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International Electrical Engineering Congress (iEECON)

作者： Tada On-khong Noppadon Sisuk Paramote Wardkein Ratchanoo Katman Kriangsak Prompak Department of Physics Faculty of Science Naresuan University Phitsanulok Thailand Program in Computer Engineering Faculty of Industrial Technology Phibunsongkhram Rajabhat University Phitsanulok Thailand Department of Telecommunication School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Program of Physics Faculty of Science and Technology Phibunsongkhram Rajabhat University Phitsanulok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

Capacitance-to-voltage conversion is essential for capacitive sensors in various industries, including touch interfaces, medical devices, and moisture measurement. However, circuit design faces challenges like non-linearity, noise, and small capacitance variations affecting voltage signals. This article proposes a phase-locked loop in free-running oscillator mode with a frequency-to-voltage converter to enhance accuracy and stability. Test results show a 97.9% measurement accuracy, demonstrating reduced noise and improved stability. The proposed circuit is ideal for high-precision applications in diverse environments, overcoming limitations of traditional methods.

关键词： Accuracy Sensitivity Moisture measurement Noise Moisture Capacitance Frequency conversion Circuit stability Phase locked loops Standards

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

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Probing bundle-wise abnormalities in patients infected with human immunodeficiency virus using fixel-based analysis:new insights into neurocognitive impairments

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Chinese Medical Journal 2023年第18期136卷 2178-2186页

作者： Jing Zhao Bin Jing Jiaojiao Liu Feng Chen Ye Wu Hongjun Li School of Biological Science and Medical Engineering Beihang UniversityBeijing 100069China Department of Radiology Beijing Youan HospitalCapital Medical UniversityBeijing 100069China School of Biomedical Engineering Capital Medical UniversityBeijing 100069China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application School of Biomedical EngineeringCapital Medical UniversityBeijing 100069China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsu 210094China

Background:Changes in white matter(WM)underlie the neurocognitive damages induced by a human immunodeficiency virus(HIV)*** study aimed to examine using a bundle-associated fixel-based analysis(FBA)pipeline for investigating the microstructural and macrostructural alterations in the WM of the brain of HIV ***:This study collected 93 HIV infected patients and 45 age/education/handedness matched healthy controls(HCs)at the Beijing Youan Hospital between January 1,2016 and December 30,*** HIV patients underwent neurocognitive evaluation and laboratory testing followed by magnetic resonance imaging(MRI)*** order to detect the bundle-wise WM abnormalities accurately,a specific WM bundle template with 56 tracts of interest was firstly generated by an automated fiber clustering method using a subset of ***-based analysis was used to investigate bundle-wise differences between HIV patients and HCs in three perspectives:fiber density(FD),fiber cross-section(FC),and fiber density and cross-section(FDC).The between-group differences were detected by a two-sample t-test with the false discovery rate(FDR)correction(P<0.05).Furthermore,the covarying relationship in FD,FC and FDC between any pair of bundles was also accessed by the constructed covariance networks,which was subsequently compared between HIV and HCs via permutation *** correlations between abnormal WM metrics and the cognitive functions of HIV patients were explored via partial correlation analysis after controlling age and ***:Among FD,FC and FDC,FD was the only metric that showed significant bundle-wise alterations in HIV patients compared to *** FD values were observed in the bilateral fronto pontine tract,corona radiata frontal,left arcuate fasciculus,left corona radiata parietal,left superior longitudinal fasciculus III,and right superficial frontal parietal(SFP)(all FDR P<0.05).In bundle-wise covariance network,HIV patients displayed

关键词： Human immunodeficiency virus Diffusion tensor imaging White matter bundle Fixel-based analysis Covariance network

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How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing 41

How Universal Polynomial Bases Enhance Spectral Graph Neural...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Keke Wang, Yu Guang Li, Ming Liò, Pietro School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China Shanghai AI Laboratory Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge United Kingdom

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these filters are predefined and remain fixed across different graphs, failing to accommodate the varying degrees of heterophily. Addressing this gap, we demystify the intrinsic correlation between the spectral property of desired polynomial bases and the heterophily degrees via thorough theoretical analyses. Subsequently, we develop a novel adaptive heterophily basis wherein the basis vectors mutually form angles reflecting the heterophily degree of the graph. We integrate this heterophily basis with the homophily basis to construct a universal polynomial basis UniBasis, which devises a polynomial filter-based graph neural network - UniFilter. It optimizes the convolution and propagation in GNN, thus effectively limiting over-smoothing and alleviating over-squashing. Our extensive experiments, conducted on datasets with a diverse range of heterophily, support the superiority of UniBasis in the universality but also its proficiency in graph explanation. Copyright 2024 by the author(s)

