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arXiv 2021年

作者： Ukwandu, Elochukwu A. Okafor, Ephraim N.C. Ikerionwu, Charles Olebara, Comfort Ugwu, Celestine Department of Computer Science Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom Department of Electrical and Electronic Engineering School of Engineering and Engineering Technology Federal University of Technology Imo State Owerri Nigeria Department of Software Engineering School of Information and Communication Technology Federal University of Technology Imo State Owerri Nigeria Department of Computer Science Faculty of Physical Science Imo State University Imo State Owerri Nigeria Department of Computer Science Faculty of Physical Science University of Nigeria Enugu State Nsukka Nigeria

The fourth industrial revolution (4IR) widely known as Industry 4.0 is a revolution many authors believe have come to stay. It is a revolution that has been fast blurring the line between physical, digital, and biological technologies. These disruptive innovative revolutionary technologies largely rely on high-speed internet connectivity, Cloud technologies, Augmented Reality, Additive Manufacturing, Data science, and Artificial Intelligence. Most developed economies have embraced the revolution while the developing economies are struggling to adopt 4IR because they lack the requisite skills, knowledge, and technology. Thus, this study investigates Nigeria as one of the developing economies to understand her readiness for 4IR and the level of preparedness to mitigate the sophisticated cyber-attacks that comes with the revolution. The investigation adopted quantitative research approach and developed an online questionnaire that was shared amongst the population of interest that includes academic, industry experts, and relevant stakeholders. The questionnaire returned 116 valid responses which were analysed with descriptive statistical tools in SPSS. Results suggest that 60% of the respondents are of the opinion that the Nigerian government at all levels are not showing enough evidence to demonstrate her preparedness to leverage these promised potentials by developing 4IR relevant laws, strong institutional frameworks and policies. They lack significant development capacity to mitigate risks associated with digital ecosystem and cyber ecosystem that are ushered in by the 4IR. In the universities, 52% of the courses offered at the undergraduate and 42% at the post-graduate levels are relevant in the development of skills required in the revolution. The study recommends that the government at all levels make adequate efforts in developing the country's intangible assets such as educational and social capitals as well as strong institutional frameworks, policies and law

关键词： Internet of things

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Companding techniques for SC-FDMA and sensor network applications

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International Journal of electronics Letters 2020年第3期8卷 241-255页

作者： Abd El-Rahman, Amal F Ahmed, Rania A Abd Elnaby, Mustafa M. Khamis, Salah Napoleon, Sameh Abd-Elnaby, Mohammed Ahmed, Zeinab A El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Electronics and Communications Department Faculty of Engineering Tanta University Tanta Egypt Training Evaluating and Updating Expert Systems Central Laboratory for Agricultural Expert System Giza Giza Egypt Department of Electronics and Electrical Communications Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Engineering College of Computers and Information Technology Taif University Al-Hawiya Saudi Arabia

The large Peak-to-Average Power Ratio (PAPR) is a serious problem in wireless communication systems for mobile and sensor network applications. This paper presents efficient non-linear techniques to solve the large PAPR problem in Single-Carrier Frequency Division Multiple Access (SC-FDMA) system and Wireless Sensor Network (WSN) systems. The performance of the proposed techniques is investigated over a realistic channel model. Simulation results reveal PAPR reduction while preserving low Bit Error Rate (BER) levels. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： companding DCT PAPR Power Amplifier SC-FDMA

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Securing Industrial Internet of Things Against Botnet Attacks Using Hybrid Deep Learning Approach

TechRxiv

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TechRxiv 2022年

作者： Hasan, Tooba Malik, Jahanzaib Bibi, Iram Khan, Wali Ullah Al-Wesabi, Fahd N. Dev, Kapal Huang, Gaojian The Department of Computer Science COMSATS University Islamabad Pakistan The SnT University of Luxembourg Luxembourg The Department of Computer Science College of Science Art at Mahayil King Khalid University Saudi Arabia The Department of information Systems Faculty of Computer and Information Technology Sana’a University Sana’a Yemen The Department of Institute of Intelligent Systems University of Johannesburg South Africa The Physics and Electronic Information Engineering Henan Polytechnic University China

Industrial Internet of Things (IIoT) formation of richer ecosystem of intelligent interconnected devices while enabling new levels of digital innovation has essentially transformed and revolutionized global manufacturing and industry 4.0. Conversely, the prevalent distributed nature of IIoT, Industrial 5G, underlying IoT sensing devices, IT/OT convergence, Edge Computing, and Time Sensitive Networking makes it an impressive and potential target for cyber-attackers. Multi-variant persistent and sophisticated bot attacks are considered catastrophic for connects IIoTs. Besides, botnet attack detection is extremely complex and decisive. Thus, efficient and timely detection of IIoT botnets is a dire need of the day. We propose a hybrid intelligent Deep Learning (DL)-enabled mechanism to secure IIoT infrastructure from lethal and sophisticated multi-variant botnet attacks. The proposed mechanism has been rigorously evaluated with latest available dataset, standard and extended performance evaluation metrics, and current DL benchmark algorithms. Besides, cross validation of our results are also performed to clearly show overall performance. The proposed mechanisms outperforms in identifying accurately multi-variant sophisticated bot attacks by achieving 99.94% detection rate. Besides, our proposed technique attains 0.066(ms) time that also shows the promising results in terms of speed efficiency. © 2022, CC BY.

