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5th International Conference on Electronic Design, ICED 2020

作者： Ghazi, Alaan Aljunid, S.A. Idrus, Syed Zulkarnain Syed Endut, R. Ali, N. Amphawan, Angela Fareed, Alaa Al-Dawoodi, Aras Noori, Awab School of Human Development and Techno-communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia Computer Technical Engineering Al-Qalam University College Iraq Centre of Excellence Universiti Malaysia Perlis Malaysia Universiti Malaysia Perlis Perlis Malaysia Lightwave Laboratory School of Engineering and Technology Sunway University Petaling Jaya Selangor47500 Malaysia School of Computing University Utara Malaysia Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Data Science Research Lab School of Computing Universiti Utara Malaysia Malaysia

This study demonstrates the implementation of multi-input-multi-output (MIMO) transmission donut modes in space wavelength division multiplexing (SWDM) over multi-mode optical fiber (MMOF) and simulating the effect of mode coupling of the transmitted signal. However, during the data transmitted over MMF by using SWDM an issue is encountered, which is the inter symbol interference (ISI) due to mode coupling. ISI has been recognized as a major obstacle to high-speed data transmission. The signal processing in SDM of donut modes over MMOF with electrical feedback equalizer is used to improve the performance for increasing the bandwidth. A data rate 80 Gbit/s up for a distance of 1600 meters is achieved. SWDM topology was designed emitted from a spatial vertical cavity surface-emitting laser (VCSEL) through MMOF in conjunction with the configurations of electrical feedback equalizer. The parameters and values used in Opt-Sim simulation topology were listed. The performance measurement of this study based on bit-error rates (BER) and eye diagram before and after electrical feedback equalization were analysed. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Space division multiple access

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Performance Analysis of ZCC-Optical-CDMA over SMF for Fiber-To-The-Home Access Network 2

Performance Analysis of ZCC-Optical-CDMA over SMF for Fiber-...

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2nd Joint International Conference on Emerging Computing Technology and Sports, JICETS 2019

作者： Ghazi, Alaan Aljunid, S.A. Fareed, Alaa Idrus, Syed Zulkarnain Syed Rashidi, C.B.M. Albayaty, Marwa Al-Dawoodi, Aras Fakhrudeen, Ahmed M. School of Human Development and Techno Communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia Computer Technical Engineering Alkitab University Iraq Internet Research Laboratory School of Computing University Utara Malaysia Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Networks Department College of Computer Science and Information Technology Kirkuk University Iraq

To provide a sustainable fiber-to-the-home (FTTH), several multiplexing techniques have been developed for this purpose. The correlation features are the main obstacle behind the network performance limitation, which imposes to a high level of multiple access interference. However, the development of multiplexing techniques helps to overcome these limitations, such as optical-code division multiple access (Optical-CDMA). Optical-CDMA is considered as one of the most powerful solutions for FTTH. This paper aims to enhance FTTH network performance by applying Zero cross-correlation code (ZCC) with Optical-CDMA with maximum link single-mode fiber. In the simulation, the system performance is demonstrated in terms of bit error rate, Q-Factor and eye diagram measurements. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Fiber to the x

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A Cloud-Based UV Monitoring System for Remote Real-Time UV Exposure Tracking

A Cloud-Based UV Monitoring System for Remote Real-Time UV E...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Dankan Gowda V V Nuthan Prasad KDV Prasad VK Satya Prasad Yogesh Mahajan Sampathirao Suneetha Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Electronics and Communication Engineering M S R I T Bangalore Karnataka India Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Symbiosis Centre for Management & Human Resource Development Symbiosis International University Pune Maharashtra India Department of Computer Science & Systems Engineering Andhra University College of Engineering Andhra University Visakhapatnam Andhra Pradesh India

Due to the widespread nature of sun-related health risks, sophisticated UV monitoring systems are urgently needed. This research study introduces a cloud-based UV monitoring system with the intent of remotely tracking and analyzing UV exposure in real time. The system incorporates a network of UV sensors deployed in various locations, continuously collecting UV radiation data. These sensors are linked to a cloud system, which allows for easy sharing and processing of collected data. As a result, we're working to perfect a cutting-edge device that can monitor vitamin D absorption, measure cumulative UV exposure, and warn of impending danger. Extensive studies have demonstrated the benefits of UV exposure for both general health and protection against the newly discovered coronavirus. However, finding a happy medium is important because too much sun exposure can cause cancer, lupus flares, and cataracts. The proposed model gives users the ability to set their own daily UV exposure limits, taking into account clinically validated thresholds for various skin tones. The developed sensor module is able to measure the UV index with high precision even in bright sunlight because it closely mimics the skin erythema spectrum.

