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Propagation Characterization and Analysis for 5G mmWave Through Field Experiments

computers, Materials & Continua 2021年第8期68卷 2249-2264页

作者： Faizan Qamar Mhd Nour Hindia Tharek Abd Rahman Rosilah Hassan Kaharudin Dimyati Quang Ngoc Nguyen Centre for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)43600BangiSelangorMalaysia Department of Electrical Engineering Faculty of EngineeringUniversity of Malaya50603Kuala LumpurMalaysia Wireless Communication Centre Faculty of Electrical EngineeringUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia Department of Computer Science and Communications Engineering Faculty of Science and EngineeringWaseda UniversityTokyo169-8050Japan

The 5G network has been intensively investigated to realize the ongoing early deployment stage as an effort to match the exponential growth of the number of connected users and their increasing demands for high throughput,bandwidth with Quality of Service(QoS),and low *** that most of the spectrums below 6 GHz are nearly used up,it is not feasible to employ the traditional spectrum,which is currently in ***,a promising and highly feasible effort to satisfy this insufficient frequency spectrum is to acquire new frequency bands for next-generation mobile *** this end,the primary effort has been focused on utilizing the millimeter-wave(mmWave)as the most promising candidate for the frequency ***,though the mmWave frequency band can fulfill the desired bandwidth requirements,it has been demonstrated to endure several issues like scattering,atmospheric absorption,fading,and especially penetration losses compared to the existing sub-6 GHz frequency ***,it is fundamental to optimize the mmWave band propagation channel to facilitate the practical 5G implementation for the network ***,this study intends to investigate the outdoor channel characteristics of 26,28,36,and 38 GHz frequency bands for the communication infrastructure at the building to the ground floor in both Line of Sight(LOS)and Non-Line of Sight(NLOS)*** experimental campaign has studied the propagation path loss models such as Floating-Intercept(FI)and Close-In(CI)for the building to ground floor environment in LOS and NLOS *** findings obtained from the field experiments clearly show that the CI propagation model delivers much better performance in comparison with the FI model,thanks to its simple setup,accuracy,and precise function.

关键词： 5G mmWave propagation channel path loss channel characterization field experiment

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An Enhanced Residual Networks Based Framework for Early Alzheimer’s Disease Classification and Diagnosis 6th

An Enhanced Residual Networks Based Framework for Early Alzh...

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6th International Conference on communication, Devices and Networking, ICCDN 2022

作者： Awotunde, Joseph Bamidele Sur, Nath Samarendra Imoize, Agbotiname Lucky Misra, Sanjay Gaber, Tarek Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University East Sikkim Rangpo Majitar737136 India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Applied Data Science Institute of Energy Technology Halden Norway Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom

ISBN: (纸本)9789819919826

Alzheimer's disease (AD) is a long-lasting, degenerative brain illness for which there is now no successful treatment. However, there are medications that can delay its growth and stop its tracks. Therefore, stopping and regulating the progression of AD depends greatly on its early diagnosis. computer-aided diagnosis (CAD) in health care has become more and more dependent on deep learning technologies. This has help in early diagnosis and detection of various disease in health care. New techniques for the early diagnosis of neurocognitive abnormalities are now possible thanks to advancements in medical imaging and computational capacity in an effort to stop or slow down cognitive aging. Therefore, this study proposes a residual network (ResNet) type 50 for early diagnosis of AD. Convolutional neural networks (CNNs) are first used in the study for features extraction and then make the ultimate diagnosis using the ResNet-50. To create a better architecture, the problem of AD diagnosis will be addressed by utilizing and incorporating ResNet-50, which improves the current categorization problem's accuracy to its maximum. With a 98.8% accuracy rate, the proposed model using ResNet-50 produces encouraging findings for AD prediction. The study reveals that the model can effectively circumvent CNNs for the diagnostic issue with AD. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer aided diagnosis

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A Deep Learning Framework with Learning without Forgetting for Intelligent Surveillance in IoT-enabled Home Environments in Smart Cities

Recent Advances in Computer Science and Communications

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Recent Advances in computer science and communications 2025年

