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arXiv 2025年

作者： Li, Haiyang Liao, Hao Wang, Ping Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Hyper-polarization of nuclear spins is crucial for advancing nuclear magnetic resonance (NMR) and quantum information technologies, as nuclear spins typically exhibit extremely low polarization at room temperature due to their small gyro-magnetic ratios. A promising approach to achieving high nuclear spin polarization is transferring the polarization of electron to nuclear spin. The nitrogen-vacancy (NV) center in diamond has emerged as a highly effective medium for this purpose, and various hyper-polarization protocols have been developed. Among these, the pulsed polarization (PulsePol) method has been extensively studied due to its robustness against static energy shifts of the electron spin. In this study, we introduce a sequential polarization protocol and identify a series of magic and digital sequences for hyper-polarizing nuclear spins. Notably, we demonstrate that some of these magic sequences exhibit significantly greater robustness compared to the PulsePol protocol in the presence of finite half π pulse duration of the protocol. This enhanced robustness positions our protocol as a more suitable candidate for hyper-polarizing nuclear spins species with large gyromagnetic ratios and also ensures better compatibility with high-efficiency readout techniques at high magnetic fields. Additionally, the generality of our protocol allows for its direct application to other solid-state quantum systems beyond the NV center. © 2025, CC BY.

关键词： Chemical shift

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Engineered Modular Neuronal Networks-on-Chip Represent Structure-Function Relationship

SSRN

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SSRN 2024年

作者： Habibey, Rouhollah Striebel, Johannes Latiftikhereshki, Roshanak Schmieder, Felix Nasiri, Rohollah Latifi, Shahrzad Universitäts-Augenklinik Bonn University of Bonn Dep. of Ophthalmology Ernst-Abbe-Straße 2 BonnD-53127 Germany CRTD - Center for Regenerative Therapies TU Dresden Dresden01307 Germany Dept. Neuroscience Italian Institute of Technology Genova Italy Department of Computer Engineering Faculty of Engineering Kermanshah Branch Azad University Kermanshah Iran Laboratory of Measurement and Sensor System Technique Faculty of Electrical and Computer Engineering TU Dresden Helmholtzstraße 18 Dresden01069 Germany Division of Nanobiotechnology Department of Protein Science Science for Life Laboratory KTH Royal Institute of Technology Solna Sweden AIMES Center for the Advancement of Integrated Medical and Engineering Sciences Department of Neuroscience Karolinska Institute Solna Sweden Department of Neurology David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Neuroscience Rockefeller Neuroscience Institute West Virginia University MorgantownWV26506 United States

Brain function is substantially linked to the highly organized structure of neuronal networks. The structures of in vitro assembled neuronal circuits often vary between samples and change over time, making it challenging to reliably record network functional output and link it to network structure. Therefore, engineering neuronal structures with predefined geometry and reproducible functional features is essential to robustly model in vivo neuronal circuits. Here, we engineered thin microchannel devices to assemble 2D and 3D modular networks. The microchannel devices were coupled with a multi-electrode array (MEA) electrophysiology system to enable long-term electrophysiology recordings from microengineered circuits. Each network consisted of 64 modules connected to their adjacent modules by micron-sized channels. The microstructures physically confined neurons to the recording electrodes, which significantly improved the efficiency of electrophysiology readout. In addition, the microstructures maintained the modular network structure for weeks. Modular circuits within microfluidic devices showed consistent spatial patterns of activity over weeks, which was lacking in the randomly formed circuits. The number of physical connections per module was shown to influence the measured activity and functional connectivity parameters, which represents the impact of network structure on its functional output. We show that microengineered 3D modular networks with profound activity and higher number of functional connections recapitulate key functional features of developing cortex. Structurally and functionally stable 2D and 3D networks mimic the modular architecture of brain circuits and provide a robust and reproducible in vitro microphysiological system for basic and translational neuroscience research. © 2024, The Authors. All rights reserved.

