Correlation-based Method Research Articles

Correlation-based ultrasound imaging of strong reflectors with phase coherence filtering

Two main metrics are usually employed to assess the quality of medical ultrasound (US) images, namely the contrast and the spatial resolution. A number of imaging algorithms have been proposed to improve one of those metrics, often at the expense of the other one. This paper presents the application of a correlation-based ultrasound imaging method, called Excitelet, to medical US imaging applications and the inclusion of a new Phase Coherence (PC) metric within its formalism. The main idea behind this algorithm, originally developed and validated for Non-Destructive Testing (NDT) applications, is to correlate a reference signal database with the measured signals acquired from a transducer array. In this paper, it is shown that improved lateral resolutions and a reduction of imaging artifacts are obtained over the Synthetic Aperture Focusing Technique (SAFT) when using Excitelet in conjunction with a PC filter. This novel method shows potential for the imaging of specular reflectors, such as invasive surgical tools. Numerical and experimental results presented in this paper demonstrate the benefit, in terms of contrast and resolution, of using the Excitelet method combined with PC for the imaging of strong reflectors.

Correlation-Based Method for Tracing Multi-dimensional Time Series Data Anomalies

Correlation-Based Method for Tracing Multi-dimensional Time Series Data Anomalies

Single-shot imaging through scattering medium with a deterministic phase-retrieval algorithm

Imaging through scattering layers has a wide range of applications. However, even a thin layer can scatter the light into a random speckle pattern thus difficult to image. Speckle correlation-based methods enable noninvasive imaging of objects hidden behind such layers whereas, still suffer from inaccurate solutions and losing the orientation information of the object. In this paper, we proposed a novel deterministic phase-retrieval algorithm to fully reconstruct the object from its speckle pattern and experimentally demonstrated that the new method can retrieve a high-quality image from a single-shot speckle pattern. Compared to other phase retrieval techniques, it maintains the orientation information correctly in a single reconstruction and is more robust in noisy conditions.

DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data

Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even resulting in poorer clustering accuracy than without feature selection. Moreover, existing methods ignore information contained in gene-gene correlations. Here, we introduce DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUBStepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. Additionally, DUBStepR was the only method to robustly deconvolve T and NK heterogeneity by identifying disease-associated common and rare cell types and subtypes in PBMCs from rheumatoid arthritis patients. DUBStepR is scalable to over a million cells, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.

Feature selection for unsupervised machine learning of accelerometer data physical activity clusters - A systematic review.

Identifying clusters of physical activity (PA) from accelerometer data is important to identify levels of sedentary behaviour and physical activity associated with risks of serious health conditions and time spent engaging in healthy PA. Unsupervised machine learning models can capture PA in everyday free-living activity without the need for labelled data. However, there is scant research addressing the selection of features from accelerometer data. The aim of this systematic review is to summarise feature selection techniques applied in studies concerned with unsupervised machine learning of accelerometer-based device obtained physical activity, and to identify commonly used features identified through these techniques. Feature selection methods can reduce the complexity and computational burden of these models by removing less important features and assist in understanding the relative importance of feature sets and individual features in clustering. We conducted a systematic search of Pubmed, Medline, Google Scholar, Scopus, Arxiv and Web of Science databases to identify studies published before January 2021 which used feature selection methods to derive PA clusters using unsupervised machine learning models. A total of 13 studies were eligible for inclusion within the review. The most popular feature selection techniques were Principal Component Analysis (PCA) and correlation-based methods, with k-means frequently used in clustering accelerometer data. Cluster quality evaluation methods were diverse, including both external (e.g. cluster purity) or internal evaluation measures (silhouette score most frequently). Only four of the 13 studies had more than 25 participants and only four studies included two or more datasets. There is a need to assess multiple feature selection methods upon large cohort data consisting of multiple (3 or more) PA datasets. The cut-off criteria e.g. number of components, pairwise correlation value, explained variance ratio for PCA, etc. should be expressly stated along with any hyperparameters used in clustering.

