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This paper is devoted to a novel multi-frequency and bispectrum-based encoding technique designed for radio communication systems. An experimental study of an interference resistance in digital communication is performed using a novel bispectrum-based strategy. Test statistics evaluated in the form of peak values of magnitude bispectrum estimates are proposed for triplet-signals discrimination. Bit error rates assessed experimentally in a radio communication link contaminated by additive Gaussian noise and fading are studied within a wide range of input signal-to-noise ratio (SNR). Advantages of the proposed bispectral-based signal processing as compared with common phase and frequency shift keying are demonstrated and discussed.

Background: Microarray data normalization is an important step for obtaining data that are reliable and usable for subsequent analysis. One of the most commonly utilized normalization techniques is the locally weighted scatterplot smoothing (LOWESS) algorithm. However, a much overlooked concern with the LOWESS normalization strategy deals with choosing the appropriate parameters. Parameters are usually chosen arbitrarily, which may reduce the efficiency of the normalization and result in non-optimally normalized data. Thus, there is a need to explore LOWESS parameter selection in greater detail. Results and discussion: In this work, we discuss how to choose parameters for the LOWESS method. Moreover, we present an optimization approach for obtaining the fraction of data points utilized in the local regression and analyze results for local print-tip normalization. The optimization procedure determines the bandwidth parameter for the local regression by minimizing a cost function that represents the mean-squared difference between the LOWESS estimates and the normalization reference level. We demonstrate the utility of the systematic parameter selection using two publicly available data sets. The first data set consists of three self versus self hybridizations, which allow for a quantitative study of the optimization method. The second data set contains a collection of DNA microarray data from a breast cancer study utilizing four breast cancer cell lines. Our results show that different parameter choices for the bandwidth window yield dramatically different calibration results in both studies. Conclusions: Results derived from the self versus self experiment indicate that the proposed optimization approach is a plausible solution for estimating the LOWESS parameters, while results from the breast cancer experiment show that the optimization procedure is readily applicable to real-life microarray data normalization. In summary, the systematic approach to obtain critica

Background: Microarray data normalization is an important step for obtaining data that are reliable and usable for subsequent analysis. One of the most commonly utilized normalization techniques is the locally weighted scatterplot smoothing (LOWESS) algorithm. However, a much overlooked concern with the LOWESS normalization strategy deals with choosing the appropriate parameters. Parameters are usually chosen arbitrarily, which may reduce the efficiency of the normalization and result in non-optimally normalized data. Thus, there is a need to explore LOWESS parameter selection in greater detail. Results and discussion: In this work, we discuss how to choose parameters for the LOWESS method. Moreover, we present an optimization approach for obtaining the fraction of data points utilized in the local regression and analyze results for local print-tip normalization. The optimization procedure determines the bandwidth parameter for the local regression by minimizing a cost function that represents the mean-squared difference between the LOWESS estimates and the normalization reference level. We demonstrate the utility of the systematic parameter selection using two publicly available data sets. The first data set consists of three self versus self hybridizations, which allow for a quantitative study of the optimization method. The second data set contains a collection of DNA microarray data from a breast cancer study utilizing four breast cancer cell lines. Our results show that different parameter choices for the bandwidth window yield dramatically different calibration results in both studies. Conclusions: Results derived from the self versus self experiment indicate that the proposed optimization approach is a plausible solution for estimating the LOWESS parameters, while results from the breast cancer experiment show that the optimization procedure is readily applicable to real-life microarray data normalization. In summary, the systematic approach to obtain critica

Linear filtering techniques have serious limitations in dealing with signals that have been created or processed by a system exhibiting some degree of nonlinearity or, in general, situations where the relevance of information cannot be specified infrequency domain. In image processing, many of these characteristics are often present, and it is no wonder that image processing is the field where nonlinear filtering techniques have first shown clear superiority over linear filters. Since nonlinear filters are all of those filters that are not linear, there is a large variety of different filters in use, and no common theory can exist. This makes filter design challenging, and optimization is meaningful only after restricting the class. It can be done in several conceptually different ways and, in this paper, we consider these techniques and the optimization methods that go together with the particular restriction. We review polynomial and rational filter classes and optimization of stack filters under structural constraints and statistical constraints.

Linear filtering techniques have serious limitations in dealing with signals that have been created or processed by a system exhibiting some degree of nonlinearity or, in general, situations where the relevance of information cannot be specified infrequency domain. In image processing, many of these characteristics are often present, and it is no wonder that image processing is the field where nonlinear filtering techniques have first shown clear superiority over linear filters. Since nonlinear filters are all of those filters that are not linear, there is a large variety of different filters in use, and no common theory can exist. This makes filter design challenging, and optimization is meaningful only after restricting the class. It can be done in several conceptually different ways and, in this paper, we consider these techniques and the optimization methods that go together with the particular restriction. We review polynomial and rational filter classes and optimization of stack filters under structural constraints and statistical constraints.

