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IEEE International Symposium on Multimedia

作者： Duc V. Nguyen Nguyen Long Quang Tran Thuy Hien Nguyen Ngoc Huyen Truong Thu Huong Pham Ngoc Nam Faculty of Engineering Tohoku Insitute of Technology Sendai Japan School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam College of Engineering and Computer Science VinUniversity Hanoi Vietnam

ISBN: (数字)9798331511111

ISBN: (纸本)9798331511128

Point cloud video streaming over networks is challenging because of the high data rate of uncompressed point cloud data. Adaptive point cloud video streaming has been proposed to deal with this challenge. However, temporal quality variation and stalling might occur under unstable network conditions, potentially degrading users’ Quality of Experience (QoE). This paper aims to evaluate and model the impacts of temporal quality variations and stalling on users’ QoE in adaptive point cloud video streaming. We first conduct a large-scale subjective study to construct a QoE database. Then, based on the constructed database, the effects of individual factors are analyzed, and two novel QoE prediction models are presented. Experiment results show that the proposed QoE models achieve high prediction performance in PLCC, SROCC, and RMSE across various point cloud videos.

关键词： Point cloud compression Adaptation models Analytical models Databases Streaming media Predictive models Quality assessment Quality of experience

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Deep Learning-Based Comparative Study to Detect Polyp Removal in Endoscopic Images 4

Deep Learning-Based Comparative Study to Detect Polyp Remova...

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4th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2022

作者： Musha, Ahmmad Hasnat, Rehnuma Mamun, Abdullah Al Ghosh, Tonmoy Pabna University of Science and Technology Dept. of Electrical and Electronic Engineering Pabna Bangladesh Multimedia University Faculty of Engineering and Technology Melaka75450 Malaysia University of Alabama Dept. of Electrical and Computer Engineering TuscaloosaAL United States

ISBN: (纸本)9781665400732

Polyps are one of the most common gastrointestinal diseases. It has the potential to cause fatal colon and rectal cancers. As a result, it must be removed during the primitive stage. In this paper, we developed an algorithm that uses endoscopy images to detect polyp removal status. We investigated convolutional neural networks such as DenseNet, ResNet, VGG, MobileNet, and others to extract features from images and then use those features to classify whether a polyp is completely removed or not. 1000 dyed resection margins and 1000 dyed and lifted polyps' images from a publicly available dataset were used to test and train the proposed models. On the testing dataset, we obtained 85% sensitivity, 88% precision, and 85% fl-scores by using MobileNet architecture. This computer-aided polyp removal method assists physicians in diagnosing polyp status in a reliable, quick, and cost-effective manner. © 2022 IEEE.

关键词： Convolutional neural networks

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A 37 GHz Millimeter-Wave Antenna Array for 5G Communication Terminals

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computers, Materials & Continua 2023年第4期75卷 1317-1330页

作者： Jalal Khan Sadiq Ullah Usman Ali Ladislau Matekovits Farooq Ahmad Tahir Muhammad Inam Abbasi Telecommunication Engineering Department University of Engineering and TechnologyMardan23200Pakistan Department of Electronics and Telecommunications Politecnico di TorinoTurin10129Italy Istituto di Elettronica e di Ingegneria Dell’Informazione e Delle Telecomunicazioni National Research Council of ItalyTurin10129Italy Department of Measurements and Optical Electronics Politehnica University TimisoaraTimisoara300006Romania Research Institute for Microwave and Millimeter-Wave Studies(RIMMS) National University of Sciences and Technology(NUST)Islamabad44000Pakistan Department of Electronics and Computer Engineering Technology(JTKEK) Faculty of Electrical and Electronic Engineering Technology(FTKEE)Universiti Teknikal Malaysia Melaka(UTeM)Hang Tuah JayaDurian TunggalMelaka76100Malaysia

This work presents,design and specific absorption rate(SAR)analysis of a 37GHz antenna,for 5th Generation(5G)*** proposed antenna comprises of 4-elements of rectangular patch and an even *** radiating element is composed of copper material supported by Rogers RT5880 substrate of thickness,0.254 mm,dielectric constant(εr),2.2,and loss tangent,*** 4-elements array antenna is compact in size with a dimension of 8mm×20mm in length and *** radiating patch is excited with a 50 ohms connector i.e.,*** antenna resonates in the frequency band of 37 GHz,that covers the 5G *** antenna behavior is studied both in free space and in the proximity of the human *** models of the human body,i.e.,belly,hand,and head(contain skin,fat,muscles,and bone)are considered for on-body *** resonant frequency,the antenna gives a boresight gain of 11.6 *** antenna radiates efficiently with a radiated efficiency of more than 90%.Also,it is observed that the antenna detunes to the lowest in the proximity of the human body,but still a good impedance matching is achieved considering the−10 dB ***,SAR is also being *** safe limit of 2 W/kg for any 10 g of biological tissue,specified by the European International Electro Technical Commission(IEC)has been *** calculated values of SAR for human body models,i.e.,belly,hand and head are 1.82,1.81 and 1.09 W/kg,*** SAR values are less than the international recommendations for the three ***,the simulated and measured results of the antenna are in close agreement,which makes it,a potential candidate for the fifth-generation smart phones and other handheld devices.

