nils-herrmann/clean_scirepeval huggingface dataset:
Face recognition by incremental learning

One of the important features for human machine 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, where 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.
Simulation based tropical cyclone sensing using space-based GNSS-R

A technique to sense tropical cyclone using the reflections from Global Navigation Satellite System (GNSS) satellites is presented in this paper. The two exponential vortex model proposed by Willoughby is employed to generate the cyclone wind profile which is used to simulate the delay-Doppler map (DDM) of the cyclone dominated sea surface based on the Zavorotny and Voronovich (Z-V) GNSS scattering model. Subsequently, the spatial integration approach (SIA) is applied to retrieve the scattering coefficients from the simulated DDMs. Two cases in which the cyclone eye co-locates with the specular point (SP) and is away from the SP are analyzed. A performance characterization including retrieval accuracy is demonstrated with respect to cyclone location. Simulated retrieval result shows that the technique proposed is promising.
Diffuse discharges of multi-needle-plane gaps sustained by repetitive nanosecond pulses at atmospheric pressure

In this paper, diffuse discharges sustained by nanosecond pulses are obtained in air gaps with a multi-needle-to-plane discharge electrode configuration at atmospheric pressure. A repetitive nanosecond generator with a rise time of 70ns and a full width at half maximum of 100ns is employed to drive diffuse discharges. Characteristics of the diffuse discharge were investigated by analyzing voltage-current waveforms and discharge images. Experimental results showed that a large-area diffuse discharge could be obtained with a pulse repetition frequency of up to 1 kHz. Furthermore, as the applied voltage increased, discharges transformed from corona discharge to diffuse mode, meanwhile the discharge current changed rapidly. It was also found that the discharge intensity varied according to the position of the needles; and discharge intensity in the center was weaker than that near the margin. In addition, the composition of the measured discharge current was calculated and analyzed.
A Two-Stage Image Segmentation Method Using Euler's Elastica Regularized Mumford-Shah Model

As one of the most important image segmentation models, the Mumford-Shah functional was developed to pursue a piecewise smooth approximation of a given image based on the regularization on the total length of curves. In this paper, we modify the Mumford-Shah model using Euler's elastic a as the regularization. A two-stage segmentation method is applied the Euler's elastic a regularized Mumford-Shah model. The first stage is to find a smooth solution of the variant Mumford-Shah functional based on augmented Lagrangian method while a thresholding is performed in the second stage to obtain different phases for the segmentation. The K-means clustering method is used as the technique to find the thresholds for the segmentation. For intensity inhomogeneous images, we eliminate the effect of the bias field by bias-corrected fuzzy c-means method. Experimental results show that as the regularization, Euler's elastic a makes the Mumford-Shah model perform better for many kinds of images, including tubular and irregular shaped, CT Angiography (CTA) and MRI images in different noise level.
Framework for real-time behavior interpretation from traffic video

Video-based surveillance systems have a wide range of applications for traffic monitoring, as they provide more information as compared to other sensors. In this paper, we present a rule-based framework for behavior and activity detection in traffic videos obtained from stationary video cameras. Moving targets are segmented from the images and tracked in real time. These are classified into different categories using a novel Bayesian network approach, which makes use of image features and image-sequence-based tracking results for robust classification. Tracking and classification results are used in a programmed context to analyze behavior. For behavior recognition, two types of interactions have mainly been considered. One is interaction between two or more mobile targets in the field of view (FoV) of the camera. The other is interaction between targets and stationary objects in the environment. The framework is based on two types of a priori information: 1) the contextual information of the camera's FoV, in terms of the different stationary objects in the scene and 2) sets of predefined behavior scenarios, which need to be analyzed in different contexts. The system can recognize behavior from videos and give a lexical output of the detected behavior. It also is capable of handling uncertainties that arise due to errors in visual signal processing. We demonstrate successful behavior recognition results for pedestrian-vehicle interaction and vehicle-checkpost interactions.
Bistatic high frequency radar ocean surface cross section for an antenna on a floating platform

