Noise factor analysis for cDNA microarrays.

The level of internal noise of the transistors, diodes, and other semiconductor components limits the successful design of any low noise electronic system. All types of noise, namely, Johnson, 1/f, and so forth, are generated due to activity of crystalline defects such as vacancies, dislocations, and others. The intensity of the electron scattering and recombination processes, inflicted by defects (traps), controls the level of noise. Dependent on the dynamic operation condition of semiconductor devices, such as external biases and level of current injection, the traps will generate certain type and level of noise. Material growth or device processing technologies could introduce all kind of defects. Therefore, characterization of the semiconductor wafer in the early stages of processing (at least before packaging) could help to predict the level of noise due to the type and density of defects present on the wafer. Sorting out bad semiconductor chips could save money and effort in the radio frequency design of low-noise circuits. This current study focuses on 1/f noise modeling, which involves most powerful generators of noise and linear defects, named dislocations. The study also examines the possibility of assessing this noise by quantitative electron beam-induced conductivity (EBIC) measurements. These defects could be found in the bulk as well as at the epitaxial interfaces of a semiconductor device. The nanoscale size of these defects makes the scanning electron beam an instrument of choice for the proposed study. Conventional EBIC produces images of the defects, where contrast is proportional to the recombination rate at the site of a defect. Since contrast is measured as a fraction of one percent, the relative nature of contract value precludes quantitative measurements of the recombination rate, thus making quantitative assessment of 1/f noise impossible. In our model, using the Boltzman continuity equation, the recombination-generation processes per unit of length of a dislocation was defined for two operational conditions of EBIC, namely, for low and high intensity of an electron beam. The experimental technique of the quantitative measurement of carrier recombination (Mil'shtein 2001) consists of taking two EBIC scans along the selected defect at two different beam intensities, digitally subtracting the first scan from the second one and normalizing the result to the size of the electron range. The value of the recombination rate, extracted from the model, could then be used to predict the level of 1/f noise in the tested semiconductor sample.

Microarray technology has become a power tool in the field of bioinformatics. It is used to measure gene expression levels and similar to any other image capturing processes is prone to noise. There are different kinds of noise, during preparation, hybridization and scanning in microarray images which usually are modeled by Gaussian noise. Since introduction of wavelets in 1970s, many more forms and extensions of this transform have been developed and used, such as stationary wavelet transform (SWT), complex wavelet transform (CWT), curvelet transform (CURV) and contourlet transform (CNT). By developing of more sparse transforms, it is important to have a perspective of how efficient the transforms are in different applications, such as microarray image analysis. In this paper, we compare the efficiency of common sparse transforms including ordinary discrete wavelet transform (DWT), SWT, CWT, CURV, CNT, Contourlet-SD decomposition, steerable pyramid (STP) and shearlet transform (SHR) for microarray image denoising. Therefore after converting microarray image into x-let transform, BayesShrink method, soft and hard thresholding are used to perform denoising of these images. Both local and general thresholds are calculated for each subband in order to evaluate the effect of incorporating intrascale dependency on top of sparsity property in statistical modeling of x-let's coefficients. Our simulation results show that CWT and SHR outperforms the others when using global thresholding and SWT is the preferred transform when using local thresholding. Although STP and SHR have better performance for some criteria like structural similarity (SSIM) index, but CWT is faster.

The contribution of type and levels of community noises to the perception of loudness, noisiness, and annoyance is being explored in ongoing research projects. Particular attention has been given to the relationship between scaling method and the conclusions it is possible to draw from the data, especially with regard to observers' abilities to differentiate noises according to the above attributes. Procedures for calibrating and possibly standardizing the perceptual scales have been attempted with preliminary success. Various types of noises have been investigated: aircraft noise under different flying conditions, traffic noise, and a number of other community noises. Besides specific scaling problems, the investigations have addressed the specific problems of observers' abilities to differentiate among noises that are combined in a complex setting as well as their ability to integrate various noises into a unitary perception of loudness and annoyance. Since the experimental setups reflect real‐life situations, the answers to these latter questions are relevant to understanding the responses of the individual who is exposed to combinations of noises in his or her community.The contribution of type and levels of community noises to the perception of loudness, noisiness, and annoyance is being explored in ongoing research projects. Particular attention has been given to the relationship between scaling method and the conclusions it is possible to draw from the data, especially with regard to observers' abilities to differentiate noises according to the above attributes. Procedures for calibrating and possibly standardizing the perceptual scales have been attempted with preliminary success. Various types of noises have been investigated: aircraft noise under different flying conditions, traffic noise, and a number of other community noises. Besides specific scaling problems, the investigations have addressed the specific problems of observers' abilities to differentiate among noises that are combined in a complex setting as well as their ability to integrate various noises into a unitary perception of loudness and annoyance. Since the experimental setups reflect real‐life situ...

