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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yonathan Eder Ravit Abel Avi Schroeder Yonina C. Eldar Faculty of Math and Computer Science Weizmann Institute of Science Israel Department of Chemical Engineering Technion Haifa Israel The Norman Seiden Multidisciplinary Program for Nanoscience and Nanotechnology Technion Haifa Israel

Gold nanoparticles (GNPs) hold promise to improve the detection and treatment of diseases such as cancer and Alzheimer's. However, detecting GNPs in the body remains an unmet technological challenge. This work introduces a new methodology for remotely localizing and tracking GNPs in various therapeutic settings, including through biological tissues, using a compact millimeter-wave radar system. By modeling GNP detection as a sparse recovery problem, we propose a method that localizes GNPs in cluttered environments as well as detects changes in concentrations that relate to changes in the intensity of the reflected signals. Our approach paves the way to a first-ever noninvasive, non-ionizing, simple approach for bioimaging and tracking GNPs inside the body.

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Measuring 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1394-1395页

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Cho, Min Gee Qian, Yiwen Gu, Junsi Rozeveld, Steve Ercius, Peter Fessler, Jeffrey A. Xu, Ting Scott, Mary Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann Arbor MI United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL United States Advanced Photon Source Argonne National Laboratory Lemont IL United States Department of Materials Science and Engineering University of California at Berkeley Berkeley CA United States National Center for Electron Microscopy Lawrence Berkeley National Laboratory Berkeley CA United States Dow Chemical Co. Midland MI United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Materials Science Division Lawrence Berkeley National Laboratory Berkeley CA United States Applied Physics Program University of Michigan Ann Arbor MI United States

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PhageBox: An Open Source Digital Microfluidic Extension with Applications for Phage Discovery

TechRxiv

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TechRxiv 2023年

作者： Albin, Dreycey Buecherl, Lukas Kochavi, Eitan Niehaus, Elise Novack, Sasha Uragoda, Shenali Myers, Chris J. Alistar, Mirela Department of Computer Science CU Boulder BoulderCO United States Biomedical Engineering Program CU Boulder United States Department of Electrical Computer and Energy Engineering and Biomedical Engineering Program CU Boulder United States ATLAS Institute The Department of Computer Science CU Boulder BoulderCO United States

Objective: Recent advancements demonstrate the significant role of digital microfluidics in automating laboratory work with DNA and on-site viral testing. However, since commercially available instruments are limited to droplet manipulation, our work addresses the need for accelerated integration of other components, such as temperature control, that can expand the application domain. Methods: We developed PhageBox— an accessible device that can be used as a biochip extension. At hardware level, PhageBox integrates temperature and electromagnetic control modules. At software level, PhageBox is controlled by embedded software containing a unique model for bio-protocol programming, and a graphical user interface for visual device feedback and operation. Results: To evaluate PhageBox’s efficacy for biomedical applications, we performed functional testing. Similarly, we validated the temperature control using thermography, obtaining a range of ±0.2◦C. The electromagnets produced a magnetic force of 15 milliTesla, demonstrating precise immobilization of magnetic beads. We show the potential of PhageBox for bacteriophage research through three initial protocols: a universal framework for PCR, T7 bacteriophage restriction enzyme digestion, and concentrating X174 RF genomic DNA. Conclusion: Our work presents an open-source hardware and software extension for digital microfluidics devices. This extension integrates temperature and electromagnetic modules, demonstrating efficacy in biomedical applications and potential for bacteriophage research. Significance: We developed PhageBox to be accessible: the components are off-the-shelf at a low cost (≤$200), and the hardware designs and software code are open-source. With the long aim of ensuring reproducibility and accelerating collaboration, we also provide a DIY-build document. (GitHub: https://***/Dreycey/PhageBox) © 2023, CC BY-SA.

关键词： Digital microfluidics

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Benefits and drawbacks of radiofrequency ablation via percutaneous or minimally invasive surgery for treating hepatocellular carcinoma

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World Journal of Gastrointestinal Surgery 2024年第11期16卷 3400-3407页

