one of the most common signals used for achieving high range resolution in radar is stepped-frequency signals. when a target is stationary in these radars, dft (discrete fourier transform) based processing is used to attain high range resolution profile (hrrp). however, in the case of moving target, due to spreading of the echo signal spectrum, dft based processing is not optimal and decreases ...

Although traditional signal-based structural health monitoring algorithms have been successfully employed for small structures, their application for large and complex bridges has been challenging due to non-stationary signal characteristics with a high level of noise. In this paper, a promising damage detection algorithm is proposed by incorporation of adaptive signal processing and Artificial...

Stochastic resonance (SR) is a phenomenon whereby the signal transmission or signal processing through certain nonlinear systems can be improved by adding noise. This paper discusses SR in nonlinear signal detection by a simple test statistic, which can be computed from multiple noisy data in a binary decision problem based on a maximum a posteriori probability criterion. The performance of det...

Modeling is a prerequisite for the most fundamental signal processing tasks of signal analysis, detection, classification, denoising, and compression. While linear models are widely used, and they allow elegant theoretical and algorithmic developments, their nonlinear alternatives offer advantages in terms of modeling power and improved performance, which often eclipse the extra cost due to inc...

A nonlinear signal-processing model is derived for the optical recording channel based on scalar diffraction theory. In this model, the signal waveform is written in closed form as an explicit function of the channel bits that are stored on an optical disk, thereby comprising both linear and nonlinear terms. Its explicit dependence on the channel bits makes this model well suited for signal-pro...

Nonlinearities are often encountered in the analysis and processing of real-world signals. Most existing approaches to nonlinear signal processing characterize the nonlinearity in the time domain or frequency domain. In fact, there are good reasons for characterizing nonlinearity using more general signal representations like the wavelet expansion. Wavelet expansions often provide very concise ...

A finite element method simulation of a carbon fibre reinforced polymer block is used to analyse the nonlinearities arising from a contacting delamination gap inside the material. The ultrasonic signal is amplified and nonlinearities are analysed by delayed Time Reversal – Nonlinear Elastic Wave Spectroscopy signal processing method. This signal processing method allows to focus the wave energy...

P ROGRESS in the theory and design of neural networks has expanded on many fronts during the past ten years. Much of that progress has a direct bearing on signal processing. In particular, the nonlinear nature of a neuron that constitutes the basic building block of neural networks—the ability of neural networks to learn from their environments in supervised as well as unsupervised ways and the...

The auditory nervous system is highly nonlinear. Some nonlinear responses arise through active processes in the cochlea, while others may arise in neural populations of the cochlear nucleus, inferior colliculus and higher auditory areas. In humans, auditory brainstem recordings reveal nonlinear population responses to combinations of pure tones, and to musical intervals composed of complex tone...