Resilient Image Fusion

The paper describes a distributed spectral-screening PCT algorithm for fusing hyper-spectral images in remote sensing applications. The algorithm provides intrusion tolerance from information warfare attacks using the notion of computational resiliency. This concept uses replication to achieve fault tolerance, but goes further to dynamically regenerate replication in response to an attack or failure. The concepts of resiliency are incorporated through library technology that is application independent. This library hides the details of communication protocols required to achieve dynamic replication and reconfiguration in distributed applications. The paper provides a status report on our progress in developing the concept and applying it to image fusion. In particular we examine the performance of the PCT algorithm and compare the results with and without resiliency to assess the associated overhead

Resilient Image Fusion

The paper describes a distributed spectral-screening PCT algorithm for fusing hyper-spectral images in remote sensing applications. The algorithm provides intrusion tolerance from information warfare attacks using the notion of computational resiliency. This concept uses replication to achieve fault tolerance, but goes further to dynamically regenerate replication in response to an attack or failure. The concepts of resiliency are incorporated through library technology that is application independent. This library hides the details of communication protocols required to achieve dynamic replication and reconfiguration in distributed applications. The paper provides a status report on our progress in developing the concept and applying it to image fusion. In particular we examine the performance of the PCT algorithm and compare the results with and without resiliency to assess the associated overhead

Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum

We have developed an algorithm called Q5 for probabilistic classification of healthy vs. disease whole serum samples using mass spectrometry. The algorithm employs Principal Components Analysis (PCA) followed by Linear Discriminant Analysis (LDA) on whole spectrum Surface-Enhanced Laser Desorption/Ionization Time of Flight (SELDI-TOF) Mass Spectrometry (MS) data, and is demonstrated on four real datasets from complete, complex SELDI spectra of human blood serum. Q5 is a closed-form, exact solution to the problem of classification of complete mass spectra of a complex protein mixture. Q5 employs a novel probabilistic classification algorithm built upon a dimension-reduced linear discriminant analysis. Our solution is computationally efficient; it is non-iterative and computes the optimal linear discriminant using closed-form equations. The optimal discriminant is computed and verified for datasets of complete, complex SELDI spectra of human blood serum. Replicate experiments of different training/testing splits of each dataset are employed to verify robustness of the algorithm. The probabilistic classification method achieves excellent performance. We achieve sensitivity, specificity, and positive predictive values above 97% on three ovarian cancer datasets and one prostate cancer dataset. The Q5 method outperforms previous full-spectrum complex sample spectral classification techniques, and can provide clues as to the molecular identities of differentially-expressed proteins and peptides

A Concurrent Spectral-Screening PCT Algorithm For Remote Sensing Applications

The paper presents a concurrent algorithm for remote sensing applications that provides significant performance and image quality enhancements over conventional uniprocessor PCT techniques. The algorithm combines spectral angle classification, principal component transform, and human centered color mapping. It is evaluated from an image quality perspective using images collected with the Hyper-spectral Digital Imagery Collection Experiment (HYDICE) sensor, an airborne imaging spectrometer. These images correspond to foliated scenes taken from an altitude of 2000 to 7500 meters at wavelengths between 400nm and 2.5 micron. The scenes contain mechanized vehicles sitting in open fields as well as under camouflage. The algorithm operates with close to linear speedup on shared memory multiprocessors and can be readily extended to operate on multiple, low-cost PC-style servers connected with high-performance networking. A simple analytical model is outlined that allows the impact on performance of practical, application-specific properties to be assessed. These properties include image resolution, number of spectral bands, increases in the number of processors, changes in processor technology, networking speeds, and system clock rates

Analysis of Long-Term Cloud Cover, Radiative Fluxes, and Sea Surface Temperature in the Eastern Tropical Pacific

Grant activities accomplished during this reporting period are summarized. The contributions of the principle investigator are reported under four categories: (1) AHVRR (Advanced Very High Resolution Radiometer) data; (2) GOES (Geostationary Operational Environ Satellite) data; (3) system software design; and (4) ATSR (Along Track Scanning Radiometer) data. The contributions of the associate investigator are reported for:(1) longwave irradiance at the surface; (2) methods to derive surface short-wave irradiance; and (3) estimating PAR (photo-synthetically active radiation) surface. Several papers have resulted. Abstracts for each paper are provided

High dimensional latent panel quantile regression with an application to asset pricing

We propose a generalization of the linear panel quantile regression model to accommodate both \textit{sparse} and \textit{dense} parts: sparse means while the number of covariates available is large, potentially only a much smaller number of them have a nonzero impact on each conditional quantile of the response variable; while the dense part is represent by a low-rank matrix that can be approximated by latent factors and their loadings. Such a structure poses problems for traditional sparse estimators, such as the $\ell_1$-penalised Quantile Regression, and for traditional latent factor estimator, such as PCA. We propose a new estimation procedure, based on the ADMM algorithm, consists of combining the quantile loss function with $\ell_1$ \textit{and} nuclear norm regularization. We show, under general conditions, that our estimator can consistently estimate both the nonzero coefficients of the covariates and the latent low-rank matrix. Our proposed model has a "Characteristics + Latent Factors" Asset Pricing Model interpretation: we apply our model and estimator with a large-dimensional panel of financial data and find that (i) characteristics have sparser predictive power once latent factors were controlled (ii) the factors and coefficients at upper and lower quantiles are different from the median

A case study using ECHO(Extraction and Classification of Homogeneous Objects) for analysis of multispectral scanner data

There are no author-identified significant results in this report

Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG