White noise

About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.

Multilevel polarization shift keying: optimum receiver structure and performance evaluation

Abstract: Multilevel digital coherent optical modulation schemes based on the state of polarization of a fully polarized lightwave are proposed and analyzed. Based on the complete statistical characterization of the Stokes parameters, extracted through appropriate signal processing in the presence of shot and additive Gaussian noise, the optimum maximum likelihood receiver operating symbol by symbol is derived. The exact performance in terms of the average symbol error probability is found. Optimum constellations for the case of equipower 4, 8, 16 and 32 signals are found on the basis of the minimization of the error probability for a given average power. Their performance turns out to be promising as compared to other standard modulation techniques. The spectral analysis of polarization modulated signals is presented. A new receiver structure, which solves the problem of the excess penalties incurred in the presence of channel dichroism, is proposed and analyzed. >

Enhanced detectability of small objects in correlated clutter using an improved 2-D adaptive lattice algorithm

Abstract: Two-dimensional (2-D) adaptive filtering is a technique that can be applied to many image processing applications. This paper will focus on the development of an improved 2-D adaptive lattice algorithm (2-D AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images. The two improvements proposed here are increased flexibility in the calculation of the reflection coefficients and a 2-D method to update the correlations used in the 2-D AL algorithm. The 2-D AL algorithm is shown to predict correlated clutter in image data and the resulting filter is compared with an ideal Wiener-Hopf filter. The results of the clutter removal will be compared to previously published ones for a 2-D least mean square (LMS) algorithm. 2-D AL is better able to predict spatially varying clutter than the 2-D LMS algorithm, since it converges faster to new image properties. Examples of these improvements are shown for a spatially varying 2-D sinusoid in white noise and simulated clouds. The 2-D LMS and 2-D AL algorithms are also shown to enhance a mammogram image for the detection of small microcalcifications and stellate lesions.

Wavelet analysis in ecology and epidemiology: impact of statistical tests

Abstract: Wavelet analysis is now frequently used to extract information from ecological and epidemiological time series. Statistical hypothesis tests are conducted on associated wavelet quantities to assess the likelihood that they are due to a random process. Such random processes represent null models and are generally based on synthetic data that share some statistical characteristics with the original time series. This allows the comparison of null statistics with those obtained from original time series. When creating synthetic datasets, different techniques of resampling result in different characteristics shared by the synthetic time series. Therefore, it becomes crucial to consider the impact of the resampling method on the results. We have addressed this point by comparing seven different statistical testing methods applied with different real and simulated data. Our results show that statistical assessment of periodic patterns is strongly affected by the choice of the resampling method, so two different resampling techniques could lead to two different conclusions about the same time series. Moreover, our results clearly show the inadequacy of resampling series generated by white noise and red noise that are nevertheless the methods currently used in the wide majority of wavelets applications. Our results highlight that the characteristics of a time series, namely its Fourier spectrum and autocorrelation, are important to consider when choosing the resampling technique. Results suggest that data-driven resampling methods should be used such as the hidden Markov model algorithm and the ‘beta-surrogate’ method.

Temporal effects in simultaneous masking and loudness.

Abstract: The intention of the research described was twofold. One purpose was to investigate the difference between threshold curves for tones masked by bands of noise and the corresponding displacement curves obtained from models of the basilar membrane. The second object of investigation was the behavior of a strange masking transient which indicates that masking of a short signal pulse by a longer masker burst is stronger at the beginning of the masker burst than later under certain circumstances, but that under other conditions masking remains constant during the total duration of the masker. The threshold of signal pulses masked by masker bursts was measured as a function of different variables such as bandwidth and center frequency of masker, delay between onset of masker and onset of signal, duration of signal, and spectrum shape of masker. The results, especially those obtained when white noise with a frequency gap was the masker, lead to the hypothesis that the masking excitation appears almost instantaneously. This hypothesis was tested and accepted on the basis of loudness judgments comparing equally long tone pulses and white‐noise pulses of very short and long duration. The conclusion that excitation occurs almost instantaneously, taken together with the known relationship between the masking effect of white noise and that of narrow‐band noise, is construed to mean that a complex spatial inhibition in the nervous system may not be responsible for the narrow sensitivity curve; in fact, a sharp mechanical filter could account for the findings.

Multiplicative stochastic resonance in linear systems: Analytical solution

Abstract: The maximum of a signal as a function of the noise intensity ("stochastic resonance") is found in a linear system subjected to multiplicative colour noise. This result is obtained for an arbitrary dichotomous noise and for a colour noise with a short autocorrelation time. Stochastic resonance does not occur for Gaussian white noise.