Analysis of the error performance of adaptive array antennas for CDMA with noncoherent M-ary orthogonal modulation in Nakagami fading

This letter presents an analytical model for evaluating the Bit Error Rate (BER) of a Direct Sequence Code Division Multiple Access (DS-CDMA) system, with M-ary orthogonal modulation and noncoherent detection, employing an array antenna operating in a Nakagami fading environment. An expression of the Signal to Interference plus Noise Ratio (SINR) at the output of the receiver is derived, which allows the BER to be evaluated using a closed form expression. The analytical model is validated by comparing the obtained results with simulation results.

Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

The utility and limitations of chloroplast DNA analysis for identifying native British oak stands and for guiding replanting strategy

We report a method using variation in the chloroplast genome (cpDNA) to test whether oak stands of unknown provenance are of native and/or local origin. As an example, a sample of test oaks, of mostly unknown status in relation to nativeness and localness, were surveyed for cpDNA type. The sample comprised 126 selected trees, derived from 16 British seed stands, and 75 trees, selected for their superior phenotype (201 tree samples in total). To establish whether these two test groups are native and local, their cpDNA type was compared with that of material from known autochthonous origin (results of a previous study which examined variation in 1076 trees from 224 populations distributed across Great Britain). In the previous survey of autochthonous material, four cpDNA types were identified as native; thus if a test sample possessed a new haplotype then it could be classed as non-native. Every one of the 201 test samples possessed one of the four cpDNA types found within the autochthonous sample. Therefore none could be proven to be introduced and, on this basis, was considered likely to be native. The previous study of autochthonous material also found that cpDNA variation was highly structured geographically and, therefore, if the cpDNA type of the test sample did not match that of neighbouring autochthonous trees then it could be considered to be non-local. A high proportion of the seed stand group (44.2 per cent) and the phenotypically superior trees (58.7 per cent) possessed a cpDNA haplotype which matched that of the neighbouring autochthonous trees and, therefore, can be considered as local, or at least cannot be proven to be introduced. The remainder of the test sample could be divided into those which did not grow in an area of overall dominance (18.7 per cent of seed stand trees and 28 per cent of phenotypically superior) and those which failed to match the neighbouring autochthonous haplotype (37.1 per cent and 13.3 per cent, respectively). Most of the non-matching test samples were located within 50 km of an area dominated by a matching autochthonous haplotype (96.0 per cent and 93.5 per cent, respectively), and potentially indicates only local transfer. Whilst such genetic fingerprinting tests have proven useful for assessing the origin of stands of unknown provenance, there are potential limitations to using a marker from the chloroplast genome (mostly adaptively neutral) for classifying seed material into categories which have adaptive implications. These limitations are discussed, particularly within the context of selecting adaptively superior material for restocking native forests.

Comparative proteomic analysis of GS-NSO murine myeloma cell lines with varying recombinant monoclonal antibody production rate

We have employed an inverse engineering strategy based on quantitative proteome analysis to identify changes in intracellular protein abundance that correlate with increased specific recombinant monoclonal antibody production (qMab) by engineered murine myeloma (NSO) cells. Four homogeneous NSO cell lines differing in qMab were isolated from a pool of primary transfectants. The proteome of each stably transfected cell line was analyzed at mid-exponential growth phase by two-dimensional gel electrophoresis (2D-PAGE) and individual protein spot volume data derived from digitized gel images were compared statistically. To identify changes in protein abundance associated with qMab clatasets were screened for proteins that exhibited either a linear correlation with cell line qMab or a conserved change in abundance specific only to the cell line with highest qMab. Several proteins with altered abundance were identified by mass spectrometry. Proteins exhibiting a significant increase in abundance with increasing qMab included molecular chaperones known to interact directly with nascent immunoglobulins during their folding and assembly (e.g., BiP, endoplasmin, protein disulfide isomerase). 2D-PAGE analysis showed that in all cell lines Mab light chain was more abundant than heavy chain, indicating that this is a likely prerequisite for efficient Mab production. In summary, these data reveal both the adaptive responses and molecular mechanisms enabling mammalian cells in culture to achieve high-level recombinant monoclonal antibody production. (C) 2004 Wiley Periodicals, Inc.

Immunohistological analysis of Tannerella forsythia-induced lesions in a murine model

