Anderson da Silva Soares

Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses

A aplicacao de tecnicas quimiometricas sofisticadas a grandes conjuntos de dados tem se tornado possivel devido aos continuos aprimoramentos tecnologicos em computadores comerciais. Recentemente, tais aprimoramentos tem sido obtidos principalmente atraves da introducao de processadores com multiplos nucleos. Contudo, o uso eficiente de hardware com multiplos nucleos requer o desenvolvimento de software apropriado para computacao paralela. Este artigo trata da implementacao de paralelismo empregando o Matlab Parallel Computing Toolbox, que requer somente pequenas modificacoes em codigos quimiometricos ja existentes de modo a explorar os beneficios do processamento em multiplos nucleos. Empregando essa ferramenta de software, mostra-se que implementacoes paralelas podem proporcionar expressivos ganhos computacionais. Em particular, considera-se o problema de selecao de variaveis empregando o algoritmo das projecoes sucessivas e o algoritmo genetico, bem como o uso de validacao cruzada em minimos quadrados parciais. Para ilustracao, duas aplicacoes analiticas sao apresentadas: determinacao de proteina em trigo por espectrometria de reflectância no infravermelho proximo e classificacao de oleos vegetais comestiveis por voltametria de onda quadrada. Empregando as implementacoes propostas para computacao paralela, ganhos computacionais de ate 204% foram obtidos. The application of sophisticated chemometrics techniques to large datasets has been made possible by continuing technological improvements in off-the-shelf computers. Recently, such improvements have been mainly achieved by the introduction of multi-core processors. However, the efficient use of multi-core hardware requires the development of software that properly address parallel computing. This paper is concerned with the implementation of parallelism using the Matlab Parallel Computing Toolbox, which requires only simple modifications to existing chemometrics code in order to exploit the benefits of multi-core processing. By using this software tool, it is shown that parallel implementations may provide substantial computational gains. In particular, the present study considers the problem of variable selection employing the successive projections algorithm and the genetic algorithm, as well as the use of cross-validation in partial least squares. For demonstration, two analytical applications are presented: determination of protein in wheat by near-infrared reflectance spectrometry and classification of edible vegetable oils by square-wave voltammetry. By using the proposed parallel computing implementations, computational gains of up to 204% were obtained.

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.

Parallelization of a Modified Firefly Algorithm using GPU for Variable Selection in a Multivariate Calibration Problem

The recent improvements of Graphics Processing Units (GPU) have provided to the bio-inspired algorithms a powerful processing platform. Indeed, a lot of highly parallelizable problems can be significantly accelerated using GPU architecture. Among these algorithms, the Firefly Algorithm (FA) is a newly proposed method with potential application in several real world problems such as variable selection problem in multivariate calibration. The main drawback of this task lies in its computation burden, as it grows polynomially with the number of variables available. In this context, this paper proposes a GPU-based FA for variable selection in a multivariate calibration problem. Such implementation is aimed at improving the computational efficiency of the algorithm. For this purpose, a new strategy of regression coefficients calculation is employed. The advantage of the proposed implementation is demonstrated in an example involving a large number of variables. In such example, gains of speedup were obtained. Additionally the authors also demonstrate that the FA, in comparison with traditional algorithms, can be a relevant contribution for the variable selection problem.

Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples

This short report proposes a sequential regression implementation for the successive projections algorithm (SPA), which is a variable selection technique for multiple linear regression. An example involving the near-infrared determination of protein in wheat is presented for illustration. The resulting model predictions exhibited a correlation coefficient of 0.989 and an RMSEP (root-mean-square error of prediction) value of 0.2% m/m in the range 10.2-16.2% m/m. The proposed implementation provided computational gains of up to five-fold.

Fault detection using Linear Discriminant Analysis with selection of process variables and time lags

This paper is concerned with the selection of process variables and time lags for fault detection. For this purpose, a feature selection technique known as Successive Projections Algorithm (SPA) is employed with Linear Discriminant Analysis (LDA) classifiers to discriminate between normal operating conditions and faults. SPA was originally designed to minimize multicollinearity among the selected features, which is a known cause of generalization problems for LDA. In the present work, a modification to the basic SPA formulation is proposed to place larger emphasis on the selection of relevant features for the classification task. The proposed SPA-LDA methodology is illustrated in a case study involving the Tennessee Eastman benchmark process. For comparison, a genetic algorithm (GA) for feature selection is also employed. In this study, the pre-selection of process variables was found to improve the accuracy of the resulting classifiers. In practice, such a pre-selection would have the extra advantage of reducing the number of sensors required to detect a given fault. Moreover, the proposed modification in SPA-LDA resulted in an improvement of average classification accuracy from 88% to 96%. This result was similar to that obtained by GA-LDA (97%). However, the SPA-LDA classifiers were found to be less sensitive to measurement noise.

