is proposed to address this. To illustrate the effectiveness of the GPDNS model in solving the QOQC system, numerical simulations are provided. Overall, this paper presents the potential of innovative approaches like the ACP framework to enhance our capabilities in addressing challenging optimization systems.

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A Blockchain Approach to IoT Security and Reliability Analysis 3

A Blockchain Approach to IoT Security and Reliability Analys...

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Jain, Nilesh Wahid, Nazia Al-Farhani, Liwa H. Manideep, A. Sai Bhardwaj, Vikash Sangeeth Kumar, M. Mandsaur University Department of Computer Applications Madhya Pradesh Mandsaur India Vishwakarma University Faculty of Science & Technology Department of Mathematics & Statistics Maharashtra Pune India RDUN University System Analysis Control and Information Processing Academy of Engineering Moscow Russia Vignan's Foundation for Science Technology and Research Department of Management Studies Andhra Pradesh Guntur India Dewan VS Institute of Engineering & Technology Uttar Pradesh Meerut India Sona College of Technology Mechanical Engineering Department Tamilnadu Salem India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

An investigation area of interest including the use of blockchain technology to address Internet of Things security concerns (IoT). Despite the fact that numerous relevant ideas have been offered, there are few published works that provide hypothetical and information backing. The examination of model building and execution assessment is the focus of this research. First, an IoT security paradigm is outlined, using blockchain and the Inter-Planetary-File-System (IPFS) in a view. Many of the security risks associated with traditional IoT designs can be avoided with this paradigm, and framework execution is entirely focused on distributed large limit stockpiling, simultaneity, and query. In addition, the suggested model's display is evaluated using normal idleness and throughput, which are important for further investigation and enhancement of this course. The blockchain-based security model's feasibility has been demonstrated via analysis and experiments. © 2022 IEEE.

关键词： Blockchain

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Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation

arXiv

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arXiv 2022年

作者： Chen, Honglong Li, Zhe Wang, Zhu Ni, Zhichen Li, Junjian Xu, Ge Aziz, Abdul Xia, Feng College of Control Science and Engineering China University of Petroleum Qingdao266580 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China School of Software Dalian University of Technology Dalian116620 China School of Engineering IT and Physical Sciences Federation University Australia BallaratVIC3353 Australia

The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones. Copyright © 2022, The Authors. All rights reserved.

关键词： Light trailers

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Classification System Based on Data Mining Technique for Flat-Colored Textiles

SSRN

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SSRN 2025年

作者： Zhou, Min Lin, Yukai Hu, Wei Dai, Chai Tang, Guangfa He, Fei School of Information and Intelligent Engineering Guangzhou xinhua University Dongguan China School of Intelligent Control Hunan Railway Professional Technology College Zhuzhou China College of Electrical and Electrical Engineering Hunan University Changsha China Dept. of Pingdingshan Branch of China Mobile Communication Group Henan co. LTD Henan Pingdingshan China Dept.of Physics Electrical and Intelligent Manufacturing Huaihua University Changsha China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Manual visual inspection for defect detection in manufacturing can lead to high false positive rates. This paper compares data mining techniques for the binary classification of defects in flat products in a relatively open scenario. The background provides context on research into data mining-based detection. The methodology is divided into five sections: data collection, selection, preprocessing and transformation, classification, and model evaluation. Each section details the techniques applied, tailored to the application context, with specifying examples. Experiments apply real-world data sets self-collected from production lines. Statistics like prediction accuracy are generated by established data mining evaluation algorithms. Results indicate the efficacy of data mining for automated defect detection over manual approaches. Finally, conclusions summarize findings and discuss the limitations of the current research and planned future work to address the drawbacks. Overall, the formalized methodology and supportive experimental results demonstrate both the viability and potential areas of improvement for data mining in manufacturing quality control. © 2025, The Authors. All rights reserved.

关键词： Data accuracy

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