Maximum Correlation Method Research Articles

Analyzing domain features of small proteins using a machine-learning method.

Small proteins (SPs) are a unique group of proteins that play crucial roles in many important biological processes. Exploring the biological function of SPs is necessary. In this study, the InterPro tool and the maximum correlation method were utilized to analyze functional domains of SPs. The purpose was to identify important functional domains that can indicate the essential differences between small and large protein sequences. First, the small and large proteins were represented by their functional domains via a one-hot scheme. Then, the MaxRel method was adopted to evaluate the relationships between each domain and the target variable, indicating small or large protein. The top 36 domain features were selected for further investigation. Among them, 14 were deemed to be highly related to SPs because they were annotated to SPs more frequently than large proteins. We found the involvement of functional domains, such as ubiquitin-conjugating enzyme/RWD-like, nuclear transport factor 2 domain, and alpha subunit of guanine nucleotide-binding protein (G-protein) in regulating the biological function of SPs. The involvement of these domains has been confirmed by other recent studies. Our findings indicate that protein functional domains may regulate small protein-related functions and predict their biological activity.

Parallel Multiple Methods with Adaptative Decision Making for Gravity-Aided Navigation

Gravity-aided navigation is an effective navigation method for underwater vehicles. However, the distribution of the gravity field may affect the measurement errors of gravity anomalies and the precision of gravity-aided navigation. In this paper, the upper and lower thresholds of the gravity field standard deviation are computed by the statistical properties of the local gravity field to classify each grid in the gravity map into different levels. A parallel multiple methods with adaptive decision making (PMMADM) for gravity-aided navigation is proposed which incorporates the gravity anomaly measurements, particle filtering, and maximum correlation method into the observation equation of the extended Kalman filter. The algorithm autonomously selects the observation variables in the filter by combining the gravity field standard deviation at the current position of the carrier with a decision tree. This approach can combine the characteristics of different gravity matching algorithms, reduce the impact of random noise in the measurements, and improve the positioning accuracy of gravity-aided navigation. Physical simulation experiments demonstrated that the proposed gravity matching algorithm achieves the high navigation accuracy and long-term stability in different gravity fields, and the mean value of positioning error is 620.72 m.

Nondestructive Testing of Bridge Stay Cable Surface Defects Based on Computer Vision

The automatically defect detection method using vision inspection is a promising direction. In this paper, an efficient defect detection method for detecting surface damage to cables on a cable-stayed bridge automatically is developed. A mechanism design method for the protective layer of cables of a bridge based on vision inspection and diameter measurement is proposed by combining computer vision and diameter measurement techniques. A detection system for the surface damages of cables is de-signed. Images of cable surfaces are then enhanced and subjected to threshold segmentation by utilizing the improved local grey contrast enhancement method and the improved maximum correlation method. Afterwards, the data obtained through diameter measurement are mined by employing the moving average method. Image enhancement, threshold segmentation, and diameter measurement methods are separately validated experimentally. The experimental test results show that the system delivers recall ratios for type-I and II surface defects of cables reaching 80.4% and 85.2% respectively, which accurately detects bulges on cable surfaces.

Two Low-Complexity DOA Estimators for Massive/Ultra-Massive MIMO Receive Array

Eigen-decomposition-based direction finding methods of using large-scale/ultra-large-scale fully-digital receive antenna arrays lead to a high or ultra-high complexity. To address the complexity dilemma, in this letter, three low-complexity estimators are proposed: partitioned subarray auto-correlation combining (PSAC), partitioned subarray cross-correlation combining (PSCC) and power iteration max correlation successive convex approximation (PI-Max-CSCA). Compared with the conventional no-partitioned direction finding method like root multiple signal classification (Root-MUSIC), in the PSAC method, the total set of antennas are equally partitioned into subsets of antennas, called subarrays, each subarray performs independent DOA estimation, and all DOA estimates are coherently combined to give the final estimation. For a better performance, the cross-correlation among sub-arrays is further exploited in the PSCC method to achieve the near-Cramer-Rao lower bound (CRLB) performance with the help of auto-correlation. To further reduce the complexity, in the PI-Max-CSCA method, using a fraction of all subarrays to make an initial coarse direction measurement (ICDM), the power iterative method is adopted to compute the more precise steering vector (SV) by exploiting the total array, and a more accurate DOA value is found using ICDM and SV through the maximum correlation method solved by successive convex approximation. Simulation results show that as the number of antennas goes to large-scale, the proposed three methods can achieve a dramatic complexity reduction over conventional Root-MUISC. Particularly, the PSCC and PI-Max-CSCA can reach the CRLB while the PSAC shows a substantial performance loss.

