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Contrast enhancement of optical videoscope images

Iran Journal of computer Science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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Novel Deep Learning Network Architectures for Time Series Forecasting

Novel Deep Learning Network Architectures for Time Series Fo...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Katariya, Jitendra Kumar Yadav, Dhananjay Gayathri, C. Kolekar, Vikas K. Daivagna, Umesh M. Pushparajesh, V. Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Atlas SkillTech University Department of UGDX Maharastra Mumbai India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350329773

This paper presents novel deep-learning network architectures for time series forecasting. First, a singular deep gaining knowledge of network architecture is proposed and tested for the usage of the Google tendencies dataset as a benchmark. The proposed structure combines 1-dimensional convolution neural networks, recurrent neural networks, and long-term cells to seize and study lengthy-term styles in the time series information. The structure is compared to current deep getting-to-know architectures and is shown to outperform them in phrases of accuracy. Moreover, the proposed structure is applied to a secondary time collection dataset, the COVID-19 confirmed infection dataset, to attain compelling effects. Eventually, an open-source implementation of the proposed architecture is made available for use in addition to research. © 2023 IEEE.

关键词： Network architecture

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Cloud-Powered Virtual Doctor Robots for Enhancing Elderly Care and Chronic Disease Management with Recurrent Neural Networks 3

Cloud-Powered Virtual Doctor Robots for Enhancing Elderly Ca...

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3rd International Conference on Electrical, electronics, Information and Communication Technologies, ICEEICT 2024

作者： Ramesh, S. Mohankumar, N. Kovarasan, Rajesh Kambattan Selvamanikandan, R. Yuvaraj, S. Sujatha, S. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India Maharashtra Nagpur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Trichy India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369083

This research introduces cloud-powered virtual doctor robots to improve elderly care and chronic illness management. This system uses recurrent neural networks (RNNs) to give personalized medical care to elderly chronic disease patients. These virtual doctor robots may integrate with cloud computing infrastructure to access huge medical databases for real-time patient data analysis and individualized treatment and care suggestions. Healthcare technology has advanced by using RNNs to learn from patient data, adapt to changing health situations, and predict prospective health issues. RNNs can interpret sequential data, thus virtual doctor robots may identify trends in patient health parameters, forecast illness development, and suggest risk mitigation strategies. Cloud-powered virtual doctor robots provide a scalable solution to conventional healthcare systems rising issues. These robots can work effectively in resource-constrained areas by offloading computational chores to distant servers, providing continuous medical expertise and lowering healthcare staff reliance. This technique might improve aged care and reduce load on overworked healthcare systems. Using proactive monitoring, timely intervention, and personalized guidance, virtual doctor robots with RNNs can improve health outcomes, empower patients to manage chronic conditions, and create a more sustainable healthcare model. © 2024 IEEE.

关键词： Medical robotics

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A Non-contact Microwave Sensor System for Characterizing a Binary-liquid Mixed Concentration

A Non-contact Microwave Sensor System for Characterizing a B...

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2023 International Electrical engineering Congress, iEECON 2023

作者： Seewattanapon, Somporn Namsang, Apirada Lerdwanittip, Reungyot Akkaraekthalin, Prayoot Rakluea, Chawalit Duangrit, Nattapong Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Nonthaburi Thailand Civil Aviation Training Center Aeronautical Engineering Division Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Rajamangala University of Technology Thanyaburi Department of Electronics and Telecommunication Engineering Pathumthani Thailand National Astronomical Research Instituite of Thailand Centre of Observatory Operation and Engineering Chiangmai Thailand

ISBN: (纸本)9798350332360

This research presents a design and analysis of an open-loop microstrip resonator which is integrated with a microwave sensor system. It is used for measuring a binary-liquid mixed concentration of sodium chloride (NaCl) and DI-water to detect salinity level. The open-loop resonator is designed to provide a band-stop bandwidth of 1 GHz, which has operated from 2 GHz to 3 GHz. The proposed microwave sensor system consists of a microwave synthesizer, a voltage detector, and an open-loop resonator that is designed to operate at 3 GHz to characterize the salinity level of the sample liquid mixture. A liquid mixture samples of NaCl and Distill-water with 5 different concentration levels of 0%, 2%, 5%, 10%, and 20% were used to assess the salinity level for the proposed microwave sensor system. © 2023 IEEE.

