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Chirped Large Mode Area Photonic Crystal Modal Fibers and its Resonance Modes Based on Finite Element Technique

Journal of Optical communications 2023年第3期44卷 333-338页

作者： Amiri, I.S. Rashed, Ahmed Nabih Zaki Sarker, Kaushik Paul, Bikash Kumar Ahmed, Kawsar Computational Optics Research Group Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of Software Engineering Daffodil International University 102 Mirpur Road Shukrabad Dhanmondi Dhaka1207 Bangladesh Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Santosh1902 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Santosh1902 Bangladesh

The study has outlined the finite element technique used for the fiber modal analysis of photonic crystal fiber (PCF) structure with a hexagonal/circular air holes arrangement on the cladding of pure fiber-optic silica. The used fibers that are namely highly nonlinear fiber (HNLF), single model silica fiber (SMSF) are combined with PCFs with different dopants concentration. Leakage loss, number of guided resonant modes, fiber birefringence, effective refractive index and cross-section areas and nonlinear coefficient parameters are measured inaccurate estimation. The optimum resonant guided modes are also estimated for different lattice infrastructure in circular PCFs. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Modal analysis

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An Analysis of Transform Coding Techniques in Image and Video Compression 15

An Analysis of Transform Coding Techniques in Image and Vide...

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

作者： Goyal, Shobhit Kuppuraj, T. Bahri, Dimple Arora, Mithhil Dongre, Y.V. Chitra, S. Quantum University Research Center Quantum University India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore India Karnataka Bangalore India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Vishwakarma Institute of Information Technology Pune India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (纸本)9798350370249

this paper c the numerous transform coding strategies used for image and video compression when it comes to their effectiveness, implementation problems, and ability packages. Transform coding is an information compression approach that reduces the number of statistics by exploiting redundancies such as spatial and spectral correlations within the data. The primary remodel coding strategies mentioned encompass the discrete cosine rework (DCT), discrete wavelet remodel (DWT), block-based movement compensation (BMC), and changed discrete cosine remodel (MDCT). Furthermore, the paper compares the extraordinary remodel coding strategies in phrases in their performance, performance, and complexity. The paper starts by introducing the motivation at the back of rework coding techniques and their position in records compression. It then discusses how the numerous rework coding techniques compare to each other and why one technique can be favored over the alternative. The paper examines the overall performance of the special strategies by thinking about parameters including top sign-to-noise ratio (PSNR), compression fee, and objective/subjective nice. Finally, the paper affords an overview of the unique applications in which transform coding may be used. Standard, this paper affords an insightful evaluation of the diverse transform coding strategies used for information compression. ©2024 IEEE.

关键词： Image compression

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QoS Improvement in Multi User Cellular-Symbiotic Radio Network Assisted by Active-Star-RIS

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IEEE Transactions on Cognitive communications and Networking 2025年

作者： Yeganeh, Rahman Saadat Omidi, Mohammad Javad Zeinali, Farshad Mili, Mohammad Robat Ghavami, Mohammad Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran Kuwait College of Science and Technology Department of Electronics and Communication Engineering Doha35003 Kuwait Tehran Iran London South Bank University Electrical and Electronic Engineering Department LondonSE1 0AA United Kingdom

In this article, we employ active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (ASRIS) to enhance the quality of 6G cellular network services. The network integrates commensal symbiotic radio (CSR) subsystems to facilitate communication between passive Internet of Things (IoT) users and active users, referred to as symbiotic backscatter devices (SBDs) and symbiotic user equipments (SUEs), respectively. Since the SBDs are passive, transmitting information to the SUEs poses significant challenges. To overcome this challenge, we harness the capabilities of massive multiple input multiple output (MIMO) antennas within the base station (BS) to relay the information transmitted by SBDs with greater power. This scheme uses the non-orthogonal multiple access (NOMA) technique for multiple access among all users, and potential interferences are eliminated using successive interference cancellation (SIC). The primary objective is to maximize the throughput between SBDs and SUEs. To achieve this, we formulate an optimization problem involving variables such as active beamforming coefficients at the BS and ASRIS, phase adjustments of ASRIS, and scheduling parameters between CSR and cellular networks. To solve this optimization problem, we used three deep reinforcement learning (DRL) methods: proximal policy optimization (PPO), twin delayed deep deterministic policy gradient (TD3), and asynchronous advantage actor critic (A3C). These methods were simulated, and the results demonstrate that A3C, TD3, and PPO have the best convergence speeds and achieve the highest increases in network throughput, respectively. Finally, the proposed scheme was evaluated using passive simultaneously transmitting and reflecting RIS (STAR-RIS), which demonstrated poorer performance compared to ASRIS. © 2025 IEEE.

