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Concept-Level Semantic Transfer and Context-Level Distribution Modeling for Few-Shot Segmentation

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Luo, Yuxuan Chen, Jinpeng Cong, Runmin Ip, Horace Ho Shing Kwong, Sam City University of Hong Kong Department of Computer Science Hong Kong Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Lingnan University Hong Kong

Few-shot segmentation (FSS) methods aim to segment objects using only a few pixel-level annotated samples. Current approaches either derive a generalized class representation from support samples to guide the segmentation of query samples, which often discards crucial spatial contextual information, or rely heavily on spatial affinity between support and query samples, without adequately summarizing and utilizing the core information of the target class. Consequently, the former struggles with fine detail accuracy, while the latter tends to produce errors in overall localization. To address these issues, we propose a novel FSS framework, CCFormer, which balances the transmission of core semantic concepts with the modeling of spatial context, improving both macro and micro-level segmentation accuracy. Our approach introduces three key modules: (1) the Concept Perception Generation (CPG) module, which leverages pre-trained category perception capabilities to capture high-quality core representations of the target class;(2) the Concept-Feature Integration (CFI) module, which injects the core class information into both support and query features during feature extraction;and (3) the Contextual Distribution Mining (CDM) module, which utilizes a Brownian Distance Covariance matrix to model the spatial-channel distribution between support and query samples, preserving the fine-grained integrity of the target. Experimental results on the PASCAL-5i and COCO-20i datasets demonstrate that CCFormer achieves state-of-the-art performance, with visualizations further validating its effectiveness. Our code is available at ***/lourise/ccformer. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Machine Learning and Intelligent Network System based Covid 19 Health Care System 3

Machine Learning and Intelligent Network System based Covid ...

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3rd International Conference on Power, Energy, control and Transmission Systems, ICPECTS 2022

作者： Basha, Amjath Ali Jakul Saravanan, K. Mangayarkarasi, T. Balarengadurai, C. University of Technology and Applied Sciences Ibra Sultanate of Oman RCC - ENGG Department Computer Engineering Electrical and Electronic Section Department of Engineering Oman R.M.D Engineering College Department of Information Technology Tamil Nadu Chennai India Sri Sairam Engineering College Department of Instrumentation and Control Engineering Tamil Nadu Chennai India Vidyavardhaka College of Engineering Department of Computer Science and Engineering Karnataka Mysuru India

ISBN: (纸本)9781665462754

COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management Systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency. © 2022 IEEE.

关键词： COVID-19

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Feature Extraction of Motor Imagination EEG Signals for a Collaborative Exoskeleton Robot Based on PSD Analysis 42

Feature Extraction of Motor Imagination EEG Signals for a Co...

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42nd Chinese control Conference, CCC 2023

作者： Wu, Yunfeng Li, Junze Ren, Zichen Lu, Bo Zhang, Changzhu Qi, Peng Tongji University Department of Control Science and Engineering No. 1239 Siping Road Shanghai200092 China Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States School of Mechanicaland Electric Engineering Soochow University Robotics and Microsystems Center Suzhou215021 China

ISBN: (纸本)9789887581543

The integration of brain-machine interface and exoskeleton robot has been widespread application in gait correction, walking assistance, and numerous other scenarios. To effectively extract the electroencephalogram (EEG) signal features of motor imagery while wearing an exoskeleton, this study proposes a frequency band pre-determination method based on power spectral density (PSD) that enables common spatial patterns (CSP) to extract features from the frequency band with the highest energy in the EEG. The signal power spectral density of all channels is obtained at or near the average frequency of the maximum short interval frequency component energy. A second round of filtering is performed on the data, and the signal components are used as input for the subsequent feature extraction step. The CSP method extracts features from the spatial domain signal and generates feature maps. Finally, Support Vector Machines (SVM) are utilized to classify the EEG signals. Based on the pre-set gait of the exoskeleton robot and the motor imagery paradigm, feature extraction and classification of motor imagery EEG data during exoskeleton use were conducted, with an average accuracy rate of 77%. The experimental results demonstrate the effectiveness of this method in extracting the motor imagery EEG features during exoskeleton use. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Electroencephalography

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Recent Technological Advances in Solar Photovoltaic System and Its Applications in Building Integrated Photovoltaic System

Recent Technological Advances in Solar Photovoltaic System a...

