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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Milovanovic, M. Pejic, J. Pejic, P. University of Niš Faculty of Electronic Engineering Department of Electronics Niš18000 Serbia University of Niš Faculty of Sciences and Mathematics Department of Computer Science Niš18000 Serbia Belgrade Metropolitan University Faculty of Information Technology Serbia

ISBN: (纸本)9798350347760

Honeybees are essential for pollination in agriculture. This paper presents a smart beehive frame for multi-parameter monitoring. The system is based on opto elements that emit IR light to the honeycomb and measure the reflection. The results showed that this kind of system can be used in practice to decrease the number of the visual on-site observations of the bee colonies. © 2023 IEEE.

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Non-Destructive pH Sensor using Honeycomb Resonator for Liquid-Mixture Characterization 4

Non-Destructive pH Sensor using Honeycomb Resonator for Liqu...

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4th Research, Invention, and Innovation Congress: Innovative Electricals and electronics, RI2C 2023

作者： Chudpooti, Sukanya Sangpet, Patchadaporn Robertson, Ian D. Akkaraekthalin, Prayoot Somjit, Nutapong Chudpooti, Nonchanutt Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand School of Electronic and Electrical Engineering University of Leeds Leeds United Kingdom

ISBN: (纸本)9798350330618

This paper presents a feasibility study of a pH sensor utilizing the microwave transmission technique within the CST Microwave Studio software. The resonator is designed based on the modified split ring resonator shape (SRR) by incorporating six segments in a circular shape. The proposed resonator shape resembles a honeycomb structure with a capacitive gap to enhance the electric field (E-field) distribution in its area. The operating frequency is set to the Industrial, Scientific, and Medical (ISM) band of 5.82 GHz. According to the simulation, the E-field demonstrates its maximum strength at the focused area, measuring 29,863 V/m. Consequently, this position is deemed suitable for selecting the sensing area for liquid-mixture detection. To investigate the performance of the proposed sensor, the industry model of a Teflon tube is imported and placed atop the focused honeycomb resonator. The performance of the proposed sensor is evaluated by modeling various liquid samples, adjusting electrical properties such as relative permittivity, Εr, and loss factor. The simulated results indicate that the proposed sensor provides a satisfactory transmission coefficient, S21, response for classifying relative permittivity, Εr, changes within the range of 1 to 81. However, for future development, the proposed sensor will undergo fabrication and further investigation to assess its performance in practical applications. ©2023 IEEE.

关键词： Microwave sensors

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Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments 5

Development of Convolutional Neural Network-Based AI-Dermato...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Kahatapitiya, Nipun Shantha Wijethunge, Akila Edirisinghe, Sajith Silva, Bhagya Nathali Jeon, Mansik Kim, Jeehyun Wijenayake, Udaya Wijesinghe, Ruchire Eranga University of Sri Jayewardenepura Faculty of Technology Dept. of Materials and Mechanical Technology Pitipana Sri Lanka University of Sri Jayewardenepura Faculty of Medical Sciences Dept. of Anatomy Gangodawila Sri Lanka University of Sri Jayewardenepura Faculty of Engineering Dept. of Computer Engineering Nugegoda Sri Lanka School of Electronic and Electrical Engineering College of IT Engineering Kyungpook National University Buk-gu Daegu Korea Republic of Sri Lanka Institute of Information Technology Faculty of Engineering Dept. of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Early detection of skin conditions is crucial, and some skin conditions can become more difficult to treat if left untreated. The gold standard Dermatoscope is a non-invasive technique used for the examination and evaluation of skin lesions, which is equipped with a magnifying lens and a light source. However, precise inspection of existing dermatoscopes has become a limitation due to the unavailability of image-analyzing methods. Herein, this study reports the successful development of a Convolutional Neural Networks (CNN) based, Artificial intelligence (AI)-Dermatoscope integrating optics and a smart illumination system to enhance the accurate examination of acne conditions of the skin. The system was trained on a large dataset of acne to accurately identify and classify skin conditions. Finally, the system utilizes CNN knowledge to predict new images of skin and provide diagnostic information to doctors and other healthcare professionals. Thus, this system will improve the accuracy and speed of skin diagnosis, and consequently, improve the health-related quality of life of patients. © 2023 IEEE.

