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IEEE Conference on Wireless Communications and Networking

作者： Faical Khennoufa Khelil Abdellatif Halim Yanikomeroglu Metin Ozturk Taissir Elganimi Ferdi Kara Khaled Rabie Department of Electrical Engineering LGEERE Lab El-Oued University El-Oued Algeria Department of Systems and Computer Engineering NTN Lab Carleton University Ottawa Canada Electrical and Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Türkiye Department of Electrical and Electronic Engineering University of Tripoli Libya Division of Communications Systems KTH Royal Institute of Technology Sweden Department of Computer Engineering Zonguldak Bulent Ecevit University Zonguldak Türkiye Department of Computer Engineering & Center for Communication Systems and Sensing King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In order to bolster future wireless networks, there has been a great deal of interest in non-terrestrial networks, especially aerial platforms including high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). These platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA). In this regard, this paper proposes a multi-layer network architecture consisting of HAPS and UAV, where the former acts as a HAPS super macro base station (HAPS-SMBS), while the latter serves as a relay node for the ground Internet of Things (IoT) devices. The UAV is equipped with active transmissive RIS, which is a novel technology with promising benefits. We also utilize multiple-input single-output (MISO) technology, i.e., multiple antennas at the HAPS-SMBS and a single antenna at the IoT devices. Additionally, we consider NOMA as the multiple access technology as well as the existence of hardware impairments as a practical limitation. We compare the proposed system model with various scenarios, all involving the HAPS-SMBS and RIS-equipped UAV relay combination, but with different types of RIS, antenna configurations, and access technologies. Sum rate and energy efficiency are used as performance metrics, and the findings demonstrate that, in comparison to all benchmarks, the proposed system yields significant performance gains. Moreover, hardware impairment limits the system performance at high transmit power levels.

关键词： NOMA System performance Reconfigurable intelligent surfaces Benchmark testing Performance gain Autonomous aerial vehicles Hardware Internet of Things MISO Relays

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Heterogeneous Integration of Lithium Niobate and Silicon Photonics for Nonlinear Optics

Heterogeneous Integration of Lithium Niobate and Silicon Pho...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Palici, Alexandra Maria Lu, Xiyuan Srinivasan, Kartik Lu, Xinda Labbe, Fabien Ding, Yunhong Laroque, Hugo Hamerly, Ryan Errando-Herranz, Carlos Pu, Minhao Englund, Dirk Heuck, Mikkel Department of Electrical and Photonics Engineering Technical University of Denmark Lyngby2800 Denmark Microsystems and Nanotechnology Division Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

We demonstrate a hybrid device consisting of a thin film lithium niobate membrane transfer-printed onto a silicon nitride ring resonator. We measure quality factors in the 105 range at telecom wavelengths. CLEO 2024 &... 详细信息

ISBN: (纸本)9781957171395

关键词： Silicon nitride

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Deep Learning Models for Potato Leaf Disease Identification: A Comparative Analysis

Deep Learning Models for Potato Leaf Disease Identification:...

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Green Energy, Computing and Sustainable Technology (GECOST), International Conference on

作者： Balasubramaniam Vadivel Rajasekaran Thangaraj P Pandiyan T Aravind K Harish E Sivaraman Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electrical and Computer Engineering Curtin University Malaysia

ISBN: (纸本)9781665486644

Plant diseases are the most crucial factor in the agriculture sector, which causes a reduction in yield and economic loss. Therefore, early and accurate detection of these diseases can control the infection spread to other crops and minimize production loss. Traditional methods use the handcrafted features of the images to detect the infection part of the leaves and infection type. Furthermore, the extraction of these features is expensive and time-consuming. However, in light of recent advances in agricultural technology, such as the use of artificial intelligence in diagnosing plant diseases, appropriate research must be conducted toward the development of agriculture in a sustainable manner. However, manually interpreting these leaf diseases can be time-consuming and laborious, and they significantly impact potato quality and yield due to diseases like early blight and late blight. In addition, this study seeks to optimize cutting-edge deep learning (DL) models for detecting potato leaf disease. The deep learning models such as ResNet50, Inception V3, VGG16, and VGG19 are evaluated and their performances are compared. The experimental findings show that the VGG19 model outperforms the other models with an accuracy of 99%.

