Intensity Errors Research Articles

Peak picking NMR spectral data using non-negative matrix factorization

BackgroundSimple peak-picking algorithms, such as those based on lineshape fitting, perform well when peaks are completely resolved in multidimensional NMR spectra, but often produce wrong intensities and frequencies for overlapping peak clusters. For example, NOESY-type spectra have considerable overlaps leading to significant peak-picking intensity errors, which can result in erroneous structural restraints. Precise frequencies are critical for unambiguous resonance assignments.ResultsTo alleviate this problem, a more sophisticated peaks decomposition algorithm, based on non-negative matrix factorization (NMF), was developed. We produce peak shapes from Fourier-transformed NMR spectra. Apart from its main goal of deriving components from spectra and producing peak lists automatically, the NMF approach can also be applied if the positions of some peaks are known a priori, e.g. from consistently referenced spectral dimensions of other experiments.ConclusionsApplication of the NMF algorithm to a three-dimensional peak list of the 23 kDa bi-domain section of the RcsD protein (RcsD-ABL-HPt, residues 688-890) as well as to synthetic HSQC data shows that peaks can be picked accurately also in spectral regions with strong overlap.

Influence of Assimilating Satellite-Derived Atmospheric Motion Vector Observations on Numerical Analyses and Forecasts of Tropical Cyclone Track and Intensity

Abstract The influence of assimilating enhanced atmospheric motion vectors (AMVs) on mesoscale analyses and forecasts of tropical cyclones (TC) is investigated. AMVs from the geostationary Multifunctional Transport Satellite (MTSAT) are processed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS, University of Wisconsin–Madison) for the duration of Typhoon Sinlaku (2008), which included a rapid intensification phase and a slow, meandering track. The ensemble Kalman filter and the Weather Research and Forecasting Model are utilized within the Data Assimilation Research Testbed. In addition to conventional observations, three different groups of AMVs are assimilated in parallel experiments: CTL, the same dataset assimilated in the NCEP operational analysis; CIMSS(h), hourly datasets processed by CIMSS; and CIMSS(h+RS), the dataset including AMVs from the rapid-scan mode. With an order of magnitude more AMV data assimilated, the CIMSS(h) analyses exhibit a superior track, intensity, and structure to CTL analyses. The corresponding 3-day ensemble forecasts initialized with CIMSS(h) yield smaller track and intensity errors than those initialized with CTL. During the period when rapid-scan AMVs are available, the CIMSS(h+RS) analyses offer additional modifications to the TC and its environment. In contrast to many members in the ensemble forecasts initialized from the CTL and CIMSS(h) analyses that predict an erroneous landfall in China, the CIMSS(h+RS) members capture recurvature, albeit prematurely. The results demonstrate the promise of assimilating enhanced AMV data into regional TC models. Further studies to identify optimal strategies for assimilating integrated full-resolution multivariate data from satellites are under way.

Practical high-speed light source for decoy-state quantum key distribution

High-speed light source is realized for decoy-state quantum key distribution (QKD) at telecom wavelength of 1.55 µm. By implementing two different electrical pulses together and triggering with 100 MHz pseudorandom number to drive the laser diode, the signal-state and the decoy-state pulses are prepared with identical pulse duration of 25 ps and similar spectral characteristics, avoiding the eavesdropper's attack by temporal and spectral analysis. The intensity fluctuation of the light source is quantified to satisfy the practical decoy-state QKD with random intensity error. The characteristics of the light source are analyzed with a high-speed single-photon detector.

