As the global semiconductor industry moving towards successive generations, the density of Integrated Circuit increased, therefore reliability issues became critically high, among them Negative Bias Temperature Instab...
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Residual Attention Networks (RANs) are a class of Convolutional Neural Networks (CNNs) that integrate attention mechanisms into deep architectures. RANs employ stacked attention modules to generate attention-aware fea...
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Deep learning advances neural decoding in functional magnetic resonance imaging (fMRI) tasks with convolution and attention-based methods. However, these methods struggle with capturing global spatiotemporal informati...
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New generation of embedded systems with superior intelligence, energy efficiency, and performance have emerged as a result of the merging of deep learning with Very-Large-Scale Integration (VLSI) technology. Methodolo...
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ISBN:
(纸本)9798331529833
New generation of embedded systems with superior intelligence, energy efficiency, and performance have emerged as a result of the merging of deep learning with Very-Large-Scale Integration (VLSI) technology. Methodologies for design, optimisation strategies, and practical uses of next-generation embedded systems are the foci of this study, which investigates the ways in which VLSI and deep learning might work together. These systems have the potential to transform several industries, such as transportation, medicine, robotics, and the IoT, by harnessing the processing power of deep neural networks with the improvements in semiconductor fabrication. Prior to delving into the advantages of bespoke hardware design for deep learning inference and training, we trace the history of very large scale integration (VLSI) technology and its incorporation with deep learning algorithms. Investigated here are the design techniques that, when applied to very large scale integration (VLSI) architectures like FPGAs and ASICs, allow for the efficient mapping of deep learning models onto these devices. We show case studies that show how these methods work and talk about the trade-offs between performance, power consumption, and adaptability. The development of next-generation embedded systems relies heavily on optimisation approaches. Model compression, quantisation, and pruning are some of the optimisation strategies that we examine;they lessen the memory and computational demands of deep learning models without drastically altering their accuracy. For embedded devices with limited resources, these methods are crucial for implementing deep learning models. Additionally, we explore the practical uses of embedded systems augmented with VLSI and deep learning. By capitalising on the complementary strengths of VLSI and deep learning, applications like autonomous driving, medical imaging, and smart home automation are revolutionising entire industries. In this paper, we examine the design
The technologies constantly try to make human lifestyles easier. The folks who are visually Impaired they faces many problems throughout navigation This paper presents the layout and implementation of a clever Cap uti...
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The rapid spread of diseases and pest infestations has a detrimental effect on plant health. Methods for disease classification and detection have gained new insights from the developments in computer vision and deep ...
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Software testing regularly involves numerous setups and user inputs, leading to a combinatorial explosion of test cases. While Combinatorial Interaction Testing (CIT) has been theoretically investigated, its effective...
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ISBN:
(纸本)9783031752001;9783031752018
Software testing regularly involves numerous setups and user inputs, leading to a combinatorial explosion of test cases. While Combinatorial Interaction Testing (CIT) has been theoretically investigated, its effectiveness in real-world scenarios remains unclear [1]. This research fills that gap by utilizing CIT in some live software projects. We led two studies: the first focuses on optimizing user input testing in jTrac, and the second focused on managing system configurations in Redmine, a comparative web application. We looked at CIT to customary testing strategies, breaking down components like test design time, test automation, test execution, suite size, and defect detection. The investigation gave valuable insights into enhancing CIT execution and reception. The results are promising. With CIT, the number of required test cases is significantly reduced, but at the same time, defect detection is improved. In the first study, the average time to detect a defect was 1.40 h (design, automation, execution, and evaluation) compared to 0.35 hwith CIT. Similar patterns emerged in the second study. These findings have important implications for both researchers and organizations. They highlight CIT's promise for software testing, including decreasing test case burden and perhaps improving defect detection rates. This study provides practical evidence for organizations and testers looking to improve their testing procedures.
Sarcasm detection, crucial for enhancing sentiment analysis and human-computer interaction, poses challenges due to its nuanced, context-dependent nature. This study investigates advanced feature engineering and model...
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Cloud automation has created a trend offering several advantages to businesses, including scalability and cost-effectiveness. Although deploying such automation in a cloud setting is fundamental, optimizing its cost e...
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This research describes a novel approach to hospital visitor screening utilizing cloud-powered deep learning using convolutional neural networks (CNNs). Our proposed approach will fill this need by improving threat de...
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