bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Adaptive Remote Sensing Extraction Algorithm for Complex Muddy Coast Waterline
Authors: Yang Ziheng , Wang Lihua , Sun Weiwei , Xu Weixin , Tian Bo , Zhou Yunxuan , Yang Gang , Chen Chao
Published: February 2022

Coastline is an important geographical element of the boundary between ocean and land. Due to the impact of the ocean-land interactions at multiple temporal-spatial scales and the intensified human activities, the waterline of muddy coast is undergoing long-term and continuous dynamic changes. Using traditional remote sensing-based waterline extraction methods, it is difficult to achieve ideal results for muddy coast waterlines, which are faced with problems such as limited algorithm stability, weak algorithm migration, and discontinuous coastlines extraction results. In response to the above challenges, three different types of muddy coasts, Yancheng, Jiuduansha and Xiangshan were selected as the study areas. Based on the Sentinel-2 MSI images, we proposed an adaptive remote sensing extraction algorithm framework for the complex muddy coast waterline, named AEMCW (Adaptive Extraction for Muddy Coast Waterline), including main procedures of high-pass filtering, histogram statistics and adaptive threshold determination, which has the capability to obtain continuous and high-precision muddy coastal waterline. NDWI (Normalized Difference Water Index), MNDWI (Modified Normalized Difference Water Index) and ED (Edge Detection) methods were selected to compare the extraction effect of AEMCW method. The length and spatial accuracy of these four methods were evaluated with the same criteria. The accuracy evaluation presented that the length errors of ED method in all three study areas were minimum, but the waterline results were offset more to the land side, due to spectral similarity, turbid water and tidal flats having similar values of NDWI and MNDWI. Therefore, the length and spatial accuracies of NDWI and MNDWI methods were lower than AEMCW method. The length errors of the AEMCW algorithm in Yancheng, Jiuduansha, and Xiangshan were 14.4%, 18.0%, and 7.7%, respectively. The producer accuracies were 94.3%, 109.6%, and 94.2%, respectively. The user accuracies were 82.4%, 92.9%, and 87.5%, respectively. These results indicated that the proposed AEMCW algorithm can effectively restrain the influence of spectral noise from various land cover types and ensure the continuity of waterline extraction results. The adaptive threshold determination equation reduced the influence of human factors on threshold selection. The further application on ZY-1 02D hyperspectral images in the Yancheng area verified the proposed algorithm is transferable and has good stability.
Assessment of Ocean Swell Height Observations from Sentinel-1A/B Wave Mode against Buoy In Situ and Modeling Hindcasts
Authors: Wang He , Mouche Alexis , Husson Romain , Grouazel Antoine , Chapron Bertrand , Yang Jingsong
Published: February 2022

Synthetic Aperture Radar (SAR) in wave mode is a powerful tool for monitoring sea states in terms of long-period ocean swells of a specific wave directional partition. Since 2016, SARs aboard Sentinel-1A/B operating in wave mode have provided ocean swell spectra dataset as Level-2 Ocean products on a continuous and global basis over open oceans. Furthermore, Level-3 swell products are processed by Copernicus Marine Environment Monitoring Services (CMEMS) taking the benefit of the unique “fireworks” analysis. In this paper, swell wave heights from Sentinel-1A/B wave mode during the period from June 2016 to June 2020 are evaluated. The reference data include the collocated in situ measurements from directional wave buoys and WaveWatch III (WW3) hindcasts. Assessment results show systematic overestimation of approximately 0.2 m in terms of the partitioned swell heights for Sentinel-1A/B Level-2 products compared to the directional buoy observations in eastern Pacific and the western Atlantic. Based on the reliable SAR-WW3 collocations after quality-controls, empirical corrections have been proposed for Sentinel-1 Level-2 swell heights. Independent comparisons against WW3 hindcasts and buoy observations demonstrate the validity of our postprocessing correction for both Level-2 and Level-3 swell heights by eliminating the biases and reducing the root mean square errors. The consistency between CMEMS Level-3 swells and buoy in situ is also examined and discussed by case studies.
A New Way for Cartesian Coordinate Transformation and Its Precision Evaluation
Authors: Yang Ronghua , Deng Chang , Yu Kegen , Li Zhao , Pan Leixilan
Published: February 2022

