nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of Algorithms for Wind Parameters Extraction From Shipborne X-Band Marine Radar Images

In this paper, curve-fitting and intensity-level-selection (ILS)-based algorithms for wind parameter extraction from shipborne X-band nautical radar images are investigated. First, to exclude the rain cases and low-backscatter images, a data quality control process is designed for both algorithms. An additional process is then introduced for the ILS-based method to improve the accuracy of wind measurements, including the recognition of blockages and islands in the temporally integrated radar images. For the low sea states, a dual-curve-fitting is proposed. These wind algorithms are tested using radar images and shipborne anemometer data collected on the east coast of Canada. It is shown that the dual-curve-fitting algorithm produces improvements in the mean differences between the radar and the anemometer results for wind direction and speed of about 5.7° and 0.3 m/s, respectively, under sea states with significant wave height lower than 2.30 m. Also, a harmonic function that is least-squares fitted to the selected range distances vector as a function of antenna look direction is applied. Compared with the original ILS-based algorithm, the modified procedure reduces the standard deviation for wind direction and speed by about 4° and 0.2 m/s, respectively. Finally, the performance of these two modified methods are compared.
Surface Current Measurements Using X-Band Marine Radar With Vertical Polarization

In this paper, the retrieval of sea surface current velocity from vertically polarized (V-pol) X-band marine radar data is presented. Three different methods, including the iterative least square approach, the normalized scalar product method, and the polar current shell algorithm, that have been used for horizontally polarized data are employed here. A comprehensive comparison of the performance of the three methods is conducted using the radar images collected by the V-pol radar deployed on the Forschungsplattformen in Nord- und Ostsee No. 3 (FINO3) offshore research platform and the acoustic Doppler current profiler (ADCP) data in the North Sea. The results indicate that all three methods are capable of providing reliable current speed and direction measurements from V-pol data, with similar performance. Based on the experimental data for which the current magnitude is less than 0.5 m/s, the radar-derived results agree best with the ADCP data at a depth of 6-8 m, with the root mean square difference for current velocity x- and y-components being 7.2-8.9 cm/s. The correlation coefficients between the radar-derived and ADCP-measured current velocity components are as high as 0.87-0.93.
The Second-Order High Frequency Radar Ocean Surface Cross Section for an Antenna on a Floating Platform

The second-order high frequency (HF) radar cross section (RCS) of the ocean surface, normalized to the area of the scattering patch, is derived for the case in which the radar transmitting and receiving antennas are mounted on a swaying platform or barge. The second-order result includes both electromagnetic and hydrodynamic contributions. The derivation for the hydrodynamic patch scatter component, for time pulsed radars, is based on the first-order RCS found in the counterpart of this paper by replacing the first-order ocean wave spectrum with the second-order ocean wave spectrum. The electromagnetic patch scatter development begins with a general expression for the bistatically received second-order electric field in which platform sway is introduced. Based on an assumption that the ocean surface can be described as a Fourier series whose coefficients are random variables, the second-order monostatic RCS is developed. The resulting second-order cross section is found to consist of Bessel functions and no singularity exists in the newly derived electromagnetic coupling coefficient. Simulation results for the new RCS are also provided to indicate the effects of barge motion under a variety of sea states.
Gallbladder modeling and simulation in laparoscopic cholecystectomy

One challenge in laparoscopic cholecystectomy surgery simulation is to construct a fast and accurate deformable gallbladder model. This paper proposed an improved multi-layer mass-spring modeling method which can adapt well to the built-in accelerating algorithms in PhysX-engine of GPU. A multi-layer mass-spring model was constructed based on the surface mesh of a gallbladder. The inner layers of the mass-spring model were generated using more geometrical information than the previous method . The parameters of the springs were configured based on the biomechanical properties of gallbladder to ensure the realism of the deformation results. Preliminary experiments demonstrate that our model can achieve better results in terms of both visual perception and time performance.
Retrieving wind speed from rain-contaminated X-band nautical radar images

In this paper, an algorithm for retrieving wind speed from rain-contaminated X-band nautical radar images is presented. Two third-order polynomial functions for the relationship between overall average backscatter intensity and wind speed for rain-free and rain-contaminated images based on least-squares fitting with rain-free and rain-contaminated data, respectively, are derived. Radar images are then classified as rain-free and rain-contaminated according to their zero-pixel percentage (ZPP). For the rain-free data, wind speed is retrieved using the rain-free wind speed model. For the rain-contaminated data, wind speed is determined using the rain-contaminated wind speed model. Experimental results obtained from field data show that, by applying the new algorithm, the wind speed standard deviation and bias can be reduced by 1.2 m/s and 1.3 m/s, respectively.
Template based rodent brain extraction and atlas mapping.

