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An Optimized Design of Antenna Arrays for the Smart Antenna systems

computers, Materials & Continua 2021年第11期69卷 1979-1994页

作者： Fahd N.Al-Wesabi Murad A.A.Almekhlafi Huda G.Iskandar Adnan Zain Saleh Alzahrani Mohammed Alamgeer Nadhem Nemri Sami Dhabi Mohammad Medani Ali.M.Al-Sharafi Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia Department of Electronics and Communication Engineering Faculty of EngineeringUniversity of AdenYemen Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA

In recent years,there has been an increasing demand to improve cellular communication services in several *** aspect that received the most attention is improving the quality of coverage through using smart antennas which consist of array *** paper investigates the main characteristics and design of the three types of array antennas of the base station for better coverage through simulation(MATLAB)which provides field and strength patterns measured in polar and rectangular coordinates for a variety of conditions including broadsides,ordinary End-fire,and increasing directivity End-fire which is typically used in smart *** method of analysis was applied to twenty experiments of process design to each antenna type separately,so sixty results were obtained from the radiation pattern indicating the parameters for each radiation ***,nineteen design experiments were described in this *** is hoped that the results obtained from this study will help engineers solve coverage problems as well as improve the quality of cellular communication networks.

关键词： Smart antenna antenna arrays patterns construction coverage

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An Examination of Cybersecurity Threats and Authentication systems 3

An Examination of Cybersecurity Threats and Authentication S...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Shakeel, Mohammed Lakshmana Rao, Chintala Shyam Prasad, T. Alam, Tanweer Rawat, Navneet Kavitha, R. Invertis University Faculty of Computer Applications Bareilly India Deemed to Be University School of Law Gitam Visakhapatnam India Anurag University Department of Cse Telangana Hyderabad India Islamic University of Madinah Faculty of Computer and Information Systems Department of Computer Science Madinah Saudi Arabia Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun248002 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode Sathyamangalam638401 India

ISBN: (纸本)9798350399264

Each and every nation in the globe has very crucial infrastructures that offer crucial services like internet activity, energy, banking and finance, crucial public services, transportation, and water management. For vital infrastructure based on sectors, each nation has a unique strategy. As IoT-based solutions proliferate, these crucial infrastructures now have network and Internet connectivity. Consequently, these crucial systems that are a part of information networks are equally vulnerable to online attacks. It is crucial to recognise the potential cyber-attack types, devise defence strategies, and implement various preventative measures. In the present day, it is critical to protect potential cyberattacks on these vital infrastructures. The most common attacks on crucial infrastructures are looked at in this study, especially those that have recently happened. Security precautions are also discussed with a view to reducing or preventing IP-based intrusions. The Internet of Things is the next step in machine-to-machine connectivity. IoT has made it possible for anything to be online nowadays. IoT also refers to the networking of uniquely identified, pervasive computing devices that have the ability to transmit data over a network without necessarily requiring or machine-to-human interaction. © 2023 IEEE.

关键词： Cybersecurity

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Light Cone Cancellation for Variational Quantum Eigensolver Ansatz

arXiv

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arXiv 2024年

作者： Yan, Xinjian Lee, Xinwei Xie, Ningyi Saito, Yoshiyuki Kurosawa, Leo Asai, Nobuyoshi Cai, Dongsheng Lau, Hoong Chuin Graduate School of Science and Technology University of Tsukuba Japan School of Computing and Information Systems Singapore Management University Singapore Graduate School of Computer Science and Engineering University of Aizu Japan School of Computer Science and Engineering University of Aizu Japan Faculty of Engineering Information and Systems University of Tsukuba Japan

Variational Quantum Algorithms (VQAs) represent a class of algorithms that utilize a hybrid approach, combining classical and quantum computing techniques. In this approach, classical computers serve as optimizers that update circuit parameters to find approximate solutions to complex problems. In this study, we apply a method known as Light Cone Cancellation (LCC) to optimize variational circuits, effectively reducing the required number of qubits and gates for circuit simulation. We then evaluate the performance of LCC one of the VQAs—the Variational Quantum Eigensolver (VQE)—to address the Max-Cut problem. Compared with the Quantum Approximate Optimization Algorithm (QAOA), VQE offers greater degrees of freedom and promising results at lower circuit depths. By applying LCC to VQE, we can shift the complexity of circuit simulation from the number of qubits to the number of edges in the graph, i.e., from exponential time to polynomial time. This enables us to solve large problems up to 100 vertices, without actually simulating the entire circuit. From our simulation in a 7-qubit and a 27-qubit fake backends, we show that LCC yields higher approximation ratios than those cases without LCC, implying that the effect of noise is reduced when LCC is applied. © 2024, CC BY.

