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Soumik Sarkar

Iowa State University, Mechanical Engineering, Faculty Member

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The Australian National University

Selçuk İşsever

Ankara University

Kwantlen Polytechnic University

Kansas State University

Jean-Michel Landry

Carleton University

Ömer Faruk Eker

Cranfield University

University of Castilla-La Mancha

Université Laval

Stanimir Stoyanov

University of Plovdiv

spyros G tzafestas

National Technical University of Athens

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Papers by Soumik Sarkar

Physical Realization of Chaotic Behavior: A Review

The chaotic behavior of uniformly hyperbolic systems is well known. Geodesic flows with negative ... more

Fractional Brownian Motion: Long range dependency, Markov property, Simulation strategy-A review

AbstractFractional Brownian motion is a very good candidate to model a wide range of natural phe... more

Model Predictive Control and Fault Detection and Diagnostics of a Building Heating, Ventilation, and Air Conditioning System

by Soumik Sarkar and S. Vichik

Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks

Occlusion edges in images which correspond to range discontinuity in the scene from the point of ... 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 (CNN) to identify occlusion edges in images and videos with both RGB-D and RGB inputs. The use of CNN avoids hand-crafting of features for automatically isolating occlusion edges and distinguishing them from appearance edges. Other than quantitative occlusion edge detection results, qualitative results are provided to demonstrate the trade-off between high resolution analysis and frame-level computation time which is critical for real-time robotics applications.

Maximally Bijective Discretization for data-driven modeling of complex systems

2013 American Control Conference, 2013

ABSTRACT Phase-space discretization is a necessary step for study of continuous dynamical systems... more ABSTRACT Phase-space discretization is a necessary step for study of continuous dynamical systems using a language-theoretic approach. It is also critical for many machine learning techniques, e.g., probabilistic graphical models (Bayesian Networks, Markov models). This paper proposes a novel discretization method - Maximally Bijective Discretization, that finds a discretization on the dependent variables given a discretization on the independent variables such that the correspondence between input and output variables in the continuous domain is preserved in discrete domain for the given dynamical system.

Symbolic identification for anomaly detection in aircraft gas turbine engines

Proceedings of the 2010 American Control Conference, 2010

Abstract This paper presents a robust and computationally inexpensive technique of fault detecti... more

A Novel Human Machine Interface for Advanced Building Controls and Diagnostics

Sensor fusion for fault detection and classification in distributed physical processes

This paper proposes a feature extraction and fusion methodology to perform fault detection and cl... more This paper proposes a feature extraction and fusion methodology to perform fault detection and classification in distributed physical processes generating heterogeneous data. The underlying concept is built upon a semantic framework for multi-sensor data interpretation using graphical models of Probabilistic Finite State Automata (PFSA). While the computational complexity is reduced by pruning the fused graphical model using an information-theoretic approach, the algorithms are developed to achieve high reliability via retaining the essential spatiotemporal characteristics of the physical processes. The concept has been validated on a simulation test bed of distributed shipboard auxiliary systems.

Suboptimal partitioning of time-series data for anomaly detection

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009

The concepts of symbolic dynamics and partitioning of time series data have been used for feature... more The concepts of symbolic dynamics and partitioning of time series data have been used for feature extraction and anomaly detection. Although much attention has been paid to modeling of finite state machines from symbol sequences, similar efforts have not been expended for partitioning of time series data to optimally generate symbol sequences. This paper addresses this issue and proposes a partitioning method based on maximum migration of data points across cell boundaries. Various aspects of the proposed partitioning tool, such as identification of evolution characteristics of dynamical systems and adaptive selection of alphabet size, are discussed. Experimental results on an electronic circuit apparatus implementing the Duffing equation show that maximum-migration partitioning yields significant improvement over existing partitioning methods (e.g., maximum entropy partitioning) for the purpose of anomaly detection.

SMART HOMES GREEN CITIES

Autonomous perception and decision-making in cyber-physical systems

2013 8th International Conference on Computer Science & Education, 2013

... AUTONOMOUS PERCEPTION AND DECISION MAKING IN CYBER-PHYSICAL SYSTEMS A Dissertation in ... Pag... more

Event-Triggered Decision Propagation in Proximity Networks

Frontiers in Robotics and AI, 2014

Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

Signal, Image and Video Processing, 2008

Symbolic dynamic filtering (SDF) has been recently reported in literature as a pattern recognitio... 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 review of SDF and its performance evaluation relative to other classes of pattern recognition tools, such as Bayesian Filters and Artificial Neural Networks, from the perspectives of: (i) anomaly detection capability, (ii) decision making for failure mitigation and (iii) computational efficiency. The evaluation is based on analysis of time series data generated from a nonlinear active electronic system.

