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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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Emotion Unleashed: Real-Time FER in Video via Advanced Deep Learning Models 1

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9th Future Technologies Conference, FTC 2024

作者： Thakkar, Bhaumikkumar Bhatt, Chintan Patel, Parth Patel, Mire Bruno, Alessandro Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University GJ Gandhinagar382007 India Department of Business Law Economics Consumer Behaviour ‘Carlo A. Ricciardi’ Faculty of Communication IULM University Milan20143 Italy

ISBN: (数字)9783031731105

ISBN: (纸本)9783031731099

While conversing, showing emotions through facial expressions comes naturally to human beings. Face expression detection is tackled by computer vision to serve psychology, human-computer interaction, and security applications. In this work, we present the software we have designed and implemented to read the emotions on people’s faces. The system allows users to discern emotions on people’s faces using artificial intelligence. It is a challenging task that has piqued the interest of scholars worldwide in facial expression analysis in recent years. Regarding some technical aspects of the proposed method, we rely on Deep learning methods, which have proved robust in various scenarios, including facial emotion recognition. This paper introduces a Deep Learning-based approach that relies on a Convolutional Neural Network (CNN) to infer knowledge from facial images and distinguish features. These features are then used to assign a label to an emotion. Deep Learning algorithms, such as DeepFace, NASNet Mobile, EfficientNet V2, and Inception V2, are investigated on FER tasks. By analyzing data from the FER-13 and Face databases, our model has learned to recognize emotions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Advancements in Sustainable Charging Infrastructure: Integrating Solar Energy and IoT for Smart E-Vehicle Charging Stations

Advancements in Sustainable Charging Infrastructure: Integra...

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2023 International Conference on Sustainable communication Networks and Application, ICSCNA 2023

作者： Sivasankar, C. Saravanan, G. Pradeepa, H. Arun, V. Ganesh, E.N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

An effective and environmentally friendly charging infrastructure is essential due to the rising popularity of Electric Vehicles (EVs). The proposed system offers an efficient approach to this problem by suggesting a solar charging station for EV incorporating the Internet of Things (IoT). In order to encourage the use of renewable energy and lessen the environmental impact, the system uses solar energy to power EV charging stations. IoT technologies improve the charging station's performance by allowing realtime monitoring and control, remote access and data analytics. Considerations for solar panel layout optimization and energy storage are central to this study's emphasis on the design and execution of a solar-powered EV charging station. In addition, the station can easily respond to changes in demand and grid circumstances thanks to dynamic load control made possible by IoT. This research proves the feasibility and efficiency of the proposed Solar with IoT Charging Station for Electric Vehicles via practical assessment and simulations. The results provide a scalable and long-term answer that promotes a cleaner and greener transportation environment by aiding the development of electric mobility and integrating renewable energy. © 2023 IEEE.

关键词： Charging Station Dynamic Load Management Electric Vehicle Internet of Things Solar Energy

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A Novel Phase Shifter for 5G Base Station Antenna 7

A Novel Phase Shifter for 5G Base Station Antenna

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7th World Conference on Computing and communication Technologies, WCCCT 2024

作者： Du, Lin Feng, Botao Ding, Xiao Gu, Wenzhe Sun, Sanshan Deng, Li Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts 999078 China Huizhou University School of Computer Science and Engineering Huizhou516007 China Sichuan Normal University College of Physics and Electronic Engineering Sichuan China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China

ISBN: (纸本)9798350372328

In this paper, a phase shifter for the base station antenna with low cost, ease of manufacturing and good performance on phase shift is presented. The phase shifter consists of a metal shell, a substrate, two dielectric plates, and two conductor lines. The phase shift is realized by moving the dielectric plates longitudinally, which cover the conductor lines. With the slots in dielectric materials, the slotted part of dielectric plates can function similarly to a quarter-wavelength impedance matching. Therefore, the function of this phase shifter is that the voltage standing wave ratio (VSWR) and power distribution ratio can be guaranteed when the dielectric plate is moved. The phase shifter exhibits a bandwidth of 1.7-2.1 GHz and the corresponding phase shift is 86.88° when the dielectric plate moves by 90 mm. Moreover, its good physical features including light weight, low cost, and easy fabrication make it well suitable for base station antenna applications. © 2024 IEEE.