Prediction of Communication Effectiveness During Media Skills Training Using Commercial Automatic Non-verbal Recognition Systems.

It is well recognised that social signals play an important role in communication effectiveness. Observation of videos to understand non-verbal behaviour is time-consuming and limits the potential to incorporate detailed and accurate feedback of this behaviour in practical applications such as communication skills training or performance evaluation. The aim of the current research is twofold: (1) to investigate whether off-the-shelf emotion recognition technology can detect social signals in media interviews and (2) to identify which combinations of social signals are most promising for evaluating trainees’ performance in a media interview. To investigate this, non-verbal signals were automatically recognised from practice on-camera media interviews conducted within a media training setting with a sample size of 34. Automated non-verbal signal detection consists of multimodal features including facial expression, hand gestures, vocal behaviour and ‘honest’ signals. The on-camera interviews were categorised into effective and poor communication exemplars based on communication skills ratings provided by trainers and neutral observers which served as a ground truth. A correlation-based feature selection method was used to select signals associated with performance. To assess the accuracy of the selected features, a number of machine learning classification techniques were used. Naive Bayes analysis produced the best results with an F-measure of 0.76 and prediction accuracy of 78%. Results revealed that a combination of body movements, hand movements and facial expression are relevant for establishing communication effectiveness in the context of media interviews. The results of the current study have implications for the automatic evaluation of media interviews with a number of potential application areas including enhancing communication training including current media skills training.

Video Image Correlation-Based Method Used for the Study of the Torsional Vibrations of an Adder Gearbox

In this paper, a study of the vibrations that appear in the transmission shafts of an adder gearbox used for a heavy truck is made. The truck has two engines on only one chassis and the power offered by these engines is summated and transmitted to the truck or the working machine. This type of transmission is used for oil production installation for the army. During the transportation of the installation to the workplace, only one engine is running, after mounting installation, both engines are running. This paper studies the vibrations of the adder gearbox, a complex construction, subjected to multiple operating requirements. In this regard, the authors first performed accuracy (accuracy) tests of the VIC 3D system on an original experimental mini-stand. Measurements performed on a mini disc demonstrated the validity and accuracy of the method, even if the cameras used were not high resolution. The authors applied the same principle in the case of the adder box from the truck, obtaining useful results for those in the field. The experimental method uses the facilities of a contactless optical measurement method (VIC-3D), which provides a high-accuracy quantitative linear and angular vibration analysis. The VIC measurement method offers, based on a frontal viewing of the disk during the resonance phenomenon and by simple calculus on the monitored linear displacements, the corresponding angular amplitude.

Copy number variation profile-based genomic typing of premenstrual dysphoric disorder in Chinese

Premenstrual dysphoric disorder (PMDD) affects nearly 5% of women of reproductive age. Symptomatic heterogeneity, together with largely unknown genetics, has greatly hindered its effective treatment. In the present study, analysis of genomic sequencing-based copy number variations (CNVs) called from 100 kb white blood cell DNA sequence windows by means of semisupervized clustering led to the segregation of patient genomes into the D and V groups, which correlated with the depression and invasion clinical types, respectively, with 89.0% consistency. Application of diagnostic CNV features selected using the correlation-based machine learning method enabled the classification of the CNVs obtained into the D group, V group, total patient group, and control group with an average accuracy of 83.0%. The power of the diagnostic CNV features was 0.98 on average, suggesting that these CNV features could be used for the molecular diagnosis of the major clinical types of PMDD. This demonstrated concordance between the CNV profiles and clinical types of PMDD supported the validity of symptom-based diagnosis of PMDD for differentiating between its two major clinical types, as well as the predominantly genetic nature of PMDD with a host of overlaps between multiple susceptibility genes/pathways and the diagnostic CNV features as indicators of involvement in PMDD etiology.