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.
Spatial Stratification Method for the Sampling Design of LULC Classification Accuracy Assessment: A Case Study in Beijing, China
Authors: Dong Shiwei , Guo Hui , Chen Ziyue , Pan Yuchun , Gao Bingbo
Published: February 2022

Spatial sampling design is important for accurately assessing land use and land cover (LULC) classification results from remote sensing data. Spatial stratification can dramatically improve spatial sampling efficiency by dividing the study area into several strata when classification correctness is spatially stratified heterogeneous. By integrating the LULC classification results from different sources and spatial resolutions, a spatial stratification method for spatial sampling of accuracy assessment is presented in this paper. Its efficiency is demonstrated in the case study using LULC data of Beijing, China, in the following steps. First, we standardized and reclassified multiresolution remote sensing data, including China’s land use/cover datasets (CLUDs) from 2017 (resolution: 30 m), 500 m MCD12Q1, and 10 m FROM-GLC10 data, into six classes. Second, we customized stratification rules, formulated a technical specification to realize 11 strata using CLUDs and MCD12Q1, and employed FROM-GLC10 as the reference data for accuracy assessment. Furthermore, six sample sets with sizes of 16,417; 1821; 652; 337; 198; and 142 were drawn using different methods, and their overall accuracy (OA), deviation accuracy (DA), root-mean-square error (RMSE), and standard deviation (STDEV) values were also evaluated to demonstrate the efficiency brought by spatial stratification. Compared with the spatial even sampling method, the OAs of the stratified even sampling method adopting the proposed stratification method was much closer to the true OA, and the corresponding RMSE and STDEV results decreased from 2.097% and 2.127% to 0.914% and 0.713%, respectively, due to the contribution of spatial stratification in the sampling scheme. The method can be used to distinguish the differences and improve the representativeness of samples, and it can be employed to select validation samples for LULC classification.
HyperLiteNet: Extremely Lightweight Non-Deep Parallel Network for Hyperspectral Image Classification
Authors: Wang Jianing , Huang Runhu , Guo Siying , Li Linhao , Pei Zhao , Liu Bo
Published: February 2022

Deep learning (DL) is widely applied in the field of hyperspectral image (HSI) classification and has proved to be an extremely promising research technique. However, the deployment of DL-based HSI classification algorithms in mobile and embedded vision applications tends to be limited by massive parameters, high memory costs, and the complex networks of DL models. In this article, we propose a novel, extremely lightweight, non-deep parallel network (HyperLiteNet) to address these issues. Based on the development trends of hardware devices, the proposed HyperLiteNet replaces the deep network by the parallel structure in terms of fewer sequential computations and lower latency. The parallel structure can extract and optimize the diverse and divergent spatial and spectral features independently. Meanwhile, an elaborately designed feature-interaction module is constructed to acquire and fuse generalized abstract spectral and spatial features in different parallel layers. The lightweight dynamic convolution further compresses the memory of the network to realize flexible spatial feature extraction. Experiments on several real HSI datasets confirm that the proposed HyperLiteNet can efficiently decrease the number of parameters and the execution time as well as achieve better classification performance compared to several recent state-of-the-art algorithms.
Tracking of Evasive Objects Using Bistatic Doppler Radar Operating in the Millimeter Wave Regime
Authors: Richter Yair , Gerasimov Jacob , Balal Nezah , Pinhasi Yosef
Published: February 2022

