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Revolutionizing UAV: Experimental Evaluation of IoT-Enabled Unmanned Aerial Vehicle-Based Agricultural Field Monitoring Using Remote Sensing Strategy

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 411-425页

作者： Narayanappa, Gireesh Babu Chandanadur Abbas, Syed Hauider Annamalai, Lavanya Meenakshi, Ramakrishnan Singh, Mangal Yadav, Tumikipalli Nagaraju Kumar, Aarthi Ramesh Department of CSE/ISE BMS Institute of Technology and Management Bengaluru India Department of Computer Science & amp Engineering Integral University Lucknow India Department of ECE Dr. MGR Educational and Research Institute Chennai India Department of CSE Chennai Institute of Technology Chennai India Department of Electronics and Telecommunication Engg Symbiosis International (Deemed University) Symbiosis Institute of Technology Pune India Department of Computer Science and Engineering Koneru Lakshmaih Education Foundation AP Vaddeswaram India Department of ECE R.M.D Engineering College R. S. M Nagar Gummidipoondi India

Agricultural monitoring has evolved significantly with the advent of unmanned aerial vehicles (UAVs), which offer innovative solutions for precision farming. Traditional agricultural practices often rely on manual inspection and limited ground-based sensors, which can be time-consuming and prone to inaccuracies. This paper explores the integration of UAV and ground-based IoT sensor data through advanced data fusion techniques to enhance data reliability and precision. Our hybrid machine learning model (HMLM), combining random forest and support vector machine algorithms, is employed for accurate crop health assessment, disease detection, and yield prediction. The implementation of UAV-based agricultural monitoring systems leverages the unique capabilities of quadcopters and fixed-wing UAVs. Quadcopters are utilized for detailed field surveys and spot checks, offering high-resolution imaging for precise inspections, while fixed-wing UAVs are deployed for large-scale monitoring, providing extensive coverage and long-flight endurance. Equipped with advanced IoT devices and sensors, including multispectral and hyperspectral cameras, GPS modules, and real-time communication systems, these UAVs enable comprehensive data collection and analysis. Our hybrid machine learning model (HMLM) improves classification accuracy and predictive power, achieving an impressive accuracy of 98.74%, leading to precise crop management and higher yields. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Agricultural monitoring Artificial intelligence (AI) Deep learning GPS modules Hybrid machine learning model (HMLM) Internet of things (IoT) Precision agriculture Sustainable farming Unmanned aerial vehicles (UAVs)

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Hybrid Routing and Energy Management for Performance Optimization in Mobile Ad-hoc Networks

Hybrid Routing and Energy Management for Performance Optimiz...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： B. Vijaya Nirmala J. Relin Francis Raj R. Santhana Krishnan V. Lavanya A. Pitchirani V. Vinoth Kumar Department of Information Technology PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Mobile Ad-hoc Networks (MANETs) encounter challenges related to dynamic topologies, energy constraints, and link instability. To enhance performance, a hybrid model integrates Ad-hoc On-demand Multipath Distance Vector (AOMDV), Power-Efficient Gathering in Sensor Information System (PEGASIS), and Deep Q-Network (DQN). OMNeT++, a discrete event simulation framework, facilitates comprehensive data collection, capturing key network parameters such as node density, signal strength, packet transmission rate, residual energy, routing paths, link breakage frequency, and packet delivery status. Stored in scalar and vector files, the dataset enables energy-efficient communication modeling and adaptive routing optimization. Data preprocessing involves cleaning, normalizing, and handling missing values using interpolation techniques while filtering noisy data through a sliding window averaging method. Feature extraction focuses on hop count, queue length, residual energy, and link expiration time, ensuring consistency and facilitating faster convergence. The hybrid model architecture integrates AOMDV for reliable multipath routing, PEGASIS for energy-efficient communication, and DQN for adaptive path optimization. Simulation results indicate a 15% improvement in throughput, a 10% rise in packet delivery ratio, a 20% reduction in end-to-end delay, and a 30% decrease in average energy consumption. Applications extend to military operations, disaster recovery, vehicular networks, IoT sensor networks, and smart city infrastructure, ensuring efficient and adaptive communication.

