Piezoelectric Ultrasonic Micromotors

This report describes development of micro-fabricated piezoelectric ultrasonic motors and bulk-ceramic piezoelectric ultrasonic motors. Ultrasonic motors offer the advantage of low speed, high torque operation without the need for gears. They can be made compact and lightweight and provide a holding torque in the absence of applied power, due to the traveling wave frictional coupling mechanism between the rotor and the stator. This report covers modeling, simulation, fabrication and testing of ultrasonic motors. Design of experiments methods were also utilized to find optimal motor parameters. A suite of 8 mm diameter x 3 mm tall motors were machined for these studies and maximum stall torques as large as 10^(- 3) Nm, maximum no-load speeds of 1710 rpm and peak power outputs of 27 mW were realized. Aditionally, this report describes the implementation of a microfabricated ultrasonic motor using thin-film lead zirconate titanate. In a joint project with the Pennsylvania State University Materials Research Laboratory and MIT Lincoln Laboratory, 2 mm and 5 mm diameter stator structures were fabricated on 1 micron thick silicon nitride membranes. Small glass lenses placed down on top spun at 100-300 rpm with 4 V excitation at 90 kHz. The large power densities and stall torques of these piezoelectric ultrasonic motors offer tremendous promis for integrated machines: complete intelligent, electro-mechanical autonomous systems mass-produced in a single fabrication process.

Maximum Entropy Discrimination

We present a general framework for discriminative estimation based on the maximum entropy principle and its extensions. All calculations involve distributions over structures and/or parameters rather than specific settings and reduce to relative entropy projections. This holds even when the data is not separable within the chosen parametric class, in the context of anomaly detection rather than classification, or when the labels in the training set are uncertain or incomplete. Support vector machines are naturally subsumed under this class and we provide several extensions. We are also able to estimate exactly and efficiently discriminative distributions over tree structures of class-conditional models within this framework. Preliminary experimental results are indicative of the potential in these techniques.

Biologically Plausible Neural Circuits for Realization of Maximum Operations

Object recognition in the visual cortex is based on a hierarchical architecture, in which specialized brain regions along the ventral pathway extract object features of increasing levels of complexity, accompanied by greater invariance in stimulus size, position, and orientation. Recent theoretical studies postulate a non-linear pooling function, such as the maximum (MAX) operation could be fundamental in achieving such invariance. In this paper, we are concerned with neurally plausible mechanisms that may be involved in realizing the MAX operation. Four canonical circuits are proposed, each based on neural mechanisms that have been previously discussed in the context of cortical processing. Through simulations and mathematical analysis, we examine the relative performance and robustness of these mechanisms. We derive experimentally verifiable predictions for each circuit and discuss their respective physiological considerations.