nils-herrmann/clean_scirepeval huggingface dataset:
An Empirical Mode Decomposition Method for Sea Surface Wind Measurements From X-Band Nautical Radar Data

In this paper, sea surface wind direction and speed are obtained from X-band nautical radar images. A data control strategy is proposed to distinguish rain-free and rain-contaminated radar data. The radar data are decomposed by an ensemble empirical mode decomposition method into several intrinsic mode functions (IMFs) and a residual. A normalization scheme is applied to the first IMF to obtain the amplitude modulation (AM) component. Wind direction is determined from the residual for the rain-free and high-wind-speed rain-contaminated data, and from the AM portion of the first IMF for the low-wind-speed rain-contaminated data, based on curve fitting a harmonic function. Wind speed is determined from a combination of the residual and the AM part of the first IMF for both rain-free and rain-contaminated data using a logarithmic relationship. Results employing ship-borne radar and anemometer data collected in a sea trial off the east coast of Canada are presented. The root-mean-square differences for wind direction and speed measurements are 11.5° and 1.31 m/s, respectively, compared with reference values from anemometers.
Statistical modeling on motion trajectories for robotic laparoscopic surgery

Learning by demonstration enables a robot to learn and perform tasks from kinesthetic demonstrations. Gaussian mixture method with constraints is applied in this work to model the motion using its trajectories and enable a robot to learn motion skills for a simple surgical task with specific requirement. Tissue dividing experiments are demonstrated on a robotic surgical simulation platform to collect motion trajectories. The demonstrations are modelled using Gaussian Mixture Model. Constraints are also imposed onto the motion model to suit the specific requirements for carrying out the surgical task on a virtual patient. The robot is demonstrated to be able to learn the surgical skills with the statistical model and execute it to complete a virtual surgical task.
Frame-based compressive sensing MR image reconstruction with balanced regularization.

This paper addresses the frame-based MR image reconstruction from undersampled k-space measurements by using a balanced ℓ(1)-regularized approach. Analysis-based and synthesis-based approaches are two common methods in ℓ(1)-regularized image restoration. They are equivalent under the orthogonal transform, but there exists a gap between them under redundant transform such as frame. Thus the third approach was developed to reduce the gap by penalizing the distance between the representation vector and the canonical frame coefficient of the estimated image, this balanced approach bridges the synthesis-based and analysis-based approaches and balances the fidelity, sparsity and smoothness of the solution. These frame-based approaches have been studied and compared for optical image restoration over the last few years. In this paper, we further study and compare these three approaches for the compressed sensing MR image reconstruction under redundant frame domain. These ℓ(1)-regularized optimization problems are solved by using a variable splitting strategy and the classical alternating direction method of multiplier (ADMM). Numerical simulation results show that the balanced approach can reduce the gap between the analysis-based and synthesis-based approaches and are even better than these two approaches under our experimental conditions.
Analysis of rain effects on wave height estimation from X-band nautical radar images

In this paper, the analysis of rain effects on wave height estimation from X-band nautical radar sea surface images is presented. A modified shadowing-analysis-based algorithm is applied to the radar data acquired in a sea trial in the North Atlantic Ocean under both rainy and rain-free conditions. The result shows that rain enhances the image pixel intensity and reduces shadowed areas, leading to the underestimation of wave heights. An increase of about 0.31 m in the bias with respect to in-situ measurements is found in the wave height retrieved from rain contaminated data compared to the results obtained from data without rain.
Audio events classification using hierarchical structure

This paper presents a novel approach using hierarchical structure with different feature sets to classify the audio signals efficiently into audio events. Most of the past researches focused on the speech (male, female), music (different genres) and environment sound (noise). To further differentiate the environment sound, this work studies the feature selection and classification. Different from that of other methods, the audio signals in our work are segmented into different lengths perceptually. So, humans can also recognize a sound based on the short segments. A top-down tree structure with the selected features is designed to classify the audio events while each node is a support vector machine trained as a classifier. Experiments show the robustness and efficiency of the method with a small set of training database.
A kernel autoassociator approach to pattern classification

Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains.
High frequency radar clutter power for mixed ionosphere-ocean propagation

