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25th IEEE international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Bril, Reinder J. Altmeyer, Sebastian Gai, Paolo Tech Univ Eindhoven TU E Eindhoven Netherlands Univ Amsterdam Amsterdam Netherlands Evidence Srl Pisa Italy

ISBN: (纸本)9781728131979

Stack sharing between tasks may significantly reduce the amount of memory required in resource-constrained real-time embedded systems. Existing work on stack sharing mainly focused on stack sharing between tasks that neither leave any data on the stack from one instance to another nor suspend themselves, i.e. tasks with a so-called single-shot execution. In this paper, we consider stack memory requirements of AUTOSAR/OSEK-compliant scheduling policies for a mixed task set, consisting of so-called basic and extended tasks. Unlike basic tasks, that have a single-shot execution, extended tasks are allowed to leave data on the stack from one instance to another and to suspend themselves. We prove that minimizing the shared stack requirement for such a mixed task set is an NP-hard problem. We subsequently provide an heuristic-based algorithm to minimize stack usage of a mixed task set, and evaluate the algorithm through a case study of an implementation of an unmanned aerial vehicle.

关键词： Task analysis Memory management real-time systems Indexes Prediction algorithms NP-hard problem Heuristic algorithms

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A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks 19

A New Clustering-Based Technique for the Acceleration of Dee...

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19th IEEE international conference on Machine Learning and applications, ICMLA 2020

作者： Vasilis Pikoulis, Erion Mavrokefalidis, Christos Lalos, Aris S. Industrial Systems Institute Greece

ISBN: (纸本)9781728184708

Deep learning and especially the use of Deep Neural Networks (DNNs) provides impressive results in various regression and classification tasks. However, to achieve these results, there is a high demand for computing and storing resources. this becomes problematic when, for instance, real-time, mobile applications are considered, in which the involved (embedded) devices have limited resources. A common way of addressing this problem is to transform the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques. Within the MCA framework, we propose a clustering-based approach that is able to increase the number of employed centroids/representatives, while at the same time, have an acceleration gain compared to conventional, k-means based approaches. this is achieved by imposing a special structure to the employed representatives, which is enabled by the particularities of the problem at hand. Moreover, the theoretical acceleration gains are presented and the key system hyper-parameters that affect that gain, are identified. Extensive evaluation studies carried out using various state-of-the-art DNN models trained in image classification, validate the superiority of the proposed method as compared for its use in MCA tasks. © 2020 IEEE.

关键词： Deep neural networks

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Message from the General and Program co-Chairs

Message from the General and Program co-Chairs

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

Message from the General and Program co-Chairs

ISBN: (数字)9781728144030

ISBN: (纸本)9781728144047

Message from the General and Program co-Chairs

关键词： real-time systems conferences embedded systems Industries Heterogeneous networks Standards Finance

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Artificial Intelligence-based Remote Diagnosis of Sleep Apnea using Instantaneous Heart Rates

Artificial Intelligence-based Remote Diagnosis of Sleep Apne...

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Prabodh Panindre Vijay Gandhi Sunil Kumar New York University Brooklyn USA

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Prolonged Sleep Apnea is a sleeping disorder that can cause arrhythmia, hypertension, and other serious health conditions leading to cardiovascular diseases and fatal strokes. Most widely used current clinical techniques for sleep apnea diagnosis are expensive, time-consuming, and cannot be performed remotely. Wearable watch-style health trackers continuously track sleep behavior, physiological data, and physical activity that can enable real-time remote diagnosis of sleep apnea. Recently, the application of Artificial Intelligence (AI) techniques within the field of medicine and remote diagnosis is gaining popularity. In this paper, several Artificial Intelligence (AI) models have been trained and tested to classify sleep apnea condition in real-time using sequential data of Instantaneous Heart Rates (IHR). Using the confusion matrix, the accuracy, precision, recall, specificity, FI Score, sensitivity, and area under the receiver operating characteristic curve of each model are computed and compared. the Bi-directional Long Short-Term Memory (LSTM) was found to be the best AI technique for classifying sleep apnea. the approach depicted in this study for diagnosing sleep apnea can allow the telemedicine, telehealth, and mHealth applications to detect several health risk factors in real-time using data streaming from the health trackers.

