Convergence analysis of chip- and fractionally spaced LMS adaptive multiuser CDMA detectors

This paper analyzes the convergence behavior of the least mean square (LMS) filter when used in an adaptive code division multiple access (CDMA) detector consisting of a tapped delay line with adjustable tap weights. The sampling rate may be equal to or higher than the chip rate, and these correspond to chip-spaced (CS) and fractionally spaced (FS) detection, respectively. It is shown that CS and FS detectors with the same time-span exhibit identical convergence behavior if the baseband received signal is strictly bandlimited to half the chip rate. Even in the practical case when this condition is not met, deviations from this observation are imperceptible unless the initial tap-weight vector gives an extremely large mean squared error (MSE). This phenomenon is carefully explained with reference to the eigenvalues of the correlation matrix when the input signal is not perfectly bandlimited. The inadequacy of the eigenvalue spread of the tap-input correlation matrix as an indicator of the transient behavior and the influence of the initial tap weight vector on convergence speed are highlighted. Specifically, a initialization within the signal subspace or to the origin leads to very much faster convergence compared with initialization in the a noise subspace.

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

A Wiener model for memory high power amplifiers using B-spline function approximation

In this paper we introduce a new Wiener system modeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener model is mainly dependent on the input signal amplitude, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplitude distortion and another for the phase shift, respectively. The Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first order derivatives recursion. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Reduced complexity set-point following in self-tuning control

An external input signal is incorporated into a self-tuning controller which, although it is based on a CARMA system model, employs a state-space framework for control law calculations. Steady-state set point following can then be accomplished even when only a recursive least squares parameter estimation scheme is used, despite the fact that the disturbance affecting the system may well be coloured.

A refined sampling strategy for intra-tooth stable isotope analysis of mammalian enamel

Serial sampling and stable isotope analysis performed along the growth axis of vertebrate tooth enamel records differences attributed to seasonal variation in diet, climate or animal movement. Because several months are required to obtain mature enamel in large mammals, modifications in the isotopic composition of environmental parameters are not instantaneously recorded, and stable isotope analysis of tooth enamel returns a time-averaged signal attenuated in its amplitude relative to the input signal. For convenience, stable isotope profiles are usually determined on the side of the tooth where enamel is thickest. Here we investigate the possibility of improving the time resolution by targeting the side of the tooth where enamel is thinnest. Observation of developing third molars (M3) in sheep shows that the tooth growth rate is not constant but decreases exponentially, while the angle between the first layer of enamel deposited and the enamel–dentine junction increases as a tooth approaches its maximal length. We also noted differences in thickness and geometry of enamel growth between the mesial side (i.e., the side facing the M2) and the buccal side (i.e., the side facing the cheek) of the M3. Carbon and oxygen isotope variations were measured along the M3 teeth from eight sheep raised under controlled conditions. Intra-tooth variability was systematically larger along the mesial side and the difference in amplitude between the two sides was proportional to the time of exposure to the input signal. Although attenuated, the mesial side records variations in the environmental signal more faithfully than the buccal side. This approach can be adapted to other mammals whose teeth show lateral variation in enamel thickness and could potentially be used as an internal check for diagenesis.

Thermopile radiometer signal conditioning for surface atmospheric radiation measurements

A highly stable microvolt amplifier for use with atmospheric broadband thermopile radiometers is described. The amplifier has a nominal gain of 500, for bipolar input signals in the range +/- 10 mV from a floating source. The noise level at the input is less than 5 mu V (at 100 k Omega input impedance), permitting instantaneous diffuse solar radiation measurements to 0.5 W m(-2) resolution with 12 bit analog to digital conversion. The temperature stability of gain is better than 5 ppm/degrees C (-4 to 20 degrees C). Averaged over a decade of use, the long term drift of the amplifier gain is less than similar to 0.02%/yr. As well as radiometers measuring solar and terrestrial radiations, the amplifier has also been successfully used with low level signals from thermocouples and ground heat flux plates.

Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

This work compares and contrasts results of classifying time-domain ECG signals with pathological conditions taken from the MITBIH arrhythmia database. Linear discriminant analysis and a multi-layer perceptron were used as classifiers. The neural network was trained by two different methods, namely back-propagation and a genetic algorithm. Converting the time-domain signal into the wavelet domain reduced the dimensionality of the problem at least 10-fold. This was achieved using wavelets from the db6 family as well as using adaptive wavelets generated using two different strategies. The wavelet transforms used in this study were limited to two decomposition levels. A neural network with evolved weights proved to be the best classifier with a maximum of 99.6% accuracy when optimised wavelet-transform ECG data wits presented to its input and 95.9% accuracy when the signals presented to its input were decomposed using db6 wavelets. The linear discriminant analysis achieved a maximum classification accuracy of 95.7% when presented with optimised and 95.5% with db6 wavelet coefficients. It is shown that the much simpler signal representation of a few wavelet coefficients obtained through an optimised discrete wavelet transform facilitates the classification of non-stationary time-variant signals task considerably. In addition, the results indicate that wavelet optimisation may improve the classification ability of a neural network. (c) 2005 Elsevier B.V. All rights reserved.

Up-regulation of c-jun mRNA in cardiac myocytes requires the extracellular signal-regulated kinase cascade, but c-Jun N-terminal kinases are required for efficient up-regulation of c-Jun protein.

Cardiac hypertrophy, an important adaptational response, is associated with up-regulation of the immediate early gene, c- jun, which encodes the c-Jun transcription factor. c-Jun may feed back to up-regulate its own transcription and, since the c-Jun N-terminal kinase (JNK) family of mitogen-activated protein kinases (MAPKs) phosphorylate c-Jun(Ser-63/73) to increase its transactivating activity, JNKs are thought to be the principal factors involved in c- jun up-regulation. Hypertrophy in primary cultures of cardiac myocytes is induced by endothelin-1, phenylephrine or PMA, probably through activation of one or more of the MAPK family. These three agonists increased c- jun mRNA with the rank order of potency of PMA approximately endothelin-1>phenylephrine. Up-regulation of c- jun mRNA by endothelin-1 was attenuated by inhibitors of protein kinase C (GF109203X) and the extracellular signal-regulated kinase (ERK) cascade (PD98059 or U0126), but not by inhibitors of the JNK (SP600125) or p38-MAPK (SB203580) cascades. Hyperosmotic shock (0.5 M sorbitol) powerfully activates JNKs, but did not increase c- jun mRNA. These data suggest that ERKs, rather than JNKs, are required for c- jun up-regulation. However, endothelin-1 and phenylephrine induced greater up-regulation of c-Jun protein than PMA and phosphorylation of c-Jun(Ser-63/73) correlated with the level of c-Jun protein. Up-regulation of c-Jun protein by endothelin-1 was attenuated by inhibitors of protein kinase C and the ERK cascade, probably correlating with a primary input of ERKs into transcription. In addition, SP600125 inhibited the phosphorylation of c-Jun(Ser-63/73), attenuated the increase in c-Jun protein induced by endothelin-1 and increased the rate of c-Jun degradation. Thus whereas ERKs are the principal MAPKs required for c- jun transcription, JNKs are necessary to stabilize c-Jun for efficient up-regulation of the protein.