Comparison of the Spectral Representation Method to Simulate Spatially Variable Ground Motions

The spectral representation method (SRM) is widely used when simulating spatially variable ground motions. It has mainly two formulas, i.e., the random amplitudes and the random phases formulas. There exist three methods for decomposing the cross spectral density matrix: Cholesky decomposition, eigen decomposition, and root decomposition. Therefore, there are six forms with respect to the different combinations of the simulation formulas and the decomposition methods. To provide researchers and engineers with the guidance on choosing simulation method, the six forms are systematically investigated from five aspects: the power intensity, response spectra, and stochastic error of auto/cross spectral density, Fourier spectra, and difference indexes for Fourier amplitudes and phases. Finally, we give the following advice: the characteristics of the ground motions simulated by the random amplitudes formula are independent of the decomposition method, while the characteristics of the ground motions simulated by random phases formula are dependent of the decomposition method. Furthermore, the root decomposition is strongly recommended when utilizing the random phases formula.

Objective Tropical Cyclone Intensity Estimation Using Analogs of Spatial Features in Satellite Data

Abstract An objective method for estimating tropical cyclone (TC) intensity using historical hurricane satellite data (HURSAT) is developed and tested. This new method, referred to as feature analogs in satellite imagery (FASI), requires a TC's center location to extract azimuthal brightness temperature (BT) profiles from current imagery as well as BT profiles from imagery 6, 12, and 24 h prior. Instead of using regression techniques, the estimated TC intensity is determined from the 10 closest analogs to this TC based on the BT profiles using a k-nearest-neighbor algorithm. The FASI technique was trained and validated using intensity data from aircraft reconnaissance in the North Atlantic Ocean, where the data were restricted to include storms that are over water and south of 45°N. This subset comprised 2016 observations from 165 storms during 1988–2006. Several tests were implemented to statistically justify the FASI algorithm using n-fold cross validation. The resulting average mean absolute intensity error was 10.9 kt (50% of estimates are within 10 kt, 1 kt = 0.51 m s−1) or 8.4 mb (50% of estimates are within 8 mb); its accuracy is on par with other objective techniques. This approach has the potential to provide global TC intensity estimates that could augment intensity estimates made by other objective techniques.

Track and Intensity Forecasting of Hurricanes: Impact of Convection-Permitting Resolution and Global Ensemble Kalman Filter Analysis on 2010 Atlantic Season Forecasts

Abstract Twice-daily 48-h tropical cyclone (TC) forecasts were produced for the fall 2010 Atlantic hurricane season using the Advanced Research core of the Weather Research and Forecasting (WRF-ARW) model on a large 4-km grid covering much of the northern Atlantic. WRF forecasts initialized from operational Global Forecast System (GFS) analyses based on the gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVAR) system and from experimental global ensemble Kalman filter (EnKF) analyses, and corresponding global GFS forecasts were intercompared. For the track, WRF forecasts show improvement over GFS forecasts using either set of initial conditions (ICs). The EnKF-initialized GFS and WRF are also better than the corresponding GSI-initialized forecasts, but the difference is not always statistically significant. At all lead times, the WRF track errors are comparable to or smaller than the National Hurricane Center (NHC) official track forecast error, with those of the EnKF WRF being smallest. For weaker TCs, more improvement comes from the model (resolution) than from the ICs. For hurricane intensity TCs, EnKF ICs produce better track forecasts than GSI ICs, with the best forecast coming from WRF at most lead times. For intensity, EnKF ICs consistently outperform GSI ICs in both models for weaker TCs. For hurricane-strength TCs, EnKF ICs produce forecasts statistically indistinguishable from GSI ICs in either model. For all TCs combined, WRF produces about half the error of the corresponding GFS simulation beyond 24 h, and at 36 and 48 h, the errors are smaller than those from NHC official forecasts. The improvement is even greater for hurricane-strength TCs. Overall, the WRF forecasts initialized with EnKF ICs have the smallest intensity error, and the difference is statistically significant compared to the GFS forecasts.

Minimized speckle noise in lens-less holographic projection by pixel separation

Images displayed by holographic methods on phase-only light modulators inevitably suffer from speckle noise. It is partly caused by multiple uncontrolled interferences between laser light rays forming adjacent pixels of the image while having a random phase state. In this work the experimental proof of concept of an almost speckle-less projection method is presented, which assumes introducing a spatial separation of the image pixels, thus eliminating the spurious interferences. A single displayed sub-frame consists of separated light spots of very low intensity error. The sub-frames with different sampling offsets are then displayed sequentially to produce a non-fragmented color final image.

