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16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023

作者： Vuckovic, Dusan M. Panajotovic, Aleksandra S. Ciric, Vladimir M. Zivkovic, Marko Z. University of Nis Faculty of Electronic Engineering Nis18000 Serbia Coming Computer Engineering Belgrade Serbia

ISBN: (纸本)9798350347029

With the increased demand for additional bandwidth, when it comes to the Metropolitan Area Networks, there is also a growing need to keep the local traffic local. Preventing the traffic of local origin from "spilling"over the MAN borders, or maybe even country borders become one of the very important tasks. This is where local Internet Exchange Points can help. This paper illustrates possibilities local IXP implementation has to offer (city of Nis, Serbia in this case) and improvements that can come out of it. © 2023 IEEE.

关键词： Metropolitan area networks

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Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

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computers, Materials & Continua 2023年第11期77卷 2009-2030页

作者： Kalpesh Jadav Vishal Sorathiya Walid El-Shafai Torki Altameem Moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui Department of Electronics&Communication Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Computer Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonSK S7N5A9Canada

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking *** shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and *** of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is *** real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above *** paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle *** method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground *** to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

关键词： Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National Institute of Health vehicle tracking

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An Improved Convolutional Neural Network Model for DNA Classification

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computers, Materials & Continua 2022年第3期70卷 5907-5927页

作者： Naglaa.F.Soliman Samia M.Abd-Alhalem Walid El-Shafai Salah Eldin S.E.Abdulrahman N.Ismaiel El-Sayed M.El-Rabaie Abeer D.Algarni Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt

Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)*** any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature *** downsampling is an improved form of the existing pooling layer to obtain better classification *** two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for *** convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature ***,there are parameters which directly affect how a CNN model is *** this paper,some issues concerned with the training of CNNs have been *** CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of *** and assessment of the performance of CNNs are carried out on 16S rRNA bacterial *** results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.

关键词： DNA classification CNN downsampling hyperparameters DL 2D DT 2D RP

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Exponential Synchronization for Mobile Robotic Formation: A Hybrid Dynamic Event-Triggered Intermittent Control Method

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-10页

作者： Chang, Zhenyu Guo, Ying Zong, Guangdeng Qi, Wenhai Zhao, Xudong School of Control Science and Engineering Tiangong University Tianjin China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Engineering Qufu Normal University Rizhao China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China

To address the formation tracking issue of mobile robotic systems, this paper constructs a novel hybrid dynamic event-triggered intermittent control strategy, which can achieve the exponential synchronization of the systems. Considering the limited control resources, the intermittent control method is introduced into the distributed control strategy to save resources, and the existing intermittent control model is reconstructed to describe the system model better. A hybrid dynamic event-triggered mechanism is developed by combining the dynamic event-triggered method with the time sampling strategy, eliminating the Zeno phenomenon. The control time sequences in the developed intermittent control strategy are automatically selected by the hybrid dynamic event-triggered sequences, rather than artificially designed in advance, which reduces a certain degree of design complexity. The developed control strategy effectively saves control resources while alleviating the burden of network communication. Sufficient conditions for achieving exponential synchronization formation tracking are provided, and the exponential convergence of the formation error is demonstrated through the proposed lemma. Finally, a control task of multi-mobile robots formation is presented to verify the effectiveness of the theoretical analysis. IEEE

关键词： Synchronization

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Towards Holistic Crime Management Through Descriptive Analysis and Predictive Forecasting 6

Towards Holistic Crime Management Through Descriptive Analys...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Wijethunge, Tinaya Edirisinghe, Sampath Jeon, Mansik Kim, Jeehyun Wijesinghe, Ruchire Silva, Bhagya Faculty of Engineering Department of Computer Engineering Nugegoda Sri Lanka Daegu Korea Republic of Faculty of Engineering Department of Electrical and Electronic Engineering Malabe Sri Lanka Malabe Sri Lanka Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

Throughout the evolution of humankind, devising effective crime prevention strategies has been a concern in fostering safer communities. Although achieving a zero-crime rate is not possible yet, using data management methods can contribute to preventing and minimizing crimes. This article presents an integrated approach to proactive crime prevention. Data insights derived from analysis and visualization provide a comprehensive overview of historical trends and scenarios, aiding understanding of past and present situations. The forecasting of crime rates extends this comprehension to the future, spotlighting the severity of the potential threat to the safety of the community. Three distinct prediction models are presented to predict the occurrence of violent/non-violent crimes, the most probable crime type, and the most probable crime location in a spatial-temporal context, facilitating optimal resource allocation of police patrolling. Combining these three analytical components, an application is designed to present this holistic solution for effective crime prevention, with a 73% accuracy in predicting violent crimes. To achieve higher accuracy in predicting the most probable crime types and locations, it is essential to go beyond spatiotemporal context and augment data on other factors that affect the occurrence of crimes. © 2024 IEEE.