作者： Ching-Lung Hsieh Cheng-Ming Peng Chun-Wen Chen Chang-Hsien Liu Chih-Tao Teng Yi-Jui Liu Department of Computer Science and Information Engineering Feng Chia UniversityTaichung 40724Taiwan Department of Surgery Chung Shan Medical University HospitalTaichung 40201Taiwan School of Medicine Chung Shan Medical UniversityTaichung 40201Taiwan Department of Radiology Taichung Armed Forces General HospitalTaichung 406Taiwan Department of Medical Imaging and Radiological Sciences Central Taiwan University of Science and TechnologyTaichung 406Taiwan Department of Radiology School of MedicineNational Defense Medical CenterTaipei 11490Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital and China Medical UniversityHsinchu 302Taiwan Institute of Nuclear Engineering and Science National Tsing Hua UniversityHsinchu 300Taiwan Department of Radiology Nantou Hospital of the Ministry of Health and WelfareNantou 540Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia UniversityTaichung 407Taiwan Department of Automatic Control Engineering Feng Chia UniversityTaichung 407Taiwan

The management of early stage hepatocellular carcinoma(HCC)presents significant *** radiofrequency ablation(RFA)has shown safety and effectiveness in treating HCC,with lower mortality rates and shorter hospital stays,its high recurrence rate remains a significant ***,achieving improved survival solely through RFA is challenging,particularly in retrospective studies with inherent *** is commonly used for guiding percutaneous RFA,but its low contrast can lead to missed tumors and the risk of HCC *** enhance the efficiency of ultrasound-guided percutaneous RFA,various techniques such as artificial ascites and contrast-enhanced ultrasound have been developed to facilitate complete tumor *** invasive surgery(MIS)offers advantages over open surgery and has gained traction in various surgical *** studies suggest that laparoscopic intraoperative RFA(IORFA)may be more effective than percutaneous RFA in terms of survival for HCC patients unsuitable for surgery,highlighting its ***,combining MIS-IORFA with these enhanced percutaneous RFA techniques may hold greater significance for HCC treatment using the MIS-IORFA *** article reviews liver resection and RFA in HCC treatment,comparing their merits and proposing a trajectory involving their combination in future therapy.

关键词： Percutaneous radiofrequency ablation Minimally invasive surgery Hepatocellular carcinoma Intraoperative radiofrequency ablation Contrast-enhanced ultrasound

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Comparison of Coherent and Incoherent Donut Beams for Deep Tissue STED Microscopy

Comparison of Coherent and Incoherent Donut Beams for Deep T...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Shanavas, Thariq McLeod, Robert R. Siemens, Mark E. Gopinath, Juliet T. Department of Physics University of Colorado BoulderCO80309 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Department of Physics University of Denver DenverCO80204 United States Materials Science Engineering Program University of Colorado BoulderCO80309 United States

We numerically compare the null quality for STED microscopy generated by Laguerre-Gaussian beams with orbital angular momentum and donut beams generated by incoherent addition of orthogonal Hermite Gaussian beams when... 详细信息

ISBN: (纸本)9781957171258

关键词： Tissue

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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Intrinsic dimension estimation using Wasserstein distance

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 14124-14160页

作者： Adam Block Zeyu Jia Yury Polyanskiy Alexander Rakhlin Department of Mathematics Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Department of Brain & Cognitive Sciences Statistics and Data Science Center Massachusetts Institute of Technology

It has long been thought that high-dimensional data encountered in many practical machine learning tasks have low-dimensional structure, i.e., the manifold hypothesis holds. A natural question, thus, is to estimate the intrinsic dimension of a given population distribution from a finite sample. We introduce a new estimator of the intrinsic dimension and provide finite sample, non-asymptotic guarantees. We then apply our techniques to get new sample complexity bounds for Generative Adversarial Networks (GANs) depending only on the intrinsic dimension of the data.

关键词： manifold hypothesis dimension estimation manifold learning intrinsic dimension Hölder GANs

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Assessing Driver Engagement Through Machine Learning Classification of Physiological Measures

Assessing Driver Engagement Through Machine Learning Classif...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Lochbihler, Aidan Wallace, Bruce Benthem, Kathleen Van Herdman, Chris Sloan, Will Brightman, Kirsten Goheen, Josh Knoefel, Frank Marshall, Shawn Carleton University Systems and Computer Engineering Ottawa Canada Carleton University Bruyère Research Institute Sam 3 Innovation Hub Systems and Computer Engineering Ottawa Canada Carleton University Department of Cognitive Science Ottawa Canada Carleton University Bruyère Research Institute Elizabeth Bruyère Hospital Systems and Computer Engineering Ottawa Canada Carleton University Bruyère Research Institute Elizabeth Bruyère Hospital Ottawa Hospital Ottawa Canada

