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A Wearable RF Energy Harvester Based on Spoof Surface Plasmonic Antenna Array

A Wearable RF Energy Harvester Based on Spoof Surface Plasmo...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Liu, Zhu Li, Jinxin Xiao, Pei Hunan Normal University Dept. Physics and Electronics Changsha China Hunan University Dept. Computer Science and Electronic Engineering Changsha China Hunan University Dept. Electrical and Information Engineering Changsha China

ISBN: (纸本)9781665452366

This paper presents a wearable RF energy harvester based on spoof surface plasmonic (SSP) antenna array. Four separate antennas are connected by the four bending SSP waveguides, and the SSP antenna array is formed. The loop is applied to excite the SSP structure to generate surface waves and converted to the radiative wave into the space. The rectify circuit is integrated with loop to output DC power and fabricated by the polyimide so that it can conform with different sensing modules. The proposed harvester is fabricated by the conductive and nylon fabrics in a compact and low-profile structure to be easily integrated with clothing. The maximum harvested RF-DC power is 126.24 μ W under a power density of 2.75 μ W/cm2 when the proposed structure is worn on phantom. The simulation and measurement results demonstrate that the wearable RF energy harvester has the potential to power body sensor network. © 2022 IEEE.

关键词： Antenna arrays

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Efficiency Redefined: The Document Scanner App for Precise Entity Identification with Customized NER

Efficiency Redefined: The Document Scanner App for Precise E...

引用

Delhi Section Conference (DELCON), IEEE

作者： Sajid Javid Mohammad Saim Saiful Islam Dept. of Computer Science & Engineering Indraprastha Institute of Information Technology Delhi Delhi India Dept. of Computer Science & Engineering University of Cincinnati Cincinnati OH USA Dept. of Computer Engineering Zakir Husain College of Engineering & Technology Aligarh Muslim University Aligarh India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

In the era of rapid advancements in technology, the efficient digitization of paper-based documents remains a crucial challenge across various domains. The traditional approach to document scanning often struggles with accurate extraction of critical information from unstructured or diverse document formats. This paper presents the development of a web-based document scanner application that leverages Named Entity Recognition (NER) to extract entities from scanned business cards efficiently. The study aims to address the challenge of digitizing and organizing physical documents, with a focus on precise entity extraction. The methodology involves creating a custom dataset of scanned business cards, preprocessing the data, and training an NER model using machine learning techniques. The application is built using the Flask framework and integrates OpenCV for text extraction and SpaCy for NER. The key findings include the model's high F1 score in identifying entities such as names, organizations, designations, emails, and contact details. The application streamlines document digitization by automatically extracting and tagging relevant metadata, minimizing the need for manual data entry. This research contributes to enhancing document management and demonstrates broader applicability for other document types apart from business cards. The implications highlight improvements in workflow automation, data accuracy, and the potential for integrating with larger document management systems.

关键词： Training Accuracy Standards organizations Pipelines Named entity recognition Manuals Machine learning Tagging Data mining Character recognition

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A Deep CNN-based Framework for Distributed Denial of Services (DDoS) Attack Detection in Internet of Things (IoT) 23

A Deep CNN-based Framework for Distributed Denial of Service...

引用

2023 Research in Adaptive and Convergent Systems, RACS 2023

作者： Gupta, Brij B. Gaurav, Akshat Arya, Varsha Kim, Pankoo Department of Computer Science and Information Engineering Asia University Taichung Taiwan Lebanese American University Beirut1102 Lebanon Ronin Institute United States Asia University Taiwan Dept. of Computer Engineering Chosun University Gwangju Korea Republic of

ISBN: (纸本)9798400702280

As the number of connected devices continues to rise, protecting those networks against DDoS attacks is more important than ever. Due to IoT devices' specific features and limited resources, traditional security procedures frequently need to be more successful in detecting and mitigating DDoS assaults in IoT settings. This research offers a new architecture for DDoS attack detection in IoT networks using Deep Convolutional Neural Networks (CNNs). Our methodology fully uses CNNs' innate strengths in detecting DDoS attacks by collecting spatial and temporal patterns in data. We develop a distributed architecture that can install lightweight CNN models to reduce the computational load on low-powered IoT devices. The suggested framework has the potential to significantly improve the safety and reliability of IoT installations, which will encourage the widespread use of this technology. © 2023 ACM.

关键词： Internet of things

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Deep Reinforcement Learning Aided Device Profiling for Anomaly Detection in IIoT Over Zero Trust Security Network

Deep Reinforcement Learning Aided Device Profiling for Anoma...