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A Novel Detection System for human Retina Based on Fuzzification Neural Network 1st

A Novel Detection System for Human Retina Based on Fuzzifica...

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1st International Conference on Applied Computing Research to Support Industry: Innovation and Technology, ACRIT 2019

作者： Alheeti, Khattab M. Ali Abdulbaqi, Azmi Shawkat Saleh, Hadeel M. Al-Ani, Muzhir Shaban Department of Computer Networking System College of Computer Sciences and Information Technology University of Anbar PO Box 55 Ramadi Iraq Department of Computer Sciences College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq College of Education for Women University of Anbar Ramadi Iraq College of Science and Technology Department of Information Technology University of Human Development Sulaymaniyah Iraq

ISBN: (纸本)9783030387518

Automated methods are based on human biometrics for the purpose of verifying a person’s identity based on the physiological or behavioural features of humans. Biometrics traits include facial, iris, retina, fingerprint, and tongue. Preserving biometrics is very important because it is one of the means of preserving the information of others. One of the most important biometrics, most accurate, and powerful methods of identity verification is human retina. The unique retina features per person can be displayed as a compact binary form. For the purpose of confirming an individual’s identity, the unique retinal features of the individual can be easily compared with the reference template. In this paper, a novel detection system is proposed for retinal images utilising the fuzzification model that integrated with deep learning neural networks. In addition, identification system based on human retina is considered the most protect scheme of modern applications. The novelty in this paper is integrated between fuzzy set and deep learning neural networks. Practically, this is first time to integrate between fuzzification model and artificial neural network. The obtained experimental results of the proposed identification system demonstrate that the suggested approach possess outstanding detection rate with decline error rate as well as reducing the number of false alarms. © 2020, Springer Nature Switzerland AG.

关键词： Biometrics

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An asynchronous wfst-based decoder for automatic speech recognition

arXiv

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arXiv 2021年

作者： Lv, Hang Chen, Zhehuai Xu, Hainan Povey, Daniel Xie, Lei Khudanpur, Sanjeev School of Computer Science Northwestern Polytechnical University Xi'an China Center of Language and Speech Processing United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States Xiaomi Corporation Beijing China SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University China

We introduce asynchronous dynamic decoder, which adopts an efficient A∗ algorithm to incorporate big language models in the onepass decoding for large vocabulary continuous speech recognition. Unlike standard one-pass decoding with on-the-fly composition decoder which might induce a significant computation overhead, the asynchronous dynamic decoder has a novel design where it has two fronts, with one performing "exploration" and the other "backfill". The computation of the two fronts alternates in the decoding process, resulting in more effective pruning than the standard one-pass decoding with an on-the-fly composition decoder. Experiments show that the proposed decoder works notably faster than the standard onepass decoding with on-the-fly composition decoder, while the acceleration will be more obvious with the increment of data complexity. Copyright © 2021, The Authors. All rights reserved.

关键词： Decoding

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Mechanism of primordial black holes production and secondary gravitational waves in α-attractor Galileon inflationary scenario

arXiv

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arXiv 2021年

作者： Teimoori, Zeinab Rezazadeh, Kazem Rasheed, Mariwan Ahmed Karami, Kayoomars Department of Physics University of Kurdistan Pasdaran Street P.O. Box 66177-15175 Sanandaj Iran P.O. Box 19395-5531 Tehran Iran University of Human Development Kurdistan Region Sulaimani Iraq Department of Computer Science College of Science and Technology University of Human Development Kurdistan Region Sulaymaniyah Iraq