关键词： 5G antenna array SAR millimeter-wave smart phones

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Continuous Wavelet Transform Spectral Image Based Epileptic Episode Classification

Continuous Wavelet Transform Spectral Image Based Epileptic ...

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Information technology (ACIT)

作者： Md. Nuhi-Alamin Ajay Krishno Sarkar Md. Simul Hasan Talukder Mohammad Aljaidi Safkat Shahrier Swapnil Department of Electrical and Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Numerous individuals globally, regardless of age, have the neurological condition epilepsy. Recurrent seizures compromised motor and sensory abilities, and a hindered normal lifestyle are all signs of epilepsy. By seeing or manually examining electroencephalogram (EEG) data, neurologists can make the diagnosis of epileptic seizures. In this paper, a continuous wavelet transform spectral image-based epileptic episode classification technique is proposed. The EEG signal was first converted into 227x227 RGB images using CWT. Then these images were used to create an image dataset, and classification was performed using the proposed deep ensemble epileptic seizure classifier (DE_ESC). DE_ESC ensembled Dense_net_169, Dense_net_201, and Inception_V3. This proposed method obtained 100% accuracy (Ac), precision (Pr), recall, and F1 Score, respectively.

关键词： Continuous wavelet transforms Accuracy Epilepsy Brain modeling Motors Electroencephalography Sleep apnea Information technology Alzheimer's disease Spectral analysis

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Resistors Over Digital Analog Converter on PRBS Threat Generator with PLL Synchronizer: Comparison Between R2R & Weighted 3

Resistors Over Digital Analog Converter on PRBS Threat Gener...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Almohammedi, Akram A. Yusof, Mohammad Hafiz Mohd Bin Tuan Muda, Saiyed Rasol Shepelev, Vladimir Balfaqih, Mohammed Shamsuddin, Solahuddin Karabuk University Electrical & Electronic Engineering Department 78050 Turkey Taiz University AlSaeed Faculty for Engineering and Information Technology Taiz Yemen Universiti Teknologi Mara UiTM Faculty of Computer and Mathematical Sciences Tapah Malaysia South Ural State University Automobile Transportation Department Chelyabinsk454080 Russia College of Computer Science and Engineering University of Jeddah Department of Computer and Network Engineering Jeddah23890 Saudi Arabia Menara Cyber Axis Jalan Impact Selangor Darul Ehsan Cyberjaya Cyberjaya63000 Malaysia

ISBN: (纸本)9798350305333

Intrusion Detection System (IDS) must be developed based on latest live traffic dataset. This will facilitate the study on new attributes of current threat. Once again, researchers are locked on the same vicious circle of inability to access live traffic dataset. This is coupled with public domain logged dataset may not be useful for a new threat research. Due to these standing hurdles, Pseudo Random Binary Sequence (PRBS) is used to generate synthetic dataset in IDS studies. One of its components is the Digital to Analog (D/A) converter. There are two types of D/A implementations;1) through Weighted and 2) R2R resistors. This paper compares the advantages and the disadvantages of designing D/A converter by using the abovementioned resistors. It is advisable to opt for R2R version over Weighted Resistor since it is easier and practical to choose the multiple types of resistors during circuit design process. Furthermore, the shifted parallel output from the circuit can be synchronized by the proposed PLL circuit. © 2023 IEEE.

关键词： Deep learning

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A hybrid optimization approach for securing cloud-based e-health systems

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Multimedia Tools and Applications 2025年第16期84卷 16525-16560页

作者： Altherwi, Abdulhadi Ahmad, Mohammad Tauheed Alam, Md. Moddassir Mirza, Hidayath Sultana, Nazia Pasha, Amjad Ali Sultana, Nishat Khan, Asif Irshad Alam, Md Mottahir Azim, Rezaul Department of Industrial Engineering College of Engineering and Computer Science Jazan University PO Box. 706 Jazan45142 Saudi Arabia College of Medicine King Khalid University Abha Saudi Arabia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al-Batin Hafr Al-Batin Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering and Computer Science Jazan University Jazan45142 Saudi Arabia Government Medical College Siddipet Ensanpalli Siddipet District Telangana502114 India Aerospace Engineering Department King Abdulaziz University Jeddah21589 Saudi Arabia Department of Business Administration Applied College Jazan University P. O. Box 706 Jazan45142 Saudi Arabia Department of Computer Science Aligarh Muslim University Uttar Pradesh Aligarh202002 India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz Jeddah21589 Saudi Arabia Department of Physics University of Chittagong Chattogram4331 Bangladesh

