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Visualizing Field-free Deterministic Magnetic Switching of all–van der Waals Spin-Orbit Torque System Using Spin Ensembles in Hexagonal Boron Nitride

arXiv

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arXiv 2025年

作者： Zhang, Xi Zhou, Jingcheng Hu, Chaowei Deng, Kuangyin Wang, Chuangtang Agarwal, Nishkarsh Jin, Hanshang Al-Matouq, Faris A. Xu, Stelo Trivedi, Roshan S. Li, Senlei Rathi, Sumedh Lu, Hanyi Jiang, Zhigang Taufour, Valentin Hovden, Robert Zhao, Liuyan Cheng, Ran Xu, Xiaodong Chu, Jiun-Haw Du, Chunhui Rita Wang, Hailong School of Physics Georgia Institute of Technology AtlantaGA30332 United States Department of Physics University of Washington SeattleWA98105 United States Department of Electrical and Computer Engineering University of California RiversideCA92521 United States Department of Physics University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Department of Physics and Astronomy University of California DavisCA95616 United States Department of Physics University of California San Diego La Jolla CA92093 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Materials Science and Engineering University of Washington SeattleWA98105 United States

Recently, optically active spin defects embedded in van der Waals (vdW) crystals have emerged as a transformative quantum sensing platform to explore cutting-edge materials science and quantum physics. Taking advantage of excellent solid-state integrability, this new class of spin defects can be arranged in controllable nanoscale proximity of target materials in vdW heterostructures, showing great promise for improving spatial resolution and field sensitivity of current sensing technologies. Building on this state-of-the-art measurement platform, here we report hexagonal boron nitride-based quantum imaging of field-free deterministic magnetic switching of room-temperature two-dimensional magnet Fe3GaTe2 in an all-vdW spin-orbit torque (SOT) system. By visualizing SOT-driven variations of nanoscale Fe3GaTe2 magnetic stray field profile under different conditions, we have revealed how the observed magnetic switching evolves from deterministic to indeterministic behavior due to the interplay between out-of-plane spins, in-plane spins and Joule heating. This understanding, which is otherwise difficult to access by conventional transport measurements, offers valuable insights on material design, testing, and evaluation of next-generation vdW spintronic devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Van der Waals forces

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Comparison Analysis of Data Sending Performance Using The Cayenne and ThingSpeak IoT Platform

Comparison Analysis of Data Sending Performance Using The Ca...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Kirana Astari Pranoto Yogi Reza Ramadhan Wahyu Caesarendra Triwiyanto Adam Glowacz Santanu Kumar Dash Bambang T Wahyono Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong Brunei Darussalam CV. Rekayasa Desain Manufaktur Semarang Indonesia Department of Electromedical Engineering Politeknik Kesehatan Surabaya Surabaya Indonesia Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland TIFAC-CORE Vellore Institute of Technology Vellore Tamil Nadu India Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia

ISBN: (纸本)9781665473286

The speed and accuracy of an IoT platform are crucial in a data transfer process. On a simple data transfer, the speed differential may not have much of an impact on the validity or accuracy. However, significant variations in data reading speed will adversely affect the accuracy of the processed data in data transfer activities that include a large volume of data and are carried out continually. This research was conducted to discover a comparison between the Cayenne IoT platform and the ThingSpeak IoT platform based on the speed of data transmission and the accuracy of data reading, which is then expected to be used to develop further projects. In this study, a simple temperature reading device was made using the DS18B20 temperature sensor as a temperature reading component controlled by the ESP8266 microcontroller. The ESP8266 also operates to process data which will then be sent to the IoT platform using the uPyCraft IDE to program the ESP8266 microcontroller. This research was accomplished by measuring the temperature of three different conditions of water as a reference value. The previous research shows that the Cayenne IoT platform displays data faster than the ThingSpeak IoT platform (about 2 to 5 seconds). However, the accuracy level refers to the result of the difference in the average temperature reading of the IoT ThingSpeak platform digital thermometer, which is more accurate than Cayenne. The difference generated by the Cayenne IoT platform in room temperature water is 2.9%, in hot water is 1.52%, and in cold water is 3.47%. On the other hand, ThingSpeak obtained the difference in room temperature water by 3.04%, hot water by 1.5%, and cold water by 3.06%. Inaccessible mode, the Cayenne IoT platform can display sensor readings according to the program created at intervals of two seconds.

