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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

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Ontological problem-solving framework for dynamically configuring sensor systems and algorithms.

Joseph Qualls¹, David J Russomanno

¹Department of Electrical and Computer Engineering, Herff College of Engineering, University of Memphis, 3720 Alumni Avenue, Memphis, TN 38152, USA. jqualls@rendermatrix.com

Sensors (Basel, Switzerland)

|December 14, 2011

Summary

This summary is machine-generated.

A new ontological framework matches sensor systems with compatible algorithms to create effective synthesized systems for specific tasks. This approach aids in discovering and integrating diverse sensing capabilities for complex missions.

Keywords:

ontological framework profiling sensors sensor networks sensor ontology

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Area of Science:

Computer Science
Artificial Intelligence
Sensor Networks

Background:

Ubiquitous sensor systems and algorithms present challenges in task compatibility and integration.
Lack of knowledge models hinders discovery and synthesis of sensor-algorithm capabilities.
Integrating heterogeneous sensing systems for specific tasks remains a significant hurdle.

Purpose of the Study:

To present a novel ontological problem-solving framework for matching sensors and algorithms.
To enable the formation of synthesized systems capable of satisfying specific tasks.
To assign these synthesized systems to high-level missions.

Main Methods:

Development of a novel ontological problem-solving framework.
Instantiation of the framework within a persistence surveillance prototype environment.
Profiling of sensor systems and algorithms to demonstrate proof-of-concept.

Main Results:

Successful matching of sensor systems to compatible algorithms.
Formation of synthesized systems capable of satisfying defined tasks.
Demonstration of the framework's utility in a prototype environment.

Conclusions:

The ontological framework effectively matches sensors to algorithms for task satisfaction.
The framework facilitates the creation and assignment of synthesized sensing systems.
The approach shows potential for application in various heterogeneous sensing environments.