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6th International Conference on Blockchain computing and Applications, BCCA 2024

作者： Sunil, Sidharth Bhat, Anjum Zameer Abbas, Ali Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada Department of Computing and Electronics Engineering Middle East College Muscat Oman

ISBN: (纸本)9798350351538

Web 3.0 technologies such as blockchain, cryptocurrency, and Non-Fungible Tokens (NFTs) are progressively making waves. Despite their adoption in various sectors, their application in the education sector, particularly in Higher Educational Institutions (HEIs), is largely unexplored. Our research proposes the development of a secure NFT platform using blockchain technology, tailored for HEIs. The primary aim of this research is to authenticate rewards, and certificates, and digitalize badges using NFTs, minimizing potential fraud and enhancing data security. Reward NFTs can enhance students' and teachers' career recognition and credit. EduNFT is a ‘first of its kind’ in the Sultanate of Oman in creating a Web 3.0 technology-enabled campus where students are rewarded with digital assets (NFTs) for their achievements by the teachers or the institution over an NFT platform in a secured environment. It runs on the Polygon blockchain which is a "layer two" or "sidechain" scaling solution that runs alongside the Ethereum blockchain - allowing speedy transactions with low fees. The research methodology incorporates an authentication process that requires users to log in via their crypto wallets which enhances security and maintains user privacy. Instead of traditional physical rewards, this digital approach allows achievements to be stored perpetually in the blockchain, reducing risks of loss or damage. The proposed research empowers an impressive expansion of NFT utilization in higher education, contributing to the ongoing growth of the NFT space. The outcome, a pioneering NFT platform powered by Web 3.0 technology, stands to revolutionize the higher education sector, not only in Oman but potentially worldwide. In addition, the research serves as a crucial step toward integrating contemporary technologies into educational systems and furthering their evolution. © 2024 IEEE.

关键词： Smart contract

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Cross-Layer Optimized HARQ Protocol for Underwater Acoustic Wireless Sensor Networks Using Stochastic Network Calculus 2

Cross-Layer Optimized HARQ Protocol for Underwater Acoustic ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Sivajayaprakash, A. Sukumaran, Rajeev Kumar, C. SRM Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore641032 India

ISBN: (纸本)9798331534950

UAWSNs face challenges such as long propagation delays, limited bandwidth, and varying channel conditions. To solve these problems, we developed a new protocol called Multi- Hop Cross-Layer Optimized Hybrid Automatic Repeat Request (CLO-HARQ) suitable for UAWSNs. This protocol automatically changes the retransmission strategy and error correction redundancy based on real-time feedback from the physical and network layers. A unique feature of this protocol is the inclusion of environmental factors such as water salinity and temperature in the decision-making process. Also, using Stochastic Network Calculus (SNC), stochastic traffic characteristics predict single-hop and multi-hop communication delay (delay), energy usage (energy usage), and throughput. In the performance evaluation of CLO-HARQ, it has been shown to provide significant improvement in efficiency, throughput, and delay over existing HARQ methods. It is well-suited for long-term underwater monitoring and is a sustainable solution for energy-constrained sensor networks. © 2024 IEEE.

关键词： Network layers

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A Novel Framework for Preserving Patient Health Record in IoT Healthcare 3

A Novel Framework for Preserving Patient Health Record in Io...

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3rd International Conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Belgaum, Mohammad Riyaz Pothureddy, Priyanka Patnam, Sai Suraksha Gadda, Parimala Deepthi G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350391565

