The rapid evolution of wireless technology has transformed communication, offering unprecedented accessibility and convenience. However, significant challenges like spectrum scarcity, security vulnerabilities, and env...
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Neural Radiation Field (NeRF) is driving the development of 3D reconstruction technology. Several NeRF variants have been proposed to improve rendering accuracy and reconstruction speed. One of the most significant va...
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With the development of 5G networks, the number and complexity of network devices have increased dramatically. Locating and predicting faulty network devices is an important way to ensure the normal operation of 5G ne...
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With the rapid development of power communication industry, optical fiber multiplexing technology is more and more widely used in power communication. This paper first introduces the principle and classification of op...
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With the rapid advancement of edge computing technology and the widespread application of artificial intelligence, the deployment of neural network inference at the edge has garnered increasing significance. However, ...
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ISBN:
(纸本)9798350386783;9798350386776
With the rapid advancement of edge computing technology and the widespread application of artificial intelligence, the deployment of neural network inference at the edge has garnered increasing significance. However, constrained by limitations in computational resources and security considerations, effectively verifying the correctness of neural network inference at the edge poses a formidable challenge. To address this challenge, this paper proposes a neural network inference verification framework based on the generalized GKR protocol, specifically tailored for edge deployment. Leveraging the bidirectional efficiency inherent in the generalized GKR protocol, this framework enables rapid and precise validation of neural network inference, thereby enhancing the reliability and security of edge-based neural network inference. Additionally, this paper tackles challenges encountered in designing the verification framework, such as handling model parameters and transforming non-linear functions, utilizing pertinent techniques. Finally, the effectiveness and performance of the framework are validated and analyzed through experimental verification.
Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term hea...
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Over the past years, numerous methods have been developed to identify anomalies in traditional VANETs networks. A survey of VANET anomaly detection and mitigation methods is presented in this analysis. This survey loo...
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Aiming at the challenges in time series data processing, this paper proposes a new deep learning model, which uses Spiking neurons with time series processing ability to organize, represent and transmit information in...
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In the process of orthopedic 2D/3D medical image registration, a spinal orthopedic locator is needed to assist registration. There is a spinal orthopedic locator image in the orthopedic X-ray image that hinders regist...
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ISBN:
(纸本)9798350386783;9798350386776
In the process of orthopedic 2D/3D medical image registration, a spinal orthopedic locator is needed to assist registration. There is a spinal orthopedic locator image in the orthopedic X-ray image that hinders registration. It is necessary to segment and remove the spinal orthopedic locator image in the Xray image to improve registration. Accuracy. In response to the above problems, this paper proposes the SR-UNet deep learning algorithm model for segmentation of spinal orthopedic locator images in orthopedic X-ray images. Based on the advantages of U-shaped network in medical image segmentation, SR-UNet network combines U-shaped network, The characteristics of residual network and asymmetric network can accurately segment the spinal orthopedic locator image. The SR-UNet network model is trained through the self-built training set and verification set, and the performance of the simulation with accurate segmentation is evaluated through the Dice Similarity Coefficient (DSC) index. The SR-UNet deep learning algorithm model ensures high-precision segmentation effects and can accurately segment spinal orthopedic locator images in orthopedic X-ray images. In the case of the same image data set, the DSC index exceeds other model networks.
networks plays a vital role in modern society and cyber security has become a critical area that needs to be acknowledged. A crucial part of cybersecurity frameworks intended to track network traffic and spot suspicio...
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