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Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 496-509页

作者： Ruhul Amin Khalil Nasir Saeed Mohammad Inayatullah Babar Tariqullah Jan Sadia Din IEEE Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar 25120Pakistan the Department of Electrical Engineering Northern Border UniversityArar 73222Saudi Arabia College of Science and Engineering Hamad Bin Khalifa UniversityDoha 34110Qatar

Localization of sensor nodes in the internet of underwater things(IoUT)is of considerable significance due to its various applications,such as navigation,data tagging,and detection of underwater ***,in this paper,we propose a hybrid Bayesian multidimensional scaling(BMDS)based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical,magnetic induction,and acoustic *** communication technologies are already used for communication in the underwater environment;however,lacking localization *** and magnetic induction communication achieves higher data rates for short *** the contrary,acoustic waves provide a low data rate for long-range underwater *** proposed method collectively uses optical,magnetic induction,and acoustic communication-based ranging to estimate the underwater sensor nodes’final ***,we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound(H-CRLB).Simulation results provide a complete comparative analysis of the proposed method with the literature.

关键词： Bayesian multidimensional scaling(BMDS) hybrid Cramer-Rao lower bound(H-CRLB) internet of underwater things(IoUT) signals of opportunity(SOA)approach

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Time-Synchronized Formation Control of Unmanned Surface Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-9页

作者： Wang, Duansong Ge, Shuzhi Sam Liang, Xiaoling Li, Dongyu School of Electrical and Photoelectronic Engineering West Anhui University LuAn China Department of Electrical and Computer Engineering National University of Singapore Singapore College of Marine Engineering Dalian Maritime University Dalian China School of Cyber Science and Technology Beihang University Beijing China

Time-synchronization (TS) formation control for unmanned surface vehicles (USVs) presents several advantages, including precise execution of tasks, broadened combat capabilities, and improved information fusion quality. To achieve this performance, a time-synchronized formation control method is presented that takes into account direct topology, external disturbances, and system uncertainties (EDSU). In contrast to prior formation control strategies, we introduce the formalized time-synchronized formation control framework, where all state components of the formation system concurrently converge to the equilibrium point at a uniform time constant, independently of their initial states. To counteract the EDSU, a fixed-time disturbance observer is designed to guarantee the convergence of all observer error components to zero. System stability is corroborated through the application of Lyapunov-like theory. Simulations and comparative experiments on three USVs are conducted to demonstrate the proposed method's superiority. IEEE

关键词： Synchronization

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A lightweight deep learning model with knowledge distillation for pulmonary diseases detection in chest X-rays

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Multimedia Tools and Applications 2025年第15期84卷 14885-14913页

作者： Asham, Mohammed A. Al-Shargabi, Asma A. Al-Sabri, Raeed Meftah, Ibrahim School of Computer Science and Engineering Central South University Hunan Changsha410083 China Department of Information Technology Collage of Computer Qassim University Buraydah51452 Saudi Arabia College of Mechanical and Electrical Engineering Central South University Changsha410017 China

Accurate and timely diagnosis of pulmonary diseases is critical in the field of medical imaging. While deep learning models have shown promise in this regard, the current methods for developing such models often require extensive computing resources and complex procedures, rendering them impractical. This study focuses on the development of a lightweight deep-learning model for the detection of pulmonary diseases. Leveraging the benefits of knowledge distillation (KD) and the integration of the ConvMixer block, we propose a novel lightweight student model based on the MobileNet architecture. The methodology begins with training multiple teacher model candidates to identify the most suitable teacher model. Subsequently, KD is employed, utilizing the insights of this robust teacher model to enhance the performance of the student model. The objective is to reduce the student model's parameter size and computational complexity while preserving its diagnostic accuracy. We perform an in-depth analysis of our proposed model's performance compared to various well-established pre-trained student models, including MobileNetV2, ResNet50, InceptionV3, Xception, and NasNetMobile. Through extensive experimentation and evaluation across diverse datasets, including chest X-rays of different pulmonary diseases such as pneumonia, COVID-19, tuberculosis, and pneumothorax, we demonstrate the robustness and effectiveness of our proposed model in diagnosing various chest infections. Our model showcases superior performance, achieving an impressive classification accuracy of 97.92%. We emphasize the significant reduction in model complexity, with 0.63 million parameters, allowing for efficient inference and rapid prediction times, rendering it ideal for resource-constrained environments. Outperforming various pre-trained student models in terms of overall performance and computation cost, our findings underscore the effectiveness of the proposed KD strategy and the integration of the Conv

关键词： Diagnosis

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An Innovation of Energy Harvesting for Small Scale Robotics in Automation Industry 2

An Innovation of Energy Harvesting for Small Scale Robotics ...