As artificial intelligence, the Internet of Things (IoT), and next-generation mobile communication continue to evolve at a rapid pace, an increasingly popular application called the Internet of Medical Things (IoT) has arisen to provide ease and practicality in healthcare. Nonetheless, privacy preservation issues are brought up by accessing patient medical information inside the IoT framework, which calls for the creation of safe and private-preserving solutions. To address these issues, we provide in this study an IoT-Framework for preserving patient health record (FFPPHR) based on ELGamal blind signatures. We provide an enhanced privacy protection medical record searching technique (FFPPHR) based on the elliptic curve discrete logarithm problem (ECDLP) and point out security vulnerabilities in the current FFPPHR. Our security research shows that by utilizing the hard problem assumption of ECDLP, the FPMR guarantees identity privacy and accuracy. Furthermore, the use of Blind ECDSA Signatures improves data security. Performance assessments confirm the FFPPHR efficiency, and theoretical findings agree with simulations. The suggested FFPPHR approach yields lower time costs when compared to state-of-the-art techniques, which makes it a potential option for safe and effective medical record searches in IoT contexts. © 2024 IEEE.

关键词： Electronic health record

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Potato Leaf Disease Detection Method Based on the YOLO Model 4

Potato Leaf Disease Detection Method Based on the YOLO Model

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Srivastava, Animesh Rawat, Bhupender Singh Bajpai, Prashant Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Coer University College of Smart Computing Roorkee India Sr Institute of Management & Technology Department of Computer Science & Engineering Lucknow India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350393354

Global potato production is at risk because of potato leaf diseases, which cause huge economic losses. To ensure crop productivity and disease management, their efficient and accurate localization is of utmost importance. The YOLOv7 deep learning model is presented in this paper as a means of disease detection and classification in potato leaves. The suggested approach uses YOLOv7's superior detection accuracy to successfully identify and categorize potato leaf diseases. To train the model, we used a wide variety of images showing both healthy and diseased potato leaves. Verified by the test results, the suggested methodology successfully identified and classified diseases affecting potato foliage with a 98.1% accuracy rate. Through early disease detection and prompt control measure implementation, this method holds great promise for enhancing the yield and quality of potato crops in precision agriculture systems. The proposed framework shows great promise for practical implementation, as the experimental results confirm that it offers enhanced accuracy in detecting and predicting potato crop diseases. © 2024 IEEE.

关键词： Losses

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Smooth Motion Planning of an Omnidirectional Mobile Robot in Dynamic Environments

Smooth Motion Planning of an Omnidirectional Mobile Robot in...

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2022 International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Šelek, Ana Seder, Marija Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this paper, a real-time smooth motion planning method for a four mecanum wheeled omnidirectional mobile robot in dynamic environments that generates a smooth collision-free trajectory is proposed. The method employs a graph-search-based path planning algorithm and a smoothing technique based on clothoids. It is suitable for real-time operation due to its computational simplicity and allows fast path replanning in case the robot encounters unknown and dynamic obstacles. Such a path is feasible for holonomic mobile robots since it does not contain sharp turns so the energy consumption is reduced. Our method is compared to the reactive model predictive control scheme in simulations. The comparison showed advantages of the proposed method in the reduced task execution time, path length, cumulative replanning time, and smooth velocity profile. © 2022 IEEE.

关键词： Motion planning

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Ship Detection in Satellite Images using You Only Look Once-Neural Architecture Search 9

Ship Detection in Satellite Images using You Only Look Once-...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Anand, R.N. Singh, Rimjhim Padam Gupta, Deepa Palaniappan, Kannappan Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical Engineering and Computer Science University of Missouri Missouri United States

ISBN: (纸本)9798350383201

Ship Detection in remotely sensed satellite imagery is a challenging task due to tiny size of the objects and low resolution of the images. Several object detection methods that perform very well for medium-sized and large-sized objects, miserable fail to perform decently in such applications. From the vast variety of the models available, few You only look once (YOLO) have proven to yield decent efficiency and accuracy in ship detection in satellite images. Hence, the paper proposes to detect and classify ships in remotely sensed satellite images by applying transfer learning to fine-tune the latest YOLO-Neural Architecture Search (YOLO-NAS) model. To the best of our knowledge this paper is the first attempt to test YOLO-NAS model for ship detection in satellite imagery. To demonstrate the strength of the YOLO NAS model, the paper also trains YOLOv5 and YOLOv8 on the same dataset allowing to comprehensively compare the efficiency of the proposed model against previous state-of-art versions. The findings demonstrate high mean average precision scores for the proposed model, indicating the effectiveness of the YOLO-NAS algorithm in accurately locating ships in satellite images. © 2023 IEEE.