PREDICTIVE VALUE ANALYSIS OF MR IMAGING FEATURES ON THE RISK OF KNEE REPLACEMENT IN PATIENTS WITH KNEE ARTHRITIS

This study used knee MR imaging features to quantify the severity of knee injury in patients and analyzed the predictive value of knee MR imaging features in their risk of knee replacement. A total of 120 patients with knee arthritis were included from a public knee arthritis database FNIH OAI. First, univariate logistic regression was used to screen clinical features to obtain the clinical risk factors. Then the minimum redundancy maximum correlation (mRMR) method was used to reduce the image features, and the LASSO method was used to further screen the retained image features to construct a Rad model. Next, the multivariate logistic regression method was used to combine the Rad model and the screened clinical risk factors to construct a combined model and its corresponding nomogram. Finally, ROC curve and its related metrics, Hosmer–Lemeshow test and Delong test were used to evaluate and compare the accuracy and consistency of the model performance. Age and body mass index (BMI) were found as significant clinical risk factors for knee replacement. After using mRMR and LASSO methods, 147 image features were reduced to 30 and 7 features, respectively, then these 7 features were linearly combined to construct the Rad model. Age, BMI, and the Rad model were combined to establish the combined model and its corresponding nomogram. The resulted two models showed high accuracy ([Formula: see text]) and consistency (H-L test: [Formula: see text]) on both the training set and test set. Finally, the comparison results showed that the prediction performance of the combined model was better than that of the Rad model, but their difference was not significant (Delong test: [Formula: see text]). This paper studied the predictive performance of MR imaging features on the risk of knee replacement and built a model that can be used to predict the potential risk of knee replacement in patients with knee arthritis. The resulting models showed good predictive accuracy and consistency. The drawn nomogram could be used as a useful tool to personalize the prognosis of patients with knee arthritis and guide clinical decisions on knee replacement.

Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images

BackgroundThe objective of this study is to explore the value of two-dimensional (2D) and three-dimensional (3D) radiomics models based on enhanced computed tomography (CT) images in predicting BRCA gene mutations in patients with epithelial ovarian cancer.MethodsThe clinical and imaging data of 106 patients with ovarian cancer confirmed by surgery and pathology were retrospectively analyzed and genetic testing was performed. Radiomics features extracted from the 2D and 3D regions of interest of the patients’ primary tumor lesions were selected in the training set using the maximum correlation and minimum redundancy method. Then, the best features were selected through Lasso tenfold cross-validation. Feature subsets were employed to establish a radiomics model. The model’s performance was evaluated via area under the receiver operating characteristic curve analysis and its clinical validity was assessed by using the model’s decision curve.ResultsOn the validation set, the area under the curve values of the 2D, 3D, and 2D + 3D combined models were 0.78 (0.61–0.96), 0.75 (0.55–0.92), and 0.82 (0.61–0.96), respectively. However, the DeLong test P values between the three pairs of models were all > 0.05. The decision curve analysis showed that the radiomics model had a high net benefit across all high-risk threshold probabilities.ConclusionsThe three radiomics models can predict the BRCA gene mutation in ovarian cancer, and there were no statistically significant differences between the prediction performance of the three models.

Effects of the Forecasting Methods, Precipitation Character, and Satellite Resolution on the Predictability of Short-Term Quantitative Precipitation Nowcasting (QPN) from a Geostationary Satellite.

The prediction of the short-term quantitative precipitation nowcasting (QPN) from consecutive gestational satellite images has important implications for hydro-meteorological modeling and forecasting. However, the systematic analysis of the predictability of QPN is limited. The objective of this study is to evaluate effects of the forecasting model, precipitation character, and satellite resolution on the predictability of QPN usingimages of a Chinese geostationary meteorological satellite Fengyun-2F (FY-2F) which covered all intensive observation since its launch despite of only a total of approximately 10 days. In the first step, three methods were compared to evaluate the performance of the QPN methods: a pixel-based QPN using the maximum correlation method (PMC); the Horn-Schunck optical-flow scheme (PHS); and the Pyramid Lucas-Kanade Optical Flow method (PPLK), which is newly proposed here. Subsequently, the effect of the precipitation systems was indicated by 2338 imageries of 8 precipitation periods. Then, the resolution dependence was demonstrated by analyzing the QPN with six spatial resolutions (0.1atial, 0.3a, 0.4atial rand 0.6). The results show that the PPLK improves the predictability of QPN with better performance than the other comparison methods. The predictability of the QPN is significantly determined by the precipitation system, and a coarse spatial resolution of the satellite reduces the predictability of QPN.

