Improving A*OMP: Theoretical and Empirical Analyses With a Novel Dynamic Cost Model

Best-first search has been recently utilized for compressed sensing (CS) by the A* orthogonal matching pursuit (A*OMP) algorithm. In this work, we concentrate on theoretical and empirical analyses of A*OMP. We present a restricted isometry property (RIP) based general condition for exact recovery of sparse signals via A*OMP. In addition, we develop online guarantees which promise improved recovery performance with the residue-based termination instead of the sparsity-based one. We demonstrate the recovery capabilities of A*OMP with extensive recovery simulations using the adaptive-multiplicative (AMul) cost model, which effectively compensates for the path length differences in the search tree. The presented results, involving phase transitions for different nonzero element distributions as well as recovery rates and average error, reveal not only the superior recovery accuracy of A*OMP, but also the improvements with the residue-based termination and the AMul cost model. Comparison of the run times indicate the speed up by the AMul cost model. We also demonstrate a hybrid of OMP and A?OMP to accelerate the search further. Finally, we run A*OMP on a sparse image to illustrate its recovery performance for more realistic coefcient distributions

Channel estimation with TCH codes for machine-type communications

TCH codes possess several properties that allow us to use them efficiently in various applications. One of these applications is channel estimation and, in this dissertation, it is studied the performance of TCH codes to estimate the channel in an Orthogonal Frequency Division Multiplexing system, regarding Machine-Type Communications. Bit error rate performance results were obtained by executing simulations that allowed the evaluation of the impact of using two different pilot techniques, such as data multiplexed and implicit pilots, different pilot power levels and different modulations, QPSK and 64-QAM. Pilots based on TCH codes are also compared with other conventional pilots. Results show that TCH codes have a very positive and reliable performance. Joint timing synchronization and channel estimation is also performed using different sparse based approaches, such as Orthogonal Matching Pursuit, L1- regularized and Iterative Reweighted L1. TCH codes are compared against different sequence types, namely Zadoff-Chu sequences and pseudorandom codewords, and variations in the pilot size, the channel length and the observation window size are executed in order to understand their effects. Results ultimately illustrate that TCH codes can be effectively used in joint channel estimation and synchronization, managing to withstand worst simulation conditions better than its counterparts. It is also proven that compressed sensing can successfully be utilized in joint synchronization and channel estimation, an area where its use has not been very explored.Os códigos TCH possuem várias propriedades que nos permitem usá-los eficientemente em diversas aplicações. Uma delas é a estimação de canal e nesta dissertação é estudado o desempenho dos códigos TCH em estimação de canal num sistema OFDM, tendo em conta as comunicações Machine-Type. Resultados que ilustram a taxa de erro de bit foram obtidos através de simulações que permitem avaliar o impacto de usar diferentes técnicas de pilotos, nomeadamente multiplexados e implícitos, diferentes valores de potência para os pilotos e diferentes modulações, QPSK e 64-QAM. Também é feita a comparação entre os pilotos TCH e pilotos convencionais. Os resultados mostram que os pilotos TCH tem um desempenho muito positivo e confiável, dentro dos parâmetros testados. Também é efetuado o estudo de sincronização e estimação de canal conjunta usando métodos esparsos como o OMP, o L1-regularized e o Iterative Reweighted L1. Os códigos TCH são comparados com outros tipos de sequências, tais como as sequências Zadoff-Chu e os códigos pseudo-aleatórios. São consideradas variações no tamanho dos pilotos, no comprimento do canal e no tamanho da janela de observação para perceber quais são os seus efeitos no desempenho. Os resultados demonstram que os códigos TCH podem ser utilizados com sucesso em estimação de canal e sincronização conjunta e conseguem aguentar condições adversas de simulação melhor que os outros pilotos utilizados. Também é provado que compressed sensing pode ser utilizado com sucesso em sincronização e estimação conjunta, que é uma área onde o seu uso ainda não foi explorado aprofundadamente

Towards Effective Codebookless Model for Image Classification

The bag-of-features (BoF) model for image classification has been thoroughly studied over the last decade. Different from the widely used BoF methods which modeled images with a pre-trained codebook, the alternative codebook free image modeling method, which we call Codebookless Model (CLM), attracted little attention. In this paper, we present an effective CLM that represents an image with a single Gaussian for classification. By embedding Gaussian manifold into a vector space, we show that the simple incorporation of our CLM into a linear classifier achieves very competitive accuracy compared with state-of-the-art BoF methods (e.g., Fisher Vector). Since our CLM lies in a high dimensional Riemannian manifold, we further propose a joint learning method of low-rank transformation with support vector machine (SVM) classifier on the Gaussian manifold, in order to reduce computational and storage cost. To study and alleviate the side effect of background clutter on our CLM, we also present a simple yet effective partial background removal method based on saliency detection. Experiments are extensively conducted on eight widely used databases to demonstrate the effectiveness and efficiency of our CLM method

Master of Science

thesisNondestructive evaluation (NDE) is a means of assessing the reliability and integrity of a structural component and provides such information as the presence, location, extent, and type of damage in the component. Structural health monitoring (SHM) is a subfield of NDE, and focuses on a continuous monitoring of a structure while in use. SHM has been applied to structures such as bridges, buildings, pipelines, and airplanes with the goal of detecting the presence of damage as a means of determining whether a structure is in need of maintenance. SHM can be posed as a modeling problem, where an accurate model allows for a more reliable prediction of structural behavior. More reliable predictions make it easier to determine if something is out of the ordinary with the structure. Structural models can be designed using analytical or empirical approaches. Most SHM applications use purely analytical models based on finite element analysis and fundamental wave propagation equations to construct behavioral predictions. Purely empirical models exist, but are less common. These often utilize pattern recognition algorithms to recognize features that indicate damage. This thesis uses a method related to the k-means algorithm known as dictionary learning to train a wave propagation model from full wavefield data. These data are gathered from thin metal plates that exhibit complex wavefields dominated by multipath interference. We evaluate our model for its ability to detect damage in structures on which the model was not trained. These structures are similar to the training structure, but variable in material type and thickness. This evaluation will demonstrate how well learned dictionaries can both detect damage in a complex wavefield with multipath interference, and how well the learned model generalizes to structures with slight variations in properties. The damage detection and generalization results achieved using this empirical model are compared to similar results using both an analytical model and a support vector machine model