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Some questions involve projects that the candidate has worked on in the past. A coding interview is intended to seek out creative thinkers and those who can adapt their solutions to rapidly changing and dynamic scenarios. [citation needed] Typical questions that a candidate might be asked to answer during the second-round interview include: [7]
After line coding, the signal is put through a physical communication channel, either a transmission medium or data storage medium. [ 3 ] [ 4 ] The most common physical channels are: the line-coded signal can directly be put on a transmission line , in the form of variations of the voltage or current (often using differential signaling ).
This process is iterated until a valid codeword is achieved or decoding is exhausted. This type of decoding is often referred to as sum-product decoding. The decoding of the SPC codes is often referred to as the "check node" processing, and the cross-checking of the variables is often referred to as the "variable-node" processing.
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As with ideal observer decoding, a convention must be agreed to for non-unique decoding. The maximum likelihood decoding problem can also be modeled as an integer programming problem. [1] The maximum likelihood decoding algorithm is an instance of the "marginalize a product function" problem which is solved by applying the generalized ...
The sliding application represents the 'convolution' of the encoder over the data, which gives rise to the term 'convolutional coding'. The sliding nature of the convolutional codes facilitates trellis decoding using a time-invariant trellis. Time invariant trellis decoding allows convolutional codes to be maximum-likelihood soft-decision ...
In 2023, building on three exciting works, [17] [18] [19] coding theorists showed that Reed-Solomon codes defined over random evaluation points can actually achieve list decoding capacity (up to n−k errors) over linear size alphabets with high probability. However, this result is combinatorial rather than algorithmic.
Arithmetic coding is a more modern coding technique that uses the mathematical calculations of a finite-state machine to produce a string of encoded bits from a series of input data symbols. It can achieve superior compression compared to other techniques such as the better-known Huffman algorithm.