A Correlation-Based Electromagnetic Time Reversal Technique to Locate Indoor Transient Radiation Sources

To overcome the multipath interference in locating transient electromagnetic (EM) radiation sources in an indoor environment, we propose a criterion that calculates the correlation between back-propagated signals from observation points, to be used in EM time reversal (EMTR) algorithms. The method introduced in this article has three main advantages with respect to classical methods that use full-wave simulations and other criteria, such as maximum field strength. First, compared with full-wave techniques in the back-propagation phase, the proposed correlation-based method utilizes approximated transfer functions from the ray-tracing technique, which can improve the computation efficiency. Second, an inverted-loss model is used for the back-propagation, which could reduce the localization error caused by multipath effects due to signal attenuation and time delay. Third, the proposed correlation criterion has weak correlation with the source characteristics, which makes it applicable to the localization in indoor reflective environments with only two observation points. Several numerical simulations are carried out to assess the performance of the proposed method. The results indicate that the proposed correlation-based EMTR technique is able to locate radiation sources accurately and efficiently in indoor reflective environments.

Measurement of kinetic constant of protein binding using microfluidics and particle diffusometry

Protein-protein interaction is widely used in biological science and biomedical engineering research Moreira et al. (2007). Accurate measurement of binding kinetics is essential for understanding protein-protein interactions. Current gold standard assays, such as surface plasmon resonance (SPR), bio-layer interferometry (BLI) and quartz crystal microbalance (QCM), can generate precise and real-time kinetics data. However, these methods usually require expensive instruments housed in core facilities and high-level expertise, which is not convenient for most labs to implement. We developed a new method based on microfluidics and particle diffusometry (PD) to measure protein binding kinetics, which only needs very general lab equipment including a fluorescent microscope to take photos, a syringe pump to inject solutions, capillary tubing, a simple chip made on a glass slide and a computer to process images. To measure the binding rate of a protein pair, both proteins are conjugated with beads of different sizes, respectively. The bead solutions are diluted to appropriate concentrations and injected into a Y-junction channel by a syringe pump. In the microchannel, the two kinds of beads will meet at the interface and bind due to surface protein interactions. Therefore, the size of the beads in solution gradually increases and the Brownian motion will be less and less drastic until the reaction is saturated. Taking photos recording this dynamic process, the apparent change in size of the beads can be measured by particle diffusometry and used for extracting binding kinetics. Particle diffusometry is a correlation-based and non-intrusive optical detection method to analyze properties of fluid and particle such as viscosity, temperature and particle diameter Chamarthy et al. (2009); Clayton et al. (2016, 2017b,a); Hohreiter et al. (2002). It was initially developed to determine errors caused by thermal noise in particle image velocimetry (PIV). PD always analyzes image pairs. A single image of a particle laden flow is first used to do auto-correlation, correlating with itself, which will generate a high and sharp peak. Then it is cross-correlated with a successive image with a known time interval ∆t. Because particles slightly deviate away from the initial positions after time ∆t, due to Brownian motion, the correlation peak is lower and broader than that of auto-correlation. Auto- and cross-correlation peaks are fit into Gaussian function to find peak widths, by which the particle size can be computed as long as the viscosity and temperature do not change. Processing the image sets of the protein-conjugated particles’ binding process, we acquire the relation of particle size and time, which can be used to solve protein binding kinetics. An equation of protein interaction and particle volume is derived to work out association rate from particle diameter data acquired by PD. In this study, we measured streptavidin-biotin binding rate. Streptavidin is conjugated with 20nm beads and biotin is immobilized onto 200nm beads. Proteins on the two kinds of beads bind rapidly after mixing in the main channel. It is necessary to choose a narrow area at the interface of particle streams that diffusion does not limit the reaction. Since the liquid is flowing, there is both Brownian motion and advection in particle images. We used EDPIV, a software package developed by Prof. Steven Wereley’s lab, to measure advection velocity. When doing PD analysis, images are shifted following the PIV data to catch up with the flow. The photos are taken at the center layer in the middle of the channel, where there is no velocity gradient. Measuring a series of photo sets along the main channel at several points with known distances to each other, the relation of complex bead size and time can be acquired. Solving for the association constant, the measured value is 1.74 × 107M−1s−1, which is close to that of current gold standard assays. This novel PD-based method is accurate and requires only general lab facilities, making protein binding kinetics measurements accessible and practical for biological and biomedical labs.