Land-based high frequency surface wave radar (HFSWR) is an established sensor for 
ocean remote sensing. Placing an HFSWR on a floating platform has the advantage of 
mobility. However, antenna motion will distort the Doppler spectrum, which is used to 
extract ocean information and to detect the signatures of targets. In this thesis, significant 
effort has been expended on establishing radar cross section (RCS) models for a fixed 
receiver and a transmitter on a floating platform, analyzing the effect of the antenna 
motion, and developing a motion compensation method to eliminate the effect of the 
platform motion. 
The first- and second-order monostatic RCSs of the ocean surface for the case of a 
pulsed dipole source on a floating platform have been previously derived in the literature, 
with the assumption that the platform motion is a single-frequency sinusoid. Following 
that work, the research in this thesis is extended to the bistatic case. The effect of platform 
motion on simulated Doppler spectra is considered for a variety of sea states. It is shown 
that the resulting motion-induced peaks are symmetrically distributed in the Doppler 
spectrum. 
Following this work, the corresponding bistatic RCS models for a frequency modulated 
continuous waveform (FMCW) source are derived. Results show that the sidelobe 
level for an FMCW source is reduced with increasing extent of range bin. 
To mimic real world scenarios, platform motion is next modelled as a combination of 
two cosine functions, based on existing research of realistic horizontal motions of moored 
floating platforms. RCSs incorporating a dual-frequency platform motion model are then 
developed. These can be extended to a general form incorporating a multi-frequency 
platform motion. It is found that the platform motion can be viewed as a modulator of the 
radar frequencies, with the modulation indices related to the amplitudes of the platform 
motion. 
Finally, to mitigate the effect of platform motion on the Doppler spectra, a motion 
compensation method is proposed. This motion compensation method can be achieved 
by a deconvolution process. Calculations involving a RCS model, incorporating external 
noise, for an antenna on a floating platform are conducted in order to simulate field data 
and to examine this motion compensation method. The external noise is characterized as 
a white Gaussian zero-mean process. By using this newly-developed RCS model with 
external noise, motion compensation results under different sea states and signal-to-noise 
ratios (SNRs) are examined. The outcomes indicate that an iterative Tikhonov regularized 
deconvolution technique is superior to other compensation methods implemented in this 
study.
Situation-Aware Patient Monitoring in and around the Bed Using Multimodal Sensing Intelligence

Due to the physical and mental impairments, frail elderly have to stay in the bed most of their time. So, serious health issues and physical injuries can occur to them if dangerous events and situations around the bed are not known in advance. This paper proposes continuous patient monitoring with situation awareness using multimodality sensors to recognize such dangerous events in and around the bed. With multimodal sensing intelligence, various contexts and situations of those bedridden elderly can be determined. By incorporating personalization and medical domain knowledge into detected events and activities, dangerous events can be recognized immediately. Then, timely intervention can be provided to the elderly with potential fall risks. So our solution supports situation aware patient monitoring and provides desirable personalized care at the institutions enabling to prevent potential health and well-being problems in and around the bed.
On the problem of antenna platform motion for high frequency surfacewave radar applications

Shore-based high frequency surface wave radars (HFSWR) have been used to monitor large areas of the coastal ocean, and there continues to be an increase in their use for estimation of marine environmental features associated with currents, waves and winds. Recently, interest has been generated in mounting the transmitting (TX) or receiving antennas (RX), or both, on ocean platforms. This provides new challenges as the motion induced in the antennas influences the phasing of the transmitted and received electric fields. Here, the development of a first-order cross section of the ocean which incorporates the effects of antenna motion is outlined.
Simulation of HF radar cross sections for swell contaminated seas

Based on HF bistatic models for the first- and second-order ocean cross sections developed earlier, a new monostatic cross section model for swell contaminated seas is proposed. The methodology of modelling is to incorporate a Wallop wave spectrum to represent the swell portion while describing the wind wave part with a typical Pierson-Moskowitz spectrum. Then, it is assumed that the total ocean surface is zero-mean Gaussian, and the general expression for the monostatic radar cross section still holds for the combined swell-wind wave spectrum. Simulated results are provided and are seen to be highly consistent with real Doppler spectra. This suggests the contamination of the swell component could be removed with refined inversion algorithms.
Stacked hidden Markov model for motion intention recognition

Motion intention recognition plays an important role in robot-assisted applications. A Stacked Hidden Markov Model (HMM) method is proposed to enable the robot to recognize the intention of a human user based on his/her motion trajectories. The Stacked HMM method is constructed based on the relationship of the observed objects. The motion intention recognition model contains multiple HMMs. Each HMM represents one motion intention in the corresponding level. Motion trajectories were collected from a Virtual Reality based surgical training platform. A two-Layered Stacked HMM intention recognition model has been built to recognize the motion intention in primitive level and subtask level. With the proposed intention recognition method, intention recognition rate for the primitive and subtask levels are 95.0±3.5% and 71.0±13.6% respectively. The proposed method is effective in the recognition of user's intention from different levels with motion trajectory.
Brain tumor grading based on Neural Networks and Convolutional Neural Networks

