signal data

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This dataset is accompanying the manuscript “Lossless Compression of Plenoptic Camera Sensor Images and of Light Field View Arrays” by Ioan Tabus and Emanuele Palma, submitted to IEEE Access in June 2020. It contains the archives and the programs for reconstructing the light field datasets publicly used in two major challenges for light field compression.

Instructions: 

This dataset is accompanying the manuscript “Lossless Compression of Plenoptic Camera Sensor Images and of Light Field View Arrays” by Ioan Tabus and Emanuele Palma, submitted to IEEE Access in June 2020. It contains the archives and the programs for reconstructing the light field datasets publicly used in two major challenges for light field compression.We propose a codec for lossless compression of plenoptic camera sensor images and then we embed the proposed codec into a full light field array codec, which encodes input sensor data and makes use specific plenoptic camera meta-information for creating lossless archives of light field view arrays. The sensor image codec takes the input lenslet image and splits it into rectangular patches, each patch corresponding to a microlens image. The codec exploits the correlation between neighbor patches using a patch-by-patch prediction mechanism, where each pixel of a patch has his own sparse predictor, designed to utilize only the relevant pixels from its neighbor patch. An intra-patch prediction mask is additionally utilized for sparse predictor design. The patches are labeled into M classes, according to several possible mechanisms, and one sparse design is performed for each pair of (class label; patch pixel). A relevant context selection mirrors the selection of relevant pixels to provide the arithmetic coding with skewed coding distributions at each context.Finally, we embed the proposed image sensor codec into a codec for the light field array of views, which is a generative mechanism, starting by encoding the sensor image or a devigneted and debayered version of it, and then including additional meta-information from the plenoptic camera, finally creating a lossless archive of the light field array of views. We exemplify the performance for two databases that were extensively used in the light field lossless compression literature, showing superior results for both cases.

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This is a dataset of Finite Difference Time Domain (FDTD) simulation results of 13 defective crystals and one non-defective crystal.  There are 4 fields in the dataset, namely: Real, Img, Int, and Attribute. The header real shows a real part of the simulated result, img shows the imaginary part, int gives the intensity all in superimposed form. Attribute denotes the label of a crystal simulated. The label 0 is for the simulated crystal, which is non-defective.  Other 13 labels, from crystal 1 to crystal 13 are assigned to the 13 different crystals whose simulations are studied.

Instructions: 

Read the abstract.

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Participants were 61 children with ADHD and 60 healthy controls (boys and girls, ages 7-12). The ADHD children were diagnosed by an experienced psychiatrist to DSM-IV criteria, and have taken Ritalin for up to 6 months. None of the children in the control group had a history of psychiatric disorders, epilepsy, or any report of high-risk behaviors.

 

Instructions: 

 

Extract the Zip files. Load the ".mat" data into MATLAB.

 

If you want to import the electrode location into EEGLAB, please use the attached".ced" file.

 

 

 

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The data set is collected from MyNeuroHealth Application developed for the detection of Seizures and Falls. Data is gathered using tri-axial accelerometer placed at the upper left arm of an individual in an unconstraint environment.

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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition.

Instructions: 

Dataset Characteristics and Filename Formats

 

The "CoMMonS_FullResolution" folder includes 6912 full-resolution images (2560x1920). The "CoMMonS_Sampled" folder includes sampled images (resolution: 300x300), which are sampled from full-resolution images with different positions (x, y), rotation angles (r), zoom levels (z), a touching direction ("pile"), a lightness condition ("l5"), and a camera function setting ("ed3u"). This "CoMMonS_Sampled" folder is an example of a dataset subset for training and testing (e.g. 5: 1). Our dataset focuses on material characterization for one material (fabric) in terms of one of three properties (fiber length, smoothness, and toweling effect), facilitating a fine-grained texture classification. In this particular case, the dataset is used for a standard supervised problem of material quality evaluation. It takes fabric samples with human expert ratings as training inputs, and takes fabric samples without human subject ratings as testing inputs to predict quality ratings of the testing samples. The texture patches are classified into 4 classes according to each surface property measured by human sense of touch. For example, the human expert rates surface fiber length into 4 levels, from 1 (very short) to 4 (long), and similarly for smoothness and toweling effect. In short, the "CoMMonS_Sampled" folder includes 9 subfolders, each of which includes both sampled images and attribute class labels.

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136 Views

As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition.

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The Costas condition on a permutation matrix, expressed as row indices as elements of a vector c, can be expressed as A*c=b, where b is a vector of integers in which no element is zero.  A particular formulation of the matrix A allows a singular value decomposition in which the eigenvalues are squared integers and the eigenvalues may be scaled to vectors with all integer elements.  This is a database of the Costas constraint matrices A, the scaled eigenvectors, and the squared eigenvalues for orders 3 through 100.

Instructions: 

Please refer to the file CC_SVD_Database_Readme.pdf for instructions on the format of the database, and its use.  The database contains one file for each order.  The files are CSV files in which each line ends with a comma, then a plain text remark that explains that line.

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150 Views

This MATLAB dataset (.mat) contains the collected real measurement data from a total of 470 access points (APs) deployed in the Linnanmaa campus of the University of Oulu, Finland. The measurements include IDs, dates of data collection, number of users, received traffic data, transmitted traffic data and location names of each AP. Each observation of traffic data and number of users provide the data value at every 10-minute interval between December 18, 2018 and February 12, 2019. Please cite this as: S. P. Sone & Janne Lehtomäki & Zaheer Khan.

Instructions: 

Major component description: There are 3 main major components: number of users connected at collected time (numb_users), received traffic data in bytes (rxbytes) and transmitted traffic data in bytes (txbytes) of each AP in this dataset. Dates and times of data collection (date) can be converted into the serial date number by using datenum() function in MATLAB.

 

Received and transmitted traffic data are in the cumulative time series format so that differencing every 2 consecutive observations is required to get the actual values at every 10-minute. It can be done by using diff() function in MATLAB, for example, "diff(ap184016.txbytes)".

 

Setup and running instructions: First, MATLAB must be installed in the computer correctly. Then, the downloaded dataset should be placed in the folder whose path is already specified in MATLAB (see https://in.mathworks.com/help/matlab/matlab_env/specify-file-names.html).

 

Once the dataset (APs_dataset.mat) is loaded correctly in MATLAB, total 470 structure arrays with the IDs of each AP will appear in MATLAB Workspace. Then, the desired time series can be called in MATLAB, for example, "Tx_data = diff(ap184016.txbytes);".

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158 Views

E-nose can be used for food authentication and adulteration assessment. Recently, halal authentication has gained attention because of cases of pork adulteration in beef. In this study, The electronic nose was built using nine MQ series gas sensors from Zhengzhou Winsen Electronics Technology Co., Ltd for detection pork adulteration in beef. The list of gas sensors are MQ2, MQ4, MQ6, MQ9, MQ135, MQ136, MQ137, and MQ138. These gas sensors were assembled with an Arduino microcontroller.

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