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This paper presents the qualitative nature of communication network operations as abstraction of typical thermodynamic parameters (e.g., order parameter, temperature, and pressure). Specifically, statistical mechanics-inspired models of critical phenomena (e.g., phase transitions and size scaling) for heterogeneous packet transmission are developed in terms(More)
This paper proposes a neural-symbolic framework for analyzing a large volume of sequential hi-speed images of combustion flame for early detection of instability that is extremely critical for engine health monitoring and prognostics. The proposed hierarchical approach involves extracting low-dimensional semantic features from images using deep(More)
This article presents a robust and computationally inexpensive technique of component-level fault detection in aircraft gas-turbine engines. The underlying algorithm is based on a recently developed statistical pattern recognition tool, symbolic dynamic filtering (SDF), that is built upon symbolization of sensor time series data. Fault detection involves(More)
— Symbolic Dynamic Filtering (SDF) has been recently reported in literature as a pattern recognition tool for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. This paper presents a comparative evaluation of SDF relative to other classes of pattern recognition tools, such as Bayesian Filters and(More)
This paper develops a distributed algorithm for decision/awareness propagation in mobile-agent networks. A time-dependent proximity network topology is adopted to represent a mobile-agent scenario. The agent-interaction policy formulated here is inspired from the recently developed language-measure-theory. Analytical results related to convergence of(More)
The concept of symbolic dynamics has been used in recent literature for feature extraction from time series data for pattern classification. The two primary steps of this technique are partitioning of time series to optimally generate symbol sequences and subsequently modeling of state machines from such symbol sequences. The latter step has been widely(More)
— This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown environments. A mobile sensor network is used for localization of regions of interest for path planning of an autonomous mobile robot in the absence of global positioning facilities. The underlying theory is an extension of a generalized(More)
—This paper develops a language-measure-theoretic distributed algorithm for decision propagation in a mobile-agent network topology. The agent interaction policy proposed here enables the control of the tradeoff between Propagation Radius and Localization Gradient. Analytical results regarding statistical moment convergence are presented and validated with(More)
A recent publication has reported a Hilbert-transform-based partitioning method, called analytic signal space partitioning (ASSP), which essentially replaces wavelet space partitioning (WSP) for symbolic analysis of time series data in dynamical systems. When used in conjunction with D-Markov machines, also reported in the recent literature, ASSP provides a(More)
Occlusion edges in images which correspond to range discontinuity in the scene from the point of view of the observer are an important prerequisite for many vision and mobile robot tasks. Although they can be extracted from range data however extracting them from images and videos would be extremely beneficial. We trained a deep convolutional neural network(More)