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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Al-Dulaimi, Aymen Mohammed Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Aliesawi, Salah A. Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq

ISBN: (纸本)9798350369106

Underwater Wireless Sensor Networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low-cost submerged sensors that are mostly connected by way of acoustic media. Sensors are relatively new to underwater research, and thanks to recent technological development, they can now function at different depths. In this article, the author explores what UWSNs must do to operate smoothly by exploring the basic features that they must meet for further development. Real-time Underwater communication remains an open problem due to underwater acoustic channels important for undersea data acquisition, undersea navigation, and undersea remote sensing. To mitigate such challenges and in light of the use of new technologies, researchers seek to improve the reliability and reliability of UWSNs. Finally, there is great potential with UWSNs for investigating and exploiting the underwater environment by improving scenarios of different domains in line with subaqueous data and communications. © 2024 IEEE.

关键词： Wireless sensor networks

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Quantum Calculus-Based Two-Dimensional Least Mean Square Algorithm 58

Quantum Calculus-Based Two-Dimensional Least Mean Square Alg...

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58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Moinuddin, Muhammad Zerguine, Azzedine Al-Saggaf, Ubaid M. Arif, Muhammad King Abdulaziz University Electrical and Computer Engineering Department The Centre of Excellence in Intelligent Engineering Systems Jeddah Saudi Arabia Kfupm Electrical Engineering Department The Interdisciplinary Research Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria The Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs Klagenfurt Austria

ISBN: (纸本)9798350354058

Recent work of the one-dimensional q-LMS algorithm give insight that the Quantum Calculus based gradient called the q-gradient is capable of improving the convergence behavior in contrast to the standard LMS algorithm. The reason for this is the fact that the q-derivative takes larger steps in minimizing a cost function as it evaluates the secant in contrast to the tangent of the cost function. Motivated by this, we propose a two-dimensional version of the q-LMS algorithm. More precisely, we employ Quantum Calculus based q-gradient in steepest descent optimization of mean-square-error (MSE) cost function in 2D adaptive filtering scenario to obtain two-dimensional q-LMS (2D q-LMS) algorithm. A thorough analytical investigation of the proposed 2D q-LMS algorithm in both mean and mean-square sense is provided. Both the transient and steady-state convergence behaviours are examined. Consequently, excess MSE (EMSE) learning curve and its steady-state expression are evaluated in closed form. Simulation results are presented for the application of noise cancelation in images to show the superiority of the proposed 2D q-LMS algorithm. © 2024 IEEE.

关键词： Cost functions

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The development of a generic IIOT framework for an industrial pneumatic system 17

The development of a generic IIOT framework for an industria...

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17th CIRP Conference on Intelligent Computation in Manufacturing engineering, CIRP ICME 2023

作者： Mallia, Jasmine Francalanza, Emmanuel Xuereb, Peter Refalo, Paul Department of Industrial and Manufacturing Engineering Faculty of Engineering University of Malta MsidaMSD2080 Malta Department of Computer Information Systems Faculty of Information & Communication Technology University of Malta MsidaMSD2080 Malta

The development of digital technologies, such as Cyber Physical systems (CPS) and Industrial Internet of Things (IIoT), under the umbrella of Industry 4.0, has increased rapidly within the manufacturing industry. Monitoring system behaviour in real time is becoming prevalent with manufacturing stakeholders to minimise downtime, maintain or improve equipment efficiency, and enhance production output. This paper proposes a generic IIoT framework for the online real time monitoring of system parameters. Such an approach has been implemented on the demand-side of a pneumatic setup, and a visualisation technique was developed for evaluation purposes. The developed framework paves the way towards further data analytics with the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques. © 2024 Elsevier B.V.. All rights reserved.

关键词： Compressed Air Industrial Internet of Things Industry 4.0 Online Monitoring Real Time Data

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Channel Estimation for 29GHz mm Wave MIMO systems in Tropical Region 7

Channel Estimation for 29GHz mm Wave MIMO Systems in Tropica...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Lu, Li Sali, Aduwati School of Microelectronics and Data Science Anhui University of Technology Maanshan243002 China Faculty of Engineering Department of Computer and Communication Systems Engineering Selangor Serdang43400 Malaysia Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350392074

This paper focuses on the 29GHz mmWave MIMO communication system in tropical environments, considering the impact of rain attenuation and providing a detailed analysis of channel estimation and system performance. Experimental results indicate that as the signal-to-noise ratio (SNR) increases, the mean square error (MSE) of channel estimation decreases. Under the same SNR conditions, MIMO systems with different antenna configurations exhibit distinct channel estimation error characteristics. Notably, in the 128x128 antenna configuration, the channel estimation error is most pronounced. Furthermore, increasing the number of training signals significantly reduces channel estimation errors, especially in the 128x128 antenna configuration. This study offers a performance evaluation of the 29GHz mmWave MIMO communication system in tropical regions, providing valuable insights for further research and optimization in this field. © 2024 IEEE.