关键词： Deep learning

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Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends

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IET communicationS 2022年第5期16卷 400-420页

作者： Elfatih, Nada M. Hasan, Mohammad Kamrul Kamal, Zeinab Gupta, Deepa Saeed, Rashid A. Ali, Elmustafa Sayed Hosain, Md. Sarwar Department of Electrical and Electronics Engineering Red Sea University Port Sudan Sudan Faculty of Information Science and Technology The National University of Malaysia (UKM) Bangi Selangor Malaysia Amity Institute of Information Technology Amity University Uttar Pradesh Delhi NCR India Department of computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Department of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Department of Information and Communication Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh

The Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) concept have emerged from IoT technology, which refers to connecting many vehicles with various applications to the internet. The 5G new radio is based on a cloud-radio access network (CRAN), considered as the communication infrastructure for IoV. However, due to the significant challenges and issues, researchers have been working on IoV and V2X. One of the main challenges for V2X is resource allocation and management for a high-speed vehicular environment. This paper discusses and provides complete detail for resource allocation and management for IoV over 5G RAN networks focusing on artificial intelligence techniques. The paper also presented reviews on integrating the multi-layers of vehicular network architecture with AI strategy to identify advancement and future directions for resource allocation and management issues.

关键词： Internet of Things Vehicle to Everything artificial intelligence techniques resource allocation IoT technology IoV radio access networks vehicle resource management V2X 5G mobile communication Internet of Vehicles communication infrastructure artificial intelligence vehicular ad hoc networks 5G RAN networks 5G new radio high-speed vehicular environment 5G networks resource management telecommunication computing

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Investigation on Enhanced Detection of Parkinson's Disease in Brain Imaging Using Ensemble Learning Approaches

Investigation on Enhanced Detection of Parkinson's Disease i...

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Integrated Circuits and communication Systems (ICICACS), IEEE International Conference on

作者： S. Sathishkumar S. P. Santhoshkumar Satyam Kumar N. Banupriya S. Hariharasudhan S. Gajalakshmi Department of Computer Science and Engineering Hindustan Institute of Technology Coimbatore India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India School of CS and IT Mahatma Gandhi Central University Motihari India Department of Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Masses of people around the world are affected by Parkinson's disease (PD), a progressive neurological disorder. Timely and precise diagnosis is critical for effective management. Yet conventional diagnostic methods often lack compassion and specificity. Advances in machine learning and cerebral imaging offer promising approaches to improve PD diagnosis. This investigation investigates an ensemble machine learning move toward to enhance the identification of Parkinson's disease using cerebral imaging data. By integrating models such as decision trees (DT), support vector machines (SVM), and neural networks, the ensemble method combines their strengths to create a reliable diagnostic system. Feature extraction and preprocessing further enhance interpretability and performance. Compared to individual models, the ensemble approach significantly improves diagnostic accuracy, sensitivity, and specificity. Brain imaging aids differential diagnosis in Parkinsonism, but no Cerebral imaging methods is currently recommended for routine clinical practice despite their proven utility. Larger trial are needed to validate the medical value and long-standing cost-effectiveness of Cerebral imaging for managing patients within healthcare systems like the NHS.

