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14th International Conference on advanced Semiconductor Devices and Microsystems, ASDAM 2022

作者： Gasparek, K. Zavodnik, T. Vavrinsky, E. Micjan, M. Donoval, M. Nevrela, J. Mateasik, A. Zabrodsky, M. Sisolak, V. Kosnacova, H. Institute of Electronics and Photonics Slovak University of Technology Faculty of Electrical Engineering and Information Technology Bratislava81219 Slovakia Advanced Progressive Technologies Institute Slovak University of Technology Faculty of Materials Science and Technology in Trnava Trnava91724 Slovakia Institute of Medical Physics Biophysics Informatics and Telemedicine Comenius University Faculty of Medicine Bratislava81272 Slovakia Comenius University Faculty of Medicine Department of Simulation and Virtual Medical Education Bratislava81272 Slovakia Cancer Research Institute Slovak Academy Sciences Biomedical Research Center Department of Genetics Bratislava84505 Slovakia

ISBN: (数字)9781665469791

ISBN: (纸本)9781665469777

The development of a high-quality photoplethysmographic sensor (PPG) is one partial tasks of our research of telemedicine equipment. This area become very critical during recent pandemic. We have previously worked on the development of patches that monitor the temperature of people in the hospital environment. The situation came up with the idea of fitting this patch with a PPG for determining the heart pulse and the blood oxygen saturation. Unfortunately, when using a PPG, we found that it would not be such an easy task and we need an extensive optimization. Each part of the human body requires a specific geometry and combination of LEDs and photodetectors, different LED intensities etc. For this reason, we created universal and advanced PPG sensor. The device contains 15 three-color LEDs, 2 photodetectors, an ambient light sensor, an accelerometer, and wireless communication. In this article, we focus only on the hardware part. © 2022 IEEE.

关键词： Photodetectors

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Concept of modern EOG system for BCI 14

Concept of modern EOG system for BCI

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14th International Conference on advanced Semiconductor Devices and Microsystems, ASDAM 2022

作者： Vavrinsky, E. Stremy, M. Horvath, D. Zavodnik, T. Gasparek, K. Vitazkova, D. Kosnacova, H. Institute of Electronics and Photonics Slovak University of Technology Faculty of Electrical Engineering and Information Technology Bratislava81219 Slovakia Institute of Medical Physics Biophysics Informatics and Telemedicine Comenius University Faculty of Medicine Bratislava81272 Slovakia Advanced Progressive Technologies Institute Slovak University of Technology Faculty of Materials Science and Technology in Trnava Trnava91724 Slovakia Comenius University Faculty of Medicine Department of Simulation and Virtual Medical Education Bratislava81272 Slovakia Cancer Research Institute Slovak Academy Sciences Biomedical Research Center Department of Genetics Bratislava84505 Slovakia

ISBN: (数字)9781665469791

ISBN: (纸本)9781665469777

In this paper, we present the concept of a modern EOG sensor. In contrast to the standard version, it is realized in the form of a headband with conductive textiles and supplemented by a photoplethysmography, electroencephalography and simplified ECG recording. The device is designed for people suffering from amyotrophic lateral sclerosis. © 2022 IEEE.

关键词： Electrophysiology

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Optimizing droplet coalescence dynamics in microchannels: A comprehensive study using response surface methodology and machine learning algorithms

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Heliyon 2025年第1期11卷 e41510页

作者： Javadpour, Seyed Morteza Kadivar, Erfan Zarneh, Zienab Heidary Kadivar, Ebrahim Gheibi, Mohammad Department of Mechanical Engineering University of Gonabad Gonabad Iran Department of Physics Shiraz University of Technology Shiraz 71555-313 Iran Institute of Ship Technology Ocean Engineering and Transport Systems University of Duisburg-Essen Duisburg 47057 Germany Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec 46117 Czech Republic Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Liberec Czech Republic Faculty of Mechanical Engineering Tarbiat Modares University Tehran 141171-3116 Iran

