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检索条件"任意字段=13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

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Adaptive Runtime Estimate of Task Execution times using Bayesian Modeling 27

Adaptive Runtime Estimate of Task Execution Times using Baye...

引用

27th ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Friebe, Anna Markovic, Filip Papadopoulos, Alessandro, V Nolte, thomas Malardalen Univ Vasteras Sweden

ISBN: (纸本)9781665441889

In the recent works that analyzed execution-time variation of real-time tasks, it was shown that such variation may conform to regular behavior. this regularity may arise from multiple sources, e.g., due to periodic changes in hardware or program state, program structure, inter-task dependence or inter-task interference. Such complexity can be better captured by a Markov Model, compared to the common approach of assuming independent and identically distributed random variables. However, despite the regularity that may be described with a Markov model, over time, the execution times may change, due to irregular changes in input, hardware state, or program state. In this paper, we propose a Bayesian approach to adapt the emission distributions of the Markov Model at runtime, in order to account for such irregular variation. A preprocessing step determines the number of states and the transition matrix of the Markov Model from a portion of the execution time sequence. In the preprocessing step, segments of the execution time trace with similar properties are identified and combined into clusters. At runtime, the proposed method switches between these clusters based on a Generalized Likelihood Ratio (GLR). Using a Bayesian approach, clusters are updated and emission distributions estimated. New clusters can be identified and clusters can be merged at runtime. the time complexity of the online step is O(N<^>2 + NC) where N is the number of states in the Hidden Markov Model (HMM) that is fixed after the preprocessing step, and C is the number of clusters.

关键词： real-time systems Hidden Markov Model Bayesian Analysis Probabilistic Timing Analysis

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real-time synthetic-to-real human detection for robotics applications 13

Real-time synthetic-to-real human detection for robotics app...

引用

13th international conference on Information, Intelligence, systems and applications, IISA 2022

作者： Tzelepi, Maria Symeonidis, Charalampos Nikolaidis, Nikos Tefas, Anastasios Aristotle University of Thessaloniki Department of Informatics Thessaloniki Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

During the recent years, Deep Learning achieved exceptional performance in various computer vision tasks, paving auspicious research directions for its application in robotics. A key component for its exceptional performance is the availability of sufficient training data. However obtaining such amount of training data constitutes a challenging task, especially considering robotics applications. thus, synthetic data have recently been regarded as a promising tool to overcoming the data availability problem. In this work we first build a synthetic human dataset, and then we train a lightweight model, capable of operating in real-time for high-resolution input on low-power GPUs, for discriminating between humans and non-humans. the target of this work is to assess the generalization of the model trained on synthetic data, to real data, and also to explore the effect of using (few) real images in the training phase. As it is shown through quantitative and qualitative results the use of only few real images can beneficially affect of the performance of the synthetic-to-real real-time model. © 2022 ieee.

关键词： Robotics

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Smart Diaper embedded with Fully Printed Sensors for Wireless Healthcare and Monitoring 5

Smart Diaper Embedded with Fully Printed Sensors for Wireles...

引用

5th ieee international conference on Flexible and Printable Sensors and systems, FLEPS 2023

作者： Ali, Shawkat Khan, Arshad Bermak, Amine College of Science and Engineering Hamad Bin Khalifa University Division of Information and Computing Technology Doha Qatar Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia

ISBN: (纸本)9781665457330

In this research, a real-time healthcare and monitoring wireless system is developed to monitor vital signs of a wearer i.e., temperature, respiration rate, and wetting volume on a smartphone. the smart diaper consists of two main units, a detachable electronic module and a disposable printed biosensors patch deployed on a commercial diaper. Custom sensors array was designed and fabricated in a single patch through printed electronics technology by using biocompatible materials on a thermal transfer substrate. the sensors patch was designed in such a way to accurately collect the vital signs data by placing each sensor at appropriate site. Individual sensors were characterized for their electrical and mechanical properties. the overall system was tested, and data was wirelessly transmitted to a smartphone via Bluetooth. the main purpose of the proposed smart diaper is to minimize the effort of caregiver and avoid infection, rashes, and discomfort. We believe this work will open a new window for the printed electronics systems for special needs, elderly, and infant healthcare. © 2023 ieee.

关键词： Wetting

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Compressing VAE-Based Out-of-Distribution Detectors for embedded Deployment

Compressing VAE-Based Out-of-Distribution Detectors for Embe...

