Rami Mangoubi

A robust failure detection and isolation algorithm

A robust failure detection and isolation (RFDI) algorithm for linear dynamic systems that is insensitive to failure mode, noise, and plant model uncertainties is presented. For robustness to failure mode uncertainties, the algorithm assumes that the failure is the output of a shaping filter, or specifically, a Gauss-Markov model. Such a model embraces a large class of failures. For robustness to noise and plant model uncertainties, the algorithm relies on either a robust risk sensitive (exponential Gaussian) or a robust H/sub /spl infin// filter for the synthesis of failure residuals. It is shown that the algorithm is a generalization of likelihood ratio tests for failure detection and isolation in dynamic systems. Numerical results are presented for an underwater vehicle application, demonstrating that the RFDI algorithm can provide good performance over the desired range of model uncertainties. By contrast, the performance of the nominally optimal likelihood ratio test can considerably degrade in the presence of the same uncertainties.

Robust Gaussian and non-Gaussian matched subspace detection

We address the problem of matched filter and subspace detection in the presence of arbitrary noise and interference or interfering signals that may lie in an arbitrary unknown subspace of the measurement space. A minmax methodology developed to deal with this uncertainty can also be adapted to situations where partial information on the interference or other uncertainties is available. This methodology leads to a hypothesis test with adequate levels of false alarm robustness and signal detection sensitivity. The robust test is applicable to a large class of noise density functions. In addition, generalized likelihood ratio (GLR) detectors are derived for the class of generalized Gaussian noise. The detectors are generalizations of the /spl chi//sup 2/, t, and F statistics used with Gaussian noise, which are themselves motivated in a new way by the robust test. For matched filter detection, these expressions are simpler and computationally efficient. The robust test reduces to the conventional test when unlearned subspace interference is known to be absent. The results demonstrate that when compared with the conventional detector, the robust one trades off some detection performance in the absence of interference for the sake of robustness in its presence.

Operational resilience: concepts, design and analysis

Building resilience into today’s complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This study proposes quantitative measures that capture and implement the definition of engineering resilience advanced by the National Academy of Sciences. The approach is applicable across physical, information, and social domains. It evaluates the critical functionality, defined as a performance function of time set by the stakeholders. Critical functionality is a source of valuable information, such as the integrated system resilience over a time interval, and its robustness. The paper demonstrates the formulation on two classes of models: 1) multi-level directed acyclic graphs, and 2) interdependent coupled networks. For both models synthetic case studies are used to explore trends. For the first class, the approach is also applied to the Linux operating system. Results indicate that desired resilience and robustness levels are achievable by trading off different design parameters, such as redundancy, node recovery time, and backup supply available. The nonlinear relationship between network parameters and resilience levels confirms the utility of the proposed approach, which is of benefit to analysts and designers of complex systems and networks.

Paracrine and epigenetic control of trophectoderm differentiation from human embryonic stem cells: the role of bone morphogenic protein 4 and histone deacetylases.

Our understanding of paracrine and epigenetic control of trophectoderm (TE) differentiation is limited by available models of preimplantation human development. Simple, defined media for selective TE differentiation of human embryonic stem cells (hESCs) were developed, enabling mechanistic studies of early placental development. Paracrine requirements of preimplantation human development were evaluated with hESCs by measuring lineage-specific transcription factor expression levels in single cells and morphological transformation in response to selected paracrine and epigenetic modulators. Bone morphogenic protein 4 (BMP4) addition to feeder-free pluripotent stem cells on matrigel frequently formed CDX2-positive TE. However, BMP4 or activin A inhibition alone also produced a mix of mesoderm and extraembryonic endoderm under these conditions. Further, BMP4 failed to form TE from adherent hESC maintained in standard feeder-dependent monolayers. Given that the efficiency and selectivity of BMP4-induced TE depended on medium components, we developed a basal medium containing insulin and heparin. In this medium, BMP4 induction of TE was dose dependent and with activin A inhibition by SB431542 (SB), approached 100% of cells. This paracrine stimulation of pluripotent cells transformed colony morphology from a cuboidal to squamous epithelium quantitatively on day 3, and produced significant multinucleated syncytiotrophoblasts by day 8. Addition of trichostatin A, a histone deacetylase (HDAC) inhibitor, reduced HDAC3, histone H3K9 methylation, and slowed differentiation in a dose-dependent manner. Modulators of BMP4- or HDAC-dependent signaling might adversely influence the timing and viability of early blastocyst developed in vitro. Since blastocyst development is synchronized to uterine receptivity, epigenetic regulators of TE differentiation might adversely affect implantation in vivo.