关键词： Adaptation models Adaptive systems Routing Ad hoc networks Energy efficiency Vectors Reliability Vehicle dynamics Mobile computing Optimization

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Blockchain Enabled Privacy- Preserved Supply-Chain Management for Tracing the Food Goods

Blockchain Enabled Privacy- Preserved Supply-Chain Managemen...

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2024 International Conference on Science, technology, engineering and Management, ICSTEM 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Tiwari, Vinamra Dhanasekaran, S. Kumar, B. Santhosh Sivaprakasam, T. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Kampar Malaysia Accenture LLC Sacramento United States Tamilnadu Srivilliputtur India Telangana Ranga Reddy501506 India Department of Electronics and Communications Engineering Karpagam Academy of Higher Education Tamilnadu Coimbatore India

ISBN: (纸本)9798350376913

A simple definition of supply chain can be said as a focus on recording activities involving the complete logistics of a given product. Food safety concerns to be an indispensable consideration for both consumers, producers and regulators. According to the World Health Organization (WHO), 420,000 people die each year as a result of food contamination, which affects one in ten people across the world. Based on the reports from WHO, each year 125,000 children are being hospitalized because of foodborne illness, potentially placing them at the highest risk [1]. Due to this, Blockchain food traceability is gaining momentum in the global agri-food sector. The capability to track the life process of products from origin through every point of contact on its journey to the consumer instantaneously, bolsters credibility, efficiency and safety. Consumers would have the ability to trace their food from fields to plate, with the help of an Id. Blockchain holds the promise of disruptive transformation, but not without potential roadblocks along the way. In a fast-paced economy, transparency can be a double-edged sword. © 2024 IEEE.

关键词： Food safety

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Novel Switched-Capacitor DC-DC Converter Achieving Highest Rational Conversion Ratios Using Inter-Stage Feedback 24

Novel Switched-Capacitor DC-DC Converter Achieving Highest R...

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24th IEEE Workshop on Control and Modeling for Power electronics, COMPEL 2023

作者： Patle, Nagesh Sahoo, Bibhu Datta University of California Berkeley Dept. of Electrical Engineering and Computer Sciences Berkeley United States Indian Institute of Technology Kharagpur Dept. of Electronics and Electrical Communication Engg Kharagpur India

ISBN: (纸本)9798350316186

This paper proposes an inter-stage feedback-based switched-capacitor (IFSC) converter that can be configured to realize any rational voltage-conversion ratio (VCR) using a minimum number of 2:1 switched-capacitor (SC) stages. The converter enables efficient dynamic voltage scaling (DVS) operation over a larger voltage range by significantly increasing the resolution of voltage levels. Additionally, a restructuring algorithm has been proposed to achieve all VCRs without requiring any additional dedicated voltage stages. The converter extends the idea of the recursive switched-capacitor (RSC) topology, which spans 2N -1 ratios with N 2:1 SC cells. Comparative analysis shows that the proposed converter outperforms the negative-output feedback-based converter in terms of bottom-plate parasitic loss for most VCRs. To validate the concept, a fully-reconfigurable hardware prototype has been developed. © 2023 IEEE.

关键词： Feedback

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Discrete Frequency Coding Waveform Sets Based on Phase-Coded Triangular FM Waveforms for MIMO Radar 3

Discrete Frequency Coding Waveform Sets Based on Phase-Coded...

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3rd International Conference on Range technology, ICORT 2023

作者： Harishankar, R. Kadambi, Anjali Kadlimatti, Ravi Birla Institute of Technology and Sciences Pilani - K. K. Birla Goa Campus Department of Electrical and Electronics Engineering Goa India University of California Department of Electrical and Computer Engineering San DiegoCA United States

ISBN: (纸本)9781665493314

This paper introduces discrete frequency-coding waveform (DFCW) sets that are based on phase-coded triangular frequency modulated (FM) waveforms. The DFCW sets are designed by discretizing the instantaneous frequencies of the triangular FM waveforms of varying slopes. The autocorrelation sidelobe peak and the cross-correlation peak vary as 1/N where N is the number of discrete frequencies. It is shown that adding a phase shift of π to one of the segments of the triangular FM based DFCW set doubles the number of codes in the set, without affecting the autocorrelation and cross-correlation properties of the set. This paper shows the design of such DFCW sets and describes their correlation, Doppler tolerance, and detection properties. These DFCW sets can be used in digital multiple-input-multiple-output (MIMO) radar and orthogonal netted radar (ONRS) systems. © 2023 IEEE.