关键词： Heart rate Recurrent neural networks Databases Computational modeling Sleep apnea real-time systems Artificial intelligence

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A Study on Energy-Process-Latency Tradeoff in embedded Artificial Intelligence 11

A Study on Energy-Process-Latency Tradeoff in Embedded Artif...

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11th international conference on Information and Communication Technology Convergence, ICTC 2020

作者： Kim, Jinhwi Galanopoulos, Apostolos Vivek Joseph, Jude Kwak, Jeongho Dgist Department of Information and Communication Engineering Korea Republic of Trinity College Dublin School of Computer Science Statistics Ireland

ISBN: (纸本)9781728167589

In this paper, we explore an impact of GPU/CPU scaling of a state-of-the-art AI embedded device on its energy consumption and AI performance. We use Nvidia Jetson TX2 as an experiment device thanks to its tractability to scale GPU/CPU and modify AI framework and libraries. Via extensive experiment in various ML (Machine Learning) scenarios, i.e., face recognition and objective detection, we demonstrate a clear tradeoff between GPU/CPU scaling, energy consumption (of GPU/CPU as well as entire device) and training/inference speed. Finally, we envision a future work aiming to optimize processing and networking resources simultaneously at an extended scenario that multiple AI embedded devices cooperate with each other for a common AI application. © 2020 IEEE.

关键词： Energy utilization

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A probabilistic calculus of cyber-physical systems

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INFORMATION AND COMPUTATION 2021年 279卷 104618-104618页

作者： Lanotte, Ruggero Merro, Massimo Tini, Simone Univ Insubria Dipartimento Sci & Alta Tecnol Via Valleggio 11 I-22100 Como Italy Univ Verona Dipartimento Informat Str Grazie 15 I-37134 Verona Italy

Cyber-Physical systems (CPSs) are integrations of networking and distributed computing systems with physical processes, where feedback loops allow physical processes to affect computations and vice versa. Although CPSs can be found in several real-world domains, their verification often relies on simulation test systems rather than formal methodologies. We propose a hybrid probabilistic process calculus for modelling and reasoning on CPSs. the dynamics of the calculus is expressed in terms of a probabilistic labelled transition system in the SOS style of Plotkin. this is used to define a bisimulation-based probabilistic behavioural semantics which supports compositional reasonings. For a more careful comparison between CPSs, we provide two compositional probabilistic metrics to formalise the notion of behavioural distance between systems, also in the case of bounded computations. Finally, we provide a non-trivial case study, taken from an engineering application, and use it to illustrate our definitions and our compositional behavioural theory for CPSs. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Cyber-physical system Hybrid probabilistic process calculus Probabilistic metric semantics

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real-time GPS tracking using serverless architecture and ARM processor 11

Real-time GPS tracking using serverless architecture and ARM...

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11th international conference on Communication systems & Networks (COMSNETS)

作者： Anand, Sundar Johnson, Annie Mathikshara, Priyanka Karthik, R. Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India

ISBN: (纸本)9781538679029

there have been many advancements in the field of technology which have helped to shape better solutions for the problems of today. One such advancement that has provided extraordinary services with minimal effort is cloud computing. the aim of this work is to build a GPS tracker for vehicles using serverless architecture. It is equipped with a suitable mobile/web application for viewing the live results. the location data is stored in a cloud database that can be used for processing. the route that the vehicle takes is plotted using google maps after the 'to and from' dates are selected by the user. this can be viewed on the website that is specially designed for this vehicle tracking application. An email is sent to the user if movement outside a specific geographical boundary is detected. these geographical boundaries or geofences are customized based on the user's desire. Incorporating cloud computing has increased the efficiency of storage and analysis of data. thereby, enhancing the features available in vehicle tracking applications.

