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Communication and Information technology (AICCIT), Al-Sadiq International Conference on

作者： Thulfiqar H. Mandeel Muhammad Imran Ahmad Noor Aldeen A. Khalid Mustafa Hamid Hassan Mohammed Hayder Kadhim Department of Computer techniques engineering College of Information Technology Imam Ja’afar Al-Sadiq University Muthanna Iraq Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Department of Medical Instruments Engineering Techniques Bilad Alrafidain University College Diyala Iraq College of Information Technology Imam Ja’afar Al-Sadiq University Muthanna Iraq Department of Information Technology Jabir Ibn Hayyan for medical and pharmaceutical sciences Najaf Iraq

In recent years, there have been many proposals to improve the performance of traditional linear discriminant analysis (LDA). One of these is subset-improving linear discriminant analysis (S-LDA), which is based on clustering the whole set of classes into subsets and performing the LDA locally on these subsets. However, this algorithm suffers from an improper mapping of the classes to the corresponding subset during the testing procedure due to the inevitable discrepancy between the images used for training and those for testing. This discrepancy is caused either by spatial distortion or noise. The wrong mapping is severe when the number of data samples is small which is a common scenario in biometric datasets. In this paper, overlapping clustering is proposed for class clustering, to overcome the aforementioned problem. The proposed algorithm outperforms the S-LDA by 35.4% in mapping accuracy when using the PolyU palmprint database, 36.96%, resp. 33.92% when using left resp. Right palm images from the IIT Delhi Touchless Palmprint Database.

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Improvement method for cervical cancer detection: A comparative analysis

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Oncology Research 2021年第5期29卷 365-376页

作者： NUR AIN ALIAS WAN AZANI MUSTAFA MOHD AMINUDIN JAMLOS AHMED ALKHAYYAT KHAIRUL SHAKIR AB RAHMAN RAMI QMALIK Faculty of Electrical Engineering&Technology Universiti Malaysia PerlisUniCITI Alam CampusSungai ChuchuhPadang BesarPerlis02100Malaysia Advanced Computing(AdvCOMP) Centre of ExcellenceUniversiti Malaysia Perlis(UniMAP)Pauh Putra CampusArauPerlis02600Malaysia Faculty of Electronic Engineering&Technology Universiti Malaysia PerlisUniCITI Alam CampusSungai ChuchuhPadang BesarPerlis02100Malaysia Department of Computer Technical Engineering College of Technical EngineeringThe Islamic UniversityNajaf54003Iraq Department of Pathology Hospital Tuanku FauziahKangarPerlis02000Malaysia Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University CollegeBabylonHillah51001Iraq

Cervical cancer is a prevalent and deadly cancer that affects women all over the *** affects about 0.5 million women anually and results in over 0.3 million *** of this cancer was previously done manually,which could result in false positives or *** researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear ***,this paper has reviewed several detection methods from the previous researches that has been done *** paper reviews pre-processing,detection method framework for nucleus detection,and analysis performance of the method *** are four methods based on a reviewed technique from previous studies that have been running through the experimental procedure using Matlab,and the dataset used is established Herlev *** results show that the highest performance assessment metric values obtain from Method 1:Thresholding and Trace region boundaries in a binary image with the values of precision 1.0,sensitivity 98.77%,speciﬁcity 98.76%,accuracy 98.77%and PSNR 25.74%for a single type of ***,the average values of precision were 0.99,sensitivity 90.71%,speciﬁcity 96.55%,accuracy 92.91%and PSNR 16.22%.The experimental results are then compared to the existing methods from previous *** show that the improvement method is able to detect the nucleus of the cell with higher performance assessment *** the other hand,the majority of current approaches can be used with either a single or a large number of cervical cancer smear *** study might persuade other researchers to recognize the value of some of the existing detection techniques and offer a strong approach for developing and implementing new solutions.

