Cornelio Yáñez

A novel approach to automatic color matching

In this paper the design and operation of an Automatic Color Matching system is presented. This novel system takes advantage of the improvements introduced by Alpha-Beta associative memories, an efficient, unconventional model of associative memory of recent creation. The results are demonstrated through experiments on a relatively small database with 1001 samples prepared by the authors. However, the approach is considered valid according to the tendency of the results obtained, in part, thanks to the performance exhibited by Alpha-Beta associative memories.

Image compression algorithm based on morphological associative memories

A new method for image compression based on Morphological Associative Memories (MAM) is proposed. We used MAM at the transformation stage of image coding, thereby replacing the traditional methods such as Discrete Cosine Transform or Wavelet Transform. After applying the MAM, the informative image data are concentrated in a minimum of values. The next stages of image coding can be obtained by taking advantage of this new representation of the image. The main advantage offered by the MAM with respect to the traditional methods is the speed of processing, whereas the compression rate and the obtained signal to noise ratios compete with the traditional methods.

Super-twisting sliding mode differentiation for improving PD controllers performance of second order systems.

Designing a proportional derivative (PD) controller has as main problem, to obtain the derivative of the output error signal when it is contaminated with high frequency noises. To overcome this disadvantage, the supertwisting algorithm (STA) is applied in closed-loop with a PD structure for multi-input multi-output (MIMO) second order nonlinear systems. The stability conditions were analyzed in terms of a strict non-smooth Lyapunov function and the solution of Riccati equations. A set of numerical test was designed to show the advantages of implementing PD controllers that used STA as a robust exact differentiator. The first numerical example showed the stabilization of an inverted pendulum. The second example was designed to solve the tracking problem of a two-link robot manipulator.

Morphological Transform for Image Compression

A new method for image compression based on morphological associative memories (MAMs) is presented. We used the MAM to implement a new image transform and applied it at the transformation stage of image coding, thereby replacing such traditional methods as the discrete cosine transform or the discrete wavelet transform. Autoassociative and heteroassociative MAMs can be considered as a subclass of morphological neural networks. The morphological transform (MT) presented in this paper generates heteroassociative MAMs derived from image subblocks. The MT is applied to individual blocks of the image using some transformation matrix as an input pattern. Depending on this matrix, the image takes a morphological representation, which is used to perform the data compression at the next stages. With respect to traditional methods, the main advantage offered by the MT is the processing speed, whereas the compression rate and the signal-to-noise ratio are competitive to conventional transforms.

Evolutive Improvement of Parameters in an Associative Classifier

This paper presents an effective method to improve some of the parameters in an associative classifier, thus increasing its performance. This is accomplished using the simplicity and symmetry of the differential evolution metaheuristic. When modifying some parameters contained in the Gamma associative classifier, which is a novel associative model for pattern classification, this model have been found to be more efficient in the correct discrimination of objects; experimental results show that applying evolutionary algorithms models the desired efficiency and robustness of the classifier model is achieved. In this first approach, improving the Gamma associative classifier is achieved by applying the differential evolution algorithm.

Hardware implementation of image recognition system based on morphological associative memories and discrete wavelet transform

The implementation of a specific image recognition technique for an artificial vision system is presented. The proposed algorithm involves two steps. First, smaller images are obtained using Discrete Wavelet Transform (DWT) after four stages of decomposition and taking only the approximations. This way the volume of information to process is reduced considerably and the system memory requirements are reduced as well. Another purpose of DWT is to filter noise that could be induced in the images. Second, the Morphological Associative Memories (MAM) are used to recognize landmarks. The proposed algorithm provides flexibility, possibility to parallelize algorithms and high overall performance of hardware implemented image retrieval system. The resulted hardware implementation has low memory requirements, needs in limited arithmetical precision and reduced number of simple operations. These benefits are guaranteed due to the simplicity of MAM learning/restoration process that uses simple morphological operations, dilation and erosion, in other words, MAM calculate maximums or minimums of sums. These features turn out the artificial vision system to be robust and optimal for the use in realtime autonomous systems. The proposed image recognition system has, among others, the following useful features: robustness to the noise induced in the patter to process, high processing speed, and it can be easily adapted to diverse operation circumstances.

Design of an evolutionary codebook based on morphological associative memories

A central issue in the use of vector quantization (VQ) for speech or image compression is the specification of the codebook. In this paper, the design of an evolutionary codebook based on morphological associative memories (MAM) is presented. The algorithm proposed for codebook generation involves two steps. First, having a set of images, one of the images is chosen to create the initial codebook. The algorithm applied to the image for codebook generation uses the morphological autoassociative memories (MAAM). Second, an evolution process of codebook creation occurs applying the algorithm on new images. This process adds the information codified of the next image to the codebook allowing to recover the images with better quality without affecting the processing speed. The performance of the generated codebook is analyzed in case when MAAM in both max and min categories are used. The presented algorithm was applied to image set after discrete cosine transformation followed by a quantization process. The proposed algorithm has a high processing speed and provides a notable improvement in signal to noise ratio.

Vector Quantization Algorithm Based on Associative Memories

This paper presents a vector quantization algorithm for image compression based on extended associative memories. The proposed algorithm is divided in two stages. First, an associative network is generated applying the learning phase of the extended associative memories between a codebook generated by the LBG algorithm and a training set. This associative network is named EAM-codebook and represents a new codebook which is used in the next stage. The EAM-codebook establishes a relation between training set and the LBG codebook. Second, the vector quantization process is performed by means of the recalling stage of EAM using as associative memory the EAM-codebook. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantages offered by the proposed algorithm is high processing speed and low demand of resources (system memory); results of image compression and quality are presented.

Skull Fractures Detection by Finite Element Method

In this paper an algorithm is developed that allows by means of the method of finite element to detect skull fractures. We intend the theoretical foundation that sustains when segmenting cranial images, these methods based on the method of finite element (FEM), for their initials in English, that allows us to detect fractures in the images. With the development of this paper the efficiency of the method of finite element is shown in the segmentation of images. Likewise, it is made notice the importance that up to now have acquired in the areas of segmentation of images due to their capacity to solve numeric methods. In turn, continuity is given to that the investigation groups that develop projects related with the analysis of images prescribe, continue to the so much of the area of finite element and they follow them incorporating in their algorithms, mainly in concerning tasks to the theory analysis of images and their applications.

Prediction of CO and NOx Levels in Mexico City Using Associative Models

Artificial Intelligence has been present since more than two decades ago, in the treatment of data concerning the protection of the environment; in particular, various groups of researchers have used genetic algorithms and artificial neural networks in the analysis of data related to the atmospheric sciences and the environment. However, in this kind of applications has been conspicuously absent from the associative models, by virtue of which the classic associative techniques exhibit very low yields. This article presents the results of applying Alpha-Beta associative models in the analysis and prediction of the levels of Carbon Monoxide (CO) and Nitrogen Oxides (NOx) in Mexico City.Artificial Intelligence has been present since more than two decades ago, in the treatment of data concerning the protection of the environment; in particular, various groups of researchers have used genetic algorithms and artificial neural networks in the analysis of data related to the atmospheric sciences and the environment. However, in this kind of applications has been conspicuously absent from the associative models, by virtue of which the classic associative techniques exhibit very low yields. This article presents the results of applying Alpha-Beta associative models in the analysis and prediction of the levels of Carbon Monoxide (CO) and Nitrogen Oxides (NOx) in Mexico City