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International Conference on Broadband communication, Wireless Sensors and Powering

作者： Hamza A. Mashaghba Hasliza A. Rahim Ping Jack Soh Mohamedfareq Abdulmalek Ismahayati Adam Muzammil Jusoh Mohd Najib Mohd Yasin Thennarasan Sabapathy Khairul Najmy Abdul Rani Advanced Communication Engineering (ACE) Centre of Excellence Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Faculty of Engineering and Information Sciences University of Wollongong in Dubai Dubai United Arab Emirates

ISBN: (数字)9781728174501

ISBN: (纸本)9781728174518

This paper proposes the design of 1×4 patch array all-textile antenna for Wireless Local Area Networks (WLAN) applications. The wearable antenna needs to have low profile and lightweight since such antenna is intended to operate in the vicinity of the human body. The key parameters are studied to determine their effects towards the performance of the antenna. The proposed design uses ShieldIt as the top radiator and ground plane, while fabric Felt is used as a substrate, sandwiched between the top radiator and ground *** obtained results show that there is improvement in the proposed array antenna in terms of gain enhancement and impedance bandwidth, maximum up to 143.6% and 19.08%, respectively, against single patch structure.

关键词： Wireless LAN Wireless sensor networks Specific absorption rate Broadband antennas Antenna arrays Sensor arrays Periodic structures

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Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

arXiv

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arXiv 2021年

作者： Wang, Shen Qureshi, M. Atif Miralles-Pechuán, Luis Huynh-The, Thien Gadekallu, Thippa Reddy Liyanage, Madhusanka The School of Computer Science University College Dublin Ireland ADAPT Centre eXplainable Analytics Group Faculty of Business Technological University Dublin Ireland The School of Computing Technological University Dublin Central Quad Grangegorman Dublin Ireland The Department of Computer and Communication Engineering Ho Chi Minh City University of Technology and Education Viet Nam The School of Information Technology and Engineering Vellore Institute of Technology India The Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made in real-time. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed, data-intensive AI decision-making beyond designers' and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This paper surveys the application of XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for 6G in the near future. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

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Bending Assessment of Dual-band Split Ring-shaped and Bar Slotted All-Textile Antenna for Off-body WBAN/WLAN and 5G Applications

Bending Assessment of Dual-band Split Ring-shaped and Bar Sl...

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International Conference on Broadband communication, Wireless Sensors and Powering

作者： Hamza A. Mashaghba Hasliza A. Rahim Ping Jack Soh Mohamedfareq Abdulmalek Ismahayati Adam Muzammil Jusoh Thennarasan Sabapathy Mohd Najib Mohd Yasin Khairul Najmy Abdul Rani Advanced Communication Engineering (ACE) Centre of Excellence Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Faculty of Engineering and Information Sciences University of Wollongong in Dubai Dubai United Arab Emirates

ISBN: (数字)9781728174501

ISBN: (纸本)9781728174518

This paper presents a dual-band split ring-shaped and bar slotted textile antenna for potential WBAN/WLAN and 5G applications. The antenna is made using textiles and features a full ground plane to possibly alleviate coupling to the human body. The overall size of the antenna is 70 × 70 mm 2 , with a patch sized at 47.2 × 31 mm 2 0.472λ×0.031λ. The antenna is made using ShieldIt Super as its conductive textile and felt as its substrate. To enable its dual-band resonance at 2.45 and 3.5 GHz a split ring-shaped and bar slots are integrated onto the patch. . The proposed antenna is evaluated when bent under different radii and at different axes to estimate its performance in terms of reflection coefficient, bandwidth, efficiency and gain. A 10-dB impedance bandwidth of 57 % or 135 MHz (from 2.39 to 2.52 GHz) and 70 % or 240 MHz (from 3.45 to 3.56 GHz) are obtained when evaluated in the planar /bent configuration. The maximum realized gain is 6 dBi for at 3.5 GHz. These performances indicate that the antenna proposed in this work can be potentially improved for applications in WBAN/WLAN and 5G bands.

关键词： Slot antennas 5G mobile communication Dual band Microstrip antennas Broadband antennas Antennas Textiles

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Artificial intelligence performance evaluation for URLLC of industrial IoT applications: A review, open challenges and future directions

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Physical communication 2025年 72卷

作者： Abdinasir Hirsi Lukman Audah Adeb Salh Mohammed Alhartomi Zhili Sun Ahmed Hammoodi Salman Ahmed Advanced Telecommunication Research Center (ATRC) Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Malaysia Faculty of Engineering Jamhuriya University of Science and Technology Mogadishu Somalia Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Faculty of Information and Communication Technology University Tunku Abdul Rahman (UTAR) Kampar 31900 Malaysia Department of Electrical Engineering University of Tabuk Tabuk 71491 Saudi Arabia Institute of Communication Systems (ICS) School of Computer Science and Electronic Engineering Faculty of Physical Science and Engineering University of Surrey UK Renewable Energy Research Centre Department University of Anbar Ramadi Iraq VLSI and Embedded Technology (VEST) Focus Group Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Malaysia

