Digital signal processing
This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent.
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SDU-Haier-ND (Shandong University-Haier-Noise Detection) is a sound dataset jointly constructed by Shandong University and Haier, which contains the operating sound of the internal air conditioner collected during the product quality inspection. We collected and marked a batch of quality inspection sounds of air conditioners in real production environments to form this data set, including normal sound samples and abnormal sound samples.
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There are hundreds of systems that create aircrafts. During design phases the validation and verification of these systems are done by using the data acquired by the flight test instrumentation system (FTI). Even though, there is no problem with the aircraft systems, if the measurement system is not capable of measuring it well, it results waste of efforts on unnecessary troubleshooting studies. Therefore, when designing an instrumentation system for flight testing purposes, sufficiency of the measurement system has to be proved before installation on the aircraft.
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Dataset asscociated with a paper in Computer Vision and Pattern Recognition (CVPR)
"Object classification from randomized EEG trials"
If you use this code or data, please cite the above paper.
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Crowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots. Crowd sounds can be characterized by frequency-amplitude features, using analysis techniques similar to those applied on individual voices, where deep learning classification is applied to spectrogram images derived by sound transformations.
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This data set is the result of model test trained on the basis of the Stanford earthquake dataset (stead): a global data set of seismic signals for AI, which can effectively get the seismic signal and the arrival time of seismic phase from the image, so as to prove the effectiveness of this model
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This dataset contains a created ST annotations for all recordings of Abdominal and Direct Fetal ECG Database (ADFECGDB).
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This dataset is in support of my Research paper 'Design of 6-DoF Combat Quadcopter'.
Preprint:
The system is basic, on existing designs.It is very simple for any graduate,degree holder.
Related Claim : Novel ß Non-Linear Theory
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