关键词： Qubits

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A Quantum Q-Learning Model for the Capacitated Vehicle Routing Problem

A Quantum Q-Learning Model for the Capacitated Vehicle Routi...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Jiahua Xu Ningyi Xie Xinwei Lee Yoshiyuki Saito Nobuyoshi Asai Dongsheng Cai Graduate School of Science and Technology University of Tsukuba Japan School of Computing and Information Systems Singapore Management University Singapore Graduate School of Computer Science and Engineering University of Aizu Japan School of Computer Science and Engineering University of Aizu Japan Faculty of Engineering Information and Systems University of Tsukuba Japan

ISBN: (数字)9798331530471

ISBN: (纸本)9798331530488

The research on Variational Quantum Algorithms (VQAs) has gained significant momentum because of their promising practicality in the noisy intermediate-scale quantum (NISQ) era. Recent studies highlight the potential of VQAs in reinforcement learning (RL) by replacing classical components with parameterized quantum circuits (PQCs). Building on this, we propose a quantum Q-learning model for the Capacitated Vehicle Routing Problem (CVRP), combining a PQC with an RL training process. We present a PQC for embedding both static and dynamic states in RL environments. The proposed PQC approximates the Q-value function by using the expectation values of different observables. Experimental results demonstrate that the proposed PQC outperforms the existing PQC for the CVRP with fewer quantum resources. Furthermore, our method shows competitive performance compared to the classical counterpart.

关键词： Training Q-learning Quantum algorithm Multicore processing Vehicle routing Buildings Noise measurement Integrated circuit modeling Vehicle dynamics Quantum circuit

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Sentiment Analysis of Tweets Using Emojis and Texts 21

Sentiment Analysis of Tweets Using Emojis and Texts

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4th International Conference on Information science and systems, ICISS 2021

作者： Saleh Alzubaidi, Mohammed Bourennani, Farid Department of Computer Science and Artificial Intelligence Faculty of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Information Systems and Technology Faculty of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9781450389136

Nowadays, social media generate massive volumes of data containing valuable information to several applications such as marketing, business, and politics which are interested to analyze opinions and sentiments of individuals. A lot of efforts have been directed towards sentiment analysis (SA) based on texts;however, limited efforts have been directed towards the study and analysis of other DTs such as emojis and other multimedia. The processing of other data types (DTs) not only can generate complementary information, but we argue that the combined processing of heterogeneous DTs (HDTs), called heterogeneous data mining (HDM), should lead to more accurate results. In this work, we propose the HDM of tweets using both texts and emojis for bi-sense SA;the SA results using HDM were more accurate than the exclusive use of either texts or emojis, and interestingly the use of emojis achieved a higher accuracy than texts. © 2021 ACM.

关键词： Sentiment analysis

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Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

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computers, Materials & Continua 2023年第7期76卷 975-992页

作者： Hadeel Alsolai Mohammed Rizwanullah Mashael Maashi Mahmoud Othman Amani A.Alneil Amgad Atta Abdelmageed Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityP.O.box 103786Riyadh11543Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil *** comes a demand for computer-related soil classification methods to support *** study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)*** presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision *** the presented GBODL-ASC technique,three major processes are *** the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature *** soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)*** design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,*** demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five *** simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.

关键词： Soil classification earth sciences machine learning parameter optimization metaheuristics

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IP SafeGuard-An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Department of Software Engineering Dhaka Daffodil Smart City1216 Bangladesh Liverpool John Moores University School of Computing and Data Sciences Oryx Universal College Cybersecurity Department Doha Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems As-Salt19385 Jordan Department of Computer Science and Engineering Dhaka1216 Bangladesh Independent Researcher As-Salt19385 Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

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A Multi-User Multi-Robot Collaboration through Augmented Reality

A Multi-User Multi-Robot Collaboration through Augmented Rea...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： João G. Martins Gabriel M. Costa Marcelo R. Petry Paulo Costa António Paulo Moreira CRIIS – Centre for Robotics in Industry and Intelligent Systems INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Current industrial environments have multiple robots working alongside humans, thus providing an operator the ability to perceive the robot’s workspace correctly and to anticipate its intentions and movements through the visualization of the robot’s digital twin is of utmost importance for safe and productive human-robot collaboration scenarios. Much has been studied regarding single human-single robot collaborative scenarios, but few address multi-user multi-robot scenarios. To this end, this paper presents a multi-robot multi-operator architecture, where the users’ awareness is enhanced through an augmented reality head-mounted display. A multi-robot, multi-user collaborative scenario is presented in a laboratory environment with two industrial robots. Besides being able to interact with both robots in the system, each user becomes more aware of the robot’s workspace and its pre-defined trajectories. Furthermore, it presents how fiducial markers can help to establish the relation between the different coordinate frames.

关键词： Visualization Service robots Robot kinematics Scalability Collaboration User interfaces Trajectory

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High-Quality Font Generation Based on StyleGAN2 and FSFont Font Generation Model

High-Quality Font Generation Based on StyleGAN2 and FSFont F...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Yuki Shimamura Michiharu Niimi Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka-shi Fukuoka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka-shi Fukuoka Japan

This paper proposes a high-quality font generation method based on StyleGAN2 and FSFont font generation model. In our previous work, we proposed a system that expresses emotions by using font changes and call it an EmoEditor (Emotion-Expressive Editor). This system uses a deep neural network based on zi2zi that is able to generate a desired rendered character by giving a fixed rendered character and a font label which distinguishes between fonts. By changing the font label, the system generates the different font characters which corresponds to several emotions. However, the system was not able to reconstruct the expected ones for our collected fonts that is needed to express emotions. Considering the rapid development of DNN technology, in this paper, we propose an extension of the StyleGAN2 model for font generation. Specifically, we enhance StyleGAN2 by incorporating an Encoder which is come from FSFont generation model and a class label input structure. By adopting a class label input, the proposed model is able to generate indicated font by users. Experimental results show that the proposed method can generate better fonts qualitatively and quantitatively than previous studies on font generation.

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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