关键词： Microfluidics

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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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Interfacial spintronic THz emission

arXiv

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arXiv 2023年

作者： Agarwal, Piyush Medwal, Rohit Dongol, Keynesh Mohan, John Rex Yang, Yingshu Asada, Hironori Fukuma, Yasuhiro Singh, Ranjan Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore637371 Singapore Center for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University Singapore639798 Singapore Department of Physics Indian Institute of Technology Uttar Pradesh Kanpur208016 India Department of Electronic Devices and Engineering Graduate School of Science and Engineering Yamaguchi University Ube755-8611 Japan Department of Physics and Information Technology Faculty of Computer Science and System Engineering Kyushu Institute of Technology Iizuka820-8502 Japan

The broken inversion symmetry at the ferromagnet (FM)/heavy-metal (HM) interface leads to spin-dependent degeneracy of the energy band, forming spin-polarized surface states. As a result, the interface serves as an effective medium for converting spin accumulation into two-dimensional charge current through the inverse Rashba-Edelstein effect. Exploring and assessing this spin-to-charge conversion (SCC) phenomenon at the FM/HM interface could offer a promising avenue to surpass the presumed limits of SCC in bulk HM layers. We utilize spintronic heterostructures as a platform to measure the spin-to-charge conversion (SCC) experienced by photoexcited spin currents. These heterostructures emit terahertz electric field when illuminated by femtosecond laser pulses, enabling us to quantitatively assess the ultrafast SCC process. Our results demonstrate a robust interfacial spin-to-charge conversion (iSCC) within a synthetic antiferromagnetic heterostructure, specifically for the NiFe/Ru/NiFe configuration, by isolating the SCC contribution originating from the interface itself, separate from the bulk heavy-metal (HM) region. Moreover, the iSCC at the NiFe/Ru interface is discovered to be approximately 27% of the strength observed in the highest spin-Hall conducting heavy-metal, Pt. Our results thus highlight the significance of interfacial engineering as a promising pathway for achieving efficient ultrafast spintronic devices. Copyright © 2023, The Authors. All rights reserved.

关键词： Electric fields

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Combining rough set-based relevance and redundancy for the ranking and selection of nominal features 24

Combining rough set-based relevance and redundancy for the r...

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24th KES International Conference on Knowledge-Based and Intelligent Information and engineering systems, KES 2020

作者： Froelich, Wojciech Hajek, Petr Institute of Computer Science University of Silesia ul. Bedzinska 39 Sosnowiec Poland Institute of System Engineering and Informatics Faculty of Economics and Administration University of Pardubice Studentska 84 Pardubice532 10 Czech Republic

In this paper, we propose a new method for features ranking and selection. Our approach is based on ranking nominal features in terms of their relevance to the assigned class and mutual redundancy with the other features. To calculate the relevance and redundancy, we propose to use a rough-set based approach. After performing the ranking, features filtering is carried out in a supervised way enabling the user to decide on the number of the retained features. The experiments revealed that thanks to our method, it is possible to filter out numerous features describing data while still maintaining satisfactory classification accuracy achieved by the classifier trained using the reduced dataset. The comparative experiments performed with the use of publicly available datasets proved the high efficiency and competitiveness of our approach. © 2020 The Authors. Published by Elsevier B.V.

关键词： Redundancy

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Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

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Informatics in Medicine Unlocked 2023年 36卷 101158页

作者： Tuncer, Ilknur Barua, Prabal Datta Dogan, Sengul Baygin, Mehmet Tuncer, Turker Tan, Ru-San Yeong, Chai Hong Acharya, U. Rajendra Elazig Governorship Interior Ministry Elazig Turkey School of Business (Information System) University of Southern Queensland Toowoomba 4350 QLD Australia Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 NSW Australia Department of Digital Forensics Engineering College of Technology Firat University Elazig Turkey Department of Computer Engineering Faculty of Engineering Ardahan University Ardahan Turkey Department of Cardiology National Heart Centre Singapore Singapore Duke-NUS Medical School Singapore School of Medicine Faculty of Health and Medical Sciences Taylor's University Subang Jaya 47500 Malaysia Ngee Ann Polytechnic Department of Electronics and Computer Engineering 599489 Singapore Department of Biomedical Engineering School of Science and Technology SUSS University Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Background: Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering. Material and method: We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm. Results: Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity. Conclusions: Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate tha

