Ricardo Barrón

Survey on Indoor Power Line Communication Channel Modeling

Power line communication (PLC) offers a convenient and inexpensive medium for data transmission, however this technology still face a difficult challenge: the channel modeling. Although enormous effort has been devoted to determining accurate channel models for the power line, so far, there is not a widely accepted model in the PLC community. In this survey we present recent development and open research issues on PLC channel modeling. The discussion is focused on the modeling of indoor PLC, and in particular, on its transfer function. Then, we discuss the most representative indoor PLC modeling approaches that we found in the recent literature.

Transforming Fundamental Set of Patterns to a Canonical Form to Improve Pattern Recall

Most results (lemmas and theorems) providing conditions under which associative memories are able to perfectly recall patterns of a fundamental set are very restrictive in most practical applications. In this note we describe a simple but effective procedure to transform a fundamental set of patterns (FSP) to a canonical form that fulfils the propositions. This way pattern recall is strongly improved. We provide numerical and real examples to reinforce the proposal.

New Associative Memories to Recall Real-Valued Patterns

In this note we describe a new set of associative memories able to recall patterns in the presence of mixed noise. Conditions are given under which the proposed memories are able to recall patterns either from the fundamental set of patterns and from distorted versions of them. Numerical and real examples are also provided to show the efficiency of the proposal.

A New Two-Level Associative Memory for Efficient Pattern Restoration

Pattern reconstruction or pattern restoration in the presence of noise is a main problem in pattern recognition. An essential feature of the noise acting on a pattern is its local nature. If a pattern is split into enough sub-patterns, a few of them will be less or more affected by noise, others will remain intact. In this paper, we propose a simple but effective methodology that exploits this fact for the efficient restoration of a pattern. A pattern is restored if enough of its sub-patterns are also restored. Since several patterns can share the same sub-patterns, the final decision is accomplished by means of a voting mechanism. Before deciding if a sub-pattern belongs to a pattern, sub-pattern restoration in the presence of noise is done by an associative memory. Numerical and real examples are given to show the effectiveness of the proposal. Formal conditions under which the proposal guaranties perfect restoration of a pattern from an unaltered or and altered version of it are also given.

Real-valued pattern classification based on extended associative memory

We propose an associative model for the classification of real-valued patterns. It is an extension of well-known associative model proposed by K. Steinbuch in 1961, which is known to be only useful in the binary case. The proposed extension is tested in several scenarios with images of realistic objects.

Dendrite Morphological Neural Networks trained by Differential Evolution

A new efficient training algorithm for a Dendrite Morphological Neural Network is proposed. Based on Differential Evolution, the method optimizes the number of dendrites and increases classification performance. This technique has two initialisation ways of learning parameters. The first selects all the patterns and opens a hyper-box per class with a length such that all the patterns of each class remain inside. The second generates clusters for each class by k-means++. After the initialisation, the algorithm divides each hyper-box and applies Differential Evolution to the resultant hyper-boxes to place them in the best position and the best size. Finally, the method selects the set of hyper-boxes that produced the least error from the least number. The new training method was tested with three synthetic and six real databases showing superiority over the state-of-the-art for Dendrite Morphological Neural Network training algorithms and a similar performance as well as a Multilayer Perceptron, a Support Vector Machine and a Radial Basis Network.

Study of the Influence of Noise in the Values of a Median Associative Memory

In this paper we study how the performance of a median associative memory is influenced when the values of its elements are altered by noise. To our knowledge this kind of research has not been reported until know. We give formal conditions under which the memory is still able to correctly recall a pattern of the fundamental set of patterns either from a non-altered or a noisy version of it. Experiments are also given to show the efficiency of the proposal.

Associative Gray Level Pattern Processing using Binary Decomposition and α β Memories

In this note we show how a binary memory can be used to recall gray-level patterns. We take as example the α β associative memories recently proposed in Yáñez, Associative Memories based on order Relations and Binary Operators(In Spanish), PhD Thesis, Center for computing Research, February of 2002, only useful in the binary case. Basically, the idea consists on that given a set of gray-level patterns to be first memorized: (1) Decompose them into their corresponding binary patterns, and (2) Build the corresponding binary associative memory (one memory for each binary layer) with each training pattern set (by layers). A given pattern or a distorted version of it, it is recalled in three steps: (1) Decomposition of the pattern by layers into its binary patterns, (2) Recalling of each one of its binary components, layer by layer also, and (3) Reconstruction of the pattern from the binary patterns already recalled in step 2. The proposed methodology operates at two phases: training and recalling. Conditions for perfect recall of a pattern either from the fundamental set or from a distorted version of one them are also given. Experiments where the efficiency of the proposal is tested are also given.

Scene Retrieval of Natural Images

Feature extraction is a key issue in Content Based Image Retrieval (CBIR). In the past, a number of describing features have been proposed in literature for this goal. In this work a feature extraction and classification methodology for the retrieval of natural images is described. The proposal combines fixed and random extracted points for feature extraction. The describing features are the mean, the standard deviation and the homogeneity (form the co-occurrence) of a sub-image extracted from the three channels: H, S and I. A K -MEANS algorithm and a 1-NN classifier are used to build an indexed database of 300 images. One of the advantages of the proposal is that we do not need to manually label the images for their retrieval. After performing our experimental results, we have observed that in average image retrieval using images not belonging to the training set is of 80.71% of accuracy. A comparison with two similar works is also presented. We show that our proposal performs better in both cases.

A New Unsupervised Learning for Clustering Using Geometric Associative Memories

Associative memories (AMs) have been extensively used during the last 40 years for pattern classification and pattern restoration. A new type of AMs have been developed recently, the so-called Geometric Associative Memories (GAMs), these make use of Conformal Geometric Algebra (CGA) operators and operations for their working. GAMs, at the beginning, were developed for supervised classification, getting good results. In this work an algorithm for unsupervised learning with GAMs will be introduced. This new idea is a variation of the k-means algorithm that takes into account the patterns of the a specific cluster and the patterns of another clusters to generate a separation surface. Numerical examples are presented to show the functioning of the new algorithm.