关键词： Phase shifters

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Traffic Congestion Prediction Based on Multivariate Modelling and Neural Networks Regressions 14

Traffic Congestion Prediction Based on Multivariate Modellin...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Fahs, Walid Chbib, Fadlallah Rammal, Abbas Khatoun, Rida Attar, Ali El Zaytoun, Issam Hachem, Joel Department of Computer and Communication Engineering IUL Wardanieh Lebanon LTCI France Faculty of Science Lebanese University Beirut Lebanon Kettering University 1700 University Ave FlintMI48504 United States

Smart traffic congestion reduction is actually a real challenge for big cities. Machine learning algorithms can play a significant role in traffic analysis, congestion prediction, and rerouting. In this paper, we propose a new prediction approach to reduce the traffic congestion problem by studying a scheme for predicting traffic flow information using four machine learning techniques: Feed Forward Neural Networks (FFNN), Radial Basis Function Neural Networks (RBFNN), simple linear regression model, and polynomial linear regression model. This prediction scheme is based on the following parameters: the average waiting time at entry and exit street pairs, the days of the week, hours of movement, holidays, and the rain rate. The results indicate that the FFNN technique overcomes the other techniques producing 97.6% prediction accuracy. © 2023 Elsevier B.V.. All rights reserved.

关键词： Traffic congestion

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AI-Powered Precision: Revolutionizing Skin Cancer Detection with Advanced Machine Learning

AI-Powered Precision: Revolutionizing Skin Cancer Detection ...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Mariam M. Abd-elaziz Basma M. Elshafie Hagar Abdullah Karen Y. Gerges Ingy A. Ghanem Khaled M. Fouad Amir El-Ghamry Ibrahim F. Moawad Sara El-Metwally Faculty of computer science and engineering New Mansoura university Dakahlia Egypt Faculty of Engineering Computer and Communication program Faculty of Computer Science and Engineering Mansoura University New Mansoura University Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Mansoura University New Mansoura University Egypt School of Engineering and Computer Science University of Hertfordshire GAF Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Artificial Intelligence College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning

关键词： Training Deep learning Adaptation models Image resolution Transfer learning Lighting Real-time systems Image preprocessing Public healthcare Skin cancer

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SVM Classifier for Hygienic Food Handling with Cloud-Connected UV-C Sterilization Solutions 10

SVM Classifier for Hygienic Food Handling with Cloud-Connect...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Arunfred, N. Ranganathan, Chitra Sabapathy Kumaresh, P.S. Maruthukannan, B. Gobinath, D. Arunagiri, Bhuvaneswari Srm Institute of Science and Technology Faculty of Management Studies Chengalpattu Chennai Tamil Nadu Kattankulathur India Mphasis Corporation ChandlerAZ United States K.L.N.College of Engineering Department of Electronics and Communication Engineering Sivagangai Tamil Nadu Pottapalayam India Npr College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Dindigul India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Technology Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research integrates a support vector machine (SVM) classifier with cloud-connected ultraviolet-C (UV-C) sterilization systems to improve food handling hygiene. To ensure food safety, this system uses machine learning to analyze ambient conditions, handling processes, and equipment cleanliness. The SVM classifier classifies hygiene levels based on input data for real-time monitoring and decision-making. Cloud-connected UV-C sterilization technologies automate and streamline food industry microorganism control. Remote monitoring and control via the cloud lets stakeholder's access vital data and take immediate action. This increases food handling cleanliness, system speed, and responsiveness. The research examines the reduction of microbial contamination in various settings by using the SVM classifier and the cloud-connected UV-C sterilization solutions. Results show a considerable increase in hygiene level predictions and a significant decrease in the presence of bacteria in food processing. The SVM classifier and cloud-connected UV-C sterilization systems might improve food safety standards. Machine learning and cloud technologies provide a complete and adaptable solution to sanitary food handling issues, providing customers with safe and high-quality food items. © 2024 IEEE.

关键词： Remote control

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Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anxiety Notification System 9th

Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anx...

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9th International Conference on Computational science and Technology, ICCST 2022

作者： Abdulmanab, Muhammad Rahiman Nor’Azman, Nur Faidz Hazirah Meon, Hasni Razak, Chempaka Seri Abdul Ab Hamid, Siti Hafizah Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Communication Faculty of Communication Visual Art and Computing Universiti Selangor Bestari Jaya Malaysia School of Pre-University Studies Taylor’s College Lakeside Campus Subang Jaya Malaysia