Accurate rodent brain extraction is the basic step for many translational studies using MR imaging. This paper presents a template based approach with multi-expert refinement to automatic rodent brain extraction. We first build the brain appearance model based on the learning exemplars. Together with the template matching, we encode the rodent brain position into the search space to reliably locate the rodent brain and estimate the rough segmentation. With the initial mask, a level-set segmentation and a mask-based template learning are implemented further to the brain region. The multi-expert fusion is used to generate a new mask. We finally combine the region growing based on the histogram distribution learning to delineate the final brain mask. A high-resolution rodent atlas is used to illustrate that the segmented low resolution anatomic image can be well mapped to the atlas. Tested on a public data set, all brains are located reliably and we achieve the mean Jaccard similarity score at 94.99% for brain segmentation, which is a statistically significant improvement compared to two other rodent brain extraction methods.
Head pose estimation by non-linear embedding and mapping

In this paper, we present a new scheme to robustly estimate the head pose from either video sequence or individual images. Developed from ISOMAP, we learn a person-independent and nonlinear embedding space (we call it a 2-D feature space) for different poses. A nonlinear interpolation is proposed to map new sequences or images into the 2-D feature space. Especially for video sequences, we propose an adaptive local fitting technique to filter unreasonable mappings. By exploring the intrinsic characteristics, we further estimate the head pose of that sequence or image. Experiments reported in this paper showed robust results.
A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction

Brain extraction is an important preprocessing step for further analysis of brain MR images. Significant intensity inhomogeneity can be observed in rodent brain images due to the high-field MRI technique. Unlike most existing brain extraction methods that require bias corrected MRI, we present a high-order and  $L_{0}$  regularized variational model for bias correction and brain extraction. The model is composed of a data fitting term, a piecewise constant regularization and a smooth regularization, which is constructed on a 3-D formulation for medical images with anisotropic voxel sizes. We propose an efficient multi-resolution algorithm for fast computation. At each resolution layer, we solve an alternating direction scheme, all subproblems of which have the closed-form solutions. The method is tested on three T2 weighted acquisition configurations comprising a total of 50 rodent brain volumes, which are with the acquisition field strengths of 4.7 Tesla, 9.4 Tesla and 17.6 Tesla, respectively. On one hand, we compare the results of bias correction with N3 and N4 in terms of the coefficient of variations on 20 different tissues of rodent brain. On the other hand, the results of brain extraction are compared against manually segmented gold standards, BET, BSE and 3-D PCNN based on a number of metrics. With the high accuracy and efficiency, our proposed method can facilitate automatic processing of large-scale brain studies.
Theoretical analysis of the first-order sea clutter in shipborne high-frequency surface wave radar

To analyze the characteristic and space-time distribution of the first-order sea clutter in shipborne high-frequency surface wave radar (HFSWR) with six degree-of-freedom (DOF) motion, a time-varying array steering vector in pulse domain is presented. The sea clutter spectrum and space-time distribution model can be derived by the classical space-time cascaded processing of the received echo signal. Simulations are carried out under given motion and radar parameters. Results show that both forward movement and six DOF motion have important influences on the first-order sea clutter, which may bring significant implication in future investigation for moving target detection and ocean remote sensing.
Learning laparoscopic surgery by imitation using robot trainer

Laparoscopic surgery requires rigorous training in order to overcome physical, spatial and visual constraints. We are developing a laparoscopic robot trainer. The robot trainer can learn the motion of the master surgeon when he is performing a virtual surgery, and drive the surgical tool by mimicking the learnt trajectory during training. This paper reports our investigation on robot learning using learning by imitation for surgical training. The feasibility of implementing this innovative learning method is demonstrated with recent results on the robot trainer, and experiments involving our laparoscopic surgery trainer box and computer vision system.
The first-order bistatic high frequency radar scattering cross section of the ocean surface for the case of a floating platform

The first-order bistatic high frequency radar cross section of ocean surface is derived for the case of a fixed receiver and a floating, but tethered, transmitter. A general expression for the bistatically received first-order electric field is obtained from earlier work based on fixed antennas. A small displacement caused by the platform motion is included in the source term to modify the fixed-antenna model. Based on the assumption that the ocean surface can be described as a Fourier series with coefficients being random variables, the first-order bistatic radar cross section is derived. The cross section model is found to contain a sum of Bessel functions, varying in order from zero to infinity. Simulation results depict the effect of platform motion in the Doppler spectrum. It is shown that the location of the motion-induced peaks are symmetrically distributed in the spectrum and the magnitude decreases with increasing order of the Bessel functions. These peaks caused by the swaying motion of the transmitter platform are seen to have less energy in the bistatic cross section than those in the monostatic case.
Motion compensation for platform-mounted high frequency surface wave radar