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2nd IEEE International Conference on Distributed computing and electrical Circuits and Electronics, ICDCECE 2023

作者： Mehrotra, Tushar Shetty, Sahana Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Robotics is a rapidly emerging technology in the automation industry, and small-scale robotics is becoming increasingly prevalent in automation applications. Small-scale robotics can be used to automate a variety of manufacturing processes, such as assembly, welding, and painting. Small-scale robotics can be used to improve efficiency and reduce labor costs, allowing companies to produce products faster and more reliably with fewer resources. Small-scale robots are typically designed with a smaller size and weight than traditional industrial robots, making them suitable for applications with limited space and weight requirements. Small-scale robots are capable of performing a wide range of tasks, including pick-and- place operations, vision-guided navigation, logistics, and even complex manipulation tasks. Small-scale robots can provide a number of benefits to automation operations. For example, they can reduce labor costs by automating manual tasks. Small-scale robots are also capable of performing highly accurate and repeatable tasks, which can improve the quality of products. Additionally, small-scale robots can be used to increase throughput and reduce cycle times, enabling companies to produce more products in less time. © 2023 IEEE.

关键词： Wages

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems Science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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Enhanced Energy Optimization of Structured Random Matrix Model in Industrial Automation 2

Enhanced Energy Optimization of Structured Random Matrix Mod...

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2nd IEEE International Conference on Distributed computing and electrical Circuits and Electronics, ICDCECE 2023

作者： Bishnoi, Amit Kumar Vivek, V. Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The Structured Random Matrix (SRM) model is an increasingly popular approach to industrial automation. This model is based on the idea that the control systems of industrial machines are "structured"in a certain way, with certain properties that can be predicted and used to model the behavior of the system. SRM incorporates the principles of random matrix theory in order to provide an efficient, reliable, and realistic model for industrial automation. The SRM model works by taking a large set of variables, such as the type of machine, the speed of the machine, the number of required parts, and the number of different components to be used in the system. These variables are then used to generate a random matrix, which can then be analyzed to identify patterns and correlations between the different variables. This allows engineers to develop more accurate models of the system, as well as to identify potential problems and solutions. In addition to providing a more accurate model, the SRM model also allows engineers to simplify the design process by reducing the number of variables used in the system. This simplification means that fewer parts are needed in the system, which reduces the cost and complexity of the system. Furthermore, by reducing the complexity of the system, engineers can reduce the amount of time spent on maintenance and repairs, which saves money and increases efficiency. © 2023 IEEE.

关键词： Automation

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An Innovation Development of Reliable Routing System Based on Industrial Conveyor Weighting and Management System 2

An Innovation Development of Reliable Routing System Based o...

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2nd IEEE International Conference on Distributed computing and electrical Circuits and Electronics, ICDCECE 2023

作者： Kanniga, Devi Kumari, Neeraj Department of Computer Science and Engineering Bangalore India Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India

ISBN: (纸本)9798350347456

Reliable routing systems for industrial conveyor weighing are essential for ensuring accurate weight readings and efficient operation. Conveyor weighing systems are used in a variety of industrial contexts, including food production and packaging, chemical processing, and the manufacturing of heavy equipment. The reliability of these systems is critical for the successful operation of these industries. The primary components of a reliable routing system for industrial conveyor weighing are the conveyor belt, the load sensors, and the control system software. The conveyor belt is the most essential part of the system, as it carries the material being weighed. The load sensors measure the weight of the material as it moves along the conveyor belt and sends this information to the control system. The software then processes the weight data and sends commands to the conveyor belt, adjusting its speed and direction to ensure accurate weight readings. A reliable routing system should also include safety features to protect operators and equipment. Sensors should be placed near the conveyor belt to detect any potential hazards and notify the operator accordingly. The system should also feature emergency stop buttons to quickly shut down the conveyor belt in the event of an emergency. Additionally, the system should be equipped with alarms and other warning systems to alert operators of any potential issues. © 2023 IEEE.

关键词： Chemical industry

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Human Fatty Liver Monitoring Using Nano Sensor and IoMT

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Intelligent Automation & Soft computing 2023年第2期35卷 2309-2323页

作者： Srilekha Muthukaruppankaruppiah Shanker Rajendiran Nagalingam Priya Murugasen Rajesh Nandaamarnath Department of Electrical and Electronics Engineering Saveetha Engineering CollegeChennai602105India Department of Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringChennai600055India Department of Physics Saveetha Engineering CollegeChennai602105India Department of Medical Gastroentrology SRM Medical College Hospital and Research CentreChennai603203India

