ANALOG-TO-DIGITAL DATA REDUCTION SYSTEM

1962 ◽  
Author(s):  
J. E. REEGAN
Keyword(s):  
Data Reduction ◽  
Digital Data ◽  
Analog To Digital ◽  
Reduction System

ELECTRONIC AREA DETECTOR DATA REDUCTION SYSTEM AT THE SRS

1986 ◽  
Vol 47 (C5) ◽  
pp. C5-109-C5-113
Author(s):  
J. W. CAMPBELL ◽  
D. CROFT ◽  
J. R. HELLIWELL ◽  
P. MACHIN ◽  
M. Z. PAPIZ ◽  
...  
Keyword(s):  
Data Reduction ◽  
Area Detector ◽  
Reduction System

Supls Photogrammetric Data Reduction System

1977 ◽  
Author(s):  
T. H. Wickstrom ◽  
M. A. Cates ◽  
R. I. Swor
Keyword(s):  
Data Reduction ◽  
Reduction System

Study of Wireless Torque Measurement System Based on CC2500

2011 ◽  
Vol 383-390 ◽  
pp. 5300-5303
Author(s):  
Wei Liu ◽  
Xiao Jie Song ◽  
Wen Gang Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Digital Data ◽  
Measuring System ◽  
Instrumentation Amplifier ◽  
Torque Sensor ◽  
Torque Measurement ◽  
Analog To Digital ◽  
Wireless Transceiver ◽  
Precision Measuring ◽  
Transmission Shaft

It’s very difficult to get high precision measuring result using contact torquemeter because of very low signal-to-noise ratio. To overcome this defect, a wireless torque measuring system is designed based on CC2500. This system uses strain gauge torque sensor to measure the surface principal stress of the transmission shaft, and get the maximum shearing stress, and then the torque that the transmission shaft bears. The weak output signal of torque sensor is magnified by the instrumentation amplifier AD623, and sent to the analog-to-digital convertor. These digital data are transmited to the portable receiving terminal by the wireless transceiver chip CC2500. The dynamic wireless torque measurement is realized by this system.

scholarly journals DESIGN AND ANALYSIS OF DIFFERENT TECHNIQUES FOR ANALOG-TO-DIGITAL CONVERSION OF VIBRATION SIGNALS FOR WIRELESS MEASUREMENT SYSTEMS

2018 ◽  
Vol 17 (1) ◽  
pp. 39
Author(s):  
Milan Dinčić ◽  
Dragan Denić ◽  
Zoran Perić
Keyword(s):  
Digital Data ◽  
Bit Rate ◽  
Rate Reduction ◽  
Digital Conversion ◽  
Vibration Signals ◽  
Analog To Digital ◽  
Measurement Systems ◽  
Analog To Digital Conversion ◽  
Uniform Quantization ◽  
Wireless Measurement

The aim of this paper is to design, analyze and compare four different systems for ADC (analog-to-digital conversion) of vibration signals. Measurement of vibration signals is of particular importance in many areas, such as predictive maintenance or structural health monitoring. Wireless systems for vibration measurements becomes very topical, due to much easier and cheaper installation compared to wired systems. Due to the lack of transmission bandwidth and energy in wireless measurement systems, the amount of digital data being sent has to be reduced; hence, we have to apply ADC systems that can achieve the required digital signal quality, reducing the bit-rate. Four ADC systems are analyzed, for possible application in wireless measurement systems: PCM (pulse code modulation) based on uniform quantization; DPCM (differential PCM) to exploit high correlation of vibration signals; two adaptive ADC systems to cope with significant variations of characteristics of vibration signals in time - APCM (adaptive PCM) with adaptation on variance and ADPCM (adaptive DPCM), with double adaptation (both on variance and correlation). These ADC models are designed and optimized specifically for vibration signals, based on the analysis of 20 vibration signals from a referent database. An experiment is done, applying designed ADC systems for digitalization of vibration signals. APCM, DPCM and ADPCM systems allow significant bit-rate reduction compared to the PCM system, but with the increasing of complexity, hence the compromise between the bit-rate reduction and complexity is needed.

