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15th International Conference on Advanced Technologies for communications, ATC 2022

作者： Kojima, Toshiharu Yoshinaka, Yuto Graduate School of Informatics and Engineering The University of Electro-Communications Department of Computer and Network Engineering 1-5-1 Chofugaoka Tokyo182-8585 Japan

ISBN: (数字)9781665451888

ISBN: (纸本)9781665451888

Modified Walsh-Hadamard code division multiplexing (MWHCDM) has an anti-blockage capability. It is thus suitable for helicopter satellite communications where the transmission channel is described as the periodic blockage channel. This paper addresses the computational complexity reduction of the initial acquisition for MWHCDM. The conventional initial acquisition scheme employs the sliding Walsh-Hadamard transform (WHT). A narrowing function based on the redundancy of MWHCDM signals is proposed to reduce the sliding WHT. computer simulation shows that the proposed narrowing function reduces the sliding WHT by about 70% without acquisition performance degradation. © 2022 IEEE.

关键词： Code division multiple access

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Influence Operations in Social Networks

arXiv

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arXiv 2025年

作者： Pastor-Galindo, Javier Nespoli, Pantaleone Ruipérez-Valiente, José A. Camacho, David Computer Systems Engineering Department Universidad Politecnica de Madrid Madrid Spain Department of Information and Communications Engineering University of Murcia Murcia Spain

An important part of online activities are intended to control the public opinion and behavior, being considered currently a global threat. This article identifies and conceptualizes seven online strategies employed in social media influence operations. These procedures are quantified through the analysis of 80 incidents of foreign information manipulation and interference (FIMI), estimating their real-world usage and combination. Finally, we suggest future directions for research on influence operations. © 2025, CC BY.

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Design and Development of a Microcontroller Based Solar PV Cooker 4

Design and Development of a Microcontroller Based Solar PV C...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Morake, Anita Samikannu, Ravi Akila, L. Karpagam, R. Rohini, R. Yahya, Abid Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Ece Chennai India Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Easwari Engineering College Department of Electrical and Electronics Engineering Ramapuram India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798331529635

This study proposes an innovative approach to improve solar cooking systems by combining microcontroller technology and deep-learning techniques. The Solar-PV Microcontroller-based Cooker concept aims to provide a sustainable and eco-friendly cooking method by harnessing the sun's power. This innovative approach addresses global energy conservation and access to clean cooking options, particularly in areas where conventional methods contribute to deforestation and indoor air pollution. The system includes inputs for cooking duration and temperature monitoring, enhancing solar energy efficiency and user experience. The study investigates how deep learning algorithms can be incorporated into the microcontroller-based solar cooker system to enhance functionality and versatility. Cooking settings can be automatically adjusted to maximize energy consumption and cooking results by analyzing real-time data from sensors and human inputs. The hybrid approach combines the best features of deep learning with microcontroller technology, demonstrating the significance of renewable energy in a cleaner, more efficient future. The Solar-Pv cooker demonstrates the possibilities of fusing culinary technology with computer algorithms, showcasing the potential of renewable energy in a cleaner, more efficient future. © 2024 IEEE.

关键词： Indoor air pollution

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Driver Drowsiness Detection Using Machine Learning Algorithm 8

Driver Drowsiness Detection Using Machine Learning Algorithm

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8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Prasath, N. Sreemathy, J. Vigneshwaran, P. Srm Institute of Science and Technology Department of Networking and Communications Tamilnadu Chennai India Sri Eshwar College of Engineering Department of Computer Science Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9781665408165

It is just a short time until self-driving vehicles become omnipresent;be that as it may, human driving management will stay a need for quite a long time. Driver Drowsiness is one of the significant reasons of roadways accidents these days. Hence fatigue and drowsiness detection play a major role in preventing the road accidents. Every year, because of this there is an increase in the number of deaths and injuries globally. Recently, in this decade, many images processing-based approaches were created and used to detect driver's drowsiness status. To minimize the number of accidents, a method is proposed in this paper. The algorithm focuses on the eye closure and yawning ratios. The driver is alarmed, if he/she is feeling sleepy. © 2022 IEEE.

