INTRODUCTION: Simultaneous multislice imaging decreases the required scan time for a given volume, which is particularly beneficial in fMRI, as well as in a variety of other applications. We propose a new method for spiral simultaneous multislice imaging that uses a z-gradient modulation during readout, along with a multiple-coil receive array. Previous methods have used a z-gradient encoding o...

The controller setting is a simple task if the process parameters are known. If model not known then modern approach adaptive method. paper shows that complex problem of iterative methods for simultaneous identification and control how can be handled.

a new class of nonlinear set-valued variationalinclusions involving $(a,eta)$-monotone mappings in a banachspace setting is introduced, and then based on the generalizedresolvent operator technique associated with$(a,eta)$-monotonicity, the existence and approximationsolvability of solutions using an iterative algorithm and fixedpint theory is investigated.

This is an experimental computational account of projection algorithms for the linear best approximation problem. We focus on the sequential and simultaneous versions of Dykstra’s algorithm and the Halpern-Lions-Wittmann-Bauschke algorithm for the best approximation problem from a point to the intersection of closed convex sets in the Euclidean space. These algorithms employ different iterative...

The Common Unified Device Architecture (CUDA) introduced in 2007 by NVIDIA is a recent programming model making use of the unified shader design of the most recent graphics processing units (GPUs). The programming interface allows algorithm implementation using standard C language along with a few extensions without any knowledge about graphics programming using OpenGL, DirectX, and shading lan...

Simultaneous subgradient projection algorithms for the convex feasibility problem use subgradient calculations and converge sometimes even in the inconsistent case. We devise an algorithm that uses seminorm-induced oblique projections onto super half-spaces of the convex sets, which is advantageous when the subgradient-Jacobian is a sparse matrix at many iteration points of the algorithm. Using...

Over ten years ago, Lu and Milios presented a probabilistic scan matching algorithm for solving the simultaneous localization and mapping (SLAM) problem with 2D laser range scans, a standard in robotics. This paper presents an extension to this GraphSLAM method. Our iterative algorithm uses a sparse network to represent the relations between several overlapping 3D scans, computes in every step ...