Fast matrix completion in epigenetic methylation studies with informative covariates.

DNA methylation is an important epigenetic mark that modulates gene expression through the inhibition of transcriptional proteins binding to DNA. As in many other omics experiments, the issue of missing values is an important one, and appropriate imputation techniques are important in avoiding an unnecessary sample size reduction as well as to optimally leverage the information collected. We consider the case where relatively few samples are processed via an expensive high-density whole genome bisulfite sequencing (WGBS) strategy and a larger number of samples is processed using more affordable low-density, array-based technologies. In such cases, one can impute the low-coverage (array-based) methylation data using the high-density information provided by the WGBS samples. In this paper, we propose an efficient Linear Model of Coregionalisation with informative Covariates (LMCC) to predict missing values based on observed values and covariates. Our model assumes that at each site, the methylation vector of all samples is linked to the set of fixed factors (covariates) and a set of latent factors. Furthermore, we exploit the functional nature of the data and the spatial correlation across sites by assuming some Gaussian processes on the fixed and latent coefficient vectors, respectively. Our simulations show that the use of covariates can significantly improve the accuracy of imputed values, especially in cases where missing data contain some relevant information about the explanatory variable. We also showed that our proposed model is particularly efficient when the number of columns is much greater than the number of rows-which is usually the case in methylation data analysis. Finally, we apply and compare our proposed method with alternative approaches on two real methylation datasets, showing how covariates such as cell type, tissue type or age can enhance the accuracy of imputed values.

Fast computation of the eigensystem of genomic similarity matrices.

The computation of a similarity measure for genomic data is a standard tool in computational genetics. The principal components of such matrices are routinely used to correct for biases due to confounding by population stratification, for instance in linear regressions. However, the calculation of both a similarity matrix and its singular value decomposition (SVD) are computationally intensive. The contribution of this article is threefold. First, we demonstrate that the calculation of three matrices (called the covariance matrix, the weighted Jaccard matrix, and the genomic relationship matrix) can be reformulated in a unified way which allows for the application of a randomized SVD algorithm, which is faster than the traditional computation. The fast SVD algorithm we present is adapted from an existing randomized SVD algorithm and ensures that all computations are carried out in sparse matrix algebra. The algorithm only assumes that row-wise and column-wise subtraction and multiplication of a vector with a sparse matrix is available, an operation that is efficiently implemented in common sparse matrix packages. An exception is the so-called Jaccard matrix, which does not have a structure applicable for the fast SVD algorithm. Second, an approximate Jaccard matrix is introduced to which the fast SVD computation is applicable. Third, we establish guaranteed theoretical bounds on the accuracy (in [Formula: see text] norm and angle) between the principal components of the Jaccard matrix and the ones of our proposed approximation, thus putting the proposed Jaccard approximation on a solid mathematical foundation, and derive the theoretical runtime of our algorithm. We illustrate that the approximation error is low in practice and empirically verify the theoretical runtime scalings on both simulated data and data of the 1000 Genome Project.

Unfolding coil localized errors from an imperfect slice profile using a structured autocalibration matrix: An application to reduce outflow effects in cine bSSFP imaging.

PURPOSE: Balanced steady-state free precession (bSSFP) imaging is susceptible to outflow effects where excited spins leaving the slice as part of the blood stream are misprojected back onto the imaging plane. Previous work proposed using slice-encoding steps to localize these outflow effects from corrupting the target slice, at the expense of prolonged scan time. This present study extends this idea by proposing a means of significantly reducing most of the outflowing signal from the imaged slice using a coil localization method that acquires a slice-encoded calibration scan in addition to the 2D data, without being nearly as time-demanding as our previous method. This coil localization method is titled UNfolding Coil Localized Errors from an imperfect slice profile using a Structured Autocalibration Matrix (UNCLE SAM). METHODS: Retrospective and prospective evaluations were carried out. Both featured a 2D acquisition and a separate slice-encoded calibration of the center in-plane k $$ k $$ -space lines across all desired slice-encoding steps. RESULTS: Retrospective results featured a slice-by-slice comparison of the slice-encoded images with UNCLE SAM. UNCLE SAM's subtraction from the slice-encoded image was compared with a subtraction from the flow-corrupted 2D image, to demonstrate UNCLE SAM's capability to unfold outflowing spins. UNCLE SAM's comparison with slice encoding showed that UNCLE SAM was able to unfold up to 74% of what slice encoding achieved. Prospective results showed significant reduction in outflow effects with only a marginal increase in scan time from the 2D acquisition. CONCLUSIONS: We developed a method that effectively unfolds most outflowing spins from corrupting the target slice and does not require the explicit use of slice-encoding gradients. This development offers a method to reduce most outflow effects from the target slice within a clinically feasible scan duration compared with the fully sampled slice-encoding technique.

