Thayer School of Engineering At Dartmouth logo
home > research > complex-systems >

Complex Systems: ECEEP

Active projects in Electrical and Computer Engineering, and Engineering Physics (ECEEP) with applications for complex systems:

Adversary intent inferencing and adversarial modeling investigates the feasibility of developing and utilizing an adversary intent inferencing model as a core element for predictive analyses and simulations to establish emergent adversarial behavior. It is our desire to use this intelligent adversary to predict adversary intentions, explain adversary goals, and predict enemy actions in an effort to generate alternative futures critical to performing course of action (COA) analysis. Such a system will allow planners to gauge and evaluate the effectiveness of alternative plans under varying actions and reactions to friendly COAs. This can also be applied in a broad range of areas.
(Faculty contact: Santos)

Agent-based systems engineering aims to successfully cross-fertilize the fields of systems engineering and artificial intelligence. Systems engineering (control, signal processing and communications) focuses primarily on physical domains that can be characterized by rich mathematical dynamics while artificial intelligence deals with human perception, decision making and action. Goals of such cross-fertilization are to explore the modeling, performance and scientific foundations of software agent systems using ideas from classical systems engineering and computer engineering.
(Faculty contacts: Cybenko, Olfati-Saber, Santos, Graves)

Bayesian knowledge bases, engineering, verification, and validation focuses on the fundamental problem of probabilistic modeling of knowledge in order to represent and reason about information in a theoretically sound manner. The world is replete with issues such as incompleteness, impreciseness, and inconsistency which makes the task of capturing even everyday tasks, processes, and activities very difficult, let alone trying to capture that of decision-making by experts or other complex phenomena. Improperly modeling uncertainty leads to numerous anomalies in reasoning as well as increased computational difficulties.
(Faculty contact: Santos)

Complex networked systems are networks of interacting agents (robots, systems, sensors, actuators, particles, humans, animals, biological organisms, investors) with diverse collective behaviors and versatile applications in large-scale engineering, biological, and social systems. The emergent behavior and performance of networked engineering systems fundamentally relies on the structure of the network. Current research topics involving design, analysis, simulation, and real-life implementation of networked systems include:

  1. Swarms: a) Coordination of networks of mobile agents/robots, unmanned autonomous vehicles (UAVs) with local interactions and b) Flocking algorithms and behavior.
  2. Sensor Networks: Distributed algorithms for cooperative information processing and sensor fusion in wireless ad-hoc sensor networks and mobile sensor networks (MANETs) embedded in an environment for monitoring, detection, and multi-target tracking purposes.
  3. Small-World Networks: Phase-transition phenomena in spectral properties of large-scale networks.
  4. Social Networks: Evolutionary dynamics of behavior and language in social networks and the origin of social norms and behavioral trends.
  5. Belief Networks: Distributed hypothesis testing and probabilistic reasoning.

(Faculty contacts: Olfati-Saber, Santos, Graves)

Computational plasma dynamics focuses on the development of computer models for diagnosing and predicting plasma, magnetofluid, and electromagnetic processes in the near-earth space environment.
(Faculty contacts: Lotko, Streltsov)

Deception detection aims to automatically detect and infer the intentions behind deceptive actions. Our objectives are to 1) develop a framework for categorizing and classifying errors that may be committed by an expert, since not all errors are deception; and 2) design algorithms for automatic deception detection capable of providing detailed evidential information and explanation of deception intent, plus analysis of the deception's impact. Like insider threat, deception detection can occur in any number of scenarios and domains, and insider threat and deception detection are often interrelated.
(Faculty contacts: Cybenko, Santos)

Distributed algorithms are being developed for flocking, data fusion in sensor networks, decision-making, and optimization.
(Faculty contact: Olfati-Saber)

Distributed information retrieval aims to develop a large-scale information retrieval architecture that can be effectively and efficiently deployed in distributed environments. Heterogeneous information (such as content, formats and sources) is the typical issue that needs to be identified and handled in the distributed environment. Our objective is to develop a unified architecture called I-FGM (intelligent foraging, gathering and matching) for dealing with the massive amount of information in a dynamic search space within large-scale distributed platforms. The system will proceed to explore the information space, and continuously identify and update promising candidate information. Specific metrics are also being developed for performance evaluation.
(Faculty contact: Santos)

Environmental fluid mechanics research studies natural fluid systems as agents for the transport and dispersion of environmental contamination. Understanding transport and dispersion processes in natural fluid flows, from the microscale to the planetary scale, serves as the basis for the development of models aimed at simulations, predictions, and ultimately sustainable environmental management. Research within this scope is diverse and can involve a variety of scientific and engineering disciplines such as civil, mechanical, and environmental engineering, meteorology, hydrology, hydraulics, limnology, and oceanography.
(Faculty contact: Cushman-Roisin)

Estimation theory and Kalman filtering: Estimation theory deals with parameter estimation for models of physical processes, or estimation and tracking of the internal "state" of a system/process given some noisy measurements of the outputs of the system. Kalman filters are one of the most effective and widely used estimation algorithms in engineering. The focus of our research is development of distributed Kalman filtering algorithms for observing and tracking
multiple events in an environment using sensor networks.
(Faculty contact: Olfati-Saber)

