Using acoustic waves to estimate fluid concentration is a promising technology due to its practicality and non-intrusive aspect, especially for medical applications. The existing approaches are exclusively based on the correlation between the reflection coefficient and the concentration. However, these techniques are limited by the high sensitivity of the reflection coefficient to environmental conditions changes, even slight ones. This introduces inaccuracies that cannot be tolerated in medical applications.

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The following dataset consists of samples of acoustic characteristics of 356 Russian-speaking subjects and measured psychological traits. All the recordings (5701 samples) were processed and acoustic characteristics were calculated. 

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The experiment is based on the open source RSRP data provided by Huawei Technologies Co., LTD. It measures RSRP of 415,244 signal receiving points in 180 dense urban communication cells. 

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The leakage data of the natural gas pipeline comes from the simulated leakage device of the pressure pipeline. The pipeline material is seamless steel pipe, the outer diameter of the pipeline is 108mm, the wall thickness of the pipeline is 4.5mm, and the length of the pipeline is about 100m. According to the specifications of the air compressor and air storage tank (the maximum allowable pressure is 2Mpa) and the standard of high-pressure and sub-high-pressure pipeline gas transmission pressure, the selected experimental pressure range is 0.5Mpa to 2Mpa.

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This repository contains the data related to the paper “CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging” (10.1109/TUFFC.2021.3131383). It contains multiple datasets used for training and testing, as well as the trained models and results (predictions and metrics). In particular, it contains a large-scale simulated training dataset composed of 31000 images for the three different imaging configuration considered (i.e., low quality, high quality, and ultrahigh quality).

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The detailed description of the data available in this repository can be found online at https://github.com/dperdios/dui-ultrafast/#data.

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EEG consists of collecting information from brain activity in the form of electrical voltage. Epileptic Seizure prediction and detection is a major sought after research nowadays. This dataset contains data from 11 patients of whom seizures are observed in EEG for 2 patients.

 

The total duration of seizures is 170 seconds. The number of channels is 16 and data is collected at 256Hz sampling rate.

 

The final dataset files in .csv format contain 87040 rows x 17 columns,

 

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 it contains the low-dose CT images used in the experiment.

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Real time barrage data of a live broadcast platform

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This dataset is related to dog activity and is sensor data.

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With the rapid deployment of indoor Wi-Fi networks, Channel State Information (CSI) has been used for device-free occupant activity recognition. However, various environmental factors interfere with the stable propagation of Wi-Fi signals indoors, which causes temporal variation of CSI data. In this study, we investigated temporal CSI variation in a real-world housing environment and its impact on learning-based occupant activity recognition.

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