Urban flooding is a common problem across the world. In India, it leads to casualties every year, and financial loss to the tune of tens of billions of rupees. The damage done due to flooding can be mitigated if the locations deserving attention are known. This will enable an effective emergency response, and provide enough information for the construction of appropriate storm water drains to mitigate the effect of floods. In this work, a new technique to detect flooding level is introduced, which requires no additional equipment, and consequent installation and maintenance costs.
Typically, a paper mill comprises three main stations: Paper machine, Winder station, and Wrapping station. The Paper machine produces paper with particular grammage in gsm (gram per square meter). The typical grammage classes in our paper mill are 48 gsm, 50 gsm, 58 gsm, 60 gsm, 68 gsm, 70 gsm. The Winder station takes a paper spool that is about 6 m width as it’s input and transfers is to customized paper rolls with particular diameter and width.
This folder contains two csv files and one .py file. One csv file contains NIST ground PV plant data imported from https://pvdata.nist.gov/. This csv file has 902 days raw data consisting PV plant POA irradiance, ambient temperature, Inverter DC current, DC voltage, AC current and AC voltage. Second csv file contains user created data. The Python file imports two csv files. The Python program executes four proposed corrupt data detection methods to detect corrupt data in NIST ground PV plant data.
A paradigm dataset is constantly required for any characterization framework. As far as we could possibly know, no paradigmdataset exists for manually written characters of Telugu Aksharaalu content in open space until now. Telugu content (Telugu: తెలుగు లిపి, romanized: Telugu lipi), an abugida from the Brahmic group of contents, is utilized to compose the Telugu language, a Dravidian language spoken in the India of Andhra Pradesh and Telangana just a few other neighboring states. The Telugu content is generally utilized for composing Sanskrit writings.
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.
Occlusion, glare and secondary reflections formed due to and on the spectacles - results in poor detection, localization, and recognition of eye/face features. We term all the problems related to the usage of spectacles as The spectacle problem. Though several studies on the spectacle detection and removal have been reported in the literature, the study focusing on spectacle problem removal is very limited. One of the main reasons being, the nonavailability of a facial image database highlighting the spectacle problems.