A Temporal Instability Measure for fMRI Quality Assurance.

BACKGROUND: There exist several fMRI quality assurance measures to assess scanner stability. Because they have practical and/or theoretical limitations, a different and more practical measure for instability would be desirable. PURPOSE: To develop and test a sensitive, reliable and widely applicable temporal instability measure (TIM) for fMRI quality assurance. STUDY TYPE: Technical development. PHANTOM: Spherical gel phantom. POPULATION: A total of 120 datasets from a local Philips scanner with two different receive-only head coils (32ch and 8ch, 60 datasets per coil) were collected as well as 29 additional datasets with three different receive-only head coils (20ch, 32ch, and 64ch) from two additional sites with GE (seven runs with 32ch) and Siemens scanners (seven runs with 32ch and Multiband imaging, five runs with 20ch, 32ch, and 64ch) were borrowed. FIELD STRENGTH/SEQUENCE: 2D Echo-planar-imaging (EPI). ASSESSMENT: A new TIM was proposed that is based on the eigenratio of the correlation coefficient matrix, where each entry of the matrix is a correlation coefficient between two time-points of the time-series. STATISTICAL TESTS: Nonparametric bootstrap resampling was used twice to estimate confidence intervals (CI) of the TIM values and to assess the improved sensitivity of this measure. Differences in coil performance were assessed via a nonparametric bootstrap two-sample t-test. P-values <0.05 were considered significant. RESULTS: The TIM values ranged between 60 parts-per-million and 10,780 parts-per-million across all 149 experiments. The mean CI was 2.96% and 2.16% for the 120 and 29 fMRI datasets, respectively (the repeated bootstrap analysis gave 2.9% and 2.19%, respectively). The 32ch coils of the local Philips data provided more stable measurements than the 8ch coil (observed two-sample t-values = 26.36, -0.2 and -6.2 for TIM, tSNR, and RDC, respectively. PtSNR = 0.58). DATA CONCLUSION: The proposed TIM is particularly useful for multichannel coils with spatially nonuniform receive sensitivity and overcomes several limitations of other measures. As such, it provides a reliable test for ascertaining scanner stability for fMRI experiments. EVIDENCE LEVEL: 5. TECHNICAL EFFICACY: Stage 1.

How Does the Powder Mixture of Ibuprofen and Caffeine Attenuate the Solubility of Ibuprofen? Comparative Study for the Xanthine Derivatives to Recognize Their Intermolecular Interactions Using Fourier-Transform Infrared (FTIR) Spectra, Differential Scanning Calorimetry (DSC), and X-ray Powder Diffractometry (XRPD).

Molecular interactions between active pharmaceutical ingredients (APIs) and xanthine (XAT) derivatives were analyzed using singular value decomposition (SVD). XAT derivatives were mixed with equimolar amounts of ibuprofen (IBP) and diclofenac (DCF), and their dissolution behaviors were measured using high-performance liquid chromatography. The solubility of IBP decreased in mixtures with caffeine (CFN) and theophylline (TPH), whereas that of DCF increased in mixtures with CFN and TPH. No significant differences were observed between the mixtures of theobromine (TBR) or XAT with IBP and DCF. Mixtures with various molar ratios were analyzed using differential scanning calorimetry, X-ray powder diffraction, and Fourier-transform infrared spectroscopy to further explore these interactions. The results were subjected to SVD. This analysis provides valuable insights into the differences in interaction strength and predicted interaction sites between XAT derivatives and APIs based on the combinations that form mixtures. The results also showed the impact of the XAT derivatives on the dissolution behavior of IBP and DCF. Although IBP and DCF were found to form intermolecular interactions with CFN and TPH, these effects resulted in a reduction of the solubility of IBP and an increase in the solubility of DCF. The current approach has the potential to predict various interactions that may occur in different combinations, thereby contributing to a better understanding of the impact of health supplements on pharmaceuticals.

