Noise characteristics of passive components for phased array applications

The results of a comparative study on noise characteristics of basic power combining/dividing and phase shifting schemes are presented. The theoretical basics of thermal noise in a passive linear multiport are discussed. A new formalism is presented to describe the noise behavior of the passive circuits, and it is shown that the fundamental results are conveniently achieved using this description. The results of analyses concerning the noise behavior of basic power combining/dividing structures (the Wilkinson combiner, 90 deg hybrid coupler, hybrid ring coupler, and the Lange coupler) are presented. Three types of PIN-diode switch phase shifters are analyzed in terms of noise performance

Evolutionary Sequence Modeling for Discovery of Peptide Hormones

There are currently a large number of “orphan” G-protein-coupled receptors (GPCRs) whose endogenous ligands (peptide hormones) are unknown. Identification of these peptide hormones is a difficult and important problem. We describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure across species and show how such models can be used to discover new functional molecules, in particular peptide hormones, via cross-genomic sequence comparisons. The computational framework incorporates a priori high-level knowledge of structural and evolutionary constraints into a hierarchical grammar of evolutionary probabilistic models. This computational method was used for identifying novel prohormones and the processed peptide sites by producing sequence alignments across many species at the functional-element level. Experimental results with an initial implementation of the algorithm were used to identify potential prohormones by comparing the human and non-human proteins in the Swiss-Prot database of known annotated proteins. In this proof of concept, we identified 45 out of 54 prohormones with only 44 false positives. The comparison of known and hypothetical human and mouse proteins resulted in the identification of a novel putative prohormone with at least four potential neuropeptides. Finally, in order to validate the computational methodology, we present the basic molecular biological characterization of the novel putative peptide hormone, including its identification and regional localization in the brain. This species comparison, HMM-based computational approach succeeded in identifying a previously undiscovered neuropeptide from whole genome protein sequences. This novel putative peptide hormone is found in discreet brain regions as well as other organs. The success of this approach will have a great impact on our understanding of GPCRs and associated pathways and help to identify new targets for drug development

On The Dynamics Of The Lre Algorithm: A Distribution Learning Approach To Adaptive Equalization

We present the general formulation for the adaptive equalization by distribution learning introduced in [Adali 94]. In this framework, adaptive equalization can be viewed as a parametrized conditional distribution estimation problem where the parameter estimation is achieved by learning on a multilayer perceptron (MLP). Depending on the definition of the conditioning event set either supervised or unsupervised (blind) algorithms in either recurrent or feedforward networks result. We derive the least relative entropy (LRE) algorithm for binary data communications and analyze its statistical and dynamical properties. Particularly, we show that LRE learning is consistent and asymptotically normal by working in the partial likelihood estimation framework, and that the algorithm can always recover from convergence at the wrong extreme as opposed to the MSE based MLP's by working within an extension of the well-formed cost functions framework of Wittner and Denker [Wittner 88]. We present si..

Modeling Nuclear Reactor Core Dynamics with Recurrent Neural Networks

A recurrent multilayer perceptron (RMLP) model is designed and developed for simulation of core neutronic phenomena in a nuclear power plant, which constitute a non-linear, complex dynamic system characterized by a large number of state variables. Training and testing data are generated by REMARK, a first principles neutronic core model [16]. A modified backpropagation learning algorithm with an adaptive steepness factor is employed to speed up the training of the RMLP. The test results presented exhibit the capability of the recurrent neural network model to capture the complex dynamics of the system, yielding accurate predictions of the system response. The performance of the network is also demonstrated for interpolation, extrapolation, fault tolerance due to incomplete data, and for operation in the presence of noise. Keywords: Nuclear reactor core dynamics; recurrent neural networks 1 This work is supported by Maryland Industrial Partnerships and S3 Technologies under grants no. ..

Decreased Plasma Apelin Levels in Pubertal Obese Children

Background Apelin is a recently defined peptide relevant to the mechanism of obesity-related disorders There has been no report so far about the levels of plasma apelin in obese childre