引用

ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Aditya Bansal Michael Yuhas Arvind Easwaran College of Computing and Data Science Nanyang Technological University Singapore Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model’s training distribution to prevent potentially unsafe actions. However, OOD detectors are often implemented using deep neural networks, which makes it difficult to meet real-time deadlines on embedded systems with memory and power constraints. We consider the class of variational autoencoder (VAE) based OOD detectors where OOD detection is performed in latent space, and apply quantization, pruning, and knowledge distillation. these techniques have been explored for other deep models, but no work has considered their combined effect on latent space OOD detection. While these techniques increase the VAE’s test loss, this does not correspond to a proportional decrease in OOD detection performance and we leverage this to develop lean OOD detectors capable of real-time inference on embedded CPUs and GPUs. We propose a design methodology that combines all three compression techniques and yields a significant decrease in memory and execution time while maintaining AUROC for a given OOD detector. We demonstrate this methodology with two existing OOD detectors on a Jetson Nano and reduce GPU and CPU inference time by 20% and 28% respectively while keeping AUROC within 5% of the baseline.

关键词： Training Quantization (signal) embedded systems Memory management Graphics processing units Detectors Machine learning real-time systems Safety Monitoring

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Overcoming Radio Map Degradation in Wi-Fi-based Positioning systems 13

Overcoming Radio Map Degradation in Wi-Fi-based Positioning ...

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13th international conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Silva, Ivo Pendao, Cristiano Torres-Sospedra, Joaquin Moreira, Adriano Universidade Do Minho Centro Algoritmi Guimarães4800-058 Portugal

ISBN: (纸本)9798350320114

Wi-Fi-based positioning systems, particularly the ones based on Wi-Fi fingerprinting, rely on a Radio Map (RM) which represents the radio environment at the time when it was collected. Over time, phenomena such as the propagation effects or adding/removing Access Points (APs) from an indoor environment may lead to significant variations in the radio environment, thus leading to errors in estimated positions. Although it is common knowledge that RMs degrade over time, it is difficult to predict and detect when degradation causes large errors. In this paper, we propose a method that continuously monitors the radio environment and uses Radial Basis Functions (RBF) interpolation to automatically enrich an old RM with new information. Before enriching the RM, AP selection is performed to remove APs that disappeared and mobile APs from the old radio map. then, the analysis of the radio environment is performed to select newly detected APs to enrich the radio map, based on predefined criteria. Our experiments with real-world data show a significant improvement over 100% in mean error when using the enriched RM. this approach presents a promising solution to overcome the RM degradation in Wi-Fi fingerprinting, with potential applications in indoor positioning and location-based services. © 2023 ieee.

关键词： Indoor Positioning Radial Basis Functions Radio Map Degradation RSS Interpolation Wi-Fi fingerprinting

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Cooperative Network-Computation Load Balancing Simulator for Vehicular Edge computing

Cooperative Network-Computation Load Balancing Simulator for...

引用

ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Juho Song BaekGyu Kim Jeongho Kwak Ji-Woong Choi Hoon Sung Chwa Department of Electrical Engineering and Computer Science DGIST Daegu Republic of Korea

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

To enhance the performance of autonomous driving, recent studies have been incorporating various tasks that require increasingly more computation. As computational demands increase, it is often difficult to achieve timely execution with the limited performance of onboard computing units alone. To address this issue, Vehicle Edge computing (VEC), which offloads computational workloads to the edge and retrieves the results back to the vehicle, is gaining significant attention. To achieve efficient offloaded analytics via VEC, it is crucial to comprehensively consider both of the computing and network conditions of the V2X systems, as well as the vehicle energy consumption and timely execution. However, current studies have not sufficiently addressed the comprehensive modeling of computational and network loads in these V2X systems. To deal with this, we propose a Cooperative Network-Computation Load Balancing Simulator for VEC.

关键词： Energy consumption Computational modeling Load management real-time systems Vehicle-to-everything Autonomous vehicles Edge computing Load modeling

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Demand Characterization of CPS with Conditionally-Enabled Sensors 27

Demand Characterization of CPS with Conditionally-Enabled Se...

引用

27th ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Willcock, Aaron Fisher, Nathan Chantem, thidapat Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Virginia Tech Dept Elect & Comp Engn Arlington VA USA