Critical phenomena and finite-size scaling in communication networks?

Proceedings of the 2010 American Control Conference, 2010

AbstractThis paper presents a statistical mechanics-based approach to investigate critical pheno... more

Spatiotemporal information fusion for fault detection in shipboard auxiliary systems

2013 American Control Conference, 2013

ABSTRACT This paper addresses the issues of data analysis and sensor fusion that are critical for... more ABSTRACT This paper addresses the issues of data analysis and sensor fusion that are critical for information management leading to (real-time) fault detection and classification in distributed physical processes (e.g., shipboard auxiliary systems). The proposed technique utilizes a semantic framework for multi-sensor data modeling, where the complexity is reduced by pruning the sensor network through an information-theoretic (e.g., mutual information-based) approach. The underlying algorithms are developed to achieve high reliability and computational efficiency while retaining the essential spatiotemporal characteristics of the physical system. The concept is validated on a simulation test bed of shipboard auxiliary systems.

Semantic sensor fusion for fault diagnosis in aircraft gas turbine engines

Proceedings of the 2011 American Control Conference, 2011

Data-driven fault diagnosis of a complex system such as an aircraft gas turbine engine requires i... more Data-driven fault diagnosis of a complex system such as an aircraft gas turbine engine requires interpretation of multi-sensor information to assure enhanced performance. This paper proposes feature-level sensor information fusion in the framework of symbolic dynamic filtering. This hierarchical approach involves construction of composite patterns consisting of: (i) atomic patterns extracted from single sensor data and (ii) relational patterns that represent the cross-dependencies among different sensor data. The underlying theories are presented along with necessary assumptions and the proposed method is validated on the NASA C-MAPSS simulation model of aircraft gas turbine engines.

Optimal partitioning of ultrasonic data for fatigue damage detection?

Proceedings of the 2011 American Control Conference, 2011

This paper presents an analytical tool for online fatigue damage detection in polycrystalline all... more This paper presents an analytical tool for online fatigue damage detection in polycrystalline alloys that are commonly used in mechanical structures. The underlying theory is built upon symbolic dynamic filtering (SDF) that optimally partitions time series data for feature extraction and pattern classification. The proposed method has been experimentally validated on a fatigue test apparatus that is equipped with ultrasonics sensors and a traveling optical microscope for fatigue damage detection.

Distributed decision propagation in mobile agent networks

49th IEEE Conference on Decision and Control (CDC), 2010

This paper develops a language-measure-theoretic distributed algorithm for decision propagation i... 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 simulation experiments.

Symbolic analysis of time series signals using generalized Hilbert transform

2009 American Control Conference, 2009

A recent publication has shown a Hilberttransform-based partitioning method, called analytic sign... more A recent publication has shown a Hilberttransform-based partitioning method, called analytic signal space partitioning (ASSP). When used in conjunction with D-Markov machines, also reported in recent literature, ASSP provides a fast tool for pattern recognition. However, Hilbert transform does not specifically address the issue of noise reduction and the usage of D-Markov machines with a small depth D could potentially lead to information loss for noisy signals. On the other hand, a large D tends to make execution of pattern recognition computationally less efficient due to an increased number of machine states. This paper explores generalization of Hilbert transform that addresses symbolic analysis of noisecorrupted dynamical systems. In this context, theoretical results are derived based on the concepts of information theory. These results are validated on time series data, generated from a laboratory apparatus of nonlinear electronic systems.

Understanding phase transition in communication networks to enable robust and resilient control

2009 American Control Conference, 2009

This paper presents an application of Statistical Mechanics for analysis of critical phenomena in... more This paper presents an application of Statistical Mechanics for analysis of critical phenomena in complex networks. Using the simulation model of a wired communication grid, the nature of phase transition is characterized and the corresponding critical exponent is computed. Network analogs of thermodynamic quantities such as order parameter, temperature, pressure, and composition are defined and the associated network phase diagrams are constructed. The notion of network eutectic point is introduced by showing characteristic similarities between the network phase diagram and the binary phase diagram. A concept of robust and resilient control in communication networks is presented based on network phase diagrams.

