IoT

This LTE_RFFI project sets up an LTE device radio frequency fingerprint identification system using deep learning techniques. The LTE uplink signals are collected from ten different LTE devices using a USRP N210 in different locations. The sampling rate of the USRP is 25 MHz. The received signal is resampled to 30.72 MHz in Matlab. Then, the signals are processed and saved in the MAT file form. More details about the datasets can be found in the README document.

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825 Views

This dataset describes ontologies relevant for the domain of systems engineering and electronics. We developed various .owl ontologies that describe the domain and collected related ontologies that are relevant. This includes: hardware like processors, sensors, actuators, processor parts, adcs, software, systems like embedded systems, automobiles and driving assistance systems. Further their properties and functions can be described. The base ontology is the GENIAL! Basic Ontology (GBO) which is based on BFO (GENIALOntBFO.owl).

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273 Views

This LTE_RFFI project sets up an LTE device radio frequency fingerprint identification system using deep learning techniques. The LTE uplink signals are collected from ten different LTE devices using a USRP N210 in different locations. The sampling rate of the USRP is 25 MHz. The received signal is resampled to 30.72 MHz in Matlab and is saved in the MAT file form. The corresponding processed signals are included in the dataset. More details about the datasets can be found in the README document.

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436 Views

IoT sensors offer a wide range of sensing capabilities, many of which have the potential for health or medical applications. Existing solutions for IoT in healthcare have notable limitations, such as limited I/O protocols, limited cloud platform support, and limited extensibility. Therefore, the development of an open-source Internet of Medical Things (IoMT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility would be highly desirable.

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236 Views

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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124 Views

In data file (.rar) contains 16 files in .mat format, where origin data after UMAP for training.mat is  the original training data and the others are the experimental result data. data1_*.mat is the model test result file containing the simulation results (test_simu_*), model output (output_test_*), and error (error_*).

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39 Views

With the growth of Internet of Things (IoT) applications, the need for accurate indoor positioning systems (IPS) has become urgent. While GPS has limitations in indoor scenarios, Visible Light Positioning (VLP) presents promising results. This paper addresses the challenge of estimating the receiver's height in three-dimensional (3D) positioning scenarios, a crucial problem in VLP. We propose a novel 3D VLP algorithm, adopting a multiple estimation strategy for height estimation to minimize random errors.

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95 Views

The increasing availability of multimodal data holds many promises for developments in millimeter-wave (mmWave) multiple-antenna systems by harnessing the potential for enhanced situational awareness. Specifically, inclusion of non-RF modalities to complement RF-only data in communications-related decisions like beam selection may speed up decision making in situations where an exhaustive search, spanning all candidate options, is required by the standard. However, to accelerate research in this topic, there is a need to collect real-world datasets in a principled manner.

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291 Views

We generated an IV fluid-specific dataset to maximize the accuracy of the measurement. We developed our system as a smartphone application, utilizing the internal camera for the nurses or patients. Thus, users should be able to capture the surface of the fluid in the container by adjusting the smartphone's position or angle to reveal the front view of the container. Thus, we collected the front view of the IV fluid containers when generating the training dataset.

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5 Views

This dataset is supplementary material for our paper "PUF for the Commons: Enhancing Embedded Security on the OS Level".

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498 Views

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