In this correspondence we describe a generalization of the state-splitting algorithm (also known as the ACH algorithm) for constructing encoders which encode arbitrary data into constrained systems of sequences. In th...
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In this correspondence we describe a generalization of the state-splitting algorithm (also known as the ACH algorithm) for constructing encoders which encode arbitrary data into constrained systems of sequences. In the generalized algorithm, we replace approximate eigenvectors with approximate eigenmatrices to yield a framework for designing encoders with smaller sliding-block windows and therefore lower error propagation.
An input-constrained channel S is defined as the set of words generated by a finite labeled directed graph. It is shown that every finite-state encoder with finite anticipation (i,e,, with finite decoding delay) for S...
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An input-constrained channel S is defined as the set of words generated by a finite labeled directed graph. It is shown that every finite-state encoder with finite anticipation (i,e,, with finite decoding delay) for S can be obtained through state-splitting rounds applied to some deterministic graph presentation of S, followed by a reduction of equivalent states, Furthermore, each splitting round can be restricted to follow a certain prescribed structure. This result, in turn, provides a necessary and sufficient condition on the existence of finite-state encoders for S with a given rate p : q and a given anticipation a. A second condition is derived on the existence of such encoders;this condition is only necessary, but it applies to every deterministic graph presentation of S, Based on these two conditions, lower bounds are derived on the anticipation of finite-state encoders, Those lower bounds improve on previously known bounds and, in particular, they are shown to be tight for the common rates used for the (1, 7)-runlength-limited (RLL) and (2,7)-RLL constraints.
An input-constrained channel is defined as the set S of finite sequences generated by a finite labeled directed graph which defines the channel. A construction based on a result of Adler, Goodwyn, and Weiss is present...
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An input-constrained channel is defined as the set S of finite sequences generated by a finite labeled directed graph which defines the channel. A construction based on a result of Adler, Goodwyn, and Weiss is presented for finite-state encoders for input-constrained channels. Let G = (V, E) denote a smallest deterministic presentation of S. For a given input-constrained channel S and for any rate p : q up to the capacity c(S) of S, the construction provides finite-state encoders of fixed-rate p : q that can be implemented in hardware with a number of gates which is at most polynomially large in \V\ When p/g < c(S), the encoders have order less than or equal to 12\V\, namely, they can be decoded by looking ahead at up to 12\V\ symbols, thus improving slightly on the order of previously known constructions. A stronger result holds when p/g less than or equal to c(S) - ((log(2) e)/(2(p)q)) and S is of finite memory, where the encoders can be decoded by a sliding-block decoder with look-ahead less than or equal to 2\V\ + 1.
Process mining techniques have been used to discover and analyze workflows in various fields, ranging from business management to healthcare. Much of this research, however, has overlooked the potential of hidden Mark...
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ISBN:
(纸本)9781509048816
Process mining techniques have been used to discover and analyze workflows in various fields, ranging from business management to healthcare. Much of this research, however, has overlooked the potential of hidden Markov models (HMMs) for workflow discovery. We present a novel alignment-guided state-splitting IIMM inference algorithm (AGSS) for discovering workflow models based on observed traces of process executions. We compared the AGSS to existing methods using four real-world medical workflow datasets and a more detailed case study on one of them. Our numerical results show that AGSS not only generates more accurate workflow models, but also better represents the underlying process. In addition, with trace alignment to guide statesplitting, AGSS is significantly more efficient (by a factor of 0(n)) than previous HMM inference algorithms. Our case study results show that our approach produces a more readable and accurate workflow model that existing algorithms. Comparing the discovered model to the hand-made expert model of the same process, we found three discrepancies. These three discrepancies were reconsidered by medical experts and used for enhancing the expert model.
In this paper, we propose the M = 9 run-length-limited (d, k) = (1, 3) code for multilevel recording channels. The code rate is 5/3 (bits/symbol), and the code has a simple 3-state encoder and a sliding block decoder....
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In this paper, we propose the M = 9 run-length-limited (d, k) = (1, 3) code for multilevel recording channels. The code rate is 5/3 (bits/symbol), and the code has a simple 3-state encoder and a sliding block decoder. During the decoding process, the maximum error propagation of input data is seven bits. The structures of the encoder and decoder of the proposed code are very simple, so it may be easily implemented for high-density multilevel recording systems.
In this paper, we propose the M = 9 run-length-limited (d, k) = (1, 3) code for multilevel recording channels. The code rate is 5/3 (bits/symbol), and the code has a simple 3-state encoder and a sliding block decoder....
详细信息
In this paper, we propose the M = 9 run-length-limited (d, k) = (1, 3) code for multilevel recording channels. The code rate is 5/3 (bits/symbol), and the code has a simple 3-state encoder and a sliding block decoder. During the decoding process, the maximum error propagation of input data is seven bits. The structures of the encoder and decoder of the proposed code are very simple, so it may be easily implemented for high-density multilevel recording systems.
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