Machine Learning

Recently, the coronavirus pandemic has made the use of facial masks and respirators common, the former to reduce the likelihood of spreading saliva droplets and the latter as Personal Protective Equipment (PPE). As a result, this caused problems for the existing face detection algorithms. For this reason, and for the implementation of other more sophisticated systems, able to recognize the type of facial mask or respirator and to react given this information, we created the Facial Masks and Respirators Database (FMR-DB).

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This multispectral remote sensing image data contained pixels of size (1024 x 1024) for the region around Kolkata city in India and was obtained with LISS-III sensor. There are four spectral bands, i.e., two from visible spectrum (green and red) and two from the infrared spectrum (near-infrared and shortwave infrared). The spatial resolution and spectral variation over the wavelength are 23.5m and 0.52 - 1.7 μm, respectively.

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This data is related to Novel window for cancer nanotheranostics: non-invasive ocular assessments of tumor growth and nanotherapeutic treatment efficacy in vivo published at https://doi.org/10.1364/BOE.10.000151 

The file also contains Deep Learning Codes for segmentation of Tumor using U-Net model. Training weights are also uploaded.

 

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Spectral data for the manuscript "Predictability of vibration loads from experimental data by means of reduced vehicle models and machine learning "

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This data is used to train ML based SINR predictor

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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.

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Measurements collected from pods M1, S1 and S2 and links L1, L2, L3 and L4. 

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This database is provided for the Fake News Detection task. In addition to being used in other tasks of detecting fake news, it can be specifically used to detect fake news using the Natural Language Inference (NLI).

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We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets c

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