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Active voltammetric microsensors with neural signal processing.

Description: Many industrial and environmental processes, including bioremediation, would benefit from the feedback and control information provided by a local multi-analyte chemical sensor. For most processes, such a sensor would need to be rugged enough to be placed in situ for long-term remote monitoring, and inexpensive enough to be fielded in useful numbers. The multi-analyte capability is difficult to obtain from common passive sensors, but can be provided by an active device that produces a spectrum-type response. Such new active gas microsensor technology has been developed at Argonne National Laboratory. The technology couples an electrocatalytic ceramic-metallic (cermet) microsensor with a voltammetric measurement technique and advanced neural signal processing. It has been demonstrated to be flexible, rugged, and very economical to produce and deploy. Both narrow interest detectors and wide spectrum instruments have been developed around this technology. Much of this technology's strength lies in the active measurement technique employed. The technique involves applying voltammetry to a miniature electrocatalytic cell to produce unique chemical ''signatures'' from the analytes. These signatures are processed with neural pattern recognition algorithms to identify and quantify the components in the analyte. The neural signal processing allows for innovative sampling and analysis strategies to be employed with the microsensor. In most situations, the whole response signature from the voltammogram can be used to identify, classify, and quantify an analyte, without dissecting it into component parts. This allows an instrument to be calibrated once for a specific gas or mixture of gases by simple exposure to a multi-component standard rather than by a series of individual gases. The sampled unknown analytes can vary in composition or in concentration, the calibration, sensing, and processing methods of these active voltammetric microsensors can detect, recognize, and quantify different signatures and support subsequent analyses. The instrument can be trained to recognize and report ...
Date: December 11, 1998
Creator: Vogt, M. C.
Partner: UNT Libraries Government Documents Department

A systematic profile/feature-based intelligence for spectral sensors.

Description: Argonne National Laboratory (ANL) has been creating a special-purpose software-engineering tool to support research and development of spectrum-output-type [chemical] sensors. The modular software system is called SAGE, the Sensor Algorithm Generation Environment and includes general-purpose signal conditioning algorithms (GP/SAGE) as well as intelligent classifiers, pattern recognizes, response accelerators, and sensitivity analyzers. GP/SAGE is an implementation of an approach for delivering a level of encapsulated intelligence to a wide range of sensors and instruments. It capitalizes on the genene classification and analysis needed to process most profile-type data. The GP/SAGE native data format is a generalized one-dimensional vector, signature, or spectrum. GP/SAGE modules form a computer-aided software engineering (CASE) workbench where users can experiment with various conditioning, filtering, and pattern recognition stages, then automatically generate final algorithm source code for data acquisition and analysis systems. SAGE was designed to free the [chemical] sensor developer from the signal processing allowing them to focus on understanding and improving the basic sensing mechanisms. The SAGE system's strength is its creative application of advanced neural computing techniques to response-vector and response-surface data, affording new insight and perspectives with regard to phenomena being studied for sensor development.
Date: October 16, 2000
Creator: Vogt, M.C.
Partner: UNT Libraries Government Documents Department

Neural network-based sensor signal accelerator.

Description: A strategy has been developed to computationally accelerate the response time of a generic electronic sensor. The strategy can be deployed as an algorithm in a control system or as a physical interface (on an embedded microcontroller) between a slower responding external sensor and a higher-speed control system. Optional code implementations are available to adjust algorithm performance when computational capability is limited. In one option, the actual sensor signal can be sampled at the slower rate with adaptive linear neural networks predicting the sensor's future output and interpolating intermediate synthetic output values. In another option, a synchronized collection of predictors sequentially controls the corresponding synthetic output voltage. Error is adaptively corrected in both options. The core strategy has been demonstrated with automotive oxygen sensor data. A prototype interface device is under construction. The response speed increase afforded by this strategy could greatly offset the cost of developing a replacement sensor with a faster physical response time.
Date: October 16, 2000
Creator: Vogt, M. C.
Partner: UNT Libraries Government Documents Department

Integrated dynamic landscape analysis and modeling system (IDLAMS) : installation manual.

Description: The Integrated Dynamic Landscape Analysis and Modeling System (IDLAMS) is a prototype, integrated land management technology developed through a joint effort between Argonne National Laboratory (ANL) and the US Army Corps of Engineers Construction Engineering Research Laboratories (USACERL). Dr. Ronald C. Sundell, Ms. Pamela J. Sydelko, and Ms. Kimberly A. Majerus were the principal investigators (PIs) for this project. Dr. Zhian Li was the primary software developer. Dr. Jeffrey M. Keisler, Mr. Christopher M. Klaus, and Mr. Michael C. Vogt developed the decision analysis component of this project. It was developed with funding support from the Strategic Environmental Research and Development Program (SERDP), a land/environmental stewardship research program with participation from the US Department of Defense (DoD), the US Department of Energy (DOE), and the US Environmental Protection Agency (EPA). IDLAMS predicts land conditions (e.g., vegetation, wildlife habitats, and erosion status) by simulating changes in military land ecosystems for given training intensities and land management practices. It can be used by military land managers to help predict the future ecological condition for a given land use based on land management scenarios of various levels of training intensity. It also can be used as a tool to help land managers compare different land management practices and further determine a set of land management activities and prescriptions that best suit the needs of a specific military installation.
Date: February 24, 1999
Creator: Li, Z.; Majerus, K. A.; Sundell, R. C.; Sydelko, P. J. & Vogt, M. C.
Partner: UNT Libraries Government Documents Department

Integrated dynamic landscape analysis and modeling system (IDLAMS) : programmer's manual.

Description: The Integrated Dynamic Landscape Analysis and Modeling System (IDLAMS) is a prototype, integrated land management technology developed through a joint effort between Argonne National Laboratory (ANL) and the US Army Corps of Engineers Construction Engineering Research Laboratories (USACERL). Dr. Ronald C. Sundell, Ms. Pamela J. Sydelko, and Ms. Kimberly A. Majerus were the principal investigators (PIs) for this project. Dr. Zhian Li was the primary software developer. Dr. Jeffrey M. Keisler, Mr. Christopher M. Klaus, and Mr. Michael C. Vogt developed the decision analysis component of this project. It was developed with funding support from the Strategic Environmental Research and Development Program (SERDP), a land/environmental stewardship research program with participation from the US Department of Defense (DoD), the US Department of Energy (DOE), and the US Environmental Protection Agency (EPA). IDLAMS predicts land conditions (e.g., vegetation, wildlife habitats, and erosion status) by simulating changes in military land ecosystems for given training intensities and land management practices. It can be used by military land managers to help predict the future ecological condition for a given land use based on land management scenarios of various levels of training intensity. It also can be used as a tool to help land managers compare different land management practices and further determine a set of land management activities and prescriptions that best suit the needs of a specific military installation.
Date: February 24, 1999
Creator: Klaus, C. M.; Li, Z.; Majerus, K. A.; Sundell, R. C.; Sydelko, P. J. & Vogt, M. C.
Partner: UNT Libraries Government Documents Department