nils-herrmann/clean_scirepeval huggingface dataset:
Quantification of the Relationship Between Sea Surface Roughness and the Size of the Glistening Zone for GNSS-R

A formulation of the relationship between sea-surface roughness and extension of the glistening zone (GZ) of a Global Navigation Satellite System Reflectometry (GNSS-R) system is presented. First, an analytical expression of the link between GZ area, viewing geometry, and surface mean square slope (MSS) is derived. Then, a strategy for retrieval of surface roughness from the delay-Doppler map (DDM) is illustrated, including details of data preprocessing, quality control, and GZ area estimation from the DDM. Next, an example for application of the proposed approach to spaceborne GNSS-R remote sensing is provided, using DDMs from the TechDemoSat-1 mission. The algorithm is first calibrated using collocated in situ roughness estimates using data sets from the National Data Buoy Center, its retrieval performance is then assessed, and some of the limitations of the suggested technique are discussed. Overall, good correlation is found between buoy-derived MSS and estimates obtained using the proposed strategy ( $r=0.73$ ).
An Algorithm for Sea-Surface Wind Field Retrieval From GNSS-R Delay-Doppler Map

In this letter, a new method is presented to retrieve sea-surface wind fields by least squares (LS) fitting the 2-D simulated global navigation satellite system reflectometry (GNSS-R) delay-Doppler maps (DDMs) to the measured data. Unlike previous methods, all the DDM points with normalized power higher than a threshold are used in the LS fitting. To reduce the computational cost of the fitting process, a variable step-size iteration is employed. Three GNSS-R data sets that were collected at two different sea-surface regions by the UK-Disaster Monitoring Constellation satellite are used to validate the proposed approach. An 18-s incoherent correlation processing is applied to each data set to reduce the noise level, and ad hoc correction is made on the simulated antenna pattern. The retrieved wind results are compared with the in situ measurements provided by the National Data Buoy Center. The results show that an error of 1 m/s in the wind speed and 30° in the wind direction can be obtained with a lower threshold set as 30% to 42% of the peak DDM point.
Motion compensation of K-band radar ocean wave measurement

Along with practicing the ocean wave measurement using K-band short-range radar, the impact of radar motion caused by a floating platform cannot be ignored. Radar motion needs to be compensated because the measurement method is based on sensing water particle motion which is apt to be influenced by radar motion. In this paper, motion compensation methods are proposed. Spectral difference method and mechanical method are designed to compensate linear motion and rotation motion respectively. The spectral difference method is evaluated using numerical simulation and then both of the two methods are validated in a wave tank. Wave period and height can be precisely measured in the numerical simulation and the wave tank experiment. The results prove that the radar motion impacts are successfully mitigated by the proposed motion compensation methods.
Measurement of Sea Surface Wind Direction Using Bistatic High-Frequency Radar

A method for extracting sea surface wind direction information from bistatic high-frequency (HF) radar Doppler spectra is presented. By analogy to the monostatic case, the ratio of the intensities of the positive and negative bistatic Bragg peaks is used to derive the (ambiguous) wind direction. For bistatic operation, the reference is taken with respect to the scattering ellipse normal rather than the radar beam direction. The method is shown to be valid based on simulated bistatic HF radar Doppler spectra. Wind direction is also extracted from the bistatic radar data collected on the Southern China coast. Comparison between the radar-measured wind directions and those obtained from the Advanced Scatterometer shows good agreement.
Guided anchor data extraction and registration from incomplete and noisy scanned surface

Accurate surface registration plays a vital role in computer vision. In order to increase the accuracy of the registration, this paper proposes a general computational framework to extract the anchor data from the scanned surface for registration. A target surface is provided to guide the extraction and the registration. First, the anchor data on the target surface is manually highlighted according to prior knowledge. After that, a fully automatic system will extract the anchor data from each scanned surface and align the scanned surface to the target surface based on the anchor data. The extraction and the registration are performed iteratively to locate the correct anchor data and increase the accuracy of the registration. In each iteration, extraction derives anchor data to enable the accurate registration, and the registration result is used to further refine the anchor data. In order to prevent infinite loops in the algorithm, orientation correction is proposed to perturb the transformation after the registration and before updating the anchor data. Experiments on different engineering parts demonstrate its robustness and convergency. Compared with traditional methods, our framework increases the registration accuracy when dealing with incomplete and noisy data.
Measuring surface wind direction by monostatic HF ground-wave radar at the eastern China Sea