Three-tiered EGFr domain risk stratification for individualized NOTCH3-small vessel disease prediction.

Cysteine-altering missense variants (NOTCH3cys) in one of the 34 epidermal growth-factor-like repeat (EGFr) domains of the NOTCH3 protein are the cause of NOTCH3-associated small vessel disease (NOTCH3-SVD). NOTCH3-SVD is highly variable, ranging from cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) at the severe end of the spectrum to non-penetrance. The strongest known NOTCH3-SVD modifier is NOTCH3cys variant position: NOTCH3cys variants located in EGFr domains 1-6 are associated with a more severe phenotype than NOTCH3cys variants located in EGFr domains 7-34. The objective of this study was to further improve NOTCH3-SVD genotype-based risk prediction by using relative differences in NOTCH3cys variant frequencies between large CADASIL and population cohorts as a starting point. Scientific CADASIL literature, cohorts and population databases were queried for NOTCH3cys variants. For each EGFr domain, the relative difference in NOTCH3cys variant frequency (NVFOR) was calculated using genotypes of 2574 CADASIL patients and 1647 individuals from population databases. Based on NVFOR cut-off values, EGFr domains were classified as either low (LR-EGFr), medium (MR-EGFr) or high risk (HR-EGFr). The clinical relevance of this new three-tiered EGFr risk classification was cross-sectionally validated by comparing SVD imaging markers and clinical outcomes between EGFr risk categories using a genotype-phenotype data set of 434 CADASIL patients and 1003 NOTCH3cys positive community-dwelling individuals. CADASIL patients and community-dwelling individuals harboured 379 unique NOTCH3cys variants. Nine EGFr domains were classified as an HR-EGFr, which included EGFr domains 1-6, but additionally also EGFr domains 8, 11 and 26. Ten EGFr domains were classified as MR-EGFr and 11 as LR-EGFr. In the population genotype-phenotype data set, HR-EGFr individuals had the highest risk of stroke [odds ratio (OR) = 10.81, 95% confidence interval (CI): 5.46-21.37], followed by MR-EGFr individuals (OR = 1.81, 95% CI: 0.84-3.88) and LR-EGFr individuals (OR = 1 [reference]). MR-EGFr individuals had a significantly higher normalized white matter hyperintensity volume (nWMHv; P = 0.005) and peak width of skeletonized mean diffusivity (PSMD; P = 0.035) than LR-EGFr individuals. In the CADASIL genotype-phenotype data set, HR-EGFr domains 8, 11 and 26 patients had a significantly higher risk of stroke (P = 0.002), disability (P = 0.041), nWMHv (P = 1.8 × 10-8), PSMD (P = 2.6 × 10-8) and lacune volume (P = 0.006) than MR-EGFr patients. SVD imaging marker load and clinical outcomes were similar between HR-EGFr 1-6 patients and HR-EGFr 8, 11 and 26 patients. NVFOR was significantly associated with vascular NOTCH3 aggregation load (P = 0.006), but not with NOTCH3 signalling activity (P = 0.88). In conclusion, we identified three clinically distinct NOTCH3-SVD EGFr risk categories based on NFVOR cut-off values, and identified three additional HR-EGFr domains located outside of EGFr domains 1-6. This EGFr risk classification will provide an important key to individualized NOTCH3-SVD disease prediction.

Posterior reversible encephalopathy syndrome and acute ischemic stroke: an underreported association.

