6,729 research outputs found
Real-time modelling and interpolation of spatio-temporal marine pollution
Due to the complexity of the interactions
involved in various dynamic systems, known physical,
biological or chemical laws cannot adequately describe
the dynamics behind these processes. The study of these
systems thus depends on measurements often taken at
various discrete spatial locations through time by noisy
sensors. For this reason, scientists often necessitate interpolative, visualisation and analytical tools to deal
with the large volumes of data common to these systems. The starting point of this study is the seminal
research by C. Shannon on sampling and reconstruction
theory and its various extensions. Based on recent work
on the reconstruction of stochastic processes, this paper
develops a novel real-time estimation method for non-
stationary stochastic spatio-temporal behaviour based
on the Integro-Di erence Equation (IDE). This meth-
odology is applied to collected marine pollution data
from a Norwegian fjord. Comparison of the results obtained by the proposed method with interpolators from
state-of-the-art Geographical Information System (GIS)
packages will show, that signifi cantly superior results are
obtained by including the temporal evolution in the spatial interpolations.peer-reviewe
Statistical Analysis of Dynamic Actions
Real-world action recognition applications require the development of systems which are fast, can handle a large variety of actions without a priori knowledge of the type of actions, need a minimal number of parameters, and necessitate as short as possible learning stage. In this paper, we suggest such an approach. We regard dynamic activities as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences which captures the similarities in their behavioral content. This measure is nonparametric and can thus handle a wide range of complex dynamic actions. Having a behavior-based distance measure between sequences, we use it for a variety of tasks, including: video indexing, temporal segmentation, and action-based video clustering. These tasks are performed without prior knowledge of the types of actions, their models, or their temporal extents
Adaptive Detection of Structured Signals in Low-Rank Interference
In this paper, we consider the problem of detecting the presence (or absence)
of an unknown but structured signal from the space-time outputs of an array
under strong, non-white interference. Our motivation is the detection of a
communication signal in jamming, where often the training portion is known but
the data portion is not. We assume that the measurements are corrupted by
additive white Gaussian noise of unknown variance and a few strong interferers,
whose number, powers, and array responses are unknown. We also assume the
desired signals array response is unknown. To address the detection problem, we
propose several GLRT-based detection schemes that employ a probabilistic signal
model and use the EM algorithm for likelihood maximization. Numerical
experiments are presented to assess the performance of the proposed schemes
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning
Although aviation accidents are rare, safety incidents occur more frequently
and require a careful analysis to detect and mitigate risks in a timely manner.
Analyzing safety incidents using operational data and producing event-based
explanations is invaluable to airline companies as well as to governing
organizations such as the Federal Aviation Administration (FAA) in the United
States. However, this task is challenging because of the complexity involved in
mining multi-dimensional heterogeneous time series data, the lack of
time-step-wise annotation of events in a flight, and the lack of scalable tools
to perform analysis over a large number of events. In this work, we propose a
precursor mining algorithm that identifies events in the multidimensional time
series that are correlated with the safety incident. Precursors are valuable to
systems health and safety monitoring and in explaining and forecasting safety
incidents. Current methods suffer from poor scalability to high dimensional
time series data and are inefficient in capturing temporal behavior. We propose
an approach by combining multiple-instance learning (MIL) and deep recurrent
neural networks (DRNN) to take advantage of MIL's ability to learn using weakly
supervised data and DRNN's ability to model temporal behavior. We describe the
algorithm, the data, the intuition behind taking a MIL approach, and a
comparative analysis of the proposed algorithm with baseline models. We also
discuss the application to a real-world aviation safety problem using data from
a commercial airline company and discuss the model's abilities and
shortcomings, with some final remarks about possible deployment directions
An adaptive stereo basis method for convolutive blind audio source separation
NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in PUBLICATION, [71, 10-12, June 2008] DOI:neucom.2007.08.02
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