The extraction of full wind vectors from data obtained by single-site (monostatic) high-frequency ground-wave radar (HFGWR) is an ongoing challenge because of the inherent directional ambiguities. Here, a new algorithm for resolving the ambiguity of wind direction from monostatic data is presented. The true wind direction is determined by minimizing the sum of the difference among three wind directions derived from three different radar look angles. The wind directions estimated by applying the algorithm to data obtained from the OSMAR2000 HFGWR situated at the Eastern China Sea are compared with values obtained from ship-borne instrumentation. The mean difference between the ground-truthed values and those obtained from the radar data is approximately 20/spl deg/. The distance limit for wind direction sensing using the OSMAR2000 is about 200 km, which is the range for which signal-to-noise ratios typically exceed about 23 dB in the relevant first-order portions of the backscatter spectra.
Compressed sensing MRI with total variation and frame balanced regularization

This paper presents a new sparse regularization model for frame-based image reconstruction in compressed sensing magnetic resonance imaging (CS-MRI). The proposed regularization includes both total variation (TV) norm and frame balanced penalty, called as TV-balanced approach. In frame-based l1-regularized image reconstruction, there are three formulations: analysis-based, synthesis-based and balance-based approaches. Although they are equivalent in orthogonal transform, they are different in redundant frame. Especially the balance-based approach can balance sparsity and smoothness of the reconstructed image by employing a penalty on distance between the sparse representation vector and analyzed coefficient vector. On the other hand, MR images commonly possess some blocky structures, and total-variation is often used to preserve image edges. Therefore by combining TV and balanced models, the proposed TV-balanced regularization can improve the reconstructed image quality from undersampled Λ-space measurements, which is solved numerically via exploiting variable splitting and alternating direction method of multiplier (ADMM) techniques. Efficiency of the proposed method has been evaluated on synthetic phantom and real human and rat brain MR images.
Multiple Objects Trackingwith Multiple Hypotheses Dynamic Updating

We present a novel and robust multi-object tracking algorithm based on multiple hypotheses about the trajectories of the objects. We represent the trajectories of the objects by a set of path graphs in which the path graphs that have the closest temporal relationship with the current frame are stored in a buffer. New hypotheses about the trajectories of the objects are continually generated based upon the spatial and temporal information of the objects. The novelty of our multi-object tracking algorithm lies in our framework in which we update these hypotheses by exploiting information in later frames and dynamically relating this information to the current set of path graphs in the buffer. Our experiments show that even with a small buffer size, our multi-object tracking algorithm achieves more than 75% accuracy in the tracking results of our test video sequences. Furthermore, we demonstrate that by a small increase of the buffer size, we are able to improve the tracking accuracy in the video sequences to above 90%.
An Algorithm for Wind Direction Retrieval From X-Band Marine Radar Images

A new method for retrieving wind direction from X-band marine radar images is presented in this letter. The new algorithm investigates radar backscatter in the wavenumber domain and obtains wind direction from the wavenumber spectrum. Different from previous algorithms that detect rain-contaminated images and discard them, the new algorithm could be applied to both rain-contaminated and rain-free images. For rain-contaminated images collected under low wind speeds (i.e., less than 8 m/s), wind directions were retrieved based on spectral components with wavenumbers of [0.01, 0.2]. For rain-contaminated images obtained under high wind speeds and rain-free images, wind directions were retrieved from the spectrum with values at zero wavenumber. The algorithm has been tested using X-band radar images and shipborne anemometer data collected on the east coast of Canada. Comparison with the anemometer data shows that the root-mean-square error of wind directions retrieved from rain-contaminated images collected under low wind speeds is reduced by 25.1°.
SenseViewer: A unified rendering interface of visual and haptic cues in medical images

We propose a new unified rendering interface - SenseViewer - that allows users to annotate and label visual, auditory and in particular haptic cues to medical images. This new interface may support pre-operation planning, surgical training and medical robot guidance better. Users can touch and feel virtual organs and tissues with haptics. The visual and auditory cues are helpful to define optimal paths for planning and guidance. SenseViewer is one of the earliest dedicated interfaces for rendering visual and haptic cues in medical images. Moreover, it employs magnetic resonance elastography for quantitative soft tissue viscoelasticity that makes haptic cues more realistic. SenseViewer will be further enhanced and integrated into our innovative Image-guided Robot Assisted Training (IRAS) system.
Segmentation of gallbladder from CT images for a surgical training system

A semi-automatic method was developed for the segmentation of 3D gallbladders (GB) from CT images, in order to construct a patient-specific model for a surgical training system. First a support vector machine (SVM) classifier was trained to extract GB region from one single 2D slice in the intermediate part of a GB by voxel classification. Then the extracted GB contour, after some morphological operations, was projected to the neighboring slices for automated re-sampling, learning and further voxel classification in these slices. This propagation procedure continued till all GB-containing slices were processed. The method was tested using 18 CT data sets and a set of quantitative measures were computed. The averaged volume overlap error of 15.56% and surface distance of 0.64 mm suggested that the method is efficient and promising.
Iterative least-squares-based wave measurement using X-band nautical radar