Feature Selection using Genetic Algorithm: An Analysis of the Bias-Property for One-Point Crossover

Genetic algorithms (GAs) have been used for feature selection with binary representation. Even if binary representation has perfect probability to include or remove a feature in the search process, some works in the field of chemometrics have reported criticism about a high number of features selected by GA implementations. Thus, in this paper, we aim to propose an investigation of the number of features selected on a point of view of the bias-property using implementations from the GA-PLS toolboxes (Genetic Algorithm with Partial Least Square). The study is performed using an one-point crossover operator and a common initialization procedure used in the matlab toolboxes. Results show the existence of such a bias that influences the increase in the number of features over the generations.

A feasibility cachaca type recognition using computer vision and pattern recognition

The problem of recognition of aging time and wood type in chacaca is presented.A new approach is introduced using a computer vision system.The developed image capture device and information processing method is presented.Results show that the new technique is cheaper and better than previous approaches. Brazilian rum (also known as cachaca) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters produced each year. It is a traditional drink with refined features and a delicate aroma that is produced mainly in Brazil but consumed in many countries. It can be aged in various types of wood for 1-3years, which adds aroma and a distinctive flavor with different characteristics that affect the price. A research challenge is to develop a cheap automatic recognition system that inspects the finished product for the wood type and the aging time of its production. Some classical methods use chemical analysis, but this approach requires relatively expensive laboratory equipment. By contrast, the system proposed in this paper captures image signals from samples and uses an intelligent classification technique to recognize the wood type and the aging time. The classification system uses an ensemble of classifiers obtained from different wavelet decompositions. Each classifier is obtained with different wavelet transform settings. We compared the proposed approach with classical methods based on chemical features. We analyzed 105 samples that had been aged for 3years and we showed that the proposed solution could automatically recognize wood types and the aging time with an accuracy up to 100.00% and 85.71% respectively, and our method is also cheaper.

Multiobjective Firefly Algorithm for Variable Selection in Multivariate Calibration

Firefly Algorithm is a newly proposed method with potential application on several real world problems, such as variable selection problem. This paper presents a Multiobjective Firefly Algorithm (MOFA) for variable selection in multivariate calibration models. The main objective is to propose an optimization to reduce the error value prediction of the property of interest, as well as reducing the number of variables selected. Based on the results obtained, it is possible to demonstrate that our proposal may be a viable alternative in order to deal with conflicting objective-functions. Additionally, we compare MOFA with traditional algorithms for variable selection and show that it is a more relevant contribution for the variable selection problem.

Multi-objective Genetic Algorithm for Variable Selection in Multivariate Classification Problems

This paper proposes multi-objective genetic algorithm for the problem of variable selection in multivariate calibration. We consider the problem related to the classification of biodiesel samples to detect adulteration, Linear Discriminant Analysis classifier. The goal of the multi--objective algorithm is to reduce the dimensionality of the original set of variables; thus, the classification model can be less sensitive, providing a better generalization capacity. In particular, in this paper we adopted a version of the Non-dominated Sorting Genetic Algorithm (NSGA-II) and compare it to a mono-objective Genetic Algorithm (GA) in terms of sensitivity in the presence of noise. Results show that the mono-objective selects 20 variables on average and presents an error rate of 14%. One the other hand, the multi-objective selects 7 variables and has an error rate of 11%. Consequently, we show that the multi-objective formulation provides classification models with lower sensitivity to the instrumental noise when compared to the mono-objetive formulation

Method for Text Entry in Smartwatches Using Continuous Gesture Recognition

This work proposes a method that allows the entry of text in smartwatches using gestures based on geometric forms. For this it is proposed the development of a prototype capable of inserting a letter with no more than two user interactions. Gesture recognition is performed using the incremental recognition algorithm. A set of gestures with lines and curves were created to be recognized by the incremental recognition algorithm, generated from the reduced equation of the line and the reduced equation of the circumference, respectively. After recognizing the gestures, they are sent to a classifier Naïve Bayes which is responsible for predicting the letter that will be inserted. The Naïve Bayes classifier was trained with a user gesture base that drew all the letters of the alphabet using only the gestures available in the set presented to them. Using the gesture base and the classifier Naïve Bayes a prototype was developed for smartwatches that automatically suggests the most likely letters to be inserted. The prototype was used to perform an experiment, during the experiment the users inserted the five most frequent letters and the five less frequent letters of the English language. The results of the experiment show that the prototype is able to recognize a letter with at most two interactions between the user and the smartwatch. The analysis of the usability and experience test shows that the prototype has generalized potential for use, since it allows the entry of text with up to two interactions and with a 100% hit rate for the most frequent letters and 95,14% For less frequent letters.