A method for mitigating antenna motion effects in high frequency radar Doppler spectra developed from ocean backscatter is proposed. Based on the established radar cross section models for a fixed antenna and for an antenna on a floating platform, the relationship between these models is developed. Through this relationship, motion compensation can be achieved by deconvolving the radar cross section data with the derived transfer function. A least squares deconvolution (LSD) method is applied in this study and shows good performance.
Wave height measurement using a short-range FMCW radar for unmanned surface craft

This paper describes a radar system designed for deployment on an unmanned surface craft under development in the Autonomous Ocean Systems Laboratory of Memorial University. The radar system includes two sub-systems: one a long-range radar that detects sea-surface objects a few kilometers away, the other a short-range frequency modulated continuous wave radar. This short-range radar not only detects the near obstacles but also senses the sea state to enhance the navigation safety of the unmanned surface craft. The short-range radar provides a useful complement to the blind zone of the long-range radar and largely determines the survival of the unmanned surface craft in oceans. The design of the short-range system and the experimental results obtained from both wave tank and seasurface tests are presented. The results show that the short-range radar are able to estimate the sea-state by measuring wave height.
Coronary artery segmentation via Hessian filter and curve-skeleton extraction

Precise coronary artery segmentation is a prerequisite for quantitatively assessing the severity of coronary artery stenosis. Extracting the centre line of the 3D volumetric coronary artery tree, also named as 3D skeletonization, plays an important role in identify the variations of cross-sectional profile. Typically there are three skeletonization methods, viz. distance transformation, Voronoi method and topological thinning method. All these three skeletonization methods were applied in this study to extract the curve-skeleton of coronary arteries, after segmenting the coronary artery tree with Hessin filter. Among them, topological thinning method is recommended, as it produces reliable and accurate curve-skeleton for vessels with varying size. This will facilitate quantitative assessment of the severity of coronary artery stenosis, help clinical diagnosis and treatment planning of coronary artery disease.
A hybrid method for surface current estimation using X-band nautical radar

In this paper, a new method is proposed to estimate surface current using X-band nautical radar. This method is referred to as the Hybrid Least Squares (HLS) method. The HLS combines the existing Iterative Least Squares (ILS) and Normalized Scalar Product (NSP) methods. The HLS is designed to inherit the short ILS computational time and the high NSP reliability. For validation, the proposed method was applied on a number of simulated X-band nautical radar image sets. Results have shown that the HLS produces accurate estimates while maintaining very short computational time.
An enhanced algorithm for wave information extraction from X-band nautical radar images

In this paper, an enhanced band-pass filtering algorithm based on the 3-D Fourier transform is presented to extract ocean wave information from X-band nautical radar images. After the current velocity is determined by an iterative least-squares method with appropriate thresholds, the components of the image spectra will be classified as the fundamental, the first-order harmonic waves and noise. In the proposed algorithm, only the fundamental components are used for wave spectra and parameter retrieval. The modified wave algorithm is first tested via numerical simulation. Further validation is then performed using field data. Unlike the previous wave analysis research that is conducted with horizontally polarized radar data, here, vertically polarized radar data is used. Retrieved results show good agreement with in-situ values obtained using a TRIAXYS wave buoy.
Combining EPI and motion correction for fMRI human brain images with big motion

Motion correction is an important component in fMRI brain image analysis. Linear registration technique is mostly used in the process based on the assumption that there is not any shape changes of human brain during imaging process. Echo planar imaging (EPI) technique has been widely adapted in fMRI imaging to shorten encoding duration and increase temporal resolution. However, due to the magnetic field inhomogeneity caused by tissues, shape distortion and signal intensity lose are brought into fMRI images by the technique. On the other hand, subject's pose in scanner has a effect on magnetic field inhomogeneity, so the EPI distortions are subject to head movement, especially when the movement is big. As a result, most current motion correction techniques, which are based on rigid registration, cannot handle the problem. In this paper, a technique that combines EPI distortion correction and motion correction to handle the above-mentioned problem is proposed. Since it is almost impossible to obtain ground truth at present, a task-related fMRI BOLD time course image with big motion is selected as experimental material to test its performance. The image is pre-processed with the proposed EPI-motion correction scheme then analyzed by FSL feat tool . Compared with another process with only motion correction and FSL feat analysis, the experimental result using the proposed method has no false activation detection. It is suggested the proposed EPI-motion correction scheme has the ability to handle the fMRI human brain images with big motion.
An alternative method for surface current extraction from X-band marine radar images