Droplet coalescence in microchannels is a complex phenomenon influenced by various parameters such as droplet size, velocity, liquid surface tension, and droplet-droplet spacing. In this study, we thoroughly investigate the impact of these control parameters on droplet coalescence dynamics within a sudden expansion microchannel using two distinct numerical methods. Initially, we employ the boundary element method to solve the Brinkman integral equation, providing detailed insights into the underlying physics of droplet coalescence. Furthermore, we integrate Response Surface Methodology (RSM) to effectively optimize droplet coalescence dynamics, harnessing the power of machine learning algorithms. Our results showcase the efficacy of these computational techniques in enhancing experimental efficiency. Through rigorous evaluation utilizing Regression Coefficient and Mean Absolute Error metrics, we ascertain the accuracy of our estimations. Our findings highlight the significant influence of key parameters, specifically the non-dimensional initial distance of the droplets (D), viscosity ratio (μ), Capillary number (Ca), and width (w), as identified by the non-dimensional final droplet-droplet spacing (DD), velocity of the first droplet (V_FD), and velocity of the second droplet (V_BD), respectively. This comprehensive approach provides valuable insights into droplet coalescence phenomena and offers a robust framework for optimizing microfluidic systems. The most influential parameters on DD are the values of A_d and D, while viscosity has the lowest influence on DD. The most influential parameters on droplet velocity are viscosity and channel width, whereas the initial distance and Ca have the least influence on droplet velocity. The comparison of different machine learning algorithms indicates that the best ones for predicting DD, V_FD, and V_BD are function, SMOreg, Lazy-IBK, and Meta-Bagging, respectively. © 2025 The

关键词： Coalescence Droplet Machine learning Microchannel Optimization

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Temperature and Electron Concentration Dependences of 1/f Noise in Hg1-xCdxTe - Evidence for a Mobility Fluctuations Mechanism

arXiv

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arXiv 2024年

作者： Rehman, Adil Petriakov, Volodymyr Yahniuk, Ivan Kazakov, Aleksandr Rogalska, Iwona Grendysa, Jakub Marchewka, Michal Haras, Maciej Wojtowicz, Tomasz Cywiński, Grzegorz Knap, Wojciech Rumyantsev, Sergey CENTERA Laboratories Institute of High Pressure Physics Polish Academy of Sciences Warsaw01-142 Poland Terahertz Center University of Regensburg Regensburg93040 Germany International Research Centre MagTop Institute of Physics Polish Academy of Sciences Warsaw02-668 Poland University of Rzeszów Institute of Materials Engineering Center for Microelectronics and Nanotechnology Rzeszów35-959 Poland Centre for Advanced Materials and Technologies CEZAMAT Warsaw University of Technology Warsaw02-822 Poland Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Advanced Materials Center Gdańsk80-233 Poland

Hg1-xCdxTe is a unique material with the band-gap tunable by the temperature, pressure, and cadmium content in a wide range, from 1.6 eV to inverted band-gap of -0.3 eV. This makes Hg1-xCdxTe one of the key materials for infrared and terahertz detectors, whose characteristics largely depend on the material noise properties. In this work, we investigated the low-frequency 1/f noise in a thick (800 nm) HgCdTe layer and in a field effect transistor (FET) with an 8 nm wide HgTe quantum well. Both structures exhibited a small contribution from contact noise and showed weak noise dependences on temperature. Investigation of the 1/f noise in HgTe quantum well FET as a function of gate voltage revealed that the noise also depends weakly on electron concentration. These findings indicate that the noise properties of Hg1-xCdxTe are similar to those of graphene, where mobility fluctuations were found to be the dominant mechanism of the 1/f noise. © 2024, CC BY.

关键词： Nanocrystals

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Parameter estimation and control of an unmanned aircraft-based transportation system for variable-mass payloads

Parameter estimation and control of an unmanned aircraft-bas...

