Adaptive lifting schemes with perfect reconstruction

In this paper, we propose a framework for constructing adaptive wavelet decompositions using the lifting scheme. A major requirement is that perfect reconstruction is possible without any overhead cost. In this paper we restrict ourselves to the update lifting stage. It is assumed that the update filter utilises local gradient information to adapt itself to the signal in the sense that smaller gradients `evoke' stronger update filters. As a result, sharp transitions in a signal will not be smoothed to the same extent as regions which are more homogeneous. The approach taken in this paper differs from other adaptive schemes found in the literature in the sense that that no bookkeeping is required in order to have perfect reconstruction

Caracterización dinámica de señales electromiográficas usando filtros adaptativos

Se presentan esquemas de caracterización dinámica aplicados sobre señales electromiográficas utilizando una estructura de bancos de filtros con variación de parámetros adaptativos. Los bancos de filtros son calculados a partir de esquemas de actualización. Los criterios de adaptabilidad utilizados para la caracterización dinámica son momentos estadísticos y vector gradiente. Para la selección de características se utiliza el módulo máximo. Los resultados de clasificación con los métodos adaptativos son superiores en un 9 % a los resultados obtenidos con bancos de filtros de parámetros fijos. Se utiliza para la clasificación un clasificador bayesiano con la distancia de Mahalanobis

Caracterización dinámica de señales electromiográficas usando filtros adaptativos

Se presentan esquemas de caracterización dinámica aplicados sobre señales electromiográficas utilizando una estructura de bancos de filtros con variación de parámetros adaptativos. Los bancos de filtros son calculados a partir de esquemas de actualización. Los criterios de adaptabilidad utilizados para la caracterización dinámica son momentos estadísticos y vector gradiente. Para la selección de características se utiliza el módulo máximo. Los resultados de clasificación con los métodos adaptativos son superiores en un 9 % a los resultados obtenidos con bancos de filtros de parámetros fijos. Se utiliza para la clasificación un clasificador bayesiano con la distancia de Mahalanobis

Caracterización dinámica de señales electromiográficas usando filtros adaptativos

Se presentan esquemas de caracterización dinámica aplicados sobre señales electromiográficas utilizando una estructura de bancos de filtros con variación de parámetros adaptativos. Los bancos de filtros son calculados a partir de esquemas de actualización. Los criterios de adaptabilidad utilizados para la caracterización dinámica son momentos estadísticos y vector gradiente. Para la selección de características se utiliza el módulo máximo. Los resultados de clasificación con los métodos adaptativos son superiores en un 9 % a los resultados obtenidos con bancos de filtros de parámetros fijos. Se utiliza para la clasificación un clasificador bayesiano con la distancia de Mahalanobis

Evolutionary Computing and Second generation Wavelet Transform optimization: Current State of the Art

The Evolutionary Computation techniques are exposed to number of domains to achieve optimization. One of those domains is second generation wavelet transformations for image compression. Various types of Lifting Schemes are being introduced in recent literature. Since the growth in Lifting Schemes is in an incremental way and new types of Lifting Schemes are appearing continually. In this context, developing flexible and adaptive optimization approaches is a severe challenge. Evolutionary Computing based lifting scheme optimization techniques are a valuable technology to achieve better results in image compression. However, despite the variety of such methods described in the literature in recent years, security tools incorporating anomaly detection functionalities are just starting to appear, and several important problems remain to be solved. In this paper, we present a review of the most well-known EC approaches for optimizing Secondary level Wavelet transformations

Evolutionary Approach to Improve Wavelet Transforms for Image Compression in Embedded Systems

A bioinspired, evolutionary algorithm for optimizing wavelet transforms oriented to improve image compression in embedded systems is proposed, modelled, and validated here. A simplified version of an Evolution Strategy, using fixed point arithmetic and a hardware-friendly mutation operator, has been chosen as the search algorithm. Several cutdowns on the computing requirements have been done to the original algorithm, adapting it for an FPGA implementation. The work presented in this paper describes the algorithm as well as the test strategy developed to validate it, showing several results in the effort to find a suitable set of parameters that assure the success in the evolutionary search. The results show how high-quality transforms are evolved from scratch with limited precision arithmetic and a simplified algorithm. Since the intended deployment platform is an FPGA, HW/SW partitioning issues are also considered as well as code profiling accomplished to validate the proposal, showing some preliminary results of the proposed hardware architecture

MASCOT : metadata for advanced scalable video coding tools : final report

The goal of the MASCOT project was to develop new video coding schemes and tools that provide both an increased coding efficiency as well as extended scalability features compared to technology that was available at the beginning of the project. Towards that goal the following tools would be used: - metadata-based coding tools; - new spatiotemporal decompositions; - new prediction schemes. Although the initial goal was to develop one single codec architecture that was able to combine all new coding tools that were foreseen when the project was formulated, it became clear that this would limit the selection of the new tools. Therefore the consortium decided to develop two codec frameworks within the project, a standard hybrid DCT-based codec and a 3D wavelet-based codec, which together are able to accommodate all tools developed during the course of the project