Diverse Non-Fundamental Information and Trading Complementarities

We investigate the use of Fourier plane nonlinear filtering for phase-encoded images. We investigate the performance of the nonlinear joint transform correlator and the nonlinearly transformed matched filter for phase-encoded images with different types of input noise. We use the peak-to-output-energy ratio, peak-to-sidelobe ratio, and discrimination ratio as the metrics for measuring the performances. We mathematically analyze the peak-to-output-energy ratio of the nonlinearly transformed matched filter for phase-encoded images with spatially nonoverlapping white noise. Computer simulations are provided to show the performance improvements of the nonlinear filtering techniques for the phase-encoded images. In comparison with linear filtering techniques, we find that the nonlinear filtering techniques substantially improve the performance metrics. From the computer-simulation results it can be seen that the nonlinear joint transform correlator performs better than the nonlinearly transformed matched filter in detecting phase-encoded targets in the presence of different types of noise, such as additive overlapping white noise, spatially nonoverlapping white background noise, spatially nonoverlapping colored background noise, and nontarget objects.

Oscillator noise is generally modeled by a power law spectral density. Thus it is possible to characterize different noise sources, each of them corresponding to a particular power law. The measurement of the contribution of these sources is necessary to know their origin and to remedy these causes in order to improve oscillator performance. Usually, an estimation of the different types of noise present in a signal is obtained by using a variance. However, the sensitivity of these variances differs for each type of noise and then limits this method. On the other hand, the use of several variances, each of them more sensitive to one type of noise, permits one to notably improve the measurement accuracy. The method suggested here uses as many different variances as there are types of noise to measure. The improvement of measurement accuracy of the noise coefficient is discussed in this paper.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this work, the effects of background noises on nonlinear dynamics of a modelled standing-wave thermoacoustic system with subcritical Hopf bifurcation behaviors are studied. These noises include (1) pressure-coupled (acoustic), (2) velocity-coupled (flow), and (3) external combustion noise. It is found that these three types of background noises play important, but different roles in changing the hysteresis width and stability boundary. In addition, the stochastic transition from stability to instability is investigated, as the noise intensity is varied. Two different stochastic P-bifurcations are identified. One is related to a craterlike probability density distribution. The other is associated with a probability density distribution characterized with two peaks and one trough. With each type of noise affecting the system's stochastic behaviors being evaluated, the effect of two different types of noises is then studied. It is shown that the combined noises (types 1 and 2) cannot only destabilize global stable thermoacoustic system, but also stabilize linearly unstable system. This depends strongly on the superimposition form of the two types of noises. In addition, when the thermoacoustic system is disturbed by the combined noise (types 3 and 1 or types 3 and 2), the transition process is dominated by the combustion noise.

Objective: to analyze speech perception in normally hearing adults when listening in silence and with different types of noise. Methods: 40 individuals of both sexes, aged 18 to 45 years, participated in the study. Speech perception was assessed with the Lists of Sentences in Portuguese test, without a competing noise and with speech-spectrum, babble, and cocktail party noise. A mixed-effects linear regression model and the 95% confidence interval were used. Results: the subjects’ performance was worse in the three types of noise than in silence. When comparing the types of noise, differences were found in all combinations (speech-spectrum X babble, speech-spectrum X cocktail party, and babble X cocktail party), with a worse performance in babble, noise, followed by cocktail party. Conclusion: all noises negatively influenced speech perception, with a worse performance in babble, followed by cocktail party and speech-spectrum.

In this work, we propose ShallowDeepNet, a novel system architecture that includes a shallow and a deep neural network. The shallow neural network has the duty of data preprocessing and generating adversarial samples. The deep neural network has the duty of understanding data and information as well as detecting adversarial samples. The deep neural network gets its weights from transfer learning, adversarial training, and noise training. The system is examined on the biometric (fingerprint and iris) and the pharmaceutical data (pill image). According to the simulation results, the system is capable of improving the detection accuracy of the biometric data from 1.31% to 80.65% when the adversarial data is used and to 93.4% when the adversarial data as well as the noisy data are given to the network. The system performance on the pill image data is increased from 34.55% to 96.03% and then to 98.2%, respectively. Training on different types of noise can benefit us in detecting samples from unknown and unseen adversarial attacks. Meanwhile, the system training on the adversarial data as well as noisy data occurs only once. In fact, retraining the system may improve the performance further. Furthermore, training the system on new types of attacks and noise can help in enhancing the system performance.

