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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chakraborty, Debamitra Mills, Bradley N. Cheng, Jing Komissarov, Ivan Gerber, Scott Sobolewski, Roman Materials Science Graduate Program University of Rochester RochesterNY14627-1299 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623 United States Department of Surgery University of Rochester Medical Center RochesterNY14642 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627-0231 United States

We developed a terahertz time-domain spectroscopy system to generate high-resolution 2-dimensional images of paraffin-embedded murine pancreatic ductal adenocarcinoma tissues using the refractive index and absorption ... 详细信息

ISBN: (纸本)9781957171258

关键词： Tissue

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System for the analysis of human balance based on accelerometers and support vector machines

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computer Methods and programs in Biomedicine Update 2023年 4卷

作者： Pinheiro, V.C. do Carmo, J.C. Nascimento, F.A. de O. Miosso, C.J. Electrical Engineering Graduate Program Department of Electrical Engineering University of Brasília DF Brasília 70910-900 Brazil Biomedical Engineering Graduate Program University of Brasília at Gama Área Especial Projeção A UnB - Setor Leste Gama DF Brasília 72444-240 Brazil

Disturbances in balance control lead to movement impairment and severe discomfort, dizziness, vertigo and may also lead to serious accidents. It is important to monitor the level of balance in order to determine the risk of a fall and to evaluate progress during treatment. Some solutions exist, but they are generally restricted to indoor environments. We propose and evaluate a system, based on accelerometers and support vector machines, that indicates the user's postural balance variation which can be used in indoor and outdoor environments. For the training phase of the system, we used the accelerometer signals acquired from a single subject under monitored conditions of balance and intentional imbalance, and used the scores provided by the SWAY®software for establishing the reference target values. Based on these targets, we trained a support vector machine to classify the signal into n levels of balance and later evaluated the performance using cross validation by random resampling. We also developed a support vector machine approach for estimating the center of pressure, by using as reference targets the results from a force platform. For validation, we performed experiments with a subject who was performing determined movements. Later other experiments were executed, so the different centers of pressure could be computed by our system and compared to the results from the force platform. We also performed tests with a dummy and a John Doe doll, in order to observe the system's behavior in the presence of a sudden drop or a lack of balance. The results show that the system can classify the acquired signals into two to seven levels of balance, with significant accuracy, and was also able to infer the centroid of each center of pressure region with an error lower than 0.9 cm. The tests performed with the dolls show that the system is able to distinguish between the conditions of a sudden drop and of a recovery of balance after losing one's balance. The results sugg

关键词： Accelerometers Balance signals processing Images processing Postural balance Support vector machines

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Real-time Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video in Compressed Domain

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APSIPA Transactions on Signal and Information Processing 2023年第4期12卷

作者： Ardianto, Sandy Hang, Hsueh-Ming Cheng, Wen-Huang Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Our goal is to develop real-time vehicle detection and tracking schemes for fisheye traffic monitoring video using the temporal information in the compressed domain without decoding the entire video. Two algorithms are proposed. The first algorithm starts with a conventional single-frame detector, but we introduce a multi-frame information fusion stage to improve the final detection and tracking accuracy, which is implemented using multi-modal bi-directional LSTM (MM bi-LSTM) network. The second algorithm first constructs multi-frame motion trail image, and then a single-image multi-head detector is designed to produce bounding boxes of an individual frame. The first scheme can be viewed as a detect-to-track design, and the second scheme is track-to-detect. We tested our proposals on the ICIP2020 VIP Cup dataset in H.265 video format. The aforementioned algorithms are applied to the motion fields and residual images in the H.265 compressed data set. It turns out that their detection and tracking performances are on par with their pixel-domain counterparts, and they can achieve the state-of-the-art accuracy of conventional video object detectors and trackers. If the decoding process for video compression is not counted, their computational complexities are much lower than the conventional pixel-domain video object detectors and trackers. © 2023 S. Ardianto, H.-M. Hang and W.-H. Cheng.

