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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Md Shohanoor Rahman Ali Nikoukar Mesut Güneş Behnam Dezfouli Communication and Networked Systems (ComSys) - Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany Department of Computer Engineering Internet of Things Research Lab Santa Clara University USA

The need of immediate fall detection, particularly within the geriatric population, is emphasized by its pivotal role in curtailing consequences post-incident. In this paper we introduce an algorithm that harnesses the subject’s acceleration, angular velocity, and orientation (pitch and roll) to ascertain fall incidents. The evaluation is based on systematic extraction and comparison of features delineated over varied temporal intervals. The presented algorithm uses data extracted from a singular sensor, amalgamating a triaxial accelerometer and a gyroscope, embedded in footwear possessing integrated computational modules. The resilience of the algorithm and efficacy are empirically validated through rigorous assessments, involving five healthy participants simulating 160 fall events and 650 activities indicative of routine locomotion.

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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Robotics, engineering, science, and Technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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Ultrasound-Coupled Electrocoagulation Based Azo Dye Fading Rate Prediction Using Deep Neural Networks

Ultrasound-Coupled Electrocoagulation Based Azo Dye Fading R...

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International KES Conference on Human Centred Intelligent systems, KES HCIS 2023

作者： Akoulih, Meryem Tigani, Smail Wakrim, Asmaa Chehri, Abdellah Saadane, Rachid Ghachtouli, Sanae El IME Lab Faculty of Science Hassan 2 University Casablanca20250 Morocco AAIR Lab Digital Engineering and Artificial Intelligence Systems High Private School Casablanca20100 Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Mathematics and Computer Science Royal Military College KingstonONK7K 7B4 Canada

ISBN: (纸本)9789819934232

This research paper presents a deep learning based predictive model for predicting the azo dye fading rate during electrocoagulation coupled with ultrasound. Electrocoagulation is a commonly used method for the treatment of wastewater contaminated with azo dyes, but the efficiency of this process can be improved by incorporating ultrasound. The proposed deep learning model is trained on a dataset of electrocoagulation experiments with varying parameters such as current density, pH, and treatment time, as well as the presence or absence of ultrasound. The model is able to accurately predict the azo dye removal rate for new sets of electrocoagulation parameters, providing a valuable tool for optimizing the treatment process. The model’s performance is evaluated using standard metrics, and the results demonstrate its effectiveness in predicting azo dye removal rate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Azo dyes

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A Review of Key Elements for Underwater Wireless Sensor Networks

A Review of Key Elements for Underwater Wireless Sensor Netw...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Ali Hamza Najim Aymen Mohammed Al-Dulaimi Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Salah A. Aliesawi Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AlKadhum College (IKC) Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Department of Communication Technical Engineering College of Engineering Techniques Al-Farahidi University Baghdad Iraq Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Computer Networking Systems College of Computer and Information Technology University of Anbar Anbar Iraq

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Underwater Wireless Sensor Networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low-cost submerged sensors that are mostly connected by way of acoustic media. Sensors are relatively new to underwater research, and thanks to recent technological development, they can now function at different depths. In this article, the author explores what UWSNs must do to operate smoothly by exploring the basic features that they must meet for further development. Real-time Underwater communication remains an open problem due to underwater acoustic channels important for undersea data acquisition, undersea navigation, and undersea remote sensing. To mitigate such challenges and in light of the use of new technologies, researchers seek to improve the reliability and reliability of UWSNs. Finally, there is great potential with UWSNs for investigating and exploiting the underwater environment by improving scenarios of different domains in line with subaqueous data and communications.

