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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Guo, Haodong Chen, Hua Liu, Wei Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong

ISBN: (纸本)9798331515669

Recently, third-order cumulants (TOCs) as a non-conventional odd-order statistics, has been introduced to direction-of-arrival (DOA) estimation with the third-order nested array (TONA), which significantly increases the number of uniform degrees-of-freedom (uDOFs) and can even be comparable to the fourth-order cumulant approach. In this paper, through analysis of the second-order sum-difference co-array, a third-order sparse linear array (SLA) design scheme suitable for TOCs is proposed, which can extend the commonly used second-order SLAs to the third-order. By selecting an appropriate generator array, the derived third-order SLA has far more uDOFs than the existing TONA, greatly improving its DOA estimation accuracy. © 2024 IEEE.

关键词： Direction of arrival

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Deep Forest-Based Fall Detection in Internet of Medical Things Environment

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computer Systems Science & engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

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Automatic recognition and classification of tumor cells in medical images based on convolutional neural networks

Journal of Biotech Research

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Journal of Biotech Research 2024年 19卷 258-268页

作者： Tan, Yanli Zhao, Yongqiang Wang, Jin Li, Guoqin Department of Electronic Engineering Taiyuan Institute of Technology Shanxi Taiyuan China Faculty of Computer and Mathematical Sciences University Teknologi MARA Selangor Shah Alam Malaysia

Tumors are one of the main causes of global mortality, and early diagnosis and precise treatment affect the survival rate of patients. Traditional image analysis relies on the professional knowledge and experience of radiologists. Manually labeling and analyzing tumor cells is laborious and subjective, which may lead to variability and inconsistency in diagnostic results. This study proposed a convolutional model based on a single-stage object detector. To address the shortcomings of the single-stage object detector model, the receptive field was introduced to improve the depth of information extraction, and an attention mechanism was also introduced to enhance its focus. The model was used to recognize pathological tissue slice images and classify tumors. The results indicated that, when the dataset size reached around 200, the performance of each model reached its maximum and showed convergence. When the dataset size was 500, the accuracy values of the single-stage target detector model, the single-stage target detector model based on receptive field, and the proposed model were 0.89, 0.91, and 0.98 with root mean square errors of 0.15, 0.14, and 0.09, respectively. Among the three models, the recognition accuracies of the proposed model for breast cancer, lung cancer, brain cancer, kidney cancer, head and neck cancer, and liver cancer were 92.3%, 93.9%, 88.8%, 82.2%, 93.1%, and 83.2%, respectively. The average recognition time for tumors was within 3 seconds. The proposed model demonstrated outstanding performance across all types of tumors, thereby providing an effective solution for medical image recognition. © (2024), (Bio Tech System). All rights reserved.

关键词： Tumors

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SubmergeStyleGAN: Synthetic Underwater Data Generation with Style Transfer for Domain Adaptation

SubmergeStyleGAN: Synthetic Underwater Data Generation with ...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Fathy, Mohamed E. Mohamed, Samer A. Awad, Mohammed I. Munim, Hossam E. Abd El Ain Shams University Mechatronics Engineering Department Faculty of Engineering Egypt Ain Shams University Computer and Systems Engineering Department Faculty of Engineering Egypt University of Bath Department of Electronic and Electrical Engineering Faculty of Engineering and Design United Kingdom

ISBN: (纸本)9798350382204

Underwater computer vision applications are challenged by limited access to annotated underwater datasets. Additionally, convolutional neural networks (CNNs) trained on in-air datasets do not perform well underwater due to the high domain variance caused by the degradation impact of the water column. This paper proposes an air-to-water dataset generator to create visually plausible underwater scenes out of existing in-air datasets. SubmergeStyleGAN, a generative adversarial network (GAN) designed to model attenuation, backscattering, and absorption, utilizes depth maps to apply range-dependent attenuation style transfer. In this work, the generated attenuated images and their corresponding original pairs are used to train an underwater image enhancement CNN. Real underwater datasets were used to validate the proposed approach by assessing various image quality metrics, including UCIQE, UIQM and CCF, as well as disparity estimation accuracy before and after enhancement. SubmergeStyleGAN exhibits a faster and more robust training procedure compared to existing methods in the literature. © 2023 IEEE.

关键词： Image enhancement

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Optimizing Photovoltaic Power Distribution for EV Charging in EV Charging Station 11

Optimizing Photovoltaic Power Distribution for EV Charging i...