关键词： Cervical cancer Detection Pap smear Images

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How Can Optical Communications Shape the Future of Deep Space Communications? A Survey

arXiv

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arXiv 2022年

作者： Karmous, Sarah Adem, Nadia Atiquzzaman, Mohammed Samarakoon, Sumudu The Department of Electrical and Electronic Engineering University of Tripoli Libya The Libyan Authority for Scientific Research Tripoli Libya The Faculty of Information Technology and Electrical Engineering University of Oulu Finland The School of Computer Science University of Oklahoma United States

With a large number of deep space (DS) missions anticipated by the end of this decade, reliable and high-capacity DS communications are needed more than ever. Nevertheless, existing technologies are far from meeting such a goal. Improving current systems does not only require engineering leadership, but also, very crucially, investigating potential technologies that overcome the unique challenges of ultra-long DS links. To the best of our knowledge, there has not been any comprehensive surveys of DS communications technologies over the last decade. Free space optical (FSO) is an emerging DS technology, proven to acquire lower communications systems size weight and power (SWaP) and achieve a very high capacity compared to its counterpart radio frequency (RF), the currently used DS technology. In this survey, we discuss the pros and cons of deep space optical communications (DSOC) and review their physical and networking characteristics. Furthermore, we provide, for the first time, thoughtful discussions about implementing orbital angular momentum (OAM) and quantum communications (QC) for DS. We elaborate on how these technologies among other field advances including interplanetary network (IPN) and RF/FSO systems improve reliability, capacity, and security. This paper provides a holistic survey of DSOC technologies gathering 247 fragmented pieces of literature and including novel perspectives aiming to set the stage for more developments in the field. © 2022, CC BY.

关键词： Optical communication

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Design of a Dual-Polarized High-Gain Yagi Antenna Array for 2G/3G/LTE Base Stations

Design of a Dual-Polarized High-Gain Yagi Antenna Array for ...

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IEEE International Conference on electronic Information and Communication technology (ICEICT)

作者： Liwei Rao Botao Feng Xiao Ding Sanshan Sun Wenzhe Gu Qingsheng Zeng College of Electronics and Information Engineering Shenzhen University Shenzhen China Faculty of Humanities and Arts Macau University of Science and Technology Macau China College of Physics and Electronic Engineering Sichuan Normal University Sichuan China School of Computer Science and Engineering Huizhou University Huizhou China College of Astronautics Nanjing University of Aeronautics and Astronautics Nanjing China

The present paper introduces a novel Yagi antenna array consisting of dual-polarized dipoles. The antenna consists of a pair of dipoles and two orthogonally mounted Yagi antennas with dielectric plates. To ensure that the antenna performance is consistent in both polarization directions, a symmetrical structure is adopted. Simulated results show a wide working frequency band ranging from 1.7 to 2.8 GHz(48.9%) with a high gain of 15±3 dBi, and the 30°-horizontal and vertical beam widths.

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TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework

arXiv

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arXiv 2023年

作者： Zhao, Yang Yang, Jiaxi Wang, Wenbo Yang, Helin Niyato, Dusit 2 Fusionopolis Way Innovis #08-04 Singapore138634 Singapore Shenzhen Institute for Advanced Study School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650050 China Department of Information and Communication Engineering Xiamen University Xiamen361005 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Industrial systems require reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. Existing studies rely on heuristic models which may struggle to capture complex temporal dependencies. This paper introduces an integrated framework that leverages the capabilities of the Transformer and Deep Reinforcement Learning (DRL) algorithms to optimize system maintenance actions. Our approach employs the Transformer model to effectively capture complex temporal patterns in IoT sensor data, thus accurately predicting the Remaining Useful Life (RUL) of equipment. Additionally, the DRL component of our framework provides cost-effective and timely maintenance recommendations. Numerous experiments conducted on the NASA C-MPASS dataset demonstrate that our approach has a performance similar to the ground-truth results and could be obviously better than the baseline methods in terms of RUL prediction accuracy as the time cycle increases. Additionally, experimental results demonstrate the effectiveness of optimizing maintenance actions. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Improving Brain MRI Image Segmentation Quality: A Hybrid Technique for Intensity Inhomogeneity Correction

Improving Brain MRI Image Segmentation Quality: A Hybrid Tec...