A new methodology for pixel-quantitative precipitation nowcasting using a pyramid Lucas Kanade optical flow approach

Short-term high-resolution Quantitative Precipitation Nowcasting (QPN) has important implications for navigation, flood forecasting, and other hydrological and meteorological concerns. This study proposes a new algorithm called Pixel-based QPN using the Pyramid Lucas-Kanade Optical Flow method (PPLK), which comprises three steps: employing a Pyramid Lucas-Kanade Optical Flow method (PLKOF) to estimate precipitation advection, projecting rainy clouds by considering the advection and evolution pixel by pixel, and interpolating QPN imagery based on the space-time continuum of cloud patches. The PPLK methodology was evaluated with 2338 images from the geostationary meteorological satellite Fengyun-2F (FY-2F) of China and compared with two other advection-based methods, i.e., the maximum correlation method and the Horn-Schunck Optical Flow scheme. The data sample covered all intensive observations since the launch of FY-2F, despite covering a total of only approximately 10 days. The results show that the PPLK performed better than the algorithms used for comparison, demonstrating less time expenditure, more effective cloud tracking, and improved QPN accuracy. (C) 2015 Elsevier B.V. All rights reserved.

Support evidence statistics for operation reliability assessment using running state information and its application to rolling bearing

Traditional reliability evaluation method generally requires a large amount of previous data and information on historical lifetime. For an individual mechanical device without historical lifetime data, it is difficult to carry out the reliability assessment by using the traditional method. To attempt exploring this difficult problem, support evidence statistics (SES) as an approach to operation reliability assessment is presented in this paper. Moreover, this presented method is also expected to indicate the physical state changes of the individual mechanical device. Since in scientific research, evidence usually goes towards supporting or rejecting a hypothesis. For a running device, evidences derived from the running state information should consistently demonstrate its current sole-running-state within a given short time interval. In practice, due to the interference of environmental noises, these evidences lose the consistency. Accordingly, they can be classified into two classes: the firm evidences and the flimsy evidences. Analogous to the support vector data description (SVDD), these firm evidences which show remarkable consistency can form a support evidence space (SESP) through one-class classification. Suppose that a SESP is obtained by using the evidences accumulated from the normal running state, the device operation reliability at any time of unknown running state can be evaluated through the statistical comparison between the normal SESP and the unknown SESP. This reliability evaluation process is named as SES. The most fundamental distinction between the proposed method and the traditional method lies in different statistical objects. The traditional methods are to analyze lifetime data while the proposed methods are to analyze running state data. Obviously, the evidence feature optimization plays a crucial role in the presented method. The maximum correlation and minimum redundancy (MCMR) method is improved by principal component analysis (PCA) to select evidence features based on vibration signals. Finally, the effectiveness of the presented method is validated through a new experiment of rolling bearing.

The Gas-to-Dust Relation in the Dark Cloud L1523—Observational Evidence for CO Gas Depletion

Correlation between gas and dust column density has been studied for the dark globule L1523. The 13CO(J=1→0) emission is used for tracing the gas, and the IR emissions, for tracing the dust constituent. In order to match the beam resolution between the images, a beam de-convolution algorithm based on the Maximum Correlation Method (MCM) was applied on the Infrared Astronomical Satellite (IRAS) data. The morphology of 13CO column density map shows a close correlation to that of 100 μm dust optical depth. The distribution of the optical depth at 100 μm follows that of gas column density more closely than does the flux map at either 60 or 100 μm. The ratio of the 13CO column density to the 100 μm optical depth shows a decreasing trend with increasing dust optical depth in the central part, indicating possible molecular gas condensation onto dust particles. The excessive decrease in the CO column density in the envelope may most probably be due to the photo-dissociation of CO molecules.

