Data preprocessing converts raw data and signals into data representation suitable for application through a sequence of operations. The objectives of data preprocessing include size reduction of the input space, smoother relationships, data normalization, noise reduction, and feature extraction. Several data preprocessing algorithms, such as data values averaging, input space reduction, and da...

The technology of functional Magnetic Resonance Imaging (fMRI) based on Blood Oxygen Level Dependent (BOLD) signal has been widely used in clinical treatments and brain function researches. The BOLD signal has to be preprocessed before being analyzed using either functional connectivity measurements or statistical methods. Current researches show that data preprocessing steps may influence the ...

<span lang="EN-US">An automated vehicle license plate recognition system using image processing techniques identifies numbers without human interference. This has significant impact because of its good application in various fields like car parking, access control, speed automatic toll collection, border security, traffic violence detection and surveillance applications. paper presents a ...

The selection of optimal preprocessing is among the main bottlenecks in chemometric data analysis. Preprocessing currently is a burden, since a multitude of different preprocessing methods is available for, e.g., baseline correction, smoothing, and alignment, but it is not clear beforehand which method(s) should be used for which data set. The process of preprocessing selection is often limited...

People often conduct preprocessing to simplify primary processing. Usually, there is a trade-off between the costs of performing preprocessing and primary processing. Therefore, the utility of preprocessing differs depending on the task complexity. We conducted three experiments to find out whether people could adaptively estimate the utility of preprocessing and then take rational action. The ...