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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年第0期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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Theoretical and Numerical Investigation of Acoustic Cavitation Bubble Based on the Impact of Ultrasound Frequency

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Bulletin of the Russian Academy of Sciences: Physics 2023年第Suppl 3期87卷 S341-S347页

作者： Ahmed K. Abu-Nab Abu-Bakr, Ali F. Zain Abu Shaeer Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow oblast Dolgoprudny141700 Russia Department of Basic Science Higher Institute of Engineering and Technology Kafr El Sheikh33511 Egypt

Abstract: Acoustic cavitation is the expansion and contraction of existing microbubbles in liquids brought on by an ultrasonic field. The dynamics of oscillations at higher pressures and temperatures when the cavitation bubbles collapse can be used to characterize cavitation dynamical behavior. This work deals with the theoretical and numerical investigation of acoustic cavitation in viscoelastic media considering the influence of ultrasound frequency. An acoustic pressure equation combined with a modified Rayleigh–Plesset equation forms the theoretical framework for ultrasound cavitation dynamics. The numerical solutions of the given cavitation bubble model were obtained taking into account the effect of acoustic pressure, using the exponential B-Spline collection method. The dynamics of the cavitation bubbles’ radius and the internal pressure of acoustic cavitation were examined at different values of viscosity and the impact of physical parameters on the monatomic, diatomic, and adiabatic gas-filled spherical bubble’s collapse dynamics for (k = 7/5, 5/3, 1) respectively. © Pleiades Publishing, Ltd. 2023. ISSN 1062-8738, Bulletin of the Russian Academy of Sciences: Physics, 2023, Vol. 87, Suppl. 3, pp. S341–S347. Pleiades Publishing, Ltd., 2023. ISSN 1062-8738, Bulletin of the Russian Academy of Sciences: Physics, 2023. Pleiades Publishing, Ltd., 2023.

关键词： Dynamics

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Custom-made Near Infrared Spectroscope as a Tool for Obtaining Information Regarding the Brain Condition

Custom-made Near Infrared Spectroscope as a Tool for Obtaini...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Anna Wieczorek Edward Jacek Gorzelańczyk Mariusz Pelc Saravanakumar Duraisamy Luis A. Leiva Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland Department of Computer Science University of Luxembourg Luxembourg Luxembourg

We can obtain valuable information about the human brain using functional Near Infrared Spectroscopy (fNIRS). This paper describes the theoretical basis associated with this neuroimaging method through a custom-made prototype of a single-channel fNIRS device. The optodes were soldered to a milled Printed Circuit Board (PCB) and enclosed in a 3D printed housing. Using this fNIRS device, we performed a preliminary study to measure emotional responses from participants. Our results suggest that fNIRS allows for accurate measurement of emotions evoked by positive and negative images.

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Orthogonal Learning Rafflesia Optimization Algorithm with Quasi-Affine Transformation Evolutionary and Its Application in Wireless Sensor Networks

Orthogonal Learning Rafflesia Optimization Algorithm with Qu...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Jeng-Shyang Pan Xin- Yi Zhang Shu-Chuan Chu Ning Zhong College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Managemen Chaoyang University of Technology Taiwan Department of Life Science and Informatics Maebashi Institute of Technology Maebashi Japan 4 Faculty of Information Technology Beijing University of Technology Beijing China

The Rafflesia Optimization Algorithm (ROA) is an optimization algorithm that mimics the growth cycle of the Rafflesia. Building upon the ROA, this study introduces a novel heuristic algorithm called Orthogonal Learning Quasi-Affine Transformation Evolutionary Rafflesia Optimization Algorithm (OLQROA). The QUATRE method and the Orthogonal Learning approach are combined in the OLQROA algorithm. Compare OLQROA with ROA algorithm, improved ROA algorithms, and other three mature algorithms using CEC2017 benchmark function. The outcomes of the experiments show that OLQROA works better than the aforementioned algorithms. Additionally, OLQROA is used to apply three-dimensional wireless sensor coverage, producing superior results in comparison to the afore-mentioned algorithms.

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Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling

arXiv

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arXiv 2023年

作者： Shahrokh, Fahime Ghadiri, Nasser Samani, Rasoul Moradi, Milad Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

Biomedical Named Entity Recognition (NER) is a fundamental task of Biomedical Natural Language Processing for extracting relevant information from biomedical texts, such as clinical records, scientific publications, and electronic health records. The conventional approaches for biomedical NER mainly use traditional machine learning techniques, such as Conditional Random Fields and Support Vector Machines or deep learning-based models like Recurrent Neural Networks and Convolutional Neural Networks. Recently, Transformer-based models, including BERT, have been used in the domain of biomedical NER and have demonstrated remarkable results. However, these models are often based on word-level embeddings, limiting their ability to capture character-level information, which is effective in biomedical NER due to the high variability and complexity of biomedical texts. To address these limitations, this paper proposes a hybrid approach that integrates the strengths of multiple models. In this paper, we proposed an approach that leverages fine-tuned BERT to provide contextualized word embeddings, a pretrained multi-channel CNN for character-level information capture, and following by a BiLSTM + CRF for sequence labelling and modelling dependencies between the words in the text. In addition, also we propose an enhanced labelling method as part of pre-processing to enhance the identification of the entity's beginning word and thus improve the identification of multi-word entities, a common challenge in biomedical NER. By integrating these models and the pre-processing method, our proposed model effectively captures both contextual information and detailed character-level information. We evaluated our model on the benchmark i2b2/2010 dataset, achieving an F1-score of 90.11. These results illustrate the proficiency of our proposed model in performing biomedical Named Entity *** Codes 68T50, 68T07 Copyright © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