关键词： Wiener filtering

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MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention

arXiv

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arXiv 2025年

作者： Wang, Tianyi Fan, Jianan Zhang, Dingxin Liu, Dongnan Xia, Yong Huang, Heng Cai, Weidong School of Computer Science The University of Sydney SydneyNSW2006 Australia National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China Department of Computer Science University of Maryland College ParkMD20742 United States

Histopathology and transcriptomics are fundamental modalities in cancer diagnostics, encapsulating the morphological and molecular characteristics of the disease. Multi-modal self-supervised learning has demonstrated remarkable potential in learning pathological representations by integrating diverse data sources. Conventional multi-modal integration methods primarily emphasize modality alignment, while paying insufficient attention to retaining the modality-specific intrinsic structures. However, unlike conventional scenarios where multi-modal inputs often share highly overlapping features, histopathology and transcriptomics exhibit pronounced heterogeneity, offering orthogonal yet complementary insights. Histopathology data provides morphological and spatial context, elucidating tissue architecture and cellular topology, whereas transcriptomics data delineates molecular signatures through quantifying gene expression patterns. This inherent disparity introduces a major challenge in aligning these modalities while maintaining modality-specific fidelity. To address these challenges, we present MIRROR, a novel multi-modal representation learning framework designed to foster both modality alignment and retention. MIRROR employs dedicated encoders to extract comprehensive feature representations for each modality, which is further complemented by a modality alignment module to achieve seamless integration between phenotype patterns and molecular profiles. Furthermore, a modality retention module safeguards unique attributes from each modality, while a style clustering module mitigates redundancy and enhances disease-relevant information by modeling and aligning consistent pathological signatures within a clustering space. Extensive evaluations on The Cancer Genome Atlas (TCGA) cohorts for cancer subtyping and survival analysis highlight MIRROR’s superior performance, demonstrating its effectiveness in constructing comprehensive oncological feature representations and be

关键词： Self-supervised learning

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SpDiff: A Speech Sensing System with Diffusion Model Based on mm Wave Radar

SpDiff: A Speech Sensing System with Diffusion Model Based o...

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IEEE Conference on Wireless Communications and Networking

作者： Can Jin Yulong Zhang Xuanheng Li Yi Sun Jie Wang Yuguang Fang School of Information and Communication Engineering Dalian University of Technology Dalian China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Information Science and Technology Dalian Maritime University Dalian China Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Voice control has become an indispensable interaction method in smart devices. Compared to traditional microphones, mm Wave radar offers a promising solution for speech sensing in noisy environments. However, most current research relies on single-view information, such as vocal cord vibrations or lip movements, to classify speech, which overlooks important details like timbre, speech rate, and intonation, limiting the application of speech sensing. To address these issues, we develop a high-quality speech sensing method based on mm Wave radar, named SpDiff. This method accurately localizes the vocalizing target and, based on the human vocal mechanism, extracts multi-view speech features according to the movement characteristics of the vocal cords, lips, and face. Additionally, to generate high-quality speech signals, we design a conditional latent diffusion model (CLDM), which uses multi-view radar information as conditional guidance, accurately capturing the complex mapping relationships between radar and speech signal distributions. To evaluate the SpDiff method, we build a mmWave system using IWR1443Boost and recruit 14 volunteers to construct a dataset. Experimental results show that SpDiff achieves high standards in speech sensing, with the generated speech directly input into existing recognition models, achieving an average character and word error rate (CER/WER) of only 2.33% and 3.05%.

关键词： Vibrations Lips Spaceborne radar Speech recognition Diffusion models Feature extraction Sensors Doppler radar Speech processing Standards

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An Effective Approach to Detect and Classify Human Monkey Pox using Inception V3

An Effective Approach to Detect and Classify Human Monkey Po...

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Delhi Section Conference (DELCON), IEEE

作者： Ritu Shree Kalyan Acharjya Khushmeen Kaur Brar M. N. Nachappa Mohammed I. Habelalmateen Saurabh Aggarwal Computer Science & Application Vivekananda Global University Jaipur India Maharishi School of Engineering & Technology MUIT Uttar Pradesh India Department of Electronics and Communication Engineering Chandigarh University Mohali Punjab India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India The Islamic university Najaf Iraq Department of Mechanical Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

The virus which generates monkeypox is now emerging as a new pandemic menace as humans overcome from COVID-19. Despite reports of incidents involving monkeypox continue to flood in on every single day, it is not believed to be as harmful or as widespread as COVID-19. The human skin infected with the monkeypox virus and that impacted region might be taken for detecting the disease early since a photograph might be utilized for learning additional information about the ailment. But as of now, there hasn't been an accurate accessible Monkeypox dataset which can be utilized for training and testing DL models. The pictures in this collection can be employed for developing and employing DL techniques with greater certainty. Additionally, we suggested and assessed a methodology, an improved DL based CNN model. The present research proposed a deep CNN model that could accurately identify monkeypox disease with accuracies of 88.82%, 75% and 85.29% for bayes net, DT and RF respectively considering the original dataset. Precision, recall and f score values for class 0 are 0.87, 0.94 and 0.908 in case of RF. While for class 1, the values are 0.90, 0.81 and 0.85 respectively.