关键词： computer vision

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Impact of Varying Laser Power in WDM Transmission Systems to Control Nonlinear Effects 1

Impact of Varying Laser Power in WDM Transmission Systems to...

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1st IEEE International Conference on Communication engineering and Emerging Technologies, ICoCET 2024

作者： Hammood, Dalal Abdulmohsin Galeb, Basim Al-Hilali, Aqeel Bashar, Mustafa Rosli, Siti Julia Electrical Engineering Technical College Middle Technical University Cybersecurity Technology Engineering Department Baghdad10022 Iraq Computer Technician Engineering Al Hikma University College Baghdad Iraq Medical Instrumentation Engineering Al-farahidi University Baghdad Iraq Uruk University Baghdad Iraq Faculty of Electronic Engineering Technology Perlis Arau02600 Malaysia

ISBN: (纸本)9798331504144

This study investigates how varied CW laser power broadcasts affect a sixteen-channel DWDM system. Constant transmission distance is sixty kilometers. The system can handle 0.649 Tbps. Launch power ranging from 0 to 10 dBm is studied to observe its effects on signal quality, crosstalk, and system efficiency. The QF assessment data shows that greater power levels boost optical signal-to-noise ratio (OSNR) and Q-factor. This improvement occurs at lower power levels. Compared to ambient noise, the signal is stronger. Due to increasing OSNR, Q-factor improves with power. When nonlinear effects become evident, progress may stagnate. Increased power levels may increase nonlinear processes including stimulated Brillouin scattering (SBS), four-wave mixing (FWM), and self-phase modulation (SPM). The Q-factor may decrease due to signal distortion and noise. According to the Bit Error Rate (BER) parameter, greater power increases signal intensity, which improves OSNR and reduces mistakes. Thus, BER drops. To increase an optical communication system's performance, laser power must be balanced to maximize the Q-factor, minimize the bit error rate (BER), and prevent substantial nonlinear aberrations. © 2024 IEEE.

关键词： Four wave mixing

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Deep Learning-Based Real-Time Face Mask Detection for Human Using Novel YOLOv2 with Higher Accuracy 5th

Deep Learning-Based Real-Time Face Mask Detection for Human ...

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Tun, Nay Kyi Myat, Aye Min Oo, Htet Ne Thu, Amar Myo Win, Kyaw Thu Faculty of Computer System and Technology Myanmar Information Institute of Technology Mandalay Myanmar Pyin Oo Lwin Myanmar Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Electronic Department Defence Services Academy Pyin Oo Lwin Myanmar

ISBN: (纸本)9789819997060

A novel deep learning-based face mask detection system is designed to enhance public safety in various environments. With the ongoing global health concerns, the need for efficient and accurate methods to identify individuals wearing or not wearing face masks has become crucial. By utilizing convolutional neural networks (CNNs) and transfer learning techniques, proposed model achieves impressive accuracy while maintaining high-speed processing capabilities. This paper outlines the architecture, training process, and performance evaluation of the proposed deep learning-based face mask detection system, highlighting its promising role in contributing to a safer and healthier society. The development of a vision-based safety system, the transfer of a small YOLO object detection model, and the creation of a CNN-based classification model are the key objectives of this study. According to experimental findings, proposed system is capable of real-time face mask detection and classification with an accuracy of over 98%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Braintransformer: Subject-Wise Patch Embed Transformer for Cross-Subject Brain Visual Information Decoding

Braintransformer: Subject-Wise Patch Embed Transformer for C...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Du Zongxin Li Lin Faculty of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Visual information decoding aims to infer the visual content perceived by a subject based on their brain responses, representing a cutting-edge area of neuroscience research. Functional magnetic resonance imaging (fMRI) provides millimeter-level spatial resolution and a relatively high signal-to-noise ratio, making it an ideal tool for neural information decoding. However, practical applications remain limited, primarily due to the reliance on subject-specific models. In this work, we propose BrainTransformer, a novel framework for cross-subject visual information decoding. To overcome the limitations, we develop a self-supervised patch embedding transformer trained using a masked signal modeling approach on large-scale fMRI data, enabling the learning of robust and generalizable neural representations across subjects. By aligning these learned fMRI representations with CLIP's image and text embeddings, BrainTransformer leverages rich semantic information from both visual and textual modalities. This alignment allows us to employ a multimodal latent diffusion model guided by CLIP's embeddings to generate images consistent with brain activity. Experimental results demonstrate BrainTransformer's effectiveness in decoding visual information across subjects, marking a significant step toward scalable and generalizable brain decoding.

