Noise Correction Algorithm Research Articles

Worker similarity-based noise correction for crowdsourcing

Crowdsourcing offers a cost-effective way to obtain multiple noisy labels for each instance by employing multiple crowd workers. Then label integration is used to infer its integrated label. Despite the effectiveness of label integration algorithms, there always remains a certain degree of noise in the integrated labels. Thus noise correction algorithms have been proposed to reduce the impact of noise. However, almost all existing noise correction algorithms only focus on individual workers but ignore the correlations among workers. In this paper, we argue that similar workers have similar annotating skills and tend to be consistent in annotating same or similar instances. Based on this premise, we propose a novel noise correction algorithm called worker similarity-based noise correction (WSNC). At first, WSNC exploits the annotating information of similar workers on similar instances to estimate the quality of each label annotated by each worker on each instance. Then, WSNC re-infers the integrated label of each instance based on the qualities of its multiple noisy labels. Finally, WSNC considers the instance whose re-inferred integrated label differs from its original integrated label as a noise instance and further corrects it. The extensive experiments on a large number of simulated and three real-world crowdsourced datasets verify the effectiveness of WSNC.

NECTAR: A New Algorithm for Characterizing and Correcting Noise in QToF-Mass Spectrometry Imaging Data.

A typical mass spectrometry imaging experiment yields a very high number of detected peaks, many of which are noise and thus unwanted. To select only peaks of interest, data preprocessing tasks are applied to raw data. A statistical study to characterize three types of noise in MSI QToF data (random, chemical, and background noise) is presented through NECTAR, a new NoisE CorrecTion AlgoRithm. Random noise is confirmed to be dominant at lower m/z values (∼50-400 Da) while systematic chemical noise dominates at higher m/z values (>400 Da). A statistical approach is presented to demonstrate that chemical noise can be corrected to reduce its presence by a factor of ∼3. Reducing this effect helps to determine a more reliable baseline in the spectrum and therefore a more reliable noise level. Peaks are classified according to their spatial S/N on the single ion images, and background noise is thus removed from the list of peaks of interest. This new algorithm was applied to MALDI and DESI QToF data generated from the analysis of a mouse pancreatic tissue section to demonstrate its applicability and ability to filter out these types of noise in a relevant data set. PCA and t-SNE multivariate analysis reviews of the top 4000 peaks and the final 744 and 299 denoised peak list for MALDI and DESI, respectively, suggests an effective removal of uninformative peaks and proper selection of relevant peaks.

Dual-View Noise Correction for Crowdsourcing

In crowdsourcing scenarios, each instance obtains a multiple noisy label set from different crowd workers on the Internet and then gets its integrated label via label integration. Although label integration algorithms are often effective, a certain level of noise still remains in the integrated labels. To reduce the impact of noise on label quality, many noise correction algorithms have been proposed in recent years. However, most of them are hard to fully utilize the joint information of the original attribute view and multiple noisy label view. Motivated by multi-view learning, in this paper, we propose a dual-view noise correction (DVNC) algorithm. Benefiting from the complementary and consensus principle, DVNC can fully utilize the joint information of two views and thus enhance the effect of noise correction. In DVNC, we at first construct the original attribute view and multiple noisy label view for each instance and respectively train a classifier on each view. Then, we use the trained two classifiers to filter each noise instance and thus obtain a clean set and noise set. Finally, we train a classifier on the clean set to correct each instance in the noise set via reclassifying it as the class with the maximum posterior probability. The experimental results on simulated and real-world datasets indicate that DVNC significantly outperforms all the other noise correction algorithms used for comparison.

Three-way decision-based noise correction for crowdsourcing

In crowdsourcing learning, a certain degree of label noise still exists in the integrated labels, which are obtained by label integration algorithms. In order to decrease the impact of label noise, many scholars focus on noise correction algorithms. Existing noise correction algorithms usually divide a data set with integrated labels into a clean set and a noise set, directly. However, there are certain noise instances in the clean set and clean instances in the noise set. To reduce the interference from noise instances and improve the utilization of clean instances, inspired by the three-way decision theory, we propose a three-way decision-based noise correction (TDNC) algorithm. Firstly, we calculate the compactness of each instance's multiple noisy label set and estimate the label certainty of each instance's integrated label. Secondly, we divide a data set with integrated labels into three disjoint sets, called positive set, boundary set and negative set, respectively. Thirdly, we further divide and add the instances in the boundary set into the positive set and the negative set according to the nearest neighbor classification. Finally, we train two heterogeneous classifiers on the new positive set to correct the noise instances in the new negative set using a consensus voting strategy. The experimental results on 34 simulated and two real-world data sets consistently show that TDNC significantly outperforms the existing state-of-the-art noise correction algorithms in terms of the noise ratio.

