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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)
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 Artificial(More)
— This brief paper presents a symbolic dynamics-based method for detection of incipient faults in gas turbine engines. The underlying algorithms for fault detection and classification are built upon the recently reported work on symbolic dynamic filtering. In particular, Markov model-based analysis of quasi-stationary steady-state time series is extended to(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)
Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of(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)
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)
This paper presents estimation of multiple faults in aircraft gas-turbine engines, based on a statistical pattern recognition tool called Symbolic Dynamic Filtering (SDF). The underlying concept is built upon statistical analysis of evidences to estimate anomalies in multiple critical parameters of the engine system; it also presents a framework for sensor(More)