nils-herrmann/clean_scirepeval huggingface dataset:
On the Development of a Second-Order Bistatic Radar Cross Section of the Ocean Surface: A High-Frequency Result for a Finite Scattering Patch

The development of a model for the second-order bistatic high-frequency (HF) radar cross section on an ocean surface patch remote from the transmitter and receiver is addressed. A new approach is taken that allows a direct comparison with existing monostatic cross sections for finite regions of the ocean surface. The derivation starts with a general expression for the bistatically received second-order electric field in which the scattering surface is assumed to be of small height and slope. The source field is taken to be that of a vertically polarized dipole, and it is assumed that the ocean surface can be described, as is usually done, by a Fourier series in which the coefficients are zero-mean Gaussian random variables. Subsequently, a bistatic cross section of the surface, normalized to patch area, is derived. The result is verified by the following two means: 1) the complete form of the bistatic HF radar cross section in backscattering case is shown to contain an earlier monostatic result that has, itself, been used extensively in radio oceanography applications; and 2) the bistatic electromagnetic coupling coefficient is shown to reduce exactly to the monostatic result when backscattering geometry is imposed. The model is also depicted and discussed based on simulated data
A dynamic hierarchical feature selection method for object-based classification of wetlands

Wetland classification has always been a challenging task among remote sensing experts. Typically, wetland classes have low accuracies regardless of the applied dataset, as they have many spectral and ecological similarities. In this paper, a method is developed particularly effective for distinguishing spectrally similar classes such as wetlands. In this method, feature selection and object-based classification are not done in one step, but instead several feature selections and classifications are applied, and in each level a target class is classified and masked out. While classifying the target class, other spectrally resembling classes are merged so that feature selection is mainly concentrated on separating two classes only. Object-based features were extracted from several SAR and optical images, including RADARSAT-2, ALOS-1, ALOS-2, RapidEye and Landsat-8 images. 15 and 10 percent improvement was obtained in wetlands' average producer and user accuracies compared to the typical feature selection by using the proposed method.
Abdominal wall extraction using constrained deformable model and abdominal context

Information about abdominal wall can be used for many applications from organ segmentation, registration, and surgical simulation. The challenges exist in abdominal wall extraction due to its varieties in shapes, connection to the internal organs and anterior layer edge formed between the muscle and fascia/fatty layer, which may distract the shape model. In this paper we present an approach to the posterior abdominal wall extraction using the shape model and other abdominal context, particularly with the rib-spine bone information and the wall image features. The shape model is constructed based on the training abdominal walls that are delineated manually. After bone information being extracted, the wall shape deforms from the prior shape model using the snake, which is constrained by the bone context and guided by the processed image energy map with the aim of removing distracted image features of anterior abdominal wall and the outer region from the original map. Meanwhile, an overall convex shape is maintained by limiting the angles of the contour points. The proposed approach is tested on abdominal CT data which provides encouraging results.
Kernel autoassociator with applications to visual classification

Autoassociator is an important issue in concept learning, and the learned concept of a particular class can be used to distinguish the class from the others. For nonlinear autoassociation, This work presents a new model referred to as kernel autoassociator. Using kernel feature space as a potential nonlinear manifold, the model formulates the autoassociation as a special reconstruction problem from kernel feature space to input space. Two methods are developed to solve the problem. We evaluate the autoassociator with artificial data, and apply it to handwritten digit recognition and multiview face recognition, yielding positive experimental results.
An algorithm for estimation of wave parameters from X-band marine radar images

An algorithm based on Radon transform (RT) is proposed to estimate wave parameters from X-band marine radar images. First, an initial guess of wave direction is determined from the first image of an image sequence. Then, sub-images located in the initial guess of wave direction from an image sequence are used to determine wave parameters, including wave direction, peak wave period, and mean wave period. The algorithm is tested using radar and buoy data collected in a sea trial in the vicinity of the east coast of Canada. Compared to the traditional Fourier-based technique, the wave parameter estimation results are shown to be improved by the proposed algorithm.
GA approach to invariant matching: under noise and geometric transformations

The paper proposes a new approach to solve the matching problem, in which the genetic algorithm is used as the search strategy to find the optimal matching between the features of the object and those of the model. Feature points matching is used as an example to discuss this kind of approach. Firstly the invariant relative attributes of features are discussed. With the invariant attributes, we analyse the expression of matching points. Secondly by using the genetic algorithm's schema we design the new approach to the invariant matching. The experiments show that the algorithm is indeed invariant to the geometric transformations and it can work in the noise case.<<ETX>>
Simulation analysis of sea surface current extraction from microwave nautical radar images