Malfunction of human liver happens due to non-alcoholic fatty *** liver measurement is used for grading hepatic steatosis,ﬁbrosis and *** various imaging techniques for measuring fatty liver are Magnetic Reso-nance Imaging,Ultrasound and Computed *** modalities lead to the exposure of harmful radiation of electromagnetic waves because of frequent *** continuous monitoring of fatty liver is never achieved through imaging *** this paper,the human fatty liver measured through a Fatty Liver Sensor(FLS).The continuous monitoring of the fatty liver is achieved through the *** is fabricated through the screen-printing with materials such as graphene and *** fatty liver sensor is placed around the liver surface for continuous measuring fatty *** signal acquired from the fatty liver sensor is processed using blind source separation,aﬁltering technique removes the random noise from the acquired *** denoised signal is pro-cessed with tunable Q wavelet transform(TQWT),of FLS based fatty liver mea-surement fatty liver *** continuous fatty liver volume measured and analysis are performed through Long-short term memory and internet of medical things(IoMT).The experimental results are validated with ultrasound lab values.

关键词： Fatty liver sensor ultrasound tunable Q wavelet transform non-alcoholic fatty liver disease long short-term memory

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Optimizing Energy Efficiencies of IoT-based Wireless Sensor Network Components for Metaverse Sustainable Development using Carry Resist Adder based Booth Recoder (CRABRA) 20

Optimizing Energy Efficiencies of IoT-based Wireless Sensor ...

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20th International Learning and Technology Conference, L and T 2023

作者： Kumar J, Charles Rajesh Majid, M.A. Effat University College of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350300307

Wireless sensing is now the spine of diverse Internet of Things (IoT) applications. In the Metaverse, the Internet of Things (IoT) can offer wireless and seamlessly integrated immersive digital experiences. Because the Metaverse's enabling technologies are considered to be energy-hungry, questions have been raised concerning the sustainability of its widespread adoption and development. IoT-based wireless sensor networks (WSN) readings are contaminated and distorted by noise. The noise in the signal causes the sensor node's (SN) computations and power consumption to rise, shortening the sensor node's longevity. To reduce noise, an efficient technique is therefore crucial. Finite-impulse response (FIR) filter is commonly employed in IoT-based WSN as a signal pre-processing stage in eliminating noise from the sensor measurements. The multiplication operation's number of adders (logic operators) and the adder steps (logic depths) determine the hardware complexities of FIR filters. The speed of the related application is determined by the multiplier's speed. By reducing the partial product (PP) row, the Booth method speeds up multiplication. The coefficients used by R8BR are ±0,±1 ,±2,±3,and ±4. As a result of the formation of odd multiples ±3, there will be a delay. The adder is required to add ±1 and ±2 for its calculations. This slowdown the multiplication procedures and reduces the recoding performance. To reduce the delay brought on by the creation of odd multiples, a carry resists adder (CRA) is used. CRA was explicitly built to achieve adding of ±2 and ±1 without carry propagation. Theoretically, it is observed that the CRA minimizes delay to 86.26% compared to carry propagation adder (CPA) approaches. Additionally, compared to a typical R8BR multiplier, the experimental findings indicated delay, area, and power reductions of 48.98%, 56.66%, and 31.2%, respectively. Without carry propagating, the CRA does addition faster, with less energy, and occupies less area.

关键词： Energy efficiency

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Integrated preparation and manipulation of high‑dimensional flying structured photons

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eLight 2024年第1期4卷 185-192页

作者： Haoqi Zhao Yichi Zhang Zihe Gao Jieun Yim Shuang Wu Natalia MLitchinitser Li Ge Liang Feng Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Materials Science and Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Electrical and Computer Engineering and Department of Physics Duke UniversityDurhamNC 27708USA Department of Engineering Science and Physics College of Staten IslandCUNYStaten IslandNY 10314USA The Graduate Center CUNYNew YorkNY 10016USA

The hope for a futuristic global quantum internet that provides robust and high-capacity quantum information transfer lies largely on qudits,the fundamental quantum information carriers prepared in high-dimensional superposition ***,preparing and manipulating N-dimensional flying qudits as well as subsequently establishing their entanglement are still challenging tasks,which require precise and simultaneous maneuver of 2(N-1)parameters across multiple degrees of ***,using an integrated approach,we explore the synergy from two degrees of freedom of light,spatial mode and polarization,to generate,encode,and manipulate flying structured photons and their formed qudits in a four-dimensional Hilbert space with high quantum fidelity,intrinsically enabling enhanced noise resilience and higher quantum data *** four eigen spin–orbit modes of our qudits possess identical spatial–temporal characteristics in terms of intensity distribution and group velocity,thereby preserving long-haul coherence within the entirety of the quantum data transmission *** leveraging the bi-photon entanglement,which is well preserved in the integrated manipulation process,we present versatile spin–orbit cluster states in an extensive dimensional Hilbert *** cluster states hold the promise for quantum error correction which can further bolster the channel robustness in long-range quantum communication.

关键词： cluster thereby preparing

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