ORCID Profile
0000-0001-7710-3073
Current Organisations
China University of Mining and Technology
,
University of Sydney
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2018
Publisher: MDPI AG
Date: 06-05-2020
DOI: 10.3390/APP10093223
Abstract: In the field of photogrammetric engineering, computer vision, and graphics, local shape description is an active research area. A wide variety of local shape descriptors (LSDs) have been designed for different applications, such as shape retrieval, object recognition, and 3D registration. The local reference frame (LRF) is an important component of the LSD. Its repeatability and robustness directly influence the descriptiveness and robustness of the LSD. Several weighting methods have been proposed to improve the repeatability and robustness of the LRF. However, no comprehensive comparison has been implemented to evaluate their performance under different data modalities and nuisances. In this paper, we focus on the comparison of weighting methods by using six datasets with different data modalities and application contexts. We evaluate the repeatability of the LRF under different nuisances, including occlusion, clutter, partial overlap, varying support radii, Gaussian noise, shot noise, point density variation, and keypoint localization error. Through the experiments, the traits, advantages, and disadvantages of weighting methods are summarized.
Publisher: MDPI AG
Date: 21-07-2022
DOI: 10.3390/RS14143507
Abstract: This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is ided into two different ranges to study the suitability of the different algorithms. A total of 10 observation variables are considered as input parameters to study the importance of in idual variables or combinations thereof. The results show that the LGBM model performs the best with an RMSE of 1.419 and a correlation coefficient of 0.849 in the low wind speed interval (0–15 m/s), while the ET model performs the best with an RMSE of 1.100 and a correlation coefficient of 0.767 in the high wind speed interval (15–30 m/s). The effects of the variables used in wind speed retrieval models are investigated using the XGBoost importance metric, showing that a number of variables play a very significant role in wind speed retrieval. It is expected that these results will provide a useful reference for the development of advanced wind speed retrieval algorithms in the future.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 12-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2007
Publisher: IEEE
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2012
Publisher: IEEE
Date: 04-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: MDPI AG
Date: 11-12-2020
DOI: 10.3390/ELECTRONICS9122117
Abstract: The weighted K-nearest neighbor (WKNN) algorithm is the most commonly used algorithm for indoor localization. Traditional WKNN algorithms adopt received signal strength (RSS) spatial distance (usually Euclidean distance and Manhattan distance) to select reference points (RPs) for position determination. It may lead to inaccurate position estimation because the relationship of received signal strength and distance is exponential. To improve the position accuracy, this paper proposes an improved weighted K-nearest neighbor algorithm. The spatial distance and physical distance of RSS are used for RP selection, and a fusion weighted algorithm based on these two distances is used for position calculation. The experimental results demonstrate that the proposed algorithm outperforms traditional algorithms, such as K-nearest neighbor (KNN), Euclidean distance-based WKNN (E-WKNN), and physical distance-based WKNN (P-WKNN). Compared with the KNN, E-WKNN, and P-WKNN algorithms, the positioning accuracy of the proposed method is improved by about 29.4%, 23.5%, and 20.7%, respectively. Compared with some recently improved WKNN algorithms, our proposed algorithm can also obtain a better positioning performance.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Korean Society for Internet Information (KSII)
Date: 30-09-2017
Publisher: IEEE
Date: 07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: SAGE Publications
Date: 05-2018
Abstract: This work reports the modification of freeze/thaw poly(vinyl alcohol) hydrogel using citric acid as the bioactive molecule for hydroxyapatite formation in simulated body fluid. Inclusion of 1.3 mM citric acid into the poly(vinyl alcohol) hydrogel showed that the mechanical strength, crystalline phase, functional groups and swelling ability were still intact. Adding citric acid at higher concentrations (1.8 and 2.3 mM), however, resulted in physically poor hydrogels. Presence of 1.3 mM of citric acid showed the growth of porous hydroxyapatite crystals on the poly(vinyl alcohol) surface just after one day of immersion in simulated body fluid. Meanwhile, a fully covered apatite layer on the poly(vinyl alcohol) surface plus the evidence of apatite forming within the hydrogel were observed after soaking for seven days. Gel strength of the soaked poly(vinyl alcohol)/citric acid-1.3 mM hydrogel revealed that the load resistance was enhanced compared to that of the neat poly(vinyl alcohol) hydrogel. This facile method of inducing rapid growth of hydroxyapatite on the hydrogel surface as well as within the hydrogel network can be useful for guided bone regenerative materials.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 2007