In this paper, an improved algorithm for extracting sea surface currents from X-band nautical radar image sequences is presented. The marine radar images are simulated by first numerically generating current-included ocean surface elevation maps for given sea states and radar parameters. Then the plan position indicator (PPI) radar images are produced via tilt and shadowing modulation processing. From the simulated radar images, current velocity is derived with a modified iterative least square (LS) fitting method in which higher harmonic wave components and aliasing effects are considered. Here, the correct current velocity is found by a proposed iteration-terminating criterion instead of using a fixed number of iterations. Simulated inversion results for a variety of current velocities and sea states validate the algorithm and show that the number of iterations may be reduced while obtaining highly precise measurement. The performance of the algorithm is also tested using real vertically polarized radar data through comparison with the results from other current methods and an acoustic Doppler current profiler (ADCP).
Research on logistics information transmission platform based on wireless mesh network

Wireless Mesh Network is a new broadband wireless network structure, That is, a high-capacity, high-speed distribution network, It is different from traditional wireless networks, can be seen as the integration of wireless LAN and mobile Ad hoc networks, and it played both advantages, is a wireless version of the Internet. Wireless Mesh networks are gradually expanding into the civilian commercial research, development and application stage. Based on wireless mesh network technology, this hot spot has become more sophisticated background, In this paper, systematic and comprehensive introduction to the major wireless Mesh network technology, applications, advantages of wireless networking solutions, topology, and logistics information transmission platform based on wireless mesh network of requirements analysis, application design case, the platform features and functions and potential applications.
The  $L_{0}$  Regularized Mumford–Shah Model for Bias Correction and Segmentation of Medical Images

We propose a new variant of the Mumford-Shah model for simultaneous bias correction and segmentation of images with intensity inhomogeneity. First, based on the model of images with intensity inhomogeneity, we introduce an L0 gradient regularizer to model the true intensity and a smooth regularizer to model the bias field. In addition, we derive a new data fidelity using the local intensity properties to allow the bias field to be influenced by its neighborhood. Second, we use a two-stage segmentation method, where the fast alternating direction method is implemented in the first stage for the recovery of true intensity and bias field and a simple thresholding is used in the second stage for segmentation. Different from most of the existing methods for simultaneous bias correction and segmentation, we estimate the bias field and true intensity without fixing either the number of the regions or their values in advance. Our method has been validated on medical images of various modalities with intensity inhomogeneity. Compared with the state-of-art approaches and the well-known brain software tools, our model is fast, accurate, and robust with initializations.
Design and implementation of a high-frequency software-defined radar for coastal ocean applications

In 1955, Crombie [1] identified that when an electromagnetic wave is transmitted from a shore-based high-frequency (HF) radar, operating at 13.56 MHz, the Doppler spectrum of sea echo yielded a resonant backscatter phenomenon known as Bragg scattering, which results from coherent reflection of the transmitted energy by ocean surface waves (SWs) whose wavelength is exactly half as long as the transmitted radar waves. Around 17 y later, Barrick introduced the first- and second-order theory for deriving the HF ocean radar cross section, paving the way for using HF-SW radar (3–30 MHz) in remote sensing of the sea state [2]–[4]. Since then, design and implementation of classical HF radars have proliferated , with Coastal Ocean Dynamics Applications Radar and Wellen/Wave Radar (WERA) probably among the most popular [6], . These radars use traditional, fixed, and dedicated hardware implementations. An outcome is that this approach limits the flexibility of these classical systems and potentially increases their cost.
Prostate boundary segment extraction using cascaded shape regression and optimal surface detection

In this paper, we proposed a new method (CSR+OSD) for the extraction of irregular open prostate boundaries in noisy extracorporeal ultrasound image. First, cascaded shape regression (CSR) is used to locate the position of prostate boundary in the images. In CSR, a sequence of random fern predictors are trained in a boosted regression manner, using shape-indexed features to achieve invariance against position variations of prostate boundaries. Afterwards, we adopt optimal surface detection (OSD) to refine the prostate boundary segments across 3D sections globally and efficiently. The proposed method is tested on 162 ECUS images acquired from 8 patients with benign prostate hyperplasia. The method yields a Root Mean Square Distance of 2.11±1.72 mm and a Mean Absolute Distance of 1.61±1.26 mm, which are lower than those of JFilament, an open active contour algorithm and Chan-Vese region based level set model, respectively.
The first-order FMCW HF radar cross section model for ionosphere-ocean propagation