作者： Dalal, Surjeet Dahiya, Neeraj Verma, Amit Faujdar, Neetu Rathee, Sarita Jaglan, Vivek Rani, Uma Le, Dac-Nhuong Department of Computer Science & Engineering Amity University Haryana Gurugram India Department of Computer Science & Engineering SRM University Sonipat Haryana Delhi-NCR India Department of Computer Science and Engineering Maharaja Agrasen University Baddi India Department of Computer Science & Applications GLA University UP Mathura India Department of Electronics and Communication Engineering School of Engineering and Technology JECRC University Rajasthan Jaipur303905 India Department of Computer Science & Engineering Amity University M.P. Gwalior India Department of Computer Science & Engineering K. R. Mangalam University Haryana Gurugram India Dean of Faculty of Information Technology Haiphong University Viet Nam

Background: Internet of Things (IoT) technology in smart urban homes has revolutionised sophisticated monitoring. This progress uses interconnected devices and systems to improve security, resource management, and resident safety. Smart cities use technology to improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring technologies are key to this goal. Objectives: Intelligent monitoring in IoT-enabled homes in smart cities improves security, convenience, and quality of life from advanced technologies. Using live monitoring and risk identification tools to quickly discover and resolve security breaches and suspicious activity to protect citizens. Intelligent devices allow homeowners to remotely control lighting, security locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and lighting based on occupancy and usage. Method: This study introduces a deep learning architecture that uses LwF (Learning without Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect and analyse data in real-time for monitoring and surveillance. They use sophisticated data pre-processing to handle IoT devices' massive data. The authors train the deep learning model with historical and real-time data and cross-validation to ensure resilience. Result: The proposed model has been validated on two different Robloflow datasets of 7382 images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF model outperforms both the original Yolo and LwF models in terms of detection speed and adaptive learning. Conclusion: By raising the bar for intelligent monitoring solutions in smart cities, the suggested system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing adaptability and knowledge retention, authors envision heightened security and safety levels in urban settings. © 2024 Bentham science Publishers.

关键词： Smart homes

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GPRS Based Health Monitoring system 1

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2nd International Conference on Advances in Information and communication Technology, ICTA 2023

作者： Deepa, A.R. Hoang, Quang-Trung Bui, Van-Tung Tran, Duc-Tan Ramaswamy, V. Sheela, C. J. Jeba Chowdhury, S. Tran, Duc-Nghia Department of Computer Science and Engineering Koneru Lakshmaih Educational Guntur Foundation India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen2400 Viet Nam Department of PG Computer Science Nesamony Memorial Christian College Marthandam India Chittoor AP Murukambattu India Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam

ISBN: (数字)9783031508189

ISBN: (纸本)9783031508172

The idea and prototype of an Internet of Things (IoT) based health monitoring gadget are introduced in this paper. We concentrate on leveraging the cloud and IoT to monitor temperature an coronary heart rate in real-time. This design can be utilized for the early diagnosis of heart illnesses given the rising number of cardiac patients throughout the world. Contrary to the majority of the current monitoring methods and systems, a variety of wireless technologies are available, but their range is constrained. IoT is being used in this session to monitor. There is no distance restriction with IoT. Through the internet, we can test a patient's coronary heart rate and temperature from anywhere. Our strategy involves using the cloud to track the temperature and heart rate in real time. Through the use of IoT technology, the recorded temperature and heart rate may be saved and instantly evaluated for prognosis and diagnosis. An integrated GPRS microcontroller on a single chip makes up the suggested hardware architecture for connecting to the internet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

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computers, Materials & Continua 2022年第2期70卷 2639-2653页

作者： V.Praveena A.Vijayaraj P.Chinnasamy Ihsan Ali Roobaea Alroobaea Saleh Yahya Alyahyan Muhammad Ahsan Raza Department of Computer Science and Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India Department of Information Technology Vignan’s Foundation for ScienceTechnology&ResearchGuntur522213India Department of Information Technology Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Community College in DwadmiShaqra University11961Saudi Arabia Department of Information Technology Bahauddin Zakariya UniversityMultan60000Pakistan

