Computer recognition of human emotional states is an important component for efficient human-computer interaction.In this paper we explore an approach for recognition of human emotion from the visual information. We perform feature selection by using The Two Dimensions Fractional Fourier Transform. As a generalization of Fourier transform, the Two Dimensions Fractional Fourier Transform (2D-FrFT) contains simultaneity the time-frequency information of the signal, and is considered as a new tool for time-frequency analysis, especially in the area of image recognition. In this paper we use the amplitude of the 2D-FrFT of an image as the feature of the images to train the FLDA multiclassifier for human emotion recognition. Simulation result shows that this method of the features extraction and classifier has high recognition rate.

Most previous imaging studies have used traditional Pearson correlation analysis to construct brain networks. This approach fails to adequately and completely account for the interaction between adjacent brain regions. In this study, we used the L1-norm linear regression model to test the small-world attributes of the brain networks of three groups of patients, namely, those with mild cognitive impairment (MCI), Alzheimer’s disease (AD), and healthy controls (HCs); we attempted to identify the method that may detect minor differences in MCI and AD patients. Twenty-four AD patients, 33 MCI patients, and 27 HC elderly subjects were subjected to functional MRI (fMRI). We applied traditional Pearson correlation and the L1-norm to construct the brain networks and then tested the small-world attributes by calculating the following parameters: clustering coefficient (Cp), path length (Lp), global efficiency (Eg), and local efficiency (Eloc). As expected, L1 could detect slight changes, mainly in MCI patients expressing higher Cp and Eloc; however, no statistical differences were found between MCI patients and HCs in terms of Cp, Lp, Eg, and Eloc, using Pearson correlation. Compared with HCs, AD patients expressed a lower Cp, Eloc, and Lp and an increased Eg using both connectivity metrics. The statistical differences between the groups indicated the brain networks constructed by the L1-norm were more sensitive to detect slight small-world network changes in early stages of AD.

Objective: Global longitudinal strain (GLS) is a sensitive and reproducible predictive factor in patients with ischemic heart disease (IHD), although its correlation with exercise tolerance is unknown. We aimed to identify the correlation between global longitudinal strain (GLS) and cardiopulmonary exercise testing (CPX) parameters and assess the prognostic implications and accuracy of GLS in predicting exercise intolerance in populations with ischemic heart disease (IHD) using CPET criteria. Methods: Prospectively, 108 patients with IHD underwent CPX and 2D speckle-tracking echocardiography. Correlation between GLS and multiple CPX variables was assessed using Spearman’s correlation analysis and univariate regression analysis. A receiver operating characteristic (ROC) curve analysis was performed on GLS to detect exercise intolerance. Results: GLS was correlated with peak oxygen uptake (peak VO2; r = −0.438, p = 0.000), %PPeak VO2 (−0.369, p = 0.000), peak metabolic equivalents (METs@peak; r = −0.438, p < 0.01), and the minute ventilation–carbon dioxide production (VE/VCO2) slope (r = 0.257, p < 0.01). Weak-to-moderate correlations were also identified for the respiratory exchange rate at the anaerobic threshold (RER@AT), end-tidal carbon dioxide at the anaerobic threshold (PETCO2@AT), oxygen consumption at the anaerobic threshold (VO2@AT), carbon dioxide production at the anaerobic threshold (VCO2@AT), and metabolic equivalents at the anaerobic threshold (METs@AT; p < 0.01). On multivariate analysis, the results showed that age, the BMI, and GLS are independent predictors for reduced exercise capacity in patients with IHD (p < 0.01). The area under the ROC curve value of GLS for identifying patients with a peak VO2 of <14 mL/kg/min was 0.73 (p = 0.000). Conclusion: As a sensitive echocardiographic assessment of patients with ischemic heart disease, global longitudinal strain is an independent predictor of reduced exercise capacity and has a sensitivity of 74.2% and a specificity of 66.7% to detect exercise intolerance.