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Posts Tagged ‘data’

Methodology

Source

Data acquisition begins with the physical phenomenon or physical property to be measured. Examples of this include temperature, light intensity, gas pressure, fluid flow, and force. Regardless of the type of physical property to be measured, the physical state that is to be measured must first be transformed into a unified form that can be sampled by a data acquisition system. The task of performing such transformations falls on devices called sensors.

A sensor, which is a type of transducer, is a device that converts a physical property into a corresponding electrical signal (e.g., a voltage or current) or, in many cases, into a corresponding electrical characteristic (e.g., resistance or capacitance) that can easily be converted to electrical signal.

The ability of a data acquisition system to measure differing properties depends on having sensors that are suited to detect the various properties to be measured. There are specific sensors for many different applications. DAQ systems also employ various signal conditioning techniques to adequately modify various different electrical signals into voltage that can then be digitized using an Analog-to-digital converter (ADC).

Signals

Signals may be digital (also called logic signals sometimes) or analog depending on the transducer used.

Signal conditioning may be necessary if the signal from the transducer is not suitable for the DAQ hardware to be used. The signal may be amplified or deamplified, or may require filtering, or a lock-in amplifier is included to perform demodulation. Various other examples of signal conditioning might be bridge completion, providing current or voltage excitation to the sensor, isolation, linearization, etc.

Analog signals tolerate almost no cross talk and so are converted to digital data, before coming close to a PC or before traveling along long cables. For analog data to have a high signal-to-noise ratio, the signal needs to be very high, and sending +-10 Volts along a fast signal path with a 50 Ohm termination requires powerful drivers. With a slightly mismatched or no termination at all, the voltage along the cable rings multiple times until it is settled in the needed precision. Digital data can have +-0.5 Volt. The same is true for DACs. Also digital data can be sent over glass fiber for high voltage isolation or by means of Manchester encoding or similar through RF-couplers, which prevent net hum.

DAQ hardware

DAQ hardware is what usually interfaces between the signal and a PC. It could be in the form of modules that can be connected to the computer’s ports (parallel, serial, USB, etc…) or cards connected to slots (S-100 bus, AppleBus, ISA, MCA, PCI, PCI-E, etc…) in the mother board. Usually the space on the back of a PCI card is too small for all the connections needed, so an external breakout box is required. The cable between this box and the PC is expensive due to the many wires, the required shielding, and because it is exotic.

DAQ cards often contain multiple components (multiplexer, ADC, DAC, TTL-IO, high speed timers, RAM). These are accessible via a bus by a microcontroller, which can run small programs. The controller is more flexible than a hard wired logic, yet cheaper than a CPU so that it is alright to block it with simple polling loops. For example: Waiting for a trigger, starting the ADC, looking up the time, waiting for the ADC to finish, move value to RAM, switch multiplexer, get TTL input, let DAC proceed with voltage ramp.

Reconfigurable computing may deliver high speed for digital signals. Digital signal processors spend a lot of silicon on arithmetic and allow tight control loops or filters. The fixed connection with the PC allows for comfortable compilation and debugging. Using an external housing a modular design with slots in a bus can grow with the needs of the user. High speed binary data needs special purpose hardware called time to digital converter and high speed 8 bit ADCs called oscilloscopes, which are typically not connected to DAQ hardware, but directly to the PC.

Not all DAQ hardware has to run permanently connected to a PC, for example intelligent stand-alone loggers and controllers, which can be operated from a PC, yet they can operate completely independent of the PC.

DAQ software

DAQ software is needed in order for the DAQ hardware to work with a PC. The device driver performs low-level register writes and reads on the hardware, while exposing a standard API for developing user applications. A standard API such as COMEDI allows the same user applications to run on different operating systems, e.g. a user application that runs on Windows will also run on Linux and BSD.

History

In 1963, IBM produced computers which were specialized for data acquisition. These include the IBM 7700 Data Acquisition System and its successor, the IBM 1800 Data Acquisition and Control System. These expensive specialized systems were surpassed in 1974 by general purpose S-100 computers and data acquisitions cards produced by Tecmar/Scientific Solutions Inc. In 1981 IBM introduced the IBM Personal Computer and Scientific Solutions introduced the first PC data acquisition products.