关键词： Wireless communication Wireless sensor networks Underwater communication Reviews Sensor phenomena and characterization Real-time systems Reliability Security Underwater acoustics Remote sensing

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Eye-tracking in Mixed Reality for Diagnosis of Neurodegenerative Diseases

arXiv

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arXiv 2024年

作者： Daniol, Mateusz Hemmerling, Daria Sikora, Jakub Jemiolo, Pawel Wodzinski, Marek Wojcik-Pedziwiatr, Magdalena The AGH University of Kraków Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland Information Systems Institute Sierre Switzerland The Andrzej Frycz Modrzewski Krakow University Department of Neurology Krakow Poland

Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements. In this paper, we present a medical scenario and outline the development of an application designed to capture eye-tracking signals through Mixed Reality technology for the evaluation of neurodegenerative diseases. Additionally, we introduce a pipeline for extracting clinically relevant features from eye-gaze analysis, describing the capabilities of the proposed system from a medical perspective. The study involved a cohort of healthy control individuals and patients suffering from Parkinson's disease, showcasing the feasibility and potential of the proposed technology for non-intrusive monitoring of eye movement patterns for the diagnosis of neurodegenerative diseases. Copyright © 2024, The Authors. All rights reserved.

关键词： Augmented reality

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Exploring PEMFCs for Powering Untethered Small-Scale Robots

Exploring PEMFCs for Powering Untethered Small-Scale Robots

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Aiswarya Lakshmi Manikandan Berfin Gürboğa Niko Münzenrieder Akash Raman Han J. G. E. Gardeniers Arturo Susarrey-Arce Leon Abelmann Islam S. M. Khalil Faculty of Electrical Engineering Mathematics and Computer Science (EEMCS) University of Twente AE Enschede The Netherlands Faculty of Engineering Metallurgical and Materials Engineering Man-isa Celal Bayar University Manisa Türkiye Faculty of Engineering Free University of Bozen-Bolzano Bozen-Bolzano Italy Faculty of Science and Technology Mesoscale Chemical Systems Group MESA+ Institute for Nanotechnology University of Twente AE Enschede The Netherlands Department of Biomechanical Engineering University of Twente En-schede AE The Netherlands

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Magnetically guided untethered devices are used in a variety of medical applications. These devices are typically powered by onboard battery units. Hydrogen fuel cells (FC) are a promising alternative power source for such small-scale devices since they rely on a sustainable fuels which produce electric power from the redox reaction of hydrogen and oxygen across a proton exchange membrane (PEM). Understanding the impact of decreasing the active electrode area in FCs is crucial for deploying FCs in untethered devices and gaining insights into the challenges of downscaling the devices. This paper investigates the performance of PEM FCs (PEMFCs) when their active area is reduced, and when the FC is supplied with reactants at different flow rates from a PEM electrolyzer. PEMFCs with three active electrode areas, 3.5 × 3.5 cm 2 , 2.7×2.7 cm 2 , and 1.6×1.6 cm 2 were designed, fabricated, and characterised. Maximum fuel cell output powers of 0.3 W, 0.09 W, and 0.03 W (maximum power densities of 0.025 W/cm 2 , 0.012 W/cm 2 , and 0.013 W/cm 2 ) corresponding to the three aforementioned areas were achieved. Mathematical modeling of the PEMFC simulated the FC response, providing insights into the activation kinetics of the fuel cell. The smallest PEMFC with an active area of 1.6 × 1.6 cm 2 was used to power an inductor coil (rated 130 mA, 150 mH, 8 Ω). This study can guide the development of FCs to power untethered devices.

关键词： Coils Performance evaluation Power system measurements Fuel cells Magnetic moments Mathematical models Numerical models Inductors Robots Load modeling

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities

The Reliability of Information Sources: Student Perceptions ...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Anita Mihalovicsne Kollar Jozsef Katona Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources; social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape.

关键词： Economics Power engineering Accuracy Social networking (online) engineering profession Education Reliability engineering Information filters Information age Reliability

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Meta-heuristic computing knacks for target angle estimation in monostatic radar system with coprime arrays

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Ain Shams engineering Journal 2024年第5期15卷

作者： Akbar, Sadiq Sohail, Muhammad Asif Zahoor Raja, Muhammad Zaman, Fawad Ullah, Rizwan Abdul Rehman Khan, Muhammad Ajavakom, Nopdanai Phanomchoeng, Gridsada Department of Mechanical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Hefei230026 China Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Electrical & Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Department of Electrical & Computer Engineering COMSATS University Islamabad Attock Campus 43600 Pakistan Human-Robot Collaboration and Systems Integration Research Unit Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