In this study, we propose a range detection (RD) ability by a continuous wave (CW) bistatic Doppler radar (RDCWB) of small and fast targets with very high range resolution. The target’s range and velocity are detected simultaneously. The scheme is based on the transmission of a continuous wave (CW) at millimeter wavelength (MMW) and the measurement of the respective Doppler shifts associated with target movements in different directions. The range resolution in this method is determined by the Doppler resolution only, without the necessity to transmit the modulated waveforms as in frequency modulation continuous wave (FMCW) or pulse radars. As the Doppler resolution in CW depends only on the time window required for processing, a very highrange resolution can be obtained. Most other systems that perform target localization use the transmission of wide-band waveforms while measuring the delay of the received signal scattered from the target. In the proposed scheme, the range resolution depends on the processed integration time of the detected signal and the velocity of the target. The transmission is performed from separated antennas and received by a single antenna. The received signal is heterodyned with a sample of the transmitted signal in order to obtain the Doppler shifts associated with the target’s movement. As in a multi-in multi-out (MIMO) configuration, the presented scheme allows for the accumulation of additional information for target classification. Data on the target’s velocity, distance, direction, and instantaneous velocity can be extracted. Using digital processing, with the additional information obtained by analyzing the difference between the resulting intermediate frequencies caused by the Doppler effect, it is possible to calculate the distance between the radar and the target at high resolution in real-time. The presented method, which was tested experimentally, proved to be highly effective, as only one receiver is required for the detection, while the transmission is carried out using a fixed, single-frequency transmission.
A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data
Authors: Msusa Anastazia Daniel , Jiang Dalin , Matsushita Bunkei
Published: February 2022

Water transparency (or Secchi disk depth: ZSD) is a key parameter of water quality; thus, it is very important to routinely monitor. In this study, we made four efforts to improve a state-of-the-art ZSD estimation algorithm that was developed in 2019 on the basis of a new underwater visibility theory proposed in 2015. The four efforts were: (1) classifying all water into clear (Type I), moderately turbid (Type II), highly turbid (Type III), or extremely turbid (Type IV) water types; (2) selecting different reference wavelengths and corresponding semianalytical models for each water type; (3) employing an estimation model to represent reasonable shapes for particulate backscattering coefficients based on the water type classification; and (4) constraining likely wavelength range at which the minimum diffuse attenuation coefficient (Kd(λ)) will occur for each water type. The performance of the proposed ZSD estimation algorithm was compared to that of the original state-of-the-art algorithm using a simulated dataset (N = 91,287, ZSD values 0.01 to 44.68 m) and an in situ measured dataset (N = 305,  ZSD values 0.3 to 16.4 m). The results showed a significant improvement with a reduced mean absolute percentage error (MAPE) from 116% to 65% for simulated data and from 32% to 27% for in situ data. Outliers in the previous algorithm were well addressed in the new algorithm. We further evaluated the developed  ZSD estimation algorithm using medium resolution imaging spectrometer (MERIS) images acquired from Lake Kasumigaura, Japan. The results obtained from 19 matchups revealed that the estimated  ZSD matched well with the in situ measured  ZSD, with a MAPE of 15%. The developed ZSD estimation algorithm can probably be applied to different optical water types due to its semianalytical features.
A Method of Vision Aided GNSS Positioning Using Semantic Information in Complex Urban Environment
Authors: Zhai Rui , Yuan Yunbin
Published: February 2022

High-precision localization through multi-sensor fusion has become a popular research direction in unmanned driving. However, most previous studies have performed optimally only in open-sky conditions; therefore, high-precision localization in complex urban environments required an urgent solution. The complex urban environments employed in this study include dynamic environments, which result in limited visual localization performance, and highly occluded environments, which yield limited global navigation satellite system (GNSS) performance. In order to provide high-precision localization in these environments, we propose a vision-aided GNSS positioning method using semantic information by integrating stereo cameras and GNSS into a loosely coupled navigation system. To suppress the effect of dynamic objects on visual positioning accuracy, we propose a dynamic-simultaneous localization and mapping (Dynamic-SLAM) algorithm to extract semantic information from images using a deep learning framework. For the GPS-challenged environment, we propose a semantic-based dynamic adaptive Kalman filtering fusion (S-AKF) algorithm to develop vision aided GNSS and achieve stable and high-precision positioning. Experiments were carried out in GNSS-challenged environments using the open-source KITTI dataset to evaluate the performance of the proposed algorithm. The results indicate that the dynamic-SLAM algorithm improved the performance of the visual localization algorithm and effectively suppressed the error spread of the visual localization algorithm. Additionally, after vision was integrated, the loosely-coupled navigation system achieved continuous high-accuracy positioning in GNSS-challenged environments.
Comparing Precipitation during Typhoons in the Western North Pacific Using Satellite and In Situ Observations
Authors: Sutton Jessica R. P. , Jakobsen Alexandra , Lanyon Kathryn , Lakshmi Venkat
Published: February 2022

