11,603 research outputs found
A Few Photons Among Many: Unmixing Signal and Noise for Photon-Efficient Active Imaging
Conventional LIDAR systems require hundreds or thousands of photon detections
to form accurate depth and reflectivity images. Recent photon-efficient
computational imaging methods are remarkably effective with only 1.0 to 3.0
detected photons per pixel, but they are not demonstrated at
signal-to-background ratio (SBR) below 1.0 because their imaging accuracies
degrade significantly in the presence of high background noise. We introduce a
new approach to depth and reflectivity estimation that focuses on unmixing
contributions from signal and noise sources. At each pixel in an image,
short-duration range gates are adaptively determined and applied to remove
detections likely to be due to noise. For pixels with too few detections to
perform this censoring accurately, we borrow data from neighboring pixels to
improve depth estimates, where the neighborhood formation is also adaptive to
scene content. Algorithm performance is demonstrated on experimental data at
varying levels of noise. Results show improved performance of both reflectivity
and depth estimates over state-of-the-art methods, especially at low
signal-to-background ratios. In particular, accurate imaging is demonstrated
with SBR as low as 0.04. This validation of a photon-efficient, noise-tolerant
method demonstrates the viability of rapid, long-range, and low-power LIDAR
imaging
Multiscale Bayesian State Space Model for Granger Causality Analysis of Brain Signal
Modelling time-varying and frequency-specific relationships between two brain
signals is becoming an essential methodological tool to answer heoretical
questions in experimental neuroscience. In this article, we propose to estimate
a frequency Granger causality statistic that may vary in time in order to
evaluate the functional connections between two brain regions during a task. We
use for that purpose an adaptive Kalman filter type of estimator of a linear
Gaussian vector autoregressive model with coefficients evolving over time. The
estimation procedure is achieved through variational Bayesian approximation and
is extended for multiple trials. This Bayesian State Space (BSS) model provides
a dynamical Granger-causality statistic that is quite natural. We propose to
extend the BSS model to include the \`{a} trous Haar decomposition. This
wavelet-based forecasting method is based on a multiscale resolution
decomposition of the signal using the redundant \`{a} trous wavelet transform
and allows us to capture short- and long-range dependencies between signals.
Equally importantly it allows us to derive the desired dynamical and
frequency-specific Granger-causality statistic. The application of these models
to intracranial local field potential data recorded during a psychological
experimental task shows the complex frequency based cross-talk between amygdala
and medial orbito-frontal cortex.
Keywords: \`{a} trous Haar wavelets; Multiple trials; Neuroscience data;
Nonstationarity; Time-frequency; Variational methods
The published version of this article is
Cekic, S., Grandjean, D., Renaud, O. (2018). Multiscale Bayesian state-space
model for Granger causality analysis of brain signal. Journal of Applied
Statistics. https://doi.org/10.1080/02664763.2018.145581
A modulation property of time-frequency derivatives of filtered phase and its application to aperiodicity and fo estimation
We introduce a simple and linear SNR (strictly speaking, periodic to random
power ratio) estimator (0dB to 80dB without additional
calibration/linearization) for providing reliable descriptions of aperiodicity
in speech corpus. The main idea of this method is to estimate the background
random noise level without directly extracting the background noise. The
proposed method is applicable to a wide variety of time windowing functions
with very low sidelobe levels. The estimate combines the frequency derivative
and the time-frequency derivative of the mapping from filter center frequency
to the output instantaneous frequency. This procedure can replace the
periodicity detection and aperiodicity estimation subsystems of recently
introduced open source vocoder, YANG vocoder. Source code of MATLAB
implementation of this method will also be open sourced.Comment: 8 pages 9 figures, Submitted and accepted in Interspeech201
On Time Delay Margin Estimation for Adaptive Control and Optimal Control Modification
This paper presents methods for estimating time delay margin for adaptive control of input delay systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent an adaptive law by a locally bounded linear approximation within a small time window. The time delay margin of this input delay system represents a local stability measure and is computed analytically by three methods: Pade approximation, Lyapunov-Krasovskii method, and the matrix measure method. These methods are applied to the standard model-reference adaptive control, s-modification adaptive law, and optimal control modification adaptive law. The windowing analysis results in non-unique estimates of the time delay margin since it is dependent on the length of a time window and parameters which vary from one time window to the next. The optimal control modification adaptive law overcomes this limitation in that, as the adaptive gain tends to infinity and if the matched uncertainty is linear, then the closed-loop input delay system tends to a LTI system. A lower bound of the time delay margin of this system can then be estimated uniquely without the need for the windowing analysis. Simulation results demonstrates the feasibility of the bounded linear stability method for time delay margin estimation
A Robust Zero-point Attraction LMS Algorithm on Near Sparse System Identification
The newly proposed norm constraint zero-point attraction Least Mean
Square algorithm (ZA-LMS) demonstrates excellent performance on exact sparse
system identification. However, ZA-LMS has less advantage against standard LMS
when the system is near sparse. Thus, in this paper, firstly the near sparse
system modeling by Generalized Gaussian Distribution is recommended, where the
sparsity is defined accordingly. Secondly, two modifications to the ZA-LMS
algorithm have been made. The norm penalty is replaced by a partial
norm in the cost function, enhancing robustness without increasing the
computational complexity. Moreover, the zero-point attraction item is weighted
by the magnitude of estimation error which adjusts the zero-point attraction
force dynamically. By combining the two improvements, Dynamic Windowing ZA-LMS
(DWZA-LMS) algorithm is further proposed, which shows better performance on
near sparse system identification. In addition, the mean square performance of
DWZA-LMS algorithm is analyzed. Finally, computer simulations demonstrate the
effectiveness of the proposed algorithm and verify the result of theoretical
analysis.Comment: 20 pages, 11 figure
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