In modern healthcare, cloud-based e-health technology offers substantial benefits but faces significant security challenges. Sensitive patient data is vulnerable to cyber threats during transmission and storage, potentially enabling unethical collaboration between healthcare professionals and cloud storage providers. This paper aims to develop a robust security framework that safeguards patient privacy and prevents unauthorized access to sensitive medical information in remote patient health monitoring systems. The study presents an Artificial Hybrid Optimization with an Attribute-Based Encryption (AHO-ABE) model. The model leverages cryptographic techniques and hybrid optimization to enhance data security. Specifically, whale optimization (WO) is used for efficient key generation, while sheep flock optimization (SFO) is employed for continuous network surveillance. Experimental results demonstrate that the AHO-ABE model significantly enhances data security, outperforming existing RPM-MEC, AID, AI-HCS, and IoT-HMS approaches. The model exhibits superior resilience against unauthorized access and data breaches, achieving a 5 ms execution time, 850 kbps throughput, 0.121 mJ energy usage, and 4 s latency. Combining cryptographic techniques with hybrid optimization algorithms, the AHO-ABE model provides a robust framework for safeguarding sensitive medical information. This approach ensures the integrity and confidentiality of patient data in remote healthcare settings, offering an effective solution against high-performance cyber attacks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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NeuroSparse: An Unsupervised Framework for Inferring Brain Connectivity in Autism Diagnosis

NeuroSparse: An Unsupervised Framework for Inferring Brain C...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Shuqi Zhang, Lijuan Lan, Qing Ma, Qiujie Huang, Huan Miao, Jiaqing Mou, Shiyu Luo, Cheng Yao, Dezhong Tan, Ying Southwest Minzu University Key Laboratory for Computer Systems of State Ethnic Affairs Commission Chengdu China Moe Key Lab for Neuroinformation University of Electronic Science and Technology of China Clinical Hospital Chengdu Brain Science Institute Chengdu China University of Malaya Faculty of Engineering Department of Biomedical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350386226

Autism spectrum disorder (ASD) is a mental disorder that severely affects social interaction and communication skills. A timely and accurate diagnosis is crucial for effective intervention. However, as an objective diagnostic tool, the existence of spurious connections and noise within the functional connectivity (FC) matrix in the brain contribute to the complexity of ASD diagnosis. In this study, we propose an unsupervised sparse graph structure learning framework, called NeuroSparse, for reasoning regarding the topological connectivity relationships between brain regions. First, the framework fuses spatiotemporal information from static and dynamic FC to provide a comprehensive representation of complex brain networks. Next, the context and neighborhood information of nodes are captured as their local and global embeddings, and the latent representations of node embeddings are learned, which are then decoded to infer the connectivity relationships among the regions of interest. Finally, we introduce three innovative loss functions to regularize and optimize the sparse representation generation process. The experimental results showed that, after learning using NeuroSparse, the FC matrix achieved state-of-the-art performance in ASD diagnosis using only a simple multilayer perceptron, indicating its wide-ranging application potential. The code is available at https://***/yangshuqigit/NeuroSparse. © 2024 IEEE.

关键词： Unsupervised learning

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Prediction of Electrical Characteristics of a-IGZO TFT Based on Transfer Learning-Based Variational Autoencoder

Prediction of Electrical Characteristics of a-IGZO TFT Based...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bea, Khean-Thye Wei-Hsuan Lin, Shih-Shin Hu Lin, Da-Zheng Hu, Hsin-Hui Chen, Xiu-Zhi Lin, Ting-Ru Chen, Yen-Lin Chen, Kun-Ming Cheng, Wai Khuen National Taipei University of Technology Dep. of Electronic Engineering Taipei106 Taiwan National Taipei University of Technology Dep. of Computer Science and Information Engineering Taipei106 Taiwan Taiwan Semiconductor Research Institute Hsinchu30078 Taiwan Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Kampar31900 Malaysia

ISBN: (纸本)9798350337020

In this study, we proposed a transfer-learning based variational autoencoder model for predicting the electrical characteristics in the parameter tuning process of a-IGZO TFT structure design. The result achieve a high R2 score of 0.9704 with a low-computing-power hardware-friendly method that reduced time consumption significantly compared to prior approaches. The findings have practical implications for mitigating the time-consuming nature of TCAD simulations, and the method can expand to various types of input data while ensuring high performance and generalization. We demonstrated significant improvement in generalization and accuracy through a k-fold validation. © 2023 IEEE.

