Microprocessor Implementation of a Time Variant Floating Mean Counting Algorithm

PDF Version Also Available for Download.

Description

Rate estimation of nuclear pulses emitted from nuclear detectors has been well documented in papers written as early as 1965 to as recently as 1990. It is well known that pulses emitted from a nuclear detector can vary with time and an accurate estimate of the count rate must be based on a sifficient number of pulse counts within a sample period as well as the recent history of pulse counts acquired in previous windows to accurately estimate the current rate. This paper will review the attributes of three popular counting methods and show the implementation of one of these ... continued below

Creation Information

Huffman, R. K. November 25, 1998.

Context

This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 11 times . More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Publisher

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Description

Rate estimation of nuclear pulses emitted from nuclear detectors has been well documented in papers written as early as 1965 to as recently as 1990. It is well known that pulses emitted from a nuclear detector can vary with time and an accurate estimate of the count rate must be based on a sifficient number of pulse counts within a sample period as well as the recent history of pulse counts acquired in previous windows to accurately estimate the current rate. This paper will review the attributes of three popular counting methods and show the implementation of one of these methods, the floating mean algorithm on an embedded controller system. The software discussion will look at how to apply the chosen algorithm on two popular platforms: the Motorola 68HC11 and the Intel 805X series embedded controllers.

Source

  • Journal Name: Nuclear Instruments and Measurements

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

  • Other: DE00002995
  • Report No.: WSRC-MS-98-00787
  • Grant Number: AC09-96SR18500
  • Office of Scientific & Technical Information Report Number: 2995
  • Archival Resource Key: ark:/67531/metadc686772

Collections

This article is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • November 25, 1998

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

Description Last Updated

  • Dec. 6, 2016, 1:26 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 11

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Huffman, R. K. Microprocessor Implementation of a Time Variant Floating Mean Counting Algorithm, article, November 25, 1998; Aiken, South Carolina. (digital.library.unt.edu/ark:/67531/metadc686772/: accessed November 15, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.