Linear filtering techniques have serious limitations in dealing with signals that have been created or processed by a system exhibiting some degree of nonlinearity or, in general, situations where the relevance of information cannot be specified infrequency domain. In image processing, many of these characteristics are often present, and it is no wonder that image processing is the field where nonlinear filtering techniques have first shown clear superiority over linear filters. Since nonlinear filters are all of those filters that are not linear, there is a large variety of different filters in use, and no common theory can exist. This makes filter design challenging, and optimization is meaningful only after restricting the class. It can be done in several conceptually different ways and, in this paper, we consider these techniques and the optimization methods that go together with the particular restriction. We review polynomial and rational filter classes and optimization of stack filters under structural constraints and statistical constraints.

The connection between the Teager energy operator and the ambiguity function is established in this correspondence after defining the Teager operator over complex-valued signals. This relation allows the use of the Teager energy in estimating the second moment angular bandwidth and the moments of a signal duration (spread) and that of its spectrum. © 1999 IEEE.

The connection between the Teager energy operator and the ambiguity function is established in this correspondence after defining the Teager operator over complex-valued signals. This relation allows the use of the Teager energy in estimating the second moment angular bandwidth and the moments of a signal duration (spread) and that of its spectrum. © 1999 IEEE.

We propose an image segmentation method based on combining unsupervised clustering in the color space with region growing in the image space. No 'a priori' knowledge is required about the number of regions in the image. The algorithm is useful for marker extraction or complete segmentation of multidimensional, and in particular color, images. The running time depends mostly upon the speed of the unsupervised clustering algorithm that is used.

We propose an image segmentation method based on combining unsupervised clustering in the color space with region growing in the image space. No 'a priori' knowledge is required about the number of regions in the image. The algorithm is useful for marker extraction or complete segmentation of multidimensional, and in particular color, images. The running time depends mostly upon the speed of the unsupervised clustering algorithm that is used.

We propose an image segmentation method based on combining unsupervised clustering in the color space with region growing in the image space. No 'a priori' knowledge is required about the number of regions in the image. The algorithm is useful for marker extraction or complete segmentation of multidimensional, and in particular color, images. The running time depends mostly upon the speed of the unsupervised clustering algorithm that is used.

A new expression for the output moments of weighted median filtered data is derived in this paper. The noise attenuation capability of a weighted median filter can now be assessed using the L-vector and M-vector parameters in the new expression. The second major contribution of the paper is the development of a new optimality theory for weighted median filters. This theory is based on the new expression for the output moments, and combines the noise attenuation and some structural constraints on the filter's behavior. In certain special eases, the optimal weighted median filter can be obtained by merely solving a set of linear inequalities. This leads in some cases to closed form solutions for optimal weighted median filters. Some applications of the theory developed in this paper, in 1-D signal processing and image processing are discussed. Throughout the analysis, some striking similarities are pointed out between linear FIR filters and weighted median filters.

A new expression for the output moments of weighted median filtered data is derived in this paper. The noise attenuation capability of a weighted median filter can now be assessed using the L-vector and M-vector parameters in the new expression. The second major contribution of the paper is the development of a new optimality theory for weighted median filters. This theory is based on the new expression for the output moments, and combines the noise attenuation and some structural constraints on the filter's behavior. In certain special eases, the optimal weighted median filter can be obtained by merely solving a set of linear inequalities. This leads in some cases to closed form solutions for optimal weighted median filters. Some applications of the theory developed in this paper, in 1-D signal processing and image processing are discussed. Throughout the analysis, some striking similarities are pointed out between linear FIR filters and weighted median filters.

A new expression for the output moments of weighted median filtered data is derived in this paper. The noise attenuation capability of a weighted median filter can now be assessed using the L-vector and M-vector parameters in the new expression. The second major contribution of the paper is the development of a new optimality theory for weighted median filters. This theory is based on the new expression for the output moments, and combines the noise attenuation and some structural constraints on the filter's behavior. In certain special eases, the optimal weighted median filter can be obtained by merely solving a set of linear inequalities. This leads in some cases to closed form solutions for optimal weighted median filters. Some applications of the theory developed in this paper, in 1-D signal processing and image processing are discussed. Throughout the analysis, some striking similarities are pointed out between linear FIR filters and weighted median filters.