关键词： Prediction models

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DSP-PUF: A Software PUF Based on Digital Signal Processor for IoT Security 17

DSP-PUF: A Software PUF Based on Digital Signal Processor fo...

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17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Yuan, Tengfei Wang, Pengjun Zhang, Yuejun Ren, Mingze Hu, Shuang Ningbo University Faculty of Electrical Engineering and Computer Science Zhejiang315211 China Wenzhou University Electrical and Electronic Engineering Wenzhou325035 China

ISBN: (纸本)9798350361834

Physical unclonable function (PUF) utilizes the tiny deviations in the chip manufacturing to generate unique identity keys. Traditional PUFs are generally implemented on dedicated circuit structures, which may not be effective in resource-constrained Internet environments. To solve the above problems, this paper proposes a software PUF based on Digital Signal Processor (DSP-PUF). The DSP-PUF uses the timing violation of the internal registers of the chip to generate random response. Primarily, the mechanism of generating abnormal information between timing paths and registers in DSP chip is analyzed. Secondly, the overclocking clock is applied to make the DSP work abnormally, and the unique and unclonable random response is generated according to the dependence of the response on the path delay. Eventually, DSP-28335 is selected as the DSP-PUF carrier, and the DSP-PUF response extraction is completed by computer, oscilloscope, DSP and other equipment. The test results indicate that the 12000 bit responses generated by DSP-PUF has passed the NIST randomness test, and the worst case reliability is 88.01% in the temperature range of 20° C-80 ° C. Moreover, the proposed DSP-PUF does not use additional circuit structure, and does not need to change the existing circuit structure. It is suitable for privacy information encryption in resource constrained Internet of things (IoT). © 2024 IEEE.

关键词： Data privacy

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Dataset formation for sem-based images of commercial minerals using ml algorithms: case study for zircon in the Egyptian mountains

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Multimedia Tools and Applications 2024年 1-19页

作者： khalil, kirolos N. R. El-Fishawy, Nawal Ali, Mohamed Mohamed, Mokhtar A. A. Computer Science & Engineering Department Faculty of Electronic Engineering Menofia University Shibin el Kom Egypt Nuclear Materials Authority P.O.Box:530 El–Maadi Cairo Egypt El Sadat City Egypt

Mineral identification holds paramount importance in geological and mineralogical endeavors, encompassing exploration, mining, and mineral processing. This work underscores the time-consuming and equipment-dependent nature of conventional identification methods, advocating for the integration of artificial intelligence techniques, particularly machine learning and computer vision. Commercial minerals, including zircon, are identified as linchpins of various industries, particularly ceramics and dentistry. The work elaborates on the pivotal role of SEM imaging techniques in discerning economic minerals in granitic rocks and pegmatite, emphasizing their utility in environmental science and mineral exploration. A novel computational approach is introduced, offering automation of mineral grain recognition, thereby mitigating the laborious and resource-intensive aspect of the process. The subsequent discussion pertains to the creation of a specialized SEM image dataset focusing on Egyptian commercial minerals, commencing with zircon, a dataset with foreseeable extensions. The authors anticipate that this dataset will significantly contribute to mineralogical research, facilitating precise mineral identification through AI techniques and enriching insights into Egypt’s geological wealth. © The Author(s) 2024.

关键词： Scanning probe microscopy

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Loss Analysis Towards Developing a Digital Twin Model of a PMSG-Based Powertrain for Wind Turbine System

Loss Analysis Towards Developing a Digital Twin Model of a P...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Shawier, Abdullah Gadoue, Shady Mehran, Kamyar Queen Mary Univierty of Lonodn School of Electronic Engineering and Computer Science London United Kingdom Alexandria Univerity Faculty of Engineering Alexandria Egypt

ISBN: (纸本)9798350364446

Wind turbine energy plays a vital role in the transition towards a cleaner energy environment. The ever-growing demand on wind turbine systems (WTS) necessitates the exploration of new technologies that offer higher reliability and efficiency. Digital twin (DT) technology has emerged as a leading trend for WTS. In this regard, this work aims to carry out a comprehensive losses analysis for the powertrain of a PMSG-based WTS to develop an accurate DT model for efficiency maximization. Leveraging finite element analysis, this study analyzes various loss components across stator, rotor, magnet, and converter under both passive and active rectifier configurations. The influence of current waveform and operating conditions on these loss components and torque ripple is also examined. The findings will contribute to training the DT model and further enhance the efficiency and performance optimization of WTS. © 2024 IEEE.

