Tuberculosis (TB) has been affecting people and high deaths from it has been recorded globally. Techniques around use of artificial intelligence (AI) based approaches are gaining momentum in early diagnosis of TB. Thi...
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Absorber working in optical wavelength draws significant attraction due to its vast field of application. A high-efficiency metamaterial absorber (MMA) is proposed in this study to utilize solar radiation perfectly. T...
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In recent years, the use of energy harvesting (EH) sensors has led to the proposal of activity sensing systems that are easy to install and do not require maintenance such as battery replacement. In this study, we aim...
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ISBN:
(纸本)9781665439299
In recent years, the use of energy harvesting (EH) sensors has led to the proposal of activity sensing systems that are easy to install and do not require maintenance such as battery replacement. In this study, we aim to construct a system that can not only sense but also recognize activities of daily living (ADLs) using only power generated by EH sensors. To achieve this goal, in this paper, we propose a fully EH-based ADL recognition system called Batterfly, which consists of EH analog PIR sensor nodes that can operate with indoor light, continuously senses human movement, and recognizes daily activities through machine learning. We applied the distributed execution method of the activity recognition model with five sensor nodes to five types of activities by five participants, and found that the system could recognize them with an average accuracy of 63.59%, comparable to the performance of the centralized model running on a gateway.
The integration of artificial intelligence (AI) into wireless communications is growing rapidly. This growth is primarily driven by machine learning (ML) techniques, which can be broadly categorized into supervised le...
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The high training costs of graph neural networks (GNNs) have limited their applicability on large graphs, e.g., graphs with hundreds of millions of vertices which have become common in the era of big data. A few recen...
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ISBN:
(数字)9781665408837
ISBN:
(纸本)9781665408837
The high training costs of graph neural networks (GNNs) have limited their applicability on large graphs, e.g., graphs with hundreds of millions of vertices which have become common in the era of big data. A few recent studies propose distributed GNN systems. However, these systems may generate high communication costs due to the extensive message passing among graph vertices stored on different machines. To address such limitations, in the paper, 1) we propose a distributed GNN computation system named EC-Graph for CPU clusters, which drastically reduces the communication costs among the machines by message compression;2) we design a requesting-end compensation method for the embeddings to mitigate the errors induced by compression in the forward propagation and a Bit-Tuner to adaptively balance the model accuracy and message size;and 3) we propose a responding-end compensation approach for the embedding gradients in the backward propagation. Extensive experiments over large real-world datasets show that EC-Graph outperforms state-of-the-art distributed GNN systems on two CPU clusters of different sizes.
Generative Pre-trained Transformer (GPT) is a state-of-the-art machine learning model capable of generating human-like text through natural language processing (NLP). GPT is trained on massive amounts of text data and...
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In order to truly evaluate the reliability of low-voltage distribution network, considering the influence of distributed energy uncertainty on the reliability evaluation of low-voltage distribution network, a reliabil...
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The ever-growing paradigms of IoT networks, cloud infrastructures, and blockchain platforms raise demands for adaptive cryptographic key management. Traditional approaches, such as Public Key Infrastructure and Shamir...
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Software-defined networking (SDN) is a transformative technology that systematically centralises and manages network resources. This paradigm shift allows for greater flexibility, agility, and efficiency in network ma...
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This research aims to explore how blockchain technology is revolutionizing the fields of cryptocurrency, supply chain management, the Internet of Things (IoT), and healthcare. The aim is to accentuate the principal ad...
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