We study the process of the Primordial Black Holes (PBHs) production in the novel framework, namely α-attractor Galileon inflation (G-inflation) model. In our framework, we take the Galileon function as G(φ) = GI(φ) (1 + GII(φ)), where the part GI(φ) is motivated from the α-attractor inflationary scenario in its original non-canonical frame, and it ensures for the model to be consistent with the Planck 2018 observations at the CMB scales. The part GII(φ) is invoked to enhance the curvature perturbations at some smaller scales which in turn gives rise to PBHs formation. By fine-tuning of the model parameters, we find three parameter sets which successfully produce a sufficiently large peak in the curvature power spectrum. We show that these parameter sets produce PBHs with masses O(10)Mo, O(10-5)Mo, and O(10-13)Mowhich can explain the LIGO events, the ultrashort-timescale microlensing events in OGLE data, and around 0.98% of the current Dark Matter (DM) content of the universe, respectively. Additionally, we study the secondary Gravitational Waves (GWs) in our setup and show that our model anticipates the peak of their present fractional energy density as ΩGW0~ 10-8for all the three parameter sets, but at different frequencies. These predictions can be located well inside the sensitivity region of some GWs detectors, and therefore the compatibility of our model can be assessed in light of the future data. We further estimate the tilts of the included GWs spectrum in the different ranges of frequency, and confirm that spectrum follows the power-law relation ΩGW0~ fnin those frequency bands. Copyright © 2021, The Authors. All rights reserved.

关键词： Gravitational effects

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A smart filtering-based adaptive optimized link state routing protocol in flying ad hoc networks for traffic monitoring

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Journal of King Saud University - computer and Information sciences 2024年第4期36卷

作者： Hosseinzadeh, Mehdi Ali, Saqib Rahmani, Amir Masoud Lansky, Jan Nulicek, Vladimir Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Sulaymaniyah Kurdistan region Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Nowadays, the use of drones as a fundamental element of smart cities has attracted the attention of many researchers to monitor and control the traffic of vehicles. Because of the high flexibility of multi-drone systems, like flying ad hoc networks (FANETs), they provide various services and improve modern life in smart cities. However, due to the unique features of FANET, especially the high speed of drones and rapid changes in network topology, communication reliability is a serious challenge in this network. Hence, traditional routing protocols, such as optimized link state routing (OLSR) scheme, cannot work well in these networks. In this paper, a smart filtering-based adaptive optimized link state routing (SFA-OLSR) scheme is proposed in FANETs. To increase adaptability to the FANET environment, SFA-OLSR provides a new solution to adjust the hello broadcast period so that each flying node specifies its broadcast period based on a new scale called cosine similarity between real and predicted positions. Furthermore, in SFA-OLSR, each flying node develops a filtering algorithm based on two parameters, namely link lifetime and remaining energy. The purpose of this algorithm is to reduce the size of the single-hop neighboring set of each flying node and minimize the search space when finding multi-point relays (MPRs). This increases the convergence speed of the algorithm. Then, SFA-OLSR exploits the sparrow search algorithm (SSA) to single out the best MPRs. This algorithm introduces a multi-objective function by focusing on three components, including energy, link lifespan, and neighbor degree. Lastly, the simulation process of SFA-OLSR is performed by the NS3 simulator. This process evaluates the performance of the proposed method and three schemes, namely Gangopadhyay et al., P-OLSR, and OLSR-ETX. These evaluations show that SFA-OLSR has a good performance in terms of three scales, namely packet delivery ratio, delay, and throughput, but its overhead is more than

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Meta-heuristic algorithms Reliability Routing

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Performance Analysis of Clan Updated Grey Wolf Optimization (CU-GWO) based Deep Ensemble Technique for Identifying Sarcasm from Social Media Data

Performance Analysis of Clan Updated Grey Wolf Optimization ...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Geeta Abakash Sahu Manoj Hudnurkar Faculty of Computer Studies (FoCS) Symbiosis Center for Research and Innovation(SCRI) Symbiosis International (Deemed University)(SIU) Pune Maharashtra India Department of Information Technology and Data Science Vidyalankar School of Information Technology (VSIT) Mumbai India Operations and Analytics Symbiosis Centre for Management and Human Resource Development(SCMHRD) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India

