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arXiv 2023年

作者： Kuckling, Jonas Luckey, Robin Avrutin, Viktor Vardy, Andrew Reina, Andreagiovanni Hamann, Heiko The Department of Computer and Information Science University of Konstanz Konstanz78457 Germany The Centre for the Advanced Study of Collective Behaviour University of Konstanz Germany The Institute of Computer Engineering University of Lübeck Lübeck Germany The Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany The Department of Computer Science The Department of Electrical and Computer Engineering Memorial University of Newfoundland St. John's Canada IRIDIA Université Libre de Bruxelles Brussels Belgium

With increasing numbers of mobile robots arriving in real-world applications, more robots coexist in the same space, interact, and possibly collaborate. Methods to provide such systems with system size scalability are known, for example, from swarm robotics. Example strategies are self-organizing behavior, a strict decentralized approach, and limiting the robot-robot communication. Despite applying such strategies, any multi-robot system breaks above a certain critical system size (i.e., number of robots) as too many robots share a resource (e.g., space, communication channel). We provide additional evidence based on simulations, that at these critical system sizes, the system performance separates into two phases: nearly optimal and minimal performance. We speculate that in real-world applications that are configured for optimal system size, the supposedly high-performing system may actually live on borrowed time as it is on a transient to breakdown. We provide two modeling options (based on queueing theory and a population model) that may help to support this reasoning. © 2023, CC BY.

关键词： Multipurpose robots

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Handcrafted and Neural Network Based Features for Outlier Modulation Detection

Handcrafted and Neural Network Based Features for Outlier Mo...

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Asilomar Conference on Signals, systems & computers

作者： Yue Qi Ruixuan Wang Mojtaba Vaezi Jonathan Francis Xun Jiao Department of Electrical and Computer Engineering Villanova University Villanova PA USA L3Harris Technologies Communications Systems - East Camden NJ USA

Outlier detection is an important topic in radio frequency signal detection and classification. In this paper, two methods for outlier signal detection in the presence of strongly and moderately confusing modulations are proposed. The first method feeds selected handcrafted features to the distance discrimination classifiers for outlier detection. By removing the redundant features, this method can reach above 95% sensitivity and specificity at signal-to-noise ratio (SNR) equals 0dB and nearly 100% at high SNRs for mild confusers. In the second method, handcrafted features and neural network based features are used as inputs to several state-of-the-art machine learning classifiers such as one-class support vector machine, isolation forest, and one-class neural networks. From the results, the first method can achieve 83.90% accuracy; isolation forest and one-class neural networks with handcrafted features can reach 79.58% and 76.76% accuracy over all outlier signals at SNR=5dB which are noticeably higher than existing works in this field.

关键词： Support vector machines Modulation Forestry Machine learning Sensitivity and specificity Feature extraction Iron

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

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Sleep Apnea detecting based on ECG and EEG signals using Discrete Wavelet Transform and Artificial Neural Network

Sleep Apnea detecting based on ECG and EEG signals using Dis...

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The 3rd International Conference on Distributed Sensing and Intelligent systems (ICDSIS 2022)

作者： A. A. Hammal T. Bonney W. A. Nassan K. Obaideen Electrical and Computer Engineering Aleppo University Syria Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates Sustainable Energy and Power Systems Research Centre RISE University of Sharjah Sharjah United Arab Emirates

Sleep apnea is a severe sleep disorder that happens when breathing stops and starts during slumber. If it goes untreated, it can cause serious health problems. In recent years, Sleep Apnea (SA) has been diagnosed using either Electrocardiogram (ECG) signals that refer the heart health or Electroencephalogram (EEG) signals that refer to brain health. This research aims to build an Artificial Neural Network (ANN) classification model to determine sleep disorders using ECG and EEG signals. Features Delta sub-band waves are extracted and processed using a multilayer perceptron (MLP) with two different topologies; hidden layers are the same. The results show that using ECG and EEG signals to classify sleep apnea achieves high accuracy of 99.97% compared to other recent related work. We anticipate our assay to be a starting point for reliable wearable diagnostic devices to control equipment that can help patients with sleep apnea avoid respiratory distress episodes.