In this paper, a novel current inversion algorithm from X-band marine radar images is proposed. The routine begins with a 3D-FFT of the radar image sequence, followed by the extraction of the dispersion shell from the 3-D image spectrum. After a polar coordinate transformation, the "polar current shell" is then analyzed to retrieve current information such as the speed and direction of encounter. Particularly, a Grubbs' test is conducted to remove outliers along each radial direction, and a robust sinusoidal curve fitting is applied to the data points along each circumferential direction. For validation, the algorithm is tested against simulated radar PPI images. The results indicate that the proposed procedure, unlike most existing current inversion schemes, is not susceptible to high current speeds and has no direction restriction. Meanwhile, the relatively low computational cost makes it an excellent choice in practical marine applications.
A First-Order HF Radar Cross-Section Model for Mixed-Path Ionosphere–Ocean Propagation With an FMCW Source

A theoretical model of the first-order high-frequency (HF) radar cross section (RCS) for the ionosphere-ocean propagation involving a frequency-modulated continuous wave (FMCW) source is presented in this paper. Based on previous modeling involving the scattering of HF electromagnetic radiation from the ocean surface, an expression for the first-order electric field with an FMCW source is derived for the mixed-path case in which scattering occurs from both the ionosphere and the ocean surface. Then, this field is Fourier transformed and the frequency distribution of the demodulated electric field indicates range information of the ionosphere-ocean propagation. Subsequently, the first-order RCS is developed by assuming the ocean surface and the ionosphere are stochastic processes. To investigate the power spectrum of this ionospheric-clutter mode and its relative intensity to that of the ocean clutter, a normalized ionospheric-clutter power is simulated. Numerical simulation results are first compared with that of a pulsed radar for the same ocean scattering patch. Subsequently, it is shown how the ionosphere velocities and wavelengths, and wind directions affect the power spectrum for this ionosphere-ocean propagation.
Designing an active motor skill learning platform with a robot-assisted laparoscopic trainer

Laparoscopic Surgery poses significant complexity in hand-eye coordination to the surgeon. In order to improve their proficiency beyond the limited exposure in the operating theatre, surgeons need to practice on laparoscopic trainers. We have constructed a robotic laparoscopic trainer with identical degrees of freedom and range of motion as a conventional laparoscopic instrument. We hypothesize that active robotic assistance through a laparoscopic trainer improves training efficacy as compared to autonomous practice. In order to test the hypothesis, we have divided the subjects into two groups. The control group practiced on two laparoscopic tasks manually without feedback or supervision. The other group practiced on the same tasks with robotic assistance. Results from the robot-assisted group show that tool orientation (pitch and yaw joint motion) in the pointing task improved by more than 15%.
The second-order monostatic HF radar cross section incorporating antenna barge motion

The second-order HF radar ocean cross section is derived for the case of the transmitting and receiving antenna undergoing platform (i.e. barge) motion. The derivation for electromagnetic patch scatter begins with a general expression for the bistatically received second-order electric field. Based on an assumption that the ocean surface can be described as a Fourier series with coefficients being zero-mean Gaussian random variables, the second-order monostatic radar cross section is developed. The derivation for the hydrodynamic patch scatter follows from an earlier first-order analysis. The second-order HF radar cross section is found to consist of Bessel functions, and no singularity exists in the new electromagnetic coupling coefficient. Simulation results depict the effects of barge motion under a variety of sea states. It is evident that first-order effects spread into the second-order region and thus could have detrimental effects on wave measurement schemes.
Multimodal Sleeping Posture Classification

Sleeping posture reveals important information for eldercare and patient care, especially for bed ridden patients. Traditionally, some works address the problem from either pressure sensor or video image. This paper presents a multimodal approach to sleeping posture classification. Features from pressure sensor map and video image have been proposed in order to characterize the posture patterns. The spatiotemporal registration of the two modalities has been considered in the design, and the joint feature extraction and data fusion is presented. Using multi-class SVM, experiment results demonstrate that the multimodal approach achieves better performance than the approaches using single modal sensing.
Wind speed determination from X-band nautical radar images

A new method is presented for estimation of wind speed from X-band nautical radar sea surface images. Ensemble empirical mode decomposition (EEMD) is first applied to the radar data. A normalization scheme is then used to obtain the amplitude modulation (AM) part of the first intrinsic mode function (IMF). Wind speed is determined from a combination of the first IMF and the residual using a logarithmic relationship. The method can be applied to both rain-free and rain-contaminated radar images. Radar and anemometer data collected in a sea trial off the east coast of Canada are employed for the test. Compared to the spectral-analysis-based method, the proposed method improves the wind speed result with an increase of about 0.06 in the correlation coefficient and a decrease of about 0.28 m/s in the root-mean-square (RMS) difference with respect to the reference.