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IEEE Instrumentation and Measurement technology Conference

作者： Amit Krishan Kumar Mansour H. Assaf Voicu Z. Groza Emil M. Petriu Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Republic of Fiji School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

To improve support system for subjects with respiratory ailments, this work analyses the respiratory patterns from spirometer and surface electromyography (sEMG). The patterns are integrated with risk and biochemical systems model to optimise the clinical decision support system (CDSS). The work considers terminal events for the spirometer and sEMG. The events and their associated patterns are correlated and projected on a hyperspace prior implementing it in decision support system. The proposed method shows that the different characteristics of breathing indicate some essential hidden information which when used as 4D scattered projection, optimises the decision making in CDSS. The analysis shows the effectiveness of bimodal respiratory patterns for different breathing capacities, which is promising for the modelled conditions.

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Peculiar spin glass phase emerging in FeCo/FePt driven via nanoconfined crystallographic distortions

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Journal of Physics D: Applied Physics 2024年第46期57卷 465304-465304页

作者： Vashisht, Garima Gandhi, Ashish C Kumar, Vishnu Mathew, Arun Jacob Dong, Chung-Li Chen, Chi-Liang Asokan, K. Wu, Sheng Yun Fukuma, Y. Annapoorni, S. Department of Physics and Information Technology Faculty of Computer Science and System Engineering Kyushu Institute of Technology Fukuoka Iizuka820- 8502 Japan Department of Physics National Dong Hwa University Hualien97401 Taiwan Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan Jakkur Bangalore560064 India Ibaraki Tsukuba305-0801 Japan Department of Physics Tamkang University Tamsui251 Taiwan National Synchrotron Radiation Research Center Hsinchu30076 Taiwan Uttarakhand Dehradun248007 India Department of Physics and Astrophysics University of Delhi Delhi110007 India

We explore the existence of spin glass phase in FeCo/FePt bilayers arising due to disordered ferromagnet. The non-ergodic and highly degenerate landscape of the spin glass phase at low temperature explains the origin of complex magnetic texture in the FeCo/FePt system. Upon cooling the bilayered system, the magnetic texture undergoes spin freezing below 120 K as evident from the bifurcations in zero field cooling and field cooling magnetizations at low magnetic field as a manifestation of broken ergodicity. The uncompensated magnetic moments originating in the spin glass state result in slow time dynamics of thermoremanent magnetization. Consequently, the bilayers demonstrate an intriguing magnetic memory effect in which the magnetic state of the system could be retrieved upon isothermal ageing below 120 K after reversing the temperature cycle. Thermal treatment deteriorates the spin glass behaviour and shows a transition to strong ferromagnetic character in FeCo/FePt bilayers. © 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

关键词： Magnetic moments

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A Bpnn-Transformer for Air Quality Index Prediction

SSRN

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SSRN 2023年

作者： Wu, Xiankui Li, Penghua Gu, Xinyu See, K.W. Li, Junhong College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Faculty of Engineering Institute for Superconducting and Electronic Materials University of Wollongong Innovation Campus WollongongNSW2500 Australia Key Laboratory for Nonlinear Science and System Structure Chongqing Three Gorges University Chongqing404000 China

The Air Quality Index (AQI) is a key metric for evaluating the extent of atmospheric pollution. Accurate forecasting of the AQI is a challenging task due to the unpredictable nature of meteorological factors and the complex interactions between various pollutants. In this paper, we proposed a novel method that combines a backward propagation neural network (BPNN) and the Transformer model for improved AQI prediction. The method incorporates single exponential smoothing (SES) and Pearson correlation coefficient (PCC) analysis to enhance the predictive capabilities of the model. The proposed method has been evaluated using a comprehensive dataset, and the results demonstrate that it can significantly reduce the maximum error of AQI prediction to less than 3.4\%, which surpassed baseline learning methods, including BPNN, Long Short-Term Memory (LSTM), and Transformer model. Furthermore, visual analysis has been performed to confirm the effectiveness of the proposed method. This approach can serve as a valuable tool for policymakers and researchers to monitor and mitigate the harmful effects of air pollution, promoting sustainable development and enhancing the quality of life worldwide. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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On the Stimulation Artifact Reduction during Electrophysiological Recording of Compound Nerve Action Potentials *

On the Stimulation Artifact Reduction during Electrophysiolo...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Raphael Panskus Lukas Holzapfel Wouter A. Serdijn Vasiliki Giagka Dept. of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology The Netherlands Dept. of System Integration and Interconnection Technologies Fraunhofer Institute for Reliability and Microintegration IZM Berlin Germany Neuroscience Dept. Erasmus Medical Center Rotterdam the Netherlands

Recording neuronal activity triggered by electrical impulses is a powerful tool in neuroscience research and neural engineering. It is often applied in acute electrophysiological experimental settings to record compound nerve action potentials. However, the elicited neural response is often distorted by electrical stimulus artifacts, complicating subsequent analysis. In this work, we present a model to better understand the effect of the selected amplifier configuration and the location of the ground electrode in a practical electrophysiological nerve setup. Simulation results show that the stimulus artifact can be reduced by more than an order of magnitude if the placement of the ground electrode, its impedance, and the amplifier configuration are optimized. We experimentally demonstrate the effects in three different settings, in-vivo and in-vitro.