Typhoons are known for causing heavy precipitation, very strong winds, and storm surges. With climate change, the occurrence, strength, and duration of typhoons are changing. Daily, weekly, and monthly precipitation from in situ stations from the NOAA Global Historical Climatological Network (GHCN) were compared in the Western North Pacific from 2000 to 2018 against two widely used datasets: NASA’s TRMM TMPA and PERSIANN-CDR. Additionally, precipitation levels during twenty-five typhoons were compared using precipitation estimates. There have been reductions in the average number of typhoons per year from 1959 to present and by month during the months of August, September, and October. Satellite-derived precipitation estimates from PERSIANN and TRMM TMPA explained approximately 50% of the variation in weekly cumulative precipitation and approximately 72% of the variation in monthly cumulative precipitation during the study period (March 2000–December 2018) when using all available stations. When analysis was completed using only stations close to the best track for the entire duration of a typhoon, 62% of the variation was explained, which is comparable to the weekly and monthly cumulative comparisons. However, most of the stations available and with sufficient data were not located in the tracks of the typhoons. It is of utmost importance to better understand typhoon events by utilizing precipitation data from satellite remote sensing in the Western North Pacific.
ClassHyPer: ClassMix-Based Hybrid Perturbations for Deep Semi-Supervised Semantic Segmentation of Remote Sensing Imagery
Authors: He Yongjun , Wang Jinfei , Liao Chunhua , Shan Bo , Zhou Xin
Published: February 2022

Inspired by the tremendous success of deep learning (DL) and the increased availability of remote sensing data, DL-based image semantic segmentation has attracted growing interest in the remote sensing community. The ideal scenario of DL application requires a vast number of annotation data with the same feature distribution as the area of interest. However, obtaining such enormous training sets that suit the data distribution of the target area is highly time-consuming and costly. Consistency-regularization-based semi-supervised learning (SSL) methods have gained growing popularity thanks to their ease of implementation and remarkable performance. However, there have been limited applications of SSL in remote sensing. This study comprehensively analyzed several advanced SSL methods based on consistency regularization from the perspective of data- and model-level perturbation. Then, an end-to-end SSL approach based on a hybrid perturbation paradigm was introduced to improve the DL model’s performance with a limited number of labels. The proposed method integrates the semantic boundary information to generate more meaningful mixing images when performing data-level perturbation. Additionally, by using implicit pseudo-supervision based on model-level perturbation, it eliminates the need to set extra threshold parameters in training. Furthermore, it can be flexibly paired with the DL model in an end-to-end manner, as opposed to the separated training stages used in the traditional pseudo-labeling. Experimental results for five remote sensing benchmark datasets in the application of segmentation of roads, buildings, and land cover demonstrated the effectiveness and robustness of the proposed approach. It is particularly encouraging that the ratio of accuracy obtained using the proposed method with 5% labels to that using the purely supervised method with 100% labels was more than 89% on all benchmark datasets.
Estimating the Pre-Historical Volcanic Eruption in the Hantangang River Volcanic Field: Experimental and Simulation Study
Authors: Hakim Wahyu Luqmanul , Ramayanti Suci , Park Sungjae , Ko Bokyun , Cheong Dae-Kyo , Lee Chang-Wook
Published: February 2022

The volcanic landforms associated with fluvial topography in the Hantangang River Volcanic Field (HRVF) have geoheritage value. The Hantangang basalt geological landform stretches along 110 km of the paleoriver channel of the Hantangang River. Since the eruption that formed this basalt occurred from 0.15 to 0.51 Ma, estimating the eruption in the HRVF that originated from two source vents in North Korea (Orisan Mountain and the 680 m peak) is challenging due to the limited recorded data for this eruption. In this study, we estimated this prehistorical eruption using 3D printing of a terrain model and Q-LavHA simulation. The results from the experiment were further analyzed using findings from an artificial neural network (ANN) and support vector machine (SVM) to classify the experimental lava area. The SVM classification results showed higher accuracy and efficiency in the computational process than the ANN algorithm. Results from the single eruptive vent scenario showed that the experiment had a higher accuracy than the Q-LavHA simulation. Further analysis of multiple vent scenarios in the Q-LavHA simulation has improved the accuracy compared with the single eruptive vent scenarios.
Automated School Location Mapping at Scale from Satellite Imagery Based on Deep Learning
Authors: Maduako Iyke , Yi Zhuangfang , Zurutuza Naroa , Arora Shilpa , Fabian Christopher , Kim Do-Hyung
Published: February 2022