The multichannel image restoration is a kind of ill-posed inverse problem, which is usually solved by the vectorial variational regularization methods based on the independent and related prior information in the channels of the image.Focus on this, this paper propose a variational regularization model for multi-polarimetric SAR image speckle reduction based on multiplicative-additive noise model. Two level alternating minimization algorithm is designed for solving this new variational regularization model. According to the scattering matrix representation model of the multi-polarimetric SAR image, the amplitude coupling term of the arbitrarily two channels of the multi-polarimetric SAR image satisfies a multiplicative-additive noise model. The distribution of the two kinds of noise is determined by the correlation coefficient of the two channels. This paper establishes a variational regularization model for denoising this kind of multiplicative-additive noise. A auxiliary variable is introduced into the observe model to decompose it into an additive noise model and a multiplicative noise model. Then the variational regularization model for restoring the two channel coupling term from the multiplicative-additive noise is obtained by using the MAP method. To solve this model, it is considered as a minimization model of the primal and the auxiliary variables. Then an alternating minimization algorithm is used to solve the problem. The sub-model with respect to the primal variable is non-convex, which is convexed by a variable substitution technique. Then according to the separation of variables, the penalty method, and the iterative reweighted least squares method, the convex model is transformed into a minimization model with respect to three variables, which is solved again by an alternating minimization algorithm. On the other hand, the sub-model with respect to the auxiliary variable is quadratic convex, which can be easily solved by the Newton iteration method. In this paper, this new model is applied to the multi-polarimetric SAR image, and a fine despeckling result is obtained.

This paper investigates the influence of the signal to noise ratio (SNR) and the type of a noise on the performance of two adaptive novelty detection methods. The evaluated methods are Learning Entropy (LE) and Error and Learning Based Novelty Detection (ELBND). The methods are compared in empirical way in classification framework. A classification based only on the error of the adaptive model was used as a reference. The research in this field is important, because a noise is present in every measured data and can drastically influence the result of tasks like the novelty detection. Moreover, various types of noise can influence the novelty detection in different ways, therefore the optimal method of adaptive novelty detection can be hard to choose. This assumption is supported by experimental results in this study.

The experimentation of footsteps as a biometric has a short history of about two decades. The process of identification of a person is based on the study of footstep signals captured when walking over a sensing area, and the registering of sounds, pressure, vibration, or a combination of these measures. Application of this biometric can emerge in security systems, that identify persons who enter or leave a space, and in providing help to elderly and disabled persons. In this paper, we are focused in the exploration of pure audio signals of footsteps and the robustness of a person’s classification under noisy conditions. We present a comparison between four wellknown classifiers and three kinds of noise, applied at different signal to noise ratio. Results are reported in terms of accuracy in the detection an users, showing different levels of sensibility according to the kind and level of noise.

Investigating the effects of different levels and types of construction noise on emotions using EEG data

DNA microarray technology has permitted the analysis of global gene expression profiles in clinical diagnosis, treatment, and environmental health research for several diseases including cancer. Microarray images analysis, processing and segmentation are decisive steps in gene expression analysis, since any error leads to improper diagnosis. These techniques increased and enhanced over the past few years. However, the segmentation of the unshaped spots within the microarray image is still a problem due to the variations of spots in shapes and sizes. Hence, we introduce a new unshaped microarray spot segmentation model, which focuses on spots detection and segmentation regardless of their size and shape using fractal dimensions transform. Real microarray images from the Stanford Microarray Images Database (SMD) and Universal Microarray Images Database (UNC) are used to evaluate the efficiency of the proposed model. The numerical and visual results show that the proposed model improves the accuracy and the efficiency of the spots segmentation process. Biological researchers can use this model in their labs and clinics, as a free priceless software instead of using expensive ones like ScanAlyze, GenePix or spot software.

