Projection-reconstruction NMR experiments have been shown to significantly reduce the acquisition time required to obtain protein backbone assignment data. To date, this concept has only been applied to smaller (15)N/(13)C-labeled proteins. Here, we show that projection-reconstruction NMR techniques can be extended to larger protonated and perdeuterated proteins. We present a suite of (4,2)D tr...

In this paper, we propose a projection based design of near perfect reconstruction QMF banks. An advantage of this method is that additional design specifications are easily implemented by defining new convex sets. To apply convex projection technique, the main difficulty is how to approximate the design specifications by some closed convex sets. In this paper, introducing a notion of Magnitude...

We study the convergence of Ishikawa iteration process for the class of asymptotically κ-strict pseudocontractive mappings in the intermediate sense which is not necessarily Lipschitzian. Weak convergence theorem is established. We also obtain a strong convergence theorem by using hybrid projection for this iteration process. Our results improve and extend the corresponding results announced by...

This paper deals with numerical Toeplitz matrix approximation. Our approach is based on (i) a projection algorithm which converges globally but slowly; and (ii) the quasi-Newton method which is faster. Hybrid methods that attempt to combine the best features of both methods are then considered.

The purpose of this article is to prove strong convergence theorems for total asymptotically strict quasi-φ-pseudo-contractions by using a hybrid projection algorithm in Banach spaces. As applications, we apply our main results to find minimizers of proper, lower semicontinuous, and convex functionals and solutions of equilibrium problems.

In this paper, a mapping which is asymptotically hemi-pseudocontractive in the intermediate sense is introduced. Hybrid projection methods are considered for the class of mappings. Strong convergence theorems for common fixed points are established in the framework of Hilbert spaces.

A hybrid architecture that includes Radial Basis Functions (RBF) and projection based hidden units is introduced together with a simple gradient based training algorithm. Classification and regression results are demonstrated on various data sets and compared with several variants of RBF networks. In particular, best classification results are achieved on the vowel classification data [1].

The goal of information visualization is to transform abstract data into efficient visual format which help humans extract useful information from the data collected. Two distinctive types of data are of particular interest to the information visualization community: one is multidimensional data in which each data point/entity is described by various attributes. The other is relational data whi...

Approaches analyzing local characteristics of an image prevail in image restoration. However, they are less effective in cases of restoring images degraded by large size point spread functions (PSFs) and heavy noise. The recently proposed learning based approaches perform well on recovering details from images degraded by large size PSFs, yet involves complicated implementation process and high...

A spin projected double-hybrid density functional theory is presented that accounts for different scaling of opposite and same spin terms in the second order correction. This method is applied to three dissociation reactions which in the unprojected formalism exhibit significant spin contamination with higher spin states. This gives rise to a distorted potential surface and can lead to poor geo...