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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Yunping Fang Chenglong Fu Xiaojiang Du Dept. of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Dept. of Software and Information Systems Univ. of North Carolina at Charlotte Charlotte NC USA

The Matter standard, formerly known as Connected Home over IP, has emerged as the preferred choice for most smart home IoT vendors and service providers for the next-generation smart home IoT systems. It enhances interoperability across different smart home ecosystems and introduces the multi-admin feature, allowing a device to be commissioned and managed by multiple platforms simultaneously. While this standard offers considerable convenience, it also presents challenges for security policy enforcement. Policy enforcement has been highlighted in various studies as a crucial countermeasure against smart home IoT system vulnerabilities. Existing smart home policy enforcement methods, designed for individual IoT admin platforms, operate under the assumption of having a global view and control over all IoT devices in a smart home. This assumption, however, is untenable in a multi-admin environment, where an admin may only have partial device access or a partial view. In this work, we thoroughly analyze these challenges in a multi-admin setting and propose the first cross-admin policy enforcement solution. Our solution can parse complex policies into deployable sub-policies for each admin, and create virtual device instances and virtual automation rules to interconnect various admins. We test our solution on a real-world testbed involving 12 IoT devices and three popular Matter-enabled IoT platforms. Our results show that our solution can enforce cross-admin policies with a 100% success rate and a very small delay.

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Mm-wave 3D-printed DRAs for Cost-effective Emerging 5G-and SatCom Applications

Mm-wave 3D-printed DRAs for Cost-effective Emerging 5G-and S...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： El-Sawaf, Heba Abdel-Wahab, Wael M. Safavi-Naeini, Safieddin University of Waterloo Dept. of Electrical and Computer Engineering WaterlooON Canada C-COM Satellite Systems Inc. Ottawa Canada

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This paper presents a demonstration for low cost dielectric resonator antenna (DRA) manufacturing, using 3D-printing technology for mm-wave applications. DRAs are high-efficient radiators, as compared to metallic antennas, especially at high frequency applications. Moreover, 3D-printing technology facilitates DRA machining and positioning on the printed circuit board. Two DRAs, designed at 20 GHz, and 30 GHz, are 3D-printed, and measured realizing good performance. © 2022 IEEE.

关键词： 3D printers

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Cooperative Multi-Agent Assignment over Stochastic Graphs via Constrained Reinforcement Learning

arXiv

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arXiv 2025年

作者： Agorio, Leopoldo Van Alen, Sean Paternain, Santiago Calvo-Fullana, Miguel Bazerque, Juan Andres Dept. of Electrical and Computer Engineering Univ. of Pittsburgh PittsburghPA United States Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Dept of Engineering Universitat Pompeu Fabra Barcelona Spain

Constrained multi-agent reinforcement learning offers the framework to design scalable and almost surely feasible solutions for teams of agents operating in dynamic environments to carry out conflicting tasks. We address the challenges of multi-agent coordination through an unconventional formulation in which the dual variables are not driven to convergence but are free to cycle, enabling agents to adapt their policies dynamically based on real-time constraint satisfaction levels. The coordination relies on a light single-bit communication protocol over a network with stochastic connectivity. Using this gossiped information, agents update local estimates of the dual variables. Furthermore, we modify the local dual dynamics by introducing a contraction factor, which lets us use finite communication buffers and keep the estimation error bounded. Under this model, we provide theoretical guarantees of almost sure feasibility and corroborate them with numerical experiments in which a team of robots successfully patrols multiple regions, communicating under a time-varying ad-hoc network. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Stereo ORB-SLAM3 Underwater Performance Evaluation

Stereo ORB-SLAM3 Underwater Performance Evaluation

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Electrical engineering (ICEENG), International Conference on

作者： Hossam Elkeshky Samer A. Mohamed Mohammed I. Awad Hossam Abd El Munim Mechatronics Engineering Dept. Faculty of Engineering Ain Shams University Cairo Egypt Electrical and Electronic Engineering Dept. Faculty of Engineering and Design University of Bath Bath UK Computer Systems and Engineering Dept. Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798331519018

ISBN: (纸本)9798331519025

Site exploration and navigation missions in hazardous locations are examples of the widespread applications of autonomous mobile robots. Efficient simultaneous localization and mapping (SLAM) is essential for the functionality of such robots. Visual SLAM (VSLAM) has become a cost-effective and attractive solution for all types of vehicles in the field. ORB-SLAM3 and its preceding versions are prevailing visual SLAM algorithms in recent literature. ORB-SLAM3 builds a three-dimensional map based on images and supplies a continuous estimate of the vehicle’s pose. This paper demonstrates an experimental evaluation of stereo ORB-SLAM3 performance in scenes with diverse backgrounds. Online ORB-SLAM3 is tested in multiple aerial and underwater environments. The performance of the algorithm is evaluated by comparing against ground-truth estimates provided by offline structure-from-motion techniques. Results show that ORB-SLAM3 can function in structured environments providing acceptable accuracy and satisfactory performance given the right conditions. The algorithm achieves average standard deviation values of 4 cm in underwater scenes depending on the complexity of the followed trajectory.

