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Flexible Inverse Design of Microwave Filter Customized on Demand With Wavelet Transform Deep Learning

IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 806-817页

作者： Xu, Kuiwen Wang, Jianguo Cai, Jialin Ma, Xuetiao Lv, Qinyi Chen, Shichang Liu, Jie Liu, Jun Hangzhou Dianzi University Innovation Center for Electronic Design Automation Technology The Key Laboratory of RF Circuits and Systems Ministry of Education Hangzhou310018 China Chang'an Campus Northwestern Polytechnical University School of Electronic Information Xi'an710060 China University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu610054 China Information Science Academy China Electronics Technology Group Corporation Beijing100846 China

Artificial intelligence (AI) techniques are increasingly being used for the inverse design of microwave devices. However, several challenges, including intensive computation costs for training samples, high-dimensional data, nonuniformity, and low-quality samples in the design space, can negatively impact the final modeling performance. To alleviate these issues, a high-quality sampling inverse design scheme incorporating wavelet transform deep learning (HQS-WTDL) is proposed to achieve customized, automated microwave filter design. In the forward simulation-based sampling, particle swarm optimization (PSO) is used to tentatively select rule-defined samples. Multilabel synthetic minority over-sampling technique (MLSMOTE) is then applied to enlarge the training samples and improve their uniformity in the design space. An inverse modeling approach using neural networks to map the nonlinear relationship between a given set of S-parameters and required filter structural parameters is presented. Given that the dimension of the S-parameters is much higher than that of the structure parameters, the corresponding neural network used in this approach is deep and complex, with multiple layers and neurons. To reduce the number of input variables, the S-parameters are subjected to wavelet transformation, allowing for more efficient representation by the neural network. The proposed method is validated using a band-pass microstrip hairpin filter as an example. Results demonstrate that the proposed approach achieves better modeling effectiveness and inverse design efficiency than conventional methods. In addition, the proposed method allows for fast customization of device parameters, such as center frequencies and bandwidths with good prediction accuracy. © 1982-2012 IEEE.

关键词： Inverse problems

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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Blood cell counting based on U-Net++ and YOLOv5

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Optoelectronics Letters 2023年第6期19卷 370-376页

作者： BAI Hua WANG Xuechun GUAN Yingjian GAO Qiang HAN Zhibo Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems School of Electronic and Information EngineeringTiangong UniversityTianjin 300387China Shenzhen Weian Co-create Biotechnology Co. Ltd.Shenzhen 518000China Tianjin Key Laboratory of Engineering Technologies for Cell Phamaceutical Tianjin 300457China National Engineering Research Center of Cell Products/AmCellGene Co. Ltd.Tianjin 300457China

Clinical information about a variety of disorders is available through blood cell counting,which is usually done by manual ***,manual methods are complex,time-consuming and susceptible to the subjective experience of *** many efforts have been made to develop automated blood cell counting algorithms,the complexity of blood cell distribution and the highly overlapping nature of some red blood cells(RBCs) remain significant challenges that limit the improvement of analytical ***,we proposed an end-to-end method for blood cell counting based on deep ***,U-Net++ was used to segment the whole blood cell image into several regions of interest(ROI),and each ROI contains only one single cell or multiple overlapping ***,YOLOv5 was used to detect blood cells in each ***,we proposed several strategies,including fine classification of RBCs,adaptive adjustment for non-maximal suppression(NMS) threshold and blood cell morphology constraints to improve the accuracy of ***,the detection outcomes for each ROI were combined and *** results show that our method can effectively address the issue of high overlap and precisely segment and detect blood cells,with a 98.18% accuracy rate for blood cell counting.

关键词： Blood consuming overlap

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Transmission Line Differential Protection Based on Numerical Solution of Partial Differential Equations

Transmission Line Differential Protection Based on Numerical...

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2023 IEEE International Conference on Advanced Power System Automation and Protection, APAP 2023

作者： Liu, Zhiquan Liu, Yu Lu, Dayou Yang, Yayu Fan, Rui Center for Intelligent Power and Energy Systems School of Information Science and Technology ShanghaiTech University China China Shanghai Maritime University Department of Electrical Engineering China University of Denver United States

ISBN: (纸本)9798350306668

Current differential protection is a widely adopted and effective high-voltage AC transmission line protection scheme. The long distance of the transmission line determines that the distributed parameters and corresponding capacitive currents are not negligible. In fact, the physical laws of a distributed parameter transmission line can be expressed as a set of partial differential equations (PDEs). In this paper, a time-domain current differential protection method based on two-terminal electrical quantities and the PDE line model is proposed. The method does not depend on the power frequency phasors, can accurately compensate line capacitive current, and can effectively improve the protection speed and sensitivity. Numerical experiments validate the effectiveness and reliability of the proposed method. Additionally, the proposed method demonstrates satisfactory real-time performance under relatively low sampling rates (4k Hz). © 2023 IEEE.