Physical Realization of Chaotic Behavior: A Review

The chaotic behavior of uniformly hyperbolic systems is well known. Geodesic flows with negative ... more

Fractional Brownian Motion: Long range dependency, Markov property, Simulation strategy-A review

AbstractFractional Brownian motion is a very good candidate to model a wide range of natural phe... more

Model Predictive Control and Fault Detection and Diagnostics of a Building Heating, Ventilation, and Air Conditioning System

by Soumik Sarkar and S. Vichik

Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks

Occlusion edges in images which correspond to range discontinuity in the scene from the point of ... 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 (CNN) to identify occlusion edges in images and videos with both RGB-D and RGB inputs. The use of CNN avoids hand-crafting of features for automatically isolating occlusion edges and distinguishing them from appearance edges. Other than quantitative occlusion edge detection results, qualitative results are provided to demonstrate the trade-off between high resolution analysis and frame-level computation time which is critical for real-time robotics applications.

Maximally Bijective Discretization for data-driven modeling of complex systems

2013 American Control Conference, 2013

ABSTRACT Phase-space discretization is a necessary step for study of continuous dynamical systems... more ABSTRACT Phase-space discretization is a necessary step for study of continuous dynamical systems using a language-theoretic approach. It is also critical for many machine learning techniques, e.g., probabilistic graphical models (Bayesian Networks, Markov models). This paper proposes a novel discretization method - Maximally Bijective Discretization, that finds a discretization on the dependent variables given a discretization on the independent variables such that the correspondence between input and output variables in the continuous domain is preserved in discrete domain for the given dynamical system.

Symbolic identification for anomaly detection in aircraft gas turbine engines

Proceedings of the 2010 American Control Conference, 2010

Abstract This paper presents a robust and computationally inexpensive technique of fault detecti... more

A Novel Human Machine Interface for Advanced Building Controls and Diagnostics

Sensor fusion for fault detection and classification in distributed physical processes

This paper proposes a feature extraction and fusion methodology to perform fault detection and cl... more This paper proposes a feature extraction and fusion methodology to perform fault detection and classification in distributed physical processes generating heterogeneous data. The underlying concept is built upon a semantic framework for multi-sensor data interpretation using graphical models of Probabilistic Finite State Automata (PFSA). While the computational complexity is reduced by pruning the fused graphical model using an information-theoretic approach, the algorithms are developed to achieve high reliability via retaining the essential spatiotemporal characteristics of the physical processes. The concept has been validated on a simulation test bed of distributed shipboard auxiliary systems.

Suboptimal partitioning of time-series data for anomaly detection

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009

The concepts of symbolic dynamics and partitioning of time series data have been used for feature... more The concepts of symbolic dynamics and partitioning of time series data have been used for feature extraction and anomaly detection. Although much attention has been paid to modeling of finite state machines from symbol sequences, similar efforts have not been expended for partitioning of time series data to optimally generate symbol sequences. This paper addresses this issue and proposes a partitioning method based on maximum migration of data points across cell boundaries. Various aspects of the proposed partitioning tool, such as identification of evolution characteristics of dynamical systems and adaptive selection of alphabet size, are discussed. Experimental results on an electronic circuit apparatus implementing the Duffing equation show that maximum-migration partitioning yields significant improvement over existing partitioning methods (e.g., maximum entropy partitioning) for the purpose of anomaly detection.

SMART HOMES GREEN CITIES

Autonomous perception and decision-making in cyber-physical systems

2013 8th International Conference on Computer Science & Education, 2013

... AUTONOMOUS PERCEPTION AND DECISION MAKING IN CYBER-PHYSICAL SYSTEMS A Dissertation in ... Pag... more

Event-Triggered Decision Propagation in Proximity Networks

Frontiers in Robotics and AI, 2014

Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

Signal, Image and Video Processing, 2008

Symbolic dynamic filtering (SDF) has been recently reported in literature as a pattern recognitio... 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 review of SDF and its performance evaluation relative to other classes of pattern recognition tools, such as Bayesian Filters and Artificial Neural Networks, from the perspectives of: (i) anomaly detection capability, (ii) decision making for failure mitigation and (iii) computational efficiency. The evaluation is based on analysis of time series data generated from a nonlinear active electronic system.

