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Non return to zero line coding with suppressed carrier in FSO transceiver systems under light rain conditions

Journal of Optical Communications 2024年 45卷 s495-s505页

作者： Rashed, Ahmed Nabih Zaki Daher, Malek G. Ahammad, S. K. Hasane Montalbo, Francis Jesmar P. Sorathiya, Vishal Asaduzzaman, Sayed Rehana, Hasin Zuhayer, Asif Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Physics Department Islamic University of Gaza P.O. Box 108 Gaza Palestine School of Physics Universiti Sains Malaysia Penang11800 Malaysia Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India College of Informatics and Computing Sciences Batangas State University Batangas4200 Philippines Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Waghodia Road 391760 Gujarat Vadodara India Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Electronics & Telecommunication Engineering Chittagong University of Engineering and Technology Chattogram4349 Bangladesh

This paper aims to simulate performance efficiency of carrier suppressed non return to zero line coding based FSO transceiver systems under light rain conditions with amplification units at 40 Gbps. The max. Q, BER and total optical power are simulated and demonstrated after FSO channel and PIN Photodetector Receiver under light rain weather conditions at maximum reach of 1.2 km at 10 Gbps. As well as the max. Q Factor variations versus max reach variations are clarified after PIN photodetector receiver under light rain weather conditions at 10, 40 Gbps with/without amplification units. Besides the total optical power variations versus max reach variations are assured after FSO channel under light rain weather conditions at 10, 40 Gbps with/without amplification units. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Rain

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Image Cryptography Using Fibonacci Bit-plane Decomposition and Quantum Chaotic Permutation

Image Cryptography Using Fibonacci Bit-plane Decomposition a...

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International Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022

作者： Ravi, Renjith V. Goyal, S.B. Djeddi, Chawki Kustov, Vladimir Department of Electronics and Communication Engineering M.E.A Engineering College Kerala Malappuram India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Department of Mathematics and Computer Science Larbi Tebessi University Tebessa Algeria LITIS Lab Rouen University Rouen France Saint Petersburg Railway Transport University of Emperor Alexander I Saint Petersburg Russia

ISBN: (纸本)9783031270987

The use of image encryption to safeguard the privacy and security of images is a good idea. This study presents a unique image encryption technique that encrypts images using Fibonacci Bitplanes as security key images. The Fibonacci numbers are used in this research to present a new encryption technique. Furthermore, a unique bit-plane decomposition for Fibonacci weights is explored, which has cryptographic advantages. The novel lossless image encryption technique provided may encrypt an image for privacy protection utilizing this new decomposition approach. It also offers two layers of encryption, allowing it to handle both pay-preview apps and encrypted communications simultaneously. The algorithm’s performance in image encryption is confirmed by modeling and analysis. Finally, the suggested algorithm’s encryption performance is shown using results obtained and security assessments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Number theory

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Medical Image Transmission Using a Secure Cryptographic Approach for IoMT Applications 1

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International Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022

作者： Ravi, Renjith V. Goyal, S.B. Djeddi, Chawki Kustov, Vladimir Department of Electronics and Communication Engineering M.E.A Engineering College Kerala India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Department of Mathematics and Computer Science Larbi Tebessi University Tebessa Algeria LITIS Lab Rouen University Rouen France Saint Petersburg Railway Transport University of Emperor Alexander I Saint Petersburg Russia

ISBN: (数字)9783031270994

ISBN: (纸本)9783031270987

Image security is very important in the field of medical imaging. To protect medical healthcare images, many studies have been undertaken. Because it eliminates data loss, encryption is the greatest solution for image secrecy. Traditional encryption methods, on the other hand, are difficult to apply to electronic health data due to data size limitations, redundancy, and scalability, especially when the data of a patient is delivered via different networks. As a result, since images are distinct from the text in terms of data loss and confidentiality, patients may loose the confidentiality and privacy of their data. Researchers discovered these security flaws and proposed different image encryption ways to remedy the problem. In order to build a safe image encryption solution for the healthcare business, this research proposes an efficient, lightweight encryption algorithm. The suggested lightweight encryption algorithm first separates the image into multiple clusters and then employs cluster-based permutations. Other procedures like diffusion and modulation could be used after that. The suggested technique is studied, analysed, and compared to generally encrypted ones in terms of information security and time complexity. The performance of the proposed method was assessed using a range of test images. Several experiments show that the proposed image cryptosystems methodology is more efficient than existing approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cryptography

