Signal Processing
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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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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Six vector maps are selected as experimental data. These maps are referred to as the coastline map, river map, building map, green land map, road map and waterway map, as shown in Fig. 9. The coastline map and river map, both in the China Geodetic Coordinate System 2000 (CGCS2000), can be downloaded from the website of the National Catalogue Service for Geographic Information of China (https://www.webmap.cn).
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This dataset contains electromagnetic field (EMF) intensity measurements recorded at half-hour intervals. The dataset spans a continuous timeline, capturing variations in electric field strength in volts per meter (V/m). It serves as a valuable resource for environmental monitoring, predictive modeling, and studying the impacts of EMF exposure. Applications include urban planning, public health assessments, and advanced regression or machine learning modeling.
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The data was collected by a tester holding a Xiaomi 13 smartphone while walking and collecting data in an underground parking lot covering a 16x70m area. The data includes 5G radio features and geomagnetic field information.
Collection Time: From 09:58 AM to 10:34 AM on July 13, 2024.
Total Samples: 12,800
Training Set (including validation set): 10,240
Test Set: 2,560
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This MATLAB script demonstrates an approach to beamforming and interference suppression in scenarios with multiple users and multiple interferers. It constructs an N-element linear array, computes beamformer weights through a generalized eigen-decomposition of summed desired and interference correlation matrices, and then runs a Monte Carlo simulation to estimate the Signal-to-Interference-plus-Noise Ratio (SINR) for one of the users under random channel conditions.
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The data is the obtained using the autotracker application of the 'Tracker' software. The data is time versus x-y coordinates of a marked particle subject to movement in presence of E X B drift. This data can be directly used to calculate the charge-to-mass ratio and electrophoretic mobility of the particle of interest.
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