Norma H. Pawley

Exploring new strategies for cellulosic biofuels production

A research program has been initiated to formulate new strategies for efficient low-cost lignocellulosic biomass processing technologies for the production of biofuels. This article reviews results from initial research into lignocellulosic biomass structure, recalcitrance, and pretreatment. In addition to contributing towards a comprehensive understanding of lignocellulosic biomass, this work has contributed towards demonstrated optimizations of existing pretreatment methods, and the emergence of new possible pretreatment strategies that remain to be fully developed.

Sparse classification of rf transients using chirplets and learned dictionaries

We assess the performance of a sparse classification approach for radiofrequency (RF) transient signals using dictionaries adapted to the data. We explore two approaches: pursuit-type decompositions over analytical, over-complete dictionaries, and dictionaries learned directly from data. Pursuit-type decompositions over analytical, over-complete dictionaries yield sparse representations by design and can work well for target signals in the same function class as the dictionary atoms. Discriminative dictionaries learned directly from data do not rely on analytical constraints or additional knowledge about the signal characteristics, and provide sparse representations that can perform well when used with a statistical classifier. We present classification results for learned dictionaries on simulated test data, and discuss robustness compared to conventional Fourier methods. We draw from techniques of adaptive feature extraction, statistical machine learning, and image processing.

Classification of transient signals using sparse representations over adaptive dictionaries

Automatic classification of broadband transient radio frequency (RF) signals is of particular interest in persistent surveillance applications. Because such transients are often acquired in noisy, cluttered environments, and are characterized by complex or unknown analytical models, feature extraction and classification can be difficult. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. Conventional representations using fixed (or analytical) orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of transients, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They do not usually lead to sparse decompositions, and require separate feature selection algorithms, creating additional computational overhead. Pursuit-type decompositions over analytical, redundant dictionaries yield sparse representations by design, and work well for target signals in the same function class as the dictionary atoms. The pursuit search however has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. Our approach builds on the image analysis work of Mairal et al. (2008) to learn a discriminative dictionary for RF transients directly from data without relying on analytical constraints or additional knowledge about the signal characteristics. We then use a pursuit search over this dictionary to generate sparse classification features. We demonstrate that our learned dictionary is robust to unexpected changes in background content and noise levels. The target classification decision is obtained in almost real-time via a parallel, vectorized implementation.

Radio frequency (RF) transient classification using sparse representations over learned dictionaries

Automatic classification of transitory or pulsed radio frequency (RF) signals is of particular interest in persistent surveillance and remote sensing applications. Such transients are often acquired in noisy, cluttered environments, and may be characterized by complex or unknown analytical models, making feature extraction and classification difficult. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. We compare two dictionary learning methods from the image analysis literature, the K-SVD algorithm and Hebbian learning, and extend them for use with RF data. Both methods allow us to learn discriminative RF dictionaries directly from data without relying on analytical constraints or additional knowledge about the expected signal characteristics. We then use a pursuit search over the learned dictionaries to generate sparse classification features in order to identify time windows that contain a target pulse. In this paper we compare the two dictionary learning methods and discuss how their performance changes as a function of dictionary training parameters. We demonstrate that learned dictionary techniques are suitable for pulsed RF analysis and present results with varying background clutter and noise levels.

APART: Automated Preprocessing for NMR Assignments with Reduced Tedium

MOTIVATION High-throughput NMR structure determination is a goal that will require progress on many fronts, one of which is rapid resonance assignment. An important rate-limiting step in the resonance assignment process is accurate identification of resonance peaks in the NMR spectra. Peak-picking schemes range from incomplete (which lose essential assignment connectivities) to noisy (which obscure true connectivities with many false ones). We introduce an automated preassignment process that removes false peaks from noisy peak lists by requiring consensus between multiple NMR experiments and exploiting a priori information about NMR spectra. This process is designed to accept multiple input formats and generate multiple output formats, in an effort to be compatible with a variety of user preferences. RESULTS Automated preprocessing with APART rapidly identifies and removes false peaks from initial peak lists, reduces the burden of manual data entry, and documents and standardizes the peak filtering process. Successful preprocessing is demonstrated by the increased number of correct assignments obtained when data are submitted to an automated assignment program. AVAILABILITY APART is available from http://sir.lanl.gov/NMR/APART.htm CONTACT npawley@lanl.gov; rmichalczyk@lanl.gov SUPPLEMENTARY INFORMATION Manual pages with installation instructions, procedures and screen shots can also be found at http://sir.lanl.gov/NMR/APART_Manual1.pdf.