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作者： Alatorre, Armando Espinoza, Eduardo S. Sánchez, Bonifacio Ordaz, Patricio Muñoz, Filiberto García Carrillo, Luis Rodolfo French-Mexican Laboratory of Informatics and Automatics Center of Research and Advanced Studies of the National Polytechnic Institute México City Mexico Automatic Control Department Center of Research and Advanced Studies of the National Polytechnic Institute México City Mexico Research Center of Information Technologies and Systems Autonomous University of the State of Hidalgo Hidalgo Mexico Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM United States

This paper deals with the development of an adaptive robust controller for an Unmanned Aircraft System with variable mass. The goal is to improve trajectory tracking and energy performance while the aircraft mass is gradually reducing due to payload release. To improve the trajectory tracking performance, we propose an algorithm that merges the concepts of least squares method and sliding mode observer for the estimation of the vehicle mass. The nonlinear observer estimates the linear velocities and accelerations, which are further used to obtain the vehicle mass. A robust adaptive pole placement controller based on the Attractive Ellipsoid Method uses this estimation to update the controllers gains due to the mass variation. To validate the effectiveness of the proposed algorithm for performing aerial transportation and deployment of payloads, we present a numerical example comparing the performance of the proposed method with respect to using Proportional Derivative controllers as well as Robust Controllers. © 2021 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Controllers

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Retrieve reusable 3D CAD objects based on hidden Markov models (HMM)

Retrieve reusable 3D CAD objects based on hidden Markov mode...

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International Conference on Information Visualisation (IV)

作者： Ahmed Fradi Borhen Louhichi Mohamed Ali Mahjoub Higher Institute of Informatics and Communication Technologies University of Sousse Sousse Tunisia Latis-Laboratory of Advanced Technology and Intelligent Systems National Engineering School of Sousse University of Sousse Sousse Tunisia ISSATSo Laboratoire de Mécanique de Sousse (LMS) University of Sousse Sousse Tunisia

ISBN: (纸本)9781665490085

Computer-aided design has been widely used in modern industry for several decades, resulting in the huge databases of 3D CAD models that specialist companies currently own. Therefore, developing a solution to retrieve a reusable 3D CAD model becomes a strategic need for companies specializing in the modern design and manufacturing industry. Recently, some research works have been launched with the aim of recognizing 3D CAD objects based on design similarity and reusability. In this context, the use of probabilistic graphical models for information retrieval was always of great importance, especially when the context is characterized by the large volume of data and the uncertainty of the result. In this paper, authors will present a new approach proposed for modeling 3D CAD objects into reusable subparts. This approach is based on the Hidden Markov Model (HMM). This model has shown improved accuracy and efficiency in recognizing reusable 3D CAD objects, compared to other previously proposed solutions.

关键词： Manufacturing industries Solid modeling Three-dimensional displays Design automation Uncertainty Computational modeling Hidden Markov models

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Synthesis of Fe3o4@Mil-101-Oh/Chitosan for Adsorption and Release of Doxorubicin

SSRN

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SSRN 2024年

作者： Najafipour, Iman Emami, Nafiseh Sadeh, Pegah Amoli, Adonis Mosleh-Shirazi, Sareh Amani, Ali Mohammad Kamyab, Hesam Chelliapan, Shreeshivadasan Kasaee, Seyed Reza Vafa, Ehsan Department of Medical Nanotechnology School of Advanced Medical Sciences and Technologies Shiraz University of Medical Sciences Shiraz Iran Department of Chemical Engineering Faculty of Engineering University of Isfahan Isfahan Iran Department of Chemical Engineering Faculty of Advanced Technologies University of Shiraz Shiraz Iran Department of Chemistry Michigan Technological University HoughtonMI49931 United States Department of Materials Science and Engineering Shiraz University of Technology Shiraz Iran Faculty of Architecture and Urbanism UTE University Calle Rumipamba S/N and Bourgeois Quito Ecuador Department of Biomaterials Saveetha Dental College and Hospital Saveetha Institute of Medical and Technical Sciences Chennai600 077 India Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Engineering Department Razak Faculty of Technology & Informatics Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Shiraz Endocrinology and Metabolism Research Center Shiraz University of Medical Sciences Shiraz Iran

