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This is a family of Las Vegas-probabilistic methods all based on the observation that it is easier to guess enough error-free positions, than it is to guess all the error-positions. The simplest form is due to Prange: Let G {\displaystyle G} be the k × n {\displaystyle k\times n} generator matrix of C {\displaystyle C} used for encoding.
A domain-specific architecture (DSA) is a programmable computer architecture specifically tailored to operate very efficiently within the confines of a given application domain. The term is often used in contrast to general-purpose architectures, such as CPUs , that are designed to operate on any computer program .
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).
For an arbitrary rate, an accurate rule of thumb is 2.5(K - 1)/(1−r) where r is the code rate. [ 1 ] However, computing the node which has accumulated the largest cost (either the largest or smallest integral path metric) involves finding the maxima or minima of several (usually 2 K -1 ) numbers, which may be time consuming when implemented ...
The Digital Signature Algorithm (DSA) is a public-key cryptosystem and Federal Information Processing Standard for digital signatures, based on the mathematical concept of modular exponentiation and the discrete logarithm problem. In a public-key cryptosystem, a pair of private and public keys are created: data encrypted with either key can ...
Arithmetic coding (AC) is a form of entropy encoding used in lossless data compression.Normally, a string of characters is represented using a fixed number of bits per character, as in the ASCII code.
In public-key cryptography, Edwards-curve Digital Signature Algorithm (EdDSA) is a digital signature scheme using a variant of Schnorr signature based on twisted Edwards curves. [1] It is designed to be faster than existing digital signature schemes without sacrificing security.
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.