Critical phenomena and finite-size scaling in communication networks?

Proceedings of the 2010 American Control Conference, 2010

AbstractThis paper presents a statistical mechanics-based approach to investigate critical pheno... more

Spatiotemporal information fusion for fault detection in shipboard auxiliary systems

2013 American Control Conference, 2013

ABSTRACT This paper addresses the issues of data analysis and sensor fusion that are critical for... more ABSTRACT This paper addresses the issues of data analysis and sensor fusion that are critical for information management leading to (real-time) fault detection and classification in distributed physical processes (e.g., shipboard auxiliary systems). The proposed technique utilizes a semantic framework for multi-sensor data modeling, where the complexity is reduced by pruning the sensor network through an information-theoretic (e.g., mutual information-based) approach. The underlying algorithms are developed to achieve high reliability and computational efficiency while retaining the essential spatiotemporal characteristics of the physical system. The concept is validated on a simulation test bed of shipboard auxiliary systems.

Semantic sensor fusion for fault diagnosis in aircraft gas turbine engines

Proceedings of the 2011 American Control Conference, 2011

Data-driven fault diagnosis of a complex system such as an aircraft gas turbine engine requires i... more Data-driven fault diagnosis of a complex system such as an aircraft gas turbine engine requires interpretation of multi-sensor information to assure enhanced performance. This paper proposes feature-level sensor information fusion in the framework of symbolic dynamic filtering. This hierarchical approach involves construction of composite patterns consisting of: (i) atomic patterns extracted from single sensor data and (ii) relational patterns that represent the cross-dependencies among different sensor data. The underlying theories are presented along with necessary assumptions and the proposed method is validated on the NASA C-MAPSS simulation model of aircraft gas turbine engines.

Optimal partitioning of ultrasonic data for fatigue damage detection?

Proceedings of the 2011 American Control Conference, 2011

This paper presents an analytical tool for online fatigue damage detection in polycrystalline all... more This paper presents an analytical tool for online fatigue damage detection in polycrystalline alloys that are commonly used in mechanical structures. The underlying theory is built upon symbolic dynamic filtering (SDF) that optimally partitions time series data for feature extraction and pattern classification. The proposed method has been experimentally validated on a fatigue test apparatus that is equipped with ultrasonics sensors and a traveling optical microscope for fatigue damage detection.

Distributed decision propagation in mobile agent networks

49th IEEE Conference on Decision and Control (CDC), 2010

This paper develops a language-measure-theoretic distributed algorithm for decision propagation i... 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 simulation experiments.

Symbolic analysis of time series signals using generalized Hilbert transform

2009 American Control Conference, 2009

A recent publication has shown a Hilberttransform-based partitioning method, called analytic sign... more A recent publication has shown a Hilberttransform-based partitioning method, called analytic signal space partitioning (ASSP). When used in conjunction with D-Markov machines, also reported in recent literature, ASSP provides a fast tool for pattern recognition. However, Hilbert transform does not specifically address the issue of noise reduction and the usage of D-Markov machines with a small depth D could potentially lead to information loss for noisy signals. On the other hand, a large D tends to make execution of pattern recognition computationally less efficient due to an increased number of machine states. This paper explores generalization of Hilbert transform that addresses symbolic analysis of noisecorrupted dynamical systems. In this context, theoretical results are derived based on the concepts of information theory. These results are validated on time series data, generated from a laboratory apparatus of nonlinear electronic systems.

Understanding phase transition in communication networks to enable robust and resilient control

2009 American Control Conference, 2009

This paper presents an application of Statistical Mechanics for analysis of critical phenomena in... more This paper presents an application of Statistical Mechanics for analysis of critical phenomena in complex networks. Using the simulation model of a wired communication grid, the nature of phase transition is characterized and the corresponding critical exponent is computed. Network analogs of thermodynamic quantities such as order parameter, temperature, pressure, and composition are defined and the associated network phase diagrams are constructed. The notion of network eutectic point is introduced by showing characteristic similarities between the network phase diagram and the binary phase diagram. A concept of robust and resilient control in communication networks is presented based on network phase diagrams.