Data are collected before and after percutaneous transluminal angiography (PTA) for dialysis patients.

Each sample is labeled as a-b-before.wav or a-b-after.wav and the associated txt, where a is the patient id and b is the location id.

The first position was the arterial-venous junction,  and the second point was 3 cm from the first position along the vein.

 The distances between the adjacent positions were also about 3 cm.

 

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Considering the ongoing works in Natural Language Processing (NLP) with the Nepali language, it is evident that the use of Artificial Intelligence and NLP on this Devanagari script has still a long way to go. The Nepali language is complex in itself and requires multi-dimensional approaches for pre-processing the unstructured text and training the machines to comprehend the language competently. There seemed a need for a comprehensive Nepali language text corpus containing texts from domains such as News, Finance, Sports, Entertainment, Health, Literature, Technology.

Instructions: 

Here's a quick way to load the .txt file in your favourite IDE.

filename = 'compiled.txt'

file = open(filename, encoding="utf-8")

text = file.read()

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The data are used to identify the  kinematic parameters deviation of Cartesian robot, train Gaussian Process Regression (GPR) model, record the compensation result of four calibration methods under different loading conditions.

Instructions: 

Compensation results file: It expresses the compensation results in 8 test points when using four calibration methods under different loading conditions. We can see Figure16 in this paper.

HCT+BD+GPR_training file: These data record 320 groups of position points of the end effector after using HCT+BD model to compensate.  We can get 320 groups of residual error data by simply calculating the difference between these data and these designated positions. And they are used to train GPR model. 10-fold cross validation results of GPR model about x and z error are obtained by using these data. They  are shown in Figure14 and Figure15 in this paper.

 

HCT+GPR_training file: These data record 320 groups of position points of the end effector after using HCT model to compensate.  We can get 320 groups of residual error data by simply calculating the difference between these data and these designated positions. And they are used to train GPR model.

Identify_kinematic_parameter_deviation file: Using nonlinear least squares method to minimize the difference  between the amended position and actual position. We can get the deviation of kinematic parameters. The procedure to identify the deviation of kinematic parameters is shown in Figure 4. And we can see the result of deviation in Table 2 in this paper.

 

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Four groups of wind speed series

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Long-term 3DC Dataset as described in 

Virtual Reality to Study the Gap Between Offline and Real-Time EMG-based Gesture Recognition

Instructions: 
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The dataset corresponds to the variables that affect the process of passing the sheet between rodsizer and the spooner section for a paper machine.

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This data is related to the article “On the Spectral Quality of Time-Resolved CMOS SPAD-Based Raman Spectroscopy with High Fluorescence Backgrounds” that have been submitted to the IEEE Sensors Journal.

 

 

Instructions: 

This data is related to the article “On the Spectral Quality of Time-Resolved CMOS SPAD-Based Raman Spectroscopy with High Fluorescence Backgrounds” that have been submitted to the IEEE Sensors Journal. The folder named “Fluorescence_to_Raman_ratio_(post-it_notes)” contains the data that was collected in the measurements where the effects of fluorescence-to-Raman ratio on the spectral quality were studied. Please, see the measurement procedures and results from the article under sections III. B and IV. A, respectively. The folder named “Recording_time_and_excitation_intensity_(oils)” contains the data that was collected in the measurement where the effects of the recording time and excitation intensity on the spectral quality was studied. Please, see the measurement procedures and results from the article under sections III. C and IV. B, respectively.

 

The measurement data is stored to the text files named as “Data.txt”. The datafiles have 8 columns and 256 rows. The columns represent the 8 time bins of the sensor and the rows in the datafiles represent the 256 spectral columns in the line sensor. The numbers in the cells of the datafiles represent the photon counts at a specific time bin and spectral column, i.e. at a specific wavenumber. The text files named as “Wavenumber_axis.txt” under the two main data folders contains the wavenumber values for each of the spectral columns in the sensor for the different measurements. The folders named as “DCR_corresction_data.txt” contains the dark count correction data for the different measurements.

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This dataset was used in the article "Dias-Audibert FL, Navarro LC, de Oliveira DN, Delafiori J, Melo CFOR, Guerreiro TM, Rosa FT, Petenuci DL, Watanabe MAE, Velloso LA, Rocha AR and Catharino RR (2020) Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers. Front. Bioeng. Biotechnol. 8:6. doi: 10.3389/fbioe.2020.00006", open access available at: https://doi.org/10.3389/fbioe.2020.00006.

Instructions: 

WGMSML-Data folder contains the mass spectra input data for the Matlab scripts which are in WGMSML-MATLAB-SourceCode folder. WGMSML-ExecutionLogsAndPlots contains logs and plots generated by the execution of the Matlab code over the input data. Main scripts are enumerated in the order of execution.

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Low light scenes often come with acquisition noise, which not only disturbs the viewers, but it also makes video compression harder. These type of videos are often encountered in cinema as a result of artistic perspective or the nature of a scene. Other examples include shots of wildlife (e.g. mobula rays at night in Blue Planet II), concerts and shows, surveillance camera footage and more. Inspired by all above, we are proposing a challenge on encoding low-light captured videos.

Last Updated On: 
Fri, 05/01/2020 - 09:40

Oxygen is one of the most adverse gas that contaminates sterile drugs in glass medicine bottle, so it is of great significance to detect oxygen concentration for glass medicine bottle in order to ensure the asepsis of drug and the stability of ingredients. Wavelength modulation spectroscopy (WMS) is applied to achieve online oxygen concentration detection by the single-line spectrum analysis for the advantages of non-contact and high sensitivity.

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