Ionospheric clutter is one of the main sources of interference affecting the performance of high frequency surface wave radar (HFSWR). One component of this arises from a portion of the transmitted radar signal which is reflected from the ionosphere to the ocean surface, from whence it is backscattered to the receiving antennas. The influence of the ionospheric reflection can be accounted for by a so-called ionosphere reflection coefficient (IRC). This paper presents three ionospheric spectral models which may be used to determine clutter power within reasonable limitations.
Application of polar-current-shell-based algorithm for surface current extraction from shipborne X-band nautical radar images

In this paper, the application of the polar-currentshell- based current inversion algorithm to shipborne X-band nautical radar images is presented. After the 3-D image spectrum is generated by a 3D-FFT of the radar image sequence, the extracted dispersion shell is converted to a polar form - the "polar current shell" - from which the current velocity of encounter is determined by incorporating a robust sinusoidal curve fitting. The algorithm is applied to shipborne radar data that were collected on the east Coast of Canada. The results are compared with those obtained by a WaMos system and agreement between the two indicates the validity of the proposed algorithm.
Retrieval of ionospheric TEC over oceans From GNSS-R delay-Doppler map

In this paper, an approach is presented to retrieve ionospheric total electron content (TEC) over oceans from Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler map (DDM). Here, an additional ionospheric delay (τI) is considered in the conventional DDM simulation process. Based on this, the least squares (LS) fitting method is employed for TEC retrieval by fitting the simulated DDMs with different τI values to the measured DDM. Meanwhile, an adaptive threshold is adopted in the fitting process to restrain the intrinsic errors due to DDM mismatching. The proposed method is validated by comparing the retrieved TEC results based on three datasets from the SSTL UK-DMC satellite with the TEC values produced by the International Reference Ionosphere (IRI)-2012 and the NeQuick 2 models. A good consistency is obtained.
A semi-automatic approach to the segmentation of liver parenchyma from 3D CT images with Extreme Learning Machine

This paper presents a semi-automatic approach to segmentation of liver parenchyma from 3D computed tomography (CT) images. Specifically, liver segmentation is formalized as a pattern recognition problem, where a given voxel is to be assigned a correct label - either in a liver or a non-liver class. Each voxel is associated with a feature vector that describes image textures. Based on the generated features, an Extreme Learning Machine (ELM) classifier is employed to perform the voxel classification. Since preliminary voxel segmentation tends to be less accurate at the boundary, and there are other non-liver tissue voxels with similar texture characteristics as liver parenchyma, morphological smoothing and 3D level set refinement are applied to enhance the accuracy of segmentation. Our approach is validated on a set of CT data. The experiment shows that the proposed approach with ELM has the reasonably good performance for liver parenchyma segmentation. It demonstrates a comparable result in accuracy of classification but with a much faster training and classification speed compared with support vector machine (SVM).
A microsecond generator based on pulse transformer and its discharge applications

With the plasma applications, kinds of high voltage (HV) pulsed generators of different principles and different parameters are developed for the increasing requirement. For the reliability and compactness of HV pulsed generators, the study on pulse transformer is required. Pulse transformer can easily achieve HV output and compress pulse width. In this paper, a wideband pulse transformer was developed with repetitive frequencies varying from 0 to 10 kHz. By calculating the magnetization characteristics and choosing the suitable core material, the volume and winding lines, turns, cross-sectional area, winding method, and insulation of pulse transformer are designed. Depending on semiconductor switches and pulse transformer, a compact repetitive frequency microsecond generator was developed, with the output voltage amplitude of up to 30 kV, repetitive frequency varying from 0 Hz to 10 kHz and pulse width of 8 μs. Finally, the generator is used for the large-area dielectric barrier discharge experiments, and discharge voltage, discharge current, and the preliminary discharge characteristics are presented.
High-frequency radar cross-sections of swellcontaminated seas for a pulsed waveform

The first- and second-order monostatic cross-sections of swell-contaminated seas for high-frequency ground wave pulsed radar operation are derived from the fundamental electric field equations. In this case, the ocean surface, a mixture of both swell and wind wave components, is first represented with a Fourier series. In one of the two approaches used, the total Fourier coefficient is expanded as a linear sum of the contribution from swell and wind waves without considering the possible interaction between the two regimes; whereas in a second, more general situation, the analysis includes coupling effects between the wind waves and swell. Then, the corresponding electric fields received from the swell-contaminated sea surface are obtained. A Fourier transform of the autocorrelation of the electric fields gives the Doppler power spectral density (PSD), and a comparison between this PSD and monostatic radar range equation yields the radar cross-sections. Simulation shows that the assumption incorporating the coupling effects leads to results that better agree with historical field observations. Overall, the model proposed here not only lays a solid foundation for the development of future swell inversion algorithms, but also reveals the existence of non-linear energy transfer between swell and wind waves from a new perspective
HF radar wave and wind measurement over the Eastern China Sea