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Rice Diseases Detection and Localization with Only Image-Level Disease Training Labels

Rice Diseases Detection and Localization with Only Image-Lev...

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International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： J. Thanawiparat T. Kasetkasem T. Patrapornnant S. Patarapuwadol Department of Electrical Engineering Faculty of Engineering Kasetsart University Bangkok Thailand National Electronics and Computer-Technology Center Pathunthani Thailand Department of Plant Pathology Faculty of Agriculture at Kamphaeng Saen Kasetsart University Nakorn Pathom Thailand

In this paper, we propose a new approach for the detection and classification of rice diseases that combines the use of deep learning and level set methods. Our approach aims to improve the performance of rice disease classification and object detection by using an efficient image processing technique with deep learning. The proposed method will be evaluated based on Mean Average Precision (mAP), Accuracy, Precision, Recall, and F1-score. The results indicate that our proposed method improved the performance for most of the 16 classes of rice diseases and also it was able to improve performance even when using a small amount of labeled data. We also found that our proposed algorithm produced segmentation maps with significantly smaller computational time and the use of bias field estimation helped in the segmentation of complicated foreground objects in images by modifying unnecessary details. Overall, this research has the potential to be a valuable tool in the field of agriculture, where early and accurate detection of rice diseases is crucial for preventing the spread of the disease and maintaining crop yields.

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Identification of Different Medicinal Plants Through Image Processing

Identification of Different Medicinal Plants Through Image P...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： J. Anvar Shathik S. Hashini A. Pandiaraj N. Ramshankar Naveen G Amirthavarshini K B AIML and Data science Anjuman Institute of Technology and Management Uttarkannada Karnataka India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Department of Computer Science and Engineering RMD Engineering College Kavaraipettai Tamilnadu Department of Electronics and Communication Madha Institute of Engineering and Technology

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Identification of medicinal plants and raw materials is crucial for various applications including pharmaceuticals, herbal remedies, and conservation efforts. Conventional methods of recognition often rely on expert intelligence and instructions investigation which container be time-timewasting and lying to oversights. In this study, we propose a novel approach for automating the identification process using image processing and machine learning algorithms. Specifically, we leverage advanced techniques such as deep learning with ResNet-50, and Random Forest algorithms to accurately classify images of medicinal plants and raw materials. Additionally, we integrate a leaf disease detection system using Keras to further enhance the functionality of the platform. The developed system includes a user-friendly website with features such as a blog page for sharing information and seamless integration of the image processing algorithms using the Django framework. Investigational results display the efficacy of the anticipated method in precisely discovering medicinal plants and raw materials, paving the way for applications in various domains.

关键词： Medicinal plants Deep learning Machine learning algorithms Medical services Raw materials Classification algorithms Sustainable development Random forests Diseases Pharmaceuticals

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Nuclei Instance Segmentation in Colon Histology Images with YOLOv7 2nd

Nuclei Instance Segmentation in Colon Histology Images with...