关键词： Visualization Simultaneous localization and mapping Accuracy Three-dimensional displays Structure from motion Pose estimation Trajectory Complexity theory State estimation Standards

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

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IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Woosung Kang Jinkyu Lee Youngmoon Lee Sangeun Oh Kilho Lee Hoon Sung Chwa Dept. of Electrical Engineering and Computer Science DGIST Republic of Korea Dept. of Computer Science and Engineering Sungkyunkwan University (SKKU) Republic of Korea Dept. of Robotics Hanyang University Republic of Korea Department of Software & Computer Engineering Ajou University Republic of Korea School of AI Convergence Soongsil University Republic of Korea

ISBN: (数字)9798350358414

ISBN: (纸本)9798350358421

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity.

关键词： Scheduling algorithms Memory management Graphics processing units Artificial neural networks Machine learning Drives Real-time systems

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Using High-Level Patterns to Estimate How Humans Predict a Robot will Behave

arXiv

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arXiv 2024年

作者： Parekh, Sagar Bramblett, Lauren Bezzo, Nicola Losey, Dylan P. Dept. of Mechanical Engineering Virginia Tech BlacksburgVA24061 United States Dept. of Systems & Information Engineering Dept. of Electrical & Computer Engineering University of Virginia CharlottesvilleVA22903 United States

Humans interacting with robots often form predictions of what the robot will do next. For instance, based on the recent behavior of an autonomous car, a nearby human driver might predict that the car is going to remain in the same lane. It is important for the robot to understand the human’s prediction for safe and seamless interaction: e.g., if the autonomous car knows the human thinks it is not merging — but the autonomous car actually intends to merge — then the car can adjust its behavior to prevent an accident. Prior works typically assume that humans make precise predictions of robot behavior. However, recent research on human-human prediction suggests the opposite: humans tend to approximate other agents by predicting their high-level behaviors. We apply this finding to develop a second-order theory of mind approach that enables robots to estimate how humans predict they will behave. To extract these high-level predictions directly from data, we embed the recent human and robot trajectories into a discrete latent space. Each element of this latent space captures a different type of behavior (e.g., merging in front of the human, remaining in the same lane) and decodes into a vector field across the state space that is consistent with the underlying behavior type. We hypothesize that our resulting high-level and course predictions of robot behavior will correspond to actual human predictions. We provide initial evidence in support of this hypothesis through proof-of-concept simulations, testing our method’s predictions against those of real users, and experiments on a real-world interactive driving dataset. © 2024, CC BY.

关键词： Prediction models

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Supervised Learning Approaches for Vibration Analysis and Anomaly Detection in Rotating Machines

Supervised Learning Approaches for Vibration Analysis and An...

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Emerging Trends in Electrical, Control, and Telecommunication engineering (ETECTE), International Conference on

作者： Syed Zulfiqar Ali Syed Hasnat Ali Anees Javeed Ihsan Ullah Sufyan Ali Memon Sajjad Manzoor Dept. of Electrical and Computer Engineering CUI Abbottabad Campus Abbottabad Pakistan Dept. of Defence Systems Engineering Sejong University Seoul Republic of Korea Dept. of Electrical Engineering MUST Mirpur AJK Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

Safety and reliability are crucial for modern systems and technologies. This paper presents an approach for real-time anomaly detection in rotating machines using vibration analysis and machine learning algorithms for predictive maintenance. Rotating machines such as motors, engines, gearboxes and turbines are widely used systems and faults in these systems can result in complete shutdowns. Preventing unexpected downtime while ensuring equipment reliability and safety is essential. In this context, anomaly detection plays a vital role in monitoring the healthy operation of rotating machines. A test-bed was designed to generate datasets for both balanced (healthy) and unbalanced (faulty) motor conditions. The experimental setup consists of a three-phase induction motor, a signal acquisition system (Raspberry Pi), a vibration sensor (MPU6050) and a coupling system capable of adjusting motor balance. Supervised machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF) were applied to classify and detect anomalies in the collected datasets. A grid search algorithm was employed for hyperparameter tuning of the SVM and RF models. The proposed technique achieved a maximum accuracy of approximately 98% using SVM and 100% using the RF algorithm leveraging nine optimally selected temporal features.

