One of the essential components of a neuromotor prosthetic device is a neural decoder that translates the activity of a set of neurons into an estimate of the intended movement of the prosthetic limb. Wiener filter style approaches model this transformation as a linear function of the number of spikes observed from a set of neurons and over a range of distinct time bins. More recently, research...

The least-mean-square (LMS) estimator is a nonlinear estimator with information dependencies spanning the entire set of data fed into it. The traditional analysis techniques used to model this estimator obscure these dependencies; to simplify the analysis they restrict the estimator to the finite set of data sufficient to span the length of its filter. Thus the finite Wiener filter is often con...

Linear transmit filters depend on current channel state information, which is available from the uplink channel estimation in time division duplex systems. For multiple antenna elements at the transmitter, we illustrate the influence of out-dated channel estimates on link level performance for symmetric and asymmetric traffic, comparing the transmit matched, zero-forcing, and Wiener filter. A W...

The paper presents speech enhancement scheme for suppression of different background noises. The objective of speech enhancement is to improve the quality of the processed speech. This paper also investigate the use of two different transforms for speech enhancement. Speech enhancement using wiener filtering approach is presented using transforms DFT and DCT. The type of transform shows differe...

Medical images or ultra sound images are typically used in the medical fields for lots of purposes. Many problems occurred in the medical images. Main problem related to this imaging technique is introduction of speckle noise which blurs or degrades the superiority of the image. Speckle noise makes the image indistinct which is tough to see clearly. There have been several techniques to de-nois...

The least-mean-square (LMS) estimator is a nonlinear estimator with information dependencies spanning the entire set of data fed into it. The traditional analysis techniques which are used to model this estimator obscure this, restricting the estimator to the finite set of data sufficient to span the length of its filter. The finite Wiener filter is thus often considered a bound on the performa...

Mrstract—The notion is exploded that to build a Kalman–Bucy filter, one needs to know tbe whole structure of the signal generating process. It is shown that the filter is constructible knowing precisely those covariwrces required to construct a Wiener filter, and no more, and that the filter is independent of the particular models of the processes generating these covariances. Performance of th...

In a number of adaptive filtering applications, non-Wiener effects have been observed for the (normalized) leastmean-square algorithm. These effects can lead to performance improvements over the fixed Wiener filter with the same model structure, and are characterized by dynamic behavior of the adaptive filter weights. Here we investigate whether such non-Wiener effects can also occur in the rec...

In this paper a novel transversal split filter configuration is proposed and the split optimum Wiener filter is introduced, as well as the symmetric and antisymmetric linear phase Wiener filter. The approach consists of combining the idea of split filtering with a linearlyconstrained optimization scheme. Then, the continuously split procedure is introduced and the multi-split adaptive filter is...

The aim of this paper is to use visual speech information to create Wiener filters for audio speech enhancement. Wiener filters require estimates of both clean speech statistics and noisy speech statistics. Noisy speech statistics are obtained from the noisy input audio while obtaining clean speech statistics is more difficult and is a major problem in the creation of Wiener filters for speech ...