987 research outputs found
Unsupervised ensembling of multiple software sensors: a new approach for electrocardiogram-derived respiration using one or two channels
While several electrocardiogram-derived respiratory (EDR) algorithms have
been proposed to extract breathing activity from a single-channel ECG signal,
conclusively identifying a superior technique is challenging. We propose
viewing each EDR algorithm as a {\em software sensor} that records the
breathing activity from the ECG signal, and ensembling those software sensors
to achieve a higher quality EDR signal. We refer to the output of the proposed
ensembling algorithm as the {\em ensembled EDR}. We test the algorithm on a
large scale database of 116 whole-night polysomnograms and compare the
ensembled EDR signal with four respiratory signals recorded from four different
hardware sensors. The proposed algorithm consistently improves upon other
algorithms, and we envision its clinical value and its application in future
healthcare
Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means
We analyze a compression scheme for large data sets that randomly keeps a
small percentage of the components of each data sample. The benefit is that the
output is a sparse matrix and therefore subsequent processing, such as PCA or
K-means, is significantly faster, especially in a distributed-data setting.
Furthermore, the sampling is single-pass and applicable to streaming data. The
sampling mechanism is a variant of previous methods proposed in the literature
combined with a randomized preconditioning to smooth the data. We provide
guarantees for PCA in terms of the covariance matrix, and guarantees for
K-means in terms of the error in the center estimators at a given step. We
present numerical evidence to show both that our bounds are nearly tight and
that our algorithms provide a real benefit when applied to standard test data
sets, as well as providing certain benefits over related sampling approaches.Comment: 28 pages, 10 figure
Development of Novel Calibrations for FT-NIR Analysis of Protein, Oil, Carbohydrates and Isoflavones in Foods
The development of calibration methodology for novel FT-NIRS analysis of soybean-based foods is presented together with high-precision NIRS spectra and composition measurements in terms of proteins, oil and carbohydrates in soybean-based foods/soy foods.

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