Improved B0‐distortion correction in diffusion MRI using interlaced q‐space sampling and constrained reconstruction

To enable high-quality correction of susceptibility-induced geometric distortion artifacts in diffusion magnetic resonance imaging (MRI) images without increasing scan time. A new method for distortion correction is proposed based on subsampling a generalized version of the state-of-the-art reversed-gradient distortion correction method. Rather than acquire each q-space sample multiple times with different distortions (as in the conventional reversed-gradient method), we sample each q-space point once with an interlaced sampling scheme that measures different distortions at different q-space locations. Distortion correction is achieved using a novel constrained reconstruction formulation that leverages the smoothness of diffusion data in q-space. The effectiveness of the proposed method is demonstrated with simulated and in vivo diffusion MRI data. The proposed method is substantially faster than the reversed-gradient method, and can also provide smaller intensity errors in the corrected images and smaller errors in derived quantitative diffusion parameters. The proposed method enables state-of-the-art distortion correction performance without increasing data acquisition time.

Improvement of vector compensation method for vehicle magnetic distortion field

Magnetic distortions such as eddy-current field and low frequency magnetic field have not been considered in vector compensation methods. A new compensation method is proposed to suppress these magnetic distortions and improve compensation performance, in which the magnetic distortions related to measurement vectors and time are considered. The experimental system mainly consists of a three-axis fluxgate magnetometer (DM-050), an underwater vehicle and a proton magnetometer, in which the scalar value of magnetic field is obtained with the proton magnetometer and considered to be the true value. Comparing with traditional compensation methods, experimental results show that the magnetic distortions can be further reduced by two times. After compensation, error intensity and RMS error are reduced from 11684.013nT and 7794.604nT to 16.219nT and 5.907nT respectively. It suggests an effective way to improve the compensation performance of magnetic distortions.

A study of the connection between tropical cyclone track and intensity errors in the WRF model

This study examines the dependence of the tropical cyclone (TC) intensity errors on the track errors in the Weather Research and Forecasting (WRF-ARW) model. By using the National Centers for Environmental Prediction global final analysis as the initial and boundary conditions for cloud-resolving simulations of TC cases that have small track errors, it is found that the 2- and 3-day intensity errors in the North Atlantic basin can be reduced to 15 and 19 % when the track errors decrease to 55 and 76 %, respectively, whereas the 1-day intensity error shows no significant reduction despite more than 30 % decrease of the 1-day track error. For the North-Western Pacific basin, the percentage of intensity reduction is somewhat similar with the 2- and 3-day intensity errors improved by about 15 and 19 %, respectively. This suggests that future improvement of the TC track forecast skill in the WRF-ARW model will be beneficial to the intensity forecast. However, the substantially smaller percentages of intensity improvement than those of the track error improvement indicate that ambient environment tends to play a less important role in determining the TC intensity as compared to other factors related to the vortex initialization or physics representations in the WRF-ARW model.

Statistical agglomeration: peak summarization for direct infusion lipidomics

Quantification of lipids is a primary goal in lipidomics. In direct infusion/injection (or shotgun) lipidomics, accurate downstream identification and quantitation requires accurate summarization of repetitive peak measurements. Imprecise peak summarization multiplies downstream error by propagating into species identification and intensity estimation. To our knowledge, this is the first analysis of direct infusion peak summarization in the literature. We present two novel peak summarization algorithms for direct infusion samples and compare them with an off-machine ad hoc summarization algorithm as well as with the propriety Xcalibur algorithm. Our statistical agglomeration algorithm reduces peakwise error by 38% mass/charge (m/z) and 44% (intensity) compared with the ad hoc method over three datasets. Pointwise error is reduced by 23% (m/z). Compared with Xcalibur, our statistical agglomeration algorithm produces 68% less m/z error and 51% less intensity error on average on two comparable datasets. The source code for Statistical Agglomeration and the datasets used are freely available for non-commercial purposes at https://github.com/optimusmoose/statistical_agglomeration. Modified Bin Aggolmeration is freely available in MSpire, an open source mass spectrometry package at https://github.com/princelab/mspire/.

