Recursive linear smoothed Newton predictors for polynomialextrapolation

Newton predictors have considerable gain at the higher frequencies, which reduces their applicability to practical signal processing where the narrowband primary signal is often corrupted by additive wideband noise. Two modifications that can be used to extrapolate low-order polynomials have been proposed. In both approaches, the highest order difference of successive input samples, approximating the constant nonzero derivative, is smoothed before it is added to the lower order differences, reducing the undesired noise gain. The linear smoothed Newton (LSN) predictor is extended in this work by including a recursive term in the basic transfer function and cascading the rest of the successive difference paths with appropriately delayed extrapolation filters of corresponding polynomial orders. This leads to computationally efficient IIR predictors with significantly lowered gain at the higher frequencies. The recursive predictor is analyzed in the time and frequency domains and compared to the other predictors     

FIR prediction using Newton's backward interpolation algorithm withsmoothed successive differences

Two alternative extensions to Newton's original backward interpolation algorithm that can be used to predict finite-order polynomials are proposed. In both approaches, the highest-order successive differences, corresponding to the constant nonzero derivatives, are smoothed before they are added to lower-order differences. The first smoother proposed is a linear lowpass filter, e.g. a moving averager that is optimal for attenuating white Gaussian and uniformly distributed noises, and the second one is a standard median filter that is optimal for double-exponentially distributed noise. These smoothers reduce the undesired gain of the entire predictor at the higher frequencies, thus making the modified Newton algorithms useful for real signal-processing applications     

Knowledge based quality-driven architecture design and evaluation

Modelling and evaluating quality properties of software is of high importance, especially when our every day life depends on the quality of services produced by systems and devices embedded into our surroundings. This paper contributes to the body of research in quality and model driven software engineering. It does so by introducing; (1) a quality aware software architecting approach and (2) a supporting tool chain. The novel approach with supporting tools enables the systematic development of high quality software by merging benefits of knowledge modelling and management, and model driven architecture design enhanced with domain-specific quality attributes. The whole design flow of software engineering is semi-automatic; specifying quality requirements, transforming quality requirements to architecture design, representing quality properties in architectural models, predicting quality fulfilment from architectural models, and finally, measuring quality aspects from implemented source code. The semi-automatic design flow is exemplified by the ongoing development of a secure middleware for peer-to-peer embedded systems.     

Clonal optimization-based negative selection algorithm with applications in motor fault detection

The Negative Selection Algorithm (NSA) and clonal selection method are two typical kinds of artificial immune systems. In this paper, we first introduce their underlying inspirations and working principles. It is well known that the regular NSA detectors are not guaranteed to always occupy the maximal coverage of the nonself space. Therefore, we next employ the clonal optimization method to optimize these detectors so that the best anomaly detection performance can be achieved. A new motor fault detection scheme using the proposed NSA is also presented and discussed. We demonstrate the efficiency of our approach with an interesting example of motor bearings fault detection, in which the detection rates of three bearings faults are significantly improved.     

Fuzzy neural network with general parameter adaptation for modelingof nonlinear time-series

By taking advantage of fuzzy systems and neural networks, a fuzzy-neural network with a general parameter (GP) learning algorithm and heuristic model structure determination is proposed in this paper. Our network model is based on the Gaussian radial basis function network (RBFN). We use the flexible GP approach both for initializing the off-line training algorithm and fine-tuning the nonlinear model efficiently in online operation. A modification of the robust unbiasedness criterion using distorter (UCD) is utilized for selecting the structural parameters of this adaptive model. The UCD approach provides the desired modeling accuracy and avoids the risk of over-fitting. In order to illustrate the operation of the proposed modeling scheme, it is experimentally applied to a fault detection application.     

Furan in the baby-food samples purchased from the Finnish markets – Determination with SPME–GC–MS

A method applying solid-phase microextraction followed by gas chromatography–mass spectrometric determination was in-house validated and used to study furan concentrations in baby-food samples purchased from the Finnish markets. The validation parameters showed that the method was well applicable for the reliable analysis of furan. Furan was analysed in 21 different baby-food samples as three independent replicates. The mean levels of furan varied between 4.7 and 90.3 μg kg⁻¹ being well in accordance with the levels reported in other studies. The mean concentrations of similar product formulas based on their ingredients were 9.2, 37.0 and 49.6 μg kg⁻¹ for fruit-, vegetables- and meat-containing baby-foods, respectively. According to the statistical analyses, fruit-based baby-food samples had significantly lower concentrations of furan as compared to other formulas. Based on our exercise, it seems that a low margin of safety exists between the extreme worst case infant exposures and the deduced NOAEL of furan on experimental animals, particularly for a clear rodent carcinogen.     

A modified Elman neural network-based power controller in mobile communications systems

It has been well recognized that power control is an important technique to combat with the harmful near-far effect as well as increase the maximum user capacity of direct sequence code division multiple access (DS/CDMA) cellular systems. In this paper, we propose a modified Elman neural network (MENN)-based power control scheme, which can regulate the received power level at the base station. Unlike the conventional “bang–bang” and fuzzy logic power control, our MENN-based controller first identifies the inverse dynamical characteristics of mobile channel by adaptive on-line learning. The inverse channel model is then employed for power regulation to reduce large overshoots and shorten long rise time. Simulations show that the fluctuation of controlled received power levels can be smoothed with small channel tracking errors. Acknowledgments The authors would like to thank the anonymous reviewers for their insightful comments and constructive suggestions that have improved this paper.     

Noise reduction in zero crossing detection by predictive digitalfiltering

A simple combination of nonlinear and linear digital signal processing methods is proposed for efficient noise reduction in zero crossing detectors. The method is very robust against strong impulsive noise, typically encountered in thyristor power converters, where reliable zero crossing detection is required for firing synchronization. A systematic design procedure is described for the proposed filter-based synchronization method, taking into account the specified line frequency tolerance. The fully digital signal processing approach allows compact implementations, and supports flexible interfacing to digital motor control systems     

Capturing quality requirements of product family architecture

Software quality is one of the major issues with software intensive systems. Moreover, quality is a critical success factor in software product families exploiting shared architecture and common components in a set of products. Our contribution is the QRF (Quality Requirements of a software Family) method, which explicitly focuses on how quality requirements have to be defined, represented and transformed to architectural models. The method has been applied to two experiments; one in a laboratory environment and the other in industry. The use of the QRF method is exemplified by the Distribution Service Platform (DiSeP), the laboratory experiment. The lessons learned are also based on our experiences of applying the method in industrial settings.