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Open Innovations Association (FRUCT), Conference of

作者： Samah Abdel Aziz Ammar Hawbani Xingfu Wang Talaat Abdelhamid Ismail Y. Maolood Saeed Alsamhi A. S. Ismail School of Computer Science and Technology University of Science and Technology of China Hefei China Faculty of Electronic Engineering Menoufia University Menoufia Egypt Ministry of Higher Education and Scientific Research Information and Communication Technology Center (ICTC)-System Information Erbil Kurdistan Region Iraq Insight Centre for Data Analytics National University of Ireland Galway Ireland

Intensity inhomogeneity is a significant issue in magnetic resonance imaging (MRI), where the presence of bias field causes distortions in pixel values, resulting in inconsistent and erroneous intensities across the image. This artifact not only hampers accurate diagnosis by radiologists but also negatively impacts the performance of computer-aided diagnosis algorithms, particularly in tasks like segmentation. In our proposed approach, we use a hybrid technique called KIFCM, which integrates K-means and Fuzzy C-means to enhance brain tumor segmentation. K-means provides computational efficiency, while Fuzzy C-means improves accuracy by detecting missed tumor cells. We employ a bias correction method based on the level set framework, removing noise with a median filter and applying the hybrid KIFCM technique for optimal segmentation. Our method effectively addresses intensity variation challenges, ensuring precise brain tumor region segmentation. We compare our results with DFCM and MFFLs, and the comparison shows the efficiency of our proposed method by highlighting the superior quality and accuracy of 81% achieved, requiring less computational time. Consequently, our results demonstrate KIFCM's potential to boost both accuracy and speed in MRI-based brain tumor detection through computer-aided diagnosis.

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Audio Security from a Chaotic Map Perspective

SSRN

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SSRN 2023年

作者： Taher, Fatma Abdelwahab, Khaled M. Emara, Heba M. Shoaib, Mohamed El-Shafai, Walid Abd El-Samie, Fathi E. Haweel, Mohammad T. College of Technological Innovative Zayed University United Arab Emirates Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Electrical Engineering Department Shaqra University Saudi Arabia

Audio signals represent an efficient tool for communication and remote access to systems such as IoT. These signals are usually used to convey information from the transmitter to the receiver. With the development of modern communication and access networks like IoT, it has become possible now to convey more secret information inside the available audio signals. This can be accomplished through the use of watermarking techniques for optical image embedding. Watermarking with images is now possible for conveying image content inside audio signals. It is now possible to embed secret messages or even images in audio signals. In this paper, an efficient technique is introduced to convey images in audio signals through a hybrid watermarking scheme that comprises both Fractional Fourier Transform (FrFT) and Singular Value Decomposition (SVD). The FrFT strength comes from the ability to use different rotation angles in the transform, which represents an added value to the security process. Moreover, the SVD is a 2D robust decomposition to degradations in the transmitted signals. Hence, the secret watermark images can be secured from degradation effects on the communication channel. In addition, the utilization of speech signals for speaker identification is common nowadays yet subject to intrusion attacks. To be able to tolerate such attacks, possible solutions like cancelable biometrics can be used. To build a robust cancelable speaker identification system for applications such as IoT, it is necessary to distort the speech signals with a well-designed transform, while keeping the ability to discriminate between speakers. In this paper, we make use of the SVD watermarking to generate the distorted speech signals to be used as cancelable templates through the utilization of large optical image watermark strengths. Simulation results of both introduced schemes ensure the robustness and security of watermarks embedded with the SVD in the FrFT domain. In addition, the present

关键词： Chaotic systems

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A Multi-Frequency Diplexer for the Sub-6 GHz Band

A Multi-Frequency Diplexer for the Sub-6 GHz Band

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IEEE International Conference on electronic Information and Communication technology (ICEICT)

作者： Xingxing Yang Botao Feng Xiao Ding Sanshan Sun Wenzhe Gu Qingsheng Zeng College of Electronics and Information Engineering Shenzhen University Shenzhen China Faculty of Humanities and Arts Macau University of Science and Technology Macau China College of Physics and Electronic Engineering Sichuan Normal University Sichuan China School of Computer Science and Engineering Huizhou University Huizhou China College of Astronautics Nanjing University of Aeronautics and Astronautics Nanjing China

This paper proposes a microstrip diplexer network that covers the frequency bands of 0.6–0.96 GHz, 1.7–2.7 GHz, 3.3–4 GHz and 4.9–6 GHz. The diplexer is composed of the multiple matching branch and the filtering branch. Its insertion loss at lower- and middle frequencies is less than 1.5 dB, and that at upper frequency is under 2.5 dB. The diplexer achieves broadband and steep-edge filtering performances. Therefore, it can be well applied in 5G indoor distribution systems.