Publisher: Elsevier BV
Date: 03-2015
Publisher: IEEE
Date: 10-2016
Publisher: IEEE
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 04-2010
Publisher: MDPI AG
Date: 14-11-2021
DOI: 10.3390/RS13224577
Abstract: The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of s les of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of s les through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, s les of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-04-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2005
Publisher: Springer London
Date: 1998
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: American Physical Society (APS)
Date: 13-11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 07-2019
Publisher: MDPI AG
Date: 31-10-2019
DOI: 10.3390/RS11212559
Abstract: Global navigation satellite system (GNSS) multipath signals received by a geodetic-quality GNSS receiver can be used to estimate the water content of soil around the antenna. The direct signals from satellite to GNSS antenna are the most valuable signals in geodetic measurement, such as positioning, navigation, GNSS control network, deformation monitoring, and so on. However, the GNSS antenna also captures the reflected signals from the ground, which contain information of surrounding environment, so that useful information about the reflection surface can be inferred by analyzing the reflected signal. This technique is termed as GNSS-interferometric reflectometry. The signal-to-noise ratio (SNR) data recorded by a receiver contains SNR component of reflected signals, which is related to the soil moisture of the ground. The changes of soil moisture content will cause the change of soil permittivity and reflectivity which are the key factors that make further change of the SNR of reflected signals. We used the measured data to evaluate the correlation between litude of multipath induced SNR time series and real soil moisture. An improved soil moisture estimation algorithm based on multipath induced SNR litude data is proposed in this paper. The performance of the proposed soil moisture estimation method is evaluated using the 15-month data recorded by PBO H2O GNSS station and a 14-day experiment in Wuhan, China. The experimental results show that the estimated soil moisture has a strong correlation with the real soil moisture and the estimation accuracy in terms of root-mean-square error (RMSE) is 0.0345 cm3cm−3 and 0.0339 cm3cm−3, respectively. Meanwhile, the application scope of the method is given.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: American Chemical Society (ACS)
Date: 08-04-2022
Publisher: Wiley
Date: 09-2017
DOI: 10.1111/PHOR.12200
Publisher: IEEE
Date: 08-2006
Publisher: MDPI AG
Date: 28-11-2020
DOI: 10.3390/RS12233905
Abstract: Snow depth and snow water equivalent (SWE) are two parameters for measuring snowfall. By exploiting the Global Navigation Satellite System reflectometry (GNSS-R) technique and thousands of existing GNSS Continuous Operating Reference Stations (CORS) deployed in the cryosphere, it is possible to improve the temporal and spatial resolutions of the SWE measurement. In this paper, a fusion model for combining multi-satellite SNR (Signal to Noise Ratio) snow depth estimations is proposed, which uses peak spectral powers associated with each of the snow depth estimations. To simplify the estimation of SWE, the complete snowfall period over a winter season is split into snow accumulation, transition, and melting period in accordance with the variation characteristics of snow depth and SWE. By extensively using in situ snow depth and SWE observations recorded by snow telemetry network (SNOTEL) and regression analysis, three empirical models are developed to describe the relationship between snow depth and SWE for the three periods, respectively. Based on the snow depth fusion model and the SWE empirical models, an SWE estimation algorithm is proposed. Three data sets recorded in different environments are used to test the proposed method. The results demonstrate that there exists good agreement between the in situ SWE measurements and the SWE estimates produced by the proposed method the root-mean-square error of SWE estimations is smaller than 6 cm when the SWE is up to 80 cm.