Gridding and spot segmentation are two critical steps in microarray gene expression data analysis. However, the problems of noise contamination and donut-shaped spots often make signal extraction process a laborintensive task. In this paper, we propose a three-step method for automatic gridding and spot segmentation. The method starts with a background removal and noise eliminating step, and then proceeds in two steps. The first step applies a fully unsupervised method to extract blocks and grids from the cleaned data. The second step applies a simple, progressive spot segmentation method to deal with inner holes and noise in spots. We tested its performance on real microarray images against a widely used software GenePix. Our results show that the proposed method deals effectively with poor-conditioned microarray images in both gridding and spot segmentation.

High-density surface electromyogram (HD-sEMG) undertakes the task of detecting the activity of motor units (MUs). However, its recordings are often mixed with power line interference and its harmonics (PLI), white Gaussian noise (WGN), baseline wandering (BW), and motion artifact (MA). The poor signal-to-noise ratio (SNR) is an obstacle for clinical applications. The surface electromyogram (sEMG) preprocessing technology rarely considers the diversity and specificity of noise, that is, the algorithm is only for a certain type of noise or only uses one method to deal with all kinds of noise. In this article, we propose a method based on variational mode decomposition (VMD) to specifically remove these four types of noise, which makes full use of the characteristics of noise. A variance-dependent adaptive parameter optimization method was proposed for PLI and WGN removal, and the higher-order statistics (HOS) were further used to adaptively suppress WGN without reference signals. The method was first evaluated using simulated data and then experimentally validated on three different databases involving not only healthy subjects but also amputees, specifically in comparison with previous methods. Both simulated and experimental results showed that the proposed method was significantly superior to the traditional methods (Wilcoxon signed-rank test, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p} \lt 0.001$ </tex-math></inline-formula>). It resulted in 14.11%, 59.71%, and 5.76%, better than traditional methods on the three experimental databases, respectively. Our proposed method has positive implications for sEMG-based or HD-sEMG-based human-robot interfaces.

Microarray image processing plays an important role among array analysis and its precision will affect the application of gene chip. However, real microarray images drawn from various experimental procedures may contain noises and other artifacts, which result in a lower image processing accuracy. Hence, in this paper, we propose a novel image denoising method by introducing the compressed sensing theory into microarray image noise reduction. To improve the matching pursuit efficiency, the correlation coefficient is adopted. Experiments on 17 simulated images and 59 real microarray images obtained from the DeRisi, SIB and BCM data sets are conducted. Qualitative and quantitative results compared with the Donoho threshold, generalized wavelet and adaptive thresholding algorithm, reveal that our proposed method performs better on reducing the noises.

Real-world classification data usually contain noise, which can affect the accuracy of the models and their complexity. In this context, an interesting approach to reduce the effects of noise is building ensembles of classifiers, which traditionally have been credited with the ability to tackle difficult problems. Among the alternatives to build ensembles with noisy data, bagging has shown some potential in the specialized literature. However, existing works in this field are limited and only focus on the study of noise based on a random mislabeling, which is unlikely to occur in real-world applications. Recent research shows that other types of noise, such as that occurring at class boundaries, are more common and challenging for classification algorithms. This paper delves into the analysis of the usage of bagging techniques in these complex problems, in which noise affects the decision boundaries among classes. In order to investigate whether bagging is able to reduce the impact of borderline noise, an experimental study is carried out considering a large number of datasets with different noise levels, and several noise models and classification algorithms. The results obtained reflect that bagging obtains a better accuracy and robustness than the individual models with this complex type of noise. The highest improvements in average accuracy are around 2–4% and are generally found at medium-high noise levels (from 15–20% onwards). The partial consideration of noisy samples when creating the subsamples from the original training set in bagging can make it so that only some parts of the decision boundaries among classes are impaired when building each model, reducing the impact of noise in the global system.

