CPS-PI Meeting 2017

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Visible to the public CPS: Small: Integrated Reconfigurable Control and Moving Target Defense for Secure Cyber-Physical Systems

Submitted by Xenofon Koutsoukos on Wed, 11/29/2017 - 4:21pm. Contributors:

Abstract: Cyber-physical systems have been increasingly subject to cyber-attacks including code injection and code reuse attacks. With the tightly coupled nature of cyber components with the physical domain, these attacks have the potential to cause significant damage if critical applications such as automobiles are compromised. Instruction Set Randomization and Address Space Randomization have been commonly proposed to address these types of attacks.

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Visible to the public CPS: Synergy: Collaborative Research: Fault Tolerant Brain Implantable Cyber-Physical System

Submitted by HittenZaveri on Wed, 11/29/2017 - 4:21pm. Contributors:

Episodic brain disorders such as epilepsy have a considerable impact on a patient's productivity and quality of life and may be life-threatening when seizures cannot be controlled with medications. We will create a second generation brain-implantable sensing and stimulating device (BISSD) based on CPS principles and practice. The BISSD will be composed of modules placed intracranially to continuously monitor brain state and vulnerability to seizure and intervene with electrical stimulation to block the development of seizure.

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Visible to the public CPS: Synergy: Coordinated Action Among Independent Mobile Cyber-Physical Systems

Submitted by Ross Knepper on Wed, 11/29/2017 - 4:21pm. Contributors:

In this project, we seek to apply principles from the field of programming languages to distributed robotics. The essence of programming languages is to design simple APIs that support compositional reasoning. We embrace that philosophy through tools from programming languages such as type theory and proof assistants to produce correct-by-construction programs that control teams of robots coordinating their actions to achieve complex tasks.

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Visible to the public CPS: Breakthrough: A Dynamic Optimization Framework for Connected Automated Vehicles in Urban Environments

Submitted by Christos Cassandras on Wed, 11/29/2017 - 4:21pm. Contributors:

Connected Automated Vehicles (CAVs), often referred to as "self-driving cars," will have a profound impact not only on transportation systems, but also in terms of associated economic, environmental, and social effects. As with any such major transformative undertaking, quantifying the magnitude of its expected impact is essential.

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Visible to the public Collaborative Research: Adaptive Intelligence for Cyber-Physical Automotive Active Safety- System Design and Evaluation

Submitted by tsiotras on Wed, 11/29/2017 - 4:21pm. Contributors:

To improve the current capabilities of automotive active safety control systems (ASCS) one needs to take into account the interactions between driver/vehicle/ASCS/environment. To achieve this goal, we are proposing a novel approach to collect data from a sensor-equipped vehicle. Motion Sensors (Inertial Measurement Units) are placed on various locations in the car, particularly around the driver's operational environment and moving car components, such as steering wheel, seat, pedals, as well as critical car components (e.g. motor, suspensions).

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Visible to the public A Cyber-Physical System for PV Monitoring and Control Cloud Movement and Shading Prediction

Submitted by spanias on Wed, 11/29/2017 - 4:21pm. Contributors:

In this paper, we describe a Cyber-Physical system approach to Photovoltaic (PV) array control. A machine learning and computer vision framework is proposed for improving the reliability of utility scale PV arrays by leveraging video analysis of local skyline imagery, customized machine learning methods for fault detection, and monitoring devices that sense data and actuate at each individual panel. Our approach promises to improve efficiency in renewable energy systems using cyber-enabled sensory analysis and fusion.

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Visible to the public A multi-scale data assimilation framework for layered sensing and hierarchical control of disease spread in field crops

Submitted by Soumik Sarkar on Wed, 11/29/2017 - 4:21pm. Contributors:

This project is focused on developing data analytics and decision-making techniques for early detection and mitigation of soybean diseases via fusing data from ground robots, UAVs and satellites. We aim to collect RGB and hyperspectral image data for soybean diseases from research farms at Iowa State and via collaboration with the Iowa Soybean Association and the NASA Jet Propulsion Lab (for satellite data).