High frequency surface wave radar (HFSWR) is becoming accepted as an important remote sensing device for sea state monitoring, and the frequency-modulated continuous wave (FMCW) has been widely used as the radar transmitted waveform. However, the performance of HFSWR may be significantly impacted by unwanted echoes, of which ionospheric clutter is one of the main sources. During transmission, a portion of the radar radiation may travel upwards to the ionosphere from the transmitting antenna. This may be partially reflected back to the receiving antennas directly or via the ocean surface, the latter being refered to as ionosphere-ocean propagation. The purpose of this paper is to investigate the physical mechanism of how the ionosphere clutter might be analytically characterized within the radar backscatter spectrum. The first-order HF radar clutter power and its radar cross section of ionosphere-ocean propagation for an FMCW source will be derived theoretically, and then simulated and compared for a variety of ionosphere velocities and wavelengths.
A Support Vector Regression-Based Method for Target Direction of Arrival Estimation From HF Radar Data

High-frequency (HF) radars have great potential for maritime surveillance, and the multiple signal classification (MUSIC) algorithm is usually used to estimate the direction of arrival (DOA) of targets for a wide-beam radar. However, the performance of the MUSIC algorithm relies on the precision of the antenna pattern, which could be contaminated by nearby electromagnetic interference. Therefore, the actual antenna pattern must be measured and used. In order to remove the requirement of antenna pattern measurement, a new method for target DOA estimation from wide-beam HF radar data using support vector regression (SVR) is proposed in this letter. A system model that relates target bearing and radar data feature is obtained through the SVR-based machine learning using the automatic identification system data and data associated with the vessels successfully detected by the HF radar. Then, such a model is used to determine the DOAs of targets from new data. The field experimental results at two sites demonstrate that the performance of the SVR method is better than that of the MUSIC algorithm.
Enhancement of the normalized scalar product method for surface current measurement using nautical radar

An algorithm to improve the normalized scalar product (NSP) method of extracting sea surface currents from X-band nautical radar image sequences is presented. In the NSP method, two-dimensional searching over a suitable range of current velocities with an appropriate resolution is required to determine the velocity that maximizes the normalized scalar product. The search may take an extremely long time if the range is wide or a high resolution is sought. In order to reduce the computation involved in the procedure, the proposed algorithm uses variable ranges and resolutions instead of fixed ones. Simulated inversion results for a variety of current velocities are obtained in order to check the internal consistency of the algorithm. With this algorithm, it is observed that the search time can be reduced significantly while obtaining high precision. The performance of the algorithm is also validated using real radar data collected on the east coast of Canada.
High-Frequency Radar Ocean Surface Cross Section Incorporating a Dual-Frequency Platform Motion Model

The first- and second-order high-frequency radar cross sections of the ocean surface are derived for an antenna on a floating platform. In this analysis, simulations are conducted for a more complicated platform motion than appear in earlier work and comparisons are made to model outputs for a fixed antenna. Results show that motion-induced peaks appear symmetrically in the Doppler frequency and have less energy in the second-order radar cross section than those in the first-order radar cross section. The magnitude and width of the Bragg peaks are seen to decrease and broaden, respectively, as compared to the case for a fixed antenna. The platform motion modulates the radar signals as a frequency modulator, and the modulation indices are related to the amplitudes of the platform motion. With a larger amplitude of platform motion, more energy is transferred from the Bragg peaks to the motion-induced peaks, and more motion-induced peaks need to be considered.
A novel hybrid MTDC model for optimal power flow using interior-point method

Original optimal power flow (OPF) model of hybrid multi-terminal high voltage direct current (hybrid MTDC) system ignores the converter transformer of voltage source converter (VSC). The modulation of VSC may reach the upper limit losing regulation and VSC station works in the nonlinear modulation area. This paper presents a novel model of hybrid MTDC for OPF solutions using primal-dual interior-point method (PDIPM). This new OPF model is constituted of current source converter (CSC), VSC and its coupling transformer which is considered to be a tap-changing transformer. The new model represents well both AC system and DC system where the VSC is not assumed as idealized, controllable voltage sources but com pound transformer devices which take account of VSC internal switching losses, ohmic losses and reactive power design limits. Finally, the accuracy and effectiveness of the proposed OPF model are demonstrated on a modified IEEE-14 Bus system.
The First-Order High Frequency Radar Ocean Surface Cross Section for an Antenna on a Floating Platform

