Automatic identification and data capture

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Automatic Identification and Data Capture (AIDC) refers to the methods of automatically identifying objects, collecting data about them, and entering that data directly into computer systems (i.e. without human involvement). Technologies typically considered as part of AIDC include bar codes, Radio Frequency Identification (RFID), biometrics, magnetic stripes, Optical Character Recognition (OCR), smart cards, and voice recognition. AIDC is also commonly referred to as utomatic Identification, uto-ID, and “Automatic Data Capture.”
AIDC is the process or means of obtaining external data, particularly through analysis of images, sounds or videos. To capture data, a transducer is employed which converts the actual image or a sound into a digital file. The file is then stored and at a later time it can be analyzed by a computer, or compared with other files in a database to verify identity or to provide authorization to enter a secured system. Capturing of data can be done in various ways; the best method depends on application.
AIDC also refers to the methods of recognizing objects, getting information about them and entering that data or feeding it directly into computer systems without any human involvement. Automatic identification and data capture technologies include barcodes, RFID, OCR, magnetic stripes, smart cards and biometrics.
In biometric security systems, capture is the acquisition of or the process of acquiring and identifying characteristics such as finger image, palm image, facial image, iris print or voice print which involves audio data and the rest all involves video data.
Radio frequency identification (RFID) is relatively a new AIDC technology which was first developed in 1980. The technology acts as a base in automated data collection, identification and analysis systems worldwide. RFID has found its importance in a wide range of markets including livestock identification and Automated Vehicle Identification (AVI) systems because of its capability to track moving objects. These automated wireless AIDC systems are effective in manufacturing environments where barcode labels could not survive.
1 The Internet of Things and the supply chain of the future Auto-ID initiative
2 See also
3 References
4 External links
The Internet of Things and the supply chain of the future Auto-ID initiative
The idea is as simple as its application is difficult. If all cans, books, shoes or parts of cars are equipped with minuscule identifying devices, daily life on our planet will undergo a transformation. Things like running out of stock or wasted products will no longer exist as we will know exactly what is being consumed on the other side of the globe. Theft will be a thing of the past as we will know where a product is at all times.
The global association Auto-ID Center was founded in 1999 and is made up of 100 of the largest companies in the world such as Wal-Mart, Coca-Cola, Gillette, Johnson & Johnson, Pfizer, Procter & Gamble, Unilever, UPS, companies working in the sector of technology such as SAP, Aliens, Sun as well as five academic research centers. These are based at the following Universities; MIT in the USA, Cambridge University in the UK, the University of Adelaide in Australia, Keio University in Japan and University of St. Gallen in Switzerland.
The Auto-ID Center suggests a concept of a future supply chain that is based on the Internet of objects, i.e. a global application of RFID. They try to harmonize technology, processes and organization. Research is focused on miniaturization (aiming for a size of 0.3 mm/chip), reduction in the price per single device (aiming at around $0.05 per unit), the development of innovative application such as payment without any physical contact (Sony/Philips), domotics (clothes equipped with radio tags and intelligent washing machines) and, last but not least, sporting events (timing at the Berlin marathon).
See also
Auto-ID Labs
Device management
Field Service Management
Mobile Enterprise
Mobile Asset Management
Ubiquitous computing
^ Waldner, Jean-Baptiste (2008). Nanocomputers and Swarm Intelligence. London: ISTE John Wiley & Sons. pp.205214. ISBN 1847040020.
External links
AIM Global
Categories: Automatic identification and data capture | Encodings | Multimodal interaction | Human-computer interaction | Radio-frequency identification
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