关键词： Wind turbines

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A two-stage progressive deep segmentation network for tumor detection in breast ultrasound images

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Multimedia Tools and Applications 2024年 1-23页

作者： Zaidkilani, Nadeem Abdel-Nasser, Mohamed Garcia, Miguel Angel Puig, Domenec Department of Computer Engineering and Mathematics University Rovira i Virgili Tarragona43007 Spain Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electronic and Communications Technology Autonomous University of Madrid Madrid Spain

Segmenting tumorous regions in breast ultrasound images is a challenging problem due to several factors, including the relatively low contrast of the available images, the presence of speckle noise, and the considerable variations in breast mass sizes and shapes. Current methods are not precise enough and prone to misdetections. An efficient deep neural model is proposed for automatically segmenting tumorous regions in breast ultrasound images. The model is constituted by two consecutive encoder-decoder (autoencoder) networks. The first autoencoder extracts a preliminary binary mask from the given image. The second autoencoder refines that mask after concatenating it with the original image. The encoders within each autoencoder can be defined by applying any state-of-the-art network. In addition, cost-sensitive learning has been used in order to focalize training on the segmentation errors of the minority class (tumor). Semantic segmentation based on advanced deep learning methods is thus applied in order to enhance tumor segmentation in breast ultrasound images. The proposed model offers advanced capabilities for automated segmentation with the aim of helping physicians identify and diagnose tumors using state-of-the-art techniques. This model outperforms recent tumor segmentation methods in the experiments conducted on two public datasets of breast ultrasound images (UDIAT and BUSI). The largest improvement for both datasets was achieved by using CoAtNet as baseline model (Dice index equal to 84.49% and 78.94%, respectively). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Ultrasonic imaging

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Software design and FPGA implementation of optimized medical image fusion techniques

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Multimedia Tools and Applications 2025年 1-31页

作者： Elshazly, E.A. El-Shafai, Walid El-Hoseny, Heba El-Rabaie, El-Sayed M Zahran, O. Abdelwahab, Safey A. S. El-Halawany, M.M. Fikry, R.M. Elaraby, S.M. Faragallah, Osama S. Mohamed, Wael A. Mahmoud, Korany R. Abd El-Samie, Fathi E. Cairo Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Faculty of Computer Studies Arab Open University 11681 Riyadh Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University 11099 Taif21944 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Benha University Benha Egypt Electronics and Communications Engineering Department Faculty of Engineering Helwan University Helwan Egypt Giza Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Nowadays, medical image fusion plays a crucial role in enhancing the diagnosis accuracy and the clinical decision-making process in various healthcare applications. This research work presents a comprehensive study of the design and implementation of optimized medical image fusion techniques using a combination of software and Field-Programmable Gate Array (FPGA) technologies. The proposed medical image fusion strategy is based on the utilization of Discrete Wavelet Transform (DWT) and Modified Central Force Optimization (MCFO). The implementation of the proposed technique as well as the traditional medical image fusion techniques is considered using an appropriate software design and FPGA. The presented techniques aim to overcome the limitations of traditional fusion techniques by integrating advanced image processing algorithms, optimization algorithms, and parallel computing capabilities offered by FPGA platforms. The first step in the proposed framework is to match the histogram of one of the images with that of the other, so that both images will have the same dynamic range. After that, the DWT is used to decompose the images that should be fused together. Based on some constraints, the MCFO optimization algorithm is used to evaluate the optimum level of decomposition and the optimum parameters for the best fusion quality. Finally, to improve the obtained visual quality and reinforce the information in the fusion result, an additional contrast enhancement step using adaptive histogram equalization is applied to the fusion result. Comparative study between the proposed optimized DWT-based fusion framework, the traditional Principal Component Analysis (PCA), Additive Wavelet Transform (AWT), and DWT-based fusion techniques is presented. Various metrics of fusion quality are considered, including average gradient, standard deviation, local contrast, entropy, edge strength, Peak Signal-to-Noise Ratio (PSNR), Qab/f, and processing time. The proposed optimized DWT-ba

关键词： MATLAB

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