Noise induced hearing loss (NIHL) as one of the major avoidable occupational related health issues has been studied for decades. To assess NIHL, the excitation pattern (EP) has been considered as one of the mechanisms to estimate the movements of the basilar membrane (BM) in the cochlea. In this study, two auditory filters, dual resonance nonlinear (DRNL) filter and rounded-exponential (ROEX) filter are applied to create two EPs, the velocity EP and the loudness EP respectively. Two noise hazard metrics are proposed based on two proposed EPs to evaluate hazardous levels caused by different types of noise. Moreover Gaussian noise and tone signals are simulated to evaluate performances of the proposed EPs and the noise metrics. The results show that both EPs can reflect the responses of the BM to different types of noise. For Gaussian noise there is a frequency shift between the velocity EP and the loudness EP. The results suggest that both EPs can be used for assessment of NIHL.

Electrocardiogram (ECG) signals are electrical signals generated corresponding to activity of heart. ECG signals are recorded and analysed to monitor heart condition. In initial raw form, ECG signals are contaminated with different types of noises. These noises may be electrode motion artefact noise, baseline wander noise and muscle noise also known as electromyogram (EMG) noise etc. In this paper, a descendent structure consists of adaptive filters is used to eliminate the three different types of noises (i.e., motion artefact noise, baseline wander noise and muscle noise). The two different adaptive filtering algorithms have been implemented; least mean square (LMS) and recursive least square (RLS) algorithm. The performance of these filters are compared on the basis of different fidelity parameters such as mean square error (MSE), normalised root mean squared error (NRMSE), signal-to-noise ratio (SNR), percentage root mean squared difference (PRD), and maximum error (ME) has been observed.

Different types of noise are studied, in particular, the color noises, flicker noise, and self-similar (fractal) noise. The results of studies of statistical stability of various noises and processes are generalized and the causes of statistical stability violation are investigated. It is found that statistically unstable processes can arise in different ways: because of inflows from the outside in an open system of matter, energy, and (or) information, as a result of nonlinear and even linear transformations, and due to wave damping.

Noise is one of the prime factors which degrade the quality of an image. Hence, image denoising is an essential image enhancement technique in the image processing domain. In this paper, we use low-pass sparse banded filter matrices for image denoising. Sparsity is the key concept in this filter design. We applied the designed low-pass filter both row-wise and column-wise to denoise the image. The proposed method is experimented on standard test images corrupted with different types of noises namely Gaussian, White Gaussian, Salt & Pepper and Speckle with noise level equals to 0.01, 0.05 and 0.1. The effectiveness of the proposed method of denoising is evaluated by the computation of standard quality metric known as Peak Signal-to-Noise Ratio (PSNR). The experimental result analysis shows that the proposed image denoising technique based on sparse banded filter matrices results in significant improvement in PSNR around 2dB to 8dB for different type of noises with noise level equal to 0.1 and is also aided by the visual analysis.

A learning strategy in Learning Classifier Systems (LCSs) defines how classifiers cover a state-action space in a problem. Previous analyses in classification problems have empirically claimed an adequate learning strategy can be decided depending on the types of noise in the problem. This issue is still arguable from two aspects. First, there lacks comparison of learning strategies in reinforcement learning problems with different types of noise. Second, when we can claim so, a further issue is how should classifiers cover the state-action space in order to improve the stability of LCS performance on as many types of noise as possible? This paper first attempts to empirically conclude these issues on a version of LCSs (i.e., the XCS classifier system). That is, we present a new concept of learning strategy for LCSs, and complement that claim by comparing it with the existing learning strategies on a reinforcement learning problem. Our learning strategy covers all state-action pairs but assigns more classifiers to the highest-return action at each state than other actions. Our results support that claim that existing learning strategies have dependencies on the types of noise in reinforcement learning problems. However, our learning strategy improves the stability of XCS performance compared with the existing strategies on all types of noise employed in this paper.

