Luis Pastor Sánchez Fernández

Noise pattern recognition of airplanes taking off: task for a monitoring system

This paper presents an original work for aircraft noise monitoring systems and it analyzes the airplanes noise signals and a method to identify them. The method uses processed spectral patterns and a neuronal network feed-forward, programmed by means of virtual instruments. The obtained results, very useful in portable systems, make possible to introduce redundancy to permanent monitoring systems. The noise level in a city has fluctuations between 50 dB (A) and 100 dB (A). It depends on the population density and its activity, commerce and services in the public thoroughfare, terrestrial and aerial urban traffic, of the typical activities of labor facilities and used machinery, which give varied conditions that must be faced of diverse ways within the corresponding normalization. The sounds or noises that exceed the permissible limits, whichever the activities or causes that originate them, are considered events susceptible to degrade the environment and the health.

Approaches to classification of multichannel images

The comparison of different approaches to classification of multichannel remote sensing images obtained by spaceborne imaging systems is presented. It is demonstrated that it is reasonable to compress original noisy images with appropriate compression ratio and then to classify the decompressed images rather than original data. Two classifiers are considered: based on radial basis function neural network and support vector machine. The latter one produces slightly better classification results.

A Fast Search Algorithm for Vector Quantization Based on Associative Memories

One of the most serious problems in vector quantization is the high computational complexity at the encoding phase. This paper presents a new fast search algorithm for vector quantization based on Extended Associative Memories (FSA-EAM). In order to obtain the FSA-EAM, first, we used the Extended Associative Memories (EAM) to create an EAM-codebook applying the EAM training stage to the codebook produced by the LBG algorithm. The result of this stage is an associative network whose goal is to establish a relation between training set and the codebook generated by the LBG algorithm. This associative network is EAM-codebook which is used by the FSA-EAM. The FSA-EAMVQ process is performed using the recalling stage of EAM. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantage offered by the proposed algorithm is high processing speed and low demand of resources (system memory), while the encoding quality remains competitive.

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.

Assessment and prediction of water quality in shrimp culture using signal processing techniques

In recent years, artificial intelligence methods have proved appropriate for the treatment of environmental problems. This paper presents a novel work for the assessment and prediction of water quality in shrimp aquaculture based on environmental pattern processing. Water quality studies are based on analyzing negative concentrations of compounds in shrimp ponds that inhibit good growth and reproduction of organisms. The physical–chemical variables are classified based on the negative ecological impact using the Gamma (Γ) classifier, which calculates the frequency and deviation of the measurements from a specific level. A fuzzy inference system processes the level classifications using a reasoning process that determines when a specific concentration is good or harmful for the organism and provides a water quality index, which describes the condition of the ecosystem: excellent, good, regular, and poor. An autoregressive model (AR) predicts a section of an environmental signal using historical information, and the set of predicted variables are assessed in order to estimate future water quality conditions in the system. This methodology emerges as a suitable and alternative tool to be used in developing effective water management plans.

Inter-turn Short Circuit and Unbalanced Voltage Pattern Recognition for Three-Phase Induction Motors

A novel diagnostic approach, by means pattern recognition, is proposed for the early detection of inter-turn short circuit on three-phase induction motor and unbalanced input voltage detection. The essential concept is that a minimum inter-turn short circuit at stator motor or unbalanced input voltage produces a slight variation that can be identified in current and rotor speed signals. To achieve this, an own motor mathematical model was created, and in order to generalize the diagnostic for a wide range of motor, is used a novel method to calculate motor parameters through data catalogue. Through this, motors of different power and poles number are emulated and, from these results, an original methodology to transform temporal response in patterns is created.

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.

Assessment of the artificial habitat in shrimp aquaculture using environmental pattern classification

This paper presents a novel model for assessing the water quality for the artificial habitat in shrimp aquaculture. The physical-chemical variables involved in the artificial habitat are measured and studied for modeling the environment of the ecosystem. A new physical-chemical index (Γ) classifies the behavior of the environmental variables, calculating the frequency and the deviations of the measurements based on impact levels. A fuzzy inference system (FIS) is used for establishing a relationship between environmental variables, describing the negative ecological impact of the concentrations reported. The FIS uses a reasoning process for classifying the environmental levels providing a new index, which describes the general status of the water quality (WQI); excellent, good, regular and poor.

Mexico City urban noise control network.

This paper presents an IP network model for monitoring urban noise emitted from mobile and fixed sources. The model is applying to build a monitoring network in the historic center of Mexico City. Design of network system includes measuring station design, network design, central of monitoring design, measuring politics and functionality, event detection modules, central database of historic information, report module design, and administration system. Mexico City Urban Noise Control Network is going to be an important tool to enforce application of local noise regulations, detection of infractions for excessive noise, detection of security events like gun shots, and monitoring of noise pollution levels. Measuring stations are developed to detect average standard levels and individual events, to generate warnings and alarms, and to detect some specific events. Network is developed over a wireless Network.

Single-Step-Ahead and Multi-Step-Ahead Prediction with Evolutionary Artificial Neural Networks

In recent years, Evolutionary Algorithms EAs have been remarkably useful to improve the robustness of Artificial Neural Networks ANNs. This study introduces an experimental analysis using an EAs aimed to evolve ANNs architectures the FS-EPNet algorithm to understand how neural networks are evolved with a steady-state algorithm and compare the Single-step-ahead SSP and Multiple-step-ahead MSP methods for prediction tasks over two test sets. It was decided to test an inside-set during evolution and an outside-set after the whole evolutionary process has been completed to validate the generalization performance with the same method SSP or MSP. Thus, the networks may not be correctly evaluated misleading fitness if the single SSP is used during evolution inside-set and then the MSP at the end of it outside-set. The results show that the same prediction method should be used in both evaluation sets providing smaller errors on average.