Publisher: MDPI AG
Date: 30-05-2017
DOI: 10.3390/S17061246
Publisher: Springer Science and Business Media LLC
Date: 12-12-2015
Publisher: MDPI AG
Date: 16-09-2022
DOI: 10.3390/RS14184634
Abstract: Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) technology has opened a new window for ocean remote sensing because of its unique advantages, including short revisit period, low observation cost, and high spatial-temporal resolution. In this article, we investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R. Three observables extracted from the DDM are introduced for swell height estimation, including delay-Doppler map average (DDMA), the leading edge slope (LES) of the integrated delay waveform (IDW), and trailing edge slope (TES) of the IDW. We propose one modeling scheme for each observable. To improve the swell height estimation performance of a single observable-based method, we present a data fusion approach based on particle swarm optimization (PSO). Furthermore, a simulated annealing aided PSO (SA-PSO) algorithm is proposed to handle the problem of local optimal solution for the PSO algorithm. Extensive testing has been performed and the results show that the swell height estimated by the proposed methods is highly consistent with reference data, i.e., the ERA5 swell height. The correlation coefficient (CC) is 0.86 and the root mean square error (RMSE) is 0.56 m. Particularly, the SA-PSO method achieved the best performance, with RMSE, CC, and mean absolute percentage error (MAPE) being 0.39 m, 0.92, and 18.98%, respectively. Compared with the DDMA, LES, TES, and PSO methods, the RMSE of the SA-PSO method is improved by 23.53%, 26.42%, 30.36%, and 7.14%, respectively.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Elsevier BV
Date: 10-1999
Publisher: MDPI AG
Date: 28-10-2020
DOI: 10.3390/RS12213532
Abstract: The global navigation satellite system (GNSS) has seen tremendous advances in measurement precision and accuracy, and it allows researchers to perform geodynamics and geophysics studies through the analysis of GNSS time series. Moreover, GNSS time series not only contain geophysical signals, but also unmodeled errors and other nuisance parameters, which affect the performance in the estimation of site coordinates and related parameters. As the number of globally distributed GNSS reference stations increases, GNSS time series analysis software should be developed with more flexible format support, better human–machine interaction, and with powerful noise reduction analysis. To meet this requirement, a new software named GNSS time series noise reduction software (GNSS-TS-NRS) was written in MATLAB and was developed. GNSS-TS-NRS allows users to perform noise reduction analysis and spatial filtering on common mode errors and to visualize GNSS position time series. The functions’ related theoretical background of GNSS-TS-NRS were introduced. Firstly, we showed the theoretical background algorithms of the noise reduction analysis (empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD)). We also developed three improved algorithms based on EMD for noise reduction, and the results of the test ex le showed our proposed methods with better effect. Secondly, the spatial filtering model supported five algorithms on a separate common model error: The stacking filter method, weighted stacking filter method, correlation weighted superposition filtering method, distance weighted filtering method, and principal component analysis, as well as with batch processing. Finally, the developed software also enabled other functions, including outlier detection, correlation coefficient calculation, spectrum analysis, and distribution estimation. The main goal of the manuscript is to share the software with the scientific community to introduce new users to the GNSS time series noise reduction and application.
