Soumik Sarkar

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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)
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)
In recent years, nanotechnology has gained significant interest for applications in the medical field. In this regard, a utilization of the ZnO nanoparticles for the efficient degradation of bilirubin (BR) through photocatalysis was explored. BR is a water insoluble byproduct of the heme catabolism that can cause jaundice when its excretion is impaired. The(More)
Light-harvesting nanohybrids (LHNs) are systems composed of an inorganic nanostructure associated with an organic pigment that have been exploited to improve the light-harvesting performance over individual components. The present study is focused on developing a potential LHN, attained by the functionalization of dense arrays of ZnO nanorods (NRs) with a(More)
This paper focuses on data-driven detection of incipient fault in commercial aircraft gas turbine engines. Detection of incipient engine fault often manifest better in transient data. This paper extends recently reported literature in the areas of symbolic dynamic filtering, i.e., Markov model based analysis of steady state data, to model and analyze(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 dynamicsbased 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)
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)
An inherent difficulty in sensor-data-driven fault detection is that the detection performance could be drastically reduced under sensor degradation (e.g., drift and noise). Complementary to traditional model-based techniques for fault detection, this paper proposes symbolic dynamic filtering by optimally partitioning the time series data of sensor(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)