Improving performance and flexibility is crucial in the field of networked embeddedsystems. Using deep learning methods, this research presents a fresh strategy for doing this. Our suggested approach involves creatin...
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ISBN:
(纸本)9798350343274
Improving performance and flexibility is crucial in the field of networked embeddedsystems. Using deep learning methods, this research presents a fresh strategy for doing this. Our suggested approach involves creating a custom deep learning architecture tailored to the specific needs of distributed embeddedsystems. This strategy makes use of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory Networks (LSTMs) to evaluate real-time data streams, expedite decision-making, and intelligently adapt to ever-changing environments. In addition, using reinforcement learning helps systems behave and use energy more efficiently, creating a more flexible and smart setting. To reduce latency and reliance on centralized servers, edge computing plays a crucial role by allowing for real-time data processing on embedded devices. The suggested technique was evaluated alongside more conventional methods in a side-by-side comparison. Imaginary numbers were utilized for demonstration purposes. The findings illustrate the higher performance of the suggested technique across several parameters. When compared to baseline deep learning methods like Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory Networks, Generative Adversarial Networks, Federated Learning, Transfer Learning, and time Series Analysis with Deep Learning, the proposed method shows marked improvements in accuracy, latency, energy efficiency, robustness, security, scalability, and resource utilization. Finally, the suggested methodology emerges as a game-changing strategy for enhancing the capabilities of networked embeddedsystems, since it is supported by deep learning methods, reinforcement learning, and edge computing. Its ushers in a new era of networked embeddedsystems with its flexibility to process sequential data, analyze picture and video material, and maximize energy efficiency. The results of the comparison study validate the superior
High-performance cyber-physical applications impose several requirements with respect to performance, functional correctness and non-functional aspects. Nowadays, the design of these systems usually follows a model-dr...
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ISBN:
(纸本)9781665493130
High-performance cyber-physical applications impose several requirements with respect to performance, functional correctness and non-functional aspects. Nowadays, the design of these systems usually follows a model-driven approach, where models generate executable applications, usually with an automated approach. As these applications might execute in different parallel environments, their behavior becomes very hard to predict, and making the verification of non-functional requirements complicated. In this regard, it is crucial to analyse and understand the impact that the mapping and scheduling of computation have on the real-time response of the applications. In fact, different strategies in these steps of the parallel orchestration may produce significantly different interference, leading to different timing behaviour. Tuning the application parameters and the system configuration proves to be one of the most fitting solutions. The design space can however be very cumbersome for a developer to test manually all combinations of application and system configurations. This paper presents a methodology and a toolset to profile, analyse, and configure the timing behaviour of highperformance cyber-physical applications and the target platforms. The methodology leverages on the possibility of generating a task dependency graph representing the parallel computation to evaluate, through measurements, different mapping configurations and select the one that minimizes response time.
Indoor positioning systems (IPS) continue to encounter significant challenges in achieving meter-level accuracy, particularly in large and intricate environments such as airports, hospitals, and industrial sites. Desp...
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The Internet of Things (IoT), which connects various systems and devices to create more innovative environments, has completely changed how we interact with technology. Sensors are essential to the success of IoT syst...
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With recent advances in computing and sensing technologies, autonomous driving has gained increasing interest and become a promising platform to support the next generation intelligent transportation systems. A critic...
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The wastewater parameters should be monitored regularly to maintain the quality in the range of allowed standard. A real-time wastewater monitoring system is required for effective monitoring. This paper presents an i...
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High-performance computing (HPC) and Artificial Intelligence (AI) are two areas of computing that are seeing fast-paced growth. The HPC systems have evolved to incorporate the capability of running mid-level AI algori...
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This paper introduces a new method for safety-aware robot learning, focusing on repairing policies using predictive models. Our method combines behavioral cloning with neural network repair in a two-step supervised le...
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ISBN:
(纸本)9798350377712;9798350377705
This paper introduces a new method for safety-aware robot learning, focusing on repairing policies using predictive models. Our method combines behavioral cloning with neural network repair in a two-step supervised learning framework. It first learns a policy from expert demonstrations and then applies repair subject to predictive models to enforce safety constraints. The predictive models can encompass various aspects relevant to robot learning applications, such as proprioceptive states and collision likelihood. Our experimental results demonstrate that the learned policy successfully adheres to a predefined set of safety constraints on two applications: mobile robot navigation, and real-world lower-leg prostheses. Additionally, we have shown that our method effectively reduces repeated interaction with the robot, leading to substantial time savings during the learning process.
The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, allowing for remote patient monitoring and personalized treatments. However, due to the resource limitations of medical devices, the...
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Thing discovery and tracking using deep learning has emerged as a cutting-edge technology with applications spanning autonomous vehicles, surveillance systems, robotics, and more. This paper provides an overview of th...
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