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11th International Conference on Power and Energy Systems engineering, CPESE 2024

作者： Le-The, Cuong Hoang, Nhat Dao-Quoc, Khanh Nguyen-Duc, Tuyen Takano, Hirotaka Fujita, Goro Truong-Thanh, Hang Hanoi Uni. of Sci. and Tech. School of EEE Hanoi Viet Nam Gifu University Faculty of Engineering Dept. of Electrical and Electronic and Computer Engineering Japan Faculty of Engineering Tokyo Japan

ISBN: (纸本)9798350377439

Photovoltaic (PV) powered Electric Vehicles Charging Station (EVCS) are currently being extensively deployed in power system networks. Nevertheless, effectively distributing PV power for the charging of many Electric Vehicles (EVs) remains difficult due to the varying State Of Charge (SOC) of each EV upon arrival at the EVCS and the plan to achieve a desired SOC when departing from the EVCS. This study proposes an Energy Management System (EMS) approach for dispersed PV units to charge EVs in a way that ensures the EVs reach their targeted SOC. This research will include the power flow between PV units, the utility grid, and EVs. Moreover, the detailed profit for the owner of EVCS will also be presented. © 2024 IEEE.

关键词： Charging stations

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An Efficient Attack Detection Framework in Software-Defined Networking using Intelligent Techniques 3

An Efficient Attack Detection Framework in Software-Defined ...

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3rd IEEE International Conference on electronic engineering, ICEEM 2023

作者： Hassan, Heba A. El-Din Hemdan, Ezz Shokair, Mona El-Samie, Fathi E. Abd El-Shafai, Walid Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Menouf32952 Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Communication Engineering Menouf32952 Egypt Prince Sultan University Security Engineering Laboratory Department of Computer Science Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350323511

Recently, Software-Defined Networking (SDN) architecture has offered great benefits due to the separation between the control and network elements such as routers and switches. Unfortunately, the enormous growth of attacks hinders the wide adoption of SDN. Intrusion Detection Systems (IDSs) are used as significant tools to detect and mitigate network attacks of anomalous nature. Several Deep Learning (DL) models, such as Convolutional Neural Networks (CNNs), have been utilized for building IDSs in cyber security in recent years, because they achieve the desired detection results. Nonetheless, little work has been introduced on IDSs in SDN systems. Hence, this paper presents an efficient framework based on intelligent techniques for predicting attacks in SDN systems. This work focuses on the early detection of abnormal attacks existing in an SDN environment. When malicious traffic is identified in an SDN topology, the Artificial Intelligence (AI) module employs Machine Learning (ML) or DL models to identify and stop the attack source. The architecture presented in this research allows for comparing several ML and DL classification techniques that can be used to identify different sorts of network attacks. The proposed framework is tested on the InSDN dataset using several learning models like Random Forest (RF), AdaBoost (AB), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT), and Logistic Regression (LR) classifiers. In addition, DL models such as Deep CNN and Long Short Term Memory (LSTM) are considered. The results demonstrate that the proposed Deep CNN model for multi-class attack data classification achieves the highest accuracy of 99.85% compared to RF, AB, KNN, NB, DT, LR, and LSTM classifiers with accuracy levels of 95%, 88 %, 97%, 93 %, 90 %, 98%, and 88.31 %, respectively. © 2023 IEEE.

关键词： Software defined networking

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Optimal Sizing of Autonomous Microgrid Using Osprey Optimization Algorithm 4

Optimal Sizing of Autonomous Microgrid Using Osprey Optimiza...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Sagor, Abidur Rahman Ahmad, Shameem Ahmed, Abir Hazari, Rifat Jahan, Effat Hossain, Chowdhury Akram Ogliari, Emanuele Mannan, Mohammad Abdul American International University-Bangladesh Faculty of Engineering Department of Electrical and Electronic Engineering Dhaka1229 Bangladesh American International University-Bangladesh Faculty of Engineering Department of Computer Engineering Dhaka1229 Bangladesh Politecnico di Milano Department of Energy Milano20156 Italy

ISBN: (纸本)9798331530938

This paper proposes an optimal size for an autonomous microgrid to satisfy the electrical load demand. The microgrid will comprise many different configurations, such as photovoltaic module, diesel generators, and energy storage technologies. By considering operational constraints to optimize the cost of energy (COE) and life cycle cost (LCC), the Osprey optimization algorithm (OOA) is applied. Furthermore, identify the most economically feasible technology that will ensure a continuous power supply, an economic study of three different energy storage technologies-namely, batteries, hydrogen storage systems, and supercapacitors-is also conducted. Additionally, provide a case study of Manpura island microgrid that validates the proposed approach. The results show that the OOA method in terms of optimal system performance and convergence speed, the loss of power supply probability is set at 0%, the LCC is 196915, the COE is 0.2959 /kWh, and the amount of GHG is 430 kgs/year. © 2025 IEEE.