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International Conference on Sustainable Technology and Advanced Computing in Electrical engineering, ICSTACE 2021

作者： Islam, Noorul Nat, Aslam Khan, Rashid Ahmed Department of Electrical Engineering ZH College of Engineering and Technology Aligarh Muslim University Aligarh202002 India Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy Department of Electrical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan

ISBN: (纸本)9789811943638

Energy has become an integral part of our life. The fossil fuel present in the environment is very much limited. On the other hand, fossil fuel emits harmful gases such as carbon dioxide, Chlorofluorocarbon (CFC), carbon monoxide, and others which harmed flora and fauna. Therefore, the concern for renewable energy application has become more vital due to the depletion of fossil fuels, rising global warming, and increased consumption of energy. Renewable energy aims to reduce pollution levels, enhancing the local economy and community sustainability. Solar energy is the most promising alternative energy source since it has various advantages over other renewable energy sources. The sun is an unquenchable source of energy capable of addressing all energy requirements of humankind. The solar energy concept has been around for a long time, and technological advancements will make it endure for a longer period. The PV-Effect is a mechanism that can produce an electric current when sunlight strikes the limiting layer of semiconductor materials. A photovoltaic (PV) system is a semiconductor device that produces electricity when exposed to luminosity. A PV module is made up of several solar cells that are coupled in parallel and series. This paper provides an extensive study of advancements in energy extraction techniques from a solar panel, analysing solar PV systems under diverse circumstances. PV technology is hastily evolving compared to alternative renewables, consequently leading to the Building Integrated Photovoltaics (BIPV) systems playing a significant role in the generation of electrical energy. BIPV system is a preferred choice as a building integrated solar energy storage system. It has been categorised into three subparts: Grid-connected BIPV system, BIPV system with a solar battery, and PV-Trombe wall. The BIPV mechanism simultaneously acts as a material for the envelope of the building and power extraction from. © 2022, The Author(s), under exclusive license

关键词： Fossil fuels

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Measurement of atmospheric nanoparticles:Bridging the gap between gas-phase molecules and larger particles

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Journal of Environmental sciences 2023年第1期123卷 183-202页

作者： Chao Peng Chenjuan Deng Ting Lei Jun Zheng Jun Zhao Dongbin Wang Zhijun Wu Lin Wang Yan Chen Mingyuan Liu Jingkun Jiang Anpei Ye Maofa Ge Weigang Wang State Key Laboratory for Structural Chemistry of Unstable and Stable Species Beijing National Laboratory for Molecular Sciences(BNLMS)CAS Research/Education Center for Excellence in Molecular SciencesInstitute of ChemistryChinese Academy of SciencesBeijing 100190China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China School of Environment Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China School of Atmospheric Sciences Sun Yat-sen UniversityZhuhaiGuangdong 519082China State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3) Department of Environmental Science&EngineeringFudan UniversityShanghai 200433China Key Laboratory for the Physics and Chemistry of Nanodevices Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China University of Chinese Academy of Sciences Beijing 100049China

Atmospheric nanoparticles are crucial components contributing to fine particulate matter(PM_(2.5)),and therefore have significant effects on visibility,climate,and human *** to the unique role of atmospheric nanoparticles during the evolution process from gas-phase molecules to larger particles,a number of sophisticated experimental techniques have been developed and employed for online monitoring and characterization of the physical and chemical properties of atmospheric nanoparticles,helping us to better understand the formation and growth of new *** this paper,we firstly review these state-of-the-art techniques for investigating the formation and growth of atmospheric nanoparticles(e.g.,the gas-phase precursor species,molecular clusters,physicochemical properties,and chemical composition).Secondly,we present findings from recent field studies on the formation and growth of atmospheric nanoparticles,utilizing several advanced ***-more,perspectives are proposed for technique development and improvements in measuring atmospheric nanoparticles.