关键词： Aspect ratio

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Community-Collaborative Solar Dryer Prototype: Development, Deployment, and Testing for Sustainable Food Preservation

Community-Collaborative Solar Dryer Prototype: Development, ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Lugnasin, Joseph Vincent B. Peña, Robert Alfie S. Dumlao, Samuel Matthew G. MacAbebe, Erees Queen B. School of Science and Engineering Ateneo de Manila University Ateneo Energy Research Laboratory Dept. of Electronics Computer and Communications Engineering Quezon City Philippines Graduate School of Energy Science Kyoto University Department of Socio-Environmental Energy Science Kyoto Japan

ISBN: (纸本)9798350350821

In the Philippines, the prevalent method for drying agricultural and aquatic products like rice, corn, and fish relies on the traditional sun-drying technique where the produce is laid out under the sun, in an open area, and exposed to environmental factors like dust, dirt, and insects. This practice persists primarily because no other alternatives can guarantee a cost-effective yield when drying agricultural products. To address this, a multilevel solar dryer with solar-powered monitoring for households was developed as a sustainable option. Inputs from farming and fisherfolk communities were gathered through interviews and consultations, which served as the basis in designing the solar dryer. A prototype was built and then deployed in a fisherfolk household in Bani, Zambales. In this paper, three species of fish, locally known as bisugo, mataan, and danggit, were dried using the solar dryer. In parallel, traditional sun drying was conducted to compare the drying time of the two methods. Data showed that the multilevel solar dryer was effective in trapping the heat inside its drying chamber. Moreover, the water activity of the dried products was obtained to determine their susceptibility to spoilage. The measured values were below the maximum threshold set by the Philippine national standard for dried fish, which indicates that the dried products can be sold commercially. Lastly, the minimum viable product of the solar dryer developed in this study costs Php 20,000.00 (USD 350.00), making this an affordable alternative. © 2024 IEEE.

关键词： Solar dryers

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Prototype Generation with the sUpervised Classifier System on kNN Matching 13

Prototype Generation with the sUpervised Classifier System o...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Yatsu, Naoya Shiraishi, Hiroki Sato, Hiroyuki Takadama, Keiki The University of Electro-Communications Department of Informatics Tokyo182-8585 Japan Yokohama National University Department of Electrical Engineering and Computer Science Yokohama240-8501 Japan University of Tokyo Interdisciplinary Information Science Research Division Chiba277-0882 Japan

ISBN: (纸本)9798350308365

This paper focuses on 'prototypes' as representative samples that can serve as a summary of the original dataset and proposes a novel Learning Classifier System (LCS) to generate a small number of prototypes that can achieve high classification accuracy in class classification. Concretely kNNUCS-PG is proposed to improve the sUpervised Classifier System (UCS) as one of LCSs by changing from the interval (rule) representation to the point representation to represent a prototype by employing k-nearest neighbors (kNN) to match the neighborhood prototypes. For a high classification accuracy with data reduction, selecting and generating prototypes that can maintain decision boundaries is necessary. Since UCS has generalization mechanisms, including rule merging, unnecessary rule deletion, and new rule discovery using niche Genetic Algorithm (GA), such techniques are highly useful for evolving indispensable prototypes as generalized prototypes. This paper compared conventional prototype-based methods with kNNUCS-PG in experiments using three benchmark problems and eight table datasets. kNNU CS-PG can maintain high classification accuracy even for complex problems with much overlap in the datasets. The results show that kNNUCS-PG is a robust PG that can maintain high classification accuracy while reducing data for various complex problems. © 2024 IEEE.

关键词： Self-supervised learning

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Efficient Deep-Learning-Based Autoencoder Denoising Approach for Medical Image Diagnosis

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computers, Materials & Continua 2022年第3期70卷 6107-6125页

作者： Walid El-Shafai Samy Abd El-Nabi El-Sayed MEl-Rabaie Anas M.Ali Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Effective medical diagnosis is dramatically expensive,especially in third-world *** of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant ***,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific ***,experimentation and utilization of different models of transfer learning do not achieve satisfactory *** this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related *** model is based on classification,denoising autoencoder,and transfer ***,pre-processing is employed to prepare the medical *** depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis ***,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect *** proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray ***,a comparative study is introduced for the classification performance with and without the denoising *** proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and *** demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN *** achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the exis

关键词： Medical images CADTra AD CT and X-ray images autoencoder

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Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases 2

