Abstract: This disclosure relates generally to physiological monitoring, and more particularly to feature set optimization for classification of physiological signal. In one embodiment, a method for physiological monitoring includes identifying clean physiological signal training set from an input physiological signal based on a Dynamic Time Warping (DTW) of segments associated with the physiological signal. An optimal features set is extracted from a clean physiological signal training set based on a Maximum Consistency and Maximum Dominance (MCMD) property associated with the optimal feature set that strictly optimizes on the objective function, the conditional likelihood maximization over different selection criteria such that diverse properties of different selection parameters are captured and achieves Pareto-optimality. The input physiological signal is classified into normal signal components and abnormal signal components using the optimal features set.

Abstract: Traditionally known classification methods of non-stationary physiological audio signals as noisy and clean involve human intervention, may involve dependency on particular type of classifier and further analyses is carried out on classified clean signals. However, in non-stationary audio signals a major portion may end up being classified as noisy and hence may get rejected which may cause missing of intelligence which could have been derived from lightly noisy audio signals that may be critical. The present disclosure enables automation of classification based on auto-thresholding and statistical isolation wherein noisy signals are further classified as highly noisy and lightly noisy through continuous dynamic learning.

Abstract: An electronic device includes a touch interface and a processor configured to identify a current touch displacement while a touch gesture of a user is received through the touch interface, to identify an action corresponding to a current state of the electronic device and the identified current touch displacement, and to perform a task according to the identified action.

Abstract: Traditionally known classification methods of non-stationary physiological audio signals as noisy and clean involve human intervention, may involve dependency on particular type of classifier and further analyses is carried out on classified clean signals. However, in non-stationary audio signals a major portion may end up being classified as noisy and hence may get rejected which may cause missing of intelligence which could have been derived from lightly noisy audio signals that may be critical. The present disclosure enables automation of classification based on auto-thresholding and statistical isolation wherein noisy signals are further classified as highly noisy and lightly noisy through continuous dynamic learning.

Abstract: A method for managing operations of applications includes identifying a first application which is active on the electronic device and an event; and displaying an action element associated with a non-active second application on the first application based on the event. The action element of the second application is dynamically driven based on at least one among a context of the first application and a content of the first application.

Abstract: An image display method is provided. The image display method includes displaying a camera preview screen that provides an image of a subject in real time, capturing one or more images by capturing the camera preview screen, and displaying a photo reel on the camera preview screen, the photo reel being generated by arranging thumbnails of the captured one or more images in a row.