The integration of fifth-generation/sixth-generation (5G/6G) ultra-reliable low-latency communication (URLLC) with industrial Internet of Things (IIoT) applications is revolutionizing Industry 4.0, and enhancing IIoT performance through artificial intelligence (AI) simulations. The critical need for low latency and high reliability in IIoT devices can be effectively addressed by leveraging AI techniques, optimizing data processing, and making decisions in real time. Traditional methods achieve some level of efficiency and safety, but AI offers significant improvements in decision-making, safety, quality prediction, and employee adoption. Integrating AI into IIoT applications enhances industrial workflows, while presenting opportunities and challenges. Machine learning (ML) and deep learning (DL) algorithms enable industrial applications to operate efficiently and intelligently. This paper outlines the requirements for reliable and low-latency communication links between IIoT devices and primary research areas where AI algorithms can be employed, such as fault diagnosis, intelligent anomaly detection, edge computing, network performance, and intrusion detection systems in IIoT applications. Special attention is paid to the role of AI techniques in enhancing IIoT system performance and efficiency, highlighting its advantages, applications, and challenges. The current state-of-the-art challenges and future directions of AI in IIoTs are discussed, providing insights for further research. Potential areas for further research include developing new techniques, integrating 5G/6G technologies, autonomous decision-making, self-optimization, addressing mission-critical applications, and shifting AI processing to the edge. This comprehensive review will benefit academics, researchers, professionals in AI and IIoT, and industries seeking to leverage AI technologies to enhance IIoT performance and efficiency.

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UHF RFID tag antennas for wearable devices

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AIP Conference Proceedings 2023年第1期2579卷

作者： Saidatul Norlyana Azemi Wan Nur Azreen Azemin Amiza Amir Rashidi Che Beson Nornikman Hassan Faculty of Electronic Engineering Technology Universiti Malaysia Perlis (UniMAP) Kampus Alam Unimap Pauh Putra 02600 Arau Perlis Malaysia Advanced Communication Engineering Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia Advanced Computing Centre of Excellence Advanced Computing (AdvComp) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia Center for Telecommunication Research and Innovation (CeTRI) Faculty of Electronic and Computer Engineering (FKEKK) Universiti Teknikal Malaysia Melaka (UTeM) Hang Tuah Jaya Durian Tunggal Melaka Malaysia

RFID technology is evolving as one of the most popular technologies in this era of technology, fast gaining attention due to high demand from users and rapidly garnering interest in scientific and commercial areas. The frequency used will be determined by the RFID application, and the power rate will change as the frequency increases. Without a straight line of sight, the RFID tag antenna can identify distinct objects. For wearable applications, several RFID tag antennas are too big to match the chip impedance. In order to overcome that problem, a small tag RFID tag antenna for UHF is designed which aimed to be operated from 865 to 867 MHz for assembling production. Impedance matching is used to transforms the impedance of the radiating antenna, to match the chip impedance. To design and simulate the designed antenna, CST Microwave Studio software has been used in this project to get the desired result which is the return loss and gain. The design for this antenna is very simple to ease the fabrication process. Overall, the construction comprises a spiral-shaped loop RFID tag antenna printed on Roger substrate RO4350 with a height of 0.8 mm and a dielectric constant of 3.3. The simulation result of the reflection coefficient of the antenna is 866 MHz at the operating frequency.

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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A Novel Approach in Fabricating TiO2 Photoanodes for Dye-Sensitized Solar Cells

SSRN

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SSRN 2022年

作者： Yeoh, Mian-En Chan, Kah-Yoong Wong, Hin-Yong Low, Pei-Ling Thien, Gregory Soon How Ng, Zi-Neng Murthy, Hanabe Chowdappa Ananda Balachandran, Ruthramurthy Centre for Advanced Devices and Systems Faculty of Engineering Multimedia University Persiaran Multimedia Selangor Cyberjaya63100 Malaysia Sdn. Bhd. Kulim Hi-Tech Park Kedah Kulim09090 Malaysia Department of Electrical and Electronics Engineering Xiamen University Malaysia Selangor Sepang43900 Malaysia Department of Applied Chemistry School of Applied Natural Science Adama Science and Technology University PO Box 1888 Adama Ethiopia Saveetha University Tamil Nadu Chennai600 077 India Department of Electronics and Communication Engineering School of Electrical Engineering and Computing Adama Science and Technology University P O Box 1888 Adama Ethiopia

Titanium dioxide (TiO2) is one of the widely utilized nanomaterials in multiple applications, particularly in photovoltaics. Thus, the hydrothermal method is a widely adopted technique in synthesizing TiO2 photoanodes for DSSCs. Despite that, most reported synthesis routes integrating the hydrothermal TiO2 photoanodes process were intricate and complex. In this work, a reinvented novel methodology for the hydrothermal synthesis of anatase-rutile mixed-phase TiO2 photoanodes was developed by omitting several intermediate steps. Hence, this rapid and simplified technique produced a time-efficient process for producing DSSCs. By employing the reinvented methodology, DSSC devices fabricated through this approach presented a comparable and excellent power conversion efficiency (η = 3.30%) when compared with the commercial TiO2-based DSSCs (η = 3.81%). Resultantly, this preliminary study provides new perspectives toward simplifying wet chemical synthesis routes for possible DSSC commercialization. © 2022, The Authors. All rights reserved.