关键词： Neuroimaging Support vector machines Accuracy Parkinson's disease Imaging Sensitivity and specificity Brain modeling Feature extraction Ensemble learning Resilience

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An Efficient Medium Access Control Mechanism for Flying Ad-hoc Networks

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computer Systems Science & engineering 2021年第7期38卷 47-63页

作者： Muhammad Asghar Khan Fazal Noor Insaf Ullah Sajjad ur Rehman Shibli Nisar Mohaira Ahmad Department of Electronic Engineering ISRA UniversityIslamabad44000Pakistan Department of Computer Science and Information Systems Islamic University of MadinahMadinah400411Saudi Arabia Hamdard Institute of Engineering&Technology Islamabad44000Pakistan Department of Electrical Engineering Namal InstituteMianwali42200Pakistan Department of Electrical Engineering National University of Sciences and Technology(NUST)Islamabad44000Pakistan

The Flying Ad-hoc Networks(FANETs)is characterized by the transition from a single large Unmanned Aerial Vehicle(UAV)to multiple small UAVs connected in an ad-hoc *** high mobility is the core feature of such networks,they are prone to route breaks within the *** issue of connectivity loss can be coped with,to some extent,by making use of omnidirectional *** modification,however,curtails Quality-of-Service(QoS)requirements of networks in terms of bandwidth,media access delay,coverage and ***,directional antennas have advantages over omnidirectional antennas such as improved transmission range,spatial reuse and high ***,its introduction raises location-dependent issues to the Medium Access Control(MAC)*** calls for an efficient MAC protocol that can cater to new directional antenna models and,at the same time,can counter the constraints associated with the dynamic ***,in this article,we consider a UAV interconnection mechanism that lets the UAVs execute the communication tasks using the directional MAC *** technique is advantageous as compared to the approach of utilizing the MAC protocol using omnidirectional *** scheme is being implemented as a case study for Industry 4.0 inventory and traceability applications in the *** modeling and simulation purposes,we use the Optimized Network engineering Tool(OPNET)and aim to seek an evaluation with respect to throughput,media access delay,retransmission attempts and data *** results obtained demonstrate the effectiveness of the proposed scheme.

关键词： FANETs QoS Industry 4.0 IEEE 802.11 MAC OPNET mobility models directional antenna

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Prediction and Analysis of Cardiovascular Disease Using Multivariate Logistics Regression Classification 4th

Prediction and Analysis of Cardiovascular Disease Using Mult...

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4th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021

作者： Mohan, Tanishka Ahmed, Gulrej Goyal, Hemlata Kirar, Bhupendra Singh Department of Computer and Communication Engineering Manipal University Jaipur Jaipur-Ajmer Express Highway Dehmi Kalan Near GVK Toll Plaza Rajasthan Jaipur303007 India Department of Electronics and Communication Engineering Indian Institute of Information Technology Madhya Pradesh Bhopal462003 India

ISBN: (纸本)9789811950360

Cardiovascular diseases are related to blood vessels and heart disorders. Throughout the world, millions of people died every year due to cardiovascular diseases. Therefore, proactive prediction of these diseases is required to attenuate fatal situations to a great extent. A machine learning approach is employed with logistics regression algorithm to predict the chance of heart attack caused by cardiovascular disorders. The logistics regression is diverse machine learning classification algorithm, which provides reasonable accuracy rate and is vividly utilized in the field of research of machine learning-based prediction models. The classification has been achieved with 85.8%, 93%, 90%, and 83% of prediction accuracy, AUC, precision, and recall, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Building Safer Work Environments for Hazard Detection in Construction Sites with IoT and Cloud Server

Building Safer Work Environments for Hazard Detection in Con...

