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Performance Enhancement of Praseodymium Doped Fiber Amplifiers

computers, Materials & Continua 2022年第12期73卷 5411-5422页

作者： Abdullah G.Alharbi Jawad Mirza Mehak Raza Salman Ghafoor Department of Electrical Engineering Faculty of EngineeringJouf UniversitySakaka42421Saudi Arabia SEECS Photonics Research Group Islamabad44000Pakistan Institute of Space Technology Islamabad44000Pakistan School of Electrical Engineering and Computer Science(SEECS) National University of Sciences and Technology(NUST)Islamabad44000Pakistan

In this paper,we report a simulation study on the performance enhancement of Praseodymium doped silica fiber amplifiers(PDFAs)in O-band(1270-1350 nm)in terms of small signal gain,power conversion efficiency(PCE),and output optical power by employing bidirectional *** PDFA performance is examined by optimizing the length of Praseodymium doped silica fiber(PDF),its mode-field diameter(MFD)and the concentration of Pr^(3+).A small-signal peak gain of 56.4 dB,power conversion efficiency(PCE)of 47%,and output optical power of around 1.6 W(32 dBm)is observed at optimized parameters for input signal wavelength of 1310 *** noise figure(NF)of 4.1 dB is observed at input signal wavelength of 1310 ***,the effect of varying the pump wavelength and pump power on output optical power of the amplifier and amplified spontaneous emission(ASE)noise is also investigated,***,the impact of ion-ion interaction(up-conversion effect)on small-signal gain of the amplifier is also studied by considering different values of up-conversion coefficient.

关键词： Praseodymium-doped fiber doped fiber amplifiers optical amplification mode field diameter noise figure

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SEES-QL: An Improved Scalable and Energy-Efficient Scheme for WSNs based on Lightweight Q-learning

SEES-QL: An Improved Scalable and Energy-Efficient Scheme fo...

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2024 Advances in Science and engineering Technology International Conferences, ASET 2024

作者： Abdul-Qawy, Antar S. H. Adnan, Ali Idarous Mohammed, Mohammed Sultan Khatri, Narendra Shikhli, Amir Jarndal, Anwar Abdulrahman Al-Sumait University Department of Mathematics and Computer Science Zanzibar Tanzania United Republic of Hodeidah University Department of Computer Engineering Al Hudaydah Yemen The State University of Zanzibar Department of Computer Science and Information Technology Zanzibar Tanzania United Republic of Universiti Teknologi Malaysia Department of Electronic and Computer Engineering Johor Bahru Malaysia Manipal Institute of Technology Manipal Academy of Higher Education Department of Mechatronics Manipal India University of Sharjah Sustainable Engineering Asset Management Research Group Sharjah United Arab Emirates University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350344134

The energy efficiency of battery-operated sensing devices in IoT is a critical research area that needs further exploration. This paper employs lightweight reinforcement learning to improve energy savings in large-scale heterogeneous WSNs. We introduce SEES-QL (a Scalable and Energy-Efficient Scheme based on Q-Learning), an enhanced version of the zonal SEES protocol, that addresses the issue of frequent data transmission by dynamically adjusting nodes' transmission cycles without the need for a predefined model. In SEES-QL, on/off periods of radio transceivers are regulated based on transmission history and reading importance of each node independently, positively affecting total energy consumption, traffic load, and overall system lifetime. Performance evaluation demonstrates that SEES-QL achieves significant advancements in energy savings and transmission count reduction, leading to a remarkable 41% increase in the overall system lifetime compared to the traditional SEES protocol. © 2024 IEEE.

关键词： Radio transceivers

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Seer: A Framework for Optimizing Traffic Camera Placement and Deep Learning Inference at the Edge for Vehicle Path Reconstruction

Seer: A Framework for Optimizing Traffic Camera Placement an...

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IEEE/ACM Symposium on Edge Computing (SEC)

作者： Siddhant Jain Kunal Jain Arun Ravindran Suresh Purini Computer Systems Group International Institute of Information Technology Hyderabad India Department of Electrical and Computer Engineering The University of North Carolina Charlotte USA

ISBN: (数字)9798350378283

ISBN: (纸本)9798350378290

Integrating traffic cameras with deep learning facilitates real-time multi-camera vehicle path reconstruction, benefiting smart city initiatives such as traffic management and public safety. The high demands on latency, bandwidth, and privacy underscore the necessity of edge computing platforms. However, the continuous operation of compute-intensive deep learning algorithms on round-the-clock camera streams can exceed the compute and power capacities of edge data centers. Therefore, it is critical to strategically deploy and activate camera streams to balance compute demands with performance requirements. Currently, there is a lack of tools to support this planning process. This paper presents Seer, a comprehensive suite of tools and algorithms that aids traffic planners in optimizing traffic camera placement and deep learning model deployment, considering compute and budgetary constraints. Seer encompasses (1) a graph-based algorithm for efficient camera placement incorporating road network topology, (2) a collaborative algorithm for vehicle path reconstruction that enables the use of less accurate but resource-efficient deep learning models, and (3) a scalable traffic simulation framework derived from open-source projects that model city road networks. Evaluations of Seer on two real-world road networks, each with thousands of intersections and simulated vehicle paths, show a 6.3x reduction in compute requirements. This is achieved through a combination of sparse camera deployment and the use of lightweight models, with an average 55% increase in the Hausdorff distance for the path reconstruction algorithm, translating to an absolute increase of 135 to 195 meters.