关键词： Deep learning Biological system modeling Computational modeling Plants (biology) Transfer learning Crops Feature extraction

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Smart Industrial Machine Management and Control System Based on IoT

Smart Industrial Machine Management and Control System Based...

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IEEE International Conference on Artificial Intelligence in engineering and Technology (IICAIET)

作者： Pritom Sadhu Arajit Saha Md. Abdul Muttalib Moon Md. Asaduzzaman Fahim Sumayea Alam Sara Sazib Hosen Salman-E Admia Munmun Akond Kh. Asekur Rahman D.M. Idid Ibne Asad Enan Department of Electrical and Electronic Engineering American International University-Bangladesh Dhaka Bangladesh Department of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Department of Computer Science and Engineering American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

Nowadays, several unexpected accidents have occurred in industrial companies, resulting in significant destruction, property damage and, most importantly, loss of worker life. At this point, worker's security and safety have become a significant concern everywhere. On the contrary, technological advancements take precautionary measures to a whole new level in today's world. Aside from this, there is significant demand for industrial sensor monitoring to ensure workers' safety. So, utilizing the Internet of Things (IoT) technology to manage industrial machinery and improve safety in the workplace, which is also known as IIoT. In addition, “Industrial Internet of Things (IIoT)” refers to a system of interconnected devices used in industrial applications essential to monitoring and managing machines and crucial elements of an industry. Therefore, an IoT -based system has been proposed to monitor and control machines. This study provides a cost-effective and reliable system consisting of a micro controller, various sensors, and software that can precisely monitor and control industrial machines and collect and store real-time data. Furthermore, the entire system is controlled by a Wi-Fi module microcontroller, which notifies the user in times of any hazardous situation with suggestions of possible solutions. The suggested system effectively monitors and controls the functionality of machines and improves workplace safety.

关键词： Industries Electricity Employment Control systems Real-time systems Software reliability Solar panels Monitoring Wireless fidelity Industrial Internet of Things

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Pareto Optimal Hybrid Beamforming for Short-Packet Millimeter-Wave Integrated Sensing and Communication

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IEEE Transactions on Communications 2024年

作者： Singh, Jitendra Naveen, Banda Srivastava, Suraj Jagannatham, Aditya K. Hanzo, Lajos Indian Institute of Technology Kanpur Department of Electrical Engineering UP Kanpur208016 India Indian Institute of Technology Jodhpur Department of Electrical Engineering Rajasthan Jodhpur342030 India University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Pareto optimal solutions are conceived for radar beamforming error (RBE) and sum rate maximization in short-packet (SP) millimeter-wave (mmWave) integrated sensing and communication (ISAC). Our ultimate goal is to realize ultra-reliable low-latency communication (uRLLC) and real-time sensing capabilities for 6G applications. The ISAC base station (BS) transmits short packets in the downlink (DL) to serve multiple communication users (CUs) and detect multiple radar targets (RTs). We investigate the performance trade-off between the sensing and communication capabilities by optimizing both the radio frequency (RF) and the baseband (BB) transmit precoder (TPC), together with the block lengths. The optimization problem considers the minimum rate requirements of the CUs, the maximum tolerable radar beamforming error (RBE) for the RTs, the unit modulus (UM) elements of the RF TPC, and the finite transmit power as the constraints for SP transmission. The resultant problem is highly non-convex due to the intractable rate expression of the SP regime coupled with the non-convex rate and UM constraints. To solve this problem, we propose an innovative two-layer bisection search (TLBS) algorithm, wherein the RF and BB TPCs are optimized in the inner layer, followed by the block length in the outer layer. Furthermore, a pair of novel methods, namely a bisection search-based majorizer and minimizer (BMM) as well as exact penalty-based manifold optimization (EPMO) are harnessed for optimizing the RF TPC in the inner layer. Subsequently, the BB TPC and the block length are derived via second-order cone programming (SOCP) and mixed integer programming methods, respectively. Finally, our exhaustive simulation results reveal the effect of system parameters for various settings on the RBE-rate region of the SP mmWave ISAC system and demonstrate a significantly enhanced performance compared to the benchmarks. © 1972-2012 IEEE.

