Teorija kodiranja — разлика између измена
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Teorija kodiranja je studija of the properties of codes and their respective fitness for specific applications. Codes are used for data compression, cryptography, error detection and correction, data transmission and data storage. Codes are studied by various scientific disciplines—such as information theory, electrical engineering, mathematics, linguistics, and computer science—for the purpose of designing efficient and reliable data transmission methods. This typically involves the removal of redundancy and the correction or detection of errors in the transmitted data.
There are four types of coding:[1]
- Data compression (or, source coding)
- Error control (or channel coding)
- Cryptographic coding
- Line coding
Data compression attempts to remove redundancy from the data from a source in order to transmit it more efficiently. For example, Zip data compression makes data files smaller, for purposes such as to reduce Internet traffic. Data compression and error correction may be studied in combination.
Error correction adds extra data bits to make the transmission of data more robust to disturbances present on the transmission channel. The ordinary user may not be aware of many applications using error correction. A typical music CD uses the Reed-Solomon code to correct for scratches and dust. In this application the transmission channel is the CD itself. Cell phones also use coding techniques to correct for the fading and noise of high frequency radio transmission. Data modems, telephone transmissions, and the NASA Deep Space Network all employ channel coding techniques to get the bits through, for example the turbo code and LDPC codes.
Istorija teorije kodiranja
In 1948, Claude Shannon published "A Mathematical Theory of Communication", an article in two parts in the July and October issues of the Bell System Technical Journal. This work focuses on the problem of how best to encode the information a sender wants to transmit. In this fundamental work he used tools in probability theory, developed by Norbert Wiener, which were in their nascent stages of being applied to communication theory at that time. Shannon developed information entropy as a measure for the uncertainty in a message while essentially inventing the field of information theory.
The binary Golay code was developed in 1949. It is an error-correcting code capable of correcting up to three errors in each 24-bit word, and detecting a fourth.
Richard Hamming won the Turing Award in 1968 for his work at Bell Labs in numerical methods, automatic coding systems, and error-detecting and error-correcting codes. He invented the concepts known as Hamming codes, Hamming windows, Hamming numbers, and Hamming distance.
In 1972, Nasir Ahmed proposed the discrete cosine transform (DCT), which he developed with T. Natarajan and K. R. Rao in 1973.[2] The DCT is the most widely used lossy compression algorithm, the basis for multimedia formats such as JPEG, MPEG and MP3.
Neuralno kodiranje
Neuralno kodiranje is a neuroscience-related field concerned with how sensory and other information is represented in the brain by networks of neurons. The main goal of studying neural coding is to characterize the relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among electrical activity of the neurons in the ensemble.[3] It is thought that neurons can encode both digital and analog information,[4] and that neurons follow the principles of information theory and compress information,[5] and detect and correct[6] errors in the signals that are sent throughout the brain and wider nervous system.
Reference
- ^ James Irvine; David Harle (2002). „2.4.4 Types of Coding”. Data Communications and Networks. стр. 18. ISBN 9780471808725. „There are four types of coding”
- ^ Nasir Ahmed. „How I Came Up With the Discrete Cosine Transform”. Digital Signal Processing, Vol. 1, Iss. 1, 1991, pp. 4-5.
- ^ Brown EN, Kass RE, Mitra PP (мај 2004). „Multiple neural spike train data analysis: state-of-the-art and future challenges”. Nat. Neurosci. 7 (5): 456—61. PMID 15114358. doi:10.1038/nn1228.
- ^ Thorpe, S.J. (1990). „Spike arrival times: A highly efficient coding scheme for neural networks” (PDF). Ур.: Eckmiller, R.; Hartmann, G.; Hauske, G. Parallel processing in neural systems and computers (PDF). North-Holland. стр. 91—94. ISBN 978-0-444-88390-2. Приступљено 30. 6. 2013.
- ^ Gedeon, T.; Parker, A.E.; Dimitrov, A.G. (пролеће 2002). „Information Distortion and Neural Coding”. Canadian Applied Mathematics Quarterly. 10 (1): 10. CiteSeerX 10.1.1.5.6365
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Stiber, M. (јул 2005). „Spike timing precision and neural error correction: local behavior”. Neural Computation. 17 (7): 1577—1601. PMID 15901408. arXiv:q-bio/0501021 . doi:10.1162/0899766053723069.
Literatura
- Elwyn R. Berlekamp (2014), Algebraic Coding Theory, World Scientific Publishing (revised edition), ISBN 978-9-81463-589-9.
- MacKay, David J. C.. Information Theory, Inference, and Learning Algorithms Cambridge: Cambridge University Press, 2003. ISBN 0-521-64298-1
- Vera Pless (1982), Introduction to the Theory of Error-Correcting Codes, John Wiley & Sons, Inc., ISBN 0-471-08684-3.
- Randy Yates, A Coding Theory Tutorial.
- Schroeder, Manfred R. (2014). „Bell Laboratories”. Acoustics, Information, and Communication: Memorial Volume in Honor of Manfred R. Schroeder. Springer. стр. 388. ISBN 9783319056609.
- Gray, Robert M. (2010). „A History of Realtime Digital Speech on Packet Networks: Part II of Linear Predictive Coding and the Internet Protocol” (PDF). Found. Trends Signal Process. 3 (4): 203—303. ISSN 1932-8346. doi:10.1561/2000000036.
- Ghanbari, Mohammed (2003). Standard Codecs: Image Compression to Advanced Video Coding. Institution of Engineering and Technology. стр. 1—2. ISBN 9780852967102.
- Wade, Graham (1994). Signal coding and processing (2 изд.). Cambridge University Press. стр. 34. ISBN 978-0-521-42336-6. Приступљено 2011-12-22. „The broad objective of source coding is to exploit or remove 'inefficient' redundancy in the PCM source and thereby achieve a reduction in the overall source rate R.”
- Salomon, David (2008). A Concise Introduction to Data Compression. Berlin: Springer. ISBN 9781848000728.
- S. Mittal; J. Vetter (2015), „A Survey Of Architectural Approaches for Data Compression in Cache and Main Memory Systems”, IEEE Transactions on Parallel and Distributed Systems, IEEE
- Tank, M.K. (2011). „Implementation of Lempel-ZIV algorithm for lossless compression using VHDL”. Implementation of Limpel-Ziv algorithm for lossless compression using VHDL. Thinkquest 2010: Proceedings of the First International Conference on Contours of Computing Technology. Berlin: Springer. стр. 275—283. ISBN 978-81-8489-988-7. doi:10.1007/978-81-8489-989-4_51.