Agile Micro-nano Satellite Attitude Estimation with Lost Measurements

Aiming at the problem of lost measurement in attitude determination process of the agile micro-nano satellite, a novel algorithm is proposed employing the Bernoulli process to describe the loss process. Firstly,the model of gyro and star sensors as well as the satellite attitude estimation system simplified with gyro is given. Since the missing observations, the modified EKF algorithm is derived by combining a Bernoulli random variable and a real-time measurement noise correction algorithm. Then the convergence of the improved algorihtm is analyzed, and the bottom boundary condition for the observation arrival rate is presented. Finally, the improved method is compared with the conventional one through digital simulation, and the experimental results show that the preciseness of the privious one is one or two orders improved compared with the latter.

A multi-view-based noise correction algorithm for crowdsourcing learning

Crowdsourcing services provide a way to obtain large amounts of labeled data, which is inexpensive and effective. In crowdsourcing scenarios, the integrated labels of instances can be deduced by implementing ground truth inference algorithms. However, those labels often contain substantial noise and, to mitigate the effects of noise, label noise handling techniques are needed. This paper proposes a novel multi-view-based noise correction algorithm (MVNC). MVNC introduces the idea of multi-view learning to make better use of the information from crowdsourced data. It adds a new view composed of multiple noise labels and then trains classifiers on two views respectively to correct noise instances. In this process, different information between the views is fully utilized to generate the disagreement between two classifiers, so that the classifiers can complement each other and make more reliable predictions for noise instances. Experimental results on 38 benchmark data sets and 6 real-world data sets show that the new view significantly enhances the effect of noise correction.

Study on Noise Correction Algorithm of Infrared Emissivity of Rock under Uniaxial Compression

In the process of uniaxial loading of rocks, the original temperature information of infrared radiation is easily submerged in the noise signal, which leads to distortion of the obtained infrared radiation response information. In this paper, we propose a multi-band pseudo-emissivity denoising algorithm. Based on the basic theory of infrared radiation, by separating the infrared temperature measurement from the emissivity of the measured object, we constructed an infrared multi-band temperature measurement vector group that does not involve the emissivity to reduce the noise interference caused by the infrared temperature measurement results and the emissivity. Under a loading experiment of rock under uniaxial compression, the change of infrared radiation (IR) characteristics with loading was observed. The research results show that the multi-band pseudo-emissivity algorithm could effectively denoise infrared images and, using the denoised rock surface MIRT, AIRT and IRV as indicators, the characteristics of infrared radiation change in the process of uniaxial compression loading and fracturing of real rocks were analyzed.

Instance difficulty-based noise correction for crowdsourcing

Crowdsourcing offers an efficient way to obtain a multiple noisy label set of each instance from different crowd workers and then label integration algorithms are used to infer its integrated label. In spite of the effectiveness of label integration algorithms, there always exists a certain degree of noise in the integrated labels, and thus noise correction algorithms have been proposed to reduce the effect of noise. However, existing noise correction algorithms seldom consider the effect of instance difficulty on noise correction. In this paper, we argue that the greater the difficulty of an instance, the fewer crowd workers can label it correctly, and the more likely the instance is a noise instance. Based on this premise, we propose a simple but very effective noise correction algorithm called instance difficulty-based noise correction (IDNC). In IDNC, we at first propose two methods to measure the difficulty of each instance. Then, we use the proposed two methods to filter the noise instances to obtain a clean set and a noise set. Finally, we build two different classifiers on the clean set to correct the noise instances in the noise set via the consensus voting. The extensive experiments on both simulated and real-world crowdsourced datasets validated the effectiveness and efficiency of our proposed IDNC.

