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MDI-QKD with resource-efficient polarization compensation

MDI-QKD with resource-efficient polarization compensation

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Bedroya, Olinka Li, Chenyang Wang, Wenyuan Hu, Jianyong Lo, Hoi-Kwong Qian, Li University of Toronto Centre for Quantum Information and Quantum Control Dept. of Physics TorontoONM5S 3G4 Canada University of Toronto Centre for Quantum Information and Quantum Control Dept. of Electrical & Computer Engineering TorontoONM5S 3G4 Canada University of Hong Kong Department of Physics Pokfulam Hong Kong 100 College St TorontoON1M5G 1L5 Canada

ISBN: (纸本)9781957171180

We implemented MDI-QKD with a novel polarization compensation scheme using discarded bits without reducing the key-sharing cycle or demanding additional resources. Polarization drift was maintained below 0.13 rad over a 40 km unisolated fibre spool for four hours, and the average secret key rate generated was 7.45×10-6bits per pulse. © 2023 The Author(s).

关键词： Polarization

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A Visual Servoing Strategy for Coastline Tracking using an Unmanned Aerial Vehicle

A Visual Servoing Strategy for Coastline Tracking using an U...

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Mediterranean Conference on control and Automation (MED)

作者： Sotirios N. Aspragkathos George C. Karras Kostas J. Kyriakopoulos Control Systems Lab School of Mechanical Engineering National Technical University of Athens Athens Greece Dept. of Computer Science and Telecommunications University of Thessaly Lamia Greece

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

In this paper, an Image-based Visual Servo (IBVS) control strategy for the autonomous surveillance of coastlines using an octocopter aerial vehicle is proposed. The implemented strategy is focused on the vision-based detection and tracking of dynamic coastlines and in the presence of waves while flying in low altitudes. For this purpose, a Deep Neural Network (DNN) for the detection of the coastline is employed. The DNN is ac-companied by an analytical formulation of an Extended Kalman Filter (EKF), which considers an approximate periodical wave motion model to provide an online estimate of the coastline motion directly in image space. The estimated feedback is provided to an appropriately formulated IBVS tracking controller for the autonomous guidance of the octocopter along the coastline, ensuring the latter is always kept inside the camera’s field of view. The efficacy of the proposed scheme is demonstrated via a set of comparative outdoor experiments using an octocopter flying along the coastline on various weather and beach settings.

关键词： Visualization Tracking Trajectory planning Surveillance Neural networks Estimation Visual servoing

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A Deep Reinforcement Learning Motion control Strategy of a Multi-rotor UAV for Payload Transportation with Minimum Swing

A Deep Reinforcement Learning Motion Control Strategy of a M...

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Mediterranean Conference on control and Automation (MED)

作者： Fotis Panetsos George C. Karras Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Lab National Technical University of Athens Athens Greece Dept. of Computer Science and Telecommunications University of Thessaly Lamia Greece

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This paper addresses the problem of controlling a multirotor UAV with a cable-suspended load. In order to ensure the safe transportation of the load, the swinging motion, induced by the strongly coupled dynamics, has to be minimized. Specifically, using the Twin Delayed Deep Deterministic Policy Gradient (TD3) Reinforcement Learning algorithm, a policy Neural Network is trained in a model-free manner which navigates the vehicle to the desired waypoints while, simultaneously, compensating for the load oscillations. The learned policy network is incorporated into the cascaded control architecture of the autopilot by replacing the common PID position controller and, thus, communicating directly with the inner attitude one. The performance of the proposed policy is demonstrated through a comparative simulation and experimental study while using an octorotor UAV.

关键词： Navigation Attitude control Transportation Reinforcement learning Water pollution Autopilot Sensors

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Multi-layer network modelling of genomic and transcriptomic data to investigate the response to checkpoint inhibitors in NSCLC

Multi-layer network modelling of genomic and transcriptomic ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Davide Mascolo Lorenzo Farina Manuela Petti MSc Program in Data Science Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

Recent innovations and developments in molecular biology and biotechnology have made it possible to acquire and store large omics datasets. In particular genomics, transcriptomics and the study of their relationship represent the key elements to understand how genotype influences phenotype. In this work we investigate the potential of multi-layer network modeling in integrating genomic and transcriptomic data of 152 advanced non-small cell lung cancer (NSCLC) patients treated with anti-PD-(L)1 therapy. For the transcriptomic layer, we performed differential expression and differential co-expression analyses in order to identify a subset of key genes in differentiating responder patients from non-responders. Adding to the genomic-transcriptomic model other three layers related to immune, myeloid and curated immunotherapy-based literature signatures we obtained a 5-layer Patient Similarity Network. The application of Similarity Network Fusion algorithm revealed a statistically significant stratification of patients (p-value ≤ 0.05) which allows the identification of two clusters characterized by patients responding and not responding to immunotherapy.

