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One-hot encoding is often used for indicating the state of a state machine.When using binary, a decoder is needed to determine the state. A one-hot state machine, however, does not need a decoder as the state machine is in the nth state if, and only if, the nth bit is high.
One-hot encoding is easy to interpret, but it requires one to maintain the arbitrary enumeration of . Given a token t ∈ T {\displaystyle t\in T} , to compute ϕ ( t ) {\displaystyle \phi (t)} , we must find out the index i {\displaystyle i} of the token t {\displaystyle t} .
In machine learning this is known as one-hot encoding. Dummy variables are commonly used in regression analysis to represent categorical variables that have more than two levels, such as education level or occupation.
Following are some of the techniques which are widely used for state encoding: In one-hot encoding, only one of the bits of the state variable is "1" (hot) for any given state. All the other bits are "0". The Hamming distance of this technique is 2. One-hot encoding requires one flip-flop for every state in the FSM.
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A Transformer network, to encode assembly opcodes are converted to one-hot encodings and concatenated to form the raw input sequence. A multilayer perceptron network, which encodes the "CPU state", that is, the states of each register and memory location for a given set of inputs,
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This code can be used with up to 8,192 source symbols in a source block, and a total of up to 65,536 encoded symbols generated for a source block. This code has an average relative reception overhead of 0.2% when applied to source blocks with 1,000 source symbols, and has a relative reception overhead of less than 2% with probability 99.9999%. [4]