Engineering topics

Bruillard, Paul J.

Publications and source records attributed to Bruillard, Paul J..

7 records

DOE OSTI2017

A Comparison of Different NDE Signal Processing Techniques Based on Waveform Entropies Applied to Long Fiber Graphite/Epoxy Plates

This study compares different approaches for imaging the internal architecture of graphite/epoxy composites using backscattered ultrasound. Two cases are studied. In the first, near-surface defects in a thin graphite/epoxy plates are imaged. The same backscattered waveforms were used to produce peak-to-peak, logarithm of signal energy, as well as entropy images of different types. All of the entropy images exhibit better border delineation and defect contrast than the either peak-to-peak or logarithm of signal energy. The best results are obtained using the joint entropy of the backscattered waveforms with a reference function. Two different references are examined. The first is a reflection of the insonifying pulse from a stainless steel reflector. The second is an approximate optimum obtained from an iterative parametric search. The joint entropy images produced using this reference exhibit three times the contrast obtained in previous studies. These plates were later destructively analyzed to determine size and location of near-surface defects and the results found to agree with the defect location and shape as indicated by the entropy images. In the second study, images of long carbon graphite fibers (50% by weight) in polypropylene thermoplastic are obtained as a first step toward ultrasonic determination of the distributions of fiber position and orientation.

Entropy Imaging↗

DOE OSTI2016

Method of generating features optimal to a dataset and classifier

A method of generating features optimal to a particular dataset and classifier is disclosed. A dataset of messages is inputted and a classifier is selected. An algebra of features is encoded. Computable features that are capable of describing the dataset from the algebra of features are selected. Irredundant features that are optimal for the classifier and the dataset are selected.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Rank 4 Premodular Categories

We consider the classification problem for rank 4 premodular categories. We uncover a formula for the 2nd Frobenius-Schur indicator of a premodular category is determined and the classification of rank 4 premodular categories (up to Grothendieck equivalence) is completed. In the appendix we show rank finiteness for premodular categories.

Bruillard, Paul J.↗

DOE OSTI2016

LINEBACKER: LINE-speed Bio-inspired Analysis and Characterization for Event Recognition

The cyber world is a complex domain, with digital systems mediating a wide spectrum of human and machine behaviors. While this is enabling a revolution in the way humans interact with each other and data, it also is exposing previously unreachable infrastructure to a worldwide set of actors. Existing solutions for intrusion detection and prevention that are signature-focused typically seek to detect anomalous and/or malicious activity for the sake of preventing or mitigating negative impacts. But a growing interest in behavior-based detection is driving new forms of analysis that move the emphasis from static indicators (e.g. rule-based alarms or tripwires) to behavioral indicators that accommodate a wider contextual perspective. Similar to cyber systems, biosystems have always existed in resource-constrained hostile environments where behaviors are tuned by context. So we look to biosystems as an inspiration for addressing behavior-based cyber challenges. In this paper, we introduce LINEBACKER, a behavior-model based approach to recognizing anomalous events in network traffic and present the design of this approach of bio-inspired and statistical models working in tandem to produce individualized alerting for a collection of systems. Preliminary results of these models operating on historic data are presented along with a plugin to support real-world cyber operations.

biosequence model↗

DOE OSTI2015

ENTROPY VS. ENERGY WAVEFORM PROCESSING: A COMPARISON ON THE HEAT EQUATION

Virtually all modern imaging devices function by collecting either electromagnetic or acoustic backscattered waves and using the energy carried by these waves to determine pixel values that build up what is basically an ”energy” picture. However, waves also carry ”informa- tion” that also may be used to compute the pixel values in an image. We have employed several measures of information, all of which are based on different forms of entropy. Numerous published studies have demonstrated the advantages of entropy, or “information imaging”, over conventional methods for materials characterization and medical imaging. Similar results also have been obtained with microwaves. The most sensitive information measure appears to be the joint entropy of the backscattered wave and a reference signal. A typical study is comprised of repeated acquisition of backscattered waves from a specimen that is changing slowing with acquisition time or location. The sensitivity of repeated experimental observations of such a slowly changing quantity may be defined as the mean variation (i.e., observed change) divided by mean variance (i.e., observed noise). We compute the sensitivity for joint entropy and signal energy measurements assuming that noise is Gaussian and using Wiener integration to compute the required mean values and variances. These can be written as solutions to the Heat equation, which permits estimation of their magnitudes. There always exists a reference such that joint entropy has larger variation and smaller variance than the corresponding quantities for signal energy, matching observations of several studies. Moreover, a general prescription for finding an “optimal” reference for the joint entropy emerges, which also has been validated in several studies.

Entropy↗

DOE OSTI2015

Prediction of multi-drug resistance transporters using a novel sequence analysis method

There are many examples of groups of proteins that have similar function, but the determinants of functional specificity may be hidden by lack of sequencesimilarity, or by large groups of similar sequences with different functions. Transporters are one such protein group in that the general function, transport, can be easily inferred from the sequence, but the substrate specificity can be impossible to predict from sequence with current methods. In this paper we describe a linguistic-based approach to identify functional patterns from groups of unaligned protein sequences and its application to predict multi-drug resistance transporters (MDRs) from bacteria. We first show that our method can recreate known patterns from PROSITE for several motifs from unaligned sequences. We then show that the method, MDRpred, can predict MDRs with greater accuracy and positive predictive value than a collection of currently available family-based models from the Pfam database. Finally, we apply MDRpred to a large collection of protein sequences from an environmental microbiome study to make novel predictions about drug resistance in a potential environmental reservoir.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Secure Image Hash Comparison for Warhead Verification

The effort to inspect and verify warheads in the context of possible future arms control treaties is rife with security and implementation issues. In this paper we review prior work on perceptual image hashing for template-based warhead verification. Furthermore, we formalize the notion of perceptual hashes and demonstrate that large classes of such functions are likely not cryptographically secure. We close with a brief discussion of fully homomorphic encryption as an alternative technique.

Bruillard, Paul J.↗