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Active touch classification using EEG signals 6

Active touch classification using EEG signals

6th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2021

作者： Aspiotis, Vasileios Peschos, Dimitrios Tzimourta, Katerina D. Tsipouras, Markos G. Al Husein, Sami Abosaleh Antoniou, Evangelos Giannakeas, Nikolaos Tzallas, Alexandros T. Glavas, Euripidis Faculty of Medicine University of Ioannina Ioannina Greece Dept. of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Open Lab Newcastle University Newcastle Upon Tyne United Kingdom Dept. of Informatics and Telecommunications University of Ioannina Kostakioi Arta Greece

ISBN: (纸本)9781665427425

Touch is a fundamental aspect of human interaction with the surrounding environment. It affects individuals' development in different manners and figures prominently in everyday operations such as the sense of presence, object recognition, performing actions, non-verbal communication and emotional state. In recent years there has been a growth of interest in researching the electrophysiological activity of the brain originating from haptic stimulation. In the present preliminary experiment, we performed a classification process of extracted EEG features acquired from four healthy participants' EEG data when they actively touched different natural textures. Each participant was asked to use their fingertips and calmly rub for one minute, each of the three different textured materials (smooth, rough and water surface). EEG recordings were acquired and processed. Next, time and frequency-based features were extracted and used as input to four classifiers to correctly identify each different texture. The results obtained show a classification performance of 63% with C4.5 algorithm and 76% with Random Forests and 10-fold cross-validation. © 2021 IEEE

关键词： Electroencephalography

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Real-Time Safety Helmet Detection Using Enhanced YOLOv5 Object Detection

Real-Time Safety Helmet Detection Using Enhanced YOLOv5 Obje...

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IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)

作者： Fatin Najihah Muhamad Zamri Mira Kartiwi Nurul Fariza Zulkurnain Nelidya Md. Yusoff Teddy Surya Gunawan Levy Olivia Nur Electrical and Computer Engineering Dept. International Islamic University Malaysia Kuala Lumpur Malaysia Information Systems Department International Islamic University Malaysia Kuala Lumpur Malaysia Razak Faculty of Technology & Informatics UTM Kuala Lumpur Kuala Lumpur Malaysia School of Electrical Engineering Telkom University Bandung Indonesia

Personal Protective Equipment (PPE) regulations require construction workers to wear safety helmets to ensure site safety. However, monitoring PPE compliance consistently in fast-paced, dynamic construction environments poses a significant challenge. In response, we developed a sophisticated object detection system that automates the real-time verification of helmet use, thereby improving safety standards and reducing the likelihood of accidents. Extensive research was conducted to analyze all feasible algorithms that can be implemented in the safety helmet detection system and compare the proposed model with an existing one to ensure the proposed system can give high accuracy and high inference speed. Therefore, YOLOv5 was identified as the ideal choice in terms of accuracy and speed, and it was then enhanced with optimized transfer learning. We began our methodology by pre-training a comprehensive Kaggle dataset before refining the model using Roboflow on a specialized dataset. Using PyTorch and YOLOv5, we conducted exhaustive model training, testing, and evaluation. Our system achieved a lightning-fast inference speed of 39.8 milliseconds and a remarkable 91.4 percent accuracy in identifying helmet compliance. The implementation of such object detection technologies has the potential to significantly increase safety helmet compliance, thereby creating a safer environment for construction workers.

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Intelligent Horizontal Autoscaling in Edge Computing Using a Double Tower Neural Network

SSRN

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SSRN 2022年

作者： Violos, John Tsanakas, Stylianos Theodoropoulos, Theodoros Leivadeas, Aris Tserpes, Konstantinos Varvarigou, Theodora School of Electrical and Computer Engineering National Technical University of Athens Zografou Greece Dept. of Informatics and Telematics Harokopio University of Athens Tavros Greece Dept. of Software and Information Technology Engineering École de Technology Supérieure Montreal Canada

Edge computing is characterised by a varying workload intensity that has a strong effect in the applications performance and their resource requirements. Thus, in order to maintain a sustainable performance a resource autoscaling mechanism is needed that will automatically add or remove computational nodes. This autoscaling mechanism must ensure that the user’s requirements in terms of Quality of Service (QoS) are satisfied, while respecting the Service Level Agreements (SLAs) and delivering always-on services with affordable ondemand computing solutions. Additionally, the autoscaling could take place proactively or reactively and the adjustability of the resources could be static or dynamic. To this end, the research goal of this paper is to design a novel autoscaling mechanism that minimizes the tasks execution and maximizes the throughput with the minimum number of resources. In this direction, we propose an Intelligent Horizontal Proactive Autoscaling (IHPA) mechanism that leverages resource usage metrics of processing Edge nodes such as CPU, RAM and Bandwidth in order to provide timely scale up and scale down decisions. The IHPA is based on a double tower Deep Learning (DL) architecture. In order to find a close to optimal DL architecture and guarantee the generality of our approach we also propose the innovative hybrid Bayesian Evolution Strategy method. We conduct an extensive simulation of the IHPA with three baseline task offloading mechanisms keeping the same statistical task generation distribution. We also compare the performance of our proposed IHPA against the reactive autoscaling and the Kubernetes Horizontal autoscaling mechanisms. The results show that our framework shows significant improvements in terms of latency and throughput, while making an optimal use of the resources. © 2022, The Authors. All rights reserved.

