Showing papers in "WSEAS Transactions on Signal Processing archive in 2007"

A supervised Bayesian method for cerebrovascular segmentation

Abstract: In this paper, we present a supervised statistical-based cerebrovascular segmentation method from Time-Of-Flight MRA. The novelty of this method is that rather than model the dataset over the entire intensity range, we at first use a low threshold to eliminate the lowest intensity region, and then use two uniform distributions to model the middle and high intensity regions, respectively. Subsequently, in order to overcome the intensity overlap between subcutaneous fat and arteries, a high order multiscale features based energy function is introduced to enhance the segmentation. Comparing with those sole intensity based segmentation method the newly proposed algorithm can solve the problem of the regional intensity variation of TOF-MRA well and improve the quality of segmentation. The experimental results also show that the proposed method can provide a better quality segmentation than sole intensity information used method.

Algorithmic transformations and peak power constraint applied to multiple-voltage low-power VLSI signal processing

Abstract: We present a multiple-voltage high-level synthesis methodology that minimizes power dissipation of VLSI signal processing. By applying algorithmic transformations, the proposed approach optimizes the power saving, in terms of the average power and peak power, for DSP applications when the resources and the latency are constrained. Our approach is motivated by the maximization of task mobilities. The mobility is defined as the distance between its as-late-as-possible (ALAP) schedule time and its as-soon-as-possible (ASAP) schedule time. The increase of mobilities may raise the possibility of assigning tasks to low-voltage components. To earn task mobilities, we use loop shrinking, retiming and unfolding techniques. The loop shrinking can reduce the iteration period bound (IPB), while the others are employed for shortening the minimum achieved sample period (MASP) as much as possible. The minimization of MASP implies high task mobilities. Thereafter, we can assign tasks with high mobilities to low-voltage components and minimize energy dissipation under resource and latency constraints. With considering the overhead of level conversion and the minimization of peak power, the proposed methodology has low complexity and can achieve significant power reduction.

A narrative approach for speech signal based MMSE estimation using quantum parameters

Abstract: In this paper, the performance of different estimators in estimating the speech signal through Quantum parameters can be analyzed. The main objective is to estimate the speech signal by a set of linear and Non-linear estimators that are proposed to be efficient in performance. The Minimax mean square error estimator is designed to minimize the worst-case MSE. In an estimation context, the objective typically is to minimize the size of the estimation error, rather than that of the data error as a cause, in many practical scenarios the least-squares estimator is known to result in a large MSE. A comparative analysis between MMSE estimator with other linear and nonlinear estimators can be performed. The analysis proved that the MMSE estimator can outperform both from linear and nonlinear estimator.