ISBN: (纸本)9781665441889

Characterizing computational demand of Cyber-Physical systems (CPS) is critical for guaranteeing that multiple hard real-time tasks may be scheduled on shared resources without missing deadlines. In a CPS involving repetition such as industrial automation systems found in chemical process control or robotic manufacturing, sensors and actuators used as part of the industrial process may be conditionally enabled (and disabled) as a sequence of repeated steps is executed. In robotic manufacturing, for example, these steps may be the movement of a robotic arm through some trajectories followed by activation of end-effector sensors and actuators at the end of each completed motion. the conditional enabling of sensors and actuators produces a sequence of Monotonically Ascending Execution times (MAE) with lower WCET when the sensors are disabled and higher WCET when enabled. Since these systems may have several predefined steps to follow before repeating the entire sequence each unique step may result in several consecutive sequences of MAE. the repetition of these unique sequences of MAE result in a repeating WCET sequence. In the absence of an efficient demand characterization technique for repeating WCET sequences composed of subsequences with monotonically increasing execution time, this work proposes a new task model to describe the behavior of real-world systems which generate large repeating WCET sequences with subsequences of monotonically increasing execution times. In comparison to the most applicable current model, the Generalized Multiframe model (GMF), an empirically and theoretically faster method for characterizing the demand is provided. the demand characterization algorithm is evaluated through a case study of a robotic arm and simulation of 10,000 randomly generated tasks where, on average, the proposed approach is 231 and 179 times faster than the state-of-the-art in the case study and simulation respectively.

关键词： real-time systems control systems cyberphysical systems

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Recovery-by-Learning: Restoring Autonomous Cyber-physical systems from Sensor Attacks 27

Recovery-by-Learning: Restoring Autonomous Cyber-physical Sy...

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27th ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Akowuah, Francis Prasad, Romesh Espinoza, Carlos Omar Kong, Fanxin Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA Syracuse Univ Dept Mech & Aerosp Engn Syracuse NY 13244 USA

ISBN: (纸本)9781665441889

Autonomous cyber-physical systems (CPS) are susceptible to non-invasive physical attacks such as sensor spoofing attacks that are beyond the classical cybersecurity domain. these attacks have motivated numerous research efforts on attack detection, but little attention on what to do after detecting an attack. the importance of attack recovery is emphasized by the need to mitigate the attack's impact on a system and restore it to continue functioning. there are only a few works addressing attack recovery, but they all rely on prior knowledge of system dynamics. To overcome this limitation, we propose Recovery-by-Learning, a data-driven attack recovery framework that restores CPS from sensor attacks. the framework leverages natural redundancy among heterogeneous sensors and historical data for attack recovery. Specially, the framework consists of two major components: state predictor and data checkpointer. First, the predictor is triggered to estimate systems states after the detection of an attack. We propose a deep learning-based prediction model that exploits the temporal correlation among heterogeneous sensors. Second, the checkpointer executes when no attack is detected. We propose a double sliding window based checkpointing protocol to remove compromised data and keep trustful data as input to the state predictor. third, we implement and evaluate the effectiveness of our framework using a realistic data set and a ground vehicle simulator. the results show that our method restores a system to continue functioning in presence of sensor attacks.

关键词： autonomous cyber-physical systems sensor attacks attack recovery attack resiliency

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One-Shot Sparse Neural Architecture Search for Resource-Constrained Devices

One-Shot Sparse Neural Architecture Search for Resource-Cons...

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ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Shenghui Song Jan-Nico Zaech Seonyeong Heo School of Computing Kyung Hee University Yongin South Korea INSAIT Sofia University Sofia Bulgaria

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

Employing high-performance neural network models is challenging for resource-constrained devices as the models require strong computing power and large memory space. One-shot neural architecture search (NAS) helps to find a suitable neural architecture more efficiently by using a single one-shot network that shares weights with sub-networks. However, the existing one-shot NAS methods pay little attention to the sparsity achievable from the one-shot network. therefore, there still may be room to reduce the resource requirement of the network. this work presents a new one-shot sparse NAS method, which tries to find an optimal sparsity for the network using soft channel masking during the architecture search. the experimental results show that the proposed method can find a more sparse architecture with little accuracy drop.

关键词： Accuracy Computational modeling Neural networks Memory management Machine learning real-time systems

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A Formally Verified Leader Election Algorithm for Autonomous Driving systems

A Formally Verified Leader Election Algorithm for Autonomous...

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ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Ryuta Kambe Benjamin Gilby Yuuki Takano TIER IV Inc. Tokyo Japan

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

As autonomous driving systems mature, the importance of fault tolerance to provide safe operation even in hazardous conditions becomes a key issue in deployment. In an autonomous driving system composed of redundant devices, it is necessary to maintain consistency across the system regarding which devices should currently be handling processing. In this paper, we present a new leader election algorithm for autonomous driving systems that guarantees consistency in the presence of the failure of any single system component. To make the algorithm practical for autonomous driving systems, we design it to both handle network link failures and guarantee liveness in the presence of a single failure. Due to the safety-critical nature of our environment and the known difficulty of testing distributed algorithms, we use formal verification to validate our proposed algorithm. Our experiments show that the algorithm completes with reasonable time in a simulated environment of an autonomous driving system.

关键词： Fault tolerance Voting Fault tolerant systems real-time systems Distributed algorithms Autonomous vehicles Formal verification Testing

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