关键词： Edge computing

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Performance Comparison of 15-Level Multilevel Inverter Topologies

Performance Comparison of 15-Level Multilevel Inverter Topol...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： G. Anusha Krishan Arora Himanshu Sharma Surya Prakash Thota Chaman Verma Maria Simona Raboaca Constantin Filote Dept. of EEE Lovely Professional University Punjab India Dept. of EEE GNITS Hyderabad India Dept. of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania

Multilevel inverters (MLI) have gained a significant emphasis in the area of renewable energy applications in recent years. The popularity of MLIs is due to their ability to efficiently convert the sustainable sources of energy, such as wind and solar power, into usable electrical energy. One key advantage of using reduced multilevel inverter configurations is that it can help to reduce the system overall cost by decreasing number of components required. The reduction of components in the inverter circuit results in simplification of design and lower expenditure. This paper proposes a fifteen-level modified multilevel inverter with cascaded h-bridge with a minimized switch count. The simulation results of the proposed inverter demonstrate that it is able to effectively attenuate harmonics and with reduced number of components required while still providing good performance. The suggested inverter design is also evaluated on its harmonic reduction characteristics, providing a cleaner output waveform and less distortion.

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A SHA-3 Co-Processor for IoT Applications

A SHA-3 Co-Processor for IoT Applications

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2020 Workshop on Communication Networks and Power Systems, WCNPS 2020

作者： Azevedo, Igor L.R. Nery, Alexandre S. Da Sena, Alexandre Dept. of Electrical Engineering University of Brasília Brazil Dept. of Informatics and Computer Science State University of Rio de Janeiro Brazil

ISBN: (纸本)9781728187914

The Secure Hash Algorithm 3 (SHA-3) is the latest member of the secure hash family of algorithms (SHA) on top of which several technologies are built upon, such as in Blockchain, security applications and protocols, including TLS, SSL, PGP, SSH, IPsec, and S/MIME. Due to tighter processing and power efficiency constraints often present in embedded applications, hardware architectures such as FPGAS (Field-Programmable Gate Array) can be employed to enable the design and implementation of efficient hardware accelerators. Thus, this work implements a SHA-3 Co-Processor in FPGA suitable for IoT applications. Performance, Circuit-area and Energy consumption results show that the Co-Processor is about 65% faster than the ARM Cortex-A9 processor that is also equipped in the FPGA chip, as well as in many IoT embedded systems. © 2020 IEEE.

关键词： Internet of things

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DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance

DCGAN-based Medical Image Augmentation to Improve ELM Classi...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Rando Noor Akhmad Setiawan Adhistya Erna Permanasari Riries Rulaningtyas Andriyan Bayu Suksmono Imas Sukaesih Sitanggang Dept. of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia Dept. of Physics Universitas Airlangga Biomedical Engineering Program Surabaya Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department of Computer Science IPB University Bogor Indonesia

ISBN: (数字)9781665460309

ISBN: (纸本)9781665460316

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%.

关键词： Satellites Extreme learning machines Medical services Feature extraction Generative adversarial networks Entropy Lesions

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Mobile Application for Visualizing the Measurements

Mobile Application for Visualizing the Measurements

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International Conference on Modern Electrical and Energy Systems (MEES)

作者： Simona Kirešová Milan Guzan Branislav Sobota Viacheslav Melnykov Rostyslav Yatsiuk Bohdan Peryvertailo Dept. of Theoretical and Industrial Electrical Engineering Technical University of Košice Košice Slovakia Dept. of Computers and Informatics Technical University of Košice Košice Slovakia Dept. of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Dept. of Computer Engineering and Electronics Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine

This paper deals with the design of an Android mobile application and visualization of the measured values of particulate matter and meteorological factors from the measurement stations. The application can in principle be effectively used in any field of data visualization. The architecture of the mobile application, the security, the use of AWS services to access the data in the InfluxDB databases, as well as the user interface and graphical visualizations of the measured data are described and illustrated. The application is user tested and the paper documents their first experiences using the mobile application.

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Application of Automated Quality Control in Smart Factories - A Deep Learning-based Approach

Application of Automated Quality Control in Smart Factories ...

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Annual IEEE Systems Conference

作者： Subbalakshmi Mandapaka Catalina Diaz Hasbanny Irisson Aditya Akundi Viviana Lopez Douglas Timmer Dept. of Computer Science University of Texas Rio Grande Valley Texas United States Informatics and Engineering Systems Dept. University of Texas Rio Grande Valley Texas United States Manufacturing and Industrial Eng. University of Texas Rio Grande Valley Texas United States

Industry 4.0 is the ongoing automation of conventional manufacturing and industrial applications using smart technology. Quality control (QC) is a set of procedures to ensure that a manufactured product adheres to a defined set of quality criteria or meets the requirements of the customer. Many applications within the manufacturing domain employ image-processing or machine learning systems but deep learning-based applications are rare. The goal of this project is to leverage deep learning methods for the automation of quality control. A visual QC automation application is proposed that utilizes a camera placed over a product assembly line containing 3-D printed product samples in a smart factory prototype setup for data collection. After model training, the model will perform object detection and recognition for analyzing complex free-form products and perform product dimension and surface analysis to identify the products that meet the quality control guidelines.

关键词： Training Analytical models Visualization Manufactured products Prototypes Quality control Object detection

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Welcome message from the Head of Department of computer and informatics engineering Politeknik Negeri Jakarta

2020 3rd International Conference on Computer and Informatic...

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2020 3rd International Conference on computer and informatics engineering, IC2IE 2020 2020年 78ADUMMY页

作者： Laya, Mauldy Dept. of Computer and Informatics Engineering

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models*

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Konstantina-Helen Tsarapatsani Antonis Sakellarios Vasilis D Tsakanikas Henrik Rudolf Hans J. Trampisch Vasileios C. Pezoulas George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas (FORTH) Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina GR 45110 Ioannina Greece University Medical Center Rostock Institute for Biostatistics and Informatics in Medicine and Ageing Research Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis’s results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided.

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