This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, with some combination operations between multiphase MRIs. Compared to other researches, which involve additional effort to design and choose feature sets, the approach used in this paper leverages the learning capability of deep learning machine. We present the grading performance on the testing data measured by the sensitivity and specificity. The results show a maximum improvement of 18% on grading performance of Convolutional Neural Networks based on sensitivity and specificity compared to Neural Networks. We also visualize the kernels trained in different layers and display some self-learned features obtained from Convolutional Neural Networks.
Retrieval of ocean wave characteristics from K-band radar

A method is proposed for the retrieval of period, height and direction for ocean waves from K-band radar signal. This method is designed for the K-band frequency modulated continuous wave radar mounted on an unmanned surface craft. The K-band radar signals transmitted, reflected and received at sea surface are analyzed. The frequency of the received signal shifts with water particle movements. The relationship between the frequency shift and the wave characteristics is established. The ocean wave characteristics can be retrieved through extracting the frequency shift and analyzing the periodicity and the amplitude of the frequency shift with the deterministic relationship. Numerical simulations about the retrieval processes under some wave conditions have been performed and the results show that the retrieval method is reasonable and feasible for the K-band radar. Besides, a sea surface experiment has also been carried out with a K-band radar and the experimental results are approximate to the measurements results of a wave rider buoy.
Evaluation and analysis of multimodal sensors for developing in and around the bed patient monitoring system

Due to the decline in physical and cognitive abilities, many frail elderly may have to lie in the bed most of their time. It is not feasible to monitor them continuously through manual observations alone. This issue can be resolved by embedding a set of multimodal sensors into the bed and providing automated activity recognition intelligence. But it is important to design and develop such multimodal sensing intelligence system desirable to the demands made by the clinicians. This paper presents the comparison and evaluation of different sensing bed configurations to observe different granularities of patient's contexts and activities in and around the bed. Based on the achievements and lessons learned from the experimental analysis, we propose improved sensing bed hardware and software systems to meet the real needs of in and around the bed patient monitoring.
Analysis and comparison of sleeping posture classification methods using pressure sensitive bed system

Pressure ulcers are common problems for bedridden patients. Caregivers need to reposition the sleeping posture of a patient every two hours in order to reduce the risk of getting ulcers. This study presents the use of Kurtosis and skewness estimation, principal component analysis (PCA) and support vector machines (SVMs) for sleeping posture classification using cost-effective pressure sensitive mattress that can help caregivers to make correct sleeping posture changes for the prevention of pressure ulcers.
Strokes recovering from static handwriting

This paper presents a novel stroke tracing approach to recovering the strokes from the handwriting images. It is carried out not only based on pixel by pixel, but considering the local relation of handwriting strokes. Firstly the primary skeletons of handwriting are produced through the binarization and thinning. Secondly, after the reliable skeletons and unreliable skeletons are classified based on the width information of handwriting, the strokes segments are obtained by the connecting and tracing on the reliable skeletons. Finally, for making the recovered strokes more natural, the post-processing of tracing is discussed. Experimental results show the effectiveness of the approach.
Sea ice detection from GNSS-R Delay-Doppler Map

In this paper, a scheme is presented for sea ice detection from Global Navigation Satellite System-Reflectometry (GNSS-R) Delay-Doppler Map (DDM). It is found that the DDM of sea ice differs from the one of seawater in the aspect of delay and Doppler spreading. This enables to distinguish sea ice from seawater through studying the number of high-value (greater than 50% of the peak power) DDM pixels. The area associated with a DDM with a number of high-value pixels less and greater than an empirical threshold (12) will be classified as covered by sea ice and seawater, respectively. The proposed method is validated by comparing the DDM-based detection results with ground-truth sea ice data and a detection accuracy of 100% is achieved.
Spaceborne GNSS-R Sea Ice Detection Using Delay-Doppler Maps: First Results From the U.K. TechDemoSat-1 Mission