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NTIRE 2023 Challenge on Image Super-Resolution (×4): Methods and Results

NTIRE 2023 Challenge on Image Super-Resolution (×4): Method...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Zhang, Yulun Zhang, Kai Chen, Zheng Li, Yawei Timofte, Radu Zhang, Junpei Zhang, Kexin Peng, Rui Ma, Yanbiao Jiao, Licheng Huang, Huaibo Zhou, Xiaoqiang Ai, Yuang He, Ran Qiu, Yajun Zhu, Qiang Li, Pengfei Li, Qianhui Zhu, Shuyuan Zhang, Dafeng Li, Jia Wang, Fan Li, Chunmiao Kim, TaeHyung Kil, Jungkeong Kim, Eon Yu, Yeonseung Lee, Beomyeol Lee, Subin Lim, Seokjae Chae, Somi Choi, Heungjun Huang, ZhiKai Chen, YiChung Chiang, YuanChun Yang, HaoHsiang Chen, WeiTing Chang, HuaEn Chen, I-Hsiang Hsieh, ChiaHsuan Kuo, SyYen Choi, Ui-Jin Conde, Marcos V. Khowaja, Sunder Ali Yoon, Jiseok Lee, Ik Hyun Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Wei, Jiayu Li, Yanfeng Sun, Jia Cheng, Zhanyi Li, Zhiyuan Yao, Xu Wang, Xinyi Li, Danxu Cui, Xuan Cao, Jun Li, Cheng Zheng, Jianbin Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Computer Vision Lab Eth Zurich Switzerland Shanghai Jiao Tong University China University of Würzburg Germany Xidian University China Mais&cripac Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China University of Science and Technology of China China Beijing Institute of Technology China School of Information Science and Technology ShanghaiTech University China School of Information and Communication Engineering University of Electronic Science and Technology of China China China Lotte Data Communication Company Seoul Korea Republic of Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States MegaStudyEdu Korea Republic of Computer Vision Lab Caidas University of Würzburg Germany University of Sindh Pakistan Iklab Inc. Tech University of Korea Siheung-Si Korea Republic of Micro-Nano Electronics Department Shanghai Jiao Tong University China Electrical Engineering Department Faculty of Engineering Assiut University Egypt Hefei University of Technology China Beijing Jiaotong University China South China University of Technology Guangdong Guangzhou China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on image super-resolution (×4), focusing on the proposed solutions and results. The task of image super-resolution (SR) is to generate a high-resolution (HR) output from a corresponding low-resolution (LR) input by leveraging prior information from paired LR-HR images. The aim of the challenge is to obtain a network design/solution capable to produce high-quality results with the best performance (e.g., PSNR). We want to explore how high performance we can achieve regardless of computational cost (e.g., model size and FLOPs) and data. The track of the challenge was to measure the restored HR images with the ground truth HR images on DIV2K testing dataset. The ranking of the teams is determined directly by the PSNR value. The challenge has attracted 192 registered participants, where 15 teams made valid submissions. They achieve state-of-the-art performance in single image super-resolution. © 2023 IEEE.

关键词： Statistical tests

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A transformer-based model for effective and exportable IoMT-based stress detection

A transformer-based model for effective and exportable IoMT-...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Moudy Sharaf Alshareef Badraddin Alturki Mona Jaber School of Electronic Engineering and Computer Science Queen Mary University of London London UK Department of Computer Science Umm Al-Qura University Makkah Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9781665435413

Pervasive e-healthcare services have evolved rapidly in the recent years with the surge of the Internet of Medical Things (IoMT). Intelligent stress monitoring assistant is such an example that uses affective computing to detect stress levels based on biological signals. Existing works use different forms of machine learning and deep learning methods to successfully detect stress in pre-defined individuals. However, such models fail to recognise stress in unseen people, a problem that is harder to solve but of critical importance when three quarters of people in our society feel unable to cope due to stress. In this work, we propose a deep learning method that leverages self-attention to compute representations of the network's input and output layers and yields a generic stress detection model. We present a comparative analysis in which we examine the efficacy of Random Forest with handcrafted features, CNN-based deep learning, and transformer network with multi-modal data. Our results show that the proposed transformer solution outperforms the state of the art in all scenarios with accuracy of 96% and F1 score of 97%. More importantly, the model is validated using a leave-one-subject-out (LOSO) approach, hence is exportable and can successfully detect the stress condition of any unseen person.

关键词： Deep learning Performance evaluation Analytical models Art Computational modeling Internet of Medical Things Transformers

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