关键词： Sodium chloride

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AMBO:All Members-Based Optimizer for Solving Optimization Problems

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computers, Materials & Continua 2022年第2期70卷 2905-2921页

作者： Fatemeh Ahmadi Zeidabadi Sajjad Amiri Doumari Mohammad Dehghani Zeinab Montazeri Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

There are many optimization problems in different branches of science that should be solved using an appropriate *** optimization algorithms are one of the most efficient approaches to solve this type of *** this paper,a new optimization algorithm called All Members-Based Optimizer(AMBO)is introduced to solve various optimization *** main idea in designing the proposedAMBOalgorithm is to use more information from the population members of the algorithm instead of just a few specific members(such as best member and worst member)to update the population ***,in AMBO,any member of the population can play a role in updating the population *** theory of AMBO is described and then mathematically modeled for implementation on optimization *** performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions,which belong to three different categories:unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO,eight other well-known algorithms have been also *** optimization results demonstrate the ability of AMBO to solve various optimization ***,comparison and analysis of the results show that AMBO is superior andmore competitive than the other mentioned algorithms in providing suitable solution.

关键词： Algorithm all members optimization optimization algorithm optimization problem population-based algorithm

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Underwater estimation of audio signal prediction using fruit fly algorithm and hybrid wavelet neural network

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Journal of Reliable Intelligent Environments 2022年第2期8卷 211-221页

作者： Sagayam, K. Martin Ghosh, Ahona Bhushan, Bharat Andrew, J. Cengiz, Korhan Elngar, Ahmed A. Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computational Science Brainware University Kolkata India School of Engineering and Technology Sharda University Greater Noida India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Electrical and Electronics Engineering Traka University Edirne Turkey Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

Underwater acoustic signal is one of its most vital features in oceanography for predicting the local variants, helps to process the signal, and also assists to discover non-stationary signals. wavelet neural network (WNN) architecture acquires a huge benefit from the artificial neural network (ANN) and time-frequency localizing features of wavelet analysis (WA). However, the system could train using the ANN model, it has tendency of falling into local minima and generating convergence. A wavelet-aided neural network is integrated with a meta-heuristic optimizer named fruit fly algorithm (FFA) that aids our proposed system in which reduces the computational complexity and improves performance measures. The main objective of the proposed work is of modelling an optimized wavelet neural network for efficient measurement of the mean absolute error and the root mean square error. The simulation of the sound signals has been performed with the use of optimized neural networks that has predicted the unknown signal. The proposed model is assessed by comparing the outcomes with the complements of it, i.e. the traditional neural networks. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Forecasting

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ThaiCoGen: Knowledge Graph Generation Enriching Suvarnabhumi’s Historical Insights

ThaiCoGen: Knowledge Graph Generation Enriching Suvarnabhumi...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Akkharawoot Takhom Chotanansub Sophaken Kantapong Vongpanich Tharathon Utasri Kanchana Saengthongpattana Thepchai Supnithi Faculty of Engineering Thammasat School of Engineering Thammasat University Thailand Faculty of Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand National Electronics and Computer Technology Center Thailand

ISBN: (数字)9798350370096

ISBN: (纸本)9798350370102

Historical tourism documentation includes guidebooks, pamphlets, and digital resources, which are collections of materials that promote the exploration of historical sites, events, and cultural heritage. These materials aim to offer insights into the history and importance of particular locations, people, and items. Within these documents, nominal historical terminologies possess valuable information, serving as significant entities that can contribute to our understanding of history. This study introduces a method for knowledge graph (KG) generation through Thai co-occurrence network analysis, called ThaiCoGen. This work improves KG generation by using three important methods: (1) term frequency-inverse document frequency (TF-IDF) to find important terms and entities; (2) Thai co-occurrence network text analysis (ThaiCoNet) to connect semantic relationships; and (3) adding to KGs using linked open data from encyclopedias. Our contributions are the generation of KGs using extracted high-n-gram entities and the highlighting of semantic enrichment. Our contributions include (1) generating KGs by identifying the extracted height n-gram entities and (2) highlighting semantic enrichment. A case study was conducted on the Thai encyclopedia *** in the domain of the Suvarnabhumi Silk Road and demonstrated the efficacy of the ThaiCoGen methodology. Despite the substantial temporal gap between historical records from the Suvarnabhumi Silk Road era (100–700 BE) and contemporary tourist documentation from the modern era (2500 BE), ThaiCoGen successfully integrates these sources into enriched KGs. The discussion of this paper intends to elaborate on the historically accurate portrayal of the Suvarnabhumi Silk Road from the enriched KG, especially Thailand’s tourism industry. Finally, this research provides the open KGs for Thailand’s cultural heritage in order to build the robustness of semantic application development.