Implementation of an RFID-Based Virtual Signal Mechanism for Indoor Location Sensing System

The variety in wireless technology and mobile computing devices has caused a growing application of location sensing systems and correspondence services. The indoor location sensing system which applies radio frequency identification technology (RFID) is the most popular research topic in the area of location sensing systems, and LANDMARC is the most representative one. However, LANDMARC suffers the drawback of inaccuracy in unstable signal intensity. A Virtual Signal Location System (VSLS) is designed and developed to overcome some drawbacks of LANDMARC. The concept of VSLS is based on the additional setting of virtual signal tags as well as the normal distribution of signal strength, analysis of sampling rate and equalization to decrease signal intensity error. Comparisons were made with LANDMARC; results showed that VSLS effectively increased the sampling quality of the signal as well as the precision of positioning.

The Theory of Using an Intensity-Correcting Algorithm to Overcome Quantization Error for Phase Measuring Profilometry

In the image process, intensity differs from its true value because the quantization process restricts image pixels to lie on an integer grid, and phase quantization error is introduced. In this paper, we propose a theory of using an intensity-correcting to overcome phase quantization error. According to the distribution of the intensity error in some pixels, the mathematical model of the intensity error is reconstructed to correct intensity values and reduce phase quantization error. Using specific example deduct the intensity-correction algorithm. At last, we compare the uncorrected quantization error and the quantization error after correction, and prove that the principle of this algorithm is right.

Improved three-dimensional Look-Locker acquisition scheme and angle map filtering procedure for T 1 estimation

The three-dimensional (3D) Look-Locker (LL) acquisition is a widely used fast and efficient T1 mapping method. However, the multi-shot approach of 3D LL acquisition can introduce reconstruction artifacts that result in intensity distortions. Traditional 3D LL acquisition generally utilizes a centric encoding scheme that is limited to a single phase-encoding direction in k space. To optimize k-space segmentation, an elliptical scheme with two phase-encoding directions is implemented for the LL acquisition. This elliptical segmentation can reduce the intensity errors in the reconstructed images and improve the final T1 estimation. One of the major sources of error in LL-based T1 estimation is a lack of accurate knowledge of the actual flip angle. A multi-parameter curve-fitting procedure can account for some of the variability in the flip angle. However, curve fitting can also introduce errors in the estimated flip angle that can result in incorrect T1 values. A filtering procedure based on goodness of fit (GOF) is proposed to reduce the effect of false flip angle estimates. Filtering based on GOF weighting can remove probable incorrect angles that result in bad curve fitting. Simulation, phantom and in vivo studies have demonstrated that these techniques can improve the accuracy of 3D LL T1 estimation.

Sensitivity test of a 3D diode array system for MLC and GANTRY errors in IMRT delivery