关键词： Image compression

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Analysis of the Brazilian Climate Data Influence on Soybean Pricing by Using Long Short-Term Memory Models

SSRN

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SSRN 2024年

作者： Oliva, Jefferson Tales Martelli, Otávio Pigozzo de Castro Barbosa, Marco Antônio Dal Molin, Viviane Casanova, Dalcimar Teixeira, Marcelo Graduate Program in Electrical and Computer Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Department of Academic Informatics Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Graduate Program in Production and Systems Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil

This work uses machine learning to verify whether the Brazilian climate influences international soybean price prediction. For this, climatic data, dollar quotes, inflation, and annual soybean production were collected from different data sources. Then, the collected data were pre-processed and grouped by Brazilian state. Afterward, data from the five most productive Brazilian states are chosen, such as Mato Grosso (MT), Mato Grosso do Sul (MS), Goiás (GO), Paraná (PR), and Rio Grande do Sul (RS). Afterward, predictive models are built using the long short-term memory (LSTM) method, which is applied in each chosen state dataset considering two different settings: with and without climatic data. Thus, two models were built for each Brazilian state, totaling ten predictors. In the experimental evaluation, when trained with climatic data, the PR model achieved the lowest values of root mean squared error (RMSE) and mean absolute error (MAE). Regarding predictors trained without climate data, the RS and PR obtained the lowest values of RMSE and MAE, respectively. After conducting statistical hypothesis tests, no significant difference was found among models, stating, with 95% certainty, that, in this study, the Brazilian climatic data did not influence the model performance for predicting soybean prices. On the other hand, one of the main contributions of work is conducting an unprecedented study about soybean price prediction using Brazilian climatic data. © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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Plasmonic BICs as Biosensors with Better Detectivity

Plasmonic BICs as Biosensors with Better Detectivity

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Conference on Lasers and Electro-Optics (CLEO)

作者： Zhichao Li Ciril S. Prasad Xielin Wang Gururaj V. Naik Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Electrical and Computer Engineering Rice University Houston TX USA

Plasmonic sensors exhibit high sensitivity due to enhanced local fields. But, their detectivity is poor because of their poor Q-factors. Using a plasmonic BIC, we experimentally demonstrate enhanced Q-factors in a pla... 详细信息

ISBN: (纸本)9781957171258

关键词： Absorption spectroscopy Field enhancement Optical sensing Plasmonic sensors Q factor Sensor performance

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SATac: A Thermoluminescence Enabled Tactile Sensor for Concurrent Perception of Temperature, Pressure, and Shear

SATac: A Thermoluminescence Enabled Tactile Sensor for Concu...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Ziwu Song Ran Yu Xuan Zhang Kit Wa Sou Shilong Mu Dengfeng Peng Xiao-Ping Zhang Wenbo Ding Shenzhen International Graduate School Tsinghua University China Shenzhen University China RISC-V International Open Source Laboratory Shenzhen China Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto ON Canada

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Most vision-based tactile sensors use elastomer deformation to infer tactile information, which can not sense some modalities, like temperature. As an important part of human tactile perception, temperature sensing can help robots better interact with the environment. In this work, we propose a novel multi-modal vision-based tactile sensor, SATac, which can simultaneously perceive information on temperature, pressure, and shear. SATac utilizes the thermoluminescence of strontium aluminate to sense a wide range of temperatures with exceptional resolution. Additionally, the pressure and shear can also be perceived by analyzing the Voronoi diagram. A series of experiments are conducted to verify the performance of our proposed sensor. We also discuss the possible application scenarios and demonstrate how SATac could benefit robot perception capabilities.

