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Mediterranean Electrotechnical Conference (MELECON)

作者： Carlos Rodrigues Miguel Correia João Abrantes Marco Rodrigues Jurandir Nadal C-BER - Centre for Biomedical Engineering Research INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal MovLab - Laboratory of Technologies for Interactions and Interfaces CICANT - Centre for Research in Applied Communication Culture and New Technologies Lisbon Portugal Department of Electronic and Systems Federal University of Pernambuco Recife Brazil Biomedical Engineering Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

This study presents variability assessment of real time measurements from in-vivo internal joint loads with instrumented implant during post-operative (PO) recovery process from total hip arthroplasty on daily living gait activities. A total of 112 trials walking supported by crutches in both hands, contralateral and ipsilateral sides, walking on treadmill at constant velocities, accelerating, decelerating and free walking, were assessed from 9 different patients ranging 0.3 to 76-month PO. Variability was assessed based on standard deviation of the vertical joint load normalized to each subject body weight with this metric adequacy to monitor PO recover.

关键词： Legged locomotion Temperature measurement Force measurement Instruments Force Time series analysis Implants

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Cross-system biological image quality enhancement based on the generative adversarial network as a foundation for establishing a multi-institute microscopy cooperative network

arXiv

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arXiv 2024年

作者： Panek, Dominik Rząca, Carina Szczypior, Maksymilian Sorysz, Joanna Misztal, Krzysztof Baster, Zbigniew Rajfur, Zenon Doctoral School of Exact and Natural Sciences Jagiellonian University ul. Lojasiewicza 11 Kraków30-348 Poland Department of Molecular and Interfacial Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate Program in Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate program in Biomedical Engineering Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Al. A. Mickiewicza 30 Building B-1 Kraków30-059 Poland Division of Computational Mathematics Faculty of Mathematics and Computer Science Jagiellonian University 6 Lojasiewicza St. Kraków Poland

High-quality fluorescence imaging of biological systems is limited by processes like photobleaching and phototoxicity, and also in many cases, by limited access to the latest generations of microscopes. Moreover, low temporal resolution can lead to a motion blur effect in living systems. Our work presents a deep learning (DL) generative-adversarial approach to the problem of obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents. We propose a generative-adversarial network (GAN) for contrast transfer between two different separate microscopy systems: a confocal microscope (producing HQ images) and a wide-field fluorescence microscope (producing LQ images). Our model proves that such transfer is possible, allowing us to receive HQ-generated images characterized by low mean squared error (MSE) values, high structural similarity index (SSIM), and high peak signal-to-noise ratio (PSNR) values. For our best model in the case of comparing HQ-generated images and HQ-ground truth images, the median values of the metrics are 6·10-4, 0.9413, and 31.87, for MSE, SSIM, and PSNR, respectively. In contrast, in the case of comparison between LQ and HQ ground truth median values of the metrics are equal to 0.0071, 0.8304, and 21.48 for MSE, SSIM, and PSNR respectively. Therefore, we observe a significant increase ranging from 14% to 49% for SSIM and PSNR respectively. These results, together with other single-system cross-modality studies, provide proof of concept for further implementation of a cross-system biological image quality enhancement. © 2024, CC BY-SA.

关键词： Fluorescence imaging

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Evaluating the Robustness of Complementary Channel Ferroelectric FETs Against Total Ionizing Dose Towards Radiation-Tolerant Embedded Nonvolatile Memory

TechRxiv

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TechRxiv 2023年

作者： Jiang, Zhouhang Guo, Zixiang Luo, Xuyi Sayed, Munazza Faris, Zubair Mulaosmanovic, Halid Duenkel, Stefan Soss, Steven Beyer, Sven Gong, Xiao Kurinec, Santosh Narayanan, Vijaykrishnan Amrouch, Hussam Zhang, Enxia Fleetwood, Daniel M. Schrimpf, Ronald D. Ni, Kai The Microsystems Engineering Ph.D. Program The Mechanical Engineering Rochester Institute of Technology RochesterNY14623 United States The Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States University of Stuttgart Stuttgart70174 Germany Germany The Munich Institute of Robotics and Machine Intelligence Arcisstrasse 21 Munich80333 Germany The GlobalFoundries Fab1 LLC Dresden Germany The Department of Electrical Computer Engineering National University of Singapore 119077 Singapore The Department of Computer Science and Engineering Penn State University University ParkPA16802 United States The Department of Electrical and Microelectric Engineering Rochester Institute of Technology RochesterNY14623 United States

In this work, a thorough assessment of the robustness of complementary channel HfO2 ferroelectric FET (FeFET) against total ionizing dose (TID) radiation is conducted, with the goal of determining its suitability for use as high-performance and energy-efficient embedded nonvolatile memory (eNVM) for space applications. We demonstrate that: i) ferroelectric HfO2 thin film is robust against X-ray and proton irradiation;ii) FeFET exhibits a polarization state dependent radiation sensitivity where the high-VTH (HVT) state sees noticeable negative VTH shift and low-VTH (LVT) is immune to irradiation, irrespective of the channel type;iii) the state dependence is ascribed to the depolarization field in the HVT, which points toward the channel and facilitates the transport and trapping of radiation-generated holes close to the channel. In the future, radiation hardening techniques need to be considered. © 2023, CC BY.

