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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Victor S. Simonov Marat S. Khairetdinov dept. Computer Engineering Novosibirsk State Technical University Novosibirsk Russian Federation dept. Geophysical Informatics Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk Russian Federation

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The emergence of large-scale computing paradigms required the evolution of software development methods, moreover, the need to effectively transform traditionally sequential applications into parallelizable and scalable architectures. This transformation is key in harnessing the computing power of modern multicore and distributed systems. Python, with its vast ecosystem and simplicity, is becoming a powerful language that facilitates this transition. This article discusses the methodologies and development of a Python preprocessor tool aimed at converting sequential applications into forms suitable for large-scale computing environments. Through detailed analysis and implementation, we demonstrate the use of Spark functions and third-party libraries to automate this transition, making large-scale computing more accessible to a wider range of applications and developers. The tool includes a set of rules that identify different patterns of code input (for example, list-sequential looping) and transform consistent code. This article introduces a Python preprocessor designed to automate the transformation of sequential Python applications into ones that harness the power of large-scale computing environments.

关键词： Performance evaluation Codes Multicore processing Scalability Transforms computer architecture Libraries

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Deep Predictive User Path-Inclusive Service Mobility in B5G/6G Edge Clouds

Deep Predictive User Path-Inclusive Service Mobility in B5G/...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Lusungu J. Mwasinga Syed M. Raza Duc-Tai Le Moonseong Kim Min Young Chung Hyunseung Choo Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Dept. of Electrical and Engineering Sungkyunkwan University Suwon Korea College of Computing and Informatics Sungkyunkwan University Suwon Korea Dept. of IT Convergence Seoul Theological University Bucheon Korea

Multi-access Edge Computing (MEC)-enabled Beyond 5G (B5G) or 6G networks require service migration to maintain service continuity and acceptable quality of experience for high-mobility User Equipment (UE). This involves migration of containerized applications to between Edge Hosts via the backhaul network as the associated UE moves away from its current gNodeB. Although crucial for both UEs and mobile network operators (MNOs), frequent migration not only prolongs service disruption time (downtime) for UEs but also increases operational expenses for the MNO. To address this problem, our study proposes a Deep Predictive User Path-Inclusive Service Mobility (DUPSM) approach for efficient management of the end-to-end migration process including deciding when and where to migrate the service. It features a deep Actor-Critic agent that learns suitable decision policy to balance cost-benefit trade-off interms of migration cost and quality of experience, respectively. As user mobility largely influences migration decisions, the proposed method incorporates user mobility path forecasting through a Generative Adversarial Network (GAN) based prediction technique to enhance ability for selecting suitable Edge Host while considering resource variations. The effectiveness of the proposed method is assessed through simulation experiments, showcasing an average reduction in service downtime of 22% when compared to a baseline scheme.

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Real-time Monitoring Using AWS Cloud Platform For Agriculture Soil

Real-time Monitoring Using AWS Cloud Platform For Agricultur...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Danar Ikram Tirtakusuma Sritrusta Sukaridhoto Rizqi Putri Nourma Budiarti Dwi Mulyo Fitratama Alfarezi Bima Briliandhika Aminputra Oktafian Sultan Hakim Dept. of Creative Multimedia Technology Politeknik Elektronika Negeri Surabaya Indonesia Dept. of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Indonesia Universitas Nahdlatul Ulama Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

In the era of modern agriculture, real-time monitoring of soil conditions is crucial for increasing productivity and efficiency. This research proposes an agricultural soil monitoring system that utilizes the AWS Cloud platform to capture, analyze, and present real-time soil data. This system is designed to integrate various sensors that can measure humidity, pH, temperature and soil nutrients, all connected via the Internet of Things (IoT). The data collected by these sensors is sent to the AWS Cloud, where it is processed using cloud computing services such as AWS Lambda and Amazon EC2. The use of the AWS Cloud enables high scalability, reliability, and data security, and facilitates big data analysis using Amazon S3 and Amazon RDS. By using an intuitive dashboard, farmers can see soil analysis results in real-time and make informed decisions based on the data. Additionally, this system is equipped with machine learning algorithms that run on the AWS Cloud to predict soil needs and provide optimal fertilizer recommendations. This research shows how implementing AWS Cloud in agriculture can bring digital transformation to agriculture, enabling farmers to increase crop yields while reducing waste and operational costs.

关键词： Temperature sensors Temperature measurement Productivity Cloud computing Soil Real-time systems Agriculture Sensors Internet of Things Monitoring

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Image Classification of Decapterus Macarellus Using Ridge Regression 8

Image Classification of Decapterus Macarellus Using Ridge Re...