The first-order high frequency surface wave radar (HFSWR) cross section of the ocean surface is derived for the case of the transmitting and receiving antenna being mounted on a floating, but otherwise fixed, ocean platform. It is assumed that the sway component of the platform or barge motion is responsible for observed differences in the cross section compared to that for the fixed antenna case. Based on earlier work, a general expression for the bistatically received first-order electric field, which consists of a two-dimensional spatial convolution, is presented and reduced to integral form. Then, it is assumed that the surface can be described by a Fourier series whose coefficients are zero-mean Gaussian random variables, and from there the analysis proceeds for the backscatter case. The integrals are taken to the time domain, with the source field being that of a barge-mounted omnidirectional vertically polarized pulsed dipole antenna. Subsequently, the first-order monostatic radar cross section is developed and found to consist of Bessel functions. Simulation results for the new cross section are also provided to show the effects of barge motion under different sea states and operating frequencies. It is seen that the results have important implications in the application of HFSWR technology to ocean remote sensing.
Hybrid classifiers ensemble with an undersampling scheme for liver tumor segmentation

In this paper, we propose a new framework, namely hybrid classifiers ensemble with random undersampling for liver tumor segmentation. Essentially, the proposed framework is working on computed tomography images in which each pixel is represented by a rich feature vector. To handle the class imbalance problem, those pixels which correspond to non-tumor region are randomly subsampled. Outcomes of three types of classifiers are then combined in a decision level for performance enhancement. Our empirical results on 19 tumor images from 11 patients show promising segmentation performance.
A second-order monostatic high frequency radar power model for mixed-path propagation

A second-order monostatic high frequency radar power model is derived for the case of mixed-path ionosphere-ocean propagation with a pulsed source. This model is investigated by considering both ionospheric reflection and second-order sea surface scattering. The latter involves both electromagnetic and hydrodynamic contributions. The second-order power spectrum of the mixed-path propagation along with an earlier first-order result is simulated with different wind speeds.
Estimation of Significant Wave Height From X-Band Marine Radar Images Based on Ensemble Empirical Mode Decomposition

In this letter, an ensemble empirical mode decomposition (EEMD)-based method is proposed to estimate significant wave height (SWH) from the X-band marine radar sea surface images. First, the data sequence in each radial direction of a radar subimage is decomposed by the EEMD into several intrinsic mode functions (IMFs). A normalization scheme is then applied to the IMFs to obtain their amplitude modulation components. Finally, by adopting a linear model, the SWH is estimated from the sum of the amplitudes from the second to the fifth modes. The method is tested using radar and buoy data collected in a sea trial off the east coast of Canada. The root-mean-square differences with respect to the buoy reference for the SWH estimations using the traditional signal-to-noise-based method, a recent shadowing-based method, and the proposed technique are 0.78, 0.48, and 0.36 m, respectively. The result indicates that the proposed technique produces improvement in the SWH measurements.
Fast TVL1-L2 MR image reconstruction using variable splitting and accelerated alternating direction method with adaptive restart

This paper presents a fast algorithm for magnetic resonance (MR) image reconstruction from undersampled k-space measurements. The underlying MR image reconstruction is formulated as solving a TVL1-L2 minimization problem whose objective function consists of total variation (TV) regularizer, wavelet-based ℓ1-norm regularizer and ℓ2 data fidelity. Our approach is based on a variable splitting strategy and an accelerated alternating direction method of multiplier (ADMM) with restart. This paper shows that our proposed algorithm is fast and efficient for solving the TVL1-L2 MR image reconstruction problem. More precisely, a variable splitting method is used to split the variable into three variables and obtain an equivalent constrained optimization formulation, which is then addressed with an accelerated ADMM with adaptive restart. This ADMM algorithm is acceleration because the next iterate is computed by employing two previous computed iterates, and the restart rule is employed to enforce monotonicity and convergence in solving weakly convex TVL1-L2 optimization. Moreover thanks to intrinsic spatial-frequency encoding in MRI data, the inverse of regularized Hessian matrix can perform efficiently by exploiting fast Fourier transform (FFT) and fast wavelet transform (or tight frame). Experimental examples also demonstrate that the proposed algorithm is fast and efficient compared to the classical ADMM in TVL1-L2 MR image reconstruction.
Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation

This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients' CT data and experiment show promising results.