Focus Area: Sensing, Communication, and Control
Sensing, Communications and Control is an interdisciplinary research area that encompasses the fields of control systems, wireless communications, signal and image processing, robotics, and mechatronics.
The image of an object taken from a varying viewpoint can be represented as a point on a low dimensional manifold. Multi-scale image representations enhance the ability to navigate the manifold for viewpoint recovery.
Focus areas include adaptive and nonlinear control, intelligent and learning control
systems, fault detection and system identification, wireless communication circuits,
computer vision and pattern recognition, sensor development, mobile manipulation and
autonomous systems. Applications can be found in renewable energy and power systems,
materials processing, sensor and control networks, bio-engineering, intelligent
structures, and geosystems. Participating graduate students come from a variety of
backgrounds, and may specialize in civil, mechanical or electrical engineering, or
engineering systems.
Sensing, Communication, and Control faculty work with a variety of research groups
and centers across CSM, including the following:
* Center for Automation, Robotics, and Distributed Intelligence (CARDI)
* Research in Delivery, Usage, and Conrol of Energy (ReDUCE)
* Advanced Control of Energy Power Systems (ACEPS)
* Center for Research and Education in Wind (CREW)
Faculty
Bill Hoff — Image Processing, Digital, Computers
Katie Johnson — Wind Energy/Control Systems
Kevin Moore — Control Systems
John Steele — Robotics, Control, Microcontrollers
Tyrone Vincent — Control Systems
Mike Wakin --- Signal Processing, Compressive Sensing
Manoja Weis — RF/Wireless Communications
Sensing, Communication, and Control Projects
Augmented Reality
Augmented Reality (AR) is technology for displaying computer graphics overlaid upon the real world. At CSM we have developed a handheld AR system that uses 3D fiducials (orange cones) for registration, along with a supplementary mems-based gyros that supply rotational velocity information. The fiducials are segmented using color and shape. A fast absolute orientation algorithm determines the camera position relative to the default triangular model of the cones. An Extended Kalman Filter is used to fuse the pose information obtained from fiducials with the gyro data to obtain a final pose estimate that is used to correctly place the virtual object in the scene. The system runs at frame rate (30Hz) on a PC with dual core processor and mid-range graphics card.
Compressive Sensing
Compressive Sensing (CS) is a new paradigm for acquiring high-resolution signals and high-dimensional data sets using very small numbers of randomized measurements.
Identification and Control of Solid Oxide Fuel Cells
In order to operate at multiple power levels, fuel cells require a control system to balance the fuel and air supply, as well as electrical load. We are investigating the design of control systems to regulate the operation of a complete system based on an solid oxide fuel cell (SOFC) technology.
Multi-scale geometric analysis for image processing, data compression, and computer vision.
Data processing of high dimensional signals such as images can be computationally demanding. The geometry of low-dimensional signal models can be exploited to create low complexity algorithms for a variety of processing and compression tasks.
Wind Turbine Management and Control
We are researching active control systems that can increase the efficiency of energy capture for single turbines as well as coordinated control for wind farms.