In recent years,progressive developments have been observed in recent technologies and the production cost has been continuously *** such scenario,Internet of Things(IoT)network which is comprised of a set of Unmanned Aerial Vehicles(UAV),has received more attention from civilian tomilitary *** network security poses a serious challenge to UAV networks whereas the intrusion detection system(IDS)is found to be an effective process to secure the UAV *** IDSs are not adequate to handle the latest computer networks that possess maximumbandwidth and data *** order to improve the detection performance and reduce the false alarms generated by IDS,several researchers have employed Machine Learning(ML)and Deep Learning(DL)algorithms to address the intrusion detection *** this view,the current research article presents a deep reinforcement learning technique,optimized by BlackWidow Optimization(DRL-BWO)algorithm,for UAV *** addition,DRL involves an improved reinforcement learning-based Deep Belief Network(DBN)for intrusion *** parameter optimization of DRL technique,BWO algorithm is *** helps in improving the intrusion detection performance of UAV *** extensive set of experimental analysis was performed to highlight the supremacy of the proposed *** the simulation values,it is evident that the proposed method is appropriate as it attained high precision,recall,F-measure,and accuracy values such as 0.985,0.993,0.988,and 0.989 respectively.

关键词： Intrusion detection UAV networks reinforcement learning deep learning parameter optimization

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Utilizing Cooperative Spectrum Sharing for High Speed Wireless Networks 3

Utilizing Cooperative Spectrum Sharing for High Speed Wirele...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Dhabliya, Dharmesh Kharbas, Vikash Kumar Sidhu, Amritpal Singh, Shweta Gowrishankar, J. Jaisudha, J. Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

Cooperative spectrum sharing is a method to allocate the radio spectrum amongst users for high-velocity wireless networks. It entails multiple unbiased customers that cooperate to percentage their respective spectra to maximize the records fee and the practical spectrum usage. The cooperative spectrum sharing technique lets non-overlapping frequency bands be utilized by multiple customers supplying a high statistics fee with minimum interference. It affords a green way of handling the radio spectrum, permitting multiple customers to share the to-be-had spectrum sources rather than having an unmarried consumer monopolize an upbeat frequency band. Cooperative spectrum sharing is likewise tremendous because of its low implementation cost and occasional power intake. Furthermore, it could be fast deployed to numerous areas to meet the increasing demand for excessive-speed Wi-Fi networks. © 2023 IEEE.

关键词： Cooperative implementation managing monopolize spectrum

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Exploring the Benefits of Deep Learning for Data science Practices 15

Exploring the Benefits of Deep Learning for Data Science Pra...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Mohit Kumar Kumar, Ritesh Sinha, Deepak Kumar Senthilkumar, K.K. Dhabliya, Dharmesh Ahluwalia, Gunveen Vivekananda Global University Department of Electrical Engineering Jaipur India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

With the growing occurrence of records technology and analytics, Deep studying has become an effective device within the records technology field. Deep mastering is a subset of artificial Intelligence that uses algorithms to construct complex fashions for huge-scale records analysis. The models created through Deep studying are often extra correct than traditional analytic techniques, as they use a wide style of capabilities and labels to discover records. Deep studying models can remedy many facts and technological know-how problems, from herbal language processing to photo class to regression modeling. Deep mastering models are compelling in conditions in which a high degree of accuracy is needed and where the inputs are complicated and varied. In brief, the blessings of Deep studying for data technological know-how consist of improved accuracy, faster processing, and automated feature extraction. © 2024 IEEE.

关键词： Economic and social effects

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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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Effective Information Capture and Retrieval Through Machine Learning-Based systems 3

Effective Information Capture and Retrieval Through Machine ...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Sharmila, P. Kadam, Rutuja S. Bhushan, Bharat Vinay Kumar, S.B. Singh, Shweta Hassan, Inzimam Ul Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

Effective information capture and retrieval is crucial for organizations to meet their business needs and remain competitive in today's fast-paced digital world. Traditional information management systems, while useful, are often limited in their ability to handle large amounts of data and provide accurate and relevant results. This paper proposes the use of machine learning-based systems to enhance the process of information capture and retrieval. These systems leverage the power of artificial intelligence to automatically learn from data patterns and improve their performance over time. This approach has shown promising results in various industries, such as finance, healthcare, and e-commerce. Through the utilization of machine learning algorithms, organizations can achieve more efficient and accurate information capture and retrieval, leading to better decision-making and improved business outcomes. The benefits and challenges associated with using machine learning-based systems for information management are discussed, along with potential future research directions in this field. Overall, this paper highlights the importance of utilizing machine learning-based systems for effective information capture and retrieval and the potential impact they can have on organizational success. © 2023 IEEE.

关键词： accurate extracting generating inferences recognizing

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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

引用

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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