Accelerating FPGA-based evolution of wavelet transform filters by optimized task scheduling

Adaptive embedded systems are required in various applications. This work addresses these needs in the area of adaptive image compression in FPGA devices. A simplified version of an evolution strategy is utilized to optimize wavelet filters of a Discrete Wavelet Transform algorithm. We propose an adaptive image compression system in FPGA where optimized memory architecture, parallel processing and optimized task scheduling allow reducing the time of evolution. The proposed solution has been extensively evaluated in terms of the quality of compression as well as the processing time. The proposed architecture reduces the time of evolution by 44% compared to our previous reports while maintaining the quality of compression unchanged with respect to existing implementations. The system is able to find an optimized set of wavelet filters in less than 2 min whenever the input type of data changes

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

Bio-inspired FPGA Architecture for Self-Calibration of an Image Compression Core based on Wavelet Transforms in Embedded Systems

A generic bio-inspired adaptive architecture for image compression suitable to be implemented in embedded systems is presented. The architecture allows the system to be tuned during its calibration phase. An evolutionary algorithm is responsible of making the system evolve towards the required performance. A prototype has been implemented in a Xilinx Virtex-5 FPGA featuring an adaptive wavelet transform core directed at improving image compression for specific types of images. An Evolution Strategy has been chosen as the search algorithm and its typical genetic operators adapted to allow for a hardware friendly implementation. HW/SW partitioning issues are also considered after a high level description of the algorithm is profiled which validates the proposed resource allocation in the device fabric. To check the robustness of the system and its adaptation capabilities, different types of images have been selected as validation patterns. A direct application of such a system is its deployment in an unknown environment during design time, letting the calibration phase adjust the system parameters so that it performs efcient image compression. Also, this prototype implementation may serve as an accelerator for the automatic design of evolved transform coefficients which are later on synthesized and implemented in a non-adaptive system in the final implementation device, whether it is a HW or SW based computing device. The architecture has been built in a modular way so that it can be easily extended to adapt other types of image processing cores. Details on this pluggable component point of view are also given in the paper

Evolutionary design and optimization of Wavelet Transforms for image compression in embedded systems

This paper describes the initial studies of an Evolution Strategy aimed at implementation on embedded systems for the evolution of Wavelet Transforms for image compression. Previous works in the literature have already been proved useful for this application, but they are highly computationally intensive. Therefore, the work described here, deals with the simplifications made to those algorithms to reduce their computing requirements. Several optimizations have been done in the evaluation phase and in the EA operators. The results presented show how the proposed algorithm cut outs still allow for good results to be achieved, while effectively reducing the computing requirements

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

Implementation of bio-inspired adaptive wavelet transforms in FPGAs. Modeling, validation and profiling of the algorithm

Providing embedded systems with adaptation capabilities is an increasing importance objective in design community. This work deals with the implementation of adaptive compression schemes in FPGA devices by means of a bioinspired algorithm. A simplified version of an Evolution Strategy using fixed point arithmetic is proposed. Specifically, a simpler than the standard (hardware friendly) mutation operator is designed, modelled and validated using a high-level language. HW/SW partitioning issues are considered and code profiling accomplished to validate the proposal. Preliminary results of the proposed hardware architecture are also show

An Evolved Wavelet Library Based on Genetic Algorithm

As the size of the images being captured increases, there is a need for a robust algorithm for image compression which satiates the bandwidth limitation of the transmitted channels and preserves the image resolution without considerable loss in the image quality. Many conventional image compression algorithms use wavelet transform which can significantly reduce the number of bits needed to represent a pixel and the process of quantization and thresholding further increases the compression. In this paper the authors evolve two sets of wavelet filter coefficients using genetic algorithm (GA), one for the whole image portion except the edge areas and the other for the portions near the edges in the image (i.e., global and local filters). Images are initially separated into several groups based on their frequency content, edges, and textures and the wavelet filter coefficients are evolved separately for each group. As there is a possibility of the GA settling in local maximum, we introduce a new shuffling operator to prevent the GA from this effect. The GA used to evolve filter coefficients primarily focuses on maximizing the peak signal to noise ratio (PSNR). The evolved filter coefficients by the proposed method outperform the existing methods by a 0.31 dB improvement in the average PSNR and a 0.39 dB improvement in the maximum PSNR

Cluster Detection by Lifting with Application to Phylogenetics

In this thesis, we propose a new algorithm which automatically detects the number of clusters in a tree structure data set by denoising some generalized node values in the tree using lifting “one coefficient at a time” (LOCAAT) algorithm introduced by Jansen et al. (2001). Our algorithm can be applied to any multidimensional data set using compactness value as a node value or to phylogenetic data sets, DNA sequences, using either compactness value or dissimilarity score as a node value. Compactness value is defined as the average distance from the centroid of each possible cluster in the tree, and the dissimilarity score is the average number of loci, where at least one of them does not share the same nucleotide between sequences under the node of interest. For multidimensional data sets, we consider each node in the tree as a possible location of a cluster after denoising the tree by LOCAAT. Thus, for each possible cluster, we check how much departure we can allow from the centroid of the cluster to assign the objects under the node of interest as a cluster. Then if a node and all its child nodes are denoised less than or equal to the allowed amount of departure from the centroid of their clusters, a cluster is located at this node. We also propose another version of our algorithm based on non-decimated lifting (Knight & Nason, 2009) in which we generate a probability of being clustered for each node. If a node and all its child nodes have a probability of being clustered less than or equal to the probability of acceptance, θ∈[0; 1], a cluster is located at this node. We provide a comparison study between our algorithms and some available internal cluster validity indices (CVIs) in the literature using some artificial data sets and a real data set. In addition, we compare the performance of each method using some available external cluster validity scores. For phylogenetic data sets, we check the performance of our algorithms and other CVIs using both compactness value and dissimilarity score as a node value. To be able to compute compactness value for a phylogenetic tree, we need to find the position of each specie in Rp using multidimensional scaling (MDS), and then we can find which species share the similar features using our algorithm. If we use the dissimilarity score as a node value, we will cluster similar species together by finding how much difference we can allow between species. We check the performance of our algorithms using some artificial and a real data sets. In the final part of our thesis, we propose a visualization tool for cophylogenetic data sets. We only consider the associated two phylogenetic trees case, and we apply our algorithm to both host and parasite trees separately to provide a summary of these data sets. We check the performance of our algorithm using two well-known cophylogenetic data sets