关键词： Computed tomography images COVID-19 classification Swin Swin-textural Textural feature extraction

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GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning

arXiv

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arXiv 2024年

作者： Kurniati, Florentina Tatrin Sembiring, Irwan Setiawan, Adi Setyawan, Iwan Huizen, Roy Rudolf Faculty of Information Technology Universitas Kristen Satya Wacana Jl. Diponegoro No.52-60 Jawa Tengah Salatiga50711 Indonesia Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Indonesia Jl. Raya Puputan No.86 Denpasar Bali80234 Indonesia Faculty of Science and Mathematics Universitas Kristen Satya Wacana Jl. Diponegoro No.52-60 Jawa Tengah Salatiga50711 Indonesia Faculty of Electronics and Computer Engineering Universitas Kristen Satya Wacana Salatiga Jl. Diponegoro No.52-60 Jawa Tengah Salatiga50711 Indonesia Department of Magister Information System Institut Teknologi dan Bisnis STIKOM Bali Indonesia Jl. Raya Puputan No.86 Denpasar Bali80234 Indonesia

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. High computing time delays can cause overall system failure. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that KNN outperforms SVM in terms of computational complexity. Specifically, KNN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Research contribution to improving computational efficiency in object detection using the GLCM method and KNN and SVM classification models to achieve high accuracy with low complexity. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness. © 2024, CC BY.

关键词： Support vector machines

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Fault Diagnosis Model of Switching Power Supply Based on Improved DBN

Fault Diagnosis Model of Switching Power Supply Based on Imp...

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International Conference on Intelligent system and Knowledge engineering, ISKE

作者： Lei Zhou Cheng Yu Hu Liu Xin Li Fenfen Li Haiyan Zhang Faculty of Computer and Software Engineering Research Center for logic and Intelligent Computation National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology Huai’an Jiangsu China College of Computer Science and Technology Nanjing University of Technology Nanjing Jiangsu China Faculty of Chemical Engineering Huaiyin Institute of Technology Huai’an Jiangsu China

ISBN: (纸本)9781665405546

Switching power supply has wide application in various electrical equipment. The failure of high-voltage filter capacitors is the main cause of frequent faults, accounting for more than 60% of switching power supply failures. In order to realize the real-time detection of switching power supply failure without disassembling the power supply, a fault diagnosis model for improving the deep belief network (DTDBN) is proposed. The model uses the data acquisition device to isolate, filter, and collect data on the grid voltage of the switching power supply, judge the failure ratio of the filter capacitor according to the degree of voltage distortion of the power supply. Experimental results show that the proposed fault detection method achieves a 100% recognition rate for the filter capacitor normal state, 50% and 90% state recognition rate is 90%. The superiority of this method for the classification and early warning of power capacitor faults is proved.

关键词： Fault diagnosis Power supplies Fault detection Capacitors Switches Voltage Distortion

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Exploring advanced materials: Harnessing the synergy of inverse gas chromatography and artificial vision intelligence

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TrAC - Trends in Analytical Chemistry 2024年 173卷

作者： Basivi, Praveen Kumar Hamieh, Tayssir Kakani, Vijay Pasupuleti, Visweswara Rao Sasikala, G. Heo, Sung Min Pasupuleti, Kedhareswara Sairam Kim, Moon-Deock Munagapati, Venkata Subbaiah Siva Kumar, Nadavala Wen, Jet-Chau Kim, Chang Woo Pukyong National University Industry-University Cooperation Foundation Pukyong National University Busan48513 Korea Republic of Faculty of Science and Engineering Maastricht University P.O. Box 616 MD Maastricht6200 Netherlands Faculty of Sciences Lebanese University Hadath Lebanon Department of Integrated System Engineering INHA University 100 Inha-ro Namgu Incheon22212 Korea Republic of Center for International Relations and Research Collaborations and School of Applied Sciences REVA University Rukmini Knowledge Park Kattigenahalli Yelahanka Karnataka Bangalore560064 India School of Computer Sciences and Applications REVA University Kattigenahalli Rukmini Knowledge Park Bangalore560064 India Department of Smart Green Technology Engineering Pukyong National University Busan48513 Korea Republic of Chungnam National University 99 Daehak-ro Yuseong-gu Daejeon34134 Korea Republic of National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Department of Chemical Engineering College of Engineering King Saud University P.O. Box - 800 Riyadh11421 Saudi Arabia Department of Nanotechnology Engineering College of Engineering Pukyong National University Busan48513 Korea Republic of