In this paper, a scheme is presented for detecting sea ice from Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler maps (DDM). Less spreading along delay and Doppler axes were observed in the DDMs of sea ice relative to those of seawater. This enables us to distinguish sea ice from seawater through studying the values of various DDM observables, which describe the extent of DDM spreading. The area associated with a DDM that results in an observable below or above a threshold value will be classified as covered by sea ice and seawater, respectively. In particular, this study applies an adaptive incoherent summation to each DDM with efforts to increase signal-to-noise ratio and avoid the averaging between DDMs collected over surfaces of different types. Accordingly, an adaptive threshold is employed for the derived observable based on the incoherent summation interval for its corresponding DDM. The proposed sea ice detection method is tested with five different DDM observables. Through comparing DDM observable-based detection results with ground-truth sea ice data, the feasibility of this method is validated with an accuracy of up to 99.73% based on the pixel number observable.
Target tracking using particle filter with X-band nautical radar

In this paper, a particle filter (PF) method is proposed for target tracking based on a practical X-band nautical radar image sequence, using two histogram-based target models: a kernel-weighted histogram model and a background-weighted histogram model. In particular, the sampling importance resampling (SIR) particle filter is implemented to provide a recursively probabilistic estimation procedure for target tracking. For the measurement model of the particle filter, a Bhattacharyya coefficient based similarity function is utilized to compare the reference target and target candidate, which are represented by the histogram of the target area in the radar image. Within the PF tracking procedure, both the kernel-weighted and background-weighted models are applied with estimated target tracks compared with the practical GPS data. The experiment shows that both models can effectively complete the target tracking task with the kernel-weighted model performing better when the reference model is accurately selected, with the background-weighted model being more flexible.
A virtual surgical training system that simulates cutting of soft tissue using a modified pre-computed elastic model

This work presents a surgical training system that incorporates cutting operation of soft tissue simulated based on a modified pre-computed linear elastic model in the Simulation Open Framework Architecture (SOFA) environment. A precomputed linear elastic model used for the simulation of soft tissue deformation involves computing the compliance matrix a priori based on the topological information of the mesh. While this process may require a few minutes to several hours, based on the number of vertices in the mesh, it needs only to be computed once and allows real-time computation of the subsequent soft tissue deformation. However, as the compliance matrix is based on the initial topology of the mesh, it does not allow any topological changes during simulation, such as cutting or tearing of the mesh. This work proposes a way to modify the pre-computed data by correcting the topological connectivity in the compliance matrix, without re-computing the compliance matrix which is computationally expensive.
Bayesian network based computer vision algorithm for traffic monitoring using video

This paper presents a novel approach to estimating the 3D velocity of vehicles from video. Here we propose using a Bayesian Network to classify objects into pedestrians and different types of vehicles, using 2D features extracted from the video taken from a stationary camera. The classification allows us to estimate an approximate 3D model for the different classes. The height information is then used with the image co-ordinates of the object and the camera's perspective projection matrix to estimate the objects 3D world co-ordinates and hence its 3D velocity. Accurate velocity and acceleration estimates are both very useful parameters in traffic monitoring systems. We show results of highly accurate classification and measurement of vehicle's motion from real life traffic video streams.
Real time head tracking and face and eyes detection

This paper presents a novel algorithm to detect face and eyes in a reliable manner with a stereo camera. With the combination of stereo tracking method for object detection and tracking and structure-based method and example-based learning approach for face detection a real time and robust system is built. Human head detection is implemented with a stereo vision system, where a layered scenery is obtained from the depth image. With head-shoulder contour model applied to the disparity image, a fast head detection algorithm is developed. On the head detected, which also tells the rough scale of it, a hybrid face detection algorithm is proposed for face pattern detection and eyes location. By applying the face structure model and facial components model, face candidates in images are obtained for the later validating of face pattern. The support vector machine (SVM) is adopted here to select the good samples that form a classification boundary to classify face and non-face patterns.
An Inversion Method for Extraction of Wind Speed From High-Frequency Ground-Wave Radar Oceanic Backscatter

A new approach to extracting sea surface wind speed from high-frequency radar Doppler spectra is presented. Based on certain appropriate approximations associated with the Doppler region close to the first-order (Bragg) peaks, the second-order radar cross section equation is differentiated. Once Doppler shifts due to ocean currents are removed from the data, an expression that relates the wind speed to the frequency position of the second-order peak is derived. The method is applied to simulated noisy data as well as to field data obtained from a Seasonde (a product of CODAR Oceans Sensors) in Breezy Point, NY. In the latter case, the retrieved wind speeds are then compared to ground truth data measured by an anemometer from a National Oceanic and Atmospheric Administration weather station located in the vicinity of the illuminated patch of ocean. Subject to certain constraints as detailed in the manuscript, the algorithm shows significant promise.