关键词： Internet of things

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An Overview on Binary and Non-Binary Codes and Challenges in low Parity-Check -Density 4

An Overview on Binary and Non-Binary Codes and Challenges in...

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4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020

作者： Mahdi, Hussain Falih Hamdi, Mustafa Maad Abood, Mohammed Salah Abdulelah, Aymen Jalil Mohammed, Ruaa Qahtan University of Diyala Faculty of Engineering Computer Engineering Department Baquba Diyala Province Iraq Al-Maarif University College Department of Computer Engineering Techniques Al-Anbar Iraq Beijing Institute of Technology School of Information and Communication Engineering Dept. of Information and Electronic Engineering Haidian District Beijing100081 China Ministry of Education Karkh Directorate of Education First Baghdad Iraq

ISBN: (纸本)9781728190907

An LDPC code is a particular linear block code class with low-density parity-check matrix H, i.e. sparse. Because of this parsity in LDPC codes, encoding is low in difficulty and therefore simple to enforce. LDPC codes also provide a wide spectrum of compensation for efficiency and complexity. The high coding complexity of LDPC codes is a major drawback. Where forward error detection and correction codes have been used in large numbers of storage applications or transmitted over many years by wireless services or wireless communication networks due to unreliable network *** this review paper, we will present the main concept of LDPC and overview on error correcting codes, decoding and encoding, binary code, and Nonbinary code. Also, this paper will explain the challenges in LDPC. © 2020 IEEE.

关键词： Binary Code Error Correcting Codes LDPC Non-Binary

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

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作者： Lianyong Qi Fan Wang Xiaolong Xu Wanchun Dou Xuyun Zhang Mohammad R.Khosravi Xiaokang Zhou School of Computer Science Qufu Normal University School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Department of Computing Macquarie University Department of Computer Engineering Persian Gulf University Department of Electrical and Electronic Engineering Shiraz University of Technology Faculty of Data Science Shiga University

With the continuous development of Io T, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies,which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacyaware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy.

关键词： Traffic flow prediction Multi-party sensors Privacy Locality-Sensitive Hashing

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Dense plasma irradiated platinum with improved spin Hall effect

arXiv

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arXiv 2023年

作者： Kumar, Sachin Manna, Sourabh Mohan, John Rex Shashank, Utkarsh Vimal, Jospeh Mishra, Mayank Gupta, Surbhi Asada, Hironori Fukuma, Yasuhiro Rawat, Rajdeep Singh Medwal, Rohit Department of Physics Indian Institute of Technology Kanpur Kanpur208016 India Natural Sciences and Science Education National Institute of Education Nanyang Technological University 637616 Singapore Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka820-8502 Japan The Ernst Ruska-Centre for Microscopy and Spectroscopy Forschungszentrum Jülich52428 Germany Department of Physics Motilal Nehru National Institute of Technology Barrister Mullah Colony Teliarganj Uttar Pradesh Prayagraj211004 India Department of Electronic Devices and Engineering Graduate School of Science and Engineering Yamaguchi University Japan Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu808-0196 Japan

The impurity incorporation in host high spin orbit coupling materials like platinum has shown improved charge to spin conversion by modifying the up-spin and down-spin electron’s trajectories by bending or skewing them in opposite directions. This enables efficient generation, manipulation, and transport of spin currents. In this study, we irradiate the platinum with non-focus dense plasma to incorporate the oxygen ion species. We systematically analyze the spin Hall angle of the oxygen plasma irradiated Pt films using spin torque ferromagnetic resonance. Our results demonstrate a 2.4 times enhancement in the spin Hall effect after plasma treatment of Pt as compared to pristine Pt. This improvement is attributed to the introduction of disorder and defects in the Pt lattice, which enhances the spin-orbit coupling and leads to more efficient charge-to-spin conversion without breaking the spin orbit torque symmetries. Our findings offer a new method of dense plasma-based modification of material for the development of advanced spintronic devices based on Pt and other heavy metals. Copyright © 2023, The Authors. All rights reserved.