关键词： Partial differential equations

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Proposing a Low-Rank Approximation Method with Mathematical Guarantees for High-Dimensional Tensor Data

Proposing a Low-Rank Approximation Method with Mathematical ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Hasegawa, Hiroki Yata, Kazuyoshi Okada, Yukihiko Kunimatsu, Jun University of Tsukuba Program in Service Engineering Tsukuba Japan Sciences University of Tsukuba Institute of Pure and Applied Tsukuba Japan University of Tsukuba Institute of Systems and Information Engineering Tsukuba Institute for Advanced Research Center for Artificial Intelligence Research Tsukuba Japan University of Tsukuba Institute of Medicine Tsukuba Japan

ISBN: (纸本)9798350362480

The analysis of high-dimensional tensor data presents significant challenges for traditional methods such as CANDECOMP/PARAFAC (CP) decomposition and Higher-Order Singular Value Decomposition (HOSVD) due to the size, complexity, and noise inherent in the data. Recent studies have emphasized the critical need for noise-robust approaches capable of reducing dimensionality while preserving data integrity, particularly for matrix-form data, underscoring the necessity of theoretical advancements in noise reduction. In this study, we propose a novel method, Noise-Reduced Higher-Order Singular Value Decomposition (NR-HOSVD), which integrates noise reduction techniques into the HOSVD framework to enhance dimensionality reduction for matrix data. In comparative simulations with CP decomposition and HOSVD, NR-HOSVD consistently demonstrated lower relative errors as the rank increased, particularly for high-dimensional tensors, surpassing the performance of conventional methods beyond a certain rank threshold. Furthermore, NR-HOSVD exhibited superior stability and faster convergence on large-scale datasets compared to existing approaches. These findings suggest that NR-HOSVD provides a more accurate and efficient approach to high-dimensional tensor data analysis, making it particularly well-suited for applications in big data and machine learning. In the future, NR-HOSVD is expected to serve as a pivotal method for enhancing the accuracy and efficiency of high-dimensional tensor data analysis, with broad applicability anticipated in fields such as large-scale data processing and precision medicine. © 2024 IEEE.

关键词： Data integrity

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Monkeypox Detection with Deep-Learning and Ensemble of Features: A Study 4

Monkeypox Detection with Deep-Learning and Ensemble of Featu...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Taniar, David Damaševičius, Robertas Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Monash University Faculty of Information Technology MelbourneVIC3800 Australia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas44249 Lithuania

ISBN: (纸本)9798350387490

There is a growing global prevalence of infectious diseases in the human population and it is imperative to have precise diagnosis and therapy in order to effectively cure these diseases. Monkeypox (Mpox) is an infectious disease that can cause varying degrees of skin infection, ranging from minor to severe. The recent spread of Mpox has been confirmed as Public Health Emergency of International Concern (PHEIC) due to its large infection rate. This study aims to develop a deep-learning (DL) scheme for Mpox identification. To enhance the detection accuracy, this study considered preprocessed skin images for the analysis and employed the binary classification with Ensemble of Deep-Features (EDF) using 5-fold cross validation. The outcome of this investigation validate that the suggested deep learning tool enables the attainment of a detection accuracy of 100% on the augmented images from the Monkeypox Skin Images Dataset (MSID). Compared to the existing results in the literature, this research provides a significant result on the chosen MSID database. © 2024 IEEE.

关键词： Classification Ensemble of deep-features Healthcare Illness Monkeypox

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Identification of optimal composition with superior electrochemical properties along the zero Mn^(3+)line in Na_(0.75)(Mn-Al-Ni)O_(2)pseudo ternary system

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Journal of Energy Chemistry 2024年第9期96卷 206-216页

作者： Hari Narayanan Vasavan Manish Badole Samriddhi Saxena Velaga Srihari Asish Kumar Das Pratiksha Gami Neha Dagar Sonia Deswal Pradeep Kumar Himanshu Kumar Poswal Sunil Kumar Department of Metallurgical Engineering and Materials Science Indian Institute of Technology IndoreSimrol 453552India High Pressure&Synchrotron Radiation Physics Division Bhabha Atomic Research Centre400085 MumbaiIndia School of Physical Sciences Indian Institute of Technology MandiHimachal Pradesh 175005India Center for Electric Vehicle and Intelligent Transport Systems Indian Institute of Technology IndoreSimrol 453552India