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8th International Conference on Education and Technology, ICET 2022

作者： Jerandu, Charmelia Yunizar Mondolang, Alicia Herlin Sianturi, Corazon Olivia Sianturi, Shine Crossifixio Ximenes, Jenita Felixia Gumelar, Agustinus Bimo Sinlae, Alfry Aristo Jansen Batarius, Patrisius Yuhana, Umi Laili Sooai, Adri Gabriel Universitas Katolik Widya Mandira Dept. of Computer Science Kupang Indonesia Universitas Negeri Malang Dept. of Mathematics Malang Indonesia Universitas Sanata Dharma Dept. of Mathematics Yogyakarta Indonesia Universitas Sanata Dharma Dept. of Computer Science Yogyakarta Indonesia Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Dept. of Informatics Engineering Surabaya Indonesia

ISBN: (数字)9781665492140

ISBN: (纸本)9781665492140

Pelagic fish such as mackerel are a source of protein in Indonesia. However, there is no decapterus macarellus as an open dataset for image processing using various classification algorithms. Where its use includes the sensor-assisted sorting process in checking fresh fish and rotten fish. For this reason, this study aims to provide a classification model for pelagic fish and their primary datasets which is available for free on the IEEE data port. Artificial intelligence is used in the process of guided classification with the help of ground truth for the preparation of fish classes. The dataset used is a primary dataset consisting of fish images, arranged in two classes, namely 71 fresh fish and 96 rotten fish. The methods used are k-NN classifiers, naive bayes and ridge regression. Experiments were drawn up to classify rotten fish and fresh fish. The preprocessing was assisted by InceptionV3 as a feature extraction method. Furthermore, the image data is trained in a ratio of 60:40 for training and testing data. Validation was performed using 2-fold cross validation with the results obtained being 99.4%, 94% and 100% accuracy using the classifiers namely k-NN, naive bayes and ridge regression, respectively. © 2022 IEEE.

关键词： Image classification

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Calorie Burn Estimator on Stationary Bike using Human Body Pose Detector

Calorie Burn Estimator on Stationary Bike using Human Body P...

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Iwap Saputra Batan Eko Mulyanto Yuniarno Mauridhi Hery Purnomo Ahmad Ramadhani Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember University Center of Excellence on Artificial Intelligence for Healthcare and Society (UCE AIHeS) Surabaya Indonesia

During the pandemic, we must maintain our body’s immunity at best. Outdoor activities have been closed; therefore, there are limitations to activities that can be done. A solution to staying healthy during the pandemic is indoor exercise. One type of indoor exercise is regularly riding stationary bikes. We maintain the body’s fitness and burn calories by riding stationary bikes. However, some stationary bikes could not show accurate calories burned; they calculated it only based on speed, resistance, and duration, so people with different weights seem to burn the same amount of calories. Therefore, this paper proposed an additional parameter essential in calorie burning: body weight. We used the human body pose estimator to detect the activity of riding the stationary bike and calculated it by adding the person’s weight. The result shows a difference between the estimated calorie burned and the data directly from the stationary bike. The difference is between 0.0225 to 2.8775. The heavier the person, the more differences in the calories burned. The proposed method helped prove that a person’s weight affects the calories burned while doing an activity, especially riding a stationary bike.

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Context-aware Container Orchestration in Serverless Edge Computing

Context-aware Container Orchestration in Serverless Edge Com...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Peiyuan Guan Chen Chen Ziru Chen Lin X. Cai Xing Hao Amir Taherkordi Dept. of Informatics University of Oslo Oslo Norway Dept. of Computer Science and Technology University of Cambridge Cambridge UK Dept. of Electrical and Computer Engineering Illinois Institute of Technology Chicago USA School of Information Science and Technology Northwest Univesity Xi’an China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art.

关键词： Wireless communication Simulation Serverless computing Bandwidth Quality of service Containers Heterogeneous networks Delays Global communication Edge computing

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A Prediction and Weak Coevolution-Based Dynamic Constrained Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Gong, Dunwei Rong, Miao Hu, Na Wang, Yan Pedrycz, Witold Yang, Shengxiang College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao Shandong China School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Electrical and Computer Engineering Dept University of Alberta Edmonton Canada School of Computer Science and Informatics De Montfort University Leicester U.K

Dynamic multi-objective evolutionary algorithms (DMOEAs) have gained great popularity in dealing with dynamic multi-objective optimization problems (DMOPs). However, existing studies have difficulties in tackling DMOPs subject to (dynamic) constraints. In this paper, we propose a prediction and weak coevolutionary multi-objective optimization algorithm (PWDCMO) to handle dynamic constrained multi-objective optimization problems (DCMOPs), where a prediction strategy is employed to forecast potential optimal regions under the new environment, with a weak coevolutionary constrained multi-objective optimization (CCMO) as the optimizer aiming at balancing exploration and convergence. The proposed method is compared with four popular dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) on six test instances from two various test suites with their convergence and the overall performance being discussed. Furthermore, the performance of the proposed prediction strategy is also investigated to observe its impact on the final results. Additionally, the PWDCMO is employed in the optimization of an integrated coal mine energy system (ICMES) to validate the proficiency in addressing real world problems. Experimental results demonstrate the superiority of PWDCMO. IEEE

关键词： Heuristic algorithms

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Normal Vector Direction-based 3D LiDAR Point Cloud Planar Surface Removal for Object Cluster Minimization in Human Activity Monitoring System

Normal Vector Direction-based 3D LiDAR Point Cloud Planar Su...