关键词： Spin Hall effect

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Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

arXiv

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arXiv 2021年

作者： Ranasinghe, Don Yasiru Lakshan Weerasooriya, H.K. Herath, S. Bandara Ekanayake, M.P. Herath, H.M.V.R. Godaliyadda, G.M.R.I. Madhujith, Terrence Department of Electrical and Electronic Engineering Faculty of Engineering University of Peradeniya Peradeniya 20400 Sri Lanka Department of Electrical and Computer Engineering University of Maryland College ParkMD21250 United States Department of Food Science and Technology Faculty of Agriculture University of Peradeniya Peradeniya 20400 Sri Lanka

Oil reheating has a significant impact on global health due to its extensive consumption, especially in South Asia, and severe health risks. Nevertheless, food image analysis using multispectral imaging systems(MISs) has not been applied to oil reheating analysis despite their vast application in rapid food quality screening. To that end, the paper discusses the application of a low-cost MSI to estimate the 'reheat cycle count classes' (number of times an oil sample is recursively heated) and identify 'critical classes' at which substantial changes in the oil sample have materialized. Firstly, the reheat cycle count class is estimated with Bhattacharyya distance between the reheated and a pure oil sample as the input. The classification was performed using a support vector machine classifier that resulted in an accuracy of 83.34 % for reheat cycle count identification. Subsequently, an unsupervised clustering procedure was introduced using a modified spectral clustering (SC) algorithm to distinguish critical classes under reheating. In addition, laboratory experiments were performed to ascertain the ramifications of the reheating process with a chemical analysis. The chemical analysis of the coconut oil samples used in the experiment coincided with the chemical analysis results and was statistically significant (p © 2021, CC BY.

关键词： Support vector machines

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Optimized energy efficient virtualization and content caching in 5G networks

arXiv

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arXiv 2021年

作者： Al-Quzweeni, Ahmed N. Lawey, Ahmed Q. Elgorashi, Taisir E.H. Elmirghani, Jaafar M.H. School of Electronic and Electrical Engineering University of Leeds LS2 9JT United Kingdom Computer Technology Department Faculty of Information Technology Imam Ja'afar Al-Sadiq University Baghdad Iraq

Network function virtualization (NFV) and content caching are two promising technologies that hold great potential for network operators and designers. This paper optimizes the deployment of NFV and content caching in 5G networks and focuses on the associated power consumption savings. In addition, it introduces an approach to combine content caching with NFV in one integrated architecture for energy aware 5G networks. A mixed integer linear programming (MILP) model has been developed to minimize the total power consumption by jointly optimizing the cache size, virtual machine (VM) workload, and the locations of both cache nodes and VMs. The results were investigated under the impact of core network virtual machines (CNVMs) inter-traffic. The result show that the optical line terminal (OLT) access network nodes are the optimum location for content caching and for hosting VMs during busy times of the day whilst IP over WDM core network nodes are the optimum locations for caching and VM placement during off-peak time. Furthermore, the results reveal that a virtualization-only approach is better than a caching-only approach for video streaming services where the virtualization-only approach compared to caching-only approach, achieves a maximum power saving of 7% (average 5%) when no CNVMs inter-traffic is considered and 6% (average 4%) with CNVMs inter-traffic at 10% of the total backhaul traffic. On the other hand, the integrated approach has a maximum power saving of 15% (average 9%) with and without CNVMs inter-traffic compared to the virtualization-only approach, and it achieves a maximum power saving of 21% (average 13%) without CNVMs inter-traffic and 20% (average 12%) when CNVMs inter-traffic is considered compared with the caching-only approach. In order to validate the MILP models and achieve real-time operation in our approaches, a heuristic was developed. The heuristic results are comparable to the MILP model results with and without CNVMs inter-traffic. © 2

关键词： Network security

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A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

arXiv

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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： Data fusion

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Fully Automatic Robotic Sensor Assembly 29

Fully Automatic Robotic Sensor Assembly

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29th International Scientific Conference electronics, ET 2020

作者： Tsenev, Valentin Nikolov, Dimitar College of Energy and Electronics Technical University of Sofia Department Applied Electronic and Computer Technology 8 Kliment Ohridski blvd Sofia1000 Bulgaria Technical University of Sofia Faculty of Electronic Engineering and Technologies Department of Electronics 8 Kliment Ohridski blvd Sofia1000 Bulgaria

ISBN: (纸本)9781728174266

The report presents robotic automatic center for sensor assembling. The way to the creation of a fully automatic robotic assembly center, compliant with the requirements of the industry standard INDUSTRY 4.0 and using Lean manufacturing, is described. The way of communication between the individual modules in the robot center and the external environment is considered. The control monitors for operator are described. Based on the shown schemes of development of the robotic assembly complex, the obtained results are measured, analyzed and presented. Conclusions are made, including for future development based on INDUSTRY 4.0 and INDUSTRY 5. © 2020 IEEE.

关键词： Robotic assembly

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