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Multiple detailed tutorials on systems engineering topics will be offered on Monday, April 18. Registration is separate from conference registration. See the fees here.

Tutorial Listing

Smart Home System Cybersecurity: Threat and Defense in a Cyber-Physical System

Time: 08:00 - 12:00
Instructor: Shiyan Hu, Stanford
Abstract: Cyber-Physical System (CPS) research addresses the close interactions and feedback loop between the cyber components such as embedded computing systems and the physical components such as energy and mechanical systems. As an exemplary CPS, smart home energy system has gained significant popularity due to the massive deployment of advanced metering infrastructure enabling a transformative shift of the classical grid into a more reliable and secure grid. Smart home is critical in this infrastructure as it controls the end use components of a grid. Despite its importance, such a system is vulnerable to various cyberattacks such as energy theft and pricing hack. In this tutorial, several of the recent works on smart home cyberthreat analysis and defense technology development will be described. It will be shown that due to the interdependence between utility pricing and customer energy load, a cyberattacker could tamper smart meters for electricity bill manipulation and energy load unbalancing, and similarly energy theft could potentially disturb the power system. We will then discuss some advanced control theoretic and algorithmic techniques to defend against those cyberattacks, including partially observable Markov decision process (POMDP) based detection and cross entropy optimization based Feeder Remote Terminal Unit (FRTU) deployment optimization. The talk will conclude with some of the future research directions on this topic.

Distributed Sensing and RF Tomography

Time: 13:00 - 15:00
Instructor: Michael Wicks, University of Dayton
Abstract: Many applications require imaging, shape reconstruction and material characterization of objects in clutter, including, for example, aircraft and airport surveillance, below ground imaging, foliage penetration (FOPEN), concealed weapons detection (CWD), crowd control, border control, through the wall surveillance (TWS), antenna and RCS measurements, as well as quality control, industrial automation, medical imaging and 3D/4D printing. Recent advances in computing, computational sciences and radio frequency (RF) technology improved the potential for successful applications tomography to these challenging problems. Tomographic systems may be supported by a variety of technologies, but they all share one common feature in that they all require viewing of the environment from a variety of angles. This is referred to as geometric diversity of illumination and observation. The technology that supports geometric diversity is based upon distributed sensors. For applications where sensing occurs using electromagnetic waves, the most common sensor is radar. Distributed sensing systems employ a single aperture that is moved to form a synthetic aperture radar (SAR) or numerous simultaneous fixed aperture systems. RF tomography is typically employs a distributed system of low-cost, reconfigurable electromagnetic transmit and receive antennas placed arbitrarily around the region of interest. RF tomography transmitters radiate known waveforms. But, sources of opportunity may also be exploited, while spatially distributed receivers sample of the scattered fields, and relay this information to a central processor. The distinctive attribute of RF tomography is its high resolution capabilities: sub-wavelength, range-independent, bandwidth-independent, resolution which is a function of the RF carrier frequency. This tutorial will present the principles of RF tomography, and the relationship between classical electromagnetics, signal processing, and applications specific phenomenology as in medical imaging, SAR, and seismic sensing. This tutorial will include results from the most recent experiments and trends with many different applications. In particular, this tutorial will demonstrate theoretical concepts using experimental results obtained via one of the first dedicated RF tomography chamber.

Data Analytics

Time: 08:00 - 12:00
Instructor: Paul C. Hershey, Raytheon Company
Abstract: This tutorial provides an in depth review of the ever evolving technical area called “Data Analytics,” a subset of “Big Data,” that encompasses data analysis, data fusion, data storage, data sources, infrastructure and technology, screening and filtering algorithms, machine learning, and complexity. These techniques will be introduced with respect to their individual contribution to data analytics and to their combined contributions to data analytics systems. Specific use cases will be presented in which participants will observe, both through presentation and videos, the individual and combined applications and value of these techniques and systems for specific commercial and Department of Defense (DoD) use cases. Participants will emerge from this tutorial with a focused understanding of data analytics principles and techniques that will enable them to apply these concepts toward building engineering systems for mission decision support. This tutorial is applicable to individuals interested in systems engineering with respect to analysis of complex systems, mission support, and automated decision aides.

Intelligent Control Architecture for Autonomous Vehicles

Time: 13:00 - 15:00
Instructor: Carlos Insaurralde, Teesside University
Abstract: The use of remotely-operated vehicles is ultimately limited by economic support costs, and the presence and skills from human operators (pilots). Unmanned craft have the potential to operate with greatly reduced overhead costs and level of operator intervention. The challenging design is for a system that deploys a team of Unmanned Vehicles (UVs) and can perform complex tasks reliably and with minimal (remote) pilot intervention. A critical issue to achieve this is to develop a system with the ability to deal with internal faults, and changes in the environment as well as their impact on sensor outputs used for the planning phase.

The tutorial objective is to present step by step the development process (from requirements to prototyping) of an Intelligent Vehicle Control Architecture (IVCA) that enables multiple collaborating UVs to autonomously carry out missions. The architectural foundation to achieve the IVCA lays on the flexibility of service-oriented computing and agent software technology. An ontological database captures the remote pilot skills, platform capabilities and, changes in the environment. The information captured (stored as knowledge) enables reasoning agents to plan missions based on the current situation. The combination of the two above paradigms makes it possible to develop an IVCA that is able to dynamically reconfigure and adapt itself in order to deal with changes in the operation environment. The ability to perform on-the-fly re-planning of activities when needed increases the chance to succeed in a given mission. The IVCA realization is underpinned by the development of fault-tolerant planning and spooling modules (fault diagnosis and recovery) as well as a module called matchmaker to link services with available capabilities.