This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) se...
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Facial recognition techniques are used extensively in areas like online payments, education, and social media. Traditionally, these applications relied on powerful cloud-based systems, but advancements in edge computi...
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Facial recognition techniques are used extensively in areas like online payments, education, and social media. Traditionally, these applications relied on powerful cloud-based systems, but advancements in edge computing have changed this, enabling fast and reliable local processing in complex and extreme environments. However, new challenges arise in availability and durability insurance to make the system run 24/7 with acceptable performance. This paper proposes a novel solution to these challenging settings. First, we use edge devices for local data processing, reducing the need for cloud communication and enhancing user privacy. Second, we implement an adaptive control strategy to improve energy management in these devices. Lastly, we establish a solar-powered energy system to facilitate long-term device operation. The experiments show our approach strikes a balance between performance, quality, and durability, enabling facial recognition systems to work energy-efficiently in complex environments. Meanwhile, considering the limited resources of devices in extreme cases, we also proposed a learning-based approach to accelerate the solution generation. IEEE
Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specificat...
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Bias fields adversely affect various automatic analysis technologies. Therefore, bias field correction is essential. However, deep learning based methods encounter challenges in obtaining ground truth. Although existi...
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Background Determining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics ...
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Background Determining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics of the general public. Although deep learning methods have successfully learned good visual features from images,correctly assessing the aesthetic quality of an image remains a challenge for deep learning. Methods To address this, we propose a novel multiview convolutional neural network to assess image aesthetics assessment through color composition and space formation(IAACS). Specifically, from different views of an image––including its key color components and their contributions, the image space formation, and the image itself––our network extracts the corresponding features through our proposed feature extraction module(FET) and the Image Net weight-based classification model. Result By fusing the extracted features, our network produces an accurate prediction score distribution for image aesthetics. The experimental results show that we have achieved superior performance.
ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transforma...
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With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction be...
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By leveraging smart devices [e.g., industrial Internet of Things (IIoT)] and real-time data analytics, organizations, such as production plants can benefit from increased productivity, reduced costs, enhanced self-mon...
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We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendr...
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We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.
Hadoop is one of the most popular platforms for distributed big data processing, and YARN is at the core of Hadoop 2.0. During data processing, malicious nodes may compromise results by sharing incorrect outputs in th...
Hadoop is one of the most popular platforms for distributed big data processing, and YARN is at the core of Hadoop 2.0. During data processing, malicious nodes may compromise results by sharing incorrect outputs in the public cloud. To address this issue, this paper introduces an efficient YARN security framework, termed CMT-YARN, which incorporates an enhanced convolutional Merkle tree (CMHT) to ensure the reliability of task execution results. In a hybrid cloud environment, CMT-YARN leverages the improved CMHT technique for dual verification of intermediate and final results, ensuring data integrity and reliability. Compared to the conventional Merkle tree, CMHT reduces computational overhead by more than 37% during verification. Qualitative and quantitative analyzes indicate that with a data sampling rate of 6.8%, CMT-YARN can ensure that the number of malicious acts undetected by the system does not exceed 5; when the sampling rate exceeds 26.9%, the framework can guarantee the detection of all malicious activities. Experimental results on a real Hadoop cluster demonstrate that CMT-YARN significantly enhances computational and storage performance compared to traditional solutions.
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