Performance of the linear models, widely used within the framework of adaptive line enhancement (ALE), deteriorates dramatically in the presence of non-Gaussian noises. On the other hand, adaptive implementation of nonlinear models, e.g. the Volterra filters, suffers from the severe problems of large number of parameters and slow convergence. Nonetheless, kernel methods are emerging solutions t...

We describe and apply a flexible, adaptive cosine packet transform to separate audio sources from instantaneous, underdetermined audio mixtures by time-frequency masking. Previously studied adaptive transform schemes have two main drawbacks: the signal can only be partitioned into dyadic intervals, and the profiles of the overlapping windows are often very short, thus tapering off very quickly....

The present work describes an application of adaptative signal filtration in the time-scale domain using a pair of reversible wavelet transformations to the precise delimitation of the nystagmus quick and slow phases in an electronystagmogram. In common used methods the main source of inaccuracies in diagnostics parameters is the imprecision in phases delimitation (nystagmus segmentation) cause...

As the population ages, prediction of falls risk is becoming an increasingly important research area. Due to built-in inertial sensors and ubiquity, smartphones provide an attractive data collection and computing platform for falls risk prediction and continuous gait monitoring. One challenge in continuous gait monitoring is that significant signal variability exists between individuals with a ...

Semantic segmentation has been widely investigated for its important role in computer vision. However, some challenges still exist. The first challenge is how to perceive semantic regions with various attributes, which can result in unbalanced distribution of training samples. Another challenge is accurate semantic boundary determination. In this paper, a contour-aware network for semantic segm...

In this paper we introduce a simple, computationally inxepentsive, adaptive recursive structure for enhancing bandpass signals highly corrupted by broad-band noise. This adaptive algorithm, enhancing input signals, enables us to estimate the center frequency and the bandwidth of the input signal. In addition, an important feature of the proposed structure is that the conventional bias existing ...

In this paper we introduce a simple, computationally inxepentsive, adaptive recursive structure for enhancing bandpass signals highly corrupted by broad-band noise. This adaptive algorithm, enhancing input signals, enables us to estimate the center frequency and the bandwidth of the input signal. In addition, an important feature of the proposed structure is that the conventional bias existing ...

Computational intelligence and signal analysis of multi-channel data form an interdisciplinary research area based upon general digital signal processing methods and adaptive algorithms. The chapter is restricted to their use in biomedicine and particularly in electroencephalogram signal processing to find specific components of such multi-channel signals. Methods presented include signal de-no...

A system to estimate the weed density between two rows of soybeans was developed. An environmentally adaptive segmentation algorithm (EASA) was used to segment the plants from the background of the image. The effect of two image data transformations on the segmentation performance of the EASA was investigated, and the RGB-IV1V2 transformation resulted in significantly higher quality segmentatio...