Introduction Delta4 device is designed for three-dimensional dose verification and shows excellent reproducibility, linearity and doserate independence. The purpose of this work was to verify the sensitivity of the Delta4 (ScandiDos) for small MLC and gantry errors in intensity modulated radiotherapy (IMRT). Materials and methods Ten IMRT plans (5 prostate and 5 head and neck) were used. Systematic MLC errors of (–0.2), (–0.5), (–0.7), (–1), (–2), (–3), (–5) and (–10) mm, were introduced for one leaf. Gantry errors of 0.2°, 0.5°, 0.7°, 1°, 2°, 2.5°, 3°, 4° and 5° were introduced for the same plans. The Delta4 system was used to acquire and analyze the data. All the measurements were acquired consecutively for each patient. To eliminate the repeatability errors, standard deviations (SD) of the maximum gamma (Gmax) were calculated for 10 successively beams with no offsets. Gmax values were noted for both global gamma (normalization dose) and local gamma (local detector dose) with pass/fail criteria of 2%/2 mm. Gmax was evaluated for each modified field when compared to the measured field with no introduced error (0 mm or 0°). Because we had a single leaf with a small offset we evaluated only the Gmax and not the mean gamma or the percentage of area gamma Results For local gamma evaluation (LGE), the maximum SD for Gmax was of 0.06. For global gamma evaluation (GGE), the maximum SD was of 0.02. The MLC and gantry offsets had a noticeable impact on the plan evaluation. For all the ten plans modifications in Gmax were observed starting with 0.2 mm (from 0.2 to 4.75 for LGE and from 0.13 to 2.45 for GGE) for MLC plans and with 0.2° for the gantry errors (from 0.54 to 9.23 for LGE and from 0.37 to 4.71 for GGE). Conclusion The Delta4 system seems to be a very sensitive instrument for the quality assurance of IMRT plans. In our study it detected submilimetric and subdegree errors for MLC and gantry, respectively. In clinical use these small errors may not be detected and could be hidden by the residual error between planned and delivered dose.

An ocean coupling potential intensity index for tropical cyclones

AbstractTimely and accurate forecasts of tropical cyclones (TCs, i.e., hurricanes and typhoons) are of great importance for risk mitigation. Although in the past two decades there has been steady improvement in track prediction, improvement on intensity prediction is still highly challenging. Cooling of the upper ocean by TC‐induced mixing is an important process that impacts TC intensity. Based on detail in situ air‐deployed ocean and atmospheric measurement pairs collected during the Impact of Typhoons on the Ocean in the Pacific (ITOP) field campaign, we modify the widely used Sea Surface Temperature Potential Intensity (SST_PI) index by including information from the subsurface ocean temperature profile to form a new Ocean coupling Potential Intensity (OC_PI) index. Using OC_PI as a TC maximum intensity predictor and applied to a 14 year (1998–2011) western North Pacific TC archive, OC_PI reduces SST_PI‐based overestimation of archived maximum intensity by more than 50% and increases the correlation of maximum intensity estimation from r2 = 0.08 to 0.31. For slow‐moving TCs that cause the greatest cooling, r2 increases to 0.56 and the root‐mean square error in maximum intensity is 11 m s−1. As OC_PI can more realistically characterize the ocean contribution to TC intensity, it thus serves as an effective new index to improve estimation and prediction of TC maximum intensity.

Near and far field flow disturbances induced by model hydrokinetic turbine: ADV and ADP comparison

Wake flows downstream of hydrokinetic turbines are characterized by hub and tip vortices, a velocity deficit and an increase in turbulence intensity. Velocity and turbulence recovery in the wakes of individual turbines constrains the density of turbines in an array and limits the amount of energy that can be produced by a turbine farm. However, few hydrokinetic turbine flow recovery studies have been conducted, especially on the far-field flow characteristics. Nor have studies evaluated the accuracy of acoustic Doppler profiler measurements in the wakes of turbines. The present study examines vertical profiles of mean velocity and turbulence, as well as longitudinal profiles of velocity deficit and turbulence levels measured at the symmetry plane of a model three-blade axial flow turbine in a large open channel flow. Mean velocity and turbulence statistics are measured using an acoustic Doppler velocimeter (ADV) and a pulse coherent acoustic Doppler profiler (ADP). ADV and corrected-ADP derived values of mean velocity, turbulence intensity and root-mean-square velocity constitute a well-documented data set that can be used to validate numerical models simulating the effects of hydrokinetic turbine arrays. We found that 80% of the flow recovery occurred about ten diameters downstream from the rotor plane, which suggests that practical values for longitudinal spacing of turbines should be between ten and fifteen diameters. Significant errors observed in mean velocity and turbulence statistics derived from ADP measurements in the near wake region raise concerns on the use of these instruments for such measurements in lab and field studies. Although the cause of some of the errors requires further investigation, we show that errors in turbulence intensity can be successfully corrected with supplemental ADV measurements.