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Triangle-free triple systems

arXiv

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arXiv 2024年

作者： Frankl, Peter Füredi, Zoltán Goorevitch, Ido Holzman, Ron Simonyi, Gábor HUN-REN Alfréd Rényi Institute of Mathematics P. O. Box 127 Budapest1364 Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Mathematics Technion-Israel Institute of Technology Haifa3200003 Israel

There are four non-isomorphic configurations of triples that can form a triangle in a 3-uniform hypergraph. Forbidding different combinations of these four configurations, fifteen extremal problems can be defined, several of which already appeared in the literature in some different context. Here we systematically study all of these problems solving the new cases exactly or asymptotically. In many cases we also characterize the extremal *** Codes Primary:05D05, Secondary:05C69, 05C76 © 2024, CC BY.

关键词： Graph theory

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Cybersecurity Revolution via Large Language Models and Explainable AI

Cybersecurity Revolution via Large Language Models and Expla...

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Security of Information and Networks (SIN), International Conference on

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Munir Ahmad Anuam Ihsan Nidal A. Al-Dmour College of Arts & Science Applied Science University Manama Kingdom of Bahrain Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul Republic of Korea Department of Computer Engineering College of Engineering Mutah University Jordan

ISBN: (数字)9798331509736

ISBN: (纸本)9798331509743

Integrating Groundbreaking advancements in AI, like language models, interpretable AI, and machine learning, opens up a world of exciting new possibilities. The Evolving face of cybersecurity and Modern cyber threats are complex and well crafted; hence, conventional cybersecurity mechanisms show difficulty in staying relevant. LLMs, especially based on Transformer architecture will noticeably increase the accuracy and speed of detecting threats. Transparency and trust are increased by XAI approaches like SHAP and LIME, which offer facts about ML model predictions. This paper explores the literature that demonstrates the integration between XAI and LLMs in cybersecurity, exemplifying how this trinity of models has the potential to help attenuate errors producing reduced false positives and improve how we detect threats. Thinking about the possibilities the challenges including performance Explainability trade-offs, the need for common evaluation metrics, and the black-box nature of AI Models, remain in place. Solving these will help to enhance AI-driven solutions in cybersecurity.

关键词： Measurement Machine learning algorithms Explainable AI Large language models Predictive models Transformers Threat assessment computer security computer crime Faces

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CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

CITR: A Coordinate-Invariant Task Representation for Robotic...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Peter So Rafael I. Cabral Muchacho Robin Jeanne Kirschner Abdalla Swikir Luis Figueredo Fares J. Abu-Dakka Sami Haddadin Munich Institute of Robotics and Machine Intelligence TUM Germany Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Libya School of Computer Science University of Nottingham UK Electronic and Informatics Department Faculty of Engineering Mondragon Unibertsitatea Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned *** this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

关键词： Learning systems Coordinate measuring machines Robot kinematics Taxonomy Fingerprint recognition Mathematical models Libraries

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A Non-Iterative LMI-Based Design of Static Output Feedback Control with $\mathcal{L}_{2}$ Gain Performance for Linear Systems

A Non-Iterative LMI-Based Design of Static Output Feedback C...

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International Conference on Electrical, Control and Instrumentation engineering (ICECIE)

作者： Hidetoshi Oya Daiki Asada Shunya Nagai Kazushi Nakano Department of Computer Science Tokyo City University Tokyo Japan Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Ehime Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

In this paper, we present a new LMI-based non-iterative design strategy for static output feedback controllers with $\mathcal{L}_{2}$ Gain Performance for linear systems. In our approach, on the basis of the well-known necessary and sufficient condition for the existence of the static output feedback control for linear systems, the non-iterative LMI-based design procedure for the proposed static output feedback controller is derived. In this paper, we show the LMI-based non-iterative design procedure for static output feedback controllers with $\mathcal{L}_{2}$ Gain Performance and the effectiveness of the proposed static output feedback control is presented through a simple illustrative example.

关键词： Linear systems Sufficient conditions Instruments Interconnected systems Control systems Output feedback Gain

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Defect Recognition based on Radon Transform in Pentacene organic thin-film Archimedean interdigitated spirals transistor

Defect Recognition based on Radon Transform in Pentacene org...

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International Conference on Clean Electrical Power (ICCEP)

作者： Grazia Lo Sciuto Sivan Linde Rafi Shikler Paweł Kowol Salvo Coco Giacomo Capizzi Department of Mechatronics Silesian University of Technology Gliwice Poland Department of Electrical Electronics and Informatics Engineering University of Catania Catania Italy Department of Electrical and Computer Engineering School of Electrical and Computer Engineering Optoelectronic Organic Semiconductor Devices Laboratory Ben-Gurion University of the Negev Beer-Sheva Israel Ilse Katz Institute for Nanoscale Science and Technology Ben-Gurion University of the Negev Beer-Sheva Israel

In this paper the problem of automatic defects detection in thin film transistor is considered in order to increase the efficiency of electronic devices. An effective approach based on defect pattern recognition in organic transistor is developed using the Radon transform. The adopted design and technology in Pentacene organic thin-film transistor exhibits two interdigitated Archimedean spirals pattern. In the conducted experiments, the images acquisition is performed with a microscope camera to capture the various defects of surface morphology on the top of the examined transistors. The defects detection is obtained by using the Radon transform on each channel (R,G,B) of these images.

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