Angle estimation of signals received by antenna arrays is a key step in adaptive beamforming, used to detect and locate targets in space. It is desirable to reduce costs by employing a smaller number of antennas while maintaining efficiency and degrees of freedom (DOF) of the system at the same time. Co-prime antenna arrays not only circumvent the operational constraints of cost and hardware complexity, but also boost the DOF by utilizing far fewer antennas as compared to traditional uniform linear arrays. In this paper, meta-heuristic computing paradigm for estimating arrival angle of targets with co-prime arrays incorporated in monostatic radar is proposed. The system's autocorrelation and Mean Square Error (MSE) are used for fitness function optimized by global/local search with Genetic Algorithm (GA) and Pattern Search (PS). Monte Carlo simulations are performed to evaluate the performance via estimation accuracy, MSE, and computational cost for varying numbers of impinging waves. © 2024 THE AUTHORS

关键词： Genetic algorithms

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A Continual Learning System with Self Domain Shift Adaptation for Fake News Detection

A Continual Learning System with Self Domain Shift Adaptatio...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Sebastián Basterrech Andrzej Kasprzak Jan Platoš Michał Woźniak Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Department of Systems and Computer Networks Wroclaw University of Science and Technology Wroclaw Poland Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava Czech Republic

Detecting fake news is currently one of the critical challenges facing modern societies. The problem is particularly relevant, as disinformation is readily used for political warfare but can also cause significant harm to the health of citizens, such as by promoting false data on the harmfulness of selected therapies. One way to combat disinformation is to treat fake news detection as a machine learning task. This paper presents such an approach, which additionally addresses an important problem related to the non-stationarity characteristics of the fake news. We elaborated a stream data with the simulation of domain shift based on two popular benchmark datasets dedicated to the fake news classification problem (Kaggle Fake News and Constraint@AAAI2021–COVID19 Fake News Detection). The proposed learning system works in a Continual Learning (CL) framework and integrates a self domain shift adaptation in a machine learning scheme. The method was built following state-of-the-art techniques, that includes Word2Vec as a feature extractor and the LSTM model as a classifier. The performance of the approach has been evaluated over the generated data stream. The convenience of our approach is showed in the results, where the accuracy gain with respect to a CL approach without domain adaptation is observed to be significant.

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A Machine Learning Approach in Predictive Maintenance in the IoT Enabled Industry 4.0

A Machine Learning Approach in Predictive Maintenance in the...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Sathiyakeerthi Madasamy B. Prabhu Shankar Rakesh Kumar Yadav Jayalakshmi K P Solution Architect Pilvi Systems Inc Lewisville TX USA Department of Computer Science and Engineering Faculty of Engineering and Technology Alliance University Karnataka India Department of Computer Science and Engineering SRM Institute of Science and Technology Ghaziabad Uttar Pradesh India Department of Electronics and Communication Engineering St Joseph Engineering College Mangaluru Karnataka India

Predictive maintenance using machine learning is a powerful technique for industries seeking to enhance their operations with minimize downtime. In an IoT-enabled Industry 4.0 environment, this approach can be taken to a new level by leveraging the vast amounts of data generated by connected devices. To implement a machine learning methodology to projecting conservation in an Industry 4.0 environment, several key steps need to be taken. First, data from IoT devices across the industrial ecosystem should be collected and centralized in a data lake or similar storage system. This data should include information on equipment health, sensor readings, and other relevant metrics. Next, the data should be preprocessed and transformed to ensure its quality and consistency. This may involve cleaning, normalization, and feature engineering to create relevant variables for use in machine learning models. Once the data has been preprocessed, a range of machine learning models can be trained on it to predict equipment failures or other maintenance issues. This may involve ongoing tuning and optimization of model hyperparameters or retraining the models on new data as it becomes available. Finally, the predictions generated by the machine learning models should be integrated into a broader maintenance management system to enable timely action. This may include triggering maintenance requests, generating work orders, or even automating maintenance tasks through the use of robots or other industrial automation technologies. By implementing a machine learning method to projecting preservation in an IoT-enabled Industry 4.0 environment, industries can optimize their operations, minimize downtime, and improve overall equipment effectiveness.

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