Computer vision for large scale building detection can be very challenging in many environments and settings even with recent advances in deep learning technologies. Even more challenging is modeling to detect the presence of specific buildings (in this case schools) in satellite imagery at a global scale. However, despite the variation in school building structures from rural to urban areas and from country to country, many school buildings have identifiable overhead signatures that make them possible to be detected from high-resolution imagery with modern deep learning techniques. Our hypothesis is that a Deep Convolutional Neural Network (CNN) could be trained for successful mapping of school locations at a regional or global scale from high-resolution satellite imagery. One of the key objectives of this work is to explore the possibility of having a scalable model that can be used to map schools across the globe. In this work, we developed AI-assisted rapid school location mapping models in eight countries in Asia, Africa, and South America. The results show that regional models outperform country-specific models and the global model. This indicates that the regional model took the advantage of having been exposed to diverse school location structure and features and generalized better, however, the global model was the worst performer due to the difficulty of generalizing the significant variability of school location features across different countries from different regions.
Learning a Fully Connected U-Net for Spectrum Reconstruction of Fourier Transform Imaging Spectrometers
Authors: Chen Tieqiao , Su Xiuqin , Li Haiwei , Li Siyuan , Liu Jia , Zhang Geng , Feng Xiangpeng , Wang Shuang , Liu Xuebin , Wang Yihao , Zou Chunbo
Published: February 2022

Fourier transform imaging spectrometers (FTISs) are widely used in global hyperspectral remote sensing due to the advantages of high stability, high throughput, and high spectral resolution. Spectrum reconstruction (SpecR) is a classic problem of FTISs determining the acquired data quality and application potential. However, the state-of-the-art SpecR algorithms were restricted by the length of maximum optical path difference (MOPD) of FTISs and apodization processing, resulting in a decrease in spectral resolution; thus, the applications of FTISs were limited. In this study, a deep learning SpecR method, which directly learned an end-to-end mapping between the interference/spectrum information with limited MOPD and without apodization processing, was proposed. The mapping was represented as a fully connected U-Net (FCUN) that takes the interference fringes as the input and outputs the highly precise spectral curves. We trained the proposed FCUN model using the real spectra and simulated pulse spectra, as well as the corresponding simulated interference curves, and achieved good results. Additionally, the performance of the proposed FCUN on real interference and spectral datasets was explored. The FCUN could obtain similar spectral values compared with the state-of-the-art fast Fourier transform (FFT)-based method with only 150 and 200 points in the interferograms. The proposed method could be able to enhance the resolution of the reconstructed spectra in the case of insufficient MOPD. Moreover, the FCUN performed well in visual quality using noisy interferograms and gained nearly 70% to 80% relative improvement over FFT for the coefficient of mean relative error (MRE). All the results based on simulated and real satellite datasets showed that the reconstructed spectra of the FCUN were more consistent with the ideal spectrum compared with that of the traditional method, with higher PSNR and lower values of spectral angle (SA) and relative spectral quadratic error (RQE).
High-Resolution ISAR Imaging Based on Plug-and-Play 2D ADMM-Net
Authors: Li Xiaoyong , Bai Xueru , Zhang Yujie , Zhou Feng
Published: February 2022