Two-dimensional correlation analysis for x-ray photoelectron spectroscopy

X-ray photoelectron spectroscopy (XPS) measures the binding energy of core-level electrons, which are well-localised to specific atomic sites in a molecular system, providing valuable information on the local chemical environment. The technique relies on measuring the photoelectron spectrum upon x-ray photoionisation, and the resolution is often limited by the bandwidth of the ionising x-ray pulse. This is particularly problematic for time-resolved XPS, where the desired time resolution enforces a fundamental lower limit on the bandwidth of the x-ray source. In this work, we report a novel correlation analysis which exploits the correlation between the x-ray and photoelectron spectra to improve the resolution of XPS measurements. We show that with this correlation-based spectral-domain ghost imaging method we can achieve sub-bandwidth resolution in XPS measurements. This analysis method enables XPS for sources with large bandwidth or spectral jitter, previously considered unfeasible for XPS measurements.

Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism

ObjectiveTechnological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology; yet, tools to fully tap their potential remain scarce. MethodsWe present here a fully unsupervised and versatile correlation-based method – termed Correlation guided Network Integration (CoNI) – to integrate multi-omics data into a hypergraph structure that allows for the identification of effective modulators of metabolism. Our approach yields single transcripts of potential relevance that map to specific, densely connected, metabolic subgraphs or pathways. ResultsBy applying our method on transcriptomics and metabolomics data from murine livers under standard Chow or high-fat diet, we identified eleven genes with potential regulatory effects on hepatic metabolism. Five candidates, including the hepatokine INHBE, were validated in human liver biopsies to correlate with diabetes-related traits such as overweight, hepatic fat content, and insulin resistance (HOMA-IR). ConclusionOur method's successful application to an independent omics dataset confirmed that the novel CoNI framework is a transferable, entirely data-driven, flexible, and versatile tool for multiple omics data integration and interpretation.

P-P and Dynamic Time Warped P-SV Wave AVA Joint-Inversion With ℓ1–2 Regularization

S-wave velocity and mass density, which are two essential parameters in distinguishing lithology and hydrocarbon detection, are very difficult to retrieve even when long-offset gather data are used. P-P and P-SV waves joint inversion has been verified as an effective tool to accurately extract such fluid related parameters. However, registering the travel-time of P-P and P-SV waves on the identical coordinate is a significant but knotty problem for the joint inversion. Therefore, an improved strategy of prestack seismic joint inversion is proposed for accurately transforming the recorded data to elastic-parameter-based interpretive information. First, to overcome the weaknesses of artificial strenching and the local cross correlation-based method, a nonstrenching and globally optimal registration algorithm, i.e., dynamic time warping (DTW), is exploited to match the P-P and P-SV waves. Then, a new method has been developed for the joint inversion method by combining the logarithmic absolute-criterion-based misfit function with <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> norm-based penalty, which can significantly improve the vertical resolution and stability of inversion results. The numerical examples demonstrate that the DTW algorithm can obtain better registered results than the conventional method. Moreover, the proposed joint inversion method performs better than the conventional prestack inversion in both resolution and accuracy, especially for the S-wave velocity.