关键词： Text analysis Terminology Roads Semantics Tourism industry Knowledge graphs Encyclopedias

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Efficient Design of a Single-Balanced Diode Mixer for 5G Communication Systems 5

Efficient Design of a Single-Balanced Diode Mixer for 5G Com...

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5th International Conference on Computing, electronics and Communications engineering. iCCECE 2022

作者： Sellidj, Abdelmounim Gaoua, Said Yagoub, Mustapha C.E. University of Science and Technology H. Boumedienne Faculty of Electronics and Computer Science Algiers Algeria University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada

ISBN: (数字)9781665489805

ISBN: (纸本)9781665489805

This work discusses the design of a broadband single-balanced down converter mixer, operating in the 5G NR FR2 bands of 24.25-29.5 GHz. With an IF frequency of 250 MHz, the mixer presents excellent port-To-port isolation over almost all the targeted band, i.e., 94.6 dB, 72.6 dB, and 25 dB for LO-IF, RF-IF, and RF-LO ports, respectively. It also exhibits low conversion loss (7.49 dB) and noise figure (with a maximum of 6.38 dB and 6.29 dB for double-and single-sideband, respectively). Furthermore, it displays a good linearity with a 1-dB compression point of 10.5 dB as well as input/output third order intermodulation products (IIP3/OIP3) of 18.68 dBm and 11.03 dBm, respectively. Such performance made the proposed design a suitable candidate for 5G applications. © 2022 IEEE.

关键词： Mixers (machinery)

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An Authentication Model Using Brainwave Panic Region Classifications from Electroencephalography 7th

An Authentication Model Using Brainwave Panic Region Classif...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Abiodun, Opeyemi Anuoluwa Oyinloye, Oghenerukevwe E. Thompson, Aderonke F. Olowoyo, Paul Imoize, Agbotiname Lucky Sur, Samarendra Nath Computer Science Department The Federal Polytechnic Ekiti State Ado-Ekiti360211 Nigeria Computer Science Department Ekiti State University Ekiti State Ado-Ekiti360211 Nigeria Cyber Security Department Federal University of Technology Ondo State Akure350106 Nigeria Federal Teaching Hospital Ekiti State Ido-Ekiti Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India

ISBN: (纸本)9789819764648

Biometric authentication scheme has been widely adopted for authentication purpose due to its uniqueness, universality, and distinctiveness. However, research has shown that these schemes are not necessarily more secured, especially with issues of coercion proliferating the cyberspace. Also, available techniques on repudiation of biometric features for authentication have not adequately explored this exciting topic. Integrating emotional state into the authentication scheme helps to mitigate coercion. This paper presents a framework for emotion as a way of biometric authentication scheme. An emotion classification model was developed by training an emotion classifier brainwave signal from eight subjects under normal state and duress using KNN machine learning algorithm. The authentication scheme grants access to users who pass both verification phases. The emotion classification model achieved an accuracy of 93.6% on the full feature set, while the reduced-feature set, as a result of feature selection, produced an improved accuracy of 94.15%. Hence, the emotion classification model can be integrated into existing biometric authentication system for improved security of critical user information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biometrics

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Hybrid VLC Systems with Terahertz Communication: A Performance Analysis 100

Hybrid VLC Systems with Terahertz Communication: A Performan...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Rajahrajasingh, Hanojhan Jayakody, Dushantha Nalin K. Muthuchidambaranathan, P. Dinis, Rui Copelabs Lusofona University Lisbon1749-024 Portugal Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Ciet Faculty of Engineering Malabe Sri Lanka National Institute of Technology Tiruchirappalli Department of Electronics and Communication Engineering India Instituto de Telecomunicações FCT-UNL Caparica2829-516 Portugal

ISBN: (纸本)9798331517786

This paper investigates the performance of dual-hop UAV-assisted communication system, employing a decode and forward relay architecture. The system utilizes Terahertz (THz) communication in the primary hop and Visible Light Communication (VLC) in the secondary hop. We conduct a thorough analysis by deriving closed-form expressions for end-to-end bit error rate (BER). Furthermore, a Monte Carlo approach will be employed to generate best fitting curves and validate the numerical expressions with simulation results. The outcomes of this work offer valuable insights into the feasibility and limitations of UAV-assisted THz-VLC systems. © 2024 IEEE.

关键词： Bit error rate

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