INTRODUCTION: Posterior reversible encephalopathy syndrome (PRES) is a rare and complex disorder with variable clinical presentation and a typical magnetic resonance imaging (MRI) pattern of vasogenic edema with typical and atypical locations. It is often triggered by other diseases and drugs and the most prototypical association is with persistently elevated arterial pressure values. Among the potential cerebrovascular complications, intracranial bleeding has been described, but ischemic stroke is uncommonly reported. METHODS: We are presenting a case of a male patient with prolonged and sustained arterial hypertension acutely presenting with lacunar ischemic stroke involving the right corona radiata and composite MRI findings with the association of chronic small vessel disease (SVD) markers, acute symptomatic lacunar stroke, and atypical, central variant, posterior fossa dominant PRES. In the MRI follow-up, the white matter hyperintensities in T2-fluid attenuated inversion recovery (FLAIR sequences) due to PRES. DISCUSSION: The pathophysiology of PRES is not yet fully known, but the association with markedly increased values of arterial pressure is typical. In this context, ischemic stroke has not been considered in the clinical and neuroradiological manifestations of PRES and it has been only occasionally reported in the literature. In this case, the main hypothesis is that sustained hypertension may have triggered both manifestations, PRES, and ischemic stroke and the last one allowed to diagnose the first one. CONCLUSIONS: Atypical variants of PRES are not so rare and it may also occur in typical triggering situations. The association with ischemic stroke is even rarer and it may add some clues to the pathomechanisms of PRES.

Tensor-tensor algebra for optimal representation and compression of multiway data.

With the advent of machine learning and its overarching pervasiveness it is imperative to devise ways to represent large datasets efficiently while distilling intrinsic features necessary for subsequent analysis. The primary workhorse used in data dimensionality reduction and feature extraction has been the matrix singular value decomposition (SVD), which presupposes that data have been arranged in matrix format. A primary goal in this study is to show that high-dimensional datasets are more compressible when treated as tensors (i.e., multiway arrays) and compressed via tensor-SVDs under the tensor-tensor product constructs and its generalizations. We begin by proving Eckart-Young optimality results for families of tensor-SVDs under two different truncation strategies. Since such optimality properties can be proven in both matrix and tensor-based algebras, a fundamental question arises: Does the tensor construct subsume the matrix construct in terms of representation efficiency? The answer is positive, as proven by showing that a tensor-tensor representation of an equal dimensional spanning space can be superior to its matrix counterpart. We then use these optimality results to investigate how the compressed representation provided by the truncated tensor SVD is related both theoretically and empirically to its two closest tensor-based analogs, the truncated high-order SVD and the truncated tensor-train SVD.

Motion Clutter Suppression for Non-Cooperative Target Identification Based on Frequency Correlation Dual-SVD Reconstruction.

Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between birds and UAVs in environments with strong motion clutter is crucial for improving target monitoring performance and ensuring flight safety. To address the impact of strong motion clutter on discriminating between UAVs and birds, we propose a frequency correlation dual-SVD (singular value decomposition) reconstruction method. This method exploits the strong power and spectral correlation characteristics of motion clutter, contrasted with the weak scattering characteristics of bird and UAV targets, to effectively suppress clutter. Unlike traditional clutter suppression methods based on SVD, our method avoids residual clutter or target loss while preserving the micro-motion characteristics of the targets. Based on the distinct micro-motion characteristics of birds and UAVs, we extract two key features: the sum of normalized large eigenvalues of the target's micro-motion component and the energy entropy of the time-frequency spectrum of the radar echoes. Subsequently, the kernel fuzzy c-means algorithm is applied to classify bird and UAV targets. The effectiveness of our proposed method is validated through results using both simulation and experimental data.

Presenilin-1 mutation position influences amyloidosis, small vessel disease, and dementia with disease stage.

INTRODUCTION: Amyloidosis, including cerebral amyloid angiopathy, and markers of small vessel disease (SVD) vary across dominantly inherited Alzheimer's disease (DIAD) presenilin-1 (PSEN1) mutation carriers. We investigated how mutation position relative to codon 200 (pre-/postcodon 200) influences these pathologic features and dementia at different stages. METHODS: Individuals from families with known PSEN1 mutations (n = 393) underwent neuroimaging and clinical assessments. We cross-sectionally evaluated regional Pittsburgh compound B-positron emission tomography uptake, magnetic resonance imaging markers of SVD (diffusion tensor imaging-based white matter injury, white matter hyperintensity volumes, and microhemorrhages), and cognition. RESULTS: Postcodon 200 carriers had lower amyloid burden in all regions but worse markers of SVD and worse Clinical Dementia Rating® scores compared to precodon 200 carriers as a function of estimated years to symptom onset. Markers of SVD partially mediated the mutation position effects on clinical measures. DISCUSSION: We demonstrated the genotypic variability behind spatiotemporal amyloidosis, SVD, and clinical presentation in DIAD, which may inform patient prognosis and clinical trials. HIGHLIGHTS: Mutation position influences Aß burden, SVD, and dementia. PSEN1 pre-200 group had stronger associations between Aß burden and disease stage. PSEN1 post-200 group had stronger associations between SVD markers and disease stage. PSEN1 post-200 group had worse dementia score than pre-200 in late disease stage. Diffusion tensor imaging-based SVD markers mediated mutation position effects on dementia in the late stage.