Capturing dynamics on multiple time scales: a multilevel fusion approach for cluttered electromagnetic data

Many problems in electromagnetic signal analysis exhibit dynamics on a wide range of time scales. Further, these dynamics may involve both continuous source generation processes and discrete source mode dynamics. These rich temporal characteristics can present challenges for standard modeling approaches, particularly in the presence of nonstationary noise and clutter sources. Here we demonstrate a hybrid algorithm designed to capture the dynamic behavior at all relevant time scales while remaining robust to clutter and noise at each time scale. We draw from techniques of adaptive feature extraction, statistical machine learning, and discrete process modeling to construct our hybrid algorithm. We describe our approach and present results applying our hybrid algorithm to a simulated dataset based on an example radio beacon identification problem: civilian air traffic control. This application illustrates the multi-scale complexity of the problems we wish to address. We consider a multi-mode air traffic control radar emitter operating against a cluttered background of competing radars and continuous-wave communications signals (radios, TV broadcasts). Our goals are to find a compact representation of the radio frequency measurements, identify which pulses were emitted by the target source, and determine the mode of the source.

A comparison of methods for estimating broadband noise in the frequency domain

Estimating the noise component of a signal that consists of sinusoids plus broadband noise is a ubiquitous problem. Most methods work in the time-domain, but frequency-domain methods can be computationally more efficient and invariant to the time-domain noise distribution. We compare two prominent frequency-domain approaches, one that computes statistics over periodograms of multiple time segments, and another that computes statistics over frequency segments from a single periodogram. We explore the accuracy-resolution tradeoff for both approaches and provide comparisons of accuracy, sample and segment size dependence, frequency resolution, and computational complexity for each method.

Detection of facilities in satellite imagery using semi-supervized image classification and auxiliary contextual observables

Detecting complex targets, such as facilities, in commercially available satellite imagery is a difficult problem that human analysts try to solve by applying world knowledge. Often there are known observables that can be extracted by pixel-level feature detectors that can assist in the facility detection process. Individually, each of these observables is not sufficient for an accurate and reliable detection, but in combination, these auxiliary observables may provide sufficient context for detection by a machine learning algorithm. We describe an approach for automatic detection of facilities that uses an automated feature extraction algorithm to extract auxiliary observables, and a semi-supervised assisted target recognition algorithm to then identify facilities of interest. We illustrate the approach using an example of finding schools in Quickbird image data of Albuquerque, New Mexico. We use Los Alamos National Laboratorys Genie Pro automated feature extraction algorithm to find a set of auxiliary features that should be useful in the search for schools, such as parking lots, large buildings, sports fields and residential areas and then combine these features using Genie Pros assisted target recognition algorithm to learn a classifier that finds schools in the image data.

Annotated Bibliography for the MATADOR Project

The MATADOR project is focused on developing methods to infer the operational mode of facilities that have the potential to be used in weapons development programs. Our central hypothesis is that by persistent, non-intrusive monitoring of such facilities, differences between various use scenarios can be reliably discovered. The impact of success in this area is that new tools and techniques for monitoring and treaty verification would make it easier to reliably discover and document weapons development activities. This document captures the literature that will serve as a basis to approach this task. The relevant literature is divided into topical areas that relate to the various aspects of expected MATADOR project development. We have found that very little work that is directly applicable for our purposes has been published, which has motivated the development of novel methods under the project. Therefore, the manuscripts referenced in this document were selected based on their potential use as foundational blocks for the methods we anticipate developing, or so that we can understand the limitations of existing methods.

Capturing dynamics on multiple time scales: A hybrid approach for cluttered electromagnetic data

Many problems in electromagnetic signal analysis exhibit dynamics on a wide range of time scales against nonstationary clutter and noise. We consider a problem in which the relevant time scales can range from nanoseconds to hours or days (12 or 13 orders of magnitude). We present a hybrid algorithm currently designed to capture the dynamic behavior at scales from nanoseconds to milliseconds (6 orders of magnitude) while remaining robust to clutter and noise. We draw from techniques of adaptive feature extraction, statistical machine learning, and discrete process modeling and present results on a simulated multimode problem. Our goals are to find a representation of the signal that allows us to identify which pulses were produced by a target emitter and to determine the operational mode of the target.