关键词： Object detection

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Blockchain-Enabled Mitigation Strategies for Distributed Denial of Service Attacks in IoT Sensor Networks:An Experimental Approach

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computers, Materials & Continua 2024年第12期81卷 3679-3705页

作者： Kithmini Godewatte Arachchige Mohsin Murtaza Chi-Tsun Cheng Bader M.Albahlal Cheng-Chi Lee Department of Engineering Technologies School of ScienceComputing and Engineering TechnologiesSwinburne UniversityMelbourne3122Australia School of Engineering Science Technology Engineering and Mathematics CollegeRMIT UniversityMelbourneVIC 3001Australia Department of Information Technology College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Asia UniversityTaichung City41354TaiwanChina

Information security has emerged as a crucial consideration over the past decade due to escalating cyber security threats,with Internet of Things(IoT)security gaining particular attention due to its role in data communication across various ***,IoT devices,typically low-powered,are susceptible to cyber ***,blockchain has emerged as a robust solution to secure these devices due to its decentralised ***,the fusion of blockchain and IoT technologies is challenging due to performance bottlenecks,network scalability limitations,and blockchain-specific security ***,on the other hand,is a recently emerged information security solution that has great potential to secure low-powered IoT *** study aims to identify blockchain-specific vulnerabilities through changes in network behaviour,addressing a significant research gap and aiming to mitigate future cybersecurity *** blockchain and IoT technologies presents challenges,including performance bottlenecks,network scalability issues,and unique security *** paper analyses potential security weaknesses in blockchain and their impact on network *** developed a real IoT test system utilising three prevalent blockchain applications to conduct *** results indicate that Distributed Denial of Service(DDoS)attacks on low-powered,blockchain-enabled IoT sensor networks cause measurable anomalies in network and device performance,specifically:(1)an average increase in CPU core usage to 34.32%,(2)a reduction in hash rates by up to 66%,(3)an increase in batch timeout by up to 14.28%,and(4)an increase in block latency by up to 11.1%.These findings suggest potential strategies to counter future DDoS attacks on IoT networks.

关键词： Blockchain cyber intrusions DDoS IoT low powered sensors vulnerabilities wireless network

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Fiber Endoscopy Using Synthetic Wavelengths for 3D tissue imaging 2

Fiber Endoscopy Using Synthetic Wavelengths for 3D tissue im...

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Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences II 2025

作者： Balaji, Muralidhar Madabhushi Cornwall, Patrick Liu, Parker Forschner, Stefan Czarske, Jürgen Willomitzer, Florian Wyant College of Optical Sciences University of Arizona United States Department of Electrical & Computer Engineering TU Dresden Germany

ISBN: (纸本)9781510684140

Optical endoscopes utilizing multi-core fiber (MCF) bundles offer the capability to image deep within the human body, making them well-suited for imaging applications in minimally invasive surgery or diagnosis. However, the optical fields relayed through each fiber core can be significantly affected by phase scrambling due to variations in fiber length or the presence of multimode cores. Moreover, obtaining high-quality endoscopic images commonly requires the fiber tip to be placed close to the target or relies on the addition of lenses. Furthermore, imaging through scattering layers after the fiber tip is commonly difficult. In this work, we address these challenges by integrating Synthetic Wavelength Imaging (SWI) into fiber endoscopy. This novel approach enables the endoscopic acquisition of holographic information from objects obscured by scattering layers, such as biological tissue. The resulting endoscopic system eliminates the need for lenses, allows for single-shot 3D imaging and is inherently robust against phase scrambling caused by scattering and fiber bending. Using this technique, we successfully demonstrate the endoscopic imaging of ∼750μm features on an object positioned behind a scattering layer. This advancement holds significant potential for enabling spatially resolved three-dimensional imaging of objects concealed beneath tissue using fiber endoscopes, expanding the capabilities of these systems for novel medical applications. © 2025 SPIE.

