Deep Learning

 

 

In the dataset, there is the electrical transmission system modeled in Simulink. It also contains the codes to generate the data from the model, extract images from data processing (in this case, a continuous wavelet transform), and image processing. Finally, the program to train the network is also provided. All codes are in M-FILE format.

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  • Artificial Intelligence
  • Last Updated On: 
    Thu, 05/28/2020 - 14:16

    Non-contrast computed tomography (NCCT) is commonly used for volumetric follow-up assessment of ischemic strokes. However, manual lesion segmentation is time-consuming and subject to high inter-observer variability. The aim of this study was to develop and establish a baseline convolutional neural network (CNN) model for automatic NCCT lesion segmentation. A total of 252 multi center clinical NCCT datasets, acquired from 22 centers, and corresponding manual segmentations were used to train  (204 datasets) and validate (48 datasets) a 3D multi scale CNN model for lesion segmentation.

    60 views
  • Artificial Intelligence
  • Last Updated On: 
    Fri, 05/15/2020 - 16:43

    PRIME-FP20 dataset is established for development and evaluation of retinal vessel segmentation algorithms in ultra-widefield fundus photography. PRIME-FP20 provides 15 high-resolution ultra-widefield fundus photography images acquired using the Optos 200Tx camera (Optos plc, Dunfermline, United Kingdom), the corresponding labeled binary vessel maps, and the corresponding binary masks for the FOV of the images.

    125 views
  • Computer Vision
  • Last Updated On: 
    Sun, 05/03/2020 - 13:13

    Several pathological phenomena are closely associated with vascular stiffness and interactions of blood flow and wall dynamics. However, conventional elastography and imaging techniques cannot easily measure local stiffness and analyze complicated interactions between multiple parameters. In this study, a new deep learning based simultaneous measurement of flow–wall dynamics (DL-SFW) is proposed by integrating and enhancing our two DL techniques for high-resolution velocimetry and strain measurement.

    65 views
  • Standards Research Data
  • Last Updated On: 
    Fri, 05/01/2020 - 07:51

    This dataset has been collected in the Patient Recovery Center (a  24-hour,  7-day  nurse  staffed  facility)  with  medical  consultant   from  the  Mobile  Healthcare  Service of Hamad Medical Corporation.

    339 views
  • Artificial Intelligence
  • Last Updated On: 
    Thu, 04/30/2020 - 11:04

    Extracting the boundaries of Photovoltaic (PV) plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. This method provides a clear delineation of the utility-scale PV plants’ boundaries for PV developers, Operation and Maintenance (O&M) service providers for use in aerial photogrammetry, flight mapping, and path planning during the autonomous monitoring of PV plants. 

    172 views
  • Artificial Intelligence
  • Last Updated On: 
    Sat, 04/11/2020 - 12:20

    This work develops a novel power control framework for energy-efficient powercontrol in wireless networks. The proposed method is a new branch-and-boundprocedure based on problem-specific bounds for energy-efficiency maximizationthat allow for faster convergence. This enables to find the global solution forall of the most common energy-efficient power control problems with acomplexity that, although still exponential in the number of variables, is muchlower than other available global optimization frameworks.

    46 views
  • Machine Learning
  • Last Updated On: 
    Thu, 04/09/2020 - 09:49

    The zizania image dataset consists of a total of 4900 zizanias. The quantity of high quality samples is 2648 and defective quality samples is 2252.

    There are four classes in the apple image dataset, which are apples with a diameter greater than 90 mm, between 80 mm and 90 mm, less than 80 mm, and diseases and insect pests. The quantity distributionin above categories are 3647 (51.19%), 2464 (34.59%), 558 (7.83%), 455 (6.39%).

    76 views
  • Artificial Intelligence
  • Last Updated On: 
    Mon, 02/24/2020 - 00:10

    Research on damage detection of road surfaces has been an active area of research, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection.

    773 views
  • Artificial Intelligence
  • Last Updated On: 
    Tue, 01/21/2020 - 14:54

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