The convergence rate of the Least Mean Square (LMS) algorithm is dependent on the eigenvalue distribution of the reference input correlation matrix. When adaptive filters are employed in low-delay over-sampled subband structures, colored subband signals considerably decelerate the convergence speed. Here, we propose and implement two promising techniques for improving the convergence rate based...

AIM This study aimed to determine the efficacy of trays made with and without reservoirs, in conjunction with time and cost evaluations, by measuring color change with home whitening procedures. MATERIALS AND METHODS Extracted human maxillary teeth (central incisors n = 20; canines n = 20; molars n = 20) and 60 artificial teeth (lateral n = 20; premolar n = 40) were mounted into ten typodonts...

Assessment of bone quality is an emerging solution for quantifying the effects of bone pathology or treatment. Perhaps one of the most important parameters characterising bone quality is the toughness behaviour of bone. Particularly, fracture toughness, is becoming a popular means for evaluating bone quality. The method is moving from a single value approach that models bone as a linear-elastic...

CONTEXT The possibility of bleaching vital teeth with peroxide-based products considerably revolutionized esthetic dentistry. AIM The aim of this clinical study was to evaluate tooth color change and dental sensitivity after exposure to preloaded film containing a 10% hydrogen peroxide whitening system (Opalescence Trθswhite Supreme). MATERIALS AND METHODS A total of 13 volunteers, aged 18 ...

The main objective of this letter is to present an e cient algorithm for robust whitening in the presence of temporally uncorrelated additive noise that may be spatially correlated. This whitening is introduced as a pre-processing step in the blind source separation process. The robust whitening consists in the eigenvalue decomposition of a positive de nite linear combination of a set a correla...

Whitened Neural Network (WNN) is a recent advanced deep architecture, which improves convergence and generalization of canonical neural networks by whitening their internal hidden representation. However, the whitening transformation increases computation time. Unlike WNN that reduced runtime by performing whitening every thousand iterations, which degenerates convergence due to the ill conditi...

In natural image understanding, the whitening step plays an important role, especially within many unsupervised feature learning algorithms. Examples of these algorithms include ICA, TICA, Auto-encoder, and so forth. Current whitening techniques include Principal Component Analysis (PCA) and Zero-phase Component Analysis (ZCA). However, the drawbacks of these approaches are obvious: the time co...