关键词： Autocorrelation

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Improvement of Task Scheduling with Energy Efficiency including Fault Tolerant with Resilient Computing System in Parallel and Distributed Communication Network 2

Improvement of Task Scheduling with Energy Efficiency includ...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Kumar, Vijay Madhuri, P. Yamini, R. Sravani, Palla Muthukumar, K. Graphic Era Hill University Department of Physics Dehradun India Hyderabad501401 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India Raghu Engineering College Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam531162 India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350325799

In parallel and distributed communication networks, task scheduling is essential for attaining the best system performance. Innovative ways that may intelligently distribute computing resources while minimizing energy consumption and maintaining system resilience are required due to the rising need for energy efficiency and fault tolerance. This system, offers a unique approach to job scheduling in parallel and distributed communication networks that integrate genetic algorithms (GA) and machine learning (ML).Using ML approaches, proposed method analyses historical task execution data to discover patterns that may be applied to task execution time prediction in various computing systems. As a result, this can assign tasks to resources effectively, maximize resource usage, and cut down on execution time as a whole. In addition, using predefined objectives like energy efficiency and fault tolerance, GA is used to find the best task-resource assignments. This applies resilient computing approaches to the job scheduling procedure to overcome problems with fault tolerance. This prevents the execution of crucial operations from being hampered by system faults or resource shortages by utilizing fault detection and recovery technologies. This system can adapt dynamically to shifting network conditions through the combination of ML, GA, and resilient computing, which raises the entire system's reliability and availability. © 2023 IEEE.

关键词： Machine learning

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Image segmentation of rail surface defects based on fractional order particle swarm optimization 2D-Otsu algorithm

Image segmentation of rail surface defects based on fraction...

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2023 International Conference on Algorithm, Imaging Processing, and Machine Vision, AIPMV 2023

作者： Geng, Na Sheng, Hu Sun, Weizhi Wang, Yifeng Yu, Tan Liu, Zihan School of Computer and Communication Engineering Dalian Jiaotong University Liaoning Province Dalian116028 China Innovation Center The 47th Research Institute of China Electronics Technology Group Corporation Liaoning Province Shenyang110000 China

ISBN: (纸本)9781510672444

Under the influence of high density operation and natural environment, the rail surface will appear abrasion damage, which will affect the safety and comfort of the train. Rail surface defect detection is an important part to ensure the safe and efficient operation of railway system. In order to distinguish whether there are defects on the rail surface, a method of rail surface defect image segmentation based on FPSO 2D-Otsu algorithm is proposed. The rail image is denoised and enhanced by adaptive fractional calculus, and then the rail image is segmented by FPSO 2D-Otsu algorithm. In order to verify the accuracy of the algorithm, the proposed algorithm is compared with PSO 2D-Otsu image segmentation algorithm. The experimental results show that the accuracy of FPSO 2D-Otsu algorithm in rail image segmentation is improved from 48.76% to 83.59% compared with PSO 2D-Otsu algorithm. © 2024 SPIE.

关键词： Surface defects

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Demonstration of a Vertically Stacked Junctionless Forksheet as Dielectric Modulated Biosensor 28

Demonstration of a Vertically Stacked Junctionless Forksheet...

