Signal Processing
This radar signal dataset comprises 10,000 simulated signals, providing a comprehensive resource for radar signal processing research and experiments. The dataset is designed to facilitate the study of various radar phenomena, including signal deinterleaving and modulation recognition analysis. The number of overlapping signals is uniformly distributed between 1 and 5, ensuring a wide range of complexity for testing algorithms under different interference conditions. Each signal file contains detailed pulse information, which supports robust analysis of radar performance. Researchers can le
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Attached Image data set from combined OCT-SLO is used to train AI models and identify features to maximize quality of data set to adjust MZI reference arm, PMT Voltage of Liquid Lens and location of object. Why adjustment is needed is explained below:
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Data associated with the article: "PM2.5 Retrieval with Sentinel-5P Data over Europe Exploiting Deep Learning"
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This dataset includes a database of 15 feature parameters such as image texture features, pixel statistical features, and geometric features extracted from scans of fixed and living cell samples of two types of cells, HeLa and SiHa, using an adaptive harmonic atomic force microscopy probe and a commercial atomic force microscope. The total number of data contained in the database is 2400.
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The data are the original data of UAV sound source localization by ODAS system consisting of 12-channel spherical microphone array and circular MEMS microphone array of dual system. The data contains four different types of UAV sound sources, and the UAV sound sources in different frequency bands are used to locate the sound sources of multiple UAVs.
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This dataset is used for machine learning. And the data set is collected in different micro-environments. In this project, ExpoM-RF 4 is used to measure the electric field strength. Four different typs of micro-environments are selected which are urban (6 high population density areas in Kuala Lumpur), suburban (7 low population density areas in Cyberjaya), park (3 park areas) and one indoor micro-environment. From the measurement campaigns, three machine learning (ML) techniques are simulated to model the Electric Field Strength in each micro-environment.
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This dataset supports the research on hybrid quantum encryption by providing simulation results for Quantum Bit Error Rate (QBER) vs. Channel Loss in Quantum Key Distribution (QKD). The dataset includes numerical values used to generate the QBER vs. Channel Loss graph, which illustrates how increasing channel loss impacts quantum encryption performance.
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This database, collected at the Neural Engineering Laboratory, Iran University of Science and Technology, comprises iEEG recordings from Wistar rats during healthy and epileptic conditions. Recordings were collected from 5 rats (3 males, 2 females, weighing 260-378 g and aged 4-5 months). iEEG signals were recorded from 3 brain sites: motor cortex (left M1), thalamus (left ANT), and hippocampus (right CA1) of freely moving rats. As a result, for each rat, a matrix with 3 columns (representing the 3 signals) is available in this dataset.
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This study adopts a comprehensive approach that integrates finite element simulations with experimental validation to investigate the potential application of ultrasonic imaging technology for downhole fallen objects detection. The study first employed finite element simulations to model the impact of fallen objects on acoustic wave propagation, with a focus on examining the correlations between the reflected signal and the fallen objects' spatial position, size, and the probe's excitation frequency.
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A collection of Python pickles objects containing a Pandas DataFrame. Each Dataframe corresponds to the postprocessed firing rate (fr) in Hz and mean amplitude of the spikes (AMP) in microV/s of the vagus nerve recordings obtained from 12 adult female Sprague-Dawley rats. Additionally, the blood-glucose level in mg/dL is included. The fr and AMP signals have 0.1 miliseconds of resolution, whereas the glucose level was measured approximately every 5 minutes. Temporal variations are due to experimental factors. The number of available glucose samples changes across recordings.
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