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International Conference on communications and Signal Processing

作者： H. Azath E. Sivanantham B. Suganthi E. Swarnalatha M. Suresh B Elango School of Computing Science and Engineering VIT Bhopal University Bhopal India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering RVS College of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad Telangana India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

This research explores real-time Internet of Things (IoT) methods to track and mitigate construction site hazards. The construction industry must innovate due to its high accident rate to protect workers. It uses IoT to create a system that watches the construction site and discovers real-time safety issues. The system uses sensors across the construction site to monitor worker location, equipment operation, ambient conditions, and structural integrity. In real-time, the IoT identifies potential threats from this data. Workers, supervisors, and other users receive timely alerts about dangers, enabling quick and focused mitigation. The system's proactive approach to building site safety reduces accidents, injuries, and deaths. With real-time IoT solutions, potential hazards can be discovered early and addressed quickly. Data collection and analysis allow the system to improve safety protocols and practices over time. It highlights the importance of real-time IoT technologies in construction site safety management. IoT devices improve hazard tracking and identification and safety culture by fostering proactive actions and informed decision-making. As buildings embrace digital transformation, this study enables safer and more efficient procedures.

关键词： Technological innovation Buildings Real-time systems Hazards Safety Sensors Internet of Things

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Label-Free Biomolecule Detection With InP/AlGaAs Charge Plasma Dielectric-Modulated Vertical TFET Using TaN as Metal Gate

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IEEE Access 2025年 13卷 89667-89684页

作者： Hashim Elshafie Abdulrahman Saad Alqahtani Azath Mubarakali Adarsh Rag S M. Venkatesh P. Parthasarathy Department of Computer Engineering College of Computer Science King Khalid University Abha Saudi Arabia College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Electronics and Communication Engineering CMR Institute of Technology Bengaluru Karnataka India

This study introduces a label-free biosensing method for biomolecule detection utilizing an InP/AlGaAs charge plasma dielectric-modulated vertical tunnel field-effect transistor (InP/AlGaAs VTFET) featuring TaN as the metal gate. The device comprises a nanocavity beneath the gate metal adjacent to the tunnelling junction, where biomolecules engage with the dielectric material, resulting in fluctuations in the drain current. A twin metal gate architecture with laterally split dielectrics is employed to eliminate short-channel effects. The biosensor has exceptional sensitivity, attaining a peak drain current sensitivity of $2.5 \times 10$ cm2 for biomolecules such as albumin (k = 7). The gadget proficiently identifies biomolecules with varying dielectric constants and charge distributions, enabling adaptable label-free detection of diverse targets. The study examines the influence of dielectric constant on critical metrics such as Energy Band Diagrams, Drain Current, Drain Sensitivity, and subthreshold swing (SS). The overlap between the source and pocket regions, along with the introduction of an auxiliary gate, optimizes the electrical characteristics. Simulation results show that the proposed InP/AlGaAs VTFET achieves a maximum sensitivity of $3.5 \times 10 ^{3}$ , outperforming configurations without overlap ( $2 \times 10 ^{3}$ ), highlights the potential of proposed InP/AlGaAs VTFET for scalable, high-sensitivity, label-free biomolecule detection.

关键词： Biosensors Logic gates Sensitivity Tunneling Dielectric constant Performance evaluation Metals Biological system modeling TFETs Electrostatics

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A Novel Machine Learning Approach of Multi-omics Data Prediction

A Novel Machine Learning Approach of Multi-omics Data Predic...

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2022 International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2022

作者： Rao, B. Srinivasa Lavanya, S. Kajendran, K. Sharma, Prince Prashant Verma, Devvret Radhakrishnan, P. Manikandan, G. Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Karpagam College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering TamilNadu Chennai India GurukulaKangri Deemed to Be University Department of Pharmaceutical Sciences Uttarakhand Haridwar India Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun248002 India Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai Thandalam602105 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

An integrated (combined) approach is needed in today's biomedical research in order to effectively use this data and obtain insights into natural systems. Genome, proteome, and metaproteomic data may be used to comprehend the complexities of molecular genetics utilising "machine learning"performance tracking methods derived from diverse omics sources. New biomarkers may be discovered by merging and analysing omics data using machine learning techniques. These biomarkers may aid in the proper identification of illness, the separation of patients, and the provision of tailored therapy. This study looks at a variety of "integrative machine learning or ML techniques"that are being used to obtain a better understanding of biological systems during natural bodily performance as well as when systems are *** data collection method has been used for this paper to gather relevant and factual data related to ML techniques of multi omics data prediction. © 2022 IEEE.

关键词： Biomarkers

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