关键词： Temperature measurement Thermometers Microcontrollers Multimedia systems Buildings Manuals Data transfer

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Low-Latency Label-Free Image-Activated Cell Sorting Using Fast Deep Learning and Ai Inferencing

SSRN

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SSRN 2022年

作者： Tang, Rui Xia, Lin Gutierrez, Bien Gagne, Ivan Munoz, Adonary Eribez, Korina Jagnandan, Nicole Chen, Xinyu Zhang, Zunming Waller, Lauren Alaynick, William Cho, Sung Hwan An, Cheolhong Lo, Yu-Hwa Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States NanoCellect Biomedical Inc. San DiegoCA92121 United States Department of Bioengineering University of California La Jolla San DiegoCA92093 United States

Classification and sorting of cells using image-activated cell sorting (IACS) systems can bring significant insight to biomedical sciences. Incorporating deep learning algorithms into IACS enables cell classification and isolation based on complex and human-vision uninterpretable morphological features within a heterogeneous cell population. However, the limited capabilities and complicated implementation of deep learning–assisted IACS systems reported to date hinder the adoption of the systems for a wide range of biomedical research. Here, we present image-activated cell sorting by applying fast deep learning algorithms to conduct cell sorting without labeling. The overall sorting latency, including signal processing and AI inferencing, is less than 3 milliseconds, and the training time for the deep learning model is less than 30 minutes with a training dataset of 20,000 images. Both values set the record for IACS with sorting by AI inference. The system provides a compact, sterile, low-cost, label-free, and low-latency cell sorting solution based on real-time AI inferencing and fast training of the deep learning model. © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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Leveraging Computational Pathology AI for Noninvasive Optical Imaging Analysis Without Retraining

arXiv

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arXiv 2024年

作者： Barash, Danny Manning, Emilie Van Vleck, Aidan Hirsch, Omri Aye, Kyi Lei Li, Jingxi Scumpia, Philip O. Ozcan, Aydogan Aasi, Sumaira Rieger, Kerri E. Sarin, Kavita Y. Freifeld, Oren Winetraub, Yonatan Department of Computer Science Ben Gurion University Be'er Sheva8499000 Israel Department of Structural Biology Stanford University StanfordCA94305 United States Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Division of Dermatology University of California Los AngelesCA90095 United States Department of Dermatology Stanford University School of Medicine StanfordCA94305 United States Department of Pathology Stanford University School of Medicine Stanford Cancer Institute StanfordCA94305 United States The Bio-X Program StanfordCA94305 United States

Noninvasive optical imaging modalities can probe patient's tissue in 3D and over time generate gigabytes of clinically relevant data per sample. There is a need for AI models to analyze this data and assist clinical workflow. The lack of expert labelers and the large dataset required (>100,000 images) for model training and tuning are the main hurdles in creating foundation models. In this paper we introduce FoundationShift, a method to apply any AI model from computational pathology without retraining. We show our method is more accurate than state of the art models (SAM, MedSAM, SAM-Med2D, CellProfiler, Hover-Net, PLIP, UNI and ChatGPT), with multiple imaging modalities (OCT and RCM). This is achieved without the need for model retraining or fine-tuning. Applying our method to noninvasive in vivo images could enable physicians to readily incorporate optical imaging modalities into their clinical practice, providing real time tissue analysis and improving patient care. © 2024, CC BY-NC-ND.

关键词： Medical imaging

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BEC-on-A chip: A physiological microfluidic system incorporating brain endothelial cell (BEC) 21

BEC-on-A chip: A physiological microfluidic system incorpora...