ISBN: (纸本)9781665493840

The assessment to determine if drivers are engaged and supervising autonomous and semi-autonomous vehicles (AV) is becoming an increasingly important task. AVs are becoming more prevalent on the road and adequate driver engagement is a must for safe operation. The full adoption of level 5 vehicles will likely take many years as they do not currently exist for consumer purchase and before this happens the roads will see a mix of level 0-4 vehicles. During this time humans will still be responsible for taking control of the vehicle if a hazardous scenario occurs and the AV does not know how to maneuver around. To have a safe handover from AV to human during these situations the driver must maintain a level of engagement even while the AV is driving. To do this physiological sensors can be used to measure signals such as heart rate and respiration rate, which are known indicators of a driver's engagement. This paper exposes drivers to non-surprise and surprise driving scenarios to assess if attentive drivers can be identified from physiological changes for manual and AV driving. Machine learning (ML) is used to understand the patterns of physiological signals and classify when a driver is engaged during a surprise scenario. Finally, the ML models show a successful ability to classify engaged versus non-engaged drivers with a 73.3% accuracy in manual driving scenarios, 86.7% accuracy in AV, and 70.0% accuracy when the data from both driving scenarios are combined showing the model's ability to generalize. © 2023 IEEE.

关键词： Autonomous vehicles

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Fuzzy Logic for Assessing the Health of a Corn Crop within Family Farming

Fuzzy Logic for Assessing the Health of a Corn Crop within F...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Vanessa Mendonça Mikael Santos Daniel M. Muñoz Carlos Humberto Llanos Department of Computer Science Federal Institute of Brasilia Department of Mechanical Engineering Mechatronics Graduate Program Department of Agronomy Federal Institute of Brasilia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Technological advancements are increasingly evident across various sectors, including automobiles, industry, and healthcare. In precision agriculture, significant progress has been made, with AgroTICs and Smart Agriculture gaining substantial traction in the market. However, a gap remains between cutting-edge technology and family farming, presenting a challenge from both social and applied research perspectives. However, there is still a gap between cutting-edge technology and family farming, which creates a challenge from a social and applied research point of view. In this context, this paper proposes a monitoring model based on Fuzzy Logic and sensor automation applied to estimate the health of a corn crop. The proposed Fuzzy inference system involves calculating an indicator of nutrients as well as the average color and area of corn plants. The nutrient indicator is automatically computed by an ESP32 microcontroller using sensor readings, while the average color and area inputs are manually entered via a mobile application. Additionally, the Fuzzy inference is integrated into the ESP32. The model underwent experimental validation on the health of the plantation, and the results were evaluated in four areas: one was designated for testing, and three were for validation. The model achieved an accuracy of 97.5% in Scenario 3, categorized as ’Very Favorable’, and an accuracy of 65% in Scenarios 2 and 4, categorized as ‘Unfavorable’. The implications of this research contribute to the advancement of AgroTICs among small producers, with the potential to enhance and automate the monitoring of their harvest production.

关键词： Fuzzy logic Accuracy Computational modeling Plantations Crops Production Color Nutrients Monitoring Fuzzy systems

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Sentiment Analysis of User Review on Indonesian Food and Beverage Group using Machine Learning Techniques

Sentiment Analysis of User Review on Indonesian Food and Bev...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Yanfi Yanfi Yaya Heryadi Lukas Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches of AI have focused on developing various algorithms that can learn from input data without explicitly being programmed. In the food industry, notably in restaurants, smartphone technology is typically used to collect data from customers efficiently such as orders or feedback toward the services provided. The use of smartphones by some restaurants is motivated by their convenience and efficiency. Furthermore, various promotions, discounts, and membership benefits offered by cellular service providers have attracted the use of smartphones as devices for improving restaurant services to their customers. This study aimed to conduct a sentiment analysis related to multi-brand restaurants in Indonesia based on customer feedback. The data retrieved from the Google Play Store consists of 756 reviews from Boga, Champs, Hangry, Ismaya, Kulo, and Union groups. In this study, TF-IDF was used as a vectorizer to represent customer feedback as numeric vectors. The polarity sentiment of customer feedback was recognized using classifier models based on machine learning such as Logistic Regression, Naive Bayes (NB), Random Forest, and Support Vector Machine (SVM) as the classifier. The empirical results showed that SVM has the best accuracy of 93.10 % average accuracy followed by Logistic Regression with 92.41 %, Random Forest with 84.14 % average accuracy, and Naive Bayes with 76.55 % average accuracy.

关键词： Support vector machines Sentiment analysis Analytical models Machine learning algorithms Food industry Companies Classification algorithms

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