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.     

Survey of reliability and availability prediction methods from the viewpoint of software architecture

Many future software systems will be distributed across a network, extensively providing different kinds of services for their users. These systems must be highly reliable and provide services when required. Reliability and availability must be engineered into software from the onset of its development, and potential problems must be detected in the early stages, when it is easier and less expensive to implement modifications. The software architecture design phase is the first stage of software development in which it is possible to evaluate how well the quality requirements are being met. For this reason, a method is needed for analyzing software architecture with respect to reliability and availability. In this paper, we define a framework for comparing reliability and availability analysis methods from the viewpoint of software architecture. Our contribution is the comparison of the existing analysis methods and techniques that can be used for reliability and availability prediction at the architectural level. The objective is to discover which methods are suitable for the reliability and availability prediction of today’s complex systems, what are the shortcomings of the methods, and which research activities need to be conducted in order to overcome these identified shortcomings. The comparison reveals that none of the existing methods entirely fulfill the requirements that are defined in the framework. The comparison framework also defines the characteristics required of new reliability and availability analysis methods. Additionally, the framework is a valuable tool for selecting the best suitable method for architecture analysis. Furthermore, the framework can be extended and used for other evaluation methods as well.     

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     

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.     

Comparison of conventional and soft computing-based power control methods in mobile communications systems

 In this paper, we give a comparison between the conventional power control scheme and soft computing-based approaches in a mobile communications application. At the base station, the `bang–bang' control strategy and a neural network-based prediction control method are employed. In addition, full power command transmission mode, single-bit command transmission mode, and fuzzy logic-based power command enhancement unit are considered. Based on simulation experiments, we quantitatively evaluate the performance of various combinations of these control methods and command transmission modes. Conclusions on the optimal configuration are finally drawn.     

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     

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     

Design and implementation of efficient IIR notch filters withquantization error feedback

Straightforward methods for the design of digital notch filters are presented. The design method is based on setting a zero of the filter at a notch frequency and placing a pole in its neighborhood such that the notch width is narrow enough while keeping the group delay of the filter sufficiently flat. A technique for efficient and well-behaved implementation with fixed-point signal processors is advanced, based on the use of quantization error feedback for roundoff noise reduction. The design approach is illustrated with numerical examples, and an assembly-language program for the family of TMS320 signal processors is provided     

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.