Signal Processing

This dataset was produced as part of the NANCY project (https://nancy-project.eu/), with the aim of using it in the fields of communication and

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GNSS-R Data for the paper: First Shipborne GNSS-R Ultra-long Observation Utilizing Oceangoing Freighter

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This dataset contains samples of the scattered field from a set of flat metallic objects. It includes several files corresponding to eight different measurement planes, where the distances between the plane containing the antennas and the plane containing the targets are 74.3913 cm, 66.4120 cm, 61.4173 cm, 56.4063 cm, 51.4131 cm, 46.3873 cm, 41.4244 cm, and 36.4289 cm. The measurement setup corresponds to a multistatic L-shaped configuration.

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The existing public datasets often suffer from small data volumes, leading to insufficient training processes that result in severe overfitting and poor generalization performance. To address this issue, a radar dataset named RadSet is constructed. During the data acquisition phase, frequency modulated continuous wave (FMCW) radar system IWR1843 Boost manufactured by Texas Instruments (TI) was used.

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144 Views

Repeated Route Naturalistic Driving Dataset (R2ND2) is a dual-perspective dataset for driver behavior analysis constituent of vehicular data collected using task-specific CAN decoding sensors using OBD port and external sensors, and (b) gaze-measurements collected using industry-standard multi-camera gaze calibration and collection system. Our experiment is designed to consider the variability associated with driving experience that depends on the time of day and provides valuable insights into the correlation of these additional metrics on driver behavior.

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211 Views

In this study, MMW data are collected using a commercial handheld scanner (Vayyar's ECS2000), focusing on localized scans of the human body. The collected data are complex-valued (CV) high-resolution local 3D pseudo-images over a volume of 13×13×10 cm with spatial resolutions of 1.6 mm, 1.6 mm, and 4.3 mm in the x, y, and z directions, respectively.  The compact, portable ECS2000 Vayyar's MMW scanner is built around a single RF board working in the frequency range of [60.4-69.9] GHz, housing transmitting and receiving antennas in a multiple-input multiple-output (MIMO) setup.

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The data set consists of exemplary results of the product of voltage noise power spectral density S(f) multiplied by frequency f and normalized to squared DC voltage U^2 recorded in the graphene back-gated Field Effect Transistor under UV light assistance (275 nm) in the selected ambient atmospheres (Figure 3) and the results of gas detection by SVM algorithm: 1) chloroform (Figure 5), 2) acetonitrile (Figure 6), and predicted gas concentrations using various number of frequency bins (Figure 7, Figure 8).

 

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The dataset, used for behavioral modeling and testing, originates from the ITU ML5G-PS-007 benchmark. It includes input and output signals obtained from a nonlinear power amplifier (PA). The input signals feature a wide bandwidth of 200 MHz and vary in average power levels. The output signals captured from the PA exhibit a variety of distortions, encompassing nonlinear effects, short-term memory effects, and most notably, significant long-term memory effects.

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This dataset was preprocessed based on fiber optic vibration data collected in Qujing City, Yunnan Province, China. Due to the fact that the original fiber optic vibration data only contains vibration intensity information and vague verbal location of the same channel. So, based on this, we perform data supplementation operations according to the signal characteristics of the original vibration data.

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118 Views

Solar insecticidal lamps (SIL) are commonly used agricultural pest control devices that attract pests through a lure lamp and eliminate them using a high-voltage metal mesh. When integrated with Internet of Things (IoT) technology, SIL systems can collect various types of data, e.g., pest kill counts, meteorological conditions, soil moisture levels, and equipment status. However, the proper functioning of SIL-IoT is a prerequisite for enabling these capabilities. Therefore, this paper introduces the component composition and fault analysis of SIL-IoT.

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