Improving data and model quality in crowdsourcing using co-training-based noise correction

Crowdsourcing makes it much faster and cheaper to obtain labels for a large amount of data used in supervised learning. In the crowdsourcing scenario, an integrated label is inferred from a multiple noisy label set for each instance using ground truth inference algorithms, which is called label integration. However, a certain level of label noise remains in the integrated dataset, which degrades the performance of the models trained on it. To the best of our knowledge, existing label noise correction algorithms only use the original attribute space and do not use the information contained in the multiple noisy label sets for building models. To solve these problems, we propose a novel integrated label noise correction algorithm called co-training-based noise correction (CTNC). In CTNC, the weight is first calculated from the information provided by the multiple noisy label set for each instance. Subsequently, a label noise filter is used to identify noisy instances; a clean set and a noisy set are thus obtained. Another attribute view of each instance in both the clean and noisy sets is then generated by the classifiers trained on the original attribute view of the clean set. Finally, a co-training framework is used to train two classifiers to relabel the integrated instances. The performance on 34 simulated datasets and 2 real-world datasets demonstrates that our proposed CTNC outperforms all state-of-the-art label noise correction algorithms used for comparison.

Latex agglutination analysis by novel ultrasound scattering techniques

The latex agglutination test is employed to visualize antigen–antibody reactions through the aggregation of antibody-coated particles in the presence of an antigen. In the present study, we developed an ultrasound scattering technique to detect latex agglutination in an optically turbid media. However, the ultrasonic technique had less sensitivity to the dilute particle suspension than the optical techniques because of its wavelength. Therefore, we applied a time-correlation approach to detect small amounts of these aggregates using a sophisticated noise correction algorithm in the frequency domain. The lowest concentration of avidin used to detect aggregations of the biotin-coated particle using the ultrasound scattering technique was found to be 0.625 μg/ml. Furthermore, since the density differences between the particle and liquid were larger for silica suspensions than for polystyrene (PS) suspensions, a larger signal was proposed to be expected from silica suspensions. Nevertheless, it was found that latex agglutinations with the PS particle were more sensitive than those with the silica particles. The dynamic ultrasound scattering analysis along the sedimentation direction also supported the presence of strongly scattered intensity components of the PS aggregates, which is proposed to be due to the resonance scattering from PS spherical particles. Therefore, this technique can be employed to enhance scattering signals from particles for application in the agglutination test using ultrasound.

Investigation and Mitigation of Noise Contributions in a Compact Heterodyne Interferometer

We present a noise estimation and subtraction algorithm capable of increasing the sensitivity of heterodyne laser interferometers by one order of magnitude. The heterodyne interferometer is specially designed for dynamic measurements of a test mass in the application of sub-Hz inertial sensing. A noise floor of / at 100 is achieved after applying our noise subtraction algorithm to a benchtop prototype interferometer that showed a noise level of / at 100 when tested in vacuum at levels of Torr. Based on the previous results, we investigated noise estimation and subtraction techniques of non-linear optical pathlength noise, laser frequency noise, and temperature fluctuations in heterodyne laser interferometers. For each noise source, we identified its contribution and removed it from the measurement by linear fitting or a spectral analysis algorithm. The noise correction algorithm we present in this article can be generally applied to heterodyne laser interferometers.

A Color Image Segmentation Method Based on Region Salient Color and Fuzzy C-Means Algorithm

This paper proposes a color image segmentation method based on region salient color and the fuzzy C-means (FCM) algorithm. The method first uses the convex hull theory based on Harris corner detection to detect the object of the image. Thus, the object and the background can be separated. Then, the quantized color histogram can be studied in the HSV color space. By calculating the number of the peak values of both the object and the background histograms, the quantity of the regional salient colors can be obtained. The quantity is the number of the clustering centroids of FCM algorithm. Finally, the FCM algorithm and the noise correction algorithm can be used in the object and the background, respectively. The obtained segmented image consists of the object and the background segmentation. It proves that the method in this paper is an effective segmentation method based on the experiments made by use of Berkeley segmentation dataset. According to the experimental results, it can be concluded that the proposed algorithm has the highest segmentation accuracy and the shortest computing time among the algorithms mentioned in this paper. The algorithm can achieve high-quality, stable and accurate color image segmentation results.

基于多尺度分析和加权最小二乘法的非制冷红外条纹噪声校正算法

基于多尺度分析和加权最小二乘法的非制冷红外条纹噪声校正算法

The Design of Ocean Turbulence Measurement with a Free Fall Vertical Profiler

The newly designed instrument Free Fall Vertical Profiler (FFVP) developed by Ocean University of China (OUC) had been deployed in the Western Pacific in March 08, 2017 and succeed to collect turbulence signals about 350-m-deep water. According to the requirements of turbulence measurement, the mechanical design was developed for turbulence platform to achieve stability and good flow tracking. By analysing the Heading, Pitch and Roll, the results suggested that the platform satisfies the requirements of stability. The power spectrum of the cleaned shear signals using the noise correction algorithm match well with the theoretical Nasmyth spectrum and the rate of turbulence dissipation are approximately 10-8 W/kg. In general, the FFVP was rationally designed and provided a good measurement platform for turbulence observation.

