DTW - DYNAMIC TIME WARPING

For a list of relevant conferences to our research axes, click here. Entries: 319 Expand all Collapse all 2021 (2) O. Zayene, R. Ingold, N. E. BenAmara, and J. Hennebert, “ICPR2020 Competition on Text Detection and Recognition in Arabic News Video Frames,” in Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10-15, 2021, Proceedings, […]

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Here we will show how to automatically find good settings for correlation optimized warping (COW). Code used in the video: %% Prepare data % Data available at https://bit.ly/2uvYtLp load WARP_GC_TIC.mat X = Xtic.data(:,2040:3130); optim_space = [5 100 2 10]; options = [0 3 50 .15]; ref = ref_select(X,[],[1 1]); %% Run the optimization [optim_pars,OS,diagnos] = optim_cow(X,optim_space,options,ref); %% [Warping,XWarped,Diagnos] = cow(ref,X,53,4); %% ax(1) = subplot(2,1,1); plot(XWarped') title('Warped'); axis tight ax(2)=subplot(2,1,2) plot(X'); title('Raw') axis tight shg linkaxes(ax,'xy'); shg

The multivariate calibration methods—principal component regression (PCR) and partial least squares (PLSs)—were employed for the prediction of total phenol contents of four Prunella species. High performance liquid chromatography (HPLC) and spectrophotometric approaches were used to determine the total phenol content of the Prunella samples. Several preprocessing techniques such as smoothing, normalization, and column centering were employed to extract the chemically relevant information from the data after alignment with correlation optimized warping (COW). The importance of the preprocessing was investigated by calculating the root mean square error (RMSE) for the calibration set of the total phenol content of Prunella samples. The models developed based on the preprocessed data were able to predict the total phenol content with a precision comparable to that of the reference of the Folin-Ciocalteu method. PLS model seems preferable, because of its predictive and describing abilities and good interpretability of the contribution of compounds to the total phenol content. Multivariate calibration methods were constructed to model the total phenol content of the Prunella samples from the HPLC profiles and indicate peaks responsible for the total phenol content successfully.

All data herein from Alex Martin, et al . [NIMH IRP]. Data for this presentation: AFNI_data5/ directory. Instant Analyses in AFNI and SUMA : Clusters and Correlations. AFNI “ Insta ” Functions. 3 new capabilities added to the interactive AFNI

AFNI, SUMA, and NIML : Interprocess Communication in FMRI Data Analysis. Robert W Cox and Ziad S Saad Statistical and Scientific Computing Core National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services, USA. Linked Programs.

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