In this study, an existing iterative least-squares (LS) method for determining ocean surface currents from X-band nautical radar images is modified for extracting ocean wave information from the same data. Within each iterative step, the image spectra samples are classified as containing contributions from fundamental, first-order and higher harmonic waves or noise. Based on the classification result, a new scheme is proposed to increase the robustness of current estimation. This involves automatically adjusting the first threshold that is used for obtaining an initial guess of the current velocity in the iterative LS method. The proposed wave algorithm directly uses the classified fundamental and first-order harmonic wave components for wave spectra and parameter retrieval. Unlike previous wave analysis techniques in which a bandpass filter is required to eliminate the non-wave contributions after the current velocity is obtained, the proposed algorithm simplifies the wave retrieval without such a bandpass filter. Algorithm verification is first conducted by using simulated radar images. Subsequently, the method is applied to field radar data and the results are compared with wave-buoy measurements. Wave parameters from both types of data show that the modified wave algorithm produces results that are close to those achieved using traditional algorithms.
Maintaining track continuity in GMPHD filter

The data association between objects and measurements is a challenging task in multiple-object tracking because of computationally expensive. This challenge can be overcame by the probability hypothesis density (PHD) filter. Recently, the Gaussian mixture probability hypothesis density (GMPHD) filter has been proposed as a closed-form of the PHD filter. However, the GMPHD filter does not include track continuity during the period of tracking. In this paper, we present a method for maintaining the continuity of state estimates of objects in the GMPHD filter. The set of labels from Gaussian components is used to create hypotheses for label association process and the Hungarian algorithm is applied to search for the best hypothesis association. The results show that the method is robust and efficient.
Multi-strategy object tracking in complex situation for video surveillance

In this paper, a novel method of multi-strategy object tracking for video surveillance is proposed. Under this framework, the moving and stationary objects are tracked separately, so that different reliable features for different types of objects can be exploited efficiently. For a moving object, the global color features called dominant color histogram (DCH) are reliable for object tracking. An efficient sequential approach is employed, which first estimates the depth order of the objects using DCH, then tracks each individual one-by-one with mean- shift and exclusion operations. For a stationary object, the image template is accurate for object representation and matching. A layer model is employed for stationary object tracking. Stationary objects are classified as "visible", "occluded", and "removed". For people stop moving in scene, they seldom stay completely motionless. Therefore, two more states, "changing pose", and "start moving" are added. Once the stationary person is detected as "start moving", he will be switched to moving object tracking seamlessly. The proposed method has been successfully applied in a real-time intelligent CCTV surveillance system for unusual event detection and tested in both real-world public sites and public datasets from PETS2006. Very encouraging results have been obtained.
A robust approach to face and eyes detection from images with cluttered background

Automatic face location in complex scenes is extremely challenging in human face recognition systems. Further more, the facial features detection also plays an important role. The paper presents a scheme for robust face and eyes detection from an image. The scheme uses the Gaussian steerable filter to search and detect the facial feature (preattentive feature) roughly in an image. The face model is investigated to locate the whole face and facial features, such as eyes, nose and mouth. Here, multiple evidences are used in the face location and eyes detection. One important feature is the structural information of the face, i.e. facial components of certain structure. The other is the symmetry property of the face, here only the front face with certain pose variation is considered. It will reduce the computation greatly. For facial components detection, some image features and PCA features are used for verification from the candidates detected before. Experiments show that the algorithm is robust and fast.
A repetitive microsecond pulse generator for atmospheric pressure plasma jets

Compared with other non-thermal plasma sources, the atmospheric-pressure plasma jet (APPJ) has advantages on simple structure, low temperatures, strong chemical activities, and convenient handling, all of which have attracted much attention. The power sources play an important role on the characteristics and the applications of the APPJ. In this article, a compact repetitive microsecond-pulse generator is designed for exciting the APPJ in helium and argon. The microsecond-pulse generator can produce repetitive pulses with output voltages of up to 20 kV, pulse width of ~8 μs, and pulse repetition frequencies (PRFs) of 1 Hz ~2 kHz. Using the designed repetitive microsecond-pulse generator, the characteristics of the APPJ are investigated by measuring the voltages and currents and obtaining images of the discharges. Experimental results show that the microsecond-pulse generator has been successfully used to sustain stable APPJs both in helium and argon. The shape of the output voltage pulses may change as the applied voltage increases. Nevertheless, the output voltages are stable at all PRFs when the applied voltage is fixed. Furthermore, the effects of flow rate, the applied voltage, and the PRF on the He/Ar APPJ are investigated. Results show that it is more likely to generate a He APPJ rather than an Ar APPJ under microsecond-pulse excitation. The length of the plasma plume is slightly affected by the PRF both in the He APPJ and the Ar APPJ.
2-layer Erroneous-Plan Recognition for dementia patients in smart homes