This study reports the synthesis and characterization of a magnetic composite metal-organic framework, Fe3O4@MIL-101-OH/Chitosan, for the adsorption and release of the anticancer drug doxorubicin (DOX). The material was prepared by the synthesis of MIL-101-OH, followed by the coating of chitosan and deposition of Fe3O4 nanoparticles on the surface. The resulting material was characterized using scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and vibrating sample magnetometry (VSM). The Fe3O4@MIL-101-OH/Chitosan demonstrated a crystalline structure with a surface area of 610.36 m2/g and a high adsorption capacity of 185 mg/g for DOX. The kinetics of adsorption conformed to a pseudo-second-order and Langmuir isotherm model. The Fe3O4@MIL-101-OH/Chitosan exhibited a controlled release over the period of 84 hours at pH 5 and reached 80% of DOX release rate after 60 hours. In conclusion, the Fe3O4@MIL-101-OH/Chitosan composite has great potential as a drug delivery system as a result of its high adsorption capacity and magnetic properties. This research provides a promising approach for the development of novel drug delivery systems for cancer therapy. © 2024, The Authors. All rights reserved.

关键词： Magnetite

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Inter-domain alignment for predicting high-resolution brain networks using teacher-student learning

arXiv

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arXiv 2021年

作者： Demir, Başar Bessadok, Alaa Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Higher Institute of Informatics and Communication Technologies University of Sousse 4011 Tunisia National Engineering School of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

Accurate and automated super-resolution image synthesis is highly desired since it has the great potential to circumvent the need for acquiring high-cost medical scans and a time-consuming preprocessing pipeline of neuroimaging data. However, existing deep learning frameworks are solely designed to predict high-resolution (HR) image from a low-resolution (LR) one, which limits their generalization ability to brain graphs (i.e., connectomes). A small body of works has focused on superresolving brain graphs where the goal is to predict a HR graph from a single LR graph. Although promising, existing works mainly focus on superresolving graphs belonging to the same domain (e.g., functional), overlooking the domain fracture existing between multimodal brain data distributions (e.g., morphological and structural). To this aim, we propose a novel inter-domain adaptation framework namely, Learn to SuperResolve Brain Graphs with Knowledge Distillation Network (L2S-KDnet), which adopts a teacher-student paradigm to superresolve brain graphs. Our teacher network is a graph encoder-decoder that firstly learns the LR brain graph embeddings, and secondly learns how to align the resulting latent representations to the HR ground truth data distribution using an adversarial regularization. Ultimately, it decodes the HR graphs from the aligned embeddings. Next, our student network learns the knowledge of the aligned brain graphs as well as the topological structure of the predicted HR graphs transferred from the teacher. We further leverage the decoder of the teacher to optimize the student network. In such a way, we are not only bringing the learned embeddings from both networks closer to each other but also their predicted HR graphs. L2S-KDnet presents the first TS architecture tailored for brain graph super-resolution synthesis that is based on inter-domain alignment. Our experimental results demonstrate substantial performance gains over benchmark methods. Our code is available a

关键词： Graphic methods

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Brain multigraph prediction using topology-aware adversarial graph neural network

arXiv

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arXiv 2021年

作者： Bessadok, Alaa Mahjoub, Mohamed Ali Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Higher Institute of Informatics and Communication Technologies University of Sousse Tunisia National Engineering School of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia School of Science and Engineering Computing University of Dundee United Kingdom

Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (GAN) focused on predicting the missing multimodal medical images from a few existing modalities. While brain graphs help better understand how a particular disorder can change the connectional facets of the brain, synthesizing a target brain multigraph (i.e, multiple brain graphs) from a single source brain graph is strikingly lacking. Additionally, existing graph generation works mainly learn one model for each target domain which limits their scalability in jointly predicting multiple target domains. Besides, while they consider the global topological scale of a graph (i.e., graph connectivity structure), they overlook the local topology at the node scale (e.g., how central a node is in the graph). To address these limitations, we introduce topology-aware graph GAN architecture (topoGAN), which jointly predicts multiple brain graphs from a single brain graph while preserving the topological structure of each target graph. Its three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs. The experimental results using five target domains demonstrated the outperformance of our method in brain multigraph prediction from a single graph in comparison with baseline approaches. © 2021, CC BY-NC-SA.

关键词： Graphic methods

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