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Transfer-appropriate processing (TAP) is a type of state-dependent memory specifically showing that memory performance is not only determined by the depth of processing (where associating meaning with information strengthens the memory; see levels-of-processing effect), but by the relationship between how information is initially encoded and how it is later retrieved.
Transfer-appropriate processing is a strategy for encoding that leads to successful retrieval. An experiment conducted by Morris and coworkers in 1977 proved that successful retrieval was a result of matching the type of processing used during encoding. [41]
Marshalling is similar to or synonymous with serialization, although technically serialization is one step in the process of marshalling an object.. Marshalling is describing the overall intent or process to transfer some live object from a client to a server (with client and server taken as abstract, mirrored concepts mapping to any matching ends of an arbitrary communication link ie.
In contrast, convolutional codes are typically decoded using soft-decision algorithms like the Viterbi, MAP or BCJR algorithms, which process (discretized) analog signals, and which allow for much higher error-correction performance than hard-decision decoding. Nearly all classical block codes apply the algebraic properties of finite fields ...
Typical level-of-processing theory would predict that picture encodings would create deeper processing than lexical encoding. "Memory over the short term and the long term has been thought to differ in many ways in terms of capacity, the underlying neural substrates, and the types of processes that support performance." [13]
To convolutionally encode data, start with k memory registers, each holding one input bit.Unless otherwise specified, all memory registers start with a value of 0. The encoder has n modulo-2 adders (a modulo 2 adder can be implemented with a single Boolean XOR gate, where the logic is: 0+0 = 0, 0+1 = 1, 1+0 = 1, 1+1 = 0), and n generator polynomials — one for each adder (see figure below).
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In information theory, data compression, source coding, [1] or bit-rate reduction is the process of encoding information using fewer bits than the original representation. [2] Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in ...