Browsing Electrical Engineering (EE) by Advisor "Seelamantula, Chandra Sekhar"
Now showing items 1-16 of 16
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Analysis of Local Field Potential and Gamma Rhythm Using Matching Pursuit Algorithm
(2017-11-17)Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. These signals also have transient ... -
Demodulation of Narrowband Speech Spectrograms
(2017-11-22)Speech is a non-stationary signal and contains modulations in both spectral and temporal domains. Based on the type of modulations studied, most speech processing algorithms can be classified into short-time analysis ... -
Generalized Analytic Signal Construction and Modulation Analysis
(2018-04-02)This thesis deals with generalizations of the analytic signal (AS) construction proposed by Gabor. Functional extensions of the fractional Hilbert Transform (FrHT) are proposed using which families of analytic signals are ... -
Interpolation of Digital Elevation Models using Generative Adversarial Networks
A digital elevation model (DEM) is a three-dimensional representation of elevation data of a terrain such as a terrestrial terrain acquired by a reconnaissance aircraft or a lunar terrain acquired using a Chandrayaan rover. ... -
On Maximizing The Performance Of The Bilateral Filter For Image Denoising
(2017-07-07)We address the problem of image denoising for additive white Gaussian noise (AWGN), Poisson noise, and Chi-squared noise scenarios. Thermal noise in electronic circuitry in camera hardware can be modeled as AWGN. Poisson ... -
Optimum Savitzky-Golay Filtering for Signal Estimation
(2018-03-21)Motivated by the classic works of Charles M. Stein, we focus on developing risk-estimation frameworks for denoising problems in both one-and two-dimensions. We assume a standard additive noise model, and formulate the ... -
Phase Retrieval and Hilbert Integral Equations – Beyond Minimum-Phase
(2018-07-18)The Fourier transform (spectrum) of a signal is a complex function and is characterized by the magnitude and phase spectra. Phase retrieval is the reconstruction of the phase spectrum from the measurements of the magnitude ... -
Robust Non-convex Penalties for Solving Sparse Linear Inverse Problems and Applications to Computational Imaging
Sparse linear inverse problems require the solution to the l-0-regularized least-squares cost, which is not computationally tractable. Approximate and computationally tractable solutions are obtained by employing ... -
Sampling of Structured Signals: Techniques and Imaging Applications
The celebrated Shannon sampling theorem is a key mathematical tool that allows one to seamlessly switch between the continuous-time and discrete-time representations of bandlimited signals. Sampling and reconstruction of ... -
Savitzky-Golay Filters and Application to Image and Signal Denoising
(2018-07-18)We explore the applicability of local polynomial approximation of signals for noise suppression. In the context of data regression, Savitzky and Golay showed that least-squares approximation of data with a polynomial of ... -
Shape-constrained Biomedical Image Segmentation and Applications
The detection, segmentation, and delineation of the targeted regions of interest in biomedical images are fundamental steps for computer-aided assessment and prescreening. In this thesis, we focus on shape-constrained ... -
Solving Inverse Problems Using a Deep Generative Prior
In an inverse problem, the objective is to recover a signal from its measurements, given the knowledge of the measurement operator. In this thesis, we address the problems of compressive sensing (CS) and compressive phase ... -
Sparsity Driven Solutions to Linear and Quadratic Inverse Problems
The problem of signal reconstruction from inaccurate and possibly incomplete set of linear/non-linear measurements occurs in a variety of signal and image processing applications. In this thesis, we develop reconstruction ... -
Sparsity Motivated Auditory Wavelet Representation and Blind Deconvolution
(2018-08-29)In many scenarios, events such as singularities and transients that carry important information about a signal undergo spreading during acquisition or transmission and it is important to localize the events. For example, ... -
Spectrotemporal Processing of Speech Signals Using the Riesz Transform
Speech signals possess a rich time-varying spectral content, which makes their analysis a challenging signal processing problem. Developing methods for accurate speech analysis has a direct impact on applications such as ... -
Sub-Nyquist Sampling and Super-Resolution Imaging
(2018-07-04)The Shannon sampling framework is widely used for discrete representation of analog bandlimited signals, starting from samples taken at the Nyquist rate. In many practical applications, signals are not bandlimited. In order ...