关键词： discrete wavelet transforms diseases electrocardiography electroencephalography medical disorders medical signal processing multilayer perceptrons pneumodynamics signal classification sleep

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Exploring the Frontiers of Synthetic Image-Based Deep Learning Training in Digital X-ray Radiography.

e-Journal of Nondestructive Testing

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e-Journal of Nondestructive Testing 2024年第8期29卷

作者： Hena, Bata Wei, Ziang Perron, Luc Castanedo, Clemente Ibarra Maldague, Xavier Department of Electrical and Computer Engineering Université Laval 1065 Ave de la Médecine Quebec CityQCG1V 0A6 Canada Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Université Laval 1065 Ave de la Médecine Quebec CityQCG1V 0A6 Canada GI Lab Solutions 475 Pruneau Ave Québec CityQCG1M 2J8 Canada

Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Nevertheless, the conventional human-based approaches to carrying out industrial radiography are prone to challenges that negatively impact the accuracy and efficiency of defect detection. To solve this challenge, numerous computer-based alternatives have been developed, including Automated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these ADR algorithms demand large volumes of qualitative data that should be representative of real-world cases to be expected during deployment. However, the availability of digital X-ray radiography data, especially for open research, is limited by non-disclosure contractual terms in the industry. In this study, we present a pipeline that is capable of modelling synthetic images based on real digital X-ray radiography images. This is achieved through a systematic analysis of the intensity distribution, considering grey value (GV) statistical uniqueness related to exposure conditions used during image acquisition, type of imaged component, material thickness variations, X-ray beam divergence, anode heel effect, scatter radiation, edge delineation, etc. The generated synthetic images were exclusively utilized to train a deep learning model, yielding an impressive model performance with mean intersection over union (IoU) of 0.93, and mean dice coefficient of 0.96 when tested on real unseen digital X-ray radiography images. The presented methodology is scalable and adaptable, making it suitable for diverse industrial applications. © 2024 The Authors.

关键词： X ray radiography

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Robust remote estimation over the collision channel in the presence of an intelligent jammer

Robust remote estimation over the collision channel in the p...

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IEEE Conference on Decision and Control

作者： Xu Zhang Marcos M. Vasconcelos LSEC Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Commonwealth Cyber Initiative and the Bradley Department of Electrical and Computer Engineering Virginia Tech

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We consider a sensor-receiver pair communicating over a wireless channel in the presence of a jammer who may launch a denial-of-service attack. We formulate a zero-sum game between a coordinator that jointly designs the transmission and estimation policies, and the jammer. We consider two cases depending on whether the jammer can sense the channel or not. We characterize a saddle-point equilibrium for the class of symmetric and unimodal probability density functions when the jammer cannot sense the channel. If the jammer can sense if the channel is being used, we provide an efficient algorithm that alternates between iterations of Projected Gradient Ascent and the Convex-Concave Procedure to find approximate First-order Nash-Equilibria. Our numerical results show that in certain cases the jammer may decide to launch a denial-of-service attack with the goal of deceiving the receiver even when the sensor decides not to transmit.

关键词： Wireless communication Wireless sensor networks Estimation Channel estimation Receivers Games Probability density function

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Control Strategies for Pursuit-Evasion Under Occlusion Using Visibility and Safety Barrier Functions

arXiv

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arXiv 2024年

作者： Zhou, Minnan Shaikh, Mustafa Chaubey, Vatsalya Haggerty, Patrick Koga, Shumon Panagou, Dimitra Atanasov, Nikolay Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States General Dynamics Mission Systems BloomingtonMN55431 United States Honda R&D Co. Ltd. Tokyo Japan Department of Robotics Department of Aerospace Engineering University of Michigan Ann ArborMI48109 United States

This paper develops a control strategy for pursuit-evasion problems in environments with occlusions. We address the challenge of a mobile pursuer keeping a mobile evader within its field of view (FoV) despite line-of-sight obstructions. The signed distance function (SDF) of the FoV is used to formulate visibility as a control barrier function (CBF) constraint on the pursuer’s control inputs. Similarly, obstacle avoidance is formulated as a CBF constraint based on the SDF of the obstacle set. While the visibility and safety CBFs are Lipschitz continuous, they are not differentiable everywhere, necessitating the use of generalized gradients. To achieve non-myopic pursuit, we generate reference control trajectories leading to evader visibility using a sampling-based kinodynamic planner. The pursuer then tracks this reference via convex optimization under the CBF constraints. We validate our approach in CARLA simulations and real-world robot experiments, demonstrating successful visibility maintenance using only onboard sensing, even under severe occlusions and dynamic evader movements. © 2024, CC BY.

