PubMed HealthSearch

PubMed · 7154675

An automated microprocessor-controlled data collection device for use with the Technicon AutoAnalyzer system.

Abstract

An automated microprocessor-controlled data collection system for use with a Technicon AutoAnalyzer in a research or clinical laboratory is described. The collection system can digitize an analog output from the AutoAnalyzer, perform mathematical operations on this digital data, detect and record peak-trough data, and perform these data acquisition and mathematical data processing operations in parallel. The data collection system can be interfaced to a variety of host computers for further data analysis. The accuracy of the data collection system was tested and compared to existing procedures using an analysis of bovine serum albumin protein. The correlation coefficient for the comparison of the results was 0.99998. Hence, the automated system is as accurate as previous methods, but is considerably faster, more efficient, and, in comparison to existing devices, less expensive.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

T P Moyles, D R Schneider, R F Erlandson. 1982. An automated microprocessor-controlled data collection device for use with the Technicon AutoAnalyzer system.. https://doi.org/10.1016/0160-5402(82)90076-6

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Analog neural nets with gaussian or other common noise distribution cannot recognize arbitrary regular languages.

We consider recurrent analog neural nets where the output of each gate is subject to gaussian noise or any other common noise distribution that is nonzero on a sufficiently large part of the state-space. We show that many regular languages cannot be recognized by networks of this type, and we give a precise characterization of languages that can be recognized. This result implies severe constraints on possibilities for constructing recurrent analog neural nets that are robust against realistic types of analog noise. On the other hand, we present a method for constructing feedforward analog neural nets that are robust with regard to analog noise of this type.

Analog-Digital Conversion

Digital images.

This tutorial introduces basic digital image processing concepts. An increasing fraction of fluoroscopic images are handled and processed using digital tools. The quality of an image in digital format depends on both the technical details of the digitization process and on the quality of the starting scene. Digital tools yield images that are difficult or impossible to achieve using analog techniques. Proper processing improves the observer's perception of clinical information.

Analog-Digital Conversion