关键词： Signal to noise ratio

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Intelligent optimisation in smart and sustainable compressed air systems: Towards support for decision-making under faulty conditions 31

Intelligent optimisation in smart and sustainable compressed...

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31st CIRP Conference on Life Cycle engineering, LCE 2024

作者： Mallia, Jasmine Francalanza, Emmanuel Xuereb, Peter Baldacchino, Daniel Refalo, Paul Department of Industrial and Manufacturing Engineering Faculty of Engineering University of Malta MsidaMSD2080 Malta Department of Computer Information Systems Faculty of Information & Communication Technology University of Malta MsidaMSD2080 Malta

The monitoring of complex industrial systems through the implementation of smart approaches provides unique opportunities, such as the characterisation of their real-time performance. Within this scope, there exists the need to support decision-making during maintenance processes, due to the presence of a multitude of faults in real-world systems and the difficulty of identifying the appropriate mitigating solutions. The proposed solution uses intelligent optimisation techniques to identify the ideal control solution within these complex systems when faults arise. This paper presents a framework based on an intelligent optimisation approach, which provides a workflow process for the support of decision-making during faulty situations. It is adapted and implemented to the demand-side of a Compressed Air System (CAS), thus providing a holistic approach in automating fault mitigation during real-time system operation. In implementing this framework, multiple intelligent optimisation techniques such as the Genetic Algorithm and the Particle Swarm Optimisation algorithms were adopted and implemented. Both algorithms were successful in providing the ideal control solution under fault conditions. For a typical production case study, the proposed optimisation approach results in a reduction of 40% in its air consumption, which directly improves its environmental performance and energy costs. This result demonstrates that this approach contributes a suitable control strategy for CAS when experiencing a pneumatic fault, which has a direct positive effect on the operational energy performance and costs. © 2024 The Author(s).

关键词： Decision making

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Artificial Butterfly Optimization based Cluster Head Selection with Energy Efficient Data Aggregation model for Heterogeneous WSN Environment 7

Artificial Butterfly Optimization based Cluster Head Selecti...

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7th International Conference on Computing Methodologies and communication, ICCMC 2023

作者： Venkatasubramanian, S. Vijay, R. HariPrasath, S. Department of Computer Science and Business Systems Saranathan College of Engineering Trichy India Department of Electrical and Electronics Engineering Saranathan College of Engineering Trichy India Department of Electronics and Communication Engineering Saranathan College of Engineering Trichy India

ISBN: (纸本)9781665464086

The WSN is a new and urgent technology with many potential uses, including but not limited to health security, environmental monitoring, etc. Due to lower battery capacity, WSN has a restricted-energy resource. In order to solve the issue of unequal energy consumption among nodes, it is necessary to choose a sensor node from a cluster with more than enough power to make up for the weaker nodes. This paper develops the idea of heterogeneous WSN (H-WSN), which provides supplementary energy to the heterogeneity network. One method that has shown promise in overcoming this difficulty is the clustering technique, which optimizes energy consumption and extends the useful life of a sensor network. Even if the existing approaches function well, the computational complexity may rise due to the usage of a single mobile sink in their studies. As an alternative to communication among each CH and sink through a separate hop, the network uses Multiple Mobile Sinks (MMSs). The combination of the data collection and aggregation mechanism (DCA) and artificial butterfly optimization (ABO) based on CH selection allows for energy-efficient data transfer using MMSs in H-WSN. The CH assortment uses the distance parameter, remaining energy, and regular energy for the suggested energy efficiency model. The NS2 platform hosts the final product of the projected H-WSN. The suggested ABO-CH-DCA approach is superior to the baseline protocols in simulations on various measures, including throughput, network lifespan, remaining energy, dead nodes, and live nodes. © 2023 IEEE.

关键词： Energy utilization

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Lung Cancer Diagnosis and Classification Using Hybrid Neural Network Techniques

Lung Cancer Diagnosis and Classification Using Hybrid Neural...

