TXT

This dataset is derived from a research paper proposing a wireless localization correction methodology based on Convolutional Neural Networks (CNN). The approach involves feature extraction from maps that depict both line of sight (LOS) and non-line of sight (NLOS) effects. The research includes four prediction tasks, categorizing CNN models based on building distribution and propagation mode, resulting in models with low prediction loss. Additionally, an error compensation scheme is designed using CNN-predicted localization errors.

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In this paper, we propose a modular security approach using 

a positioning security engine featuring GPS location features that can 

uniquely identify the IoT user device. We propose the modular security 

scheme to reinforce the security and viability of IoT-centric solutions for 

various innovative applications, including IoT Mobile payment, Smart city 

heterogeneous networks, communication services, safety, and locationbased services integration. To achieve our goal of securitization and 

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81 Views

Oblivion Results is a TXT dataset which contains the report files generated in the experimental phase of Oblivion's development. Knowing the SHA256 hash of a file of interest, if this file is present in our list, the relative report can be consulted.

The set is organized as follows:

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Data based on capacitive and pressure measurement on physical vascular model developed at out university. We used capacitve sensing for dielectric volume change of artificial arterial segment on two measurement sites. For verification we used pressure sensors commonly used in medical applications to measure blood pressure or fluid pressure within the human body. Overall, your approach combining capacitive sensing and pressure measurements provides a comprehensive analysis of the dielectric volume changes in the artificial arterial segment.

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高频信号恢复图

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For the UNB-GCN  classification model, UNB as the input feature vector to the graph structure node, phenotypic information, such as gender, age and APOE genetic information, as the weight of the edges between nodes.

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For the UNB-GCN  classification model, UNB as the input feature vector to the graph structure node, phenotypic information, such as gender, age and APOE genetic information, as the weight of the edges between nodes.

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Supplementary material to the article "Improving the teaching of real-world software practices by means of course integration"

 

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Image coordinates data of AprilTag's four edges

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