India is known for its highly disciplined foreign policies, strategic location, vibrant and massive Diaspora. India envisages enhancing its scope of cooperation, trade and widens its sphere of relations with the Pacific. As a result, the world is witnessing the rise of Indo-Pacific ties. Before the 1980’s the keystone of the universe was called the Atlantic, but now a radical shift to the east is noticed by the term “Indo-Pacific‟.

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176 Views

YonseiStressImageDatabase is a database built for image-based stress recognition research. We designed an experimental scenario consisting of steps that cause or do not cause stress; Native Language Script Reading, Native Language Interview, Non-native Language Script Reading, Non-native Language Interview. And during the experiment, the subjects were photographed with Kinect v2. We cannot disclose the original image due to privacy issues, so we release feature maps obtained by passing through the network.

Instructions: 

 

Database Structure

- YonseiStressImageDatabase

         - Subject Number (01~50)

                  - Data acquisition phase

                    (Native Language Script Reading, Native Language Interview, Non-native Language Script Reading, Non-native Language Interview)

                           - Data (*.npy, the filename is set to the time the data was acquired; YYYYMMDD_hhmmss_ms)

 

In the case 'Non-native_Language_Interview' data of subject 26, it was not acquired due to equipment problems.

 

Citing YonseiStressImageDatabase

If you use YonseiStressImageDatabase in a scientific publication, we would appreciate references to the following paper:

Now Reviewing.

 

Usage Policy

Copyright © 2019 AI Hub, Inc., https://aihub.or.kr/

AI data provided by AI Hub was built as part of a business National Information Society Agency's 'Intelligent information industry infrastructure construction project' in Korea, and the ownership of this database belongs to National Information Society Agency.

Specialized field AI data was built for artificial intelligence technology development and prototype production and can be used for research purposes in various fields such as intelligent services and chatbots.

 

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356 Views

The DREAM (Data Rang or EArth Monitoring): a multimode database including optics, radar, DEM and OSM labels for deep machine learning purposes.

DREAM, is a multimodal remote sensing database, developed from open-source data.

The database has been created using the Google Earth Engine platform, the GDAL python library; the “pyosm” python package developed by Alexandre Mayerowitz (Airbus, France). If you want to use this dataset in your study, please cite:

Instructions: 

The two datasets are stored in two separate zip files: USA_DREAM_MULTIMODAL.zip and France_DREAM_MULTIMODAL.zip.

After unzip, each directory contain different sub directories with different areas. Each available tile is a 1024x1024 tile GeoTiffs format.

In France:

  • CoupleZZ_S2_date1_date2_XX_YY (Uint16 GeoTiff, UTM, RGB)
  • CoupleZZ_SRTM_V2_XX_YY (Int16 GeoTiff)
  • CoupleZZ_S1_date2_date1_XX_YY (Float32 GeoTiff 2 bands, Red:VV, Green: HV)
  • CoupleZZ_S1moy_date2__dual_XX_YY (Float32 GeoTiff 2 bands, Red:VV, Green: HV)
  • CoupleZZ_OSMraster_XX_YY (Uint8 3 bands RGB GeoTIff)

In the USA There are directories named zoneZ that include following subdirectories

  • optique     contains    *_pauli_x***_y***_optique.tif 
    • Ex: SanAnd_09018_18038_017_180730_L090_CX_01_pauli_x000_y002_optique.tif
  • radar                            *_pauli_x***_y***.tif 
    • Ex: SanAnd_09018_18038_017_180730_L090_CX_01_pauli_x000_y002.tif
  • S1                                 *_pauli_x***_y***_S1moy.tif 
    • Ex: SanAnd_09018_18038_017_180730_L090_CX_01_pauli_x000_y002_S1moy.tif
  • S2                                 *_pauli_x***_y***_S2mosa.tif 
    • Ex: SanAnd_09018_18038_017_180730_L090_CX_01_pauli_x000_y002_S2mosa.tif
  • SRTM                           *__x***_y***_hgt.tif
    • Ex:  SanAnd_09018_18038_017_180730_L090_CX_01__x000_y002_hgt.tif

 

 

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380 Views

The data-set used in the paper titled "Short-Term Load Forecasting Using an LSTM Neural Network."

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633 Views

Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures.

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624 Views

Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

Instructions: 

Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

4) Leaf Spot

5) Downy Mildew

The Quality parameters (Healthy/Diseased) and also classification based on the texture and color of leaves. For the object detection purpose researcher using an algorithm like Yolo,  TensorFlow, OpenCV, deep learning, CNN

I had collected a dataset from the region Amravati, Pune, Nagpur Maharashtra state the format of the images is in .jpg.

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1631 Views

This dataset contains 1216 data, which are scanned by HIS-RING PACT system.

the data sampling rate of our system is 40 MSa/s, a 128-elements 2.5MHz full-view ring-shaped transducer with 30mm radius. 

 continuous updating.....

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230 Views

Dataset of fluorescent mice brain vessels Confocal 3D volumes aligned to Light-Field images.

Confocal:

  • Single volume dimension: 1287x1287x64.
  • Number of samples: 362
  • Voxel size: 0.086x0.086x0.9 um.
  • Objective: 40x/1.3 Oil.
  • Stain: tomato lectin (DyLight594 conjugated, DL-1177, Vector Laboratories).

 

LightField:

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103 Views

The dataset contains:
1. We conducted a A 24-hour recording of ADS-B signals at DAB on 1090 MHz with USRP B210 (8 MHz sample rate). In total, we got the signals from more than 130 aircraft.
2. An enhanced gr-adsb, in which each message's digital baseband (I/Q) signals and metadata (flight information) are recorded simultaneously. The output file path can be specified in the property panel of the ADS-B decoder submodule.
3. Our GnuRadio flow for signal reception.
4. Matlab code of the paper, wireless device identification using the zero-bias neural network.

Instructions: 

1. The "main.m" in Matlab code is the entry of simulation.
2. The "csv2mat" is a CPP program to convert raw records (adsb_records1.zip) of our gr-adsb into matlab manipulatable format. Matio library (https://github.com/tbeu/matio) is required.
3. The Gnuradio flowgraph is also provided with the enhanced version of gr-adsb, in which you are supposed to replace the original one (https://github.com/mhostetter/gr-adsb). And, you can specify an output file path in the property panel of the ADS-B decoder submodule.
4. Related publication: Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices, IEEE IoTJ (accepted for publication on 21 August 2020), DOI: 10.1109/JIOT.2020.3018677

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913 Views

Intelligent Hybrid model to Enhance Time Series Models for Predicting Network Traffic, the proposed research has used the clustering approach to handle the ambiguity from the entire network data for enhancing the existing time series models.

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842 Views

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