Singular-Value-Decomposition-Based Matrix Surgery.

This paper is motivated by the need to stabilise the impact of deep learning (DL) training for medical image analysis on the conditioning of convolution filters in relation to model overfitting and robustness. We present a simple strategy to reduce square matrix condition numbers and investigate its effect on the spatial distributions of point clouds of well- and ill-conditioned matrices. For a square matrix, the SVD surgery strategy works by: (1) computing its singular value decomposition (SVD), (2) changing a few of the smaller singular values relative to the largest one, and (3) reconstructing the matrix by reverse SVD. Applying SVD surgery on CNN convolution filters during training acts as spectral regularisation of the DL model without requiring the learning of extra parameters. The fact that the further away a matrix is from the non-invertible matrices, the higher its condition number is suggests that the spatial distributions of square matrices and those of their inverses are correlated to their condition number distributions. We shall examine this assertion empirically by showing that applying various versions of SVD surgery on point clouds of matrices leads to bringing their persistent diagrams (PDs) closer to the matrices of the point clouds of their inverses.

3-D Data Interpolation and Denoising by an Adaptive Weighting Rank-Reduction Method Using Multichannel Singular Spectrum Analysis Algorithm.

Addressing insufficient and irregular sampling is a difficult challenge in seismic processing and imaging. Recently, rank reduction methods have become popular in seismic processing algorithms for simultaneous denoising and interpolating. These methods are based on rank reduction of the trajectory matrices using truncated singular value decomposition (TSVD). Estimation of the ranks of these trajectory matrices depends on the number of plane waves in the processing window; however, for the more complicated data, the rank reduction method may fail or give poor results. In this paper, we propose an adaptive weighted rank reduction (AWRR) method that selects the optimum rank in each window automatically. The method finds the maximum ratio of the energy between two singular values. The AWRR method selects a large rank for the highly curved complex events, which leads to remaining residual errors. To overcome the residual errors, a weighting operator on the selected singular values minimizes the effect of noise projection on the signal projection. We tested the efficiency of the proposed method by applying it to both synthetic and real seismic data.

Performance of a Double RIS Communication System Aided by Partially Active Elements.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance the spectral efficiency of wireless communication systems. However, if there are many obstacles between the RIS and users, a single RIS may not provide sufficient performance. For this reason, a double RIS-aided communication system is proposed in this paper. However, this system also has a problem: the signal is attenuated three times due to the three channels created by the double RIS. To overcome these attenuations, an active RIS is proposed in this paper. An active RIS is almost the same as a conventional RIS, except for the included amplifier. Comprehensively, the proposed system overcomes various obstacles and attenuations. In this paper, an active RIS is applied to the second RIS. To reduce the power consumption of active elements, a partially active RIS is applied. To optimize the RIS elements, the sum of the covariance matrix is found by using channels related to each RIS, and the right singular vector is exploited using singular value decomposition for the sum of the covariance matrix. Then, the singular value of the sum of the covariance value is checked to determine which element is the active element. Simulation results show that the proposed system has better sum rate performance compared to a single RIS system. Although it has a lower sum rate performance compared to a double RIS with fully active elements, the proposed system will be more attractive in the future because it has much better energy efficiency.

Precision Heart Rate Estimation Using a PPG Sensor Patch Equipped with New Algorithms of Pre-Quality Checking and Hankel Decomposition.

