convolutional neural network
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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342 Views
This paper proposes a novel Recursive Convolutional Target Detector (RCTD) for Frequency-Modulated Continuous-Wave (FMCW) radar in complex automotive scenarios. Leveraging a lightweight convolutional neural network, RCTD efficiently localizes multiple targets despite strong interference. Detailed simulations and a hardware prototype on an FPGA-based deep learning processor demonstrate real-time feasibility, low false alarm rates, and higher detection accuracy under stringent resource constraints.
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29 Views
Dataset for PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models
KTH Royal Institute of Technology, SCI; AstraZeneca, Sweden Operations
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293 Views
This dataset comprises 33,800 images of underwater signals captured in aquatic environments. Each signal is presented against three types of backgrounds: pool, marine, and plain white. Additionally, the dataset includes three water tones: clear, blue, and green. A total of 12 different signals are included, each available in all six possible background-tone combinations.
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461 Views
This dataset comprises Terahertz (THz) images collected to support the research presented in the IEEE Access paper titled Diagnosing Grass Seed Infestation: Convolutional Neural Network Based Terahertz Imaging. The dataset is intended for the detection and classification of grass seeds embedded in biological samples, specifically ham, covered with varying thicknesses of wool. The images were captured at different frequencies within the THz spectrum, providing valuable data for the development of deep-learning models for seed detection.
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42 Views
The terahertz communications band in the 252 to325 GHz range has been recently explored for its potential to meet the stringent requirements for the emerging sixth generation of wireless communications. However, there are several challenges including noise and nonlinearity that hinder efficient implementations. We aim to address this limitation in terahertz communications through convolutional neural networks (CNN) enhanced by the domain knowledge from traditional Volterra filters.
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286 Views
In our work, we propose an innovative system to accurately infer and track occluded target locations using mmWave beat frequency signals. Our approach combines a classic direction-finding method with advanced deep learning techniques, specifically a convolutional neural network (CNN), to enhance detection capabilities. The dataset includes raw beat frequency signal data from the TI IWR6843ISK rev B with TI mmWAVEICBOOST and the TI DCA1000EVM capture board. Corresponding ground truth data (target position) from the Realsense L515 RGB-D camera is also provided.
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393 Views
The advancement of machine and deep learning methods in traffic sign detection is critical for improving road safety and developing intelligent transportation systems. However, the scarcity of a comprehensive and publicly available dataset on Indian traffic has been a significant challenge for researchers in this field. To reduce this gap, we introduced the Indian Road Traffic Sign Detection dataset (IRTSD-Datasetv1), which captures real-world images across diverse conditions.
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1615 Views
This dataset contains the Supplementary Information of the article "Discovering Mathematical Patterns Behind HIV-1 Genetic Recombination: a new methodology to identify viral features" (Manuscript DOI: 10.1109/ACCESS.2023.3311752).
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307 Views
1.The spectrum of the dataset is obtained by applying force to the tactile sensor based on Chirped Bragg gratings.
2.The applied force ranges from 0N to 10N on the sensing pad of 4cm×4cm.
3.The folder name (x, y) represents the specific coordinates of the point at which the force is applied, and the xN name of the subfolder represents the xN force applied at that point.
4.A total of 120 spectral data were collected in each applied force state.
5.The first column of each spectrum is wavelength and the second column is intensity.
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192 Views