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2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Singh, Anamika Dhanaraj, Rajesh Kumar Hsu, Ching-Hsien Sharma, Anupam Kumar Kumar, Pardeep School of Computing Science and Engineering Galgotias University U.P Greater Noida India Pune India Asia University Dept. Computer Science and Information Engineering Taiwan Manav Rachna International Institute of Research and Studies India

ISBN: (纸本)9798350341072

The application of internet of things in industries is the fourth revolution of industries and is referred to as industry 4.0 or industrial internet of things. The shifting of manual and conventional approach to a digital platform is witnessed as a key consideration for the emergence of industrial internet of things. Industrial internet of things works around thousands of devices connected remotely and the overall monitoring, functioning and storage is performed virtually by and on devices, hence more prone to cyber threads. With the amplification in the growth and application of more complicated networks, the requirements for the security are also increased significantly. Zero trust security stands as an efficient solution for all the security questions. Zero trust security involves continuous monitoring of devices for verifying the authenticity before permitting the access to network. Continuous monitoring for detecting the honest and malicious device adversely effects the productivity of the IIoT environment. The proposed paper presents a novel approach to generate a unique profile of devices via deep reinforcement learning to avoid irrelevant authentication cycles and produce more optimized results. The proposed work surpasses the existing approaches (CRH-SRP) [1] and Cyber Security via Determinism [2] with enhanced data confidentiality rate by 3.25% and reduced false positive rate by 30.5% respectively. © 2023 IEEE.

关键词： Anomaly detection

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Design Optimization and Performance Analysis of Rectangular Structured Moving Magnet Linear Actuator 4

Design Optimization and Performance Analysis of Rectangular ...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Ahmad, Hussain Jawad, Muhammad Ahmad, Zahoor Ahmad, Aftab Sarhad University of Science & Information Technology Dept. Electrical Engineering Peshawar Pakistan School of Electrical Engineering Southeast University Nanjing China Comsats University Islamabad Abbottabad Campus Dept. of Electrical and Computer Engineering Abbottabad Pakistan School of Electrical Engineering Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9798350335316

This article examines a new linear oscillating actuator (LOA) design that uses rectangular-shaped core materials and permanent magnets (PMs). The paper's primary objective is to examine a novel LOA topology with a rectangular-shaped core and PMs as an alternative to the tubular LOA. A Static core material is housed between the mover's components, and two stator cores with two coils each are placed on either side of the mover. The dimensions of all the parameters are swept for optimization, and the optimal parameter dimension is chosen based on the optimal value of the electromagnetic (EM) force. An analysis is done for output parameters like EM force and stroke. EM force per PM mass of the investigated design of LOA is 67 percent higher than of conventional rectangular-shaped LOA. Additionally, the proposed design's EM force density is 23.8 percent higher than that of the conventional design of LOA. Furthermore, the stroke of the proposed LOA is also feasible and more than most of the designs of the LOA. Additionally, the proposed design of the LOA is simple structure, low-cost, and feasible for fabrication. © 2023 IEEE.

关键词： Topology

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Assessment of IT Governance Maturity Level for Yemeni Telecom Sector Using COBIT 2019 Framework 1

Assessment of IT Governance Maturity Level for Yemeni Teleco...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Ghallab, Abdullatif Alswari, Tahani Alhazbi, Saleh Al-Fuhaidi, Belal Hadwan, Mohammed Al-Fahaidy, Farouk Al-Dowail, Mohammed Maqtary, Naseebah Department of Computer Science University of Science and Technology Sana’a Yemen Department of IT Open University Malaysia Sana’a Yemen Dept. of Computer Science & Eng. Qatar University Doha Qatar Department of IT Qassim University Buraydah Saudi Arabia Department of Electrical Engineering Ibb University Ibb Yemen Department of Information Systems University of Science and Technology Sana’a Yemen

ISBN: (纸本)9798331533557

information technology (IT) plays a crucial role in business operations most organizations rely on IT resources for gaining competitive advantages and creating innovative and continuous values. This increased the importance of enterprise IT governance (EGIT) and developed the recent version of the control objectives for information and related technology (COBIT), COBIT 2019 framework. It identifies the seven components of an effective EGIT system: processes, organizational structures, information flows and items, people, skills and competencies, policies and procedures, culture, ethics and behavior, services, infrastructure, and applications. The EGIT maturity level (ITGML) assessment helps to align the EGIT with strategic objectives. This study aims to assess the ITGML of the Yemeni telecom sector using COBIT 2019 framework. A quantitative method was applied, with a 73.6% response rate (148 out of 201 questionnaires collected). The study revealed that the current ITGML is 3.48, reflecting a defined maturity level. The findings suggested that IT governance practices based on COBIT 2019 are well documented and standardized within the Yemeni telecom sector. © 2024 IEEE.

