Multidimensional Systolic Arrays of LMS AlgorithmAdaptive (FIR) Digital Filters

A multidimensional systolic arrays realization of LMS algorithm by a method of mapping regular algorithm onto processor array, are designed. They are based on appropriately selected 1-D systolic array filter that depends on the inner product sum systolic implementation. Various arrays may be derived that exhibit a regular arrangement of the cells (processors) and local interconnection pattern, which are important for VLSI implementation. It reduces latency time and increases the throughput rate in comparison to classical 1-D systolic arrays. The 3-D multilayered array consists of 2-D layers, which are connected with each other only by edges. Such arrays for LMS-based adaptive (FIR) filter may be opposed the fundamental requirements of fast convergence rate in most adaptive filter applications

An Evaluation Of The Explicit Fuzzy Method Using Parametric And Non-Parametric Approaches For Supervised Classification Of Multispectral Remote Sensing Data

Fuzzy Classification is of great interest because of its capacity to provide more useful information for Geographic Information Systems. This paper describes an Explicit Fuzzy Supervised Classification method, which consists of three steps. The explicit fuzzyfication is the first step where the pixel is transformed into a matrix of membership degrees representing the fuzzy inputs of the process. Then, in the second step, a MIN fuzzy reasoning rule followed by a rescaling operation are applied to deduce the fuzzy outputs, or in other words, the fuzzy classification of the pixel. Finally, a defuzzyfication step is carried out to produce a hard classification. The classification results ofLandsat TM data show the promising performance of the method and, particularly, the classification time. These results are compared with those produced by the Maximum Likelihood method and a non-parametric method based on the use of Artificial Neural Networks