Geomagnetically-induced currents (GICs) can occur in technological networks such as railroads, power transmission lines, and pipelines. Large-scale currents flowing overhead in the ionosphere induce electric and magnetic fields on the surface of the Earth which in turn trigger GICs. During electromagnetic storm periods caused by the Sun these GICs can be large, often exceeding several hundred Amperes, and cause catastrophic consequences to the system in which they flow. Researchers are attempting to predict the occurence of GICs using physics-based models of the global magnetosphere, ionosphere, and Earth conductivity together with input from a satellite located in the upstream solar wind.
(Faculty contact: Shepherd)

Geospace environment modeling research focuses on the development of computer models and satellite- and ground-based data streams for diagnosing and predicting plasma, magnetofluid, electromagnetic and radiation processes in geospace, including Earth's magnetosphere and ionosphere. Geospace encompasses the aerospace environment for hundreds of communication, navigation, meteorological, military, remote sensing, and research satellites.
(Faculty contact: Lotko)

High-performance search and optimization aims to develop new models and algorithms for solving challenging engineering problems in domains such as mission planning and logistics, manufacturing process optimization, composite materials production, distributed plant scheduling and management, and policy evaluation, to name a few.
(Faculty contact: Santos)

Influence of culture and society on attitudes and behaviors aims to build and employ social, cultural, and political data-driven models to explore and explain attitudes and behaviors. The efforts involve classifying the factors that play significant roles in attitudes and behaviors, abstracting general rules from traditional research such as sociological case studies, studying the inferencing structures that allow different factors to influence decision-making, reasoning from different points of view, and applying them in predicting behavior.
(Faculty contact: Santos)

Information processing and summarization are critical areas of research that study how we can develop stand-alone algorithms as well as algorithms fused with humans to handle and process information in a variety of forms. The goal is to be able to extract the meaning (or semantics) of the information in order to better manipulate/reason and present it to the human user. This is fundamental to solving problems such as avoiding information overload and providing effective summarization.
(Faculty contact: Santos)

Insider threat and deception detection are two areas that focus on user actions and their impacts upon the systems with which they interact. Insider threat aims to understand and prevent malicious activities that are instigated by "trusted" users on complex computer/information systems. Such activities cover a broad spectrum ranging from simple theft of confidential data to the more subtle alteration of system performance and/or information. For the latter, examples can include minor perturbation of a component specification in a manufacturing process resulting in a rippling effect of final component failure to influencing the decision-makers by modifying their information flow and content. The goal is to model insider threat in order to predict behavior and ultimately infer their goals and intentions.
(Faculty contacts: Cybenko, Santos)

Interacting micro-robots are designed, fabricated, and studied in the microengineering cleanroom laboratory as part of our MEMS effort. Current research includes studies of compliant interactions, control strategies for multiple robots, and collective behaviors, as well as materials and fabrication issues. This work is in collaboration with the Dartmouth and Duke Computer Science Departments. Control is constrained to a few classes of robots, with a number of instances of each class. Typical robots are made of silicon 60x250 microns across, and under 10 microns thick, with an average step size of 12 nanometers.
See Dartmouth College Micro-Robots
(Faculty contact: Levey)

Ionospheric electric fields, created by a combination of reconnection and viscous processes occurring at the magnetopause and in the magnetotail, map down geomagnetic field lines into the high-latitude ionosphere where they cause the plasma to drift. Together with scientists at the Johns Hopkins University's Applied Physics Laboratory researchers are using the SuperDARN incoherent scatter radars to study ionospheric convection and how it responds to changes in the solar wind. Measuring the motion of the ionospheric plasma can greatly increase our understanding of the magnetospheric processes responsible for the convection.
(Faculty contact: Shepherd)

Process query systems have applications that involve using databases or datastreams of events to detect instances of processes. In those applications, events provide evidence that is used to infer the existence and estimate the states of the various processes of interest. Examples of such applications include: network and computer security; network management; sensor network tracking; military situational awareness; and critical infrastructure monitoring and protection.
(Faculty contacts: Cybenko, Santos)

Space plasma physics research is focused on:

  • generation and dynamics of ULF and ELF electromagnetic emissions in space
  • formation and dynamics of thin plasma current sheets and boundary layers in space
  • collisionless wave-particle acceleration processes and plasma heating
  • auroral and polar phenomena

Interests also include hardware efforts that focus on ground- and space-based instrumentation related to these topics as well as the development of a student-built satellite.
(Faculty contacts: Lotko, Streltsov)

Unexploded ordnance (UXO) detection and discrimination approaches are being developed to solve the Department of Defense's (DoD) most pressing environmental problems: UXO cleanup and humanitarian de-mining. The program combines advanced forward and inverse EM sensing approaches with statistical signal processing methodologies to solve these complex and challenging problems. See also UXO Research Group.
(Faculty contact: Shubitidze)

User modeling and user intent inferencing involves building dynamic cognitive user models that can predict the goals and intentions of a user in order to understand and ultimately provide proactive assistance with user tasks, such as information gathering. The key is to capture the user's intent by answering questions such as: what is the user's current focus, why is the user pursuing certain goals, and how will the user achieve them? The efforts involve machine learning, knowledge representation, intent inferencing, and establishment of proper evaluation metrics. This work has been applied to assisting with intelligent information retrieval and enhancing the effectiveness of intelligence analysts.
(Faculty contact: Santos)

Wireless sensor networks are being studied within the context of complex networked systems.
(Faculty contact: Olfati-Saber)