PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 7108616

An interface between several cameras and two computers, using differential analog transmission.

F S Prato·E Gauci·T D Cradduck
View original source ↗Export metadata ↓

Abstract

A system has been built to allow up to four scintillation cameras to be connected in parallel with two computers, yet provide for selection of individual pairs of camera/computer combinations with the exclusion of unwanted connections. Since the distances involved were relatively long, signal drivers were used to transmit the analog signals in a differential mode over multiple twin-paired lines rather than coaxial cable. By transmitting the signals differentially, any noise induced on the signal was subtracted at the receiver. The principal features of this system, which has been installed in two institutions, are described. A problem that occurred in connection with one camera is presented (also its solution), with a suggestion concerning parameters to be assessed at the time of system acceptance.

Explore related subjects

Computers, AnalogElectronicsRadionuclide ImagingTechnologyTelecommunicationsComparative StudyJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

F S Prato, E Gauci, T D Cradduck. 1982. An interface between several cameras and two computers, using differential analog transmission.. https://pubmed.ncbi.nlm.nih.gov/7108616/

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

KEEP EXPLORING

Related citations

PubMed2006

Analog neural network for support vector machine learning.

An analog neural network for support vector machine learning is proposed, based on a partially dual formulation of the quadratic programming problem. It results in a simpler circuit implementation with respect to existing neural solutions for the same application. The effectiveness of the proposed network is shown through some computer simulations concerning benchmark problems.

Computers, Analog↗
PubMed2006

Design and implementation of multipattern generators in analog VLSI.

In recent years, computational biologists have shown through simulation that small neural networks with fixed connectivity are capable of producing multiple output rhythms in response to transient inputs. It is believed that such networks may play a key role in certain biological behaviors such as dynamic gait control. In this paper, we present a novel method for designing continuous-time recurrent neural networks (CTRNNs) that contain multiple embedded limit cycles, and we show that it is possible to switch the networks between these embedded limit cycles with simple transient inputs. We also describe the design and testing of a fully integrated four-neuron CTRNN chip that is used to implement the neural network pattern generators. We provide two example multipattern generators and show that the measured waveforms from the chip agree well with numerical simulations.

Computers, Analog↗
PubMed2004

An analog neural network implementation in fixed time of adjustable-order statistic filters and applications.

In this paper, we show a neural network implementation in fixed time of adjustable order statistic filters, including sorting, and adaptive-order statistic filters. All these networks accept an array of N numbers Xi = S(Xi)M(Xi)2E(Xi) as input (where S(Xi) is the sign of Xi, M(Xi) is the mantissa normalized to m digits, and Ex is the exponent) and employ two kinds of neurons, the linear and the threshold-logic neurons, with only integer weights (most of the weights being just +1 or -1) and integer threshold. Therefore, this will greatly facilitate the actual hardware implementation of the proposed neural networks using currently available very large scale integration technology. An application of using minimum filter in implementing a special neural network model neural network classifier (NNC) is given. With a classification problem of l classes C1, C2,.. ., C1, NNC classifies in fixed time an unknown vector to one class using a minimum-distance classification technique.

Computers, Analog↗