Software in autonomous systems, owing to performance requirements, is deployed on heterogeneous hardware comprising task specific accelerators, graphical processing units, and multicore processors. But performing timi...
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ISBN:
(数字)9781665453448
ISBN:
(纸本)9781665453448
Software in autonomous systems, owing to performance requirements, is deployed on heterogeneous hardware comprising task specific accelerators, graphical processing units, and multicore processors. But performing timing analysis for safety critical control software tasks with such heterogeneous hardware is becoming increasingly challenging. Consequently. a number of recent papers have addressed the problem of stability analysis of feedback control loops in the presence of timing uncertainties (cf., deadline misses). In this paper, we address a different class of safety properties, viz., whether the system trajectory deviates too much from the nominal trajectory, withthe latter computed for the ideal timing behavior. Verifying such quantitative safety properties involves performing a reachability analysis that is computationally intractable, or is too conservative. To alleviate these problems we propose to provide statistical guarantees over behavior of control systems with timing uncertainties. More specifically, we present a Bayesian hypothesis testing method based on Jeffreys's Bayes factor test that estimates deviations from a nominal or ideal behavior. We show that our analysis can provide, with high confidence, tighter estimates of the deviation from nominal behavior than using known reachability based methods. We also illustrate the scalability of our techniques by obtaining bounds in cases where reachability analysis fails to converge, thereby establishing the former's practicality.
Many real-timesystems run in dynamic environments where exogenous factors inform task workloads and deadlines, which may not be known prior to job release. A job of a task that would otherwise miss its deadline may a...
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the growing computing power of embeddedsystems has led to an increase in the use of general-purpose Operating systems (OSs) such as Linux. However, the substantial attack surface arising from their complexity makes t...
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this paper details developing and characterizing a soft capacitive sensor patch designed for force measurement in soft robotics and orthosis applications. the 6x6 matrix sensor was fabricated using silicone, selected ...
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ISBN:
(纸本)9798331517519;9798331517526
this paper details developing and characterizing a soft capacitive sensor patch designed for force measurement in soft robotics and orthosis applications. the 6x6 matrix sensor was fabricated using silicone, selected for its viscoelasticity and strain resistance, making it well-suited for human-interactive applications. the sensing mechanism is based on capacitance changes when force is applied and measured using a MUCA board. the system also incorporates an SHT30 sensor to monitor environmental conditions like temperature and humidity, ensuring the sensor's performance under varying conditions. Data from the capacitive sensor is processed via an Arduino Nano 33 IoT, with all measurements wirelessly transmitted and displayed in real-time using a custom dashboard on Arduino Cloud. the sensor can detect forces in the range of 1 to 4 N. Machine learning techniques, specifically linear regression, were used to characterize the sensor, yielding a highly accurate R-2 value of 0.9932. Capacitance and environmental data are automatically saved and processed using Python scripts, allowing for efficient data analysis. this capacitive sensor shows great promise in applications requiring precise force measurement and enhanced human-machine interaction. Potential future uses include wearable orthosis devices to improve human comfort and soft robotic systems designed to mimic natural movements. the ability to integrate this sensor into wearable technology highlights its potential in fields such as medical rehabilitation and prosthetics, offering improved interaction and adaptability in human-centered environments.
Recent advances in Augmented and Virtual reality show that they will play a critical role in our daily lives. However, interaction in such virtual environments is challenging as digital contents cannot be physically f...
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Large Language Models (LLMs) have attracted a lot of attention due to their success in natural language processing tasks. this paper provides a thorough overview by examining the architecture, applications, problems, ...
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the fixed preemption point (FPP) model has been studied as an alternative to fully preemptive and non-preemptive models, as restricting preemptions to specific, predictable locations within a task's execution can ...
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the edge computing paradigm extends the architectural space of real-timesystems by bringing the capabilities of the cloud to the edge. Unlike cloud-native systems designed for mean response times, real-time industria...
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this paper introduces an innovative architecture designed to enhance the execution of Artificial Intelligence (AI) software on edge devices, which are often constrained by limited hardware resources. the core of propo...
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ISBN:
(纸本)9798350376975;9798350376968
this paper introduces an innovative architecture designed to enhance the execution of Artificial Intelligence (AI) software on edge devices, which are often constrained by limited hardware resources. the core of proposal is to dynamically adapt AI models through server-mediated parameter updates and learning, thus allowing edge devices to efficiently process AI tasks in real-time and adapt to various operational conditions. By leveraging the computational power of cloud resources for the heavy lifting of AI model training, the computational burden on edge devices is alleviated, enabling them to focus on inference tasks with updated models. this approach significantly improves the operational efficiency and adaptability of edge computing in AI applications. Our architecture employs server-based emulation to monitor and dynamically update edge devices, ensuring their execution is optimized for current conditions. Experimental results demonstrate a substantial reduction in operational time up to 75% compared to traditional edge devices without accelerators and 49% when compared to devices equipped with accelerators. Moreover, proposed model shows an ability to improve accuracy by 20% in scenarios with biased inputs through continuous learning and parameter updating, highlighting its adaptability to changing environments. this research contributes to the field of edge computing by demonstrating a viable solution for deploying sophisticated AI models in resource-constrained environments. By offloading computationally intensive tasks to the cloud, proposed architecture ensures that edge devices can operate more efficiently and handle a broader range of AI applications. this study not only underscores the potential of integrating cloud and edge computing to overcome the limitations of edge devices but also opens new avenues for future research in intelligent edge computingsystems.
the proceedings contain 38 papers. the special focus in this conference is on embedded Computer systems: Architectures, Modeling, and Simulation. the topics include: QCEDA: Using Quantum Computers for EDA;real-Ti...
ISBN:
(纸本)9783031783760
the proceedings contain 38 papers. the special focus in this conference is on embedded Computer systems: Architectures, Modeling, and Simulation. the topics include: QCEDA: Using Quantum Computers for EDA;real-time Linux on RISC-V: Long-Term Performance Analysis of PREEMPT_RT Patches;RV-VP2: Unlocking the Potential of RISC-V Packed-SIMD for embedded Processing;A Novel System Simulation Framework for HBM2 FPGA Platforms;ONNX-To-Hardware Design Flow for Adaptive Neural-Network Inference on FPGAs;efficient Post-training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments;pooling On-the-Go for NoC-Based Convolutional Neural Network Accelerator;Vitamin-V: Serverless Cloud computing Porting on RISC-V;Design and Implementation of an Open Source OpenGL SC 2.0.1 Installable Client Driver and Offline Compiler;Plan Your Defense: A Comparative Analysis of Leakage Detection Methods on RISC-V Cores;iVault: Architectural Code Concealing Techniques to Protect Cryptographic Keys;I2DS: FPGA-Based Deep Learning Industrial Intrusion Detection System;ACRA: A Cutting-Edge Analytics Platform for Advanced real-time Corruption Risk Assessment and Investigation Prioritization;post Quantum Cryptography Research Lines in the Italian Center for Security and Rights in Cyberspace;advancing Future 5G/B5G systems: the Int5Gent Approach;RISC-V Accelerators, Enablement and applications for Automotive and Smart Home in the ISOLDE Project;PMDI: An AI-Enabled Ecosystem for Cooperative Urban Mobility;Open Source Software Randomisation Framework for Probabilistic WCET Prediction on Multicore CPUs, GPUs and Accelerators;a Hypervisor Based Platform for the Development and Verification of Reliable Software applications.
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