关键词： Telecommunication industry

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Electricity Generation From Food Waste Using IOT

Electricity Generation From Food Waste Using IOT

引用

Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： M Arunkumar S. Agalya M Bhuvanaeshwari Dept. of Information Technology IFET College of Engineering Villupuram India Dept. of Computer Science and Engineering Surya Group of Institutions Villupuram India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

One inventive solution is to turn food waste into power using the Internet of Things (IoT) to meet the growing need for sustainable energy sources. It turns thermal energy from trash into electricity by utilizing thermoelectric devices and the Seebeck effect. Real-time monitoring and control are made possible with IoT integration. By improving the extraction of energy from liquid or semi-solid waste, this technique reduces waste and promotes a circular energy economy. The system makes use of hardware components such as transformers and voltage regulators. The outcomes of experiments demonstrate its efficacy and possible role in renewable energy. This creative idea, which provides a sustainable route ahead for the production of power from food waste, highlights the importance of IoT in improving waste-to-energy systems.

关键词： Food waste Renewable energy sources Regulators Production Transformers Thermoelectric devices Real-time systems

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Fortifying IoT Devices: AI-Driven Intrusion Detection via Memory-Encoded Audio Signals

Fortifying IoT Devices: AI-Driven Intrusion Detection via Me...

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IEEE Cybersecurity Development (SecDev)

作者： Ramyapandian Vijayakanthan Karley M Waguespack Irfan Ahmed Aisha Ali-Gombe Dept. of Computer & Information Sciences Towson University Dept. of Computer Science & Engineering Louisiana State University Dept. of Computer Science Virginia Commonwealth University

IoT devices have become an increasingly accessible target for evasive attacks, such as botnets, due to insecure network services, deprecated software components, unencrypted data communication, and other vulnerabilities. To address these security concerns, our work makes several significant contributions toward curating datasets and designing and developing a robust and effective Host-Based Intrusion Detection algorithm (HIDS) for IoT devices. The proposed algorithm leverages memory-based fingerprints to train a convolutional neural network (CNN) model. Our approach is based on the premise that despite the heterogeneity of IoT devices, the functionality of each IoT device is often unique and remains relatively constant throughout its lifespan. Thus, to develop an effective IDS algorithm based on anomaly detection, we encode the dynamic IoT device memory into sound wave signals, extract discriminable features such as MFCCs and Chroma features, and pass these deterministic fingerprints as feature vectors to a device-specific CNN model. When trained using three different testbed datasets, our model achieved 100% accuracy for known and anomaly memory instances. Additionally, the evaluation of our features and feature engineering process showed that our model, which is trained with the features from memory-encoded signals, is more reliable and robust than those algorithms that leverage raw memory bytes as feature vectors.

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Improving Fake News Detection with a Mixture of Experts: Leveraging Sentiment Analysis and Sarcasm Detection as Core Expert Models

Improving Fake News Detection with a Mixture of Experts: Lev...

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Advancements in Computing (ICAC), International Conference on

作者： Tharindu Gunasekara Disira Thihan Sansika Kodithuwakku Dinuka Dissanayake Harinda Fernando Samitha Vidhanaarachchi Archchana Sindhujan dept. of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka dept. of Computer Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka dept. of Computer Science and Electronic Engineering University of Surrey United Kingdom

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods.

关键词： Sentiment analysis Analytical models Accuracy Bidirectional control Logic gates Linguistics Transformers Encoding Vectors Fake news

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A Framework for Smart & Secure Vehicle Intelligent Life Monitoring System 1

A Framework for Smart & Secure Vehicle Intelligent Life Moni...

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Dhulipudi, Ramesh Dangeti, Srinivasa Rao Pynam, Venkateswarlu Daniya, T. Das, Prasenjit Maram, Balajee Godavari Institute of Engineering and Technology Department of Cse Rajahmundry India Shri Vishnu Engineering College for Women Department of Information Technology India Jntugv Cev Dept. of It Vizianagaram India AP Rajam India Chandigarh University Dept. of Computer Science and Engineering Punjab Mohali140413 India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal506371 India

ISBN: (纸本)9798350338423

In today's world, vehicle safety has become one of the most concerns for private. Nowadays, existing alarm systems of vehicles can be easily replaced as the necessary tools are equally available. While traveling safety is everyone's primary concern. This analysis explains the model of an efficient data model that can monitor and secure the condition of the vehicle / car. The use of IoT for tracking and monitoring is the components. Internet of Things (IoT) is significant for observing the vehicles, medical treatment and manufacturing approaches. By the uninterrupted growth of Internet of Things, many 'things' are capable. In this framework, a new IoT based framework depended on Field Programmable Gate Array (FPGA) and System on Chip (SoC) that gives unified access for IoT is low as well as high-speed devices with extensibility and configurability. Hence, this model performs better accuracy and security. © 2023 IEEE.

关键词： System-on-chip

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