A new method for accurately estimating heart rates based on a single photoplethysmography (PPG) signal and accelerations is proposed in this study, considering motion artifacts due to subjects' hand motions and walking. The method comprises two sub-algorithms: pre-quality checking and motion artifact removal (MAR) via Hankel decomposition. PPGs and accelerations were collected using a wearable device equipped with a PPG sensor patch and a 3-axis accelerometer. The motion artifacts caused by hand movements and walking were effectively mitigated by the two aforementioned sub-algorithms. The first sub-algorithm utilized a new quality-assessment criterion to identify highly noise-contaminated PPG signals and exclude them from subsequent processing. The second sub-algorithm employed the Hankel matrix and singular value decomposition (SVD) to effectively identify, decompose, and remove motion artifacts. Experimental data collected during hand-moving and walking were considered for evaluation. The performance of the proposed algorithms was assessed using the datasets from the IEEE Signal Processing Cup 2015. The obtained results demonstrated an average error of merely 0.7345 ± 8.1129 beats per minute (bpm) and a mean absolute error of 1.86 bpm for walking, making it the second most accurate method to date that employs a single PPG and a 3-axis accelerometer. The proposed method also achieved the best accuracy of 3.78 bpm in mean absolute errors among all previously reported studies for hand-moving scenarios.

New Contrast Enhancement Method for Multiple Sclerosis Lesion Detection.

Multiple sclerosis (MS) is one of the most serious neurological diseases. It is the most frequent reason of non-traumatic disability among young adults. MS is an autoimmune disease wherein the central nervous system wrongly destructs the myelin sheath surrounding and protecting axons of nerve cells of the brain and the spinal cord which results in presence of lesions called plaques. The damage of myelin sheath alters the normal transmission of nerve flow at the plaques level, consequently, a loss of communication between the brain and other organs. The consequence of this poor transmission of nerve impulses is the occurrence of various neurological symptoms. MS lesions cause mobility, vision, cognitive, and memory disorders. Indeed, early detection of lesions provides an accurate MS diagnosis. Consequently, and with the adequate treatment, clinicians will be able to deal effectively with the disease and reduce the number of relapses. Therefore, the use of magnetic resonance imaging (MRI) is primordial which is proven as the relevant imaging tool for early diagnosis of MS patients. But, low contrast MRI images can hide important objects in the image such lesions. In this paper, we propose a new automated contrast enhancement (CE) method to ameliorate the low contrast of MRI images for a better enhancement of MS lesions. This step is very important as it helps radiologists in confirming their diagnosis. The developed algorithm called BDS is based on Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) and Singular Value Decomposition with Discrete Wavelet Transform (SVD-DWT) techniques. BDS is dedicated to improve the low quality of MRI images with preservation of the brightness level and the edge details from degradation and without added artifacts or noise. These features are essential in CE approaches for a better lesion recognition. A modified version of BDS called MBDS is also implemented in the second part of this paper wherein we have proposed a new method for computing the correction factor. Indeed, with the use of the new correction factor, the entropy has been increased and the contrast is greatly enhanced. MBDS is specially dedicated for very low contrast MRI images. The experimental results proved the effectiveness of developed methods in improving low contrast of MRI images with preservation of brightness level and edge information. Moreover, performances of both proposed BDS and MBDS algorithms exceeded conventional CE methods.

Optimal High-order Tensor SVD via Tensor-Train Orthogonal Iteration.

This paper studies a general framework for high-order tensor SVD. We propose a new computationally efficient algorithm, tensor-train orthogonal iteration (TTOI), that aims to estimate the low tensor-train rank structure from the noisy high-order tensor observation. The proposed TTOI consists of initialization via TT-SVD [1] and new iterative backward/forward updates. We develop the general upper bound on estimation error for TTOI with the support of several new representation lemmas on tensor matricizations. By developing a matching information-theoretic lower bound, we also prove that TTOI achieves the minimax optimality under the spiked tensor model. The merits of the proposed TTOI are illustrated through applications to estimation and dimension reduction of high-order Markov processes, numerical studies, and a real data example on New York City taxi travel records. The software of the proposed algorithm is available online (https://github.com/Lili-Zheng-stat/TTOI).

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning.

It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically. The usual approach to data compression is to collect data first, then compress it; however, we cannot ensure the correctness and efficiency of the data. Based on sparse Bayesian optimization block learning, this research provides a method for compression reconstruction and fault diagnostics of diesel engine vibration data. This method's essential contribution is combining compressive sensing technology with fault diagnosis. To achieve a better diagnosis effect, we can effectively improve the wireless transmission efficiency of the vibration signal. First, the dictionary is dynamically updated by learning the dictionary using singular value decomposition to produce the ideal sparse form. Second, a block sparse Bayesian learning boundary optimization approach is utilized to recover structured non-sparse signals rapidly. A detailed assessment index of the data compression effect is created. Finally, the experimental findings reveal that the approach provided in this study outperforms standard compression methods in terms of compression efficiency and accuracy and its ability to produce the desired fault diagnostic effect, proving the usefulness of the proposed method.

