17,374 research outputs found
Anomaly Detection in Paleoclimate Records using Permutation Entropy
Permutation entropy techniques can be useful in identifying anomalies in
paleoclimate data records, including noise, outliers, and post-processing
issues. We demonstrate this using weighted and unweighted permutation entropy
of water-isotope records in a deep polar ice core. In one region of these
isotope records, our previous calculations revealed an abrupt change in the
complexity of the traces: specifically, in the amount of new information that
appeared at every time step. We conjectured that this effect was due to noise
introduced by an older laboratory instrument. In this paper, we validate that
conjecture by re-analyzing a section of the ice core using a more-advanced
version of the laboratory instrument. The anomalous noise levels are absent
from the permutation entropy traces of the new data. In other sections of the
core, we show that permutation entropy techniques can be used to identify
anomalies in the raw data that are not associated with climatic or
glaciological processes, but rather effects occurring during field work,
laboratory analysis, or data post-processing. These examples make it clear that
permutation entropy is a useful forensic tool for identifying sections of data
that require targeted re-analysis---and can even be useful in guiding that
analysis.Comment: 15 pages, 7 figure
Spectral analysis for long-term robotic mapping
This paper presents a new approach to mobile robot mapping in long-term scenarios. So far, the environment models used in mobile robotics have been tailored to capture static scenes and dealt with the environment changes by means of ‘memory decay’. While these models keep up with slowly changing environments, their utilization in dynamic, real world
environments is difficult.
The representation proposed in this paper models the environment’s spatio-temporal dynamics by its frequency spectrum. The spectral representation of the time domain allows to identify, analyse and remember regularly occurring environment processes in a computationally efficient way. Knowledge of the periodicity of the different environment processes constitutes the model predictive capabilities, which are especially useful for long-term mobile robotics scenarios.
In the experiments presented, the proposed approach is applied to data collected by a mobile robot patrolling an indoor
environment over a period of one week. Three scenarios are investigated, including intruder detection and 4D mapping. The results indicate that the proposed method allows to represent arbitrary timescales with constant (and low) memory requirements, achieving compression rates up to 106 . Moreover, the representation allows for prediction of future environment’s state with ∼ 90% precision
Incorporating Relaxivities to More Accurately Reconstruct MR Images
Purpose
To develop a mathematical model that incorporates the magnetic resonance relaxivities into the image reconstruction process in a single step.
Materials and methods
In magnetic resonance imaging, the complex-valued measurements of the acquired signal at each point in frequency space are expressed as a Fourier transformation of the proton spin density weighted by Fourier encoding anomalies: T2⁎, T1, and a phase determined by magnetic field inhomogeneity (∆B) according to the MR signal equation. Such anomalies alter the expected symmetry and the signal strength of the k-space observations, resulting in images distorted by image warping, blurring, and loss in image intensity. Although T1 on tissue relaxation time provides valuable quantitative information on tissue characteristics, the T1 recovery term is typically neglected by assuming a long repetition time. In this study, the linear framework presented in the work of Rowe et al., 2007, and of Nencka et al., 2009 is extended to develop a Fourier reconstruction operation in terms of a real-valued isomorphism that incorporates the effects of T2⁎, ∆B, and T1. This framework provides a way to precisely quantify the statistical properties of the corrected image-space data by offering a linear relationship between the observed frequency space measurements and reconstructed corrected image-space measurements. The model is illustrated both on theoretical data generated by considering T2⁎, T1, and/or ∆B effects, and on experimentally acquired fMRI data by focusing on the incorporation of T1. A comparison is also made between the activation statistics computed from the reconstructed data with and without the incorporation of T1 effects.
Result
Accounting for T1 effects in image reconstruction is shown to recover image contrast that exists prior to T1 equilibrium. The incorporation of T1 is also shown to induce negligible correlation in reconstructed images and preserve functional activations.
Conclusion
With the use of the proposed method, the effects of T2⁎ and ∆B can be corrected, and T1 can be incorporated into the time series image-space data during image reconstruction in a single step. Incorporation of T1 provides improved tissue segmentation over the course of time series and therefore can improve the precision of motion correction and image registration
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