关键词： Microgrids

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Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

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computers, Materials & Continua 2022年第1期70卷 1683-1697页

作者： Mohamed Esmail Karar Marwa Ahmed Shouman Claire Chalopin Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Computer Engineering and Networks College of Computing and Information TechnologyShaqra UniversityShaqra11961Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Innovation Center for Computer Assisted Surgery(ICCAS) Universit鋞LeipzigLeipzig04103Germany

The novel Coronavirus disease 2019(COVID-19)pandemic has begun in China and is still affecting thousands of patient livesworldwide *** X-ray and Computed Tomography are the gold standardmedical imaging modalities for diagnosing potentially infected COVID-19 cases,applying Ultrasound(US)imaging technique to accomplish this crucial diagnosing task has attracted many physicians *** this article,we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images,based on generative adversarial neural networks(GANs).The proposed image classifiers are a semi-supervised GAN and a modifiedGANwith auxiliary *** one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique,keeping its main function of defining the“realness”of tested *** tests have been successfully conducted on public dataset of US images acquired with a convex US *** study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 *** results of our proposed GAN models showed that high accuracy values above 91.0%were obtained under different sizes of limited training data,outperforming other deep learning-based methods,such as transfer learning models in the recent ***,the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

关键词： COVID-19 medical imaging machine learning applications adversarial neural networks ultrasound

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Optimization of EV Scheduling in a Microgrid Integrated with a Rooftop Solar System Using Forward Dynamic Programming 10

Optimization of EV Scheduling in a Microgrid Integrated with...

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10th International Conference on Power and Energy Systems engineering, CPESE 2023

作者： Cuong, Le The Nhat, Hoang Duc, Tuyen Nguyen Takano, Hirotaka Hanoi University of Science and Technology School of Electronic and Electrical Engineering Hanoi Viet Nam Gifu University Faculty of Engineering Dept. of Electrical Electronic and Computer Engineering Japan

ISBN: (纸本)9798350327625

Electric vehicles (EVs) have been a trend in terms of transportation for a decade because of the energy transition. However, as the number of EVs increases, various problems related to the power quality, such as voltage and load profile, arise. To reduce the negative impact of EV charging, several solutions are suggested, with the integration of renewable energy sources into the charging station combined with Vehicle-to-Grid technology. This paper presents an algorithm to minimize the charging cost of all EVs and flatten the load curve, considering the travel schedule of EV owners and voltage constraints in the power grid. © 2023 IEEE.

关键词： Dynamic programming

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MSIsensor-RNA:Microsatellite Instability Detection for Bulk and Single-cell Gene Expression Data

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Genomics, Proteomics & Bioinformatics 2024年第3期22卷 85-90页

作者： Peng Jia Xuanhao Yang Xiaofei Yang Tingjie Wang Yu Xu Kai Ye Department of Gynecology and Obstetrics Center for Mathematical MedicalThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China

Microsatellite instability(MSI)is an indispensable biomarker in cancer ***,MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method for MSI ***,the MSI detection method on expression data,especially single-cell expression data,is still lacking,limiting the scope of clinical application and prohibiting the investigation of MSI at a single-cell ***,we developed MSIsensor-RNA,an accurate,robust,adaptable,and standalone software to detect MSI status based on expression values of MSI-associated *** demonstrated the favorable performance and promise of MSIsensor-RNA in both bulk and single-cell gene expression data in multiplatform technologies including RNA sequencing(RNA-seq),microarray,and single-cell ***-RNA is a versatile,efficient,and robust method for MSI status detection from both bulk and single-cell gene expression data in clinical studies and ***-RNA is available at https://***/xjtu-omics/msisensor-rna.

关键词： Microsatellite instability Gene expression Single-cell RNA-seq RNA-seq Microarray

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