Publisher: IEEE
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: IEEE
Date: 09-2006
Publisher: MDPI AG
Date: 09-2013
DOI: 10.3390/RS11172056
Abstract: Two estimation methods using a dual GNSS (Global Navigation Satellite System) receiver system are proposed. The dual-frequency combination method combines the carrier phase observations of dual-frequency signals, whereas the single-frequency combination method combines the pseudorange and carrier phase observations of a single-frequency signal, both of which are geometry-free strictly combination and free of the effect of ionospheric delay. Theoretical models are established in the offline phase to describe the relationship between the spectral peak frequency of the combined sequence and the antenna height. A field experiment was conducted recently and the data processing results show that the root mean squared error (RMSE) of the dual-frequency combination method is 5.04 cm with GPS signals and 6.26 cm with BDS signals, which are slightly greater than the RMSE of 4.16 cm produced by the single-frequency combination method of L1 band with GPS signals. The results also demonstrate that the proposed two combination methods and the SNR method achieve similar performance. A dual receiver system enables the better use of GNSS signal carrier phase observations for snow depth estimation, achieving increased data utilization.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: American Physical Society (APS)
Date: 25-11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 17-09-2019
Publisher: Emerald
Date: 16-01-2017
Abstract: Nowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further improve the performance of WiFi indoor positioning based on RSS, this paper aims to propose a novel position estimation strategy which is called radius-based domain clustering (RDC). This domain clustering technology aims to avoid the issue of access point (AP) selection. The proposed positioning approach uses each in idual AP of all available APs to estimate the position of target point. Then, according to circular error probable, the authors search the decision domain which has the 50 per cent of the intermediate position estimates and minimize the radius of a circle via a RDC algorithm. The final estimate of the position of target point is obtained by averaging intermediate position estimates in the decision domain. Experiments are conducted, and comparison between the different position estimation strategies demonstrates that the new method has a better location estimation accuracy and reliability. Weighted k nearest neighbor approach and Naive Bayes Classifier method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of the two strategies are affected by AP selection strategies and inappropriate selection of APs may degrade positioning performance considerably. The RDC positioning approach can improve the performance of WiFi indoor positioning, and the issue of AP selection and related drawbacks is avoided. The RSS-based effective WiFi indoor positioning system can makes up for the indoor positioning weaknesses of global navigation satellite system. Many indoor location-based services can be encouraged with the effective and low-cost positioning technology. A novel position estimation strategy is introduced to avoid the AP selection problem in RSS-based WiFi indoor positioning technology, and the domain clustering technology is proposed to obtain a better accuracy and reliability.
Publisher: Springer Science and Business Media LLC
Date: 08-2023
Publisher: IGI Global
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2013
DOI: 10.1109/TMC.2012.124
Publisher: Springer Science and Business Media LLC
Date: 11-12-2019
DOI: 10.1038/S41467-019-13406-9
Abstract: Recent theoretical works have proposed atomic clocks based on narrow optical transitions in highly charged ions. The most interesting candidates for searches of physics beyond the Standard Model are those which occur at rare orbital crossings where the shell structure of the periodic table is reordered. There are only three such crossings expected to be accessible in highly charged ions, and hitherto none have been observed as both experiment and theory have proven difficult. In this work we observe an orbital crossing in a system chosen to be tractable from both sides: Pr $${}^{9+}$$ 9 + . We present electron beam ion trap measurements of its spectra, including the inter-configuration lines that reveal the sought-after crossing. With state-of-the-art calculations we show that the proposed nHz-wide clock line has a very high sensitivity to variation of the fine-structure constant, $$\\alpha$$ α , and violation of local Lorentz invariance and has extremely low sensitivity to external perturbations.
Publisher: IEEE
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2013
Publisher: IEEE
Date: 10-2012
Publisher: Copernicus GmbH
Date: 07-04-2027
DOI: 10.5194/NPG-2019-48
Abstract: Abstract. Recently, various models have been developed, including the fractional Brownian motion (fBm), to analyse the stochastic properties of geodetic time series, together with the extraction of geophysical signals. The noise spectrum of these time series is generally modeled as a mixed spectrum, with a sum of white and coloured noise. Here, we are interested in modelling the residual time series, after deterministically subtracting geophysical signals from the observations. This residual time series is then assumed to be a sum of three random variables (r.v.), with the last r.v. belonging to the family of Levy processes. This stochastic term models the remaining residual signals and other correlated processes. Via simulations and real time series, we identify three classes of Levy processes: Gaussian, fractional and stable. In the first case, residuals are predominantly constituted of short-memory processes. Fractional Levy process can be an alternative model to the fBm in the presence of long-term correlations and self-similarity property. Stable process is characterized by a large variance, which can be satisfied in the case of heavy-tailed distributions. The application to geodetic time series implies potential anxiety in the functional model selection where missing geophysical information can generate such residual time series.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
Publisher: IEEE
Date: 09-2014
Publisher: IEEE
Date: 10-2016
Publisher: Copernicus GmbH
Date: 24-06-2020
DOI: 10.5194/NPG-2020-23
Abstract: Abstract. Recently, various models have been developed, including the fractional Brownian motion (fBm), to analyse the stochastic properties of geodetic time series, together with the estimated geophysical signals. The noise spectrum of these time series is generally modelled as a mixed spectrum, with a sum of white and coloured noise. Here, we are interested in modelling the residual time series, after deterministically subtracting geophysical signals from the observations. This residual time series is then assumed to be a sum of three stochastic processes, including the family of Lévy processes. The introduction of a third stochastic term models the remaining residual signals and other correlated processes. Via simulations and real time series,we identify three classes of Lévy processes: Gaussian, fractional and stable. In the first case, residuals are predominantly constituted of short-memory processes. Fractional Lévy process can be an alternative model to the fBm in the presence of long-term correlations and self-similarity property. Stable process is here restrained to the special case of infinite variance, which can be only satisfied in the case of heavy-tailed distributions in the application to geodetic time series. Therefore, it implies potential anxiety in the functional model selection where missing geophysical information can generate such residual time series.