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Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification

arXiv

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arXiv 2023年

作者： Gao, Zan Wei, Shengxun Guan, Weili Zhu, Lei Wang, Meng Chen, Shengyong The Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China The Faculty of Information Technology Monash University Clayton Campus Australia The School of Electronic and Information Engineering Tongji University Shanghai200092 China The school of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Cloth-changing person reidentification (ReID) is a newly emerging research topic aimed at addressing the issues of large feature variations due to cloth-changing and pedestrian view/pose changes. Although significant progress has been achieved by introducing extra information (e.g., human contour sketching information, human body keypoints, and 3D human information), cloth-changing person ReID remains challenging because pedestrian appearance representations can change at any time. Moreover, human semantic information and pedestrian identity information are not fully explored. To solve these issues, we propose a novel identity-guided collaborative learning scheme (IGCL) for cloth-changing person ReID, where the human semantic is effectively utilized and the identity is unchangeable to guide collaborative learning. First, we design a novel clothing attention degradation stream to reasonably reduce the interference caused by clothing information where clothing attention and mid-level collaborative learning are employed. Second, we propose a human semantic attention and body jigsaw stream to highlight the human semantic information and simulate different poses of the same identity. In this way, the extraction features not only focus on human semantic information that is unrelated to the background but are also suitable for pedestrian pose variations. Moreover, a pedestrian identity enhancement stream is proposed to enhance the identity importance and extract more favorable identity robust features. Most importantly, all these streams are jointly explored in an end-to-end unified framework, and the identity is utilized to guide the optimization. Extensive experiments on six public clothing person ReID datasets (LaST, LTCC, PRCC, NKUP, Celeb-reID-light, and VC-Clothes) demonstrate the superiority of the IGCL method. It outperforms existing methods on multiple datasets, and the extracted features have stronger representation and discrimination ability and are weakly corre

关键词： Semantics

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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Adaptive Fourier Decomposition for Multi-Channel Signal Analysis

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IEEE Transactions on Applied Superconductivity 2022年第4期32卷

作者： Wang, Ze Wong, Chi Man Rosa, Agostinho Qian, Tao Wan, Feng Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Taipa999078 China Department of Bioengineering LaSEEB-System and Robotics Institute Instituto Superior Tecnico University of Lisbon Lisbon1049-001 Portugal Macao Center for Mathematical Sciences Macau University of Science and Technology Taipa China

Evolved from the conventional Fourier decomposition based on a pre-defined basis, Adaptive Fourier decomposition (AFD) uses adaptive basis to achieve the fast energy convergence. This paper extends the AFD to the multi-channel case, which finds common adaptive basis across all channels. The proposed multi-channel AFD (MAFD) scheme includes the multi-channel core AFD for general signals and the multi-channel unwinding AFD for specific signals that have common inner functions. Owing to the merits of the original AFD, the MAFD can provide sparse joint time-frequency distribution by computing the transient time frequency distribution (TTFD) across channels. Simulations on synthetic and real-world signals demonstrate that the proposed scheme can find and apply the common adaptive basis with desired properties maintained by the AFD, showing high potentials in real-world applications. © 1991-2012 IEEE.

关键词： Signal analysis

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Protocol for the fabrication of self-standing (nano)cellulose-based 3D scaffolds for tissue engineering

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STAR Protocols 2025年第1期6卷 103583页

作者： Mohan, Tamilselvan Bračič, Matej Bračič, Doris Lackner, Florian Nagaraj, Chandran Štiglic, Andreja Dobaj Kargl, Rupert Kleinschek, Karin Stana Graz University of Technology Institute for Chemistry and Technology of Biobased System (IBioSys) Stremayrgasse 9 Graz 8010 Austria University of Maribor Faculty of Mechanical Engineering Laboratory for Characterization and Processing of Polymers Smetanova Ulica 17 Maribor 2000 Slovenia Department of Internal Medicine Division of Pulmonology Medical University of Graz Graz Austria Institute of Automation Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Members of the European Polysaccharide Network of Excellence (EPNOE) Austria

Three-dimensional (3D) and porous scaffolds made from nanocellulosic materials hold significant potential in tissue engineering (TE). Here, we present a protocol for fabricating self-standing (nano)cellulose-based 3D scaffolds designed for in vitro testing of cells from skin and cartilage tissues. We describe steps for preparation of nanocellulose ink, scaffold formation using 3D printing, and freeze-drying. We then detail post-processing procedures to enhance mechanical properties, stability, and biocompatibility. This protocol offers researchers a framework for developing versatile and sustainable biomaterials for regenerative medicine. For complete details on the use and execution of this protocol, please refer to Mohan et al.¹ and Štiglic et al.² © 2025 The Authors

关键词： Biotechnology and bioengineering Chemistry Material sciences Tissue engineering

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Performance of Supercapacitor Using Carbon Electrode Prepared by Microwave Activation of Japanese Distilled Liquor Waste