关键词： Radio frequency COVID-19 Training Accuracy Pandemics Neural networks Skin Bayes methods Diseases Testing

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A cross Transformer for image denoising

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Information Fusion 2024年 102卷

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Shichao Zhang, Yanning Lin, Chia-Wen School of Software Northwestern Polytechnical University Xi'anShaanxi710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'anShaanxi710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi'an710129 China Department of Electrical Engineering and the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://***/hellloxiaotian/CTNet. © 2023 Elsevier B.V.

关键词： Image denoising

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Organic-inorganic hybrid piezotronic bipolar junction transistor for pressure sensing

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Microsystems & Nanoengineering 2024年第3期10卷 229-244页

作者： Emad Iranmanesh Zihao Liang Weiwei Li Congwei Liao Shunyu Jin Chuan Liu Kai Wang Shengdong Zhang Charalampos Doumanidis Gehan A.J.Amaratunga Hang Zhou Guangdong Provincial Key Laboratory of In-Memory Computing Chips School of Electronic and Computer EngineeringPeking University Shenzhen Graduate SchoolShenzhen 518055P.R.China School of Mechanical Engineering Guangdong Technion-Israel Institute of TechnologyShantou 515063P.R.China State Key Laboratory of Microelectronics Device and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029P.R.China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 23000PR China School of Electronics and Information Technology Sun Yat-sen UniversityNo.132 East Waihuan RoadGuangzhou 510006P.R.China Department of Mechanical Aerospaceand Biomedical EngineeringUniversity of South AlabamaShelby Hall3128MobileAL 36688USA Electrical Engineering Division Department of EngineeringUniversity of Cambridge9 JJ Thomson AvenueCambridge CB30FAUnited Kingdom Zhejiang University International CampusHainingChina

With the rapid development of the Internet of Things(IoTs),wearable sensors are playing an increasingly important role in daily monitoring of personal health and *** signal-to-noise-ratio has become the most critical performance factor to *** enhance it,on the one hand,good sensing materials/devices have been employed;on the other hand,signal amplification and noise reduction circuits have been ***,most of these devices and circuits work in an active sampling mode,requiring frequent data acquisition and hence,entailing high-power *** this scenario,a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great ***,we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor(PBJT)that acts as an autonomous and highly sensitive,current-and/or voltage-mediated pressure *** PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collectorbase *** force exertion on the emitter side,as a result of the piezoelectric effect,the emitter-base diode is forward biased while the collector-base diode is reverse *** to the inherent BJT amplification effect,the PBJT achieves record-high sensitivities of 139.7 kPa^(-1)(current-based)and 88.66 kPa^(-1)(voltage-based)in sensing *** PBJT also has a fast response time of<110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger ***,the PBJT advances the state of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous,making it promising for pressure sensing and other IoT applications.

关键词： bipolar transistor IoT

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The Performance Analysis of Distributed Denial of Service Attacks in Wireless Networks

The Performance Analysis of Distributed Denial of Service At...

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International Conference on Futuristic Technologies (INCOFT)

作者： Trapty Agrawal R Raghavendra K. Ranjith Singh Rajiv Gandhi N Dharmesh Dhabliya Jitendra Kumar Katariya Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Indi Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science & Application Vivekananda Global University Jaipur India

Complete studies paper that examines the outcomes that malicious 0.33-celebration actors will have on Wi-Fi networks via dispensed denial of carrier (DDoS) attacks. This paper explores the available numerous assault models and the potential countermeasures that may be applied if you want to mitigate the damage due to such assaults. In addition, this paper focuses on the capability blessings related to deploying preventative measures to lessen the impact of such assaults. Moreover, the paper investigates existing attack trends. It affords an outline of the safety techniques to be had for limiting the harm caused by DDoS attacks in wireless networks. By comparing and analyzing the performance of diverse protection mechanisms, this paper offers an evidenceprimarily based evaluation of the effectiveness of DDoS attack safety answers. ultimately, this paper concludes with the aid of supplying recommended measures for strengthening and ensuring the safety of Wi-Fi networks against malicious actors.

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