Inverse gas chromatography (IGC) has emerged as a highly sensitive, adaptable, and effective technology for material analysis. Through employing thermochemical approaches, IGC provides crucial insight into physicochemical information of materials such as dispersive surface free energy, Gibbs surface energy components and Guttamann Lewis acid-base parameters. In this comprehensive review, we delve into the historical background, instrumentation, and diverse applications of IGC. Researchers and practitioners will find valuable information on the selection and description of numerous models used in IGC experiments. The applications of IGC span various domains, including polymers, medicines, minerals, surfactants, and nanomaterials. Furthermore, IGC facilitates the measurement of important parameters such as sorption enthalpy and entropy, surface energy components (dispersive and specific), co/adhesion work, glass transition temperature, surface heterogeneity, miscibility, solubility parameters, and specific surface area. These insights contribute to a deeper understanding of material behavior and aid in the design and optimization of advanced materials. Moreover, the integration of computer vision and image processing techniques with IGC has enhanced our understanding of materials intricate surface texture, roughness, and related properties. This convergence of IGC with computer vision and artificial intelligence (AI) presents exciting opportunities for future exploration of chemical materials, opening new avenues for research and discovery. This paper not only provides a comprehensive overview of IGC, its techniques, and applications but also highlights the synergistic potential of combining IGC with AI and computer vision. The informative content and insights presented here will benefit researchers, scientists, and professionals in the field of advanced materials, enabling them to leverage IGC and AI for innovative materials discovery and development. © 2024 Elsevier

关键词： Interfacial energy

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Analysis of Deep Learning Algorithms for Prawn Aquaculture in a Challenging Environment

Analysis of Deep Learning Algorithms for Prawn Aquaculture i...

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Applied Computational Intelligence in Information systems (ACIIS), International Conference on

作者： Wafiq Zariful Najeebah Az-Zahra Tashim Tiong Hoo Lim Aida Maryam Basri Suriayati Chuprat Seno Adi Putra Pengcheng Liu Hanif Fakhrurroja Electrical and Electronic Engineering Universiti Teknologi Brunei Bandar Seri Begawan Brunei School of Applied Sciences and Mathematics Universiti Teknologi Brunei Bandar Seri Begawan Brunei Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Johor Bahru Malaysia School of Industrial and System Engineering Telkom Universiti Bandung Indonesia Department of Computer Science University of York York United Kingdom Research Center for Smart Mechatronics National Research and Innovation Agency Telkom University Bandung Indonesia

Deep Learning plays a vital role in various domains, spanning surveillance, agriculture, and healthcare. However, the current landscape lacks comprehensive comparative studies, underscoring the needs to explore various convolutional neural networks (CNNs) for aquaculture industries. This study conducts a comprehensive comparative analysis on state-of-the-art object detection models to identify the most suitable approach for detecting freshwater prawns, specifically in challenging turbid and murky water conditions as there is a lack of comparison studies for underwater images of freshwater prawns. The evaluated models, including YOLOv8, Faster R-CNN (FRCNN), Single Shot Detector (SSD), and EfficientDet, are assessed based on the following metrics namely inference time, accuracy, image cropping size, and model memory footprint. Using a dataset comprising 2976 manually collected images of freshwater prawns, augmented to a total of 7148 images, our results demonstrate that YOLOv8 outperforms its counterparts, achieving an impressive mean Average Precision (mAP) accuracy of 83.71% with an inference time of 32.9 milliseconds. This positions YOLOv8 as a dependable choice for real-time object detection in challenging underwater environments and contributes to addressing the limited research comparing YOLOv8 with other popular object detection algorithms, thereby aiding model selection for similar object detection applications.

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