关键词： Integer programming

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Integrated AI and RFID Tracking Systems: State-of-the-Art Review

Integrated AI and RFID Tracking Systems: State-of-the-Art Re...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Anand Vardhan Bhalla Rajiv Dey Sandeep Gupta Padmanabh Thakur Department of Electronics & Communication Engineering School of Engineering Eklavya University Damoh Madhya Pradesh India Department of Electronics & Computer Engineering SOET BML Munjal University Gurugram Haryana India Department of Electrical Engineering Graphic Era (Deemed to be University) Dehradun Uttarakhand India

RFID is one of the admired, powerful, and effectual technology in the field of communication, defense and detection in the Indoor RFID localization and tracking systems. The remoteness between RFID reader and object is measured from acknowledged signal strength indication (RSSI) values received from RFID reader. Radio Frequency Identification (RFID) and Artificial Intelligence (AI) have attracted considerable attention in recent years for ubiquitous computing and their use revolutionizes diverse applications in present and future system. Fast, accurate, cost-effective, simple to use, stable, and dependable systems are in high demand today. AI-based RFID has demonstrated its potential and can get beyond some of the drawbacks of traditional systems. Security systems based on RFID have previously been put in place. The many known algorithms and their potential uses are covered in this overview. The comparison is provided in terms of the method/algorithm employed, the applicability, the measurement criteria, the benefits, and the limitations.

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A Machine Learning Approach for Detecting Fake News in Hindi

A Machine Learning Approach for Detecting Fake News in Hindi

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IEEE UP Section Conference on electrical computer and electronics (UPCON)

作者： Shreyas Nagesh Aditya N Sampath Pradhumna Guruprasad Salai Sanjay S Jeyakumar G Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Several solutions exist to detect whether English news articles are fake or not. However, in a country such as India, news articles are published in numerous regional languages too. There has been little research work done on regional languages such as Hindi. This work proposes a framework to detect Hindi fake news articles. A dataset of nearly 4500 news articles has been collected in Hindi, written using the Devanagari Script. These news articles are scraped using BeautifulSoup and Scrapy libraries. The IndicBERT and MuRIL libraries, developed earlier in Hindi, are used to generate the word embedding. SVM, Logistic Regression, and a simple CNN are used as classifiers. Extensive experiments have been methodically conducted in order to find the best combination to detect fake news in Hindi. The best combination was found to be MuRIL and SVM, yielding 98.58\% classification accuracy. A standalone GUI running on a local host is built to demonstrate the working model.

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Assessing the Efficacy of Symbolic Regression for Scientific Data Classification and Regression

Assessing the Efficacy of Symbolic Regression for Scientific...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Pradhumna Guruprasad Shreyas Nagesh Aditya N Sampath Salai Sanjay S Jeyakumar G Department of Electrical and Electronics Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

Symbolic regression (SR) is a subdomain of evolutionary computing which aims to generate mathematical equations which best fit a given dataset. This is done with the help of genetic algorithms. Symbolic Regression is especially useful in cases where features in a dataset are known to have distinct mathematical relations to each other and its output. Therefore, this paper aims to compare and provide insight on the results of using SR on two datasets with two python libraries. These results are further compared with other ML models to assess their performance in classification and regression tasks. The detailed methodology and results are discussed further test metrics and obtained equations. The results show that symbolic regression can perform very well on certain datasets whose target outputs have a mathematical connection to the input features. SR is found to work better than other ML models in the classification and regression tasks.

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Quantum Wrapper Networking

arXiv

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arXiv 2023年

作者： Ben Yoo, S.J. Singh, Sandeep Kumar On, Mehmet Berkay Gül, Gamze Kanter, Gregory S. Proietti, Roberto Kumar, Prem The Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States The Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy The Center for Photonic Communication and Computing Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States

We introduce a new concept of Quantum Wrapper Networking, which enables control, management, and operation of quantum networks that can co-exist with classical networks while keeping the requirements for quantum networks intact. The quantum wrapper networks (QWNs) enable the transparent and interoperable transportation of quantum wrapper datagrams consisting of quantum payloads and, notably, classical headers to facilitate the datagram switching without measuring or disturbing the qubits of the quantum payload. Furthermore, QWNs can utilize the common network control and management for performance monitoring on the classical header and infer the quantum channel quality. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum entanglement

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Heterogeneous Integration of Lithium Niobate and Silicon Photonics for Nonlinear Optics

Heterogeneous Integration of Lithium Niobate and Silicon Pho...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Silicon photonics

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