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Performance of the H-Butterworth, CLAHE, and HE Methods for Adenocarcinoma Images

Performance of the H-Butterworth, CLAHE, and HE Methods for ...

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Information technology and Computing (ICITCOM), International Conference on

作者： Laila Ma'rifatul Azizah Siti Nurul Aqmariah Bt. Mohd. Kanafiah Yessi Jusman Rafikha Aliana A. Raof Anani Aila Mat Zin Mohd Yusoff Bin Mashor Computer Engineering (Information Technology) University Malaysia Perlis Universitas Muhammadiyah Yyogyakarta Arau Perlis Malaysia Mechatronic Engineering Faculty of Electrical Engineering & Technology University Malaysia Perlis Arau Perlis Malaysia Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Computer Engineering Faculty of Electronic Engineerin & Technology University Malaysia Perlis Arau Perlis Malaysia Pathology Hospital Universiti Sains Malaysia Kubang Kerian Kelantan Malaysia Faculty of Electronic Engineering & Technology University Malaysia Perlis Perlis Malaysia

ISBN: (数字)9798350379839

ISBN: (纸本)9798350379846

Comprehensive analysis and improvement of adenocarcinoma images are essential for efficient medical diagnosis and treatment strategy. This study analyses the performance of three contrast enhancement techniques-H-Butterworth filtering, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Histogram Equalization (HE). H-Butterworth filters enhance high-frequency components, which are ideal for emphasizing intricate structures, while CLAHE adapts locally to improve contrast without compromising global image characteristics. HE globally redistributes pixel intensities to enhance overall brightness and contrast, albeit potentially introducing artifacts. Using qualitative and quantitative analyses, this research systematically compares the efficacy of these methods in enhancing adenocarcinoma images, which are characterized by heterogeneous textures and structures. Findings indicate that H-Butterworth generally achieves superior results with the lowest MSE, the highest PSNR, and the highest SSIM compared to CLAHE and HE in improving overall image quality, especially in full-colour images (RGB). So, H-Butterworth can improve the image of adenocarcinoma. This study provides valuable insights for clinicians and researchers seeking optimal approaches to enhance diagnostic accuracy and treatment outcomes in adenocarcinoma imaging.

关键词： Image quality Histograms Image color analysis Statistical analysis Microscopy Brightness Adaptive equalizers Medical diagnosis Information technology Image reconstruction

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Comparison of Contrast Enhancement Methods in Adenocarcinoma Cancer Images

Comparison of Contrast Enhancement Methods in Adenocarcinoma...

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electronic and Electrical engineering and Intelligent System (ICE3IS), International Conference on

作者： Laila Ma'rifatul Azizah Siti Nurul Aqmariah Bt. Mohd. Kanafiah Yessi Jusman Rafikha Aliana A. Raof Anani Aila Mat Zin Mohd Yusoff Bin Mashor Computer Engineering (Information Technology) University Malaysia Perlis Universitas Muhammadiyah Yogyakarta Arau Perlis Malaysia Mechatronic Engineering Faculty of Electrical Engineering & Technology University Malaysia Perlis Arau Perlis Malaysia Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Computer Engineering Faculty of Electronic Engineerin & Technology University Malaysia Perlis Arau Perlis Malaysia Pathology Hospital Universiti Sains Malaysia Kubang Kerian Kelantan Malaysia Faculty of Electronic Engineering & Technology University Malaysia Perlis Perlis Malaysia

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

This study quantifies the extent to which various image enhancement methods can improve the quality and visibility of adenocarcinoma cells on cytology slide images. The primary goal is to overcome the problems of low contrast and blurring inherent in microscopic imaging of such cells. The researchers analyzed the Partial Contrast, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Histogram Equalization (HE) methods. Then, it was applied to a dataset of 418 cytology images, including benign and malignant adenocarcinoma cells. The results show that Partial Contrast offers the most superior performance. Partial contrast achieved the lowest Mean Squared Error (MSE) and the highest values in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). This method improves the overall brightness and contrast between light and dark areas of the image and maintains a better level of fine detail and structural integrity. Thus, Partial Contrast proved to be the best approach to improve the appearance of adenocarcinoma cells on cytology slides, offering better clarity and better contrast, which has great potential in improving the diagnosis of adenocarcinoma.

关键词： Measurement Histograms PSNR Image color analysis Microscopy Adaptive equalizers Indexes Intelligent systems Image enhancement Cancer

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