关键词： Radiation effects

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Passive Versus Active: Frameworks of Active Learning for Linking Humans to Machines 43

Passive Versus Active: Frameworks of Active Learning for Lin...

引用

43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Lim, Jaeseo Jo, Hwiyeol Zhang, Byoung-Tak Park, Jooyong Interdisciplinary Program in Cognitive Science Seoul National University Seoul Korea Republic of Institute of Computer Technology Seoul National University Seoul Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Department of Psychology Seoul National University Seoul Korea Republic of

There are numerous studies that show that the more learner actively participate in the learning process, the more they learn. Although the use of active learning to increase learning outcomes has been recently introduced in a variety of methods, empirical experiments are lacking. In this study, we introduce two frameworks of human active learning and then conducted two experiments to determine how these frameworks can be used as leaning tools. In experiment 1, we compared three types of active learning and passive learning in order to empirically confirm the effect of active learning. In experiment 2, based on the results of experiment 1, we explored through simulation on machine learning with the frameworks that the more active the learners are, the better outcomes can be obtained. Both experiments showed that active learning both also effective in human and machine learning. Therefore, our analyses of the two experiments fit within the taxonomy and classification of the frameworks of active learning. This result is further significant in that it gives practical implications on human and machine learning methods. © Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Knowledge management

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Aldian Nurcahyo Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Compuer Science Binus Graduate Program 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 Compuer Science Departement Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices.

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Sensing property of Ga2O3-based extended-gate field-effect transistors for a living cell viability sensor

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Sensors and Actuators A: Physical 2023年 349卷

作者： Chou, Hsin-Yu Chiang, Jung-Lung Yu, Chang-Tze Ricky Chen, Jo-Mei Maureen Wuu, Dong-Sing Department of Materials Science and Engineering National Chung Hsing University Taichung40227 Taiwan Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung411030 Taiwan Department of Applied Chemistry National Chi-Nan University Nantou54561 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan

In this study, gallium oxide (Ga2O3) nanorods were deposited onto an indium tin oxide (ITO) glass substrate to develop a real-time living cell viability sensor. Ga2O3 nanorods had characteristics of cell population sensing based on cellular metabolism to detect cell viability. The pH sensing characteristics of Ga2O3-based extended-gate field-effect transistors (EGFETs) with and without the annealing process were measured from pH 1–11. Three different sensor heads, ITO/glass, GaOOH/ITO/glass, and Ga2O3/ITO/glass, were produced as EGFETs and immersed into a pH standard buffer and high-glucose Dulbecco's modified Eagle's medium (DMEM) to investigate hydrogen ion sensing. Results showed that the Ga2O3/ITO/glass probe had the best hydrogen ion sensing sensitivity and stability in different sample buffers. In contrast, GaOOH/ITO/glass had instability and lower sensitivity due to interference with other components in the medium. The drift characteristic of sensors was analyzed to detect long-term stability. The Ga2O3/ITO/glass probe showed lower drift properties, which can potentially monitor the DMEM situation in real-time for a long-term period. During in vitro detection, the Ga2O3/ITO/glass probe revealed a 70 μA/cells change in IDS-VDS analysis and a 10 μV/cells change in IDS-VGS analysis, which was superior linearly dependent within 10,000 cells. The biocompatibility of the Ga2O3/ITO/glass probe was also detected by 3-(4,5-dimethylthiazol-2-yl)− 2,5-diphenyltetrazolium bromide assay and showed no cytotoxicity. Overall, the Ga2O3/ITO/glass EGFET sensor has potential applications as a living cell viability device in microenvironmental studies in the future. © 2022 Elsevier B.V.

关键词： Nanorods

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Churn Prediction in Financial Risk Management using Deep Learning Techniques

Churn Prediction in Financial Risk Management using Deep Lea...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication engineering (ICATIECE), International Conference on

作者： V Sunil Kumar Sowmyashree S Piyush Kumar Pareek Ashish Khanna Victor Hugo Costa de Albuquerque Deepak Gupta Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Computer Science & Engineering East West Institute of Technology Bengaluru Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Cearά Fortaleza Brazil CSE dept Maharaja Agrasen Institute of Technology GGSIPU Department of Teleinformatics Engineering Federal University of Cearά Brazil