High-frequency (HF) radar can be employed to measure sea surface state parameters such as waveheight, wind field, and surface current velocity. This paper describes the application of the HF ground wave radar in remote sensing the surface conditions over the Eastern China Sea in October 2000. The radar, referred to as the OSMAR2000, was developed by Wuhan University. Preliminary wave spectra, waveheights, and wind fields estimated from the collected data are presented and compared with ship-recorded measurements where such are available. The range for wind direction sensing is up to 200 km. Wave information and wind speed can be provided up to a range of 120 km. The mean difference between radar- and ship-measured significant waveheight is 0.323 m; wind direction is measured within 20/spl deg/; and wind speed to within 0.6 m/s. With such agreement being fairly reasonable, the feasibility of the inversion algorithm and the ocean state real-time sensing capability of OSMAR2000 are demonstrated.
A comparison of DTI pre-processing tools on a dataset of chronic subcortical stroke rehabilitation patients

In this paper, we compared the performance of a number of Diffusion Tensor Imaging (DTI) pre-processing tools on a dataset of chronic subcortical stroke patients during rehabilitation exercise. In the comparison, acquired Diffusion-Weighted Images (DWI) are pre-processed by different pipelines, which compose of different DTI pre-processing tools, independently. And a DTI measure, FA (fractional anisotropy), is derived from each processed image. Then, a group-based DTI tractography analysis tool, Tract-Based Spatial Statistics (TBSS), is used to localize brain changes in white matter that correlate to the behavior changes during stroke rehabilitation. Although the ground-truth of the dataset is unavailable, it can be observed that there exist significant variations in the obtained hot spot maps that come with different DTI pre-processing pipelines. It suggests that the imaging technicians and scientists should choose the tools carefully according to the acquisition methods and parameters.
Face detection and precise eyes location

Presents a robust and precise scheme for face detection and precise facial feature location. Multiscale filters are used to obtain the pre-attentive features of objects, based on which different models are investigated to locate the face and facial features such as eyes, nose and mouth. The structural model is used to characterize the geometric pattern of facial components. The texture and feature models are used to verify the face candidates detected before. Since the eyeballs are the only features that are salient and have strong invariant property, the distance between them is used to normalize faces for recognition. Motivated from this, with the face detected and the structural information extracted, a precise eyes location algorithm is applied using contour and region information. It detects, with a subpixellic precision, the center and the radius of the eyeballs of a person. The detected result can be used as an accurate normalization of images, which reduces greatly the number of possible scales used during the face recognition process.
Tracking a Variable Number of Human Groups in Video Using Probability Hypothesis Density

We apply a multi-target recursive Bayes filter, the probability hypothesis density (PHD) filter, to a visual tracking problem: tracking a variable number of human groups in video. First, we use background subtraction to detect human groups which appear as foreground blobs. The PHD filter is implemented using sequential Monte Carlo methods; and the centroids of the foreground blobs are used as the measurements to update the PHD filter. Our experimental results show that when human groups appear, merge, split, and disappear in the field of view of a camera, our method can track them correctly
Sea surface current measurements at long range using lower-band high-frequency radar

Surface current measurements on the Grand Banks, Newfoundland, Canada using two long-range (> 300 km) high-frequency surface-wave radars are presented. The radars operated in the lower HF band at 3.2 MHz and 3.45 MHz. During the three-month experiment, three drifters were also deployed for data comparison. Though the data available for comparison are somewhat limited, experimental results show the radar-derived radial currents and vector currents agree well with the drifter data.
Sea surface wind retrieval from GNSS delay-Doppler map using two-dimension least-squares fitting

In this paper, a novel method is presented to retrieve sea surface wind speed and direction by fitting the two-dimensional simulated GNSS-R DDMs to measured data. The signal scattering model of Z-V and the sea surface roughness model of Cox and Munk are employed for the DDM simulation, and an 18-second incoherent correlation is performed on the measured signal to reduce the noise level. Meanwhile, a variable step-size iteration as well as a fitting threshold are used to reduce the computational cost and error rate of the fitting procedure, respectively. Unlike previous methods, all the DDM points with normalized power higher than the threshold are used in the least-square fitting. An optimal fitting threshold is also proposed. To validate the proposed method, the retrieving results based on a dataset from the UK-DMC satellite are compared with the in-situ measurements provided by the National Data Buoy Center, and good correlation is observed between the two.
2-D continuous wavelet-based algorithm for extracting wave information from nautical radar images

