The detectability for a noise-enhanced composite hypothesis testing problem according to different criteria is studied. In this work, the noise-enhanced detection problem is formulated as a noise-enhanced classical Neyman–Pearson (NP), Max–min, or restricted NP problem when the prior information is completely known, completely unknown, or partially known, respectively. Next, the detection perfo...

The optimal detection of a signal of known form hidden in additive white noise is examined in the framework of stochastic resonance and noise-aided information processing. Conditions are exhibited where the performance in the optimal detection increases when the level of the additive (non-Gaussian bimodal) noise is raised. On the additive signal–noise mixture, when a threshold quantization is p...

The paper addresses a problem of tracking multiple number of frequencies using Regularized Autoregressive (RAR) approximation. The RAR procedure allows to decrease approximation bias, comparing to other AR-based frequency detection methods, while still providing competitive variance of sample estimates. We show that the RAR estimates of multiple periodicities are consistent in probabilit...

Knowledge Graphs (KGs) play a key role in many artificial intelligence applications. Large KGs are often constructed through a noisy automatic knowledge extraction process. Noise detection is, therefore, an important task for having high-quality KGs. We argue that the current noise detection approaches only focus on a specific type of noise (i.e., fact checking) whereas knowledge extraction met...

Edge detection is an important aspect in image processing. When a noisy image is presented for edge detection, the noise creates problem in the process of edge detection using conventional methods. One of the disadvantages of the conventional methods is that the noise is not removed automatically. The present paper proposes a novel approach for noise removal cum edge detection for both gray sca...

We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the limits of small signal and weak noise correlation, the optimal test statistic and its performance are derived under general conditions, especially concerning the type of noise. We also analyze, for a threshold nonlinearity-a key component of a neural model, the conditions for noise-enhanced perfo...

Corrosion of structural steel in an SRB containing environment for as long as 9 months, is studied using the Electrochemical Noise Analysis technique. The results show that the activity of the system, which was very low initially, started to increase after 6 months exposure to the environment. The power spectral density curves of the Electrochemical Potential Noise of the system, using the maxi...

In speech recognition, a speech/non-speech detection must be robust to noise. In this work, a new method for speech/nonspeech detection using a Linear Discriminant Analysis (LDA) applied to Mel Frequency Cepstrum Coefficients (MFCC) is presented. The energy is the most discriminant parameter between noise and speech. But with this single parameter, the speech/non-speech detection system detects...

Data association target tracking algorithms assume knowledge of target detection probability and sensor measurement noise. They are unknown and/or variable. Target signal is unknown due to unknown and time-varying target radar cross section as well as multi-path signal attenuation and variable propagation signal losses. Noise power is unknown due to the presence of unknown clutter signal whose ...