See also

Signal processing

Data analysis

Test method

Input devices:

3D scanner

Analog to digital converter

Time to digital converter

Hardware:

CAMAC – Computer Automated Measurement and Control

Industrial Ethernet

Industrial USB

LAN eXtensions for Instrumentation

NIM

PowerLab

PCI eXtensions for Instrumentation

VMEbus

VXI

Software:

Comedi

EPICS

LabChart

LabVIEW

MATLAB

References

^ COMDEX FALL November 18, 1981 Las Vegas, NV, “Tecmar shows 20 IBM PC option cards.. LabMaster,LabTender,DADIO,DeviceTender,IEEE-488..”

^ PC Magazine Vol1 No.1, “Taking the Measure” by David Bunnell, “Tecmar deployed 20 option cards for the IBM PC”

^ PC Magazine Vol1 No.5, “Tecmar Triumph” by David Bunnell, Scientific Solutions releases 20 new products for the PC

^ BYTE Vol7 No.1 “Scientific Solutions – Advertisement for data acquisition boards, stepper controllers, IEEE-488 products

^ Test&Meausrement; World Vol11 No 10 Decade of Progress Award: Scientific Solutions – LabMaster First in PC Data Acquisition

Further reading

Simon McBeath (2002). Competition Car Data Logging: A Practical Handbook. J. H. Haynes & Co.. ISBN 1-85960-653-9. 

Simon S. Young (2001). Computerized Data Acquisition and Analysis for the Life Sciences. Cambridge University Press. ISBN 0-521-56570-7. 

W. R. Leo (1994). Techniques for Nuclear and Particle Physics Experiments. Springer. ISBN 3-540-57280-5. 

Charles D. Spencer (1990). Digital Design for Computer Data Acquisition. Cambridge University Press. ISBN 0-521-37199-6. 

B.G. Thompson & A. F. Kuckes (1989). IBM-PC in the laboratory. Cambridge University Press. ISBN 0-521-32199-9. 

Buddy Fey (1996). Data Power: Using Racecar Data Acquisition. Towery Pub. ISBN 1-88109-601-7. 

Categories: Data collectionHidden categories: Articles with unsourced statements from September 2009 | All articles with unsourced statements

Learn more at www.sqlpower.ca/architect SQL Power Architect is a user-friendly Open Source data modeling tool. Created by data warehouse designers, it has many unique features geared specifically for the data warehouse architect. It allows users to reverse-engineer existing databases, perform data profiling on source databases, and auto-generate ETL metadata.

Learn more at www.sqlpower.ca/architect SQL Power Architect is a user-friendly Open Source data modeling tool. Created by data warehouse designers, it has many unique features geared specifically for the data warehouse architect. It allows users to reverse-engineer existing databases, perform data profiling on source databases, and auto-generate ETL metadata.

We tried to get document about a particular data format from a vendor, but they refused to provide it. Can we read that data file through reverse engineering and still sell our software application? Thanks for help.

Prototype and simulate data behaviour with Data masters

Data masters can be understood as lists of data fields. A data master is a single list, whose columns or attributes can be defined by the user. A very good example of this is a book store application. Imagine you need to create a prototype of an application to manage a book store, which needs to manage all the books. For that purpose, you would create a data master called Book, and add the attributes ISBN, Name, Author, Edition and Description. You would later use this data master to create screens with lists, creation and edition forms, etc.

Creating forms through Data masters

Drag and drop a data master from the pane to a screen or template to create a set of input fields with all the attributes of this data master in a form structure. Each field is linked to the data master’s attribute.

Drag and drop a single attribute of a data master to a screen or template to create a single input field linked to that data master’s attribute.

Add datagrids to your wireframes

Datagrid builds a list of data. In order to create a data grid for a particular data master, follow the steps:

Drag the data grid icon from the Screen Components pane and drop it in the canvas at the desired position. Use the size feedback to position the data grid to the desired location. Enter the data grid identifier and select the data master you want to list from the Data Master drop down. Once the data master is selected, all its attributes appear in the Data Field left list. Select the attributes you want to display in the data grid as columns.

Simulate datagrids with your own data

In the tab View and Edit Records you can add data instances of your data master. To do so, click on the plus button and add them manually. Also, you can import data from CSV files and export current data to CSV using the import and export utilities provided. When importing, the utility lets you select amongst three CSV separators: semicolon, comma and tabulator. Also, it lets you add the imported file records to the current or overwrite them.