It will start in 40 minutes [Thesis Oral: Prateek Tandon]

[Artur Dubrawski]

Date: 13 July 2015
Time: 4:00 p.m.
Place: NSH 1305
Type: Thesis Oral
Who:  Prateek Tandon
Topic: Bayesian Aggregation of Evidence for Detection and Characterization
          of Patterns in Multiple Noisy Observations

Abstract:
Effective use of Machine Learning to support extracting maximal 
information from limited sensor data is one of the important research 
challenges in robotic sensing. This thesis develops techniques for 
detecting and characterizing patterns in noisy sensor data. Our Bayesian 
Aggregation (BA) algorithmic framework can leverage data fusion from 
multiple low Signal-To-Noise Ratio (SNR) sensor observations to boost 
the capability to detect and characterize the properties of a signal 
generating source or process of interest.

We illustrate our research with application to the nuclear threat 
detection domain. Developed algorithms are applied to the problem of 
processing the large amounts of spectrometry data that can be produced 
in real-time by mobile radiation sensors. The thesis experimentally 
shows BA's capability to boost sensor performance in detecting radiation 
sources of interest, even if the source is faint, partially-occluded, or 
enveloped in the noisy and variable radiation background characteristic 
of urban scenes.

In addition, BA provides simultaneous inference of source parameters 
such as the source intensity or source type while detecting it. The 
thesis demonstrates this capability as well as develops techniques to 
efficiently optimize these parameters over large possible setting 
spaces. Methods developed in this thesis are demonstrated both in 
simulation and in a radiation-sensing backpack that applies robotic 
localization techniques to enable indoor surveillance of radiation sources.

The thesis further improves the BA algorithm's capability to be robust 
under various detection scenarios. First, we augment BA with appropriate 
statistical models to improve estimation of signal components in low 
photon count detection, where the sensor may receive limited photon 
counts from either source and/or background. Second, we develop methods 
for online sensor reliability monitoring to create algorithms that are 
resilient to possible sensor faults in a data pipeline containing one or 
multiple sensors.

Finally, we develop Retrospective BA, a variant of BA that allows 
reinterpretation of past sensor data in light of new information about 
percepts. These Retrospective capabilities include the use of Hidden 
Markov Models in BA to allow automatic correction of a sensor pipeline 
when sensor malfunction may be occur, an Anomaly-Match search strategy 
to efficiently optimize source hypotheses, and prototyping of a 
Multi-Modal Augmented PCA to more flexibly model background and nuisance 
source fluctuations in a dynamic environment.


Thesis Committee Members:
Artur Dubrawski, Chair
Manuela Veloso
Paul Scerri
Simon Labov, Lawrence Livermore National Laboratory

A copy of the thesis document is available at:
https://goo.gl/zAEoXj