Real-time Diagnosis Of Physical Failures Using Causation-based AI

Every system in existence is prone to failure and analysis and early detection of said failures (for Predictive Maintenance) is becoming a crucial aspect of modern systems design. Most catastrophic issues start from the smallest parts of a component within the system (physical failures) and minute changes to certain sensor readings from this level may indicate that an incipient failure will occur. Much of this information and system knowledge is often captured during typical RAM activities but is not repurposed for diagnostics. Recent endeavours have been made to utilize AI-based number crunchers to analyse such measurements to notify that an anomaly is observed, however the nature of such correlation-based methods have a limit both on the information on such events and the reliability of the analysis due to spurious correlation. In this paper, we present a novel strategy to 'catch' failures before they happen using a combination of both correlation and causation, i.e. a causation-based AI and demonstrate its advantage over ‘classic’ correlation based methodology.

A correlation-based inversion method for aerosol property (CIMAP) retrieval from AERONET measurements

We developed a principal-component based inversion approach – CIMAP, to retrieve aerosol properties from AERONET measurements of aerosol optical depths (AODs) and sky radiances. Capitalizing on the correlation in aerosol properties, CIMAP reduces the aerosol retrieval parameter space and saves retrieval time by more than 80% from adopting 7-8 principal components in the retrieval process, compared to retrievals in regular parameter space. As algorithm test, CIMAP is implemented to retrieve 60 sets of AERONET observations in several selected areas in southern California, southern Africa, and north China around Beijing, and the results are compared to the AERONET measurement and operational retrieval products. The mean absolute differences are found to be 0.004, 0.019, 0.032, 0.039, 0.003 and 0.044µm for AOD, single scattering albedo, Ångström exponent, real and imaginary parts of refractive index and effective radius of aerosols, respectively. These differences are within or close to AERONET measurement or retrieval uncertainties.

Shall genomic correlation structure be considered in copy number variants detection?

Copy number variation has been identified as a major source of genomic variation associated with disease susceptibility. With the advent of whole-exome sequencing (WES) technology, massive WES data have been generated, allowing for the identification of copy number variants (CNVs) in the protein-coding regions with direct functional interpretation. We have previously shown evidence of the genomic correlation structure in array data and developed a novel chromosomal breakpoint detection algorithm, LDcnv, which showed significantly improved detection power through integrating the correlation structure in a systematic modeling manner. However, it remains unexplored whether the genomic correlation exists in WES data and how such correlation structure integration can improve the CNV detection accuracy. In this study, we first explored the correlation structure of the WES data using the 1000 Genomes Project data. Both real raw read depth and median-normalized data showed strong evidence of the correlation structure. Motivated by this fact, we proposed a correlation-based method, CORRseq, as a novel release of the LDcnv algorithm in profiling WES data. The performance of CORRseq was evaluated in extensive simulation studies and real data analysis from the 1000 Genomes Project. CORRseq outperformed the existing methods in detecting medium and large CNVs. In conclusion, it would be more advantageous to model genomic correlation structure in detecting relatively long CNVs. This study provides great insights for methodology development of CNV detection with NGS data.

Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring

Wireless sensors are the key components of structural health monitoring systems. During the signal transmission, sensor failure is inevitable, among which, data loss is the most common type. Missing data problem poses a huge challenge to the consequent damage detection and condition assessment, and therefore, great importance should be attached. Conventional missing data imputation basically adopts the correlation-based method, especially for strain monitoring data. However, such methods often require delicate model selection, and the correlations for vehicle-induced strains are much harder to be captured compared with temperature-induced strains. In this article, a novel data-driven generative adversarial network (GAN) for imputing missing strain response is proposed. As opposed to traditional ways where correlations for inter-strains are explicitly modeled, the proposed method directly imputes the missing data considering the spatial–temporal relationships with other strain sensors based on the remaining observed data. Furthermore, the intact and complete dataset is not even necessary during the training process, which shows another great superiority over the model-based imputation method. The proposed method is implemented and verified on a real concrete bridge. In order to demonstrate the applicability and robustness of the GAN, imputation for single and multiple sensors is studied. Results show the proposed method provides an excellent performance of imputation accuracy and efficiency.