关键词： Deep learning Smart cities Computational modeling Roads Traffic control Reconstruction algorithms Cameras Real-time systems Streams Edge computing

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Pre-trained Deep Learning Networks for Advanced Visible Imagery Drone Detection and Recognition 15

Pre-trained Deep Learning Networks for Advanced Visible Imag...

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15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Al Dawasari, Hassan J. Bilal, Muhammad Moinuddin, Muhammad Arshad, Kamran Assaleh, Khaled King Abdulaziz University Centre of Excellence in Intelligent Engineering Systems and Electrical and Computer Engineering Department Jeddah Saudi Arabia Ajman University Artificial Intelligence Research Centre Ajman United Arab Emirates

ISBN: (纸本)9798350324433

In this paper, we introduce a state-of-the-art deep learning technique designed to accurately differentiate between drones and birds. This technique is particularly effective in reducing hazards associated with unauthorized drones, especially in airport environments where such drones can cause significant flight disruptions. Our approach involves the utilization of a meticulously compiled image dataset for testing, yielding results that surpass previous detection methods outlined in existing literature. Among the models evaluated, ResNet18 emerges as a standout, achieving an impressive average precision (AP) of 0.739 in medium area ratios. A key feature of our method is its ability not only to detect drones but also to precisely distinguish them from birds. The dataset employed in this research is derived from the publicly accessible real-world data of the 2020 Drone vs. Bird Detection Challenge. © 2023 IEEE.

关键词： Drones

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Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environment

Deep Attention Driven Reinforcement Learning (DAD-RL) for Au...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Chowdhury, Jayabrata Shivaraman, Venkataramanan Dangi, Sumit Sundaram, Suresh Sujit, P.B. Indian Institute of Science Robert Bosch Centre for Cyber-Physical Systems Bangalore India Indian Institute of Science Department of Aerospace Engineering Bangalore India Indian Institute of Science Education and Research Department of Data Science & Engineering and Electrical Engineering & Computer Sci-ence Bhopal India

ISBN: (纸本)9781665410205

Autonomous Vehicle (AV) decision-making in ur-ban environments is inherently challenging due to the dynamic interactions with surrounding vehicles. For safe planning, AV/ego must understand the weightage of various spatiotemporal interactions in a scene. Contemporary works use colos-sal transformer architectures to encode interactions mainly for trajectory prediction, resulting in increased computational complexity. To address this issue without compromising spatiotemporal understanding and performance, we propose the simple Deep Attention Driven Reinforcement Learning (DAD-RL) framework, which dynamically assigns and incorporates the significance of surrounding vehicles into the ego's RL-driven decision-making process. We introduce an AV-centric spatiotemporal attention encoding (STAE) mechanism for learning the dynamic interactions with different surrounding vehicles. To understand map and route context, we employ a context encoder to extract features from context maps. The spatiotemporal representations combined with contextual encoding provide a comprehensive state representation. The resulting model is trained using the Soft-Actor Critic (SAC) algorithm. We evaluate the proposed framework on the SMARTS urban benchmarking scenarios without traffic signals to demonstrate that DAD-RL outperforms recent state-of-the-art methods. Furthermore, an ablation study underscores the importance of the context-encoder and spatiotemporal attention encoder in achieving superior performance. © 2024 IEEE.

关键词： Deep reinforcement learning

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Validation Of Wind Turbine Model Software Simulation Using Real Time Windspeed Measurements 14

Validation Of Wind Turbine Model Software Simulation Using R...

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14th International Conference on electrical and Electronics engineering, ELECO 2023

作者： Polycarpou, Alexis Shiacallis, Sotiris Christofides, Nicholas Papadakis, Antonis Papadopoulos, Theofilos Ceylan, Oguzhan Frederick University Frederick Research Center Department of Electrical Computer Engineering and Informatics Nicosia Cyprus T.P. Aeolian Dynamics Ltd Cyprus Kyamos Multiphysics Nicosia Cyprus Democritus University of Thrace Electrical and Computer Engineering Department Greece Kadir Has University Deparment of Management Information Systems Istanbul Turkey

ISBN: (纸本)9798350360493

Based on the achievements regarding the 2020 targets, the Renewable Energy Directive established a new, higher target for 2030, reflecting a significant increase of interest for renewable energy sources (RES). As a result, various new software simulation packages, featuring models of RES, have been developed and being used in academia as well as in industry for research, educational or training purposes. In this paper Wind turbine (WT) characteristics and wind speed data obtained from a commissioned wind farm in Cyprus are used to implement a simulation model in two different software programs featuring RES simulation capabilities. Power System computer Aided Design (PSCAD) is used to investigate the electrical power output of a WT model, and KYAMOS software is used to investigate the modelling of the blades, the resulting primary torque and the resulting rotor rotational speed. The investigation result novelty is the establishment of the ability of the proposed, under development, software program to satisfy the three required characteristics. Therefore, Deviation from measured values of the turbine characteristics is observed for both programs, user interface and blade modelling, and the potential of integrating results of one software into the other is investigated. © 2023 IEEE.