Machine Learning Models for Brain Signal Classification: A F...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ali, Ali M Nashwan, Shadi Al-Qerem, Ahmad Almomani, Ammar Sakhnini, Mahmoud Al Aldweesh, Amjad AlAhliyya Amman University Communications And Computer Engineering Department Faculty Of Engineering Amman19328 Jordan Cybersecurity Department Faculty Of Information Technology Amman11831 Jordan Zarqa University Computer Science Department Faculty Of Information Technology Zarqa13110 Jordan School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan Shaqra University College Of Computing And It Shaqra Saudi Arabia

ISBN: (纸本)9798350394962

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. This study presents a machine learning framework for classifying normal and epileptic EEG recordings. The approach involves feature extraction using discrete wavelet transform (DWT) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. Specifically, DWT decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. Supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. This epilepsy EEG classification framework demonstrates the potential for AI to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Statistical and Machine Learning Approaches to Predict the Next Purchase Date: A Review 4

Statistical and Machine Learning Approaches to Predict the N...

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4th International Conference on Applied Automation and Industrial Diagnostics, ICAAID 2022

作者： Sharma, Akul Randhawa, Princy Alharbi, Hadeel Fahad Manipal University Jaipur Department of Electronics and Communications Rajasthan Jaipur India Manipal University Jaipur Department of Mechatronics Engineering Rajasthan Jaipur India University of ha'Il College of Computer Science and Engineering Department of Computer Science Hail1234 Saudi Arabia

ISBN: (纸本)9781728189390

In the era of the digital revolution, customer expectations have drastically changed. To meet and exceed customer expectations, companies are transforming their marketing strategies by anticipating the date when a particular customer is likely to purchase a specific product. By doing so, companies can predict a customer's next order and can target the particular customer at the right time. This would ensure that the specific product which is likely to be bought by the customer doesn't go out of stock on the predicted date of purchase. The customer also benefits by experiencing a personalized touch by seeing fewer and relevant ads. However, predicting the next date of purchase still remains a challenge as the cost of wrong predictions would directly lead to unfavourable outcomes for the company. There has been a significant number of researches with an aim of reducing the difference between the actual date of purchase and the predicted date of purchase. The researchers experimented with numerous statistical techniques to estimate when a particular customer would re-purchase a specific product. However, statistical models were not flexible enough to adapt to dynamic consumer buying behaviour. Researchers wanted to build a robust model and thus, experimented with multiple machine learning algorithms such as Linear Regression, XGBoost model, Neural Network, and Recurrent Neural Network. Among these algorithms, neural network model outperformed others. This paper attempts to review each of the statistical and machine learning models developed by researchers for predicting the next purchase date (NPD) of a customer-product pair. © 2022 IEEE.

关键词： Neural network models

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Affinity-Based Power Flow Optimization in Reconfigurable Picogrid

Affinity-Based Power Flow Optimization in Reconfigurable Pic...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Kokubu, Ryota Oeda, Kaito Kawakita, Yuusuke Yokogawa, Shinji Tobe, Yoshito Ichikawa, Haruhisa Graduate School of Science and Engineering Aoyama Gakuin University Sagamihara Japan Kanagawa Institute of Technology Department of Information and Computer Sciences Atsugi Japan Info-Powered Energy System Research Center The University of Electro-Communications Chofu Japan

ISBN: (纸本)9798350312072

In recent years, there has been an increasing demand to adopt renewable energy resources to power grids for environmental issues and energy concerns. A virtual grid system, which is designed to utilize decentralized resources, is proposed as an approach. This system enables users to construct their power grids with virtual grid hubs (VG Hub) which interconnect via commercially available cables according to USB Power Delivery (USB PD). A prototype of VG Hub is capable of synthesizing and distributing DC power for connected devices, and the power flow control is managed by a microcontroller. Our previous study proposed a method called Minimum-Hop Power Path Routing (MHPPR). This method determines the power flow that uses the closest source in candidates which can fulfill the demand when a load connects to the network to minimize power loss due to transmission. However, this could result in unproductive power flow, such as a flow that consumes power stored in batteries prior to surplus power from generators. Therefore, we assumed that there are factors to value a source for a load other than demand and supply capacity, and inferred that the factor derives from the type of device. We defined this factor as the affinity between devices. The objective of this study is to solve the power path optimization problem while taking affinity into account. In this study, we discussed device classification and affinity between types and defined affinity coefficients. We then extended MHPPR by incorporating this affinity coefficient. The results have shown that the proposed method selected the sources following the priority defined by the affinity. © 2024 IEEE.

关键词： Renewable energy

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