SSRN

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SSRN 2024年

作者： Tashima, Daisuke Izaki, Akio Eguchi, Takuya Tsubota, Toshiki Kumagai, Seiji Department of Electrical Engineering Graduate School of Engineering Fukuoka Institute of Technology 3-30-1 Wajirohigashi Higashi-ku Fukuoka-shi Fukuoka811-0295 Japan College of Engineering Nihon University 1 Nakagawara Tokusada Tamuramachi Fukushima Prefecture Koriyama963-8642 Japan Department of Materials Science Faculty of Engineering Kyushu Institute of Technology 1-1 Sensui-cho Tobata-ku Kitakyushu-shi Fukuoka804-8550 Japan Department of Mathematical Science and Electrical-Electronic-Computer Engineering Akita University 1-1 Tegatagakuen-machi Akita010-8502 Japan

Japanese distilled liquor (Shochu) is an alcoholic beverage widely produced in the kyushu region in Japan. Our research group is developing an inexpensive electrode material for supercapacitors using waste generated during the production of Japanese distilled liquor. In a previous study, Japanese distilled liquor waste was carbonized and activated in an electric furnace. However, the process required long production times and high running costs. Therefore, the present study investigates the use of microwaves to activate carbon with reduced process time and cost. In this study, activated carbon was prepared in a microwave, using Japanese distilled liquor waste carbide and potassium carbonate with a weight ratio of 1:3, an output power of 800 W, and different activation times. The performance of the activated carbon was evaluated and compared by imaging their surface structure and evaluating their specific surface areas and pore volumes. The specific capacitance and charge transfer resistance were also measured. The results reveal a maximum specific surface area of 1,063 m2/g and specific capacitance of 229 F/g, obtained at an activation time of 9 min. Additionally, the cost of power consumption for microwave activation was reduced by approximately 61.9% compared to that of activation via an electric furnace. © 2024, The Authors. All rights reserved.

关键词： Activated carbon

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AI-optimized electrochemical aptasensors for stable, reproducible detection of neurodegenerative diseases, cancer, and coronavirus

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Heliyon 2025年第1期11卷 e41338页

作者： Tayfour Ahmed, Amira Elsir Dhahi, Th.S. Attia, Tahani A. Elhassan Ali, Fawzia Awad Elobaid, Mohamed Elshaikh Adam, Tijjani Gopinath, Subash C.B. Department of Information System College of Science & Arts King Khalid University Asser Mohyel Saudi Arabia Health and Medical Technicals College Southern Technical University Basrah Iraq Department of Computer Engineering College of Computer Science and Engineering University of Ha'il Saudi Arabia DEEE Faculty of Engineering University of Khartoum Sudan Imam Abdulrahman Bin Faisal University Applied College Computer Department Saudi Arabia Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Perlis Arau 02600 Malaysia Institute of Nano Electronic Engineering Universiti Malaysia Perlis Perlis Kangar 01000 Malaysia Micro System Technology Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia Center for Global Health Research Saveetha Medical College & Hospital Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Tamil Nadu Chennai 602 105 India Faculty of Chemical Engineering & Technology Universiti Malaysia Perlis (UniMAP) Perlis Arau 02600 Malaysia

AI-optimized electrochemical aptasensors are transforming diagnostic testing by offering high sensitivity, selectivity, and rapid response times. Leveraging data-driven AI techniques, these sensors provide a non-invasive, cost-effective alternative to traditional methods, with applications in detecting molecular biomarkers for neurodegenerative diseases, cancer, and coronavirus. The performance metrics outlined in the comparative table illustrate the significant advancements enabled by AI integration. Sensitivity increases from 60 to 75 % in ordinary aptasensors to 85–95 %, while specificity improves from 70-80 % to 90–98 %. This enhanced performance allows for ultra-low detection limits, such as 10 fM for carcinoembryonic antigen (CEA) and 20 fM for mucin-1 (MUC1) using Electrochemical Impedance Spectroscopy (EIS), and 1 pM for prostate-specific antigen (PSA) with Differential Pulse Voltammetry (DPV). Similarly, Square Wave Voltammetry (SWV) and potentiometric sensors have detected alpha-fetoprotein (AFP) at 5 fM and epithelial cell adhesion molecule (EpCAM) at 100 fM, respectively. AI integration also enhances reproducibility, reduces false positives and negatives (from 15-20 % to 5–10 %), and significantly decreases response times (from 10-15 s to 2–3 s). These advancements improve data processing speeds (from 10 to 20 min per sample to 2–5 min) and calibration accuracy (<2 % margin of error compared to 5–10 %), while expanding application scope to multi-target biomarker detection. This review highlights how these advancements position AI-optimized electrochemical aptasensors as powerful tools for personalized treatment, point-of-care testing, and continuous health monitoring. Despite a higher cost ($500-$1,500/unit), their enhanced portability and diagnostic performance promise to revolutionize healthcare, environmental monitoring, and food safety, ultimately improving public health outcomes. © 2024

关键词： AI Aptasensors Cancer Coronavirus Electrochemical Integrated biosensor Molecular cells Neurodegenerative

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