Far‐Infrared Study of IRAS 00494+5617 and IRAS 05327−0457

High angular resolution far-infrared observations at 143 and 185 μm, using the Tata Institute of Fundamental Research 1 m balloon-borne telescope, are presented for two Galactic star-forming complexes associated with IRAS 00494+5617 and IRAS 05327-0457. The latter map also reveals the cold dust in OMC-3. Both these regions are illuminated at the edges by high-mass stars with substantial UV flux. The HIRES (High-Resolution processing using Maximum Correlation Method) processed IRAS maps at 12, 25, 60, and 100 μm have also been presented for comparison. The present study is aimed at quantifying the role of the nearby stars vis-a-vis embedded young stellar objects in the overall heating of these sources. Based on the FIR observations at 143 and 185 μm carried out simultaneously with almost identical angular resolution, reliable dust temperature and optical depth maps have been generated for the brighter regions of these sources. Radiative transfer modeling in spherical geometry has been carried out to extract physical parameters of these sources by considering the observational constraints, such as spectral energy distribution, angular size at different wavelengths, dust temperature distribution, etc. It is concluded that for both IRAS 00494+5617 and IRAS 05327-0457, the embedded energy sources play the major role in heating them with finite contribution from the nearby stars. The best-fit model for IRAS 00494+5617 is consistent with a simple two-phase clump-interclump picture with ~5% volume filling factor (of clumps) and a density contrast of ≈80.

L1448 IRS 2: A HIRES‐identified Class 0 Protostar

We present far-infrared and submillimeter images and millimeter-continuum photometry for L1448 IRS 2, a low-luminosity (5.2 L☉), embedded young stellar object discovered by IRAS. New far-infrared maps were produced from the archival IRAS data via high-resolution (HIRES) processing, the name given to the implementation of the maximum correlation method for image construction from the IRAS data streams. The HIRES-processed images of L1448 IRS 2 presented here have effective resolutions of ~35'' × 28'', ~35'' × 37'', and ~45'' × 40'' at 25, 60, and 100 μm, respectively, which represent an order of magnitude improvement over previously available far-infrared maps. L1448 IRS 2 was mapped at 450 and 850 μm, and we acquired continuum photometry at 1.3 mm. With these new data, we plot the most complete spectral energy distribution for L1448 IRS 2 to date. We also present sensitive 12CO J=1→0 emission-line maps of a 47' × 7' area of the L1448 dark cloud, centered on L1448 IRS 2, obtained with the on-the-fly imaging capability of the NRAO 12 m telescope. As a direct consequence of this mapping, we have discovered a parsec-scale molecular outflow associated with L1448 IRS 2. The combined continuum and molecular line data confirm the classification of L1448 IRS 2 as a new member of the rare, short-lived (a few × 104 yr), Class 0 phase of protostellar evolution. The use of HIRES imaging of IRAS data as a promising tool for identifying protostellar candidates is demonstrated by the example of L1448 IRS 2.

PARALLELIZATION AND ALGORITHMIC ENHANCEMENTS OF HIGH RESOLUTION IRAS IMAGE CONSTRUCTION

ABSTRACT The Infrared Astronomical Satellite carried out a nearly complete survey of the infrared sky, and the survey data are important for the study of many astrophysical phenomena. However, many data sets at other wavelengths have higher resolutions than that of the co-added IRAS maps, and high resolution IRAS images are strongly desired both for their own information content and their usefulness in correlation studies. The HIRES program was developed by the Infrared Processing and Analysis Center (IPAC) to produce high resolution (~1') images from IRAS data using the Maximum Correlation Method (MCM). We describe the port of HIRES to the Intel Paragon, a massively parallel supercomputer, other software developments for mass production of HIRES images, and the IRAS Galaxy Atlas, a project to map the Galactic plane at 60 and 100 microns. Images produced from the MCM algorithm sometimes suffer from visible striping and ringing artifacts. Correcting detector gain offsets and using a Burg entropy metric in the reconstruction scheme were found to be effective in suppressing these artifacts. A variation of the destriping algorithm was used to subtract zodiacal emission.

