Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics

Digital Coding of Waveforms

This article reviews various procedures used in the analysis of circadian rhythms at the populational, organismal, cellular and molecular levels. The procedures range from visual inspection of time plots and actograms to several mathematical methods of time series analysis. Computational steps are described in some detail, and additional bibliographic resources and computer programs are listed.

/pdf/procedures-for-numerical-analysis-of-circadian-rhythms-2s10ln0129.pdf

Procedures for numerical analysis of circadian rhythms.

Viewers are attracted and attached to multimedia content due to their emotional experience associated with it. In this paper, we present an emotion recognition method of EEG signals elicited while watching music videos. The emotional features are extracted from EEG signals using dual-tree complex wavelet packet transform (DT-CWPT). The most discriminating emotional features are selected by Singular value decomposition (SVD), QR factorization with column pivoting (QRcp) and F-ratio based method. The selected features are used to train support vector machine (SVM) to classify emotion for each video using leave-one-out cross-validation strategy.

Recognition of emotions induced by music videos using DT-CWPT

Feature selection using singular value decomposition and QR factorization with column pivoting for text-independent speaker identification

Traditionally, the clinical diagnosis of a respiratory disease is made from a careful clinical examination including chest auscultation. Objective analysis and automatic interpretation of the lung sound based on its physical characters are strongly warranted to assist clinical practice. In this paper, a new method is proposed to distinguish between the normal and the abnormal subjects using the morphological complexities of the lung sound signals. The morphological embedded complexities used in these experiments have been calculated in terms of texture information (lacunarity), irregularity index (sample entropy), third order moment (skewness), and fourth order moment (Kurtosis). These features are extracted from a mixed data set of 10 normal and 20 abnormal subjects and are analyzed using two different classifiers: extreme learning machine (ELM) and support vector machine (SVM) network. The results are obtained using 5-fold cross-validation. The performance of the proposed method is compared with a wavelet analysis based method. The developed algorithm gives a better accuracy of 92.86% and sensitivity of 86.30% and specificity of 86.90% for a composite feature vector of four morphological indices.

/pdf/detection-of-lungs-status-using-morphological-complexities-2my5ue21xy.pdf

Detection of lungs status using morphological complexities of respiratory sounds.

During the recording time of lung sound (LS) signals from the chest wall of a subject, there is always heart sound (HS) signal interfering with it. This obscures the features of lung sound signals and creates confusion on pathological states, if any, of the lungs. A novel method based on empirical mode decomposition (EMD) technique is proposed in this paper for reducing the undesired heart sound interference from the desired lung sound signals. In this, the mixed signal is split into several components. Some of these components contain larger proportions of interfering signals like heart sound, environmental noise etc. and are filtered out. Experiments have been conducted on simulated and real-time recorded mixed signals of heart sound and lung sound. The proposed method is found to be superior in terms of time domain, frequency domain, and time-frequency domain representations and also in listening test performed by pulmonologist.

Reduction of heart sound interference from lung sound signals using empirical mode decomposition technique.

Previous work shows the presence of scale invariance and long-range correlations in ongoing and spontaneous activity of large scale brain responses (i.e. EEG), and such scaling behavior can also be modulated by simple sensory stimulus. However, little is known whether such alteration but not destruction in scaling properties also occurs during complex cognitive processing and if neuroplasticity plays any role in mediating such changes. In this study, we addressed these issues by investigating scaling properties of multivariate EEG signals obtained from two broad groups – artists and non-artists – while they performed complex tasks of perception and mental imagery of visual art objects. We found that brain regions showing increased correlation properties from rest were similar for both tasks, suggesting that brain networks responsible for visual perception are reactivated for mental imagery. Further, we observed that the two groups could be differentiated by scaling exponents and an artificial neural network based classifier achieved a classification efficiency of over 80%. These results altogether suggest that specific complex cognitive task demands and task-specific expertise can modify the temporal scale-free dynamics of brain responses.

Goutam Saha

Papers

Recognition of emotions induced by music videos using DT-CWPT

Feature selection using singular value decomposition and QR factorization with column pivoting for text-independent speaker identification

Detection of lungs status using morphological complexities of respiratory sounds.

Reduction of heart sound interference from lung sound signals using empirical mode decomposition technique.

Investigating long-range correlation properties in EEG during complex cognitive tasks