The assembly gap between components is very vital for the evaluation of assembly quality of aircrafts. Due to the limits of gap size and operation space, the assembly gap needs to be indirectly calculated by the measurements of surface of components instead of plug gauge test. However, the surface constituted of point cloud is usually mixed with different types of noise ,which severely affects the evaluation of assembly gap. To remove these different types of noise simultaneously with high efficiency, a classified denoising method combining with an improved bilateral filtering and median filtering was proposed. Firstly, based on the principal component analysis, a new coordinate system was established to achieve the homogeneity of coordinates of point cloud. Then, an improved median filtering method on the basis of region segmentation (RSMF) was used to remove large-scale noise. Next, the fast bilateral filtering method based on threshold segmentation (TSBF) was proposed to remove small-scale noise. Finally, a measurement experiment of aircraft component was performed to verify the effectiveness of the proposed method. Experimental results showed that the proposed method could not only reduce measurement error including RMSE (Root Mean Square Error), but also improve SNR (Signal Noise Ratio) and PSNR (Peak Signal to Noise Ratio) of point cloud data.

Compositional model for speech denoising based on source/filter speech representation and smoothness/sparseness noise constraints

In subjective evaluation of distorted images, human observers usually consider the type of distortion, the amount of distortion, and the distribution of error. We recently proposed an image quality measure, M-SVD, for gray-scale images that can be used as a graphical tool to predict the distortion based on these three factors, and also as a numerical tool to assess the overall visual quality of the distorted image. It performs better than two state-of-the-art metrics, Q and MSSIM, especially when we compute the correlation with mean opinion score across different types of noise. The test image was degraded using six types of noise (JPEG, JPEG 2000, Gaussian blur, Gaussian noise, sharpening and DC-shifting), each with five different levels of intensity. In this paper, we extend M-SVD to full color images using a color model which decouples the color and gray-scale information in an image. Our experiments show that using only the luminance component, the measure outperforms Q and MSSIM. When we also use the two chrominance layers, the performance of M-SVD becomes slightly higher whereas the performance of Q and MSSIM is degraded. This indicates that the colo r components may also contribute to the performance of the proposed measure. Keywords: image quality, objective measures, subjective evaluation, PSNR, local error measurement, human visual system, visual threshold, singular value decomposition

The strength and shape of the dose-response relationship between different types of noise and myocardial infarction (MI) remain unclear. Therefore, we aimed to summarize the evidence of the association between various types of noise and MI incidence and mortality through a dose-response meta-analysis. We performed a systematic search of the PubMed, Embase, and Web of Science databases up to December 19, 2021. The generalized least-squares method and restricted cubic splines were used to assess the potential linear and nonlinear dose-response relationships between noise exposure and the risk of MI events. Twenty observational studies with 34 reports met the eligibility criteria. In the linear models, the pooled relative risk and corresponding 95% confidence interval (CI) for MI incidence was 1.04 (95% CI: 1.02 - 1.05), and the MI mortality was 1.02 (95% CI: 1.02 - 1.03) for each 10dB(A) increase in noise exposure. In addition, we observed an approximately J-shaped dose-response relationship between noise and MI mortality (Pnonlinearity = 0.0037), whereas the threshold for the statistical impact of noise on MI mortality may be 42dB(A). Our findings support the notion that various types of noise exposure have a positive dose-response relationship with the risk of MI incidence and mortality.

In self-propelled particle models, the interactions are generally subject to some type of noise, which is introduced either during or after the interactions between the particles. Here, self-propelled particles are subject to two different types of noise. If the orientation between them is less than a certain rate [Formula: see text], a vectorial noise is introduced in each particle–particle interaction, otherwise, an angular noise is introduced in the average direction of motion, already calculated, of neighboring particles. We vary the rate [Formula: see text] over a wide range ([Formula: see text]) and study the change in the nature of the phase transition. The product-moment correlation coefficient between the order parameter and the hybrid noise, is calculated. We found that in the combination of the two types of noise, the critical noise decreases with increasing the parameter [Formula: see text], and by analysis of the Binder cumulant we estimate the value of [Formula: see text] as from which begins to appear the effects of the vectorial noise on the phase transition.