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28th International Symposium on VLSI Design and Test, VDAT 2024

作者： Gandhi, Navneet Jaisawal, Rajeewa Kumar Kondekar, P.N. Dixit, Ankit Kumar, Naveen Georgiev, Vihar Bagga, Navjeet PDPM-IIITDM Vlsi Lab Electronics & Communication Department Jabalpur India University of Glasgow Device Modelling Group James Watt School of Engineering United Kingdom Indian Institute of Technology School of Electrical and Computer Sciences Bhubaneswar India

ISBN: (纸本)9798350380101

In this paper, we demonstrated the performance metrics of a Junctionless vertically stacked Forksheet FET for biosensing applications. Through the use of well-calibrated TCAD models, the N-and P-side of a Forksheet FET is designed with a cavity region where the presence/absence of the biomolecule is sensed using a permittivity modulation approach. The label-free electrical detection of different biomolecules, including streptavidin (K=2.1), biotin (K=2.63), APTES (K=3.57), and keratin (K=8) is obtained by analyzing the variations in threshold voltage, OFF current, and the ION/IOFF ratio as sensing performance metrics. The proposed Junctionless sensor exhibits superior electrical characteristics and sensitivity at lower metal gate work functions compared to other available Junctionless devices operating at higher metal gate work functions. Thus, the study underscores the potential of the proposed Junctionless Forksheet FET for efficient and accurate biosensing applications. © 2024 IEEE.

关键词： Biomolecules

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Revisiting the K-Fold Approach for a Stable Model on Amyotrophic Lateral Sclerosis Prediction Scheme using LSTM and Attention Mechanism 23

Revisiting the K-Fold Approach for a Stable Model on Amyotro...

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Proceedings of the 2023 10th International Conference on Biomedical and Bioinformatics engineering

作者： Nur Achmad Sulistyo Putro Cries Avian Setya Widyawan Prakosa Jenq-Shiou Leu Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan and Department of Computer Science and Electronics Universitas Gadjah Mada Indonesia Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan

ISBN: (纸本)9798400708343

A progressive neurodegenerative disease affecting motor neurons, Amyotrophic Lateral Sclerosis (ALS) requires early diagnosis as quickly as possible. For such situations, surface electromyography (S-EMG) is widely used as a non-invasive diagnostic tool to measure muscles' activity through electrodes placed on the skin's surface. Artificial intelligence (AI) approaches can be employed to analyze captured signals and distinguish abnormal patterns. However, previous work focused primarily on spatial information. It does not consider temporal information, effectively capturing the dynamic nature of muscle activity and identifying subtle abnormalities that might indicate ALS. Therefore, we fill the gap in this study by proposing a combination of CNN, Long-Short-Term Memory Networks (LSTM), and attention mechanisms to exploit temporal information in EMG signals. Stability assessment using K-fold cross-validation ensures reliable model performance. Our results demonstrate that combining spatial and temporal information can enhance performance and acquire 98.15% and 98.45% for CNN and LSTM, and CNN, LSTM, and Attention combination. In addition, our proposed model remains stable compared to previous work.

关键词： Amyotrophic Lateral Sclerosis detection Attention K-Fold cross validation LSTM temporal information

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IoT-driven Smart Packaging for Pharmaceuticals: Ensuring Product Integrity and Patient Safety 1

IoT-driven Smart Packaging for Pharmaceuticals: Ensuring Pro...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Raman, Ramakrishnan Dhivya, K. Sapra, Pooja Gurpur, Shashikala Maniraj, S.P. Murugan, S. Maharashtra Pune India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Maharashtra Pune India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350330915

In this paper, an IoT-driven smart packaging in the pharmaceutical industry is developed to maintain product integrity and improve patient safety. To monitor essential environmental and handling conditions, the research incorporates several sensors into pharmaceutical packaging, including temperature, humidity, light, tamper detection, gas, GPS, accelerometer, RFID tags, moisture indicator sensors, and shock/vibration sensors. These sensors monitor temperature, humidity, light, tamper detection, and gas. IoT-enabled smart packaging may proactively detect deviations from ideal storage and transportation conditions, decrease risks of product deterioration, and prevent tampering by collecting and analyzing data in real-time. This enables smart packaging to avoid tampering. The deployment of such intelligent packaging technologies can revolutionize medication management, improve patient outcomes by assuring the delivery of effective and legitimate pharmaceuticals, and boost supply chain visibility. This study highlights the opportunities and obstacles of implementing IoT-driven smart packaging in the pharmaceutical business. As a result, the research paves the way for better and safer healthcare practices. © 2023 IEEE.

关键词： Healthcare Applications Real-time Monitoring Smart Packaging Supply Chain Management Wireless Communication

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