Deep Learning
Some novel methods for imaging based on synthetic aperture radar can result in images contaminated by artifacts as a consequence of pushing the limits of the algorithms. In order to mitigate the impact of this artifacts, image translation techniques can be exploited enabling to turn the SAR image into a cleaner one. For this purpose, multiple techniques can be used such as convolutional neural networks or generative adversial networks. However, the training of those systems can require a high number of images, which can be computationally expensive to generate.
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315 Views
The anomaly detection in photovoltaic (PV) cell electroluminescence (EL) image is of great significance for the vision-based fault diagnosis. Many researchers are committed to solving this problem, but a large-scale open-world dataset is required to validate their novel ideas. We build a PV EL Anomaly Detection (PVEL-AD) dataset for polycrystalline solar cell, which contains 36,543 near-infrared images with various internal defects and heterogeneous background. This dataset contains anomaly-free images and anomalous images with 10 different categories.
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6352 Views
The experiment is based on the open source RSRP data provided by Huawei Technologies Co., LTD. It measures RSRP of 415,244 signal receiving points in 180 dense urban communication cells.
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1217 Views
A commonly used definition of spatial disorientation (SD) in aviation is "an erroneous sense of one’s position and motion relative to the plane of the earth’s surface". There exists a wide range of SD use-cases dictated by situational factors, therefore SD has been predominantly studied using reduced motion detection experimental contexts in isolation. The study of SD by use-case makes it difficult to understand general SD occurrence and thus provide viable solutions. To investigate SD in a generalized manner, a two-part Human Activity Recognition (HAR) study was performed.
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338 Views
This repository contains the data related to the paper “CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging” (10.1109/TUFFC.2021.3131383). It contains multiple datasets used for training and testing, as well as the trained models and results (predictions and metrics). In particular, it contains a large-scale simulated training dataset composed of 31000 images for the three different imaging configuration considered (i.e., low quality, high quality, and ultrahigh quality).
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3211 Views
This dataset is related to dog activity and is sensor data.
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821 Views
This LoRa-RFFI project builds a LoRa radio frequency fingerprint identification (RFFI) system based on deep learning techniques. The RF signals are collected from 60 commercial-off-the-shelf LoRa devices. The packet preamble part and device labels are provided. The dataset consists of 19 sub-datasets and please refer to the README document for more detailed collection settings for all the sub-datasets.
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8290 Views
ATTENTION: THIS DATASET DOES NOT HOST ANY SOURCE VIDEOS. WE PROVIDE ONLY HIDDEN FEATURES GENERATED BY PRE-TRAINED DEEP MODELS AS DATA
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6682 Views
In this project, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the proposed testbed is organized into seven layers, including, Cloud Computing Layer, Network Functions Virtualization Layer, Blockchain Network Layer, Fog Computing Layer, Software-Defined Networking Layer, Edge Computing Layer, and IoT and IIoT Perception Layer.
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21362 Views
Research data associated with paper: A Semantic Segmentation Model for Lumbar MRI Images using Divergence Loss, comprising the python code, a trained model and empirical results.
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367 Views