We propose a deep learning architecture, dubbed Plug-and-play 2D ADMM-Net (PAN), by combining model-driven deep networks and data-driven deep networks for effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging with various signal-to-noise ratios (SNR) and incomplete data scenarios. First, a sparse observation model of 2D ISAR imaging is established, and a 2D ADMM algorithm is presented. On this basis, using the plug and play (PnP) technique, PnP 2D ADMM is proposed, by combining the 2D ADMM algorithm and the deep denoising network DnCNN. Then, we unroll and generalize the PnP 2D ADMM to the PAN architecture, in which all adjustable parameters in the reconstruction layers, denoising layers, and multiplier update layers are learned by end-to-end training through back-propagation. Experimental results showed that the PAN with a single parameter set can achieve noise-robust ISAR imaging with superior reconstruction performance on incomplete simulated and measured data under different SNRs.
The Wavelength-Shifting Optical Module
Authors: Bastian-Querner Benjamin , Binn Lucas S. , Böser Sebastian , Brostean-Kaiser Jannes , Hebecker Dustin , Helbing Klaus , Karg Timo , Köpke Lutz , Kowalski Marek , Peiffer Peter , Pollmann Anna , Rack-Helleis John , Rongen Martin , Schlickmann Lea , Thomas Florian , Vocke Anna
Published: February 2022

The Wavelength-shifting Optical Module (WOM) is a novel photosensor concept for the instrumentation of large detector volumes with single-photon sensitivity. The key objective is to improve the signal-to-noise ratio, which is achieved by decoupling the photosensitive area of a sensor from the cathode area of its photomultiplier tube (PMT). The WOM consists of a transparent tube with two PMTs attached to its ends. The tube is coated with wavelength-shifting paint that absorbs ultraviolet photons with nearly 100% efficiency. Depending on the environment, e.g., air (ice), up to 73% (41%) of the subsequently emitted optical photons can be captured by total internal reflection and propagate towards the PMTs, where they are recorded. The optical properties of the paint, the geometry of the tube, and the coupling of the tube to the PMTs have been optimized for maximal sensitivity based on theoretical derivations and experimental evaluations. Prototypes were built to demonstrate the technique and to develop a reproducible construction process. Important measurable characteristics of the WOM are the wavelength-dependent effective area, the transit time spread of detected photons, and the signal-to-noise ratio. The WOM outperforms bare PMTs, especially with respect to the low signal-to-noise ratio with an increase of a factor up to 8.9 in air (5.2 in ice). Since the gain in sensitivity is mostly in the UV regime, the WOM is an ideal sensor for Cherenkov and scintillation detectors.
Mobility-Aware Proactive Edge Caching Optimization Scheme in Information-Centric IoV Networks
Authors: Musa Salahadin Seid , Zennaro Marco , Libsie Mulugeta , Pietrosemoli Ermanno
Published: February 2022

Edge caching is a promising approach to alleviate the burden on the backhaul of network links. It has a significant role in the Internet of Vehicle (IoV) networks performance by providing cached data at the edge and reduce the burden of the core network caused by the number of participating vehicles and data volume. However, due to the limited computing and storage capabilities of edge devices, it is hard to guarantee that all contents are cached and every requirement of the device are satisfied for all users. In this paper, we design an Information-Centric Network (ICN) with mobility-aware proactive caching scheme to provide delay-sensitive services on IoV networks. The real-time status and interaction of vehicles with other vehicles and Roadside Units (RSU) is modeled using a Markov process. Mobility aware proactive edge caching decision that maximize network performance while minimizing transmission delay is applied. Our numerical simulation results show that the proposed scheme outperforms related caching schemes in terms of latency by 20–25% in terms of latency and by 15–23% in cache hits.
Digital Twin and Smart Manufacturing in Industries: A Bibliometric Analysis with a Focus on Industry 4.0
Authors: Moiceanu Georgiana , Paraschiv Gigel
Published: February 2022

Technology is being used in our society in all areas, mostly in industry, and generates the most interest in current research since it is a part of day-to-day activities. The main objective of this research was to use bibliometric analysis to analyze the production of scientific literature on digital twin and smart manufacturing with a focus on Industry 4.0, using information from the Web of Science database. To conduct the study, the keywords necessary for data selection were chosen, and then analyzed based on different variables such as author productivity, citations, most productive institutions, publishers with the highest number of publications, scientific document classification, countries with the highest number of publications, and a network analysis using VOSviewer. The results showed Tao F. and Soderberg R. were the main authors, that China was the country with the highest knowledge, and Elsevier was the main publisher. Although the subject has only been in publication for five years, digital twin will constitute an important part of future technologies due to its rapid ascension, proof of this being its yearly productivity (2020 producing the highest number of materials). Papers published in 2021 were excluded, but the difference between the numbers of materials found and those analyzed shows that 2021 will be even more productive than 2020.
An Indoor Positioning Method Based on UWB and Visual Fusion
Authors: Peng Pingping , Yu Chao , Xia Qihao , Zheng Zhengqi , Zhao Kun , Chen Wen
Published: February 2022