Biphasic layered oxide cathodes,known for their superior electrochemical performance,are prime candidates for commercializing in Na-ion ***,we unveil a series of P3/P2 monophasic and biphasic Al-substituted Na_(3/4)Mn_(5-x/8)Al_(2x/8)Ni_(3-x/8)O_(2)layered oxide cathodes that lie along the‘zero Mn^(3+)line’in the Na_(3/4)(Mn-Al-Ni)O_(2)pseudo-ternary *** structural analysis showed a larger Na^(+)conduction bottleneck area in both P3 and P2 structures with a higher Al3+content,which enhanced their rate *** each composition,the P3/P2 biphasic compound with nearly equal fractions of P3 and P2 phases outperformed their monophasic counterparts in almost all electrochemical performance *** synchrotron XRD measurements obtained for the monophasic P3 and biphasic P2/P3 samples revealed the absence of the O3 phase during *** high structure stability and faster Na^(+)transport kinetics in the biphasic samples underpins the enhancement of electrochemical properties in the Al-substituted P3/P2 *** results highlight fixed oxidation state lines as a novel tool to identify and design layered oxide cathodes for Na-ion batteries in pseudo-ternary diagrams involving Jahn-Teller active cations.

关键词： Layered oxides Biphasic cathode Operando Synchrotron XRD Electrochemical behavior Cyclic performance

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Human Gait Recognition:A Deep Learning and Best Feature Selection Framework

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Computers, Materials & Continua 2022年第1期70卷 343-360页

作者： Asif Mehmood Muhammad Attique Khan Usman Tariq Chang-Won Jeong Yunyoung Nam Reham R.Mostafa Amira ElZeiny Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Information Systems Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt Department of Information Systems Faculty of Computers and InformationDamietta UniversityDamiettaEgypt

Background—Human Gait Recognition(HGR)is an approach based on biometric and is being widely used for *** is adopted by researchers for the past several *** factors are there that affect the system performance such as the walking variation due to clothes,a person carrying some luggage,variations in the view ***—In this work,a new method is introduced to overcome different problems of HGR.A hybrid method is proposed or efficient HGR using deep learning and selection of best *** major steps are involved in this work-preprocessing of the video frames,manipulation of the pre-trained CNN model VGG-16 for the computation of the features,removing redundant features extracted from the CNN model,and *** the reduction of irrelevant features Principal Score and Kurtosis based approach is proposed named *** that,the features of PSbK are fused in one ***,this fused vector is fed to the One against All Multi Support Vector Machine(OAMSVM)classifier for the final ***—The system is evaluated by utilizing the CASIA B database and six angles 00◦,18◦,36◦,54◦,72◦,and 90◦are used and attained the accuracy of 95.80%,96.0%,95.90%,96.20%,95.60%,and 95.50%,***—The comparison with recent methods show the proposed method work better.

关键词： Human gait recognition deep features extraction features fusion features selection

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ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

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Computer Modeling in engineering & Sciences 2024年第4期139卷 361-382页

作者： Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang Ultrasonic Department Zhongda Hospital Affiliated to Southeast UniversityNanjing210009China School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia State Key Laboratory of MillimeterWaves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal UniversityNanjing211200China School of Software Engineering Quanzhou Normal UniversityQuanzhou362000China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques ***:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid ***,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask ***,we propose the DualLoss function,tailored to the thyroid nodule localization and classification *** balances the learning of the localization and classification tasks to help improve the model’s generalization ***,we introduce strategies for augmenting the ***,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid ***:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and *** results show that ThyroidNet outperformed these methods in localizing and classifying thyroid *** achieved improved accuracy of 3.9%and 1.5%,***:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis *** research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

关键词： ThyroidNet deep learning TransUnet multitask learning medical image analysis

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Femoropopliteal Artery Stent Detection in Fluoroscopy Using Otsu's Preprocessing and CNN Segmentation 4

Femoropopliteal Artery Stent Detection in Fluoroscopy Using ...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Damaševičius, Robertas Taniar, David Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania Monash University Faculty of Information Technolog MelbourneVIC3800 Australia

ISBN: (纸本)9798350387490

Computer algorithm supported medical image analysis is a prevalent method in clinics, which effectively alleviates the diagnostic burden associated with traditional image assessment methods. This study aims to create a method that utilizes a Convolutional-Neural-Network (CNN) to accurately segment the Femoropopliteal Artery Stent (FAS) in Digital Fluoroscopy Images (DFI). In order to improve the accuracy of segmentation, this study used the combination of Otsu's tri-level thresholding and the Lévy-Flight Distribution Algorithm (LFDA). The suggested technique consists of many stages: collecting and resizing images and masks, boosting the DFI using Otsu and LFDA, using CNN-segmentation to extract the stent region, comparing segmented stent with mask, and calculating the required performance metrics. The efficacy of the suggested system is validated by analyzing the raw and thresholded DFI. The investigational results of this work affirm that this method achieves a higher level of accuracy (>96%) in segmenting images when the Otsu's preprocessed image is utilized. The experimental study employs the UNet-variants, and in the future, alternative CNN-segmentation methodologies can be utilized to validate the effectiveness of the presented scheme. © 2024 IEEE.

关键词： Convolutional neural networks

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