Signal Processing
This dataset consists of 2579 image pairs (5158 images in total) of wood veneers before and after drying. The high-resolution .png images (generally over 4000x4000) have a white background. The data has been collected from a real plywood factory. Raute Corporation is acknowledged for making this dataset public. The manufacturing process is well visualized here: https://www.youtube.com/watch?v=tjkIYCEVXko.
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Three raw (i.e., In-Phase and Quadrature data with a software radio, and observation files) GNSS dataset were recorded using a LabSat Version 3 inside of the West Virginia University greenhouse and two outside recordings were also made to provide a quality reference and comparison. The outdoor location had to be an ideal location for satellite signal reception and the indoor location was a greenhouse room where satellite visibility was limited, susceptible to attenuation, occlusion and multipath.
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The data set is collected using Neurosky MindWave 2.0 Headset. It uses a single dry electrode placed at FP-1 position for the acquisition of EEG signals. The data is collected from Healthy Individuals and Epileptic Patients performing different Activities of Daily Living (ADLs) in an unconstraint environment.
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A qualitative and quantitative extension of the chaotic models used to generate self-similar traffic with long-range dependence (LRD) is presented by means of the formulation of a model that considers the use of piecewise affine onedimensional maps. Based on the disaggregation of the temporal series generated, a valid explanation of the behavior of the values of Hurst exponent is proposed and the feasibility of their control from the parameters of the proposed model is shown.
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We introduce a novel dataset specifically designed for the evaluation of “alarm flood classification” (AFC) methods within process plants. The growing complexity of industrial systems and the heightened demands for operational safety and efficiency underscore the critical need for advanced diagnostic tools capable of handling alarm floods—situations where numerous alarms are triggered simultaneously.
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A wide range of wearable sensors exist on the market for continuous physiological health monitoring. The type and scope of health data that can be gathered is a function of the sensor modality. Blumio presents a dataset of synchronized data from a reference blood pressure device along with several wearable sensor types: PPG, applanation tonometry, and the Blumio millimeter-wave radar. Data collection was conducted under set protocol with subjects seated at rest. 115 study subjects were included (age range 20-67 years), resulting in over 19 hours of data acquired.
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This dataset of 7200 channels is generated at different locations in the room area of 30x15x4 m3, where the locations are separated by 0.25m in both horizontal and vertical directions. Each AP uses 10 dBm TX power and 2D BF. In the concurrent mmWave BT scenario, all APs are operating, while in the single mmWave BT scenario, we consider a single AP fixed on the center of the room’s ceiling
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WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.
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WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.
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