Allan de Medeiros Martins

Variance fluctuations in nonstationary time series: a comparative study of music genres

An important problem in physics concerns the analysis of audio time series generated by transduced acoustic phenomena. Here, we develop a new method to quantify the scaling properties of the local variance of nonstationary time series. We apply this technique to analyze audio signals obtained from selected genres of music. We find quantitative differences in the correlation properties of high art music, popular music, and dance music. We discuss the relevance of these objective findings in relation to the subjective experience of music.

Classification System of Pathological Voices Using Correntropy

This paper proposes the use of a similarity measure based on information theory called correntropy for the automatic classification of pathological voices. By using correntropy, it is possible to obtain descriptors that aggregate distinct spectral characteristics for healthy and pathological voices. Experiments using computational simulation demonstrate that such descriptors are very efficient in the characterization of vocal dysfunctions, leading to a success rate of 97% in the classification. With this new architecture, the classification process of vocal pathologies becomes much more simple and efficient.

Performance evaluation of the correntropy coefficient in automatic modulation classification

Abstract Automatic modulation classification (AMC) techniques have applications in a variety of wireless communication scenarios, such as adaptive systems, cognitive radio, and surveillance systems. However, a common requirement to most of the AMC techniques proposed in the literature is the use of signal preprocessing modules, which can increase the computational cost and decrease the scalability of the AMC strategy. This work proposes the direct use of a similarity measure based on information theory for the automatic recognition of digital modulations, which is known as correntropy coefficient. The performance of correntropy in AMC applied to channels subject to additive white Gaussian noise (AWGN) is evaluated. Specifically, the influence of the kernel size on the classifier performance is analyzed, since it is the only free parameter in correntropy. Besides, a relationship between its respective value and the signal-to-noise ratio (SNR) of the channel is also proposed. Considering the investigated modulation techniques, numerical results obtained by simulation demonstrate that there are high accuracy rates in classification, even at low SNR values. By using correntropy, AMC task becomes simpler and more efficient.

Cyclostationary correntropy: Definition and applications

Abstract Information extraction is a frequent and relevant problem in digital signal processing. In the past few years, different methods have been utilized for the parameterization of signals and the achievement of efficient descriptors. When the signals possess statistical cyclostationary properties, the Cyclic Autocorrelation Function (CAF) and the Spectral Cyclic Density (SCD) can be used to extract second-order cyclostationary information. However,second-order statistics tightly depends on the assumption of gaussianity, as the cyclostationary analysis in this case should comprise higher-order statistical information. This paper proposes a new mathematical formulation for the higher-order cyclostationary analysis based on the correntropy function. The cyclostationary analysis is revisited focusing on the information theory, while the Cyclic Correntropy Function (CCF) and Cyclic Correntropy Spectral Density (CCSD) are also presented. The CCF has different properties compared with CAF that can be very useful in non-gaussian signal processing, especially in the impulsive noise environment which implies in the expansion of the class of problems addressed by the second-order cyclostationary analysis. In particular, we prove that the CCF contains information regarding second- and higher-order cyclostationary moments, being a generalization of the CAF. The performance of the aforementioned functions in the extraction of higher-order cyclostationary characteristics is analyzed in a wireless communication system in which non-gaussian noise is present. The results demonstrate the advantages of the proposed method over the second-order cyclostationary.

Comparative study on dimension reduction techniques for cluster analysis of microarray data

This paper proposes a study on the impact of the use of dimension reduction techniques (DRTs) in the quality of partitions produced by cluster analysis of microarray datasets. We tested seven DRTs applied to four microarray cancer datasets and ran four clustering algorithms using the original and reduced datasets. Overall results showed that using DRTs provides a improvement in performance of all algorithms tested, specially in the hierarchical class. We could see that, despite Principal Component Analysis (PCA) being the most widely used DRT, its was overcome by other nonlinear methods and it did not provide a substantial performance increase in the clustering algorithms. On the other hand, t-distributed Stochastic Embedding (t-SNE) and Laplacian Eigenmaps (LE) achieved good results for all datasets.