Fast Localization and Characterization of Underground Targets with a Towed Transient Electromagnetic Array System.

A fast inversion algorithm combined with the transient electromagnetic (TEM) detection system has important significance for improving the detection efficiency of unexploded ordnance. The traditional algorithms, such as differential evolution or Gauss-Newton algorithms, usually require tens to thousands of iterations to locate the underground target. A new algorithm with a magnetic gradient tensor and singular value decomposition (SVD) to estimate the target position and characterization quickly and accurately is proposed in this paper. Two modes of magnetic gradient tensor are constructed to accurately locate shallow and deep targets, respectively. The SVD algorithm is applied to the responses to estimate the electromagnetic characteristics of the target quickly and accurately. To verify the performance of the proposed algorithm, a towed TEM sensor is designed, which is constructed with three transmitting coils and nine three-component receiving coils arranged in a 3 × 3 array. Field experiments in survey and cued modes were taken to verify the performance of the proposed algorithm and the towed system. Results show that the magnetic gradient tensor algorithm proposed in this paper can accurately locate a single target within 2.0 m depth, and the error of depth is no more than 8 cm. Even for overlapping response of multi targets, the error of depth is no more than 12 cm. The underground target can be accurately characterized by the SVD algorithm. For targets with depths over 2.0 m, the signal-to-noise ratio of characteristic response estimated by SVD is higher than that of the traditional method. The proposed method needs approximately 40 ms, only 1% of the traditional one, considerably improving detection efficiency and laying a theoretical and experimental foundation for real-time data processing.

Efficient Model Assisted Probability of Detection Estimations in Eddy Current NDT with ACA-SVD Based Forward Solver.

Model assisted probability of detection (MAPoD) is crucial for quantifying the inspection capability of a nondestructive testing (NDT) system which uses the coil or probe to sense the size and location of the cracks. Unfortunately, it may be computationally intensive for the simulation models. To improve the efficiency of the MAPoD, in this article, an efficient 3D eddy current nondestructive evaluation (ECNDE) forward solver is proposed to make estimations for PoD study. It is the first time that singular value decomposition (SVD) is used as the recompression technique to improve the overall performance of the adaptive cross approximation (ACA) algorithm-based boundary element method (BEM) ECNDE forward solver for implementation of PoD. Both the robustness and efficiency of the proposed solver are demonstrated and testified by comparing the predicted impedance variations of the coil with analytical, semi-analytical and experimental benchmarks. Calculation of PoD curves assisted by the proposed simulation model is performed on a finite thickness plate with a rectangular surface flaw. The features, which are the maximum impedance variations of the coil for various flaw lengths, are obtained entirely by the proposed model with selection of the liftoff distance as the uncertain parameter in a Gaussian distribution. The results show that the proposed ACA-SVD based BEM fast ECNDE forward solver is an excellent simulation model to make estimations for MAPoD study.

A Vital Signs Fast Detection and Extraction Method of UWB Impulse Radar Based on SVD.

The identification of weak vital signs has always been one of the difficulties in the field of life detection. In this paper, a novel vital sign detection and extraction method with high efficiency, high precision, high sensitivity and high signal-to-noise ratio is proposed. Based on the NVA6100 pulse radar system, the radar matrix which contains several radar pulse detection signals is received. According to the characteristics of vital signs and radar matrices, the Singular Value Decomposition (SVD) is adopted to perform signal denoising and decomposition after preprocessing, and the temporal and spatial eigenvectors of each principal component are obtained. Through the energy proportion screening, the Wavelet Transform decomposition and linear trend suppression, relatively pure vital signs in each principal component, are obtained. The human location is detected by the Energy Entropy of spatial eigenvectors, and the respiratory signal and heartbeat signal are restored through a Butterworth Filter and an MTI harmonic canceller. Finally, through an analysis of the performance of the algorithm, it is proved to have the properties of efficiency and accuracy.