Robust estimation in fault detection

Modeling errors present a significant and difficult challenge in the design of analytic fault detection mechanisms. The authors discuss the sensitivity to model uncertainty of estimator-based failure detection techniques. In particular, they discuss desired statistical properties for the decision variable, and why these characteristics are difficult to achieve in situations involving significant uncertainty in the noise, fault, or plant dynamic modeling assumptions. This discussion motivates the use of robust estimation techniques in failure detection. An aircraft example is presented to illustrate the effect of modeling error on the failure detection performance of detection test designs based on a Kalman filter and an H/sub infinity // mu estimator.<<ETX>>

Functional MRI activity characterization using response time shift estimates from curve evolution

Characterizing the response of the brain to a stimulus based on functional magnetic resonance imaging data is a major challenge due to the fact that the response time delay of the brain may be different from one stimulus phase to the next and from pixel to pixel. To enhance detectability, this work introduces the use of a curve evolution approach that provides separate estimates of the response time shifts at each phase of the stimulus on a pixel-by-pixel basis. The approach relies on a parsimonious but simple model that is nonlinear in the time shifts of the response relative to the stimulus and linear in the gains. To effectively use the response time shift estimates in a subspace detection framework, we implement a robust hypothesis test based on a Laplacian noise model. The algorithm provides a pixel-by-pixel functional characterization of the brains response. The results based on experimental data show that response time shift estimates, when properly implemented, enhance detectability without sacrificing robustness.

Performance evaluation of multiresolution texture analysis of stem cell chromatin

We apply texture image analysis to automated classification of stem cell nuclei, based on the observation that chromatin in human embryonic stem cells becomes more granular during differentiation. Using known probability models for texture multiresolution decompositions, we derive likelihood ratio test statistics. We also derive the probability density functions of these non-Gaussian statistics and use them to evaluate the performance of the classification test. Results indicate that the test can distinguish with probability 0.95 between nuclei that are pluripotent and those with varying degrees of differentiation. The test recognizes nuclei with similar differentiation level even if prior information says the contrary. This approach should be useful for classifying genome-wide epigenetic changes and chromatin remodeling during human development. Finally, the test statistics and their density functions are applicable to a general texture classification problem.

Stochastic interpretation of H/sub /spl infin// and robust estimation

H/sub /spl infin// and robust estimation methods are discussed from a deterministic as well as a stochastic point of view. The relationship between H/sub /spl infin// and risk sensitivity for systems with known plant dynamics is reviewed. This relationship is extended to the more general case of estimators that are robust to noise and plant model uncertainties. Specifically, it is shown that a stochastic equivalent to the robust H/sub /spl infin// estimator exists. An example is used to compare the estimators in the deterministic sense, using the frequency response of the transfer function between the inputs and the error, as well as in the stochastic sense, using the probability density function of the output error residual.<<ETX>>

Robust Subspace Learning and Detection in Laplacian Noise and Interference

We address the problem of maximum likelihood subspace learning and detection in the presence of Laplacian noise and interference whose subspace may be known or unknown. For subspace learning, the Laplacian problem reduces to a maxmin convex mathematical program with polyhedral cost. The minimization involves projection of the measurements onto a subspace orthogonal to the signal and interference spaces and has linear constraints, while the maximization produces the subspace and has polyhedral constraints. The Laplacian noise model for subspace detection and estimation, motivated by applications in functional magnetic resonance imaging and applicable in other areas, yields maximum likelihood detectors and learned subspaces with unique structure due to the presence of corners in the polyhedrally convex optimization. For instance, the optimal learned subspace can consist of vectors whose elements take values of +1 or -1 only. Emergence of such a quantization attests to the robustness property of Laplacian learning, meaning that the solution is insensitive to perturbation in the data set. The resulting detectors are similarly robust to false alarms and have computationally attractive properties.

Adaptive robust constrained matched filter and subspace detection

A minimax methodology for formulating adaptively robust and sensitive matched filter and subspace detectors is provided, where the signal and interference of structured noise are partially known. The signal and interference subspaces are assumed to reside in conical regions of the measurement space, and the gain parameters can have bounded magnitude. It is shown that the minimax approach permits the design of a rich variety of matched filter and subspace detectors that vary in the degree of robustness and sensitivity.