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2nd International Conference on Advanced engineering, technology and Applications, ICAETA 2023

作者： Yıldız, Serdar Memiş, Abbas Varlı, Songül Department of Computer Engineering Faculty of Electrical and Electronics Engineering Yıldız Technical University İstanbul Turkey Department of Software Engineering Faculty of Engineering and Natural Sciences İstanbul Health and Technology University İstanbul Turkey Health Institutes of Türkiye İstanbul Turkey BİLGEM TÜBİTAK Kocaeli Turkey

ISBN: (纸本)9783031509193

In histology image analysis, instance-based nuclei segmentation is one of the challenging tasks within the segmentation-guided studies since it is quite troublesome to detect each distinct nuclei instance of each nuclei type in images in contrast to the semantic segmentation in which all the image pixels of a nuclei type are labelled with the same mask ID although the segmented region may comprise of multiple instances. In this paper, an instance-based medical image segmentation task is addressed, and in this context, instances of multiple types of nuclei in colon histology images are aimed to be delineated distinctly. For the instance-based segmentation of the nuclei in colon histology images, the YOLOv7 algorithm and its built-in instance segmentation module are utilized. In the experimental studies performed on Colon Nuclei Identification and Counting (CoNIC) Challenge 2022 colon histology image dataset by using a 5-fold cross-validation performance evaluation strategy, nuclei instances belonging to 6 classes as the neutrophil, epithelial, lymphocyte, plasma, eosinophil and connective were segmented. To calculate the overall system accuracy, the quantification metrics of mean average precision (mAP) and mean panoptic quality (mPQ) were measured. In performance evaluations, quite promising accuracy values were obtained. The mAP values of 0.2885 and 0.2903, and mPQ values of 0.1659 and 0.1704 were observed by using the YOLOv7 algorithm. To the best of our knowledge, this is the first nuclei instance segmentation study with YOLOv7. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Colon histology images CoNIC 2022 dataset Nuclei instance segmentation Nuclei segmentation YOLOv7

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Detection of Human Stress Using Optimized Feature Selection and Classification in ECG Signals

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Mathematical Problems in engineering 2023年第1期2023卷

作者： Narmadha, G. Deivasigamani, S. Vellaisamy, Muthukumar Freitas, Lídio Inácio Ahmad, Badlishah R. Sakthivel, B. Department of Electrical and Electronics Engineering Sethu Institute of Technology Tamil Nadu Virudhunagar626115 India Faculty of Engineering Technology and Built Environment UCSI University Cheras Kuala Lumpur56000 Malaysia School of Engineering Faculty of Computing and Engineering Quest International University Ipoh Perak30250 Malaysia Department of Mechanical Engineering Dili Institute of Technology Dili Timor-leste Computer and Communication System University Malaysia Perlis Arau Malaysia Department of Electronics and Communication Engineering PSY Engineering College Tamil Nadu Sivagangai630561 India

An autonomic nervous system (ANS) of humans is majorly affected by psychological stress. The changes in ANS may cause several chronic diseases in humans. The electrocardiogram (ECG) signal is used to observe the variation in ANS. Numerous techniques are presented for an ECG stress signal handling feature extraction and classification. This work managed a heart rate variability feature acquired from smaller peak waveforms such as P, Q, S, and T waves. Also, the R peak is detected, which is a significant part of the ECG waveform. In this work, the proposed stress classification work has been categorized into two main processes: feature selection (FS) and classification. The main aim of the proposed work is to propose an optimized FS and classifier model for the detection of stress in ECG signals. The Metaheuristics model of the African vulture optimization (AVO) technique is presented to perform an FS. This selection is made to choose the required features and minimize the data for classification. The AVO-based modified Elman recurrent neural network (MERNN) technique is proposed to perform an efficient classification. The AVO is used for fine-tuning the weight of the MERNN technique. The experimental result of this technique is evaluated in terms of Recall (91.56%), Accuracy (92.43%), Precision (92.78%), and F1 score (95.86%). Thus, the proposed performance achieved a superior result than the conventional techniques. © 2023 Narmadha G et al.

关键词： Classification (of information)

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Investigation of Beam Steering Using a Rotatable All-Dielectric Hemispherical Lens Assembly with an Ultra-Wideband Antenna

Investigation of Beam Steering Using a Rotatable All-Dielect...

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Research, Invention, and Innovation Congress (RI2C)

作者： Patchadaporn Sangpet Virote Pirajnanchai Suramate Chalermwisutkul Sukanya Chudpooti Prayoot Akkaraekthalin Nonchanutt Chudpooti Deparment of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Industrial Physics and Medical Instrumentation King Mongkut's University of Technology North Bangkok Bangkok Thailand Deparment of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Bangkok Thailand

ISBN: (数字)9798331506018

ISBN: (纸本)9798331506025

This study presents a 3D-printed all-dielectric lens assembly integrated with an ultra-wideband (UWB) antenna, designed specifically for beam-steering applications. The proposed system achieves beam steering by combining a rotatable 3D-printed all-dielectric lens assembly with a hemispherical lens and a UWB antenna. The UWB antenna is fabricated on an FR-4 substrate, while the hemispherical lens and rotatable stage assembly are made from Acrylonitrile Butadiene Styrene (ABS). The lens assembly includes a set of 3D-printed lenses capable of rotating to specific angles, with predefined positions at ±60° and a step size of 30°. Based on the simulated results, at the lower frequency of 3.1 GHz, the UWB antenna integrated with the 3D-printed all-dielectric hemispherical lens assembly does not achieve effective beam steering, as the main beam direction deviates from the rotatable lens angle. However, at intermediate frequencies, such as 7.0 GHz, the system demonstrates successful beam steering by adjusting the rotation angle of the hemispherical lens on the rotatable stage.

关键词： Technological innovation Beam steering Accuracy Directive antennas Ultra wideband antennas Frequency measurement Assembly Substrates Frequency control Lenses

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Label-free Identification of Nonelectrogenic Cancer Cells using Adhesion Noise

Label-free Identification of Nonelectrogenic Cancer Cells us...

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BioSensors Conference (BioSensors), IEEE

作者： Maximilian Ell Ralf Zeitler Roland Thewes Günther Zeck TU Wien Faculty of Electrical Engineering and Information Technology Institute of Biomedical Electronics Vienna Austria zerodev Stuttgart Germany TU Berlin Faculty of Electrical Engineering and Computer Science Chair of Sensor and Actuator Systems Berlin Germany

Time-continuous detection of cells on substrates without the need of optical microscopic imaging is of broad interest in biotechnological applications. We present a method how to detect cancer cells using voltage noise caused by cell adhesion which is recorded by high-density CMOS-based microelectrode arrays. We analyze our data in terms of spectral power density for two different types of arrays, each of them being optimized in a different working regime.

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Magnetization Dynamics in Nanofiber Networks 11

Magnetization Dynamics in Nanofiber Networks

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11th IEEE International Conference "Nanomaterials: Applications and Properties", NAP 2021

作者： Blachowicz, Tomasz Steblinski, Pawel Grzybowski, Jacek Ehrmann, Andrea Institute of Physics Center for Science and Education Silesian University of Technology Gliwice Poland Faculty of Electronics and Informatics Koszalin University of Technology Koszalin Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Faculty of Engineering and Mathematics Bielefeld University of Applied Sciences Bielefeld Germany

ISBN: (纸本)9781665439077

Magnetic nanofibers are of great interest for applications like data transport and storage as well as in basic research. Especially bent nanofibers, which can unambiguously be produced by electrospinning, show a broad spectrum of possible magnetization reversal processes, depending on bending radius, geometry, magnetic field orientation, etc. Besides these quasistatic processes, dynamic investigations are necessary for investigating data transport properties of magnetic nanofibers. We report on domain wall transport through coupled bent nanowires, forming networks with multiple data inputs and outputs. Our results show diverse phenomena which have to be taken into account during these dynamic processes, such as domain wall instabilities or interference between converging signals, and suggest possible architectures of nanowire-based logics. ©2021 IEEE

关键词： Nanofibers

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