Ankit Parekh

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Total variation (TV) denoising is an effective noise suppression method when the derivative of the underlying signal is known to be sparse. TV denoising is defined in terms of a convex optimization problem involving a quadratic data fidelity term and a convex regularization term. A non-convex regularizer can promote sparsity more strongly, but generally(More)
This paper proposes an EEG processor for sleep spindle detection algorithms. It non-linearly separates the raw EEG signal into non-oscillatory transient and sustained rhythmic oscillation components using long and short windows for the short-time Fourier transform. The processor utilizes the fact that sleep spindles can be sparsely represented via the(More)
BACKGROUND This paper addresses the problem of detecting sleep spindles and K-complexes in human sleep EEG. Sleep spindles and K-complexes aid in classifying stage 2 NREM human sleep. NEW METHOD We propose a non-linear model for the EEG, consisting of a transient, low-frequency, and an oscillatory component. The transient component captures the(More)
This letter considers the problem of signal denoising using a sparse tight-frame analysis prior. The l1 norm has been extensively used as a regularizer to promote sparsity; however, it tends to under-estimate non-zero values of the underlying signal. To more accurately estimate non-zero values, we propose the use of a non-convex regularizer, chosen so as to(More)
With the development of electronic commerce, a lot of companies have established online trading platforms of their own such as e-tickets, online booking, online shopping, etc. Virtual Keyboard is used for authentication on such web based platform. However Virtual Keyboard still suffers from numerous other fallacies that an attacker can take advantage of.(More)
We propose a convex formulation of the fused lasso signal approximation problem consisting of non-convex penalty functions. The fused lasso signal model aims to estimate a sparse piecewise constant signal from a noisy observation. Originally, the ℓ1 norm was used as a sparsity-inducing convex penalty function for the fused lasso signal approximation(More)
STUDY OBJECTIVES Emerging evidence suggests a role for sleep in contributing to the progression of Alzheimer disease (AD). Slow wave sleep (SWS) is the stage during which synaptic activity is minimal and clearance of neuronal metabolites is high, making it an ideal state to regulate levels of amyloid beta (Aβ). We thus aimed to examine relationships between(More)
In educational institutes a huge amount of data is being generated. The produced data does not provide enough information which obscures important details of data that may help in better understanding of available data and its utility. If the data is analyzed efficiently, it can provide many insights, specific information regarding various facets of data(More)