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21st International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2017

作者： Jeong, Sehoon Kim, Sunja Buonocore, John Park, Jaewon Welsh, Jane Li, Jianrong Han, Arum Department of Biomedical Engineering Texas A&M University United States Department of Veterinary Integrative Biosciences Texas A&M University United States Department of Electrical and Computer Engineering Texas A&M University United States South University of Science and Technology of China China

ISBN: (纸本)9780692941836

We have developed a microfluidic organ-on-a-chip that can better mimic the in vivo physiological conditions in the blood-brain barrier (BBB) structure. To investigate cell to cell interactions, a multi-compartment coculture microsystem is developed to have the ability to culture multiple cell types and to be able to form tight junctions. The improved BEC-on-a-chip is enabled by applying in vivo level of shear stress, as well as through the use of primary mouse endothelial cells with astrocytes. Furthermore, drug testing was demonstrated with the BEC chip, where interestingly histamine attenuated barrier functions in the monoculture, but not in the coculture. © 17CBMS-0001.

关键词： Endothelial cells

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Computational miniature mesoscope for single-shot 3d fluorescence imaging

Computational miniature mesoscope for single-shot 3d fluores...

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Optics and the Brain, BRAIN 2020

作者： Xue, Yujia Davison, Ian G. Boas, David A. Tian, Lei Department of Electrical and Computer Engineering Boston University MA02215 United States Department of Biology Boston University MA02215 United States Department of Biomedical Engineering Boston University MA02215 United States Neurophotonics Center Boston University MA02215 United States

We report a single-shot computational 3D fluorescence mesoscope. Our design is based on a finite conjugate imaging geometry with a microlens array. It achieves high resolution extended depth-of-field across a wide fie... 详细信息

ISBN: (纸本)9781943580743

关键词： Fluorescence imaging

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A Clinical Decision Support System of Pressure Ulcers Tissue Classification 21

A Clinical Decision Support System of Pressure Ulcers Tissue...

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5th International Conference on Medical and Health Informatics, ICMHI 2021

作者： Chang, Dun-Hao Chu, Po-Jui Li, Yi-Jhen Ning, Chun-Kai Chien, Ting-Ying Department of Information Management Yuan Ze University Taoyuan City Taiwan Department of Plastic and Aesthetic Surgery Far Eastern Memorial Hospital Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9781450389846

Pressure ulcers (PUs) are a common problem associated with great morbidity and cost. Because of the lack of professional personnel in the institutions or home care system, the diagnosis and treatment of PUs were sometimes delayed. We aim to develop an automatic tissue classification and severity evaluation system of PUs using AI technology. All PU images were collected from patients in the Far Eastern Memorial Hospital (FEMH) in recent 3 years and labeled according to different tissue types. The labeled images were used to train the tissue segmentation model with U-net convolutional neural network (CNN). The percentage of the different tissue can be calculated automatically and would be compared with the manually labeled one. The output of tissue classification was transformed to a clinical decision support system (CDSS) with a rule-base formula. Our study showed that proposed system can classify each tissue automatically and reach an accuracy about 93.6%. However, the CDSS's accuracy rate was only 64% for the referral suggestion and 62% for the care recommendation. Hence, the system should be modified with more data and information. © 2021 ACM.

关键词： Tissue

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Differential privacy for industrial internet of things: Opportunities, applications and challenges

arXiv

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arXiv 2021年

作者： Jiang, Bin Li, Jianqiang Yue, Guanghui Song, Houbing College of Computer Science and Software Engineering Shenzhen University Shenzhen518000 China School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518000 China Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona BeachFL32114 United States

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Especially, some common algorithms in IIoT technology, such as deep models, strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this article, we conduct a comprehensive survey on the opportunities, applications, and challenges of differential privacy in IIoT. We first review related papers on IIoT and privacy protection, respectively. Then, we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up research on industrial differential privacy. Copyright © 2021, The Authors. All rights reserved.