Publisher: Springer Science and Business Media LLC
Date: 12-07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-04-2018
Publisher: MDPI AG
Date: 11-02-2022
DOI: 10.3390/RS14040864
Abstract: High-precision coordinate transformation is vital for high-quality data fusion involving different coordinate systems. The transformation precision is mainly evaluated by the transformation parameters’ estimation precision, the root mean square error (RMSE) of the conversion of common points, or the RMSE of the conversion of check points. However, there are a number of issues associated with the rotation parameters’ precision estimated by the existing transformation methods. First, the estimated precision is related to the rotation matrix, so it is not suitable for scenarios where different rotation matrices are used. Second, the RMSE of the conversion of check points may not be consistent with the RMSE of the conversion of common points, so that the RMSE of the conversion of common points should not be used as a transformation precision index. In addition, some engineering applications do not have check points, and many applications need to know which range of points can meet our requirements. To deal with these limitations, this paper proposes a new way to calculate the translation parameters and evaluate the transformation precision. A lot of experimental data was used to verify the effectiveness and applicability of the proposed transformation model.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: MDPI AG
Date: 16-11-2020
DOI: 10.3390/RS12223760
Abstract: This paper focuses on sea surface wind speed estimation based on cyclone global navigation satellite system reflectometry (GNSS-R) data. In order to extract useful information from delay-Doppler map (DDM) data, three delay waveforms are presented for wind speed estimation. The delay waveform without Doppler shift is defined as central delay waveform (CDW), and the integral of the delay waveforms with different Doppler shift values is defined as integral delay waveform (IDW), while the difference between normalized IDW (NIDW) and normalized CDW (NCDW) is defined as differential delay waveform (DDW). We first propose a data filtering method based on threshold setting for data quality control. This method can select good-quality DDM data by adjusting the root mean square (RMS) threshold of cleaned DDW. Then, the normalized bistatic radar scattering cross section (NBRCS) and the leading edge slope (LES) of IDW are calculated using clean DDM data. Wind speed estimation models based on NBRCS and LES observations are then developed, respectively, and on this basis, a combination wind speed estimation model based on determination coefficient is further proposed. The CYGNSS data and ECMWF reanalysis data collected from 12 May 2020 to 12 August 2020 are used, excluding data collected on land, to evaluate the proposed models. The evaluation results show that the wind speed estimation accuracy of the piecewise function model based on NBRCS is 2.3 m/s in terms of root mean square error (RMSE), while that of the double-parameter and triple-parameter models is 2.6 and 2.7 m/s, respectively. The wind speed estimation accuracy of the double-parameter and triple-parameter models based on LES is 3.3 and 2.5 m/s. The results also demonstrate that the RMSE of the combination method is 2.1 m/s, and the coefficient of determination is 0.906, achieving a considerable performance gain compared with the in idual NBRCS- and LES-based methods.
Publisher: Elsevier BV
Date: 12-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: IEEE
Date: 08-2017
Publisher: Elsevier BV
Date: 09-2006
Publisher: IEEE
Date: 04-2012
Publisher: IEEE
Date: 05-2008
Publisher: MDPI AG
Date: 08-2021
DOI: 10.3390/RS13153021
Abstract: Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly ides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it ides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.