Investigation of Aerosol Inhomogeneities Parameters in Planetary Boundary Layer in UV-A Spectral Region

For today necessity of atmosphere wind remote sensing for wide pool of applications require development of new measurement methods and improvement of already existing.Currently doppler methods for wind speed measuring have largest sensing range. Correlation-based methods are provide lower sensing range for wind speed measurement. However, doppler-based lidar are expensive in opposite to simply designed correlation lidars that additionally allows to measure wind profile along sensing direction.Development of wind correlation lidars required knowledge of atmosphere aerosol inhomogeneities parameters.The most experimental research till now in this area are related to visible spectral range.One of the perspective eye-safety spectral range for wind lidar is UV spectral region. There not so much experimental research works of aerosol inhomogeneities parameters in UV.This paper is related to field experiments of aerosol inhomogeneities parameters in planetary boundary layer on 0.355 um operating wavelength.Block diagram and parameters of lidar with 0.355 operating wavelength is shown. Process of experiments is described. Typical atmosphere backscattering echo-signal for single laser pulse is demonstrated.Procedures if lidar signals processing are described, example of two-dimensional field of relative fluctuations of volume backscattering coefficient for different atmosphere conditions is shown.Results of measurement data processing shown that in planetary boundary layer average contrast of aerosol inhomogeneities in most cases has values in range 0,4% – 10 % (in case of snow up to 25%), and size in range 1,5 – 20 m. Dependency of observation number of aerosol inhomogeneities by range z from lidar to backscattering volume is descending with 1/z manner that the same for dependency for signal-noise ratio SNR(z).

Quantum-Optical Spectrometry in Relativistic Laser–Plasma Interactions Using the High-Harmonic Generation Process: A Proposal

Quantum-optical spectrometry is a recently developed shot-to-shot photon correlation-based method, namely using a quantum spectrometer (QS), that has been used to reveal the quantum optical nature of intense laser–matter interactions and connect the research domains of quantum optics (QO) and strong laser-field physics (SLFP). The method provides the probability of absorbing photons from a driving laser field towards the generation of a strong laser–field interaction product, such as high-order harmonics. In this case, the harmonic spectrum is reflected in the photon number distribution of the infrared (IR) driving field after its interaction with the high harmonic generation medium. The method was implemented in non-relativistic interactions using high harmonics produced by the interaction of strong laser pulses with atoms and semiconductors. Very recently, it was used for the generation of non-classical light states in intense laser–atom interaction, building the basis for studies of quantum electrodynamics in strong laser-field physics and the development of a new class of non-classical light sources for applications in quantum technology. Here, after a brief introduction of the QS method, we will discuss how the QS can be applied in relativistic laser–plasma interactions and become the driving factor for initiating investigations on relativistic quantum electrodynamics.

High-speed train fault detection with unsupervised causality-based feature extraction methods

With the development of smart sensors, large amount of operating data collected from a complex system as a high-speed train providing opportunities in efficient and effective fault detection and diagnosis (FDD). The data brings also challenges in the FDD modelling process, since the various signals may be redundant, useless and noisy for the FDD modelling of a specific sub-system. The data-driven methods suffer also from the curse of dimensionality. Feature dimension reduction can reduce the dimension of the monitoring dataset and eliminate the useless information. Different from the classical methods based on the correlation among variables, recent studies have shown that causality-based methods can make the FDD model more explanatory and robust. From the adjacency matrix of the causal network diagram, three unsupervised causality-based feature extraction methods for FDD in the braking system of a high-speed train are proposed in this paper. By constructing the causal network diagram among the raw monitoring feature variables through the causal discovery algorithm, the proposed methods extract informative features based on the causal adjacency matrix or the full causal adjacency matrix proposed in this work. These methods are adopted for fault detection with real dataset collected from the braking system in a high-speed train to verify their effectiveness. The experimental results show that the proposed causality-based feature extraction methods are effective and have certain advantages in comparison with the classical correlation-based methods. Especially, the feature extraction method based on the correlation matrix constructed from full causal adjacency matrix achieves better and stable results than the benchmark methods in the experiment.