关键词： Training Visualization Semantics Functional magnetic resonance imaging Brain modeling Transformers Diffusion models Data models Decoding Spatial resolution

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Two-Operation Buffering based on Dynamic Optical RAM 17

Two-Operation Buffering based on Dynamic Optical RAM

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2024

作者： Semenov, Andriy Tsyrulnyk, Serhii Baraban, Serhii Semenova, Olena Rudyk, Andrii Khloba, Anton Vinnytsia National Technical University Faculty of Information Electronic Systems Vinnytsia Ukraine Vinnytsia National Agrarian University Department of Computer Sciences and Digital Economy Vinnytsia Ukraine Poznan University of Technology Faculty of Computing and Telecommunications Poznan Poland National University of Water and Environmental Engineering Department of Automation Electrical Engineering and Computer-Integrated Technologies Rivne Ukraine

ISBN: (纸本)9798331520564

The authors of this paper propose a block diagram for a dual-operating buffer on a dynamic optical RAM. The buffer consists of two dynamic optical RAMs, each controlled separately to allow for simultaneous writing and reading of data, thereby improving performance. This setup enables data to be read from the buffer while new data is being written to it. Implementing a dual-operating buffer on a dynamic optical RAM has the potential to enhance the performance of high-speed data transmission in telecommunications networks. The authors conducted numerical calculations to compare failure probabilities between single-operation and dual-operation buffers on dynamic optical RAM. The results indicated that using a dual-operating buffer significantly reduces the probability of failure with the same buffer memory capacity compared to a single-operating buffer. Additionally, the authors analyzed the effectiveness of the proposed dual-operating buffer. The analysis confirmed that exploiting dynamic optical memory for buffering enhances data transfer speed and efficiency due to the inherent properties of optical data transfer, such as high bandwidth and low latency. © 2024 IEEE.

关键词： Dynamic random access storage

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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

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Neural Computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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IoT Authentication Protocols: Classification, Trend and Opportunities

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 515-533页

作者： N.alsheavi, Amar Hawbani, Ammar Wang, Xingfu Othman, Wajdy Zhao, Liang Liu, Zhi Alsamhi, Saeed Hamood Al-Qaness, Mohammed A. A. University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China Nankai University Haihe Laboratory of ITAI School of Cyber Science Tianjin300459 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan University of Galway Insight Centre for Data Analytics Galway Ireland IBB University Faculty of Engineering Ibb Yemen Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions. © 2016 IEEE.

关键词： Authentication Protocol

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Enhanced Robotic Vision System Based on Deep Learning and Image Fusion

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computers, Materials & Continua 2022年第10期73卷 1845-1861页

作者： E.A.Alabdulkreem Ahmed Sedik Abeer D.Algarni Ghada M.El Banby Fathi E.Abd El-Samie Naglaa F.Soliman Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafrelsheikh33511Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt

Image fusion has become one of the interesting fields that attract researchers to integrate information from different image *** is involved in several *** of the recent applications is the robotic *** application necessitates image enhancement of both infrared(IR)and visible *** paper presents a Robot Human Interaction System(RHIS)based on image fusion and deep *** basic objective of this system is to fuse visual and IR images for efficient feature extraction from the captured ***,an enhancement model is carried out on the fused image to increase its *** image enhancement models such as fuzzy logic,Convolutional Neural Network(CNN)and residual network(ResNet)pre-trained model are utilized on the fusion results and they are compared with each other and with the state-of-the-art *** results prove that the fuzzy logic enhancement gives the best results from the image quality ***,the proposed system can be considered as an efficient solution for the robotic vision problem with multi-modality images.

关键词： Deep learning fuzzy logic image fusion IR images

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