Synapsing variable-length crossover: Meaningful crossover for variable-length genomes

The synapsing variable-length crossover (SVLC algorithm provides a biologically inspired method for performing meaningful crossover between variable-length genomes. In addition to providing a rationale for variable-length crossover, it also provides a genotypic similarity metric for variable-length genomes, enabling standard niche formation techniques to be used with variable-length genomes. Unlike other variable-length crossover techniques which consider genomes to be rigid inflexible arrays and where some or all of the crossover points are randomly selected, the SVLC algorithm considers genomes to be flexible and chooses non-random crossover points based on the common parental sequence similarity. The SVLC algorithm recurrently "glues" or synapses homogenous genetic subsequences together. This is done in such a way that common parental sequences are automatically preserved in the offspring with only the genetic differences being exchanged or removed, independent of the length of such differences. In a variable-length test problem, the SVLC algorithm compares favorably with current variable-length crossover techniques. The variable-length approach is further advocated by demonstrating how a variable-length genetic algorithm (GA) can obtain a high fitness solution in fewer iterations than a traditional fixed-length GA in a two-dimensional vector approximation task.

A n-Bit Reconfigurable Scalar Quantiser

A reconfigurable scalar quantiser capable of accepting n-bit input data is presented. The data length n can be varied in the range 1... N-1 under partial-run time reconfiguration, p-RTR. Issues as improvement in throughput using this reconfigurable quantiser of p-RTR against RTR for data of variable length are considered. The quantiser design referred to as the priority quantiser PQ is then compared against a direct design of the quantiser DIQ. It is then evaluated that for practical quantiser sizes, PQ shows better area usage when both are targeted onto the same FPGA. Other benefits are also identified.

An LMS style variable tap-length algorithm for structure adaptation

Searching for the optimum tap-length that best balances the complexity and steady-state performance of an adaptive filter has attracted attention recently. Among existing algorithms that can be found in the literature, two of which, namely the segmented filter (SF) and gradient descent (GD) algorithms, are of particular interest as they can search for the optimum tap-length quickly. In this paper, at first, we carefully compare the SF and GD algorithms and show that the two algorithms are equivalent in performance under some constraints, but each has advantages/disadvantages relative to the other. Then, we propose an improved variable tap-length algorithm using the concept of the pseudo fractional tap-length (FT). Updating the tap-length with instantaneous errors in a style similar to that used in the stochastic gradient [or least mean squares (LMS)] algorithm, the proposed FT algorithm not only retains the advantages from both the SF and the GD algorithms but also has significantly less complexity than existing algorithms. Both performance analysis and numerical simulations are given to verify the new proposed algorithm.

Optimum decision delay of the finite-length DFE

This work investigates the optimum decision delay and tap-length of the finite-length decision feedback equalizer. First we show that, if the feedback filter (FBF) length Nb is equal to or larger than the channel memory v and the decision delay Δ is smaller than the feedforward filter (FFF) length Nf, then only the first Δ+1 elements of the FFF can be nonzero. Based on this result we prove that the maximum effective FBF length is equal to the channel memory v, and if Nb ≥ v and Nf is long enough, the optimum decision delay that minimizes the MMSE is Nf-1.

An adaptive linear equalizer with optimum filter length and decision delay

The power of an adaptive equalizer is maximized when the structural parameters including the tap-length and decision delay can be optimally chosen. Although the method for adjusting either the tap-length or decision delay has been proposed, adjusting both simultaneously becomes much more involved as they interact with each other. In this paper, this problem is solved by putting a linear prewhitener before the equalizer, with which the equivalent channel becomes maximum-phase. This implies that the optimum decision delay can be simply ¯xed at the tap-length minus one, while the tap-length can then be chosen using a similar approach as that proposed in our previous work.

Flexible MMSE DFE using length and LAG adaptation

This paper investigates how to choose the optimum tap-length and decision delay for the decision feedback equalizer (DFE). Although the feedback filter length can be set as the channel memory, there is no closed-form expression for the feedforward filter length and decision delay. In this paper, first we analytically show that the two dimensional search for the optimum feedforward filter length and decision delay can be simplified to a one dimensional search, and then describe a new adaptive DFE where the optimum structural parameters can be self-adapted.