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs) are an efficient tool to identify nonlinear systems. In these structures, features related to fuzzy logic, wavelet functions, and neural networks are combined in an architecture similar to the Adaptive Neurofuzzy Inference Systems (ANFIS). In practical applications, the experimental data set used in the identification task often contains unknown noise and outliers, which decrease the FWNN model reliability. In order to reduce the negative effects of these erroneous measurements, this work proposes the direct use of a similarity measure based on information theory in the FWNN learning procedure. The Mean Squared Error (MSE) cost function is replaced by the Maximum Correntropy Criterion (MCC) in the traditional error backpropagation (BP) algorithm. The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. The results demonstrate the advantages of the proposed cost function using the MCC as compared to the MSE. This work also investigates the influence of the kernel size on the performance of the MCC in the BP algorithm, since it is the only free parameter of correntropy.

Automatic segmentation and classification of blood components in microscopic images using a fuzzy approach

INTRODUCTION: Automatic detection of blood components is an important topic in the field of hematology. Segmentation is an important step because it allows components to be grouped into common areas and processed separately. This paper proposes a method for the automatic segmentation and classification of blood components in microscopic images using a general and automatic fuzzy approach. METHODS: During pre-processing, the supports of the fuzzy sets are automatically calculated based on the histogram peaks in the green channel of the RGB image and the Euclidean distance between the leukocyte nuclei centroids and the remaining pixels. During processing, fuzzification associates the degree of pertinence of the gray level of each pixel in the regions defined in the histogram with the proximity of the leukocyte nucleus centroid closest to the pixel. The fuzzy rules are then applied, and the image is defuzzified, resulting in the classification of four regions: leukocyte nuclei, leukocyte cytoplasm, erythrocytes and blood plasma. In post-processing, false positives are reduced and the leukocytes (including the nucleus and cytoplasm), erythrocytes and blood plasma are segmented. RESULTS: A total of 530 microscopic images of blood smears were processed, and the results were compared with the results of manual segmentation by experts and the accuracy rates of other approaches. CONCLUSION: The method demonstrated average accuracy rates of 97.31% for leukocytes, 95.39% for erythrocytes and 95.06% for blood plasma, avoiding the limitations found in the literature and contributing to the practice of the segmentation of blood components.

Information-theoretic clustering: A representative and evolutionary approach

This paper proposes a new perspective on non-parametric entropy-based clustering. We developed a new cost evaluation function for clustering that measures the cross information potential (CIP) between clusters on a dataset using representative points, which we called representative CIP (rCIP). We did this based on the idea that optimizing the cross information potential is equivalent to minimizing cross entropy between clusters. Our measure is different because, instead of using all points in a dataset, it uses only representative points to quantify the interaction between distributions without any loss of the original properties of cross information potential. This brings a double advantage: decreases the computational cost of computing the cross information potential, thus drastically reducing the running time, and uses the underlying statistics of the space region where representative points are in order to measure interaction. With this, created a useful non-parametric estimator of entropy and makes possible using cross information potential in applications where it was not. Due to the nature of clustering problems, we proposed a genetic algorithm in order to use rCIP as cost function. We ran several tests and compared the results with single linkage hierarchical algorithm, finite mixture of Gaussians and spectral clustering in both synthetic and real image segmentation datasets. Experiments showed that our approach achieved better results compared to the other algorithms and it was capable of capture the real structure of the data in most cases regardless of its complexity. It also produced good image segmentation with the advantage of a tuning parameter that provides a way of refining segmentation.

Complex Correntropy: Probabilistic Interpretation and Application to Complex-Valued Data

Recent studies have demonstrated that correntropy is an efficient tool for analyzing higher order statistical moments in non-Gaussian noise environments. Although correntropy has been used with complex data, no theoretical study was pursued to elucidate its properties, nor how to best use it for optimization. By using a probabilistic interpretation, this work presents a novel similarity measure between two complex random variables, which is defined as complex correntropy. A new recursive solution for the maximum complex correntropy criterion is introduced based on a fixed-point solution. This technique is applied to a system identification, and the results demonstrate prominent advantages when compared against three other algorithms: the complex least mean square, complex recursive least squares, and least absolute deviation. By the aforementioned probabilistic interpretation, correntropy can now be applied to solve several problems involving complex data in a more straightforward way.

Comparative study on information theoretic clustering and classical clustering algorithms

This paper proposes a comparative empirical study on algorithms for clustering. We tested the method proposed in [2] using distinct synthetic and real (gene expression) datasets. We chose synthetic datasets with different spatial complex to verify the applicability of the algorithm. We also evaluated the IT algorithm in real-life problems by using microarray gene expression datasets. Compared with simple but still spread used classical algorithms k-means, hierarchical clustering and finite mixture of Gaussians, the IT algorithm showed to be more robust for both proposed scenarios.