Postprocessing Model-Predicted Rainfall Fields in the Spectral Domain Using Phase Information from Radar Observations

Abstract In an attempt to combine the short-term skill of radar nowcasting and the long-term skill of numerical models, successive 15-min rainfall accumulations obtained from the U.S. national radar composites and predicted by the Weather Research and Forecasting (WRF) model are corrected by adjusting the phases of Fourier components in the spectral domain while keeping the amplitudes invariant. The phase information is first obtained by decomposing the radar and model fields into Fourier space. Then, to correct the model outputs, a new set of Fourier components is constructed using the phases from the radar image and amplitudes from the model image. The corrected model image can be obtained by inverse transformation of the new set of Fourier components. Scales of wavelengths starting from 50 to 500 km are playing a crucial role in correcting the positional and intensity errors of continental-scale precipitating systems predicted by the WRF model, while scales greater than 500 km are contributing very little, which is negligible. The model errors are characterized in terms of phase and power. Compared to power/amplitude correction, phase correction plays a major role in eliminating the positional and intensity errors. Scales that are responsible for the errors or not predicted by the model have a decorrelation time of about 2 h in both the zonal and meridional directions. The higher critical success index (CSI) and smaller RMS error values of the radar-extrapolated and phase-corrected fields compared with the WRF model–predicted fields indicate that the extrapolated phase information can be used to correct the model outputs up to a lead time of about 4 h.

A Dynamical Initialization Scheme for Real-Time Forecasts of Tropical Cyclones Using the WRF Model*

Abstract To improve the initial conditions of tropical cyclone (TC) forecast models, a dynamical initialization (DI) scheme using cycle runs is developed and implemented into a real-time forecast system for northwest Pacific TCs based on the Weather Research and Forecasting (WRF) Model. In this scheme, cycle runs with a 6-h window before the initial forecast time are repeatedly conducted to spin up the axisymmetric component of the TC vortex until the model TC intensity is comparable to the observed. This is followed by a 72-h forecast using the Global Forecast System (GFS) prediction as lateral boundary conditions. In the DI scheme, the spectral nudging technique is employed during each cycle run to reduce bias in the large-scale environmental field, and the relocation method is applied after the last cycle run to reduce the initial position error. To demonstrate the effectiveness of the proposed DI scheme, 69 forecast experiments with and without the DI are conducted for 13 TCs over the northwest Pacific in 2010 and 2011. The DI shows positive effects on both track and intensity forecasts of TCs, although its overall skill depends strongly on the performance of the GFS forecasts. Compared to the forecasts without the DI, on average, forecasts with the DI reduce the position and intensity errors by 10% and 30%, respectively. The results demonstrate that the proposed DI scheme improves the initial TC vortex structure and intensity and provides warm physics spinup, producing initial states consistent with the forecast model, thus achieving improved track and intensity forecasts.

PROPRIOCEPTIVE TRAINING AS AN ADJUNCT IN OSTEOARTHRITIS OF KNEE

Osteoarthritis (OA) is steadily becoming the most common cause of disability with advancing age. So, this study was aimed to prevent and rehabilitate the patient with such disability. Proprioception along with other deficits have been established with threat to the concerned joint. Hence, objective of the study was set to show the efficacy of proprioceptive training with conventional physiotherapy versus conventional physiotherapy. A Pre test- Post test single blind experimental study was designed with 44 patients having knee OA and randomly divided in two groups. Outcome measures were pain intensity on NRS, functional disability on Reduced WOMAC and joint position sense (JPS) error on Electronic Goniometer. Results between group comparisons showed significant improvement in pain intensity (p < 0.05), WOMAC score (p < 0.05) and JPS error (p < 0.05). However both the groups improved significantly but there was more significant improvement in group with proprioception intervention. Hence, it can be concluded that proprioceptive training should be included along with conventional physiotherapy in knee OA rehabilitation.