Luminita State

New Approaches in Image Compression and Noise Removal

The increasing use of location-based services has raised many issues of decision support and resource allocation. A crucial problem is how to solve queries of Group k-Nearest Neighbour (GkNN). A typical example of a GkNN query is finding one or many nearest meeting places for a group of people. Existing methods mostly rely on a centralised base station. However, mobile P2P systems offer many benefits, including self-organization, fault-tolerance and load-balancing. In this study, we propose and evaluate a novel P2P algorithm focusing on GkNN queries, in which mobile query objects and static objects of interest are of two different categories. The algorithm is evaluated in the MiXiM simulation framework with both real and synthetic datasets. The results show the practical feasibility of the P2P approach for solving GkNN queries for mobile networks.The Discrete Fourier Transform (DFT) can be viewed as the Fourier Transform of a periodic and regularly sampled signal. The Non-Uniform Discrete Fourier Transform (NuDFT) is a generalization of the DFT for data that may not be regularly sampled in spatial or temporal dimensions. This flexibility allows for benefits in situation where sensor placement cannot be guaranteed to be regular or where prior knowledge of the informational content could allow for better sampling patterns than a regular one. NuDFT is used in applications such as Synthetic Aperture Radar (SAR), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Direct calculation of NDFT is time consuming and, in general, Non-uniform Fast Fourier Transform (NuFFT) is used. The key of computing NuFFT is to interpolate the non-uniformly sampled data onto a uniform grid and then use the Fast Fourier Transform. The interpolation process, called re-gridding or data-translation, is known to be the most time consuming (over 90% of the overall computation time of NuFFT) [1]. FPGA have been shown in prior work to be a power efficient way to perform this re-gridding as in [1]. We propose a novel memory-efficient FPGA based technique based on grouping the source points together in on-chip memory and hence reducing the number of memory accesses. The proposed architecture exhibits high performance for the re-gridding process. A speed-up of over 7.5 X was achieved when compared with existing FPGA-based technique for a target grid of size 256 atimes; 256. The basic procedure for re-gridding is based on updating all the target points within a specified distance of the source point using an interpolation kernel function. In this paper, we refer to this specified distance as interpolation threshold and its value is expressed in terms of the number of target points. Our proposed architecture is based on dividing the 2- Dimensional (2D) uniform target grid T into smaller 2D sub-grid. These sub-grids are called tiles. Corresponding to each tile, a block memory based FIFO is used. The idea is to group the source points that affect a tile into the FIFO corresponding to the tile. FIFOs are read one at a time and the tile corresponding to the FIFO being read is fetched from the external memory into the device. Performance of the proposed architecture is evaluated by simulating and computing the number of clock cycles required. Using a clock frequency of 50 MHz, which is chosen to be less then the achieved maximum frequency of 60.16 MHz, computation time for the translation process is calculated. Based on this computed time, throughput is calculated in terms of frames per second (fps).Principal Component Analysis is a well-known statistical method for feature extraction and it has been broadly used in a large series of image processing applications. The multiresolution support provides a suitable framework for noise filtering and image restoration by noise suppression. The procedure used is to determine statistically significant wavelet coefficients and from this to specify the multiresolution support. In the third section, we introduce the algorithms Generalized Multiresolution Noise Removal, and Noise Feature Principal Component Analysis. The algorithm Generalized Multiresolution Noise Removal extends the Multiresolution Noise Removal algorithm to the case of general uncorrelated Gaussian noise, and Noise Feature Principal Component Analysis algorithm allows the restoration of an image using a noise decorrelation process. A comparative analysis of the performance of the algorithms Generalized Multiresolution Noise Removal and Noise Feature Principal Component Analysis is experimentally performed against the standard Adaptive Mean Variance Restoration and Minimum Mean Squared Error algorithms. In the fourth section, we propose the Compression Shrinkage Principal Component Analysis algorithm and its model-free version as Shrinkage-Principal Component Analysis based methods for noise removal and image restoration. A series of conclusive remarks are supplied in the final section of the paper.

On a certain class of algorithms for noise removal in image processing: a comparative study

The effectiveness of restoration techniques mainly depends on the accuracy of the image modeling. One of the most popular degradation models is based on the assumption that the image blur can be modeled as a superposition with an impulse response H that may be space variant and its output is subject to an additive noise. Our research aimed at the use of statistical concepts and tools for developing a new class of image restoration algorithms. Several variants of a heuristic scatter matrix based algorithm (HSBA), the algorithm HBA that uses the Bhattacharyya coefficient for image restoration, the heuristic regression based algorithm for image restoration and new approaches of image restoration based on the innovation algorithm are reported. The LMS type algorithm AMVR is presented. A comparative study is performed and reported on the quality and efficiency of the presented noise removal algorithms.