关键词： Convex optimization

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A Dynamic Series Voltage Regulator for Load Protection in Bipolar DC Power System

A Dynamic Series Voltage Regulator for Load Protection in Bi...

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IEEE Canada Electric Power Conference, EPEC

作者： Ramin Babazadeh-Dizaji Mohammad Hassan Ghaderi Mohsen Ghafouri Mohsen Hamzeh Concordia Ins. for Inf. Systems Eng. Concordia University Montreal Canada Department of Electrical Engineering University of Texas at Dallas Richardson TX USA School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665463195

The low-voltage DC distribution systems are comprised of various components with a wide range of power ratings. In such systems, compared with unipolar configurations, bipolar structures bring considerable superiority in terms of efficiency, safety, and compatibility. However, the scarcity of protection methods has restricted the expansion of bipolar DC grids. In line with this trend, this paper presents a dynamic series regulator (DSVR) for the protection of sensitive loads in bipolar DC distribution systems. The proposed DSVR injects dynamic voltage for a set of selected sensitive loads to suppress the probable voltage abnormalities in the system. Therefore, it provides desirable satisfaction of voltage quality metrics in bipolar DC power systems. The proposed DSVR is composed of a multi-active bridge converter with one primary and two secondary windings followed by two full-bridge DC-DC converters. To evaluate the feasibility and effectiveness of the proposed concept, various simulations of several case studies are carried out in the PLECS (Plexim) software.

关键词： Regulators Voltage fluctuations Power system dynamics Windings Bridge circuits DC-DC power converters Software

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An Adaptive Cooperative Manipulation Control Framework for Multi-Agent Disturbance Rejection

An Adaptive Cooperative Manipulation Control Framework for M...

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IEEE Conference on Decision and Control

作者： Victor Aladele Carlos R. De Cos Dimos V. Dimarogonas Seth Hutchinson Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The success of a cooperative manipulation process depends on the level of disturbance rejection between the cooperating agents. However, this attribute may be jeopardized due to unexpected behaviors, such as joint saturation or internal collisions. This leads to deterioration in the performance of the manipulation task. In this paper, we present an adaptive distributed control framework that directly mitigates these internal disturbances, both in the joint (and task) spaces. With our approach, we show that including the manipulator-load coupling in the definition of the task error yields improved performance and robustness. To validate this statement, we provide stability guarantees and simulation results for two implementation cases.

关键词： Torque Simulation Stability criteria Decentralized control Collaboration Robustness Hardware

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Aerial Intelligent Reflecting Surface Enabled Terahertz Covert Communications in Beyond-5G Internet of Things

TechRxiv

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TechRxiv 2021年

作者： Mamaghani, Milad Tatar Hong, Yi The Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University MelbourneVIC3800 Australia

Unmanned aerial vehicles (UAVs) are envisioned to be extensively employed for assisting wireless communications in the Internet of Things (IoT). On the other hand, terahertz (THz) enabled intelligent reflecting surface (IRS) is expected to be one of the core enabling technologies for forthcoming beyond-5G wireless communications that promise a broad range of data-demand applications. In this paper, we propose a UAV-mounted IRS (UIRS) communication system over THz bands for confidential data dissemination from an access point (AP) towards multiple ground user equipments (UEs) in IoT networks. Specifically, the AP intends to send data to the scheduled UE, while unscheduled UEs may behave as potential adversaries. To protect information messages from the privacy preservation perspective, we aim to devise an energy-efficient multi-UAV covert communication scheme, where the UIRS is for reliable data transmissions, and an extra UAV is utilized as an aerial cooperative jammer, opportunistically generating artificial noise (AN) to degrade unscheduled UEs detection, leading to communication covertness improvement. This poses a novel max-min optimization problem in terms of minimum average energy efficiency (mAEE), aiming to improve covert throughput and reduce UAVs’ propulsion energy consumption, subject to satisfying some practical constraints such as the covertness requirements for which we obtain analytical expressions. Since the optimization problem is non-convex, we tackle it via the block successive convex approximation (BSCA) approach to iteratively solve a sequence of approximated convex sub-problems, designing the binary user scheduling, AP’s power allocation, maximum AN jamming power, IRS beamforming, and both UAVs’ trajectory and velocity planning. Finally, we present a low-complex overall algorithm for system performance enhancement with complexity and convergence analysis. Numerical results are provided to verify the analysis and demonstrate significant outperfo

关键词： Antennas

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