关键词： Nanoparticle Measurement techniques Size distributions Physical properties Chemical composition

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A CNN Approach To Predicting Non-Invasive Blood Pressure of Surgical Patients 5

A CNN Approach To Predicting Non-Invasive Blood Pressure of ...

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5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Ko, Pin-You Lai, Lien-Fu Ku, Tien-Hsiung Lin, Yue-Der National Changhua University of Education Department of Computer Science and Information Engineering Changhua Taiwan Changhua Christian Hospital Ai Development Center Department of Anesthesiology Changhua Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

The occurrence of intraoperative hypertension/ hypotension may cause danger to a patient. Therefore, the monitoring of blood pressure change during surgical operation is momentous for anesthetized patient undergoing surgical procedures. The invasive method of measuring arterial blood pressure (ABP) provides accurate and much information, but it requires evaluation by an anesthesiologist and with its long-term use, it also brings a higher risk of infection, and even some patients have pain and discomfort at the catheter placement site after surgery, so it is not suitable for all types of patients. On the other hand, non-invasive method of measuring cuff blood pressure provides little information because of non-continuous measurement. With the emerging of deep learning, there has been more and more discussion on whether non-invasive Photoplethysmography (PPG) can replace invasive ABP as an alternative to continuous BP measurement and even BP prediction. This paper aims to implement a deep learning model to predict SBP/DBP signal by PPG, PPG extended data and Electrocardiography (ECG) data. We trained the CNN-based architecture on preprocessed data including ECG, PPG, 1st and 2nd derivative PPG signal extracted from MIMIC-III(Medical Information Mart for Intensive Care III) waveform database matched subset to predict continuous ABP signals using real world data. The value of SBP and DBP are directly predicted to effectively estimate the accuracy of the predictions. The prediction results fulfill the grade A in British Hypertension Society (BHS) standard and most part of Association for the Advancement of Medical Instrumentation (AAMI)'s standard. The proposed model has been applied for CHANGHUA CHRISTIAN HOSPITAL's IRB NO:191240. The results depict that our proposed model could effectively predict SBP/DBP signal and be deployed to the operating room realistically. © 2022 IEEE.

关键词： Blood pressure

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Adoption of Extended Reality as a Teaching Tool 1

Adoption of Extended Reality as a Teaching Tool

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1st International Conference on Computational science and Technology, ICCST 2022

作者： Mangay Arkarasi, T. Madhumita, S. Suretha, S. Harini, M. Ananthi, M. Hariharan, V. Sri Sairam Engineering College Department of Instrumentation and Control Chennai India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India Founder Space Zee Technologies Private Limited Chennai India

ISBN: (纸本)9781665476553

Extended reality (XR) facets aid pupils in understanding difficult topics. Regular education has been reliant on 2D technology for a very long time. It no longer draws in pupils due to its lack of immersion and engagement assistance. The suggested remedy involves using AR to develop 3D models and educational components. The augmented reality (AR) application is more appealing to pupils than other approaches and can be utilized as an interactive tool to clarify difficult concepts in the classroom. The functions of the following human anatomy parts-the brain, heart, and pancreas-were supplied in a piece of note, and audio was added predominately. The 3d models were created using the unity3d application as a support system, and Sketchfab was used to import them. © 2022 IEEE.

关键词： Augmented reality

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Impact of Parameters for the In-situ Measurement of the Electric Field Strength of the SSB Signal in a 5G NR System Using the Frequency-Selective Method in Zero-Span Mode

Impact of Parameters for the In-situ Measurement of the Elec...

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International Conference on Smart Systems and Technologies (SST)