People with dementia lose their ability to learn, solve problems, and communicate. And they are all around us. To potentially replace some of their diminished memory and problem-solving abilities, Erroneous-Plan Recognition (EPR) aims to detect defects or faults in the execution of correct plans by the dementia patient, and send timely audio and visual prompts to the dementia patient and caregiver in order to correct these faults. The scope of this work is for the patient who lives alone in a smart home. One challenge is that the definition of plan can be very subjective. It is necessary to regard a plan as an Activity of Daily Living (ADL), choose the ADLs to monitor, and deploy available sensors to acquire data. With the sensor data, there can be activity recognition, followed by plan recognition. Another challenge is the highly random and erroneous behaviour of dementia patients. Multiple, sequential, and independent layers of error detection can be arranged in a prioritised manner to detect specific errors first, and provide an error probability if no specific errors are detected. On the whole, most of the EPR results are very good as they are at least 0.9, indicating that the data is linearly separable. The 2-layer EPR system, which uses the blacklist and whitelist as Layer 1 and naive Bayes classifier as Layer 2, is significantly more accurate than each individual layer. In fact, 5 out of 6 actors have an accuracy above 0.9. With the encouraging results, there will be more technical and domain challenges which we can address in the near future.
Neural Networks Based Sea Ice Detection and Concentration Retrieval From GNSS-R Delay-Doppler Maps

In this paper, a neural networks (NN) based scheme is presented for detecting sea ice and retrieving sea ice concentration (SIC) from global navigation satellite system reflectometry delay-Doppler maps (DDMs). Here, a multilayer perceptron neural network with back-propagation learning is adopted. In practice, two NN were separately developed for sea ice detection and concentration retrieval purposes. In the training phase, DDM pixels were employed as an input. The SIC data obtained by Nimbus-7  After the training process using a dataset collected around February 4, 2015, these networks were used to produce corresponding detection and concentration estimation for other four sets of DDM data, which were collected around February 12, 2015, February 20, 2015, March 16, 2015, and April 17, 2015, respectively. Results show high accuracy in sea ice detection and concentration estimation with DDMs using the proposed scheme. On average, the accuracy for sea ice detection is about 98.4%. In terms of estimated SIC, the mean absolute error is less than 9%, whereas the correlation coefficient is as high as 0.93 compared with the reference data. It was also found that low sea state and wind speed could lead to an overestimation of SIC for partially ice-covered region.
Quantification of coronary artery Stenosis by Area Stenosis from cardiac CT angiography.

Non-invasive cardiac computed tomography angiography (CTA) is widely used to assess coronary artery stenosis and give clinical decision-making support to clinicians. The severity of stenosis lesion is commonly graded by a range of percent Diameter Stenosis (DS), which can introduce false positive diagnoses or over-estimation, triggering unnecessary further procedures. In this paper, a system and the associate methods to quantify stenosis by the percent Area Stenosis (AS) from cardiac CTA is presented. In the process, coronary artery tree is segmented and the centerline is extracted by Hessian filtering and the minimal path method. After a serial of 2D cross-sectional artery images along the artery centerline are obtained, lumen areas are segmented by ellipse-fitting with deformable models, and consequently to compute the lesion's AS. Experimental results on 5 CTA data sets show that compared to DS, AS better correlates to the reference standard for stenosis quantification, suggesting the efficacy of the proposed system.
The Effect of Radar Ocean Surface Sampling on Wave Spectrum Estimation Using X-Band Marine Radar

In this paper, the effect of the ocean surface sampling process on the ocean wave spectral estimation using the Cartesian Fourier transform (CFT) method on <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-band marine radar data is investigated. Our analysis shows that the ocean surface sampling process involves a spatial averaging process that might be described as a 2-D low pass filter. Furthermore, a filter referred to as the inverse sampling averaging filter (ISAF) is proposed to be integrated with the CFT method in order to mitigate the effect of the sampling process. For validation, the CFT-with-ISAF method as well as the CFT-without-ISAF method were used to estimate ocean wave spectra and sea state parameters from <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-band marine radar field data. The estimates from both methods were compared to ground truth estimates generated using TRIAXYS wave buoy data. The results show that the ISAF improves the CFT method in estimating ocean wave spectra. The recorded accuracy improvements in estimating the non-directional wave spectrum, the peak wave period, the mean wave period, the zero-crossing wave period, and the peak wave direction were 11%, 12%, 21%, 17%, and 34%, respectively. The performances of significant wave height estimation using the ISAF method and the standard CFT method were validated against ground truth estimates and found to be comparable.