The role of prostaglandins (PG), renin-angiotensin system (RAS) and calcium (Ca) in the control of renal hemodynamics and glomerular filtration rate (GFR) in chronic hypercalcemia (serum Ca 12.8 mg%) was studied. Renal blood flow (RBF, 6.39 ml/min per gram kidney weight [gkw]) and GFR (0.52 ml/min per gkw) were significantly decreased in hypercalcemic rats when compared with normocalcemic rats ...

An estimated 115,000 firearm injuries occur annually in the United States, and approximately 70% are nonfatal (1). Retained bullet fragments (RBFs) are an infrequently reported, but important, cause of lead toxicity; symptoms are often nonspecific and can appear years after suffering a gunshot wound (2,3). Adult blood lead level (BLL) screening is most commonly indicated for monitoring of occup...

We present a new constructive algorithm for building Radial-Basis-Function (RBF) network classiiers and a tree based associated algorithm for fast processing of the network. This method, named Constructive Tree Radial-Basis-Function (CTRBF), allows to build and train a RBF network in one pass over the training data set. The training can be in supervised or unsupervised mode. Furthermore, the al...

A novel reduced complexity Radial Basis Function (RBF) neural network based equaliser, referred to as the In-phase/Quadrature-phase RBF Equaliser (I/Q-RBFEQ), is proposed. The I/Q-RBF-EQ is employed in the context of turbo equalisation (TEQ) assisted by iterative channel estimation. The performance of the I/Q-RBF-TEQ is characterized in a noise limited environment over an equally weighted, symb...

In this paper, the chaotic time series RBF neural network model was designed. A prediction method for underwater acoustic chaotic signal based on RBF neural network is proposed in this paper according to the characteristics of chaotic signal with the short-term prediction. Typical Henon chaotic signal and the actual underwater acoustic chaotic signal are respectively predicted by the RBF neural...

Radial basis neural (RBF) networks provide an excellent solution to many pattern recognition and classi cation problems. However, RBF networks are also a local representation technique that enables the easy conversion of the hidden units into symbolic rules. This paper examines rules extracted from RBF networks. We use the iris ower classication task and a vibration diagnosis classi cation task...

This paper presents a turbo equalization (TEQ) scheme, which employs a radial basis function (RBF)-based equalizer instead of the conventional trellis-based equalizer of Douillard et al. Structural, computational complexity, and performance comparisons of the RBF-based and trellis-based TEQs are provided. The decision feedback-assisted RBF TEQ is capable of attaining a similar performance to th...

The Radial Basis Function method (RBF) can be used not only for reconstruction of a surface from scattered data but for reconstruction of damaged images, filling gaps and for restoring missing data in images, too. The basic idea of reconstruction algorithm with RBF and very interesting results from reconstruction of images damaged by noise are presented. Feasibility of the RBF method for image ...

Convolutional Radial Basis Function (RBF) networks are introduced for smoothing out irregularly sampled signals. Our proposed technique involves training a RBF network and then convolving it with a Gaussian smoothing kernel in an analytical manner. Since the convolution results in an analytic form, the computation necessary for numerical convolution is avoided. Convolutional RBF networks need t...

By visual inspection, both models produce good quality RBF images. However, comparison of regional averages to RBF values that were calculated from regional TACs (Figure C1), show that RBV must be included in the model (three-parameter model) if similar RBF estimates are required. RBF may be very sensitive to RBV, and if RBV is not fitted as the third parameter, or not corrected based on [O]CO ...