Instance Selection

2011 ◽  
pp. 1041-1045
Author(s):  
Huan Liu
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Data Reduction ◽  
Effective Means ◽  
Digital Data ◽  
Data Selection ◽  
Instance Selection ◽  
Redundant Data ◽  
Performance Deterioration ◽  
Digital Data Storage

The amounts of data become increasingly large in recent years as the capacity of digital data storage worldwide has significantly increased. As the size of data grows, the demand for data reduction increases for effective data mining. Instance selection is one of the effective means to data reduction. This article introduces basic concepts of instance selection, its context, necessity and functionality. It briefly reviews the state-of-the-art methods for instance selection. Selection is a necessity in the world surrounding us. It stems from the sheer fact of limited resources. No exception for data mining. Many factors give rise to data selection: data is not purely collected for data mining or for one particular application; there are missing data, redundant data, and errors during collection and storage; and data can be too overwhelming to handle. Instance selection is one effective approach to data selection. It is a process of choosing a subset of data to achieve the original purpose of a data mining application. The ideal outcome of instance selection is a model independent, minimum sample of data that can accomplish tasks with little or no performance deterioration.

A Global Survey on Data Deduplication

2018 ◽  
Vol 10 (4) ◽  
pp. 43-66 ◽  
Author(s):  
Shubhanshi Singhal ◽  
Pooja Sharma ◽  
Rajesh Kumar Aggarwal ◽  
Vishal Passricha
Keyword(s):  
Data Reduction ◽  
Cloud Storage ◽  
Storage Systems ◽  
Distributed Storage ◽  
Digital Data ◽  
Data Deduplication ◽  
Computationally Efficient ◽  
Redundant Data ◽  
Single Instance ◽  
Local Storage

This article describes how data deduplication efficiently eliminates the redundant data by selecting and storing only single instance of it and becoming popular in storage systems. Digital data is growing much faster than storage volumes, which shows the importance of data deduplication among scientists and researchers. Data deduplication is considered as most successful and efficient technique of data reduction because it is computationally efficient and offers a lossless data reduction. It is applicable to various storage systems, i.e. local storage, distributed storage, and cloud storage. This article discusses the background, components, and key features of data deduplication which helps the reader to understand the design issues and challenges in this field.

Specplot - An Interactive Data Reduction and Display Program for Spectral Data

1979 ◽  
Vol 23 ◽  
pp. 305-311 ◽  
Author(s):  
Raymond P. Goehner
Keyword(s):  
Spectral Data ◽  
Data Reduction ◽  
Usual Method ◽  
Spectral Lines ◽  
Digital Data ◽  
Strip Chart Recorder ◽  
Automatic Data ◽  
Chart Recorder ◽  
Rapid Pace ◽  
Interactive Data

The automation of analytical equipment is proceeding at a rapid pace, particularly since the introduction of inexpensive microcomputer systems. Most of this equipment has one characteristic in common, that is, they produce digital spectral data. The usual method of recording spectral data has been the strip chart recorder. Strip charts require the hand encoding of position and intensities of the spectral lines. This requires that all of the lines be on scale or that the sample be run several times in order to amplify weaker lines. This problem is eliminated by recording the data digitally. Digital data can then be rapidly plotted on a cathode ray terminal to any desired scale. The user of digital data has access to a great variety of automatic data reduction programs.

The EPOS Automated Selective Chemistry Analyzer evaluated.

1986 ◽  
Vol 32 (1) ◽  
pp. 165-169
Author(s):  
G C Moses ◽  
G O Lightle ◽  
J F Tuckerman ◽  
A R Henderson
Keyword(s):  
Alkaline Phosphatase ◽  
Creatine Kinase ◽  
Lactate Dehydrogenase ◽  
Data Reduction ◽  
Analytical Performance ◽  
Gamma Glutamyltransferase ◽  
External Data ◽  
Reduction System ◽  
Oriented System

Abstract We evaluated the analytical performance of the EPOS (Eppendorf Patient Oriented System) Automated Selective Chemistry Analyzer, using the following tests for serum analytes: alanine and aspartate aminotransferases, lactate dehydrogenase, creatine kinase, gamma-glutamyltransferase, alkaline phosphatase, and glucose. Results from the EPOS correlated well with those from comparison instruments (r greater than or equal to 0.990). Precision and linearity limits were excellent for all tests; linearity of the optical and pipetting systems was satisfactory. Reagent carryover was negligible. Sample-to-sample carryover was less than 1% for all tests, but only lactate dehydrogenase was less than the manufacturer's specified 0.5%. Volumes aspirated and dispensed by the sample and reagent II pipetting systems differed significantly from preset values, especially at lower settings; the reagent I system was satisfactory at all volumes tested. Minimal daily maintenance and an external data-reduction system make the EPOS a practical alternative to other bench-top chemistry analyzers.

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