Publisher: MDPI AG
Date: 12-12-2019
DOI: 10.3390/RS11242993
Abstract: This work mainly discusses an innovative teaching platform on Unmanned Aerial Vehicle digital mapping for Remote Sensing (RS) education at Wuhan University, underlining the fast development of RS technology. Firstly, we introduce and discuss the future development of the Virtual Simulation Experiment Teaching Platform for Unmanned Aerial Vehicle (VSETP-UAV). It includes specific topics such as the Systems and function Design, teaching and learning strategies, and experimental methods. This study shows that VSETP-UAV expands the usual content and training methods related to RS education, and creates a good synergy between teaching and research. The results also show that the VSETP-UAV platform is of high teaching quality producing excellent engineers, with high international standards and innovative skills in the RS field. In particular, it develops students’ practical skills with technical manipulations of dedicated hardware and software equipment (e.g., UAV) in order to assimilate quickly this particular topic. Therefore, students report that this platform is more accessible from an educational point-of-view than theoretical programs, with a quick way of learning basic concepts of RS. Finally, the proposed VSETP-UAV platform achieves a high social influence, expanding the practical content and training methods of UAV based experiments, and providing a platform for producing high-quality national talents with internationally recognized topics related to emerging engineering education.
Publisher: Elsevier BV
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institution of Engineering and Technology (IET)
Date: 2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2009
Publisher: Copernicus GmbH
Date: 03-06-2016
DOI: 10.5194/ISPRS-ARCHIVES-XLI-B1-525-2016
Abstract: Abstract. This paper presents a Tsunami lead wave reconstruction method using noisy sea surface height (SSH) measurements such as observed by a satellite-carried GNSS reflectometry (GNSS-R) sensor. It is proposed to utilize wavelet theory to mitigate the strong noise in the GNSS-R based SSH measurements. Through extracting the noise components by high-pass filters at decomposition stage and shrinking the noise by thresholding prior to reconstruction, the noise is greatly reduced. Real Tsunami data based simulation results demonstrate that in presence of SSH measurement error of standard deviation 50 cm the accuracy in terms of root mean square error (RMSE) of the lead wave height (true value 145.5 cm) and wavelength (true value 592.0 km) estimation is 21.5 cm and 56.2 km, respectively. The results also show that the proposed wavelet based method considerably outperforms the Kalman filter based method on average. The results demonstrate that the proposed wave reconstruction approach has the potential for Tsunami detection and parameter estimation to assist in achieving reliable Tsunami warning.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: IOP Publishing
Date: 14-12-2020
Abstract: Fingerprinting localization based on Wi-Fi received signal strength (RSS) is the most widely used indoor localization method. It typically includes offline training and online matching phases. The selection of the RSS characteristic value is a key step. The weighted K nearest neighbor (WKNN) algorithm is the most commonly used position-determination algorithm. The mean value of the RSS data collected over a time interval is usually taken as its characteristic value. However, the RSS measurements contain Gaussian and non-Gaussian noise, which cannot be filtered out effectively by the mean value method. The traditional WKNN algorithm adopts a fixed K . However, reference points far away from the test point (TP) may be selected as the nearest neighbors to participate in the position calculation, which may result in accuracy degradation. This paper proposes the weighted dynamic K nearest neighbor algorithm (WDKNN-HF), which utilizes a hybrid of particle filtering and Kalman filtering to extract the RSS characteristic value. In the online matching phase, a dynamic K matching algorithm based on Euclidean distances is developed to determine the coordinates of TPs. Two experiments are conducted in two different indoor scenes. Experimental results demonstrate that the proposed algorithm can obtain better positioning accuracy than existing algorithms, such as KNN, WKNN, enhanced-WKNN (EWKNN) and self-adaptive weighted K nearest neighbor (SAWKNN).
