This research presents adaptive imputation methods for handling missing Internet of Things (IoT) data, specifically in reefer container monitoring. It proposes an enhanced Vector Auto-Regressive (VAR) model for real-t...
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In recent times the use of small computing devices for gathering information from the real world is increasing day by day. The devices like wireless sensors, RIFD tags, embedded devices and IoT devices are required to...
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With the changes in modern families and the needs of infants, traditional parenting methods are no longer able to meet the needs of parents. In order to provide a more advanced and convenient parenting method, this st...
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The Multi-access Edge computation (MEC) paradigm is being regarded as a viable alternative for handling data and computation requirements of complex real-time and safety-critical applications in upcoming IoT systems. ...
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32.768kHz (32kHz) crystal oscillators (XOs) are widely used in real-time clocks (RTC) embedded in various electronic systems. Their performance is critical in battery-powered Internet-of-Things (IoT) sensor nodes when...
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Multi-agent systems (MAS) applied to embeddedsystems enable cognitive agents to act in the physical world. However, the application of these systems has been little explored to automate communication during crisis ev...
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Automated detection of road hazards such as speed bumps, has become an important area of research due to its potential to improve road safety in autonomous driving. Various techniques have been introduced to detect th...
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The proceedings contain 79 papers. The topics discussed include: design of a bistable compliant locking mechanism for new generation mirror actuators;a novel design of a brushless DC motor integrated with an embedded ...
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ISBN:
(纸本)0791847411
The proceedings contain 79 papers. The topics discussed include: design of a bistable compliant locking mechanism for new generation mirror actuators;a novel design of a brushless DC motor integrated with an embedded planetary gear train;a runtime support environment for mobile agents;digitally controlled optimal self-calibration for a laser-photodiode array based vehicle detection system;a scheduling algorithm of time-triggered period tasks for distributed embedded system;distributed modeling and framework for collaborative embedded system design;an embedded system to be applied to neural network predictive control in an electric arc furnace;and model-driven programmable logic controller design and FPGA-based hardware.
New generation of embeddedsystems 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 embeddedsystems 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 embeddedsystems 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 embeddedsystems 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 embeddedsystems 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 increasing demand for energy-efficient digital circuits in resource-constrained environments like mobile devices has driven the need for optimized low-power designs. This research focuses on the design of a 4-bit ...
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