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2023 International Conference on Sustainable communication Networks and Application, ICSCNA 2023

作者： Yasmin, M. Andrew Roobert, A. Francis Xavier Engineering College Department of Computer Science and Business Systems Tamil Nadu Tirunelveli India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350313987

Lung cancer remains a formidable global health challenge, with early and accurate diagnosis being a critical determinant of patient outcomes. In response to this pressing need, this research presents a novel approach for lung cancer diagnosis and classification utilizing hybrid neural network techniques. By harnessing the synergistic power of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we have developed an integrated model capable of effectively processing diverse data sources to enhance diagnostic precision. The first component, CNNs, is employed for feature extraction from medical imaging data, such as X-ray images and computed tomography (CT) scans. These networks excel in capturing intricate patterns and subtle abnormalities in medical images, contributing to the early detection of lung cancer. The second component, RNNs, leverages sequential patient data, including clinical histories, genetic information, and longitudinal records, providing valuable contextual information for comprehensive analysis. The hybrid neural network model facilitates a holistic approach to lung cancer diagnosis and classification, as it unifies the strengths of both data-driven imaging analysis and sequential data interpretation. Experimental evaluations of this approach reveal its potential to achieve superior accuracy, outperforming traditional methods, and thus allowing for timely and precise cancer diagnosis. This advancement holds significant promise in improving the prognosis and management of lung cancer patients, potentially leading to more personalized treatment strategies and ultimately reducing the burden of this devastating disease. As such, this research contributes to the ongoing efforts to enhance lung cancer diagnosis, making strides towards improved patient care and survival rates. © 2023 IEEE.

关键词： computed tomography Scan Images Convolution Neural Networks Lung Cancer Recurrent Neural Networks X-ray Images

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Data Security Storage Mechanism Based on Blockchain Network

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computers, Materials & Continua 2023年第3期74卷 4933-4950页

作者： Jin Wang WeiOu Wenhai Wang RSimon Sherratt Yongjun Ren Xiaofeng Yu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Control Science and Engineering Zhejiang UniversityHangzhou310058China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China School of Business Nanjing UniversityNanjing210093China

With the rapid development of information technology,the development of blockchain technology has also been deeply *** performing block verification in the blockchain network,if all transactions are verified on the chain,this will cause the accumulation of data on the chain,resulting in data storage *** the same time,the security of data is also challenged,which will put enormous pressure on the block,resulting in extremely low communication efficiency of the *** traditional blockchain system uses theMerkle Tree method to store *** verifying the integrity and correctness of the data,the amount of proof is large,and it is impossible to verify the data in batches.A large amount of data proof will greatly impact the verification efficiency,which will cause end-to-end communication delays and seriously affect the blockchain system’s stability,efficiency,and *** order to solve this problem,this paper proposes to replace the Merkle tree with polynomial commitments,which take advantage of the properties of polynomials to reduce the proof size and communication *** realizing the ingenious use of aggregated proof and smart contracts,the verification efficiency of blocks is improved,and the pressure of node communication is reduced.

关键词： Blockchain cryptographic commitment smart contract data storage

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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An Efficient and Lightweight Commitment Scheme for IoT Data Streams 19

An Efficient and Lightweight Commitment Scheme for IoT Data ...

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19th Annual International Conference on Distributed Computing in Smart systems and the Internet of Things, DCOSS-IoT 2023

作者： Katsika, Angeliki Papageorgiou, Konstantinos Fakis, Alexandros Plagianakos, Vassilis Spathoulas, Georgios University of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece University of the Aegean Department of Information and Communication Systems Engineering Samos Greece Department of Information Security and Communication Technology Gjøvik Norway

ISBN: (纸本)9798350346497

Data integrity is a crucial property for offering Internet of Things (IoT) services. Usually, this is achieved by assuming a high level of trust in the service provider, a practice that induces negative implications. In the present paper, an efficient and lightweight commitment scheme for IoT data streams is presented. The proposed scheme combines KZG polynomial commitments and a blockchain smart contract to enable full data integrity while requiring trust only at the IoT device level. Given that the IoT device operates properly, no tampering can happen at any other point of the IoT workflow, without being noticed, while the protocol offers the option for automated settlement of cases where the operator tampers with users' data. A number of experiments have been conducted to assess the computation/gas fees requirements of the scheme and validate its real-world applicability. © 2023 IEEE.

关键词： Internet of things

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