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Yvan Heyden studies Electromagnetic Fields and Microwave Techniques, Biomaterials and Tissue Engineering, and Biosensors.

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DTW (Dynamic Time Warping) python module. Contribute to pierre-rouanet/dtw development by creating an account on GitHub.

Dynamic Time Warping (DTW) library implementing lower bounds (LB_Keogh, LB_Improved...) - lemire/lbimproved

Follow my podcast: http://anchor.fm/tkorting In this video we describe the DTW algorithm, which is used to measure the distance between two time series. It was originally proposed in 1978 by Sakoe and Chiba for speech recognition, and it has been used up to today for time series analysis. DTW is one of the most used measure of the similarity between two time series, and computes the optimal global alignment between two time series, exploiting temporal distortions between them. Source code of graphs available at https://github.com/tkorting/youtube/blob/master/how-dtw-works.m The presentation was created using as references the following scientific papers: 1. Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustic Speech and Signal Processing, v26, pp. 43-49. 2. Souza, C.F.S., Pantoja, C.E.P, Souza, F.C.M. Verificação de assinaturas offline utilizando Dynamic Time Warping. Proceedings of IX Brazilian Congress on Neural Networks, v1, pp. 25-28. 2009. 3. Mueen, A., Keogh. E. Extracting Optimal Performance from Dynamic Time Warping. available at: http://www.cs.unm.edu/~mueen/DTW.pdf Subscribe to my channel!

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A two-dimensional (2-D) correlation optimized warping (COW) algorithm has been developed to align 2-D gas chromatography coupled with time-of-flight mass spectrometry (GC x GC/TOF-MS) data. By partitioning raw chromatographic profiles and warping the grid points simultaneously along the first and se …

Correlation optimized warping has been the most used technique to correct the drifts in peak positions. COW aligns the unaligned chromatogram to the reference chromatogram provided slack (t) and segment lengths (m) are optimized. However, several combinations of m and t need to be tested before finding the o

I have a Financial Time Series from a database and intend to cluster the time series based on their similarity. How would it be different to cluster them based on their pairwise Correlation and clu...

Two different algorithms for time‐alignment as a preprocessing step in linear factor models are studied. Correlation optimized warping and dynamic time warping are both presented in the literature as methods that can eliminate shift‐related artifacts from measurements by correcting a sample vector towards a reference. In this study both the theoretical properties and the practical implications of using signal warping as preprocessing for chromatographic data are investigated. The connection between the two algorithms is also discussed. The findings are illustrated by means of a case study of principal component analysis on a real data set, including manifest retention time artifacts, of extracts from coffee samples stored under different packaging conditions for varying storage times. We concluded that for the data presented here dynamic time warping with rigid slope constraints and correlation optimized warping are superior to unconstrained dynamic time warping; both considerably simplify interpretation of the factor model results. Unconstrained dynamic time warping was found to be too flexible for this chromatographic data set, resulting in an overcompensation of the observed shifts and suggesting the unsuitability of this preprocessing method for this type of signals. Copyright © 2004 John Wiley & Sons, Ltd.

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In this paper the performance of three alignment algorithms, correlation optimized warping, parametric time warping and semi-parametric time warping, is compared on real chromatograms. Among these, parametric time warping is the simplest and fastest; generally less than 1s is required to align two c …

Computed tomographic colonography (CTC) combined with a computer aided detection system has the potential for improving colonic polyp detection and increasing the use of CTC for colon cancer screening. In the clinical use of CTC, a true colonic polyp will be confirmed with high confidence if a radiologist can find it on both the supine and prone scans. To assist radiologists in CTC reading, we propose a new method for matching polyp findings on the supine and prone scans. The method performs a colon registration using four automatically identified anatomical salient points and correlation optimized warping (COW) of colon centerline features. We first exclude false positive detections using prediction information from a support vector machine (SVM) classifier committee to reduce initial false positive pairs. Then each remaining CAD detection is mapped to the other scan using COW technique applied to the distance along the centerline in each colon. In the last step, a new SVM classifier is applied to the candidate pair dataset to find true polyp pairs between supine and prone scans. Experimental results show that our method can improve the sensitivity to 0.87 at 4 false positive pairs per patient compared with 0.72 for a competing method that uses the normalized distance along the colon centerline (p

Mass spectrometry imaging (MSI) is a technique that provides comprehensive molecular information with high spatial resolution from tissue. Today, there is a strong push toward sharing data sets through public repositories in many research fields where MSI is commonly applied; yet, there is no standardized protocol for analyzing these data sets in a reproducible manner. Shifts in the mass-to-charge ratio (m/z) of molecular peaks present a major obstacle that can make it impossible to distinguish one compound from another. Here, we present a label-free m/z alignment approach that is compatible with multiple instrument types and makes no assumptions on the sample’s molecular composition. Our approach, MSIWarp (https://github.com/horvatovichlab/MSIWarp), finds an m/z recalibration function by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra. MSIWarp requires only centroid spectra to find the recalibration function and is thereby readily applicable to almost any MSI data set. To deal with particularly misaligned or peak-sparse spectra, we provide an option to detect and exclude spurious peak matches with a tailored random sample consensus (RANSAC) procedure. We evaluate our approach with four publicly available data sets from both time-of-flight (TOF) and Orbitrap instruments and demonstrate up to 88% improvement in m/z alignment.