convolutional neural network
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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263 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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344 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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965 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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296 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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177 Views
This dataset is made for traditional, machine learning, and deep neural-network-based virtual sensor development and evaluation.
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61 Views
The human gait is unique and so is the impact of a walking human on the propagation of wireless signals within a wireless network. Using appropriate pattern recognition techniques, a person can thus be identified just from a time series of Received Signal Strength (RSS) measurements. This dataset holds bidirectional RSS measurements recorded within a mesh network of four Bluetooth sensor devices. During the measurements, a total of 14 subjects walked individually through the setup. A total of more than 10,000 recordings are provided.
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617 Views
Miniature mobile robots in multi-robotic systems require robust environmental perception for successful navigation, especially when operating in real-world environments. One of the sensors that have recently become accessible for miniature mobile robots due to their size and cost-effectiveness is a multi-zone time-of-flight (ToF) sensor. The object of classification in the dataset is a miniature mobile robot on a sand-like terrain with rocks. The dataset was acquired with the ST VL53L5CX ToF sensor.
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662 Views
In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory.
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5590 Views
This dataset consists of the training and the evaluation datasets for the LiDAR-based maritime environment perception presented in our journal publication "Maritime Environment Perception based on Deep Learning." Within the datasets, LiDAR raw data are processed using Deep Neural Networks (DNN). In the training dataset, we introduce the method for generating training data in Gazebo simulation. In the evaluation datasets, we provide the real-world tests conducted by two research vessels, respectively.
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1676 Views