ISBN: (纸本)9789811984051

As anxiety becomes increasingly prevalent among youths especially university students, early prevention via anxiety disorder profiling is crucial. Nevertheless, most screening tools to date are not automated, labour-intensive and time-consuming. Mental health detection in online social networks (OSNs) using artificial intelligence (AI) can provide means for capturing overlooked behavioural attributes, but few are centred around anxiety detection. Internet of Things (IoT)’s increasing popularity enables bio-signals monitoring to aid in mental health care. However, minimum research has been devoted to IoT-based assistive technologies for anxiety, which currently are only used by individuals already diagnosed and actively monitored. This study proposes a novel approach to anticipatory anxiety detection for Malaysian university students aged between 18 and 25 inclusive as an early notification system using AI and IoT for bilingual Malay and English tweets and vital signs analysis respectively. Our result shows that Logistic Regression with Bag-of-Words (BoW) is the overall best performing model with 81.06% accuracy. The Arduino smartwatch sensor readings from the MAX30102 pulse oximeter sensor are used to determine different body conditions using a rule-based approach. The comparison test result between the prototype and conventional measuring devices shows the prototype’s ability to accurately distinguish vital signs with a relatively low Mean Absolute Percent Error (MAPE) rate of 10.92% and 1.51% for pulse rate and temperature respectively. Together, our proof-of-concept results indicate the potential of AI and IoT in deriving new measures of anxiety, by analysing social data combined with bio-signals monitoring to alert users regarding their anxiety state. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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A Deep Reinforcement Learning Deep Q Network-Based Approach for Systematic Detection of Distributed Denial of Service Attacks in Software-Defined Networking

SSRN

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SSRN 2024年

作者： Eke, Roberts Ndukwe Onyejegbu, Laeticia Nneka Information and Communication Engineering Center of Information & Communication Technology Faculty of Engineering University of Port Harcourt Rivers State Nigeria Computer Science Department Faculty of Computing University of Port Harcourt Nigeria

Network security is now seriously threatened by cyberattacks, particularly in contexts where software-defined networking (SDN) is used. Deep Reinforcement Learning (DRL) has become a potent method for handling challenging decision-making situations in recent years. In this work, a machine learning model was developed using Deep Q-Network (DQN), which is a kind of DRL algorithm known as DRL-DQN, to forecast cyberattacks in SDN. In comparison to the conventional DQN, which had 0.9900 accuracy, assessment time of 5.40 s, and training time of 290.5 s, the proposed DQN model produced an accuracy of 0.99935 with evaluation time of 1.126 seconds (s) and training time of 100 s at 20 epochs. In order to validate the improved DQN model, the result was compared with the traditional machine learning (ML) algorithms. The proposed DQN has a higher accuracy and lower training time. Additionally, it is clear that the proposed DQN performed better than the other ML algorithms in terms of recall measures and training times, which is the performance metric used in this work. This methodology is proactive for detecting and mitigating the impact of Distributed Denial of Service (DDoS) attacks in SDN. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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A CH9 Low-Profile Circularly-Polarized Antenna for Indoor Positioning 7

A CH9 Low-Profile Circularly-Polarized Antenna for Indoor Po...

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7th World Conference on Computing and communication Technologies, WCCCT 2024

作者： Zhang, Zhenfeng Feng, Botao Gu, Wenzhe Ding, Xiao Sun, Sanshan Deng, Li Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Huizhou University School of Computer Science and Engineering Huizhou516007 China Macau University of Science and Technology Faculty of Humanities and Arts 999078 China Sichuan Normal University College of Physics and Electronic Engineering Sichuan China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China

ISBN: (纸本)9798350372328

This paper presents a low-profile CH9 circularly polarized 3-element multiple input multiple output (MIMO) antenna for precise positioning applications. The feeding structure of the antenna employs a conventional wilkinson power divider to achieve circular polarization, where the feeding structure and the radiating patch are located on the same layer of the dielectric substrate with a grounded backside. The -10-dB bandwidth of each antenna element ranges from 7.04 to 8.55 GHz, with a gain larger than 2.21 dBi within the desired bandwidth. In order to enhance localization accuracy and minimize the impact of spatial correlation, the distance interval between antenna elements is designed to be 12.5 mm. Experimental results show that when ports 1, 2 and 3 are excited with equal amplitude phase differences of 0 °, 90 ° and -90 °, respectively, the frequency bandwidth of the MIMO antenna is ranging from 7.61 to 8.48 GHz, with corresponding gain fluctuating from 5.39 to 8.07 dBi. In addition, the circularly polarized bandwidth is from 7 to 8.89 GHz, which fully covers the CH9 frequency band. © 2024 IEEE.

关键词： Bandwidth

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