In this paper, a 2-D continuous wavelet transform-based algorithm for the extraction of wave information from X-band nautical radar images is presented. By reviewing the theory of the wavelet transform for image processing, a sophisticated scheme for the wavelet design is developed to obtain image spectra. First, the Morlet wavelet is selected and simplified as the mother wavelet. Then, a configuration and sampling strategy, which enables the non-dimensional continuous wavelet to discretely process the digital radar images in dimensional space, is proposed. Wavelet parameters are derived theoretically and calibrated numerically. Similar techniques employed in the Fourier analysis are incorporated into the proposed algorithm. These include image normalization and a modulation transfer function for the retrieval of wave spectra and wave parameters from the image spectra derived by wavelet. The algorithm is first evaluated via numerical simulation and then applied to horizontally-polarized radar field data collected on the East Coast of Canada. Good agreement of the results with the in-situ record from a TRIAXYS wave buoy indicates that the proposed algorithm is effective for retrieving ocean wave information.
Wind speed extraction from rain-contaminated X-band nautical radar data

In this paper, a new method for retrieving wind speeds from rain-contaminated X-band nautical radar images is presented. The method mitigates rain influence by applying the gamma correction to rain-contaminated images, and then relates the average intensities of radar images to the measured wind speeds with a logarithmic function. The method has been tested using X-band nautical radar images and shipborne anemometer data. A comparison with the anemometer data shows that the root mean square error of wind speeds retrieved from rain-contaminated radar images is reduced by 5.4 m/s.
A Spectra-Analysis-Based Algorithm for Wind Speed Estimation From X-Band Nautical Radar Images

In this letter, a new method for estimating wind speeds from X-band nautical radar images is presented. Wind speeds are determined from the wavenumber spectra of radar backscatter using a logarithmic relationship between the spectral strength and the wind speed. The method can be applied to both rain-contaminated and rain-free radar data. The method has been tested using shipborne X-band nautical data collected over the North Atlantic Ocean. A comparison with the anemometer data shows that the root mean square errors of wind speeds estimated from rain-contaminated radar data using the proposed method and that by Lund et al. are 1.6 and 7.5 m/s, respectively. The wind speed estimation accuracy is improved by 5.9 m/s with the new method.
A Monostatic Ocean Scattering Cross Section for the Case of Surface Wave Radar Operating from a Floating Barge

An understanding of the parameters affecting the high frequency surface wave radar (HFSWR) cross sections of the ocean surface is essential to employing such formulations in remote sensing models. Available techniques to date have not explicitly included the effect of antenna motion on the ocean clutter spectra derived from HF Doppler radar data. Here, a model, which assumes the incident radiation to be a simple pulse and accounts for the fact that there may be antenna motion in the radar look direction, is presented. Simulations indicate that the motion of the antennas may give rise to additional, significant spectral content which cannot be ignored when the Doppler spectra are used for ocean parameter estimation.
Face recognition by incremental learning for robotic interaction

One of the important features for human-robot interaction is its ability to recognize human faces. This paper presents a novel architecture suitable for real time robotic face recognition by learning a person's face incrementally. The Gabor features at respective feature locations of a face are used to derive a similarity measurement. A face tracking followed by a clustering technique is used to learn a person's face appearance variance when the system interacts with the person. The recognition by learning proposed in this paper is similar to the partial memory incremental learning method, where we proposed a novel approach to the learning and updating process. Experiment shows significant improvement in the face recognition performance after learning over the time and with more interaction between a person and the system.
Affine object tracking with kernel-based spatial-color representation

This paper presents a new visual tracking method that can achieve accurate estimation of affine transformation and precise spatial-color representation. The estimation of transformation provides more information than translation for better motion understanding and also helps maintain the precise representation; the precise representation enables tracking objects in highly-cluttered environment. The basis of the method is a kernel-based similarity measure called affine matching that describes the relationship between image regions with respect to affine transformation parameters. Based on the similarity measure, a mathematical solution is derived for estimating the transformation parameters for moving objects in videos. Various experiments have yielded positive results.