Publisher: Copernicus GmbH
Date: 15-02-2021
Abstract: Abstract. Recently, various models have been developed, including the fractional Brownian motion (fBm), to analyse the stochastic properties of geodetic time series together with the estimated geophysical signals. The noise spectrum of these time series is generally modelled as a mixed spectrum, with a sum of white and coloured noise. Here, we are interested in modelling the residual time series after deterministically subtracting geophysical signals from the observations. This residual time series is then assumed to be a sum of three stochastic processes, including the family of Lévy processes. The introduction of a third stochastic term models the remaining residual signals and other correlated processes. Via simulations and real time series, we identify three classes of Lévy processes, namely Gaussian, fractional and stable. In the first case, residuals are predominantly constituted of short-memory processes. The fractional Lévy process can be an alternative model to the fBm in the presence of long-term correlations and self-similarity properties. The stable process is here restrained to the special case of infinite variance, which can be only satisfied in the case of heavy-tailed distributions in the application to geodetic time series. Therefore, the model implies potential anxiety in the functional model selection, where missing geophysical information can generate such residual time series.
Publisher: Elsevier BV
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Publisher: Institution of Engineering and Technology (IET)
Date: 2009
Publisher: IEEE
Date: 2005
Publisher: Elsevier BV
Date: 05-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2008
Publisher: IEEE
Date: 2008
DOI: 10.1109/ICC.2008.186
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
DOI: 10.1109/TMC.2011.24
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 11-07-2021
Publisher: Springer Science and Business Media LLC
Date: 20-08-2022
Publisher: IEEE
Date: 10-2012
Publisher: MDPI AG
Date: 27-03-2022
DOI: 10.3390/RS14071605
Abstract: This article presents a review on spaceborne Global Navigation Satellite System Reflectometry (GNSS-R), which is an important part of GNSS-R technology and has attracted great attention from academia, industry and government agencies in recent years. Compared with ground-based and airborne GNSS-R approaches, spaceborne GNSS-R has a number of advantages, including wide coverage and the ability to sense medium- and large-scale phenomena such as ocean eddies, hurricanes and tsunamis. Since 2014, about seven satellite missions have been successfully conducted and a large number of spaceborne data were recorded. Accordingly, the data have been widely used to carry out a variety of studies for a range of useful applications, and significant research outcomes have been generated. This article provides an overview of these studies with a focus on the basic methods and techniques in the retrieval of a number of geophysical parameters and the detection of several objects. The challenges and future prospects of spaceborne GNSS-R are also addressed.
Publisher: Institute of Rock Structure and Mechanics, AS CR
Date: 13-10-2017
Publisher: IEEE
Date: 05-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2016
Publisher: IEEE
Date: 05-2008
Publisher: IEEE
Date: 09-2008
Publisher: Elsevier BV
Date: 11-2021
Publisher: Elsevier BV
Date: 02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Publisher: IEEE
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Elsevier BV
Date: 02-2017
Publisher: American Physical Society (APS)
Date: 17-11-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-12-2016
Publisher: IEEE
Date: 07-2018
Publisher: Korean Society for Internet Information (KSII)
Date: 30-11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2003
Publisher: Springer Singapore
Date: 2021
Publisher: Springer Singapore
Date: 2017
Publisher: Mary Ann Liebert Inc
Date: 10-2011
Publisher: Elsevier BV
Date: 05-2017
Publisher: IEEE
Date: 2004
Publisher: Institution of Engineering and Technology (IET)
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: MDPI AG
Date: 12-07-2017
DOI: 10.3390/S17071614
Publisher: IEEE
Date: 07-2016
Publisher: Springer Science and Business Media LLC
Date: 08-06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: MDPI AG
Date: 14-11-2020
DOI: 10.3390/RS12223751
Abstract: Two effective machine learning-aided sea ice monitoring methods are investigated using 42 months of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data collected by the TechDemoSat-1 (TDS-1). The two-dimensional delay waveforms with different Doppler spread characteristics are applied to extract six features, which are combined to monitor sea ice using the decision tree (DT) and random forest (RF) algorithms. Firstly, the feature sequences are used as input variables and sea ice concentration (SIC) data from the Advanced Microwave Space Radiometer-2 (AMSR-2) are applied as targeted output to train the sea ice monitoring model. Hereafter, the performance of the proposed method is evaluated through comparing with the sea ice edge (SIE) data from the Special Sensor Microwave Imager Sounder (SSMIS) data. The DT- and RF-based methods achieve an overall accuracy of 97.51% and 98.03%, respectively, in the Arctic region and 95.46% and 95.96%, respectively, in the Antarctic region. The DT- and RF-based methods achieve similar accuracies, while the Kappa coefficient of RF-based approach is slightly larger than that of the DT-based approach, which indicates that the RF-based method outperforms the DT-based method. The results show the potential of monitoring sea ice using machine learning-aided GNSS-R approaches.