关键词： Global Positioning System Microcontrollers Cloud computing real-time systems Computer architecture Hardware Software

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Mixed-Trust computing for real-time systems 25

Mixed-Trust Computing for Real-Time Systems

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25th IEEE international conference on embedded and real-time computing systems and applications (RTCSA)

作者： de Niz, D. Andersson, B. Klein, M. Lehoczky, J. Vasudevan, A. Kim, H. Moreno, G. Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781728131979

Verifying complex Cyber-Physical systems (CPS) is increasingly important given the push to deploy safety-critical autonomous features. Unfortunately, traditional verification methods do not scale to the complexity of these systems and do not provide systematic methods to protect verified properties when not all the components can be verified. To address these challenges, this paper proposes a real-time mixed-trust computing framework that combines verification and protection. the framework introduces a new task model, where an application task can have both an untrusted and a trusted part. the untrusted part allows complex computations supported by a full OS with a real-time scheduler running in a VM hosted by a trusted hypervisor. the trusted part is executed by another scheduler within the hypervisor and is thus protected from the untrusted part. If the untrusted part fails to finish by a specific time, the trusted part is activated to preserve safety (e.g., prevent a crash) including its timing guarantees. this framework is the first allowing the use of untrusted components for CPS critical functions while preserving logical and timing guarantees, even in the presence of malicious attackers. We present the framework design and implementation along with the schedulability analysis and the coordination protocol between the trusted and untrusted parts. We also present our Raspberry Pi 3 implementation along with experiments showing the behavior of the system under failures of untrusted components, and a drone application to demonstrate its practicality.

关键词： Task analysis real-time systems Computational modeling Timing Safety Runtime Complexity theory

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Industrial Control via Application Containers: Migrating from Bare-Metal to IAAS 11

Industrial Control via Application Containers: Migrating fro...

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11th IEEE Int Conf on Cloud Comp Technol and Sci (CloudCom) / 19th IEEE Int Conf Comp and Informat Technol (CIT) / 2019 Int Workshop on Resource Brokering with Blockchain (RBchain) / Asia-Pacific Serv Comp Conf (APSCC)

作者： Hofer, Florian Sehr, Martin A. Lannopollo, Antonio Ugalde, Ines Sangiovanni-Vincentelli, Alberto Russo, Barbara Free Univ Bolzano Bozen Fac Comp Sci Bolzano Italy Siemens Corp Corp Technol Berkeley CA 94704 USA Univ Calif Berkeley EECS Dept Berkeley CA 94720 USA

ISBN: (纸本)9781728150116

We explore the challenges and opportunities of shifting industrial control software from dedicated hardware to bare-metal servers or cloud computing platforms using off the shelf technologies. In particular, we demonstrate that executing time-critical applications on cloud platforms is viable based on a series of dedicated latency tests targeting relevant real-time configurations.

关键词： Industrial Control systems real-time IAAS Containers Determinism

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A real-time Server Based Approach for Safe and timely Intersection Crossings 25

A Real-Time Server Based Approach for Safe and Timely Inters...

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25th IEEE international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Oza, Pratham Chantem, thidapat Virginia Tech Dept Elect & Comp Engn Blacksburg VA 24061 USA

ISBN: (纸本)9781728131979

Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. this paper focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to queue spillbacks, which disrupt traffic flows across the entire network and increase congestion. While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles cannot adequately adjust to dynamic traffic conditions. this paper presents a technology-agnostic adaptive real-time server based approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. this work is also the first to provide worst-case bounds on wait time making our approach more dependable and predictable. the proposed approach was validated in simulations and on a realistic hardware testbed with robots mimicking human driving behaviors. Compared to the pre-timed traffic control and an adaptive scheduling based traffic control, our algorithm is able to avoid spillbacks under highly dynamic traffic conditions and improve the average crossing delay in most cases by 10-50%.

关键词： Delays real-time systems Roads Adaptation models Vehicle dynamics Control systems

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