关键词： Wind turbines

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A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FOR HAND-REHABILITATION: A CASE STUDY 24

A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FO...

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24th International Conference on engineering Design, ICED 2023

作者： Bos, Mikaël Ivar Karahanoǧlu, Armaǧan Haarman, Juliet Nizamis, Kostas Chair of Systems Engineering and Multidisciplinary Design Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Interaction Design Research Group Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Human Media Interaction Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Home hand rehabilitation for stroke is becoming increasingly important due to logistic and financial challenges. Developing Daily-life Integrated Hand-rehabilitation Products (DIHP) aims to enable the application of at-home rehabilitation. The materials of these products are essential for their success, however, selecting materials for DIHP has not been investigated yet. Previous research on material selection showed that it is done strictly on material properties or based on a human-centered approach. Hence, in this study, we propose a hybrid model for choosing materials for DIHP. To achieve this, we first combined the findings of previous material selection processes into a comprehensive material selection model. We applied this model in a case study, in which we first selected three materials based on their properties. Following, we 3d printed a DIHP out of the chosen materials and tested the feeling of the materials with multiple expert groups. Our findings suggest that the proposed material selection method is promising and highlights that our comprehensive model provides more insights when compared to a strict material property-based selection. © The Author(s), 2023. Published by Cambridge University Press.

关键词： 3D printing

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Variable Reduction Strategy Integrated Variable Neighborhood Search and NSGA-II Hybrid Algorithm for Emergency Material Scheduling

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Complex System Modeling and Simulation 2023年第2期3卷 83-101页

作者： Zhen Shu Aijuan Song Guohua Wu Witold Pedrycz College of Systems Engineering National University of Defense TechnologyChangsha 410073China School of Traffic and Transportation Engineering Central South UniversityChangsha 410075China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2V4Canadaand also with the Systems Research InstitutePolish Academy of SciencesWarsaw 01447Poland

Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property ***-world emergency material scheduling(EMS)problems are typically large-scale and possess complex *** evolutionary algorithm(EA)is one of the effective methods for solving EMS ***,the existing EAs still face great challenges when dealing with large-scale EMS problems or EMS problems with equality *** handle the above challenges,we apply the idea of a variable reduction strategy(VRS)to an EMS problem,which can accelerate the optimization process of the used EAs and obtain better solutions by simplifying the corresponding EMS ***,we define an emergency material allocation and route scheduling model,and a variable neighborhood search and NSGA-II hybrid algorithm(VNS-NSGAII)is designed to solve the ***,we utilize VRS to simplify the proposed EMS model to enable a lower dimension and fewer equality ***,we integrate VRS with VNS-NSGAII to solve the reduced EMS *** prove the effectiveness of VRS on VNS-NSAGII,we construct two test cases,where one case is based on a multi-depot vehicle routing problem and the other case is combined with the initial 5∙12 Wenchuan earthquake emergency material support *** results show that VRS can improve the performance of the standard VNS-NSGAII,enabling better optimization efficiency and a higher-quality solution.

关键词： emergency material scheduling evolutionary algorithm variable reduction strategy

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Modeling the Effect of Gate-Drain Parasitic Capacitance of a SiC MOSFET in a Half-Bridge During the Soft Turn-Off and Hard Turn-On Transition

Modeling the Effect of Gate-Drain Parasitic Capacitance of a...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Ayodhya Somiruwan Gamwari Ayooluwa Ajiboye Rakesh Resalayyan Alireza Khaligh Department of Electrical and Computer Engineering The Institute for Systems Research University of Maryland College Park USA

A novel frequency-domain approach to quantify the effect of the gate-drain parasitic capacitance of a SiC MOSFET is presented in this paper. The effect of this parasitic capacitance during the soft turn-off and hard turn-on transition of a MOSFET half bridge can cause an undesired spurious turn-on in the soft turned-off switch. The proposed model based on Miller’s theorem accurately quantifies the effect and the behavior of the gate loop irrespective of the usage of spurious turn-on mitigating active Miller clamp. The insights drawn in this paper can be used in designing the gate loop of the power MOSFET to avoid undesired spurious turn-on of the MOSFET due to crosstalk. Moreover, the analytical model is validated with hardware experimental results.

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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