关键词： Speckle

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Reflect-and-Amplify: a Novel SWIPT Technique through Hybrid Active/Passive RIS

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IEEE Internet of Things Journal 2025年第12期12卷 22073-22089页

作者： Ata, Yalcin Zedini, Emna Vegni, Anna Maria Alouini, Mohamed-Slim Ostim Technical University Ostim Department of Electrical and Electronics Engineering Yenimahalle Ankara06374 Turkey University of Michigan-Flint College of Innovation and Technology FlintMI United States Roma Tre University Department of Industrial Electronics and Mechanical Engineering Rome Italy Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia

This paper presents a novel technique for the simultaneous wireless information and power transfer (SWIPT) toward low-power devices, by means of Reconfigurable Intelligent Surfaces (RISs). Our approach is based on a Reflect-and-Amplify protocol meaning that, based on the RIS beam-steering functionality and assuming different reflection coefficients for the RIS elements, it allows to reflect and amplify the impinging wireless signal from the transmitter toward the receiver, which will be able to both collect energy and download data. Specifically, by considering a hybrid RIS comprised of both passive and active elements, the proposed SWIPT approach is defined Active/Passive Switching (APS). The reflection of the wireless beam toward the receiver is accomplished by means of passive RIS elements, while an amplification of the wireless signal is provided through active RIS elements. Such amplification allows the receiver to convert the incoming wave power into a DC voltage, but also causes an increase of power consumption due to active elements. To assess the performance of the proposed SWIPT approach, we derive the closed-form expression of the joint data transmission and energy harvesting outage probability (OP) in case of both a single and multiple users. We compare the proposed technique to other SWIPT approaches from the literature, such as the power splitting and the time switching, under power fairness conditions. Our results validate the benefits of using hybrid RISs for SWIPT systems, as compared to other existing approaches showing reduced performance, also in case of asymptotic analysis for high power and high number of RIS elements. © 2014 IEEE.

关键词： Inductive power transmission

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RAMRL: Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning 1

RAMRL: Towards Robust On-Ramp Merging via Augmented Multimod...

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1st IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Bagwe, Gaurav Yuan, Xiaoyong Chen, Xianhao Zhang, Lan Michigan Technological University Department of Electrical and Computer Engineering HoughtonMI United States Michigan Technological University College of Computing HoughtonMI United States University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

ISBN: (纸本)9798350319958

Despite the success of AI-enabled onboard perception, on-ramp merging has been one of the main challenges for autonomous driving. Due to limited sensing range of onboard sensors, a merging vehicle can hardly observe main road conditions and merge properly. By leveraging the wireless communications between connected and automated vehicles (CAVs), a merging CAV has potential to proactively obtain the intentions of nearby vehicles. However, CAVs can be prone to inaccurate observations, such as the noisy basic safety messages (BSM) and poor quality surveillance images. In this paper, we present a novel approach for Robust on-ramp merge of CAVs via Augmented and Multi-modal Reinforcement Learning, named by RAMRL. Specifically, we formulate the on-ramp merging problem as a Markov decision process (MDP) by taking driving safety, comfort driving behavior, and traffic efficiency into account. To provide reliable merging maneuvers, we simultaneously leverage BSM and surveillance images for multi-modal observation, which is used to learn a policy model through proximal policy optimization (PPO). Moreover, to improve data efficiency and provide better generalization performance, we train the policy model with augmented data (e.g., noisy BSM and noisy surveillance images). Extensive experiments are conducted with Simulation of Urban MObility (SUMO) platform under two typical merging scenarios. Experimental results demonstrate the effectiveness and efficiency of our robust on-ramp merging design. © 2023 IEEE.

关键词： Reinforcement learning

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