Ricardo Rodriguez Jorge

Monitoring of Cardiac Arrhythmia Patterns by Adaptive Analysis

In this paper, a study and development of a monitoring adaptive system based on dynamic quadratic neural unit are presented. The system is trained with a recurrent learning method, sample-by-sample in real time. This model will help to the prediction of possible cardiac arrhythmias in patients between 23 to 89 years old, age range of the electrocardiogram signals obtained from the Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database. By means of the implementation of this adaptive monitoring system the model is capable of processing heart rate signals in real time and to recognize patterns that predict cardiac arrhythmias up to 1 second ahead. The Dynamic Quadratic Neural Unit in real time has demonstrated presenting greater efficiency and precision comparing with multilayer perceptron-type neural networks for pattern classification and prediction; in addition, this architecture has demonstrated in developed research, to be superior to other different type of adaptive architectures.

Particle Swarm Optimization as a New Measure of Machine Translation Efficiency

The present work proposes a new approach to measuring efficiency of evolutionary algorithm-based Machine Translation. We implement some attributes of evolutionary algorithms performing cosine similarity objective function of a Particle Swarm Optimization (PSO) algorithm then, we evaluate an English text set for translation precision into the Spanish text as a simulated benchmark, and explore the backward process. Our results show that PSO algorithm can be used for translation of multiple language sentences with one identifier only, in other words the technology presented is language-pair independent. Specifically, we indicate that our cosine similarity objective function improves the velocity attribute of the PSO algorithm, making the complex cost functions unnecessary.

Adaptive Threshold, Wavelet and Hilbert Transform for QRS Detection in Electrocardiogram Signals

This paper combines Hilbert and Wavelet transforms and an adaptive threshold technique to detect the QRS complex of electrocardiogram signals. The method is performed in a window framework. First, the Wavelet transform is applied to the ECG signal to remove noise. Next, the Hilbert transform is applied to detect dominant peak points in the signal. Finally, the adaptive threshold technique is applied to detect R-peaks, Q, and S points. The performance of the algorithm is evaluated against the MIT-BIH arrhythmia database, and the numerical results indicated significant detection accuracy.

Predicting the Short-Term Exchange Rate Between United State Dollar and Czech Koruna Using Hilbert-Huang Transform and Fuzzy Logic

In this paper, the combination of the Hilbert-Huang Transform, fuzzy logic and an embedding theorem is described to predict the short-term exchange rate from United States dollar to Czech Koruna. By Using the Hilbert-Huang Transform as an adaptive filter, the proposed method decreases the embedding dimension space from five (original samples) to four (de-noising samples). This dimension space provides the number of inputs to the fuzzy rule base system, which causes the number of rules, the time for training and the inference process to decrease. Experimental results indicated that this method achieves higher accuracy prediction than the direct use of original data.

Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units

Adaptive predictive models can use conventional and nonconventional neural networks for highly non-stationary time series prediction. However, conventional neural networks present a series of known drawbacks. This paper presents a brief discussion about this concern as well as how the basis of higher-order neural units can overcome some of them; it also describes a sliding window technique alongside the batch optimization technique for capturing the dynamics of non-stationary time series over a Quadratic Neural Unit, a special case of higher-order neural units. Finally, an experimental analysis is presented to demonstrate the effectiveness of the proposed approach.

Source-target mapping model of streaming data flow for machine translation

Streaming information flow allows identification of linguistic similarities between language pairs in real time as it relies on pattern recognition of grammar rules, semantics and pronunciation especially when analyzing so called international terms, syntax of the language family as well as tenses transitivity between the languages. Overall, it provides a backbone translation knowledge for building automatic translation system that facilitates processing any of various abstract entities which combine to specify underlying phonological, morphological, semantic and syntactic properties of linguistic forms and that act as the targets of linguistic rules and operations in a source language following professional human translator. Streaming data flow is a process of mining source data into target language transformation during which any inference impedes the system effectiveness by producing incorrect translation. We address a research problem of exploring streaming data from source-target parallels for detection of linguistic similarities between languages originated from different groups.

An Enhanced Petri Net Model to Verify and Validate a Neural-Symbolic Hybrid System

As the Neural-Symbolic Hybrid Systems (NSHS) gain acceptance, it increases the necessity to guarantee the automatic validation and verification of the knowledge contained in them. In the past, such processes were made manually. In this article, an enhanced Petri net model is presented to the detection and elimination of structural anomalies in the knowledge base of the NSHS. In addition, a reachability model is proposed to evaluate the obtained results of the system versus the expected results by the user. The validation and verification method is divided in two stages: 1) it consists of three phases: rule normalization, rule modeling and rule verification. 2) It consists of three phases: rule modeling, dynamic modeling and evaluation of results. Such method is useful to ensure that the results of a NSHS are correct. Examples are presented to demonstrate the effectiveness of the results obtained with the method. [Article copies are available for purchase from InfoSci-on-Demand.com]

Verification and validation of a Neural-Symbolic Hybrid System using an enhanced Petri net

As the neural-symbolic hybrid systems (NSHS) gain acceptance, it increases the necessity to guarantee the automatic validation and verification of the knowledge contained in them. In the past, such processes were made manually. In this paper, an enhanced Petri net model is presented to the detection and elimination of structural anomalies in the knowledge base of the NSHS. In addition, a reachability model is proposed to evaluate the obtained results of the system versus the expected results by the user. The validation and verification method is divided in two stages: 1) it consists of three phases: rule normalization, rule modeling and rule verification. 2) It consists of three phases: rule modeling, dynamic modeling and evaluation of results. Such method is useful to ensure that the results of a NSHS are correct. Examples are presented to demonstrate the effectiveness of the results obtained with the method.