The Western Ghats (WG) is one of the great escarpments that extend ~1500 km parallel to the west coast of India in the direction of NNW-SSE. We deployed a network of seven broadband seismological stations along the WG to decipher its evolution. In the present study, we investigate the characteristics of different kinds of noises at the stations by utilizing the power spectral density (PSD) measurements. Further, the results are compared with the global standard noise models to assess the data quality. The PSD results reveal that the short period (0.1–1 s) cultural noise is more prominent at stations AGMB, SDPR and MGLI when compared to the other stations, especially during day hours of Indian Standard Time (IST) zone since these sites were in the proximity of roads. The seasonal variations are observed especially in the microseismic period range and noise levels are more prominent in the months of July to August since the western part of India experiences peak monsoon during this period. These variations are observed especially at PCH and KNUR stations as their locations were near the coastline. Furthermore, the results indicate that the noise levels are more prominent in vertical components than that of horizontal components in the microseismic period range whereas it is reversed in the short and long period ranges. Later, the results indicate that the noise levels at all the localities of stations are within the global standard noise models, which suggests that our installation of broadband seismological stations has been successful at all seven locations and has good data quality.

Due to sensor instability and atmospheric interference, hyperspectral images (HSIs) often suffer from different kinds of noise which degrade the performance of downstream tasks. Therefore, HSI denoising has become an essential part of HSI preprocessing. Traditional methods tend to tackle one specific type of noise and remove it iteratively, resulting in drawbacks including inefficiency when dealing with mixed noise. Most recently, deep neural network-based models, especially generative adversarial networks, have demonstrated promising performance in generic image denoising. However, in contrast to generic RGB images, HSIs often possess abundant spectral information; thus, it is non-trivial to design a denoising network to effectively explore both spatial and spectral characteristics simultaneously. To address the above issues, in this paper, we propose an end-to-end HSI denoising model via adversarial learning. More specifically, to capture the subtle noise distribution from both spatial and spectral dimensions, we designed a Residual Spatial-Spectral Module (RSSM) and embed it in an UNet-like structure as the generator to obtain clean images. To distinguish the real image from the generated one, we designed a discriminator based on the Multiscale Feature Fusion Module (MFFM) to further improve the quality of the denoising results. The generator was trained with joint loss functions, including reconstruction loss, structural loss and adversarial loss. Moreover, considering the lack of publicly available training data for the HSI denoising task, we collected an additional benchmark dataset denoted as the Shandong Feicheng Denoising (SFD) dataset. We evaluated five types of mixed noise across several datasets in comparative experiments, and comprehensive experimental results on both simulated and real data demonstrate that the proposed model achieves competitive results against state-of-the-art methods. For ablation studies, we investigated the structure of the generator as well as the training process with joint losses and different amounts of training data, further validating the rationality and effectiveness of the proposed method.

Background and PurposeSpeech-in-noise perception deficits have been demonstrated in patients with mild cognitive impairment (MCI). However, it remains unclear whether the impairment of speech perception varies between MCI subtypes. The purpose of this study was twofold: 1) to compare speech perception performance among MCI subgroups, and 2) to identify the cognitive domains specifically related to speech-in-noise perception.MethodsWe studied 46 patients with MCI and 39 hearing-threshold-matched cognitively normal elderly (CNE) subjects. Two different patient classifications were used: 1) patients with amnestic mild cognitive impairment (aMCI) (n=21) or nonamnestic mild cognitive impairment (naMCI) (n=25), and 2) patients with frontal-executive dysfunction (FED) (n=16) or without FED (n=30). All of the subjects underwent audiometric, neuropsychological, and speech perception assessments. Speech-in-noise perception was measured using sentence recognition tests in the presence of two types of background noise at four levels.ResultsFirst, as the level of background noise increased, the MCI with FED group scored lower than both the MCI without FED and CNE groups under both types of noise. Second, both the naMCI and aMCI groups scored lower than the CNE group, but there were no differences between the naMCI and aMCI groups in sentence recognition under any noise conditions. Third, significant correlations were found between sentence recognition and executive function scores both in the MCI groups and in the CNE group.ConclusionsOur findings suggest that frontal-executive function is strongly related to speech-in-noise perception and that MCI patients with FED have greater deficits in speech-in-noise perception compared to other subgroups of MCI.