Ann M. Bouchard

Systems and methods for biometric identification using the acoustic properties of the ear canal

The present invention teaches systems and methods for verifying or recognizing a persons identity based on measurements of the acoustic response of the individuals ear canal. The system comprises an acoustic emission device, which emits an acoustic source signal s(t), designated by a computer, into the ear canal of an individual, and an acoustic response detection device, which detects the acoustic response signal f(t). A computer digitizes the response (detected) signal f(t) and stores the data. Computer-implemented algorithms analyze the response signal f(t) to produce ear-canal feature data. The ear-canal feature data obtained during enrollment is stored on the computer, or some other recording medium, to compare the enrollment data with ear-canal feature data produced in a subsequent access attempt, to determine if the individual has previously been enrolled. The system can also be adapted for remote access applications.

Dynamic self-assembly and computation: From biological to information systems

We present two ways in which dynamic self-assembly can be used to perform computation, via stochastic protein networks and self-assembling software. We describe our protein-emulating agent-based simulation infrastructure, which is used for both types of computations, and the few agent properties sufficient for dynamic self-assembly. Examples of protein-network-based computation and self-assembling software are presented. We describe some novel capabilities that are enabled by the inherently dynamic nature of the self-assembling executable code.

Electronic structure classifications using scanning tunneling microscopy conductance imaging

The electronic structure of atomic surfaces is imaged by applying multivariate image classification techniques to multibias conductance data measured using scanning tunneling microscopy. Image pixels are grouped into classes according to shared conductance characteristics. The image pixels, when color coded by class, produce an image that chemically distinguishes surface electronic features over the entire area of a multibias conductance image. Such “classed” images reveal surface features not always evident in a topograph. This article describes the experimental technique used to record multibias conductance images, how image pixels are grouped in a mathematical, classification space, how a computed grouping algorithm can be employed to group pixels with similar conductance characteristics in any number of dimensions, and finally how the quality of the resulting classed images can be evaluated using a computed, combinatorial analysis of the full dimensional space in which the classification is performed.

Dynamic self-assembly in living systems as computation

Biochemical reactions taking place in living systems that map different inputs to specific outputs are intuitively recognized as performing information processing. Conventional wisdom distinguishes such proteins, whose primary function is to transfer and process information, from proteins that perform the vast majority of the construction, maintenance, and actuation tasks of the cell (assembling and disassembling macromolecular structures, producing movement, and synthesizing and degrading molecules). In this paper, we examine the computing capabilities of biological processes in the context of the formal model of computing known as the random access machine (RAM) [Dewdney AK (1993) The New Turing Omnibus. Computer Science Press, New York], which is equivalent to a Turing machine [Minsky ML (1967) Computation: Finite and Infinite Machines. Prentice-Hall, Englewood Cliffs, NJ]. When viewed from the RAM perspective, we observe that many of these dynamic self-assembly processes – synthesis, degradation, assembly, movement – do carry out computational operations. We also show that the same computing model is applicable at other hierarchical levels of biological systems (e.g., cellular or organism networks as well as molecular networks). We present stochastic simulations of idealized protein networks designed explicitly to carry out a numeric calculation. We explore the reliability of such computations and discuss error-correction strategies (algorithms) employed by living systems. Finally, we discuss some real examples of dynamic self-assembly processes that occur in living systems, and describe the RAM computer programs they implement. Thus, by viewing the processes of living systems from the RAM perspective, a far greater fraction of these processes can be understood as computing than has been previously recognized.

Chapter 7 The “Programming Language” of Dynamic Self-Assembly

Abstract We show how protein dynamic self-assembly processes map naturally onto a formal model of computing, the random access machine (RAM), which is equivalent to a Turing machine. Specifically, we show that many biological processes that do not appear to be information processing, such as synthesis of macromolecules, structural assembly, transport, disassembly, and degradation of molecules, actually do perform computation when viewed from the RAM perspective. With this view that dynamic self-assembly processes are performing computation, we then look at this relationship from two perspectives. First, if self-assembly processes in living systems are performing computation, what algorithms are they carrying out? We discuss examples of algorithms implemented by living systems at multiple hierarchy levels. Second, if we can understand the “programming language” of dynamic self-assembly, can we design programs to self-assemble novel structures or materials? We give example computer simulations of programmed dynamic self-assembly of nanostructures.

Novel collaboration and situational awareness environment for leaders and their support staff via self assembling software.

This is the final report on the Sandia Fellow LDRD, project 117865, 08-0281. This presents an investigation of self-assembling software intended to create shared workspace environment to allow online collaboration and situational awareness for use by high level managers and their teams.

Risk Assessment Meta Tool LDRD Final Report

The goal of this project was to develop a risk analysis meta tool--a tool that enables security analysts both to combine and analyze data from multiple other risk assessment tools on demand. Our approach was based on the innovative self-assembling software technology under development by the project team. This technology provides a mechanism for the user to specify his intentions at a very high level (e.g., equations or English-like text), and then the code self-assembles itself, taking care of the implementation details. The first version of the meta tool focused specifically in importing and analyzing data from Joint Conflict and Tactical Simulation (JCATS) force-on-force simulation. We discuss the problem, our approach, technical risk, and accomplishments on this project, and outline next steps to be addressed with follow-on funding.