Jesús Figueroa Nazuno

Bio: Jesús Figueroa Nazuno is an academic researcher from Instituto Politécnico Nacional. The author has contributed to research in topics: Bigram & Syllable. The author has an hindex of 2, co-authored 5 publications receiving 3434 citations.

Cellular Mathematical Morphology

Abstract: In this work basic mathematical morphology operations, such as dilation, erosion, opening, and closing, are reformulated and characterized by means of equivalent cellular automata. In this manner, it becomes possible to take advantage of the broad extent of solid results of theory and applications of cellular automata in creating new algorithms where mathematical morphology is applied, with the advantages of its cellular formulation.

Speech recognition using energy parameters to classify syllables in the spanish language

Abstract: This paper presents an approach for the automatic speech re-cognition using syllabic units. Its segmentation is based on using the Short-Term Total Energy Function (STTEF) and the Energy Function of the High Frequency (ERO parameter) higher than 3,5 KHz of the speech signal. Training for the classification of the syllables is based on ten related Spanish language rules for syllable splitting. Recognition is based on a Continuous Density Hidden Markov Models and the bigram model language. The approach was tested using two voice corpus of natural speech, one constructed for researching in our laboratory (experimental) and the other one, the corpus Latino40 commonly used in speech researches. The use of ERO parameter increases speech recognition by 5% when compared with recognition using STTEF in discontinuous speech and improved more than 1.5% in continuous speech with three states. When the number of states is incremented to five, the recognition rate is improved proportionally to 97.5% for the discontinuous speech and to 80.5% for the continuous one.

Color Matching using Time Series Searching in Color Databases

Abstract: THE GOAL OF THIS PAPER IS TO DOCUMENT THE USE OF TIME SERIES IN COLOR MATCHING. ALTHOUGH TIME SERIES ARE NOT COMMONLY USED FOR THIS PURPOSE, THE RESULTS OBTAINED, BASED ON THE PRINCIPLE OF SIMILARITY IN TIME SERIES USING THE EUCLIDEAN DISTANCE, ESTABLISH THE VALIDITY OF ITS USE FOR COLOR MATCHING APPLICATIONS. THE ACCURACY OF COLOR MATCHING WAS BASED ON THE MEASURES OF REFLECTIVITY VERSUS WAVELENGTH OF SAMPLES GIVEN BY A SPECTROPHOTOMETER. THE ERROR ESTIMATION WAS CALCULATED USING A DATABASE OF 1001 ELEMENTS. THE MATCHING MODULE HAS BEEN TESTED WITH SIX SAMPLES INCLUDED AND NOT INCLUDED IN THE DATABASE. ALL OF THEM GAVE AN ERROR LOWER THAN THE ESTIMATED ABSOLUTE ERROR OF 11.36.

Speech recognition using energy, MFCCs and rho parameters to classify syllables in the spanish language

Abstract: This paper presents an approach for the automatic speech re-cognition using syllabic units. Its segmentation is based on using the Short-Term Total Energy Function (STTEF) and the Energy Function of the High Frequency (ERO parameter) higher than 3,5 KHz of the speech signal. Training for the classification of the syllables is based on ten related Spanish language rules for syllable splitting. Recognition is based on a Continuous Density Hidden Markov Models and the bigram model language. The approach was tested using two voice corpus of natural speech, one constructed for researching in our laboratory (experimental) and the other one, the corpus Latino40 commonly used in speech researches. The use of ERO and MFCCs parameter increases speech recognition by 5.5% when compared with recognition using STTEF in discontinuous speech and improved more than 2% in continuous speech with three states. When the number of states is incremented to five, the recognition rate is improved proportionally to 98% for the discontinuous speech and to 81% for the continuous one.

Laplacian Eigenmaps for dimensionality reduction and data representation

Abstract: One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low-dimensional manifold embedded in a high-dimensional space. Drawing on the correspondence between the graph Laplacian, the Laplace Beltrami operator on the manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for representing the high-dimensional data. The algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality-preserving properties and a natural connection to clustering. Some potential applications and illustrative examples are discussed.

The Graph Neural Network Model

Abstract: Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.

Electricity price forecasting: A review of the state-of-the-art with a look into the future

Abstract: A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures and (iii) statistical testing of the significance of the outperformance of one model by another.