The database was created with records of psychosocial risk level colombian teachers school using physiological variables from May 2016 to December 2017 in five municipalities of a metropolitan area of city in Colombia. The application of physiological variables was made to the people who voluntarily participated in the study. The names and personal data were kept by the researcher.
The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj.
Please refer to the readme and documentation files for a full description.
The complete documentation of the dataset is available in the following article:
Some experiments were performed on the dataset-1 and the results can be found in the following article: [To download the dataset-1 as processed and categorized in this article, refer to the zip file named "DATASET-1 (224 X 224) Categorized.zip"]
You can visit our website for more details and related resources:
This data is divided into two sets.
DATASET-1 consists of 8079 images of date bunches that were taken using two color cameras from different angles and scales during one season in six imaging sessions (recording times) over the period of Jun-Sep 2016. The date bunches belong to five date varieties: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj.
DATASET-1 zip files:
---- DATASET-1.zip (42 GB)
High resolution images (8079 images)
---- DATASET-1_(224 X 224).zip (108 MB)
A preview of the 8079 images. Images size: 224 X 224.
---- DATASET-1 (224 X 224) Categorized.zip (649 MB)
This file contains the images of dataset-1 resized to 224x224 pixels and categorized into subfolders according to date fruit type, maturity state, and the harvesting decision. This categorization, and image resolution, is related to the experiments described in ref .
---- DATASET-1_(annotation files).zip (104 KB)
The annotation (labeling) files for type classification, maturity analysis, and harvesting decision applications. The labeling instructions and rules are explained in detail in Ref. .
DATASET-2 contains images, videos, and weight measurements that were acquired during the harvesting period of Barhi dates.
---- DATASET-2_images_Barhi_date_bunches_on_orchard.zip (16.44 GB)
Images of 152 Barhi date bunches before and during harvesting.
---- DATASET-2_images_Barhi_date_bunches_front_graph_paper.zip (88.16 MB)
Images of the 152 Barhi date bunches in front of graph paper.
---- DATASET-2_weight_measurements_Barhi_date_bunches.xlsx (683.99 KB)
The weight measurements of the 152 Barhi date bunches.
---- DATASET-2_videos_Barhi_date_palms.zip (4.49 GB)
360-degree videos of the nine Barhi date palms (120 date bunches).
---- DATASET-2_images&measurements_Sullaj_date_bunches_front_graph_paper.zip (76 MB)
Images of 11 Sullaj date bunches in front of graph paper with weight & dimensions measurements.
---- DATASET-2_images&measurements_individual_dates&bunches_stalks.zip (549.18 MB)
Images of individual dates and bunches' stalks with weight & dimensions measurements.
The dataset contains Software Development Effort Estimation (SDEE) metrics values extracted from around 1800 Open Source Software (OSS) repositories of GitHub.
The present dataset is based on implementing of 3 approaches with respect to the acquisition of driver data. The same one that we propose to use a sensor of concentration of alcohol in the environment (physiological), a sensor that measure the temperature of the defined points on driver’s face (biological) and another one that allows to identify and recognize the thickness of the pupil (visual characteristics).
The present dataset is based on implementing 3 approaches with respect to the acquisition of driver data. The same one that we propose to use a sensor of the concentration of alcohol in the environment (physiological), a sensor that measures the temperature of the defined points on driver’s face (biological) and another one that allows to identify and recognize the thickness of the pupil (visual characteristics).
Number of Instances: 390 (217 for no alcohol presence
173 for alcohol presence with
Number of Attributes: 5 numeric, predictive attributes and the class
1. acohol concentration in the car environment in ml/L
2. car environment temperature in degrees Celsius
3. face temperature min in degrees Celsius
4. face temperature max in degrees Celsius
5. pupil ratio
1 No acohol presence
2 acohol presence
Copyright (C) 2016-2019 Shandong UniversityDataset for "An artificial fusion intelligence-driven approach to energy-aware scheduling of stochastic nonlinear heterogeneous super-systems”
Design of novel RF front-end hardware architectures and their associated measurement algorithms.Research objectives, includes:RO1: Novel architecture based upon Adaptive Wavelet Band-pass Sampling (AWBS) of RF Analog-to-Information Conversion (AIC).RO2: Integration of AWBS for increasing the wideband sensing capabilities of real-time spectrum analyzers by using AICs.RO3: Propose online calibration methods and algorithms for front-end hardware non-idealities compensation.RO4: Design a hardware prototype aimed to perform real-time spectrum sensing with 1 GHz real-time bandwidth.
This database has five different linter quality classes (short cotton fibers), linter has wide applicability in the production of surgical tissue, paper money among other applications. The images available were used to classify the product in an industrial process, through the use of computer vision techniques.
This database can be used in the classification and quality analysis of the linter in the manufacturing process, as well as the basis for image processing methodologies.
We photographed Giemsa-stained thick blood smear slides from 150 P. falciparum infected patients at Chittagong Medical College Hospital, Bangladesh, using a smartphone camera for the different microscopic field of views. Images are captured with 100x magnification in RGB color space with a 3024×4032 pixel resolution. An expert slide reader manually annotated each image at the Mahidol-Oxford Tropical Medicine Research Unit (MORU), Bangkok, Thailand. We de-identified all images andtheir annotations, and archived them at the National Library of Medicine (IRB#12972).