关键词： Temperature sensors Strontium Temperature distribution Deformation Tactile sensors Elastomers Sensors

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Inorganic nanoparticles for photothermal treatment of cancer

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Journal of Materials Chemistry B 2024年第15期12卷 3569-3593页

作者： Thirumurugan, Senthilkumar Ramanathan, Susaritha Muthiah, Kayalvizhi Samuvel Lin, Yu-Chien Hsiao, Michael Dhawan, Udesh Wang, An-Ni Liu, Wai-Ching Liu, Xinke Liao, Mei-Yi Chung, Ren-Jei No. 1 Sec. 3 Zhongxiao E. Rd Taipei10608 Taiwan School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Genomics Research Center Academia Sinica Taipei115 Taiwan Department and Graduate Institute of Veterinary Medicine School of Veterinary Medicine National Taiwan University Taipei10617 Taiwan Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom Scrona AG Grubenstrasse 9 Zürich8045 Switzerland Faculty of Science and Technology Technological and Higher Education Institute of Hong Kong New Territories Hong Kong999077 Hong Kong College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Applied Chemistry National Pingtung University Pingtung90003 Taiwan Taipei106 Taiwan

In recent years, inorganic nanoparticles (NPs) have attracted increasing attention as potential theranostic agents in the field of oncology. Photothermal therapy (PTT) is a minimally invasive technique that uses nanoparticles to produce heat from light to kill cancer cells. PTT requires two essential elements: a photothermal agent (PTA) and near-infrared (NIR) radiation. The role of PTAs is to absorb NIR, which subsequently triggers hyperthermia within cancer cells. By raising the temperature in the tumor microenvironment (TME), PTT causes damage to the cancer cells. Nanoparticles (NPs) are instrumental in PTT given that they facilitate the passive and active targeting of the PTA to the TME, making them crucial for the effectiveness of the treatment. In addition, specific targeting can be achieved through their enhanced permeation and retention effect. Thus, owing to their significant advantages, such as altering the morphology and surface characteristics of nanocarriers comprised of PTA, NPs have been exploited to facilitate tumor regression significantly. This review highlights the properties of PTAs, the mechanism of PTT, and the results obtained from the improved curative efficacy of PTT by utilizing NPs platforms. © 2024 The Royal Society of Chemistry

关键词： Cancer cells

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of Medical Things Telecommunications Convolutional neural networks

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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Telecommunications Conference (ITC-Egypt), International

作者： Mohamed M. El-Sheikh Aya Bayoumy Nada M. Ahmed Amany Mohamed Eldeeb Fatma I. Rabea Amira A. Nawar Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively.

关键词： Deep learning Accuracy Magnetic resonance imaging Soft sensors Medical services Feature extraction Regulation

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Mxene/Polydimethylsiloxane (PDMS) Based Implantable and Flexible Bioelectrodes for Neural Sensing

Mxene/Polydimethylsiloxane (PDMS) Based Implantable and Flex...

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Advances in Science and engineering Technology International Conferences (ASET)

作者： Kashif Rast Baz Khan Amani Al-Othman Hasan Al-Nashash Mohammad Al-Sayah Biomedical Engineering Graduate Program American University of Sharjah Sharjah UAE Department of Chemical and Biological Engineering American University of Sharjah Sharjah UAE Department of Electrical Engineering American University of Sharjah Sharjah UAE Chemistry and Environmental Sciences American University of Sharjah Sharjah UAE

Research applications of neural sensing and muscle stimulation have been advanced by the development of bioelectrodes that are biocompatible, flexible and long lasting. The existing metallic made bioelectrodes exhibit extremely high electrochemical resistances that would interfere with the neural-electrode bridge and suffer from mechanical and immunological mismatch. Novel, flexible, implantable bioelectrodes based on MXenes (Ti 3 C 2 ) and Polydimethylsiloxane (PDMS) composite are proposed. Mxenes possess the advantage of having metallic ultra-conductive transition metals consisting of large groups of carbides, nitrides, or carbonitrides, while PDMS has inherent biostability, flexibility and biocompatibility. In the current research, PDMS has been used as the supporting matrix for conductive fillers such as PMMA, PEDOT, and Mxenes. A series of characterization techniques were performed such as Electrochemical Impedance Spectroscopy (EIS), Cyclic Voltammetry (CV) and mechanical testing. The average results of triplicate samples exhibited samples' bulk impedance of 0.913 kΩ, an impedance of 1.612 MΩ, at 1 kHz frequency and a conductivity of 1.41x10 -4 S/cm, charge storage capacity of 0.056 mC/cm 2 and Young's modulus of 3.81 MPa.

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