Shodhganga University: Based on Email request and MoU signed on 20th August 2015.
http://hdl.handle.net/10603/48078
Based on Email request and MoU signed on 20th August 2015.2019-09-17T01:55:31ZMicrowave imaging for breast cancer detection using 3d level set based optimization FDTD method and method of moments
http://hdl.handle.net/10603/233913
Title: Microwave imaging for breast cancer detection using 3d level set based optimization FDTD method and method of moments
Abstract: Microwave imaging is emerging as new diagnostic option for breast cancer detection because of non-ionizing nature of microwave radiation and significant contrast between dielectric properties of healthy and malignant breast tissues. Class III and IV breasts have more than 50% fibro-glandular tissues. So, it is very difficult to detect cancer in class III and IV breasts by using X-ray based mammography. Microwave imaging is very promising for cancer detection in case of dense breasts. Complex permittivity profile of breasts is reconstructed in three dimensions for microwave breast imaging. 3D level set based optimization proposed in this thesis is able to reconstruct proper shape and dielectric property values of breast tissues. Multiple frequency inverse scattering problem formulation improves computational efficiency and accuracy of microwave imaging system because complex number computations are avoided. Measurements of scattered electric fields are taken at five equally spaced frequencies in the range 0.5-2.5 GHz. Class III numerical breast phantom and Debye model are used in multiple frequency inverse scattering problem formulation. There are three unknowns per cell of numerical breast phantom due to Debye model. Linear relationships between Debye parameters are applied to get only static permittivity as unknown per cell of numerical breast phantom. Two level set functions are used to detect breast cancer in 3D level set based optimization. Pixel based reconstruction is replaced by initial guess about static permittivity solution in this modified four stage reconstruction strategy. Frequency hopping method is used to avoid local minima present at particular frequency in the 3D level set based optimization. 3D FDTD solves forward problem efficiently during each iteration of 3D level set method which leads to better reconstruction of static permittivity profile.
newlineDownsampling of Signals on Graphs An Algebraic Perspective
http://hdl.handle.net/10603/226797
Title: Downsampling of Signals on Graphs An Algebraic Perspective
Abstract: Real-world data such as weather data, seismic activity data, sensor networks data and social network data can be represented and processed conveniently using a mathematical structure called Graph. Graphs are a collection of vertices and edges. The relational structure between the vertices can be represented in form of a matrix called the adjacency matrix. A Graph Signal is a signal supported on a given graph. The framework of processing of signals on graphs is called Graph Signal Processing (GSP). Various signal processing concepts (e.g. Fourier Transform, filtering, translation, downsampling) need to be defined in the context of graphs. A common approach is to define a Fourier Transform for a graph (called Graph Fourier Transform - GFT), and use it to define other signal processing concepts. In this thesis, we analyze a class of graphs called Bipartite Graphs from down sampling perspective and then provide a GFT based approach to down sample signals on arbitrary graphs.
newlineSpectrum Sensing for Cognitive Radio
http://hdl.handle.net/10603/226796
Title: Spectrum Sensing for Cognitive Radio
Abstract: Due to the rapid growth of new wireless communication services and applications,need for radio frequency (RF) spectrum is continuously increasing. Most of the available RF spectrum is already been licensed to the existing wireless systems.On the other hand, it is found that spectrum is significantly under utilized due to the static frequency allocation to the dedicated users and hence the spectrum holes or spectrum opportunities arise. Considering the scarce RF spectrum,supporting new services and applications is a challenging task that requires innovative technologies capable of providing new ways of exploiting the available radio spectrum. Cognitive Radio (CR) has received immense research attention,both in the academia and industry, as it is considered a promising solution to the problem of spectrum scarcity by introducing the notion of opportunistic spectrum usage. An alternative to antenna diversity is the cooperative spectrum sensing where multiple secondary users also known as cooperating secondary users collaborate
newlineby sharing their sensing information for the detection of the spectrum opportunities.
newlineFinally, in our last work, we propose novel detection algorithm for cooperative
newlinewideband spectrum sensing. We make use of hard combining for data fusion since it minimizes the bandwidth requirements of the control channel. We show that the proposed algorithm performs better than algorithm without cooperative
newlinesensing.
newline
newlineAuditory Representation Learning
http://hdl.handle.net/10603/226795
Title: Auditory Representation Learning
Abstract: Representation learning (RL) or feature learning has a huge impact in the field
newlineof signal processing applications. The goal of the RL approaches is to learn the
newlinemeaningful representation directly from the data that can be helpful to the pattern
newlineclassifier. Specifically, the unsupervised RL has gained a significant interest in
newlinethe feature learning in various signal processing areas including the speech and
newlineaudio processing. Recently, various RL methods are used to learn the auditory like
newlinerepresentations from the speech signals or its spectral representations. In this thesis, we propose a novel auditory representation learning model based on the Convolutional Restricted Boltzmann Machine (ConvRBM). The auditory like sub band filters are learned when the model is trained directly on the raw
newlinespeech and audio signals with arbitrary lengths.
newline