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An Intelligent Battery Management System for an Electric Vehicle Powered by Solar PV Array

An Intelligent Battery Management System for an Electric Veh...

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International Universities Power engineering Conference (UPEC)

作者： Ali Falih Challoob Nur Azzammudin Bin Rahmat Vigna K. Ramachandaramurthy Nagham Saeed Amjad Jaleel Humaidi Institute of Power Engineering Universiti Tenaga Nasional Strategic Planning Centre Malaysia School of Computing and Engineering University of West London London United Kingdom Control and Systems Eng. Dept. University of Technology Baghdad Iraq

ISBN: (数字)9798350379730

ISBN: (纸本)9798350379747

Implementing a Battery Management System (BMS) in Electric Vehicles (EVs) offers several benefits that enhance the efficiency, security, and durability of the vehicle's battery system. The BMS oversees and controls the performance of each cell in the battery pack, guaranteeing that they function within predetermined voltage and temperature thresholds to maintain safety. In this paper, an intelligent BMS for an EV model powered by a solar PV array was proposed using a fuzzy logic (FL) controller. The proposed method was applied to EVs consisting of four Lithium-ion batteries. The simulation results are achieved using MATLAB/Simulink software. The estimation of the state of charge (SOC) for each battery was used as input to the FL which controls the discharging/ charging mode for each battery via power semiconductor switches. Three different scenarios are used to verify the suggested method. A full control FL operation was obtained, and the batteries were protected successfully against overcharging and over-discharging.

关键词： Fuzzy logic Lithium-ion batteries Battery management systems Electric vehicles Threshold voltage Software Temperature control State of charge MATLAB Voltage control

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Analysis of Equilibrium Point and Genetic Algorithm Design of Observer-Based PID controller for Membrane Bioreactor

Analysis of Equilibrium Point and Genetic Algorithm Design o...

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2021 International Automatic control Conference, CACS 2021

作者： Changsila, Yanita Banjerdpongchai, David Intelligent Control Automation of Process Systems Research Unit Dept. of Electrical Engineering Faculty of Engineering Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok10330 Thailand

ISBN: (纸本)9781665444125

The operation of most wastewater treatment systems is an open-loop system. There is limited feedback control of membrane bioreactors (MBR) which makes it impossible to regulate the quality of the treated water. The objectives of this paper are twofold. First, we analyze the equilibrium point of the MBR nonlinear model. Second, we utilize output feedback control with state observer and design optimal PID controllers. We determine the optimum equilibrium point and derive a linearized model of MBR. Then, we design the state observer to estimate the non-measurable state using the pole placement method. Both the measurable output and estimated state are fed back through PID controller. The objectives of PID control consist of stabilization and performance improvement. Genetic Algorithm is applied to find the optimal PID parameters that minimize the integral of absolute error and satisfy the specified time-domain criteria. We apply the observer-based PID controller to the linearized model of MBR. Numerical results demonstrate the design of PID controller and the improvement of the closed-loop response and quality of the treated water. © 2021 IEEE

关键词： Bioreactors

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Mitigating Epilepsy by Stabilizing Linear Fractional-Order systems

Mitigating Epilepsy by Stabilizing Linear Fractional-Order S...

