45,240 research outputs found
Bayesian off-line detection of multiple change-points corrupted by multiplicative noise : application to SAR image edge detection
This paper addresses the problem of Bayesian off-line change-point detection in synthetic aperture radar images. The minimum mean square error and maximum a posteriori estimators of the changepoint positions are studied. Both estimators cannot be implemented because of optimization or integration problems. A practical implementation using Markov chain Monte Carlo methods is proposed. This implementation requires a priori knowledge of the so-called hyperparameters. A hyperparameter estimation procedure is proposed that alleviates the requirement of knowing the values of the hyperparameters. Simulation results on synthetic signals and synthetic aperture radar images are presented
Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks
We present a novel learning-based approach to estimate the
direction-of-arrival (DOA) of a sound source using a convolutional recurrent
neural network (CRNN) trained via regression on synthetic data and Cartesian
labels. We also describe an improved method to generate synthetic data to train
the neural network using state-of-the-art sound propagation algorithms that
model specular as well as diffuse reflections of sound. We compare our model
against three other CRNNs trained using different formulations of the same
problem: classification on categorical labels, and regression on spherical
coordinate labels. In practice, our model achieves up to 43% decrease in
angular error over prior methods. The use of diffuse reflection results in 34%
and 41% reduction in angular prediction errors on LOCATA and SOFA datasets,
respectively, over prior methods based on image-source methods. Our method
results in an additional 3% error reduction over prior schemes that use
classification based networks, and we use 36% fewer network parameters
A Spatio-Temporal Point Process Model for Ambulance Demand
Ambulance demand estimation at fine time and location scales is critical for
fleet management and dynamic deployment. We are motivated by the problem of
estimating the spatial distribution of ambulance demand in Toronto, Canada, as
it changes over discrete 2-hour intervals. This large-scale dataset is sparse
at the desired temporal resolutions and exhibits location-specific serial
dependence, daily and weekly seasonality. We address these challenges by
introducing a novel characterization of time-varying Gaussian mixture models.
We fix the mixture component distributions across all time periods to overcome
data sparsity and accurately describe Toronto's spatial structure, while
representing the complex spatio-temporal dynamics through time-varying mixture
weights. We constrain the mixture weights to capture weekly seasonality, and
apply a conditionally autoregressive prior on the mixture weights of each
component to represent location-specific short-term serial dependence and daily
seasonality. While estimation may be performed using a fixed number of mixture
components, we also extend to estimate the number of components using
birth-and-death Markov chain Monte Carlo. The proposed model is shown to give
higher statistical predictive accuracy and to reduce the error in predicting
EMS operational performance by as much as two-thirds compared to a typical
industry practice
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