作者： Vanja Mandrić Slavko Rupčić Bogdan Pavković Josip Ilakovac Department of Communication Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Measuring 5G systems is a major challenge for measurement engineers, who need to be aware of all the limitations of the new generation The parameters of the frequency-selective measurement system (spectrum analyzer - ROHDE & SCHWARZ FSH8) were investigated in the measurement of 5G NR SSB signals (3.4 – 3.8 GHz). The focus was on the setting parameters which, according to the literature, have an influence on the measurement: RBW, VBW, SWEEP TIME and the duration of the measurement. All measurements were carried out in zero-span mode. In the second part of the measurement, these SSB synchronization signal measurement results were compared with the 5G signal measurement results at the same location using the NARDA SRM-3006 device equipped with a decoder for the 5G NR system (FR1) to determine the optimal method for measuring these signals with a spectrum analyzer (SA).The measurement results show that the resolution bandwidth (RBW) has the largest variation in the measured electric field (EF) strength values of the SSB 5G NR signal of 17.20 dB (MH/RMS) and 17.70 (RMS/AGV10). It is therefore necessary to pay particular attention to the setting of this measurement parameter. In addition, the video resolution (VBW) shows a significantly lower field strength variation of 2.50 dB, but also the same deviation from the reference value of 2.50 dB (RMS/AGV10). Other analyzed parameters have a negligible influence on the measurement of the EF *** post-processing was performed in the work to determine the final (maximum) value of the EF strength at the points of assumed highest exposure, which is used to assess the impact and compliance with the permissible values according to the relevant standards.

关键词： Time-frequency analysis 5G mobile communication Measurement uncertainty Electric variables measurement Amplitude modulation Time measurement Frequency measurement Decoding Electric fields Signal resolution

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Magnetoelastic coupling driven magnon gap in a honeycomb antiferromagnet

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Physical Review B 2024年第10期110卷 104419页

作者： Jeongheon Choe Andrey Baydin Gaihua Ye Bowen Ma Cynthia Nnokwe Martin Rodriguez-Vega David Lujan Swati Chaudhary Fuyang Tay Jiaming He Hiroyuki Nojiri Jianshi Zhou Gregory A. Fiete Junichiro Kono Rui He Xiaoqin Li Department of Physics Center of Complex Quantum Systems Center for Dynamics and Control of Materials Department of Electrical and Computer Engineering Smalley–Curl Institute Department of Physics Applied Physics Graduate Program Smalley–Curl Institute Department of Mechanical Engineering Institute for Materials Research Department of Physics and Astronomy Department of Materials Science and NanoEngineering

Cobalt titanate, CoTiO3, is a honeycomb antiferromagnet recently confirmed experimentally to host Dirac magnons, topological spin-orbit excitons, and chiral phonons. Here, we investigate a magnon gap at the zone center which calls for a refined spin Hamiltonian. We propose a microscopic model for the magnon gap and attribute it to a lattice-distortion (phonon)-induced higher-order spin interaction. Strong magnetoelastic coupling in CoTiO3 is also evident in Raman spectra, in which the magnetic order exerts a stronger influence on phonons corresponding to in-plane ionic motions than those with out-of-plane motions. We further examine the evolution of the zone-center magnons in a high magnetic field up to 18.5 T via THz absorption spectroscopy measurements. Based on this field dependence, we propose a spin Hamiltonian that not only agrees with magnon dispersion measured by inelastic neutron scattering but also includes fewer exchange constants and a realistic anisotropy term. Our work highlights the broad implications of magnetoelastic coupling in the study of topologically protected bosonic excitations.

关键词： Magnetism Magnons Topological materials Raman spectroscopy Terahertz spectroscopy

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A Cyclic Small Phase Theorem for MIMO LTI Systems*

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IFAC-PapersOnLine 2023年第2期56卷 1883-1888页

作者： Chao Chen Wei Chen Di Zhao Jianqi Chen Li Qiu Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Mechanics and Engineering Science & State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Control Science and Engineering & Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

This paper introduces a brand-new phase definition called the segmental phase for multi-input multi-output linear time-invariant systems. The underpinning of the definition lies in the matrix segmental phase which, as its name implies, is graphically based on the smallest circular segment covering the matrix normalized numerical range in the unit disk. The matrix segmental phase has the crucial product eigen-phase bound, which makes itself stand out from several existing phase notions in the literature. The proposed bound paves the way for stability analysis of a cyclic feedback system consisting of multiple subsystems. A cyclic small phase theorem is then established as our main result, which requires the loop system phase to lie between -π and π. The proposed theorem complements the celebrated small gain theorem.

关键词： Small phase theorem segmental phase cyclic feedback systems stability analysis lead-lag compensation

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