关键词： Titanium dioxide

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Corrigendum to “Hybridizing flower pollination algorithm with particle swarm optimization for enhancing the performance of IPv6 intrusion detection system” [Alex. Eng. J. 104 (2024) 504–514]

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Alexandria Engineering Journal 2025年 110卷 376-376页

作者： Adnan Hasan Bdair AIghuraibawi Selvakumar Manickam Zaid Abdi Alkareem Alyasseri Rosni Abdullah Ayman Khallel Riyadh Rahef Nuiaa Al Ogaili Fahd N. Al-Wesabi Abdulsamad Ebrahim Yahya National Advanced IPv6 Centre (NAv6) Universiti Sains Malaysia Penang 11800 Malaysia Baghdad College of Economic Sciences University Baghdad Iraq Information Technology Research and Development Center University of Kufa Najaf Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq School of Computer Sciences Universiti Sains Malays Penang 11800 Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia Department of Cybersecurity Engineering Technology Al Hikma University College Baghdad Iraq Department of Computer Science/ College of Computer Science and Information Technology Wasit University Kut Wasit 52001 Iraq Department of Computer Science College of Science & Art at Mahayil King Khalid University Saudi Arabia Department of Information Technology College of Computing and Information Technology Northern Border University Arar Saudi Arabia

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Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming

arXiv

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arXiv 2021年

作者： Zheng, Yizhen Jin, Ming Pan, Shirui Li, Yuan-Fang Peng, Hao Li, Ming Li, Zhao Department of Data Science and AI Faculty of IT Monash University ClaytonVIC3800 Australia School of Information and Communication Technology Griffith University Australia SouthportQLD4222 Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University Jinhua321004 China Alibaba Group Hangzhou311100 China

Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information, which is normally expensive to obtain in the real world. Although some existing works aim to effectively learn graph representations in an unsupervised manner, they suffer from certain limitations, such as the heavy reliance on monotone contrastiveness and limited scalability. To overcome the aforementioned problems, in light of the recent advancements in graph contrastive learning, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme. Specifically, this mechanism enables G-Zoom to explore and extract self-supervision signals from a graph from multiple scales: micro (i.e., node-level), meso (i.e., neighborhood-level), and macro (i.e., subgraph-level). Firstly, we generate two augmented views of the input graph via two different graph augmentations. Then, we establish three different contrastiveness on the above three scales progressively, from node, neighboring, to subgraph level, where we maximize the agreement between graph representations across scales. While we can extract valuable clues from a given graph on the micro and macro perspectives, the neighboring-level contrastiveness offers G-Zoom the capability of a customizable option based on our adjusted zooming scheme to manually choose an optimal viewpoint that lies between the micro and macro perspectives to better understand the graph data. Additionally, to make our model scalable to large graphs, we employ a parallel graph diffusion approach to decouple model training from the graph size. We have conducted extensive experiments on real-world datasets, and the results demonstrate that our proposed model outperforms state-of-the-art methods consistently. © 2021, CC BY.

关键词： Graph neural networks

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Pap smear images classification based on surrounding tissues: A comparative study

引用

AIP Conference Proceedings 2024年第1期2934卷

作者： Mohammed Alsalatie Wan Azani Mustafa Lugain Nammneh Feras Fares Almashakbah Hiam Alquran Yasmin Mohd Yacob The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman 11855 Jordan Faculty of Electrical Engineering Technology Campus Pauh Putra Universiti Malaysia Perlis 02000 Arau Perlis Malaysia Advanced Computing Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) 02000 Arau Perlis Malaysia Faculty of Medicine Jordan University of Science and Technology Ramtha Irbid 22110 Jordan Information Technology Faculty Internet Technology Department Zarqa University Zarqa 13110 Jordan Department of Biomedical Engineering Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556 Irbid 21163 Jordan Faculty of Electronic Engineering Technology Campus Pauh Putra Universiti Malaysia Perlis 02000 Arau Perlis Malaysia

Cervical cancer is one of the most known health problems faced by women around the world. Early detection of cervical cancer may reduce the mortality rate. Pap smear images are new techniques used for screening cervical cancer. This paper excludes the nucleus of pap smear images, and the resultant images are classified into seven classes based on the surrounding region nucleus. Automated features are extracted using three pre-trained convolutional neural networks (CNN). The resultant features are twenty-one. The principal component analysis reduces the dimensionality and selects the most significant features into ten features. These features are fed to two types of machine learning algorithms: support vector machine (SVM) classifier and random forest classifier. The support vector machine classifier achieved the highest accuracy for seven classes, reaching 93.1%. This method will help the physicians in the diagnosis of cervical cancer depending on the tissues, not the nucleus. Furthermore, the result can be enhanced using a huge amount of data.

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