Sarcasm is often considered as a prevalent linguistic element in online content that conveys strongly held subjectivity and viewpoints. The detection of sarcasm is a significant consideration over the dominant NLP appliances as sentiment analysis, which is utilizing for advertising and opinion mining is a precious tool for figuring out the people sentiments and attitudes. The proposed approach describes automatic detection of sarcasm as an ease classification of text challenges. This paper aims to develop a sarcasm detection model that categorizes words into either sarcastic or non-sarcastic expressions. Initially, the text input involves processing, here the stop word removal and tokenization process are carried out. Subsequently, various features like information gain, chisquare, mutual information, and symmetrical uncertaintybased features are extracted from the preprocessed data. The curse of dimensionality is a crucial issue, thereby the selection of optimal features is conducted by employing hybrid optimization model called Clan Updated Grey Wolf Optimization (CU-GWO). The optimal features are utilized for sarcasm detection by using ensemble technique. The ensemble techniques involve the classifiers such as NN, SVM, RF and DCNN. The final output of DCNN indicates whether the sarcasm is present. The effectiveness of the proposed approach demonstrated through diverse metrices.

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Spiral-Phased Laguerre-Gaussian Modes Generation in SWDM over Few Mode Fiber based on Electrical Equalization 2

Spiral-Phased Laguerre-Gaussian Modes Generation in SWDM ove...

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2nd Joint International Conference on Emerging Computing Technology and Sports, JICETS 2019

作者： Ghazi, Alaan Fareed, Alaa Masunda, Tendai Aljunid, S.A. Idrus, Syed Zulkarnain Syed Amphawan, Angela Al-Dawoodi, Aras Rashidi, C.B.M. Fadhel, Ibrahim Eskandar Ibrahim School of Human Development and Techno-communication Universiti Malaysia Perlis Malaysia School of Computer and Communication Engineering Universiti Malaysia Perlis Malaysia Computer Technical Engineering Alkitab University Iraq School of Computing Universiti Utara Malaysia Malaysia Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA United States Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Department of Computer and Information Systems National Institute of Administrative Science Yemen

Space division multiplexing, in conjunction with wavelength division multiplexing, is a powerful mechanism that increases spatial channels significantly in a single optical fiber. Spiral Phased-Laguerre-Gaussian (SP-LG) modes indicate that they effectively reduce mode coupling, thereby increasing the channel response and performance positively. However, mode-coupling influence optical fiber system leads to inter-symbol interference (ISI) between the channels and reduces both capacity and distance. In this paper, we apply the SP-LG modes in a space wavelength division multiplexing over a few-mode fiber system. The results demonstrate significant improvements in reducing channels effects and ISI. The performance evaluations are based on the bit-error-rate (BER), eye diagram and spectrum analyzer. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Spectrum analyzers

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Predicting Heart Disease Using Machine Learning Techniques on Electronic Health Records Data

Predicting Heart Disease Using Machine Learning Techniques o...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Waheeb Baddah Hamzah Ali Qasem Ayman Alsabry Abdulrahman Mohammed Hussein obaid Awadh abdo Mohammed F. E. Hanash Department of Information Technology Azal University for Human Development Sana'a Yemen International University of Technology Twintech 21 September university of medical and applied sciences Sana'a Yemen Department of Computer Science International University of Technology Twintech Sana'a Yemen medical information technology 21 September university of medical and applied sciences Sana'a Yemen biomedical equipments technology 21 September university of medical and applied sciences Sana'a Yemen International University of Technology Twintech Sana'a Yemen

heart disease is a prevalent cause of mortality worldwide, and the ability to identify and prevent this ailment at an early stage is crucial for enhancing patient outcomes. Recently, researchers have focused on utilizing Machine-Learning (ML) techniques to predict risk factors and the occurrence of heart disease. This study aims to identify the optimal parameters for accurately predicting the occurrence of heart disease through the fine-tuning of ML models' parameters. This study incorporated a blend of datasets from Cleveland, Hungary, Switzerland, and Long Beach V, which consisted of patients' medical histories and laboratory test outcomes. The collected data were employed to predict the occurrence of heart disease through diverse ML algorithms, such as KNN, decision tree, and SVM. Bayesian optimization was also utilized to fine-tune the hyperparameters of the ML algorithms for heart disease prediction. The findings demonstrated that the ML models achieved high accuracy in predicting the occurrence of heart disease, with the optimized KNN model achieving the highest accuracy rate of 100%.

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