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IEEE Instrumentation and Measurement Technology Conference

作者： Nova Eka Budiyanta Eko Mulyanto Yuniarno Tsuyoshi Usagawa Mauridhi Hery Purnomo Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Science and Electrical Engineering Graduate School of Science and Technology Kumamoto University Kumamoto Japan Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember University Center of Excellence on Artificial Intelligence for Healthcare and Society (UCE AIHeS) Surabaya Indonesia

The use of 3-Dimensional Light Detection and Ranging (3D LiDAR) point cloud as the alternative data to reduce privacy exposure in monitoring systems has been carried out in several studies. Unfortunately, various challenges in using point clouds intersect with the amount of data and computational costs. Several studies attempted to optimize the point cloud processing approach by segmenting the ground plane to get the object clusters separated. However, many unnecessary points can still burden the computation process. Since the ground plane mainly represents the horizontal planar plane on the x, y axis, this study tried to reduce the points on the vertical planar plane on the x, z and y, z axes with the x, y horizontal planar plane as well based on the surface normal vector direction of each point. The proposed approach has successfully reduced the raw point cloud by 79.29% removing the point cloud that indicates the planar surface of the three axes while maintaining the essential object of the monitoring system on the KITTI raw dataset. Therefore, the object cluster can be minimized, supporting the computational costs for further research in human activity monitoring systems.

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Lidar-based Human Detection for a Mobile Robot in Social Environment using Deep Learning

Lidar-based Human Detection for a Mobile Robot in Social Env...

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International Electronics Symposium (IES)

作者： Alif Wicaksana Ramadhan Bima Sena Bayu Dewantara Setiawardhana Fursan Akzada Departmen of Electrical Engineering Politeknik Elektronika Negeri Surabaya (PENS) Surabaya Indonesia Dept. of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya (PENS) Surabaya Indonesia

Nowadays, robotics implementation spans industries such as manufacturing, healthcare, and logistics, integrating AI-driven capabilities to enhance efficiency and collaboration in diverse operational environments. The ability to respond and interact while considering human presence is crucial for a mobile robot to operate effectively and be accepted in a human social environment. It is necessary to make a robot that can detect the presence of humans around it to make it capable of navigating while considering the social environment. Meanwhile, considering that the lidar sensor is almost always available in many robot navigation cases, it becomes an accessible tool for detecting the presence of humans in its surroundings. We brought the one-dimensional lidar data into two-dimensional image data, then involved some object selection based on weak characteristics, such as the acceptable range radius of the object and the acceptable distance between two legs. The result of the process is a group of human candidates. We use a deep learning model with a pretrained mobilenetv3 model as the feature extractor to ensure whether the candidates are humans and a fully connected layer of neural networks as the classifier. We defined several scenarios in an indoor environment to test the performance of the proposed algorithm. The experiment results show the system’s performance with a precision score of 0.902, a recall score of 0.782, and an F1 score of 0.837.

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Multi-Objective Design Optimization of Piezoelectric Energy Harvesting System for Unmanned Aerial Vehicles 6

Multi-Objective Design Optimization of Piezoelectric Energy ...

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

作者： Foutsitzi, Georgia Gogos, Christos Magklaras, Aris Antoniadis, Nikolaos Dept. of Informatics and Telecommunications University of Ioannina Arta47100 Greece Dept. of Electrical and Computer Engineering University of Patras Greece26500 Greece

ISBN: (纸本)9781665427425

In this work, the optimal design of an unmanned aerial vehicle (UAV) wing spar by multi-objective optimization genetic algorithms is studied. An electromechanical finite element model (FEM) for piezolaminated bimorph cantilever structure with embedded piezoceramics is used combined with multiobjective genetic algorithms. The FEM formulation is based on laminated plate theory combined with the first-order shear deformation theory (FSDT) for which each piezoelectric layer has one additional electrical degree of freedom. Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Generalized Differential Evolution 3 (GDE3) algorithm have been used to optimize the geometric and electric circuit parameters of a UAV generator for maximum power output and minimum mass added by the embedded piezoceramics. It is shown that the proposed algorithms are effective in developing optimal Pareto front curves for maximum electrical power output of the generator spar and minimum mass add by the embedded piezoceramics. Comparison of the generated solutions on the Pareto Front for UAV wing spar design shows that all three algorithms achieve solutions that closely resemble a constructed reference Pareto optimal front. © 2021 IEEE

关键词： Multiobjective optimization

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