关键词： Support vector machines Vibrations Radio frequency Machine learning algorithms Approximation algorithms Vectors Safety Classification algorithms Reliability Anomaly detection

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Train a Robot to Climb Staircase Using Vision-Base System 10

Train a Robot to Climb Staircase Using Vision-Base System

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10th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2022

作者： Jayawardana, J.T.H. Dilshan, H.V.V. Wijethilaka, R.G.K.H Balasooriya, T.D. Rajapaksha, U.U.S. Harshanath, S.M.B. Jayawardena, Chandimal Sri Lanka Institute of Information Technology Dept. Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. Information Technology Malabe Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

Currently, robots are used for different types of work, such as the manufacturing industry, healthcare, and the hotel industry. According to the current epidemic situation, the usage of robots was increased because of the need to reduce human interaction. As a result, they have to walk around the workplace, because of that, they may have to climb staircases. The world has many types of robots. Here the selected robot is a humanoid. This proposal is concerned with how to detect the staircase, count steps, get dimensions of it, and move the robot on it by keeping body balance. First, want to know what the objects are, then walk. The identified images from the image sensors will get as input. The technology stack that is used for image analysis is a method related to computer vision in deep learning. Other than that, while climbing the stairs robot needs to identify whether the staircase is over or not. Here we introduce a new concept: get the number of steps required to climb before climbing the stairs. It is related to how humans identify things by seeing and making decisions. The need to take the dimensions of the stairs is that when considering the stairs, they have their height, width, and range. Therefore, it is imperative to calculate the dimensions of each staircase separately. Creating a balance system similar to human balance is a great advantage in robotics. To implement such a system, there is an upright pose controller to allow the robot to walk stably by preventing tilting of the robot during walking on uneven floor. In general, for us to do that we need to calculate the global inclination of the floor is a key factor. It can be measured with a 2-axis accelerator meter, and it is installed in the inertial sensor. © 2022 IEEE.

关键词： Stairs

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Downlink Covariance Estimation in URA FDD Massive MIMO systems

Downlink Covariance Estimation in URA FDD Massive MIMO Syste...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Salime Bameri Khalid Almahrog Ramy H. Gohary Amr El-Keyi Yahia Ahmed Dept. of Systems and Computer Engineering Carleton University Canada Ericsson Canada

We propose a low-complexity downlink channel covariance matrix estimation for massive multiple-input multiple-output systems in which the base station (BS) is equipped with a uniform rectangular antenna array (URA). This scheme can be expressed in the form of an affine transformation which depends only on the uplink and downlink carrier frequencies, and the BS array configurations. An upper bound on the estimation error is derived, which shows that the accuracy of the proposed scheme increases with the number of URA antennas and the compactness and differentiability class of the periodic extension of a non-linearly transformed version of the angular power spread. The performance superiority of the proposed scheme over its existing counterparts is confirmed through simulations.

关键词： Geometry Estimation error Upper bound Massive MIMO Signal processing Downlink Numerical simulation

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Embracing the Future: Unveiling the Revolution of Human-AI Interaction in the Digital Education Era

Embracing the Future: Unveiling the Revolution of Human-AI I...

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Virtual Campus (JICV), X International Conference on

作者： Andrea E. Cotino Arbelo Carina S. González-González Jezabel M. Molina Gil Dept. of Computer Engineering and Systems University of La Laguna Tenerife Spain

ISBN: (数字)9798350343441

ISBN: (纸本)9798350343458

The recent and emerging introduction of generative AI in all areas of society poses new challenges to be overcome, particularly in the educational setting. The intimate and affective relationship that may develop between students and generative AI devices raises critical questions about the psychological and emotional impact of such interactions. This issue becomes particularly significant when the interactions with AI-based systems begin at an early age, and these systems assume roles as digital tutors, digital secretaries, motivator agents, and/or mentors. The aim of this work in progress was to address a recent challenge within the digital educational context: the psychological and emotional impact that the introduction of AI-based systems in digital education may have on students. However, we have faced limitations in terms of data availability and resources, particularly when addressing the issue from early ages, where there is a notable lack of studies. Although the results may not have been significant, the research has provided a general overview and a solid theoretical foundation for future studies in this field. Therefore, we suggest conducting more rigorous studies with representative samples and complete data, alongside developing reliable and validated methodologies and assessment tools to address the issues involved.

关键词： Generative AI Education Knowledge based systems Psychology Medical services Reliability theory Solids

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