Noise correction algorithm for nonlinear turbulent shear velocity time series based on energy spectrum of singular values

Noise correction algorithm for nonlinear turbulent shear velocity time series based on energy spectrum of singular values

The impact of physiologic noise correction applied to functional MRI of pain at 1.5 and 3.0 T

This study quantified the impact of the well-known physiologic noise correction algorithm RETROICOR applied to a pain functional magnetic resonance imaging (FMRI) experiment at two field strengths: 1.5 and 3.0 T. In the 1.5-T acquisition, there was an 8.2% decrease in time course variance ( σ) and a 227% improvement in average model fit (increase in mean R 2 a). In the 3.0-T acquisition, significantly greater improvements were seen: a 10.4% decrease in σ and a 240% increase in mean R 2 a. End-tidal carbon dioxide data were also collected during scanning and used to account for low-frequency changes in cerebral blood flow; however, the impact of this correction was trivial compared to applying RETROICOR. Comparison between two implementations of RETROICOR demonstrated that oversampled physiologic data can be applied by either downsampling or modification of the timing in the RETROICOR algorithm, with equivalent results. Furthermore, there was no significant effect from manually aligning the physiologic data with corresponding image slices from an interleaved acquisition, indicating that RETROICOR accounts for timing differences between physiologic changes and MR signal changes. These findings suggest that RETROICOR correction, as it is commonly implemented, should be included as part of the data analysis for pain FMRI studies performed at 1.5 and 3.0 T.

Effects of model-based physiological noise correction on default mode network anti-correlations and correlations

Previous studies have reported that the spontaneous, resting-state time course of the default-mode network is negatively correlated with that of the “task-positive network”, a collection of regions commonly recruited in demanding cognitive tasks. However, all studies of negative correlations between the default-mode and task-positive networks have employed some form of normalization or regression of the whole-brain average signal (“global signal”); these processing steps alter the time series of voxels in an uninterpretable manner as well as introduce spurious negative correlations. Thus, the extent of negative correlations with the default mode network without global signal removal has not been well characterized, and it is has recently been hypothesized that the apparent negative correlations in many of the task-positive regions could be artifactually induced by global signal pre-processing. The present study aimed to examine negative and positive correlations with the default-mode network when model-based corrections for respiratory and cardiac noise are applied in lieu of global signal removal. Physiological noise correction consisted of (1) removal of time-locked cardiac and respiratory artifacts using RETROICOR (Glover, G.H., Li, T.Q., Ress, D., 2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162–167), and (2) removal of low-frequency respiratory and heart rate variations by convolving these waveforms with pre-determined transfer functions (Birn et al., 2008; Chang et al., 2009) and projecting the resulting two signals out of the data. It is demonstrated that negative correlations between the default-mode network and regions of the task-positive network are present in the majority of individual subjects both with and without physiological noise correction. Physiological noise correction increased the spatial extent and magnitude of negative correlations, yielding negative correlations within task-positive regions at the group-level (p<0.05, uncorrected; no regions at the group level were significant at FDR=0.05). Furthermore, physiological noise correction caused region-specific decreases in positive correlations within the default-mode network, reducing apparent false positives. It was observed that the low-frequency respiratory volume and cardiac rate regressors used within the physiological noise correction algorithm displayed significant (but not total) shared variance with the global signal, and constitute a model-based alternative to correcting for non-neural global noise.

Laser frequency stabilisation for LISA: Experimental progress

In all space-borne laser interferometric gravitational wave detectors, inevitable differences in the lengths of the interferometer arms will potentially allow laser frequency noise to give rise to apparent gravitational wave signals. Active frequency stabilisation to a reference optical cavity and use of a frequency noise correction algorithm are both required to allow the target gravitational wave sensitivity to be achieved. Here we report on an experiment to frequency stabilise a laser over timescales of several hours, and to measure the excess noise using an independent analysing cavity. The residual frequency noise, at the spectral frequencies of interest to LISA, is shown to be significantly above the desired specification. However, in this relatively simple experiment it appears that the residual frequency noise can largely be accounted for by thermal drifts in the frequency discriminator cavity pathlength. Thermal stabilisation of the reference cavity can be expected to yeild a major improvement in absolute laser stability.