A Version of the FastICA Algorithm Based on the Secant Method Combined with Simple Iterations Method

The work proposes a new algorithm for the estimation of the ICA model, an algorithm based on secant method and successive approximations. The first sections briefly present the standard FastICA algorithm based on the Newton method and a new version of the FastICA algorithm. The proposed algorithm to estimate the independent components combines the secant iterations with successive approximations technique. The final section presents the results of a comparative analysis experimentally derived conclusions concerning the performance of the proposed method. The tests were performed of several samples of signal files.

Successive Approximations-Based Algorithm for Independent Component Analysis

The reported work proposes a new algorithm for the estimation of the ICA model, an algorithm based on successive approximations. We want to mathematically substantiate the successive approximations for the bidimensional case, a case which presents interest from a practical point of view, as well as to establish the performances of the proposed algorithm to estimate the independent components. Comparative analysis is performed and experimentally derived conclusions concerning the performance of the proposed method are reported in the final section of the paper for signals recognition applications.

Heuristic Attempts to Improve the Generalization Capacities in Learning SVMs

The paper reports some new variants of gradient ascent type in learning SVMs. The theoretical development is presented in the third section of the paper. The performance analysis of the proposed variants, in terms of recognition accuracy and generalization capacity, is experimentally evaluated and the results are presented and commented in the final part of the paper.

An Improved Algorithm for Estimating the ICA Model Concerning the Convergence Rate

The aim of the present paper is to propose a estimation algorithm of the ICA model, an algorithm based on successive approximations. The convergence rate of the successive approximations method are substantiated for the bidimensional case, a case which presents interest from a practical point of view, and we want to establish the performances of the proposed algorithm to estimate the independent components. Comparative analysis is done and experimentally derived conclusions on the performance of the proposed method are drawn in the last section of the paper for signals recognition applications.

GA-based attempts to improve the recognition rate and generalization capacity of the nonlinear soft margin support vector machines

The aim of the paper is to report a new method based on genetic computation of designing a nonlinear soft margin SVM yielding to significant improvements in discriminating between two classes. The design of the SVM is performed in a supervised way, in general the samples coming from the classes being nonlinearly separable. The experimental analysis was performed on artificially generated data as well as on Ripley and MONKs datasets reported in the fourth section of the paper. The tests proved real improvements of both the recognition rate and generalization capacities without significantly increasing the computational complexity.

A Faster Gradient Ascent Learning Algorithm for Nonlinear SVM

We propose a refined gradient ascent method including heuristic parameters for solving the dual problem of nonlinear SVM. Aiming to get better tuning to the particular training sequence, the proposed refinement consists of the use of heuristically established weights in correcting the search direction at each step of the learning algorithm that evolves in the feature space. We propose three variants for computing the correcting weights, their effectiveness being analyzed on experimental basis in the final part of the paper. The tests pointed out good convergence properties, and moreover, the proposed modified variants proved higher convergence rates as compared to Platt’s SMO algorithm. The experimental analysis aimed to derive conclusions on the recognition rate as well as on the generalization capacities. The learning phase of the SVM involved linearly separable samples randomly generated from Gaussian repartitions and the WINE and WDBC datasets. The generalization capacities in case of artificial data were evaluated by several tests performed on new linearly/nonlinearly separable data coming from the same classes. The tests pointed out high recognition rates (about 97%) on artificial datasets and even higher recognition rates in case of the WDBC dataset.

A Probabilistic Model-Free Approach in Learning Multivariate Noisy Linear Systems

The paper provides a series of results concerning the learning from data a linear regressive model in a multivariate framework. The parameter estimates of the regressive model are determined using the maximum likelihood principle and the adaptive learning algorithms are derived using the gradient ascent technique. The predicted output is expressed as the sum of a linear combination of the entries of the input and the random vector that represents the effects of the unobservable factors and noise. In the second section of the paper the mathematical arguments for the estimation scheme based exclusively on a finite size set of observations is provided. The third section of the paper is focused on experimental evaluation of the quality of the resulted learning scheme in order to establish conclusions concerning their accuracy and generalization capacities, the evaluation being performed in terms of metric, probabilistic and informational criterion functions. The final section of the paper contains a series of conclusions and suggestions for further work.

A New Learning Algorithm of SVM from Linear Separable Samples

Training a SVM corresponds to solving a linearly constrained quadratic problem (QP) in a number of variables equal to the number of data points, this optimization problem becoming challenging when the number of data points exceeds few thousands. Because the computational complexity of the existing algorithms is extremely large in case of few thousands support vectors and therefore the SVM QP-problem becomes intractable, several decomposition algorithms that do not make assumptions on the expected number of support vectors have been proposed instead. In this paper we propose a heuristic learning algorithm of gradient type for learning a SVM using linear separable data, and analyze its performance in terms of accuracy and efficiency. In order to evaluate the efficiency of our learning method, several tests were performed against the Platt’s SMO method, and the conclusions are formulated in the final section of the paper.