The spatial variation of the infrared-to-radio ratio in spiral galaxies

We have produced two-dimensional maps of the intensity ratio, Q(sub 60), of 60 micron infrared to 20 cm radio continuum emission, for a set of 25 nearby galaxies, mostly spirals. The ratio maps were obtained from infrared images made using IRAS data with the maximum correlation method, and radio images made using VLA data. Before taking the ratio, the radio images were processed so as to have the same resolution properties as the infrared images; the final spatial resolution in all cases is approximately 1 min, corresponding to 1 - 2 kpc for most galaxies. This resolution represents a significant improvement over previous studies. Our new high-resolution maps confirm the slow decrease of Q(sub 60) with increasing radial distance from the nucleus, but show additional structure which is probably associated with separate sites of active star formation in the spiral arms. The maps show Q(sub 60) to be more closely related to infrared surface brightness than to the radial distance r in the galaxy disk. We note also that the Q(sub 60) gradients are absent (or at least reduced) for the edge-on galaxies, a property which can be attributed to the dilution of contrast due to the averaging of the additional structure along the line of sight. The results are all in qualitative agreement with the suggestion that the radio image represents a smeared version of the infrared image, as would be expected on the basis of current models in which the infrared-radio correlation is driven by the formation of massive stars, and the intensity distribution of radio emission is smeared as a result of the propagation of energetic electrons accelerated during the supernova phase.

Critical Assessment of the Least Square Error Method Used in Derivation of Morison’s Force Coefficients

The least square error (LSE) method is frequently used in determination of Morison’s coefficients from the analysis of measured wave forces on circular cylinders. It will be proved in this paper that Morison’s coefficients determined from the LSE method always lead to predicted forces which have smaller variances (2nd-order statistical moments) than those of the measured forces. In other words, it is proved that predicted forces from this method are of lower intensity compared to the measured forces, and hence can be unconservative. A new method, termed the maximum correlation (MC) method, has been introduced. This method has all the advantages of the LSE method, but leads to predicted forces whose variances are equal to those of the measured forces.

An atlas of high-resolution IRAS maps on nearby galaxies

An atlas of far-infrared IRAS maps with near 1 arcmin angular resolution of 30 optically large galaxies is presented. The high-resolution IRAS maps were produced with the Maximum Correlation Method (MCM) image construction and enhancement technique developed at IPAC. The MCM technique, which recovers the spatial information contained in the overlapping detector data samples of the IRAS all-sky survey scans, is outlined and tests to verify the structural reliability and photometric integrity of the high-resolution maps are presented. The infrared structure revealed in individual galaxies is discussed. The atlas complements the IRAS Nearby Galaxy High-Resolution Image Atlas, the high- resolution galaxy images encoded in FITS format, which is provided to the astronomical community as an IPAC product.

Optimum frame synchronization and performance over binary symmetric channel

The fixed frame length maximum correlation method which is the optimum method over the binary symmetric channel has been proved. With Poisson distribution, the frame sync acquisition probability is obtained and followed by computer simulation. By Markov modeling techniques, the probability and the mean time into lock mode are given. Finally, it is shown that the optimum frame sync method is far superior to conventional, and it is very useful in practice.

Satellite-Image-derived Gulf Stream Currents Compared with Numerical Model Results

Abstract Sequential infrared satellite imagery is used to objectively compute surface currents in the Gulf Stream region using the maximum correlation (MCC) method. The infrared images, filtered for cloud cover, are used to find the displacement of surface temperature patterns by locating the maximum cross correlation in windowed portions of the image pair. Statistical significance and next-neighbor filter techniques are applied to remove fictitious surface current vectors due to the presence of residual cloud or other nonadvective processes. The core of the Gulf Stream is found to require special treatment due to the high local velocities and the weak sea surface temperature gradients. For the central Gulf Stream, currents are inferred by the MCC tracking of features along the northern edge of the stream. Other special MCC techniques are applied to the strong rotational motions in the Gulf Stream rings. To test the validity of the MCC technique in this geographic region where no in situ measurements were...

A maximum correlation method for image construction of IRAS survey data

An algorithm is presented for the construction of images using linear array data with nonuniform scan coverage of object space and nonuniform detector responses. The algorithm achieves the maximum correlation between adjacent pixels, i.e., the smoothest image, consistent with the data and data uncertainties. For high spatial data density and signal-to-noise ratio, the achievable spatial resolution can exceed the diffraction limit of the optics. The capability of the algorithm is illustrated using 60-micron data from the region centered on the galaxy M101, obtained during the all-sky survey performed by the Infrared Astronomical Satellite. The 60-micron map produced has a resolution of about 36 arcsec and allows the identification of many H II regions by position and aperture photometry for the brighter ones. The achieved resolution is discussed in terms of the a priori estimate of the mean correlation length of the data, the directly measured FWHM in the final image, and the results of aperture photometry of M101 H II regions NGC 5447, 5455, 5461, 5462 and 5471.