Continuous positioning and tracking of multi-pedestrian targets is a common concern for large indoor space security, emergency evacuation, location services, and other application areas. Among the sensors used for positioning, the ultra-wide band (UWB) is a critical way to achieve high-precision indoor positioning. However, due to the existence of indoor Non-Line-of-Sight (NLOS) error, a single positioning system can no longer meet the requirement for positioning accuracy. This research aimed to design a high-precision and stable fusion positioning system which is based on the UWB and vision. The method uses the Hungarian algorithm to match the identity of the UWB and vision localization results, and, after successful matching, the fusion localization is performed by the federated Kalman filtering algorithm. In addition, due to the presence of colored noise in indoor positioning data, this paper also proposes a Kalman filtering algorithm based on principal component analysis (PCA). The advantage of this new filtering algorithm is that it does not have to establish the dynamics model of the distribution hypothesis and requires less calculation. The PCA algorithm is firstly used to minimize the correlation of the observables, thus providing a more reasonable Kalman gain by energy estimation and the denoised data, which are substituted into Kalman prediction equations. Experimental results show that the average accuracy of the UWB and visual fusion method is 25.3% higher than that of the UWB. The proposed method can effectively suppress the influence of NLOS error on the positioning accuracy because of the high stability and continuity of visual positioning. Furthermore, compared with the traditional Kalman filtering, the mean square error of the new filtering algorithm is reduced by 31.8%. After using the PCA-Kalman filtering, the colored noise is reduced and the Kalman gain becomes more reasonable, facilitating accurate estimation of the state by the filter.
Dual-Beam Steerable TMAs Combining AM and PM Switched Time-Modulation
Authors: Maneiro-Catoira Roberto , Brégains Julio , García-Naya José A. , Castedo Luis
Published: February 2022

Wireless sensor networks (WSN) are increasingly requiring directional antennas that not only provide higher capacity, security, transmission range or robustness against interference, but also contribute with smart antenna capabilities such as adaptive beamforming or multi beam radiation patterns. Standard phased arrays provide these features, but employing feeding networks based on digitally controlled variable phase shifters (VPSs) which have the disadvantage of high cost and limited angular resolution. Instead, time-modulated arrays (TMAs) use switched feeding networks governed by digital periodic sequences which allow harmonic patterns to be radiated and endows (TMAs) with attractive multifunctional capabilities. In this paper, we analyze and properly combine (TMA) switched feeding networks capable of time-modulating an antenna array with discretized amplitude modulation (AM) and phase modulation (PM) waveforms. The advantages of the proposed innovative dual-beam (TMA) with respect to the competing architectures are, on the one hand, its power efficiency and beamsteering (BS) phase sensitivity and, on the other, its hardware simplicity, which allows for an excellent relative cost advantage.
Symmetric-Key-Based Authentication among the Nodes in a Wireless Sensor and Actuator Network
Authors: Vandervelden Thibaut , De Smet Ruben , Steenhaut Kris , Braeken An
Published: February 2022

To enable today’s industrial automation, a significant number of sensors and actuators are required. In order to obtain trust and isolate faults in the data collected by this network, protection against authenticity fraud and nonrepudiation is essential. In this paper, we propose a very efficient symmetric-key-based security mechanism to establish authentication and nonrepudiation among all the nodes including the gateway in a distributed cooperative network, without communicating additional security parameters to establish different types of session keys. The solution also offers confidentiality and anonymity in case there are no malicious nodes. If at most one of the nodes is compromised, authentication and nonrepudiation still remain valid. Even if more nodes get compromised, the impact is limited. Therefore, the