Publisher: Institution of Engineering and Technology (IET)
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 11-07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 10-07-2014
Publisher: IEEE
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: IEEE
Date: 2002
Publisher: MIT Press - Journals
Date: 06-2008
Abstract: We report here two cases of two young diplegic patients with cystic periventricular leukomalacia who systematically, and with high sensitivity, perceive translational motion of a random-dot display in the opposite direction. The apparent inversion was specific for translation motion: Rotation and expansion motion were perceived correctly, with normal sensitivity. It was also specific for random-dot patterns, not occurring with gratings. For the one patient that we were able to test extensively, contrast sensitivity for static stimuli was normal, but was very low for direction discrimination at high spatial frequencies and all temporal frequencies. His optokinetic nystagmus movements were normal but he was unable to track a single translating target, indicating a perceptual origin of the tracking deficit. The severe deficit for motion perception was also evident in the seminatural situation of a driving simulation video game. The perceptual deficit for translational motion was reinforced by functional magnetic resonance imaging studies. Translational motion elicited no response in the MT complex, although it did produce a strong response in many visual areas when contrasted with blank stimuli. However, radial and rotational motion produced a normal pattern of activation in a subregion of the MT complex. These data reinforce the existent evidence for independent cortical processing for translational, and circular or radial flow motion, and further suggest that the two systems have different vulnerability and plasticity to prenatal damage. They also highlight the complexity of visual motion perception, and how the delicate balance of neural activity can lead to paradoxical effects such as consistent misperception of the direction of motion. We advance a possible explanation of a reduced spatial s ling of the motion stimuli and report a simple model that simulates well the experimental results.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2009
Publisher: Elsevier BV
Date: 12-2014
Publisher: Elsevier BV
Date: 12-2014
Publisher: IEEE
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2012
DOI: 10.1109/TMC.2011.119
Publisher: Springer Singapore
Date: 2019
Publisher: Emerald
Date: 21-01-2019
Abstract: This paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays, the received signal strength-based Wi-Fi indoor positioning, a low-cost indoor positioning approach, has attracted a significant attention from both academia and industry. The local principal gradient direction is introduced and used to define the weighting function and an average algorithm based on k-means algorithm is used to estimate the local principal gradient direction of each access point. Then, correlation distance is used in the new method to find the k nearest calibration points. The weighted squared Euclidean distance between the nearest calibration point and target point is calculated and used to estimate the position of target point. Experiments are conducted and the results indicate that the proposed Wi-Fi indoor positioning approach considerably outperforms the weighted k nearest neighbor method. The new method also outperforms support vector regression and extreme learning machine algorithms in the absence of sufficient fingerprints. Weighted k nearest neighbor approach, support vector regression algorithm and extreme learning machine algorithm are the three classic strategies for location determination using Wi-Fi fingerprinting. However, weighted k nearest neighbor suffers from dramatic performance degradation in the presence of multipath signal attenuation and environmental changes. More fingerprints are required for support vector regression algorithm to ensure the desirable performance and labeling Wi-Fi fingerprints is labor-intensive. The performance of extreme learning machine algorithm may not be stable. The new weighted squared Euclidean distance-based Wi-Fi indoor positioning strategy can improve the performance of Wi-Fi indoor positioning system. The received signal strength-based effective Wi-Fi indoor positioning system can substitute for global positioning system that does not work indoors. This effective and low-cost positioning approach would be promising for many indoor-based location services. A novel Wi-Fi indoor positioning strategy based on the weighted squared Euclidean distance is proposed in this paper to improve the performance of the Wi-Fi indoor positioning, and the local principal gradient direction is introduced and used to define the weighting function.
Publisher: IEEE
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: American Physical Society (APS)
Date: 09-12-2019
Publisher: IEEE
Date: 09-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 11-07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
No related grants have been discovered for Kegen Yu.