ISBN: (纸本)9781665493970

It is essential to develop an efficient Client Relationship Model in order to make use of the estimations about customer turnover (CRM). Finding and breaking down the facts surrounding the business information understanding is necessary for client produce prediction, which should be made possible effectively by altering the strategies used for business insight. The tools that are provided by company insight allow for the historical, current, and prospective perspectives of business activity to be anticipated and dissected. It is possible to deal with it in an efficient way by providing the information mining approaches that choose the data from the created informative index that is the most helpful. Fundamental leadership should be possible to achieve in a fruitful way if one has this information. The actions that consumers have taken in the recent past are going to be analyzed, and an accurate forecast is going to be formed about those customers who are likely going to get unhappy in the near future based on the results of that prediction. The problem is that deciding on a certain period to investigate the client's activities and also establishing on a specific day and age for each of the clients won't be appropriate6. This is due to the fact that the problem is caused by the fact that deciding on a certain period to investigate the client's activities will take too long. The challenge lies just in this aspect. How to decide on a certain amount of time to spend researching the activities of the customer. For instance, when planning a model, 100 customers are used, 70 of those customers are considered “active,” which indicates that they are still conducting business with the organisation, and the remaining 30 customers are considered “agitate,” which indicates that they have severed their ties with the organisation. The primary purpose of the work that has been suggested is to carry out exploratory research and management as a multicriteria basic leadership issue, a

关键词： Leadership Roads Companies Predictive models Media Planning Data mining

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Parallel Approximations of the Tukey g-and-h Likelihoods and Predictions for Non-Gaussian Geostatistics

Parallel Approximations of the Tukey g-and-h Likelihoods and...

引用

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Sagnik Mondal Sameh Abdulah Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Kingdom of Saudi Arabia Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology

Maximum likelihood estimation is an essential tool in the procedure to impute missing data in climate/weather applications. By defining a particular statistical model, the maximum likelihood estimation can be used to understand the underlying structure of given geospatial data. The Gaussian random field has been widely used to describe geospatial data, as one of the most popular models under the hood of maximum likelihood estimation. Computation of Gaussian log-likelihood demands operations on a dense symmetric positive definite matrix, often parameterized by the Matérn correlation function. This computation of the log-likelihood requires $\mathcal{O}(n^{2})$ storage and $\mathcal{O}(n^{3})$ operations, which can be a huge task considering that the number of geographical locations, $n$ , now commonly reaches into the millions. However, despite its appealing theoretical properties, the assumptions of Gaussianity may be unrealistic since real data often show signs of skewness or have some extreme values. Herein, we consider the Tukey ${g-}$ and $-h$ (TGH) random field as an example of a non-Gaussian random field that shows more robustness in modeling geospatial data by including two more parameters to incorporate skewness and heavy tail features in the model. This work provides the first HPC implementation of the TGH random field's inference on parallel hardware architectures. Using task-based programming models associated with dynamic runtime systems, our implementation leverages the high concurrency of current parallel systems. This permits to run the exact log-likelihood evaluation of the Tukey g-and-h (TGH) random fields for a decent number of geospatial locations. To tackle large-scale problems, we provide additionally an implementation of the given model using two different low-rank approximations. We compress the aforementioned positive-definite symmetric matrix for computing the log-likelihood and rely on the Tile Low-Rank (TLR) and the Hierarchical Off-Diagon

关键词： Maximum likelihood estimation Symmetric matrices Runtime Computational modeling Linear algebra Tail Predictive models

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Adaptive Curriculum Learning for Semi-supervised Segmentation of 3D CT-Scans 28th

Adaptive Curriculum Learning for Semi-supervised Segmentati...

引用

28th International Conference on Neural Information Processing, ICONIP 2021

作者： Nartey, Obed Tettey Yang, Guowu Agyapong, Dorothy Araba Yakoba Wu, JinZhao Sarpong, Asare K. Frempong, Lady Nadia Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Electronic Information Guangxi University Nanning530004 China Biomedical Engineering Program Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Medicine Department of Radiology and Imaging Sciences Emory University 1364 Clitton Rd. AtlantaGA30322 United States

ISBN: (纸本)9783030921842

Semi-supervised learning algorithms make use of both labelled training data and unlabelled data. However, the visual domain gap between these sets poses a challenge which prevents deep learning models from obtaining the results they have achieved most especially in the field of medical imaging. Recently, self-training with deep learning has become a powerful approach to leverage labelled training and unlabelled data. However, a challenge of generating noisy pseudo-labels and placing over-confident labelling belief on incorrect classes leads to deviation from the solution. To solve this challenge, the study investigates a curriculum-styled approach for deep semi-supervised segmentation which relaxes and treats pseudo-labels as continuous hidden variables by developing an adaptive pseudo-label generation strategy to jointly optimized the pseudo-label generation and selection process. A regularization scheme is further proposed to smoothen the probability outputs and sharpen the less represented pseudo-label regions. The proposed method was evaluated on three publicly available computer Tomography (CT) scan benchmarks and extensive experiments on all modules have demonstrated the efficacy of the proposed method. © 2021, Springer Nature Switzerland AG.

关键词： Curricula

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review

Critical Factors of Immersive Learning in Higher Education: ...

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International Conference on ICT For Smart Society (ICISS)

作者： Sunardi Meyliana Arief Ramadhan Yusep Rosmansyah Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Departement School of information system Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of electrical engineering and informatics Bandung Institute of Technology Bandung Indonesia

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education.

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