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American control Conference (ACC)

作者： Emily A. Reed Guilherme Ramos Paul Bogdan Sérgio Pequito Ming Hsieh Electrical and Computer Engineering Department University of Southern California USA Dept. of Computer Science and Engineering Instituto Superior Técnico University of Lisbon Portugal and Instituto de Telecomunicações Lisbon Portugal Department of Information Technology Division of Systems and Control Uppsala University Uppsala Sweden

Epilepsy affects approximately 50 million people worldwide. Despite its prevalence, the recurrence of seizures can be mitigated only 70% of the time through medication. Furthermore, surgery success rates range from 30% - 70% because of our limited understanding of how a seizure starts. However, one leading hypothesis suggests that a seizure starts because of a critical transition due to an instability. Unfortunately, we lack a meaningful way to quantify this notion that would allow physicians to not only better predict seizures but also to mitigate them. Hence, in this paper, we develop a method to not only characterize the instability of seizures but also to leverage these conditions to stabilize the system underlying these seizures. Remarkably, evidence suggests that such critical transitions are associated with long-term memory dynamics, which can be captured by considering linear fractional-order systems. Subsequently, we provide for the first time tractable necessary and sufficient conditions for the global asymptotic stability of discrete-time linear fractional-order systems. Next, we propose a method to obtain a stabilizing control strategy for these systems using linear matrix inequalities. Finally, we apply our methodology to a real-world epileptic patient dataset to provide insight into mitigating epilepsy and designing future cyber-neural systems.

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Recurrent Graph Neural Network-Based Identification of Replay Attacks in Power Networks

Recurrent Graph Neural Network-Based Identification of Repla...

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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Faaiz Joad Rachad Atat Hayat Mbayed Abdulrahman Takiddin Dept. of Electrical & Computer Engineering Florida State University Tallahassee FL USA Computer Science Department Lebanese American University Beirut Lebanon Center for Advanced Power Systems Florida State University Tallahassee FL USA

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

As cyber-physical systems, power networks exchange data to ensure smooth operation. Through communication networks, the exchanged data becomes vulnerable to cyberattacks where malicious entities establish malevolent connections to falsify the data, which exploit the data integrity, leading to network instability. Machine learning-based solutions have been proposed to identify such malevolent actions, including false data injection attacks. This paper aims to (a) enhance the attack detection performance and (b) quantify the performance of non-recurrent and recurrent machine learning-based attack detection systems against multiple replay attacks that involve falsifying data via presenting normal operation patterns from previous timestamps. It turns out that capturing temporal aspects from the data using recurrent models outperforms non-recurrent models by 5-32% in accuracy (ACC). Specifically, we propose a recurrent graph neural network (RGNN) model that captures the temporal and spatial aspects of the network data. The RGNN model outperforms non-recurrent models by 14 – 32% and other recurrent models by 6 – 13% in ACC. The experiments are conducted on IEEE 14, 39, and 118-bus power networks where the model performance is enhanced (by up to 8% in ACC) in larger systems due to capturing more spatial and temporal features.

关键词： Mechatronics Accuracy Data integrity Computational modeling Cyber-physical systems Data models Graph neural networks Communication networks computer crime

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Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

引用

29th Working Group Reports on Innovation and Technology in computer Science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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Design and Implementation of 2-DOF Arm Manipulator Robot as Robotic Kinematic Learning Platform

Design and Implementation of 2-DOF Arm Manipulator Robot as ...

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Vocational Education and Electrical engineering, International Conference on

作者： Parama Diptya Widayaka Pradini Puspitaningayu Sayyidul Aulia Alamsyah Rifqi Firmansyah Muktiadji Arif Widodo Rifqi Abdillah Dept. of Electrical Engineering Universitas Negeri Surabaya Surabaya Indonesia Dept. of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Graduate school of Interdisciplinary Science and Engineering in Health Systems Okayama University Okayamas Japan Dept. of Informatics Institut Teknologi Sepuluh Nopember Surabaya Surabaya Indonesia

Technological developments have impacts and challenges in human life. Technological advances have made human life easier. The impact of technological developments is the industrial manufacturing process getting faster with the help of robotics technology. Robots can shorten manufacturing process time and efficient. On the other hand, challenges with technological developments force us to keep learning and adaptive to every changes around us, especially in the education sector. In education sector, formal educational institutions or campuses must be able to facilitate students to be able to learn comprehensively in the field of technology, especially robotics, where we can make an innovative learning platform based on robotics technology. In this study, we discussed a 2-DOF arm manipulator robot as kinematics learning platform of industrial robot to increase student competencies in preparation for facing the industrial environment in century. Inverse kinematics equation has been applied in the robot by providing input coordinates on the x-axis and y-axis where the accuracy value on the x-axis is 77% and the accuracy on the y-axis is 83.1%.

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