关键词： Data privacy

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Hyperplastic and tubular polyp classification using machine learning and feature selection

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Intelligence-Based Medicine 2024年 10卷 100177-100177页

作者： Doğan, Refika Sultan Akay, Ebru Doğan, Serkan Yılmaz, Bülent Department of Bioengineering Faculty of Life and Natural Sciences Abdullah Gül University Kayseri 38080 Turkey Biomedical Instrumentation and Signal Analysis Laboratory Abdullah Gül University Kayseri 38080 Turkey Pathology Clinic Kayseri City Hospital Kayseri 38080 Turkey Gastroenterology Clinic Kayseri City Hospital Kayseri 38080 Turkey GUST Engineering and Applied Innovation Research Center (GEAR) Electrical and Computer Engineering Department Gulf University for Science and Technology (GUST) Hawally 32093 Kuwait

Purpose: The aim of this study is to develop an effective approach for differentiating between hyperplastic and tubular adenoma colon polyps, which is one of the most difficult tasks in colonoscopy procedures. The main research challenge is how to improve the classification of these polyp subtypes applying various focusing levels on the polyp images, data preprocessing approaches, and classification algorithms. Methods: This study employed 202 colonoscopy videos from a total of 201 patients, focusing on 59 videos containing hyperplastic and tubular adenoma polyps. Manually extract key frames and several feature extraction and classification techniques were applied. The influence of different datasets with various focuses as well as data preprocessing steps on the performance of classification was examined, and AUC values were calculated using ten classifiers. Results: The study discovered that the optimal dataset, data preprocessing method, and classification algorithm all had significant effects on classification results. The Random Forest model with the Recursive Feature Elimination (RFE) feature selection approach, for example, consistently outperformed other models and achieved the highest AUC value of 0.9067. In terms of accuracy, F1 score, recall, and AUC, the suggested model outperformed a gastroenterologist, nevertheless precision remained slightly lower. Conclusion: This study emphasizes the importance of dataset selection, data preprocessing, and feature selection in enhancing the classification of difficult colon polyp subtypes. The suggested model offers a promising model for the clinical differentiation of hyperplastic and tubular adenoma polyps, potentially improving diagnostic accuracy in gastroenterology. © 2024 The Authors

关键词： Classification Colonoscopy Colorectal polyps Feature extraction Feature selection Machine learning

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Diagnostic Tools for Detecting Autism Spectrum Disorder: A Review

Diagnostic Tools for Detecting Autism Spectrum Disorder: A R...

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electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Khosro Rezaee Mohammad R. Khosravi Hossein Ghayoumi Zadeh Mohammad Kazem Moghimi Ghassan Samara Hani Attar Sattam Almatarneh Deprtement of Biomedical Enginering Meybod University Meybod Iran Weifang University of Science and Technology Weifang Shandong China Department of Electrical Engineering Vali-E-Asr University of Rafsanjan Rafsanjan Iran Department of Communication Engineering University of Sistan and Baluchestan Zahedan Iran Department of Computer Science Zerqa University Zerqa Jordan Department of Energy Engineering Faculty of Enineering Zarqa University Zerqa Jordan Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zerqa Jordan

The autism spectrum disorder (ASD) is a developmental disability caused by abnormalities in the brain. Different methods have been used to diagnose ASD in the past, each with its own advantages and disadvantages. Prior research has focused mainly on algorithms and processing methods, rather than identification tools, to improve the automated diagnosis of ASD. This article identifies and describes diagnostic tools in the ASD literature, including facial features, EEG recordings, speech signals, and neuroimaging, in order to assist researchers interested in developing statistical, computational, and sound clinical approaches to ASD data mining. Accordingly, based on the responses obtained, this review study intends to assess several aspects of the key automatic diagnostic tools for autism spectrum disorders, including diagnostic accuracy, privacy protection, uncertainty, cost, efficiency, absence of methodological interference, and prospective clinical and therapeutic conditions.

关键词： Neuroimaging Autism Privacy Costs Uncertainty Brain modeling Electroencephalography

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