Early breast cancer in women can be detected efficiently, by processing Mammograms in an effective way. Mammographic images are affected by noise which has low contrast and poor radiographic resolution based on illperformance of X-ray hardware systems. This leads to improper visualization of lesion detail. Generally Non-linear filters are preferred for image enhancement applications. Because th...

This paper presents an overview of several gradienttype recursive algorithms for adaptive nonlinear filters equipped with bilinear system models. Bilinear models are attractive because they can approximate a large class of nonlinear systems with great parsimony in the use of coefficients. Two algorithms of complexity O(N3) (N is the memory span of the bilinear system model used in the adaptive ...

Linear Recursive Filters and also Recurrent Neural Networks can be adapted on-line but sometimes with instability problems. Stability control techniques exist for the linear case but they are either computationally expensive or non-robust. For the nonlinear case, stability control is simply usually not performed in applications. This paper presents a new stability control method for IIR adaptiv...

This paper studies a class of algorithms called Natural Gradient (NG) algorithms, and their approximations, known as ANG algorithms. The LMS algorithm is derived within the NG framework, and a family of LMS variants that exploit sparsity is derived. Mean squared error (MSE) analysis of the family of ANG algorithms is provided, and it is shown that if the system is sparse, then the new algorithm...

This paper develops a framework for the mean-square analysis of adaptive lters with general data and error nonlinearities. The approach relies on energy conservation arguments and is carried out without restrictions on the probability distribution of the input sequence. In particular, for adaptive lters with diagonal matrix nonlinearities, we provide closed form expressions for the steady-state...

Correlation filters have attracted growing attention due to their high efficiency, which have been well studied for binary classification. However, by setting the desired output to be a fixed Gaussian function, the conventional multi-class classification based on correlation filters becomes problematic due to the under-fitting in many real-world applications. In this paper, we propose an adapti...

Over the last years, particle filters have been applied with great success to a variety of state estimation problems. We present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets on-the-fly. The key idea of the KLD-sampling method is to bound the approximation error introduced by the sample-based representation of the particle filter. Th...

{ In some adaptive ltering applications, the least-mean-square (LMS) algorithm may be too computationally-and memory-intensive to implement. In this paper , we analyze two adaptive algorithms that update only a portion of the coeecients of the adaptive lter per iteration. These algorithms use decimated versions of the error and regressor signals, respectively. Simulations verify the accuracy of...

The pros and cons of a quadratic error measure in the context of various applications have often been discussed. In this tutorial, we argue that it is not only a suboptimal but definitely the wrong choice when describing the stability behavior of adaptive filters. We take a walk through the past and recent history of adaptive filters and present 14 canonical forms of adaptive algorithms and eve...

New porphyrin-based charge storage materials will be synthesized, including porphyrin monomers, dyads, and triple-decker sandwich coordination compounds. Novel approaches for development of charge-transfer layers also will be investigated. Molecular-based charge-storage materials will be incorporated into a variety of novel nanodevice designs in the Misra lab. Material samples will be delivered...