Tannerella forsythia has been implicated as a defined periodontal pathogen. In the present study a mouse model was used to determine the phenotype of leukocytes in the lesions induced by subcutaneous injections of either live (group A) or nonviable (group B) T. forsythia. Control mice (group C) received the vehicle only. Lesions were excised at days 1, 2, 4, and 7. An avidin-biotin immunoperoxidase method was used to stain infiltrating CD4(+) and CD8(+) T cells, CD14(+) macrophages, CD19(+) B cells, and neutrophils. Hematoxylin and eosin sections demonstrated lesions with central necrotic cores surrounded by neutrophils, macrophages and lymphocytes in both group A and group B mice. Lesions from control mice exhibited no or only occasional solitary leukocytes. In both groups A and B, neutrophils were the dominant leukocyte in the lesion 1 day after injection, the numbers decreasing over the 7-day experimental period. There was a relatively low mean percent of CD4(+) and CD8(+) T cells in the lesions and, whereas the percent of CD8(+) T cells remained constant, there was a significant increase in the percent of CD4(+) T cells at day 7. This increase was more evident in group A mice. The mean percent of CD14(+) macrophages and CD19(+) B cells remained low over the experimental period, although there was a significantly higher mean percent of CD19(+) B cells at day 1. In conclusion, the results showed that immunization of mice with live T. forsythia induced a stronger immune response than nonviable organisms. The inflammatory response presented as a nonspecific immune response with evidence of an adaptive (T-cell) response by day 7. Unlike Porphyromonas gingivalis, there was no inhibition of neutrophil migration.

Adaptive stepsize based on control theory for stochastic differential equations

The numerical solution of stochastic differential equations (SDEs) has been focussed recently on the development of numerical methods with good stability and order properties. These numerical implementations have been made with fixed stepsize, but there are many situations when a fixed stepsize is not appropriate. In the numerical solution of ordinary differential equations, much work has been carried out on developing robust implementation techniques using variable stepsize. It has been necessary, in the deterministic case, to consider the best choice for an initial stepsize, as well as developing effective strategies for stepsize control-the same, of course, must be carried out in the stochastic case. In this paper, proportional integral (PI) control is applied to a variable stepsize implementation of an embedded pair of stochastic Runge-Kutta methods used to obtain numerical solutions of nonstiff SDEs. For stiff SDEs, the embedded pair of the balanced Milstein and balanced implicit method is implemented in variable stepsize mode using a predictive controller for the stepsize change. The extension of these stepsize controllers from a digital filter theory point of view via PI with derivative (PID) control will also be implemented. The implementations show the improvement in efficiency that can be attained when using these control theory approaches compared with the regular stepsize change strategy. (C) 2004 Elsevier B.V. All rights reserved.

Performance comparison between uniformly and nonuniformly spaced adaptive antennas with respect to tolerance to pointing errors

The paper presents theoretical and experimental investigations into performances of narrowband uniformly and nonuniformly spaced adaptive linear dipole array antennas that are subjected to pointing errors. The analysis focuses on the array's output Signal to Interference plus Noise Ratio. The presence of mutual coupling between the array elements is taken into account. It is shown that the array's tolerance to pointing errors can be enhanced by controlling the interelement spacing. (c) 2006 Wiley Periodicals, Inc.

Analysis of line x environment interactions for yield in navy beans. 2. Pattern analysis of lines and environment within years

Seven years of multi-environment yield trials of navy bean (Phaseolus vulgaris L.) grown in Queensland were examined. As is common with plant breeding evaluation trials, test entries and locations varied between years. Grain yield data were analysed for each year using cluster and ordination analyses (pattern analyses). These methods facilitate descriptions of genotype performance across environments and the discrimination among genotypes provided by the environments. The observed trends for genotypic yield performance across environments were partly consistent with agronomic and disease reactions at specific environments and also partly explainable by breeding and selection history. In some cases, similarities in discrimination among environments were related to geographic proximity, in others management practices, and in others similarities occurred between geographically widely separated environments which differed in management practices. One location was identified as having atypical line discrimination. The analysis indicated that the number of test locations was below requirements for adequate representation of line x environment interaction. The pattern analyses methods used were an effective aid in describing the patterns in data for each year and illustrated the variations in adaptive patterns from year to year. The study has implications for assessing the number and location of test sites for plant breeding multi-environment trials, and for the understanding of genetic traits contributing to line x environment interactions.

Performance analysis using equivariant kernel density estimator in nonlinear mixture

This paper investigates the performance analysis of separation of mutually independent sources in nonlinear models. The nonlinear mapping constituted by an unsupervised linear mixture is followed by an unknown and invertible nonlinear distortion, are found in many signal processing cases. Generally, blind separation of sources from their nonlinear mixtures is rather difficult. We propose using a kernel density estimator incorporated with equivariant gradient analysis to separate the sources with nonlinear distortion. The kernel density estimator parameters of which are iteratively updated to minimize the output independence expressed as a mutual information criterion. The equivariant gradient algorithm has the form of nonlinear decorrelation to perform the convergence analysis. Experiments are proposed to illustrate these results.

Investigating the performance analysis of EASI algorithm and EKENS algorithm in nonlinear model

This paper investigates the performance of EASI algorithm and the proposed EKENS algorithm for linear and nonlinear mixtures. The proposed EKENS algorithm is based on the modified equivariant algorithm and kernel density estimation. Theory and characteristic of both the algorithms are discussed for blind source separation model. The separation structure of nonlinear mixtures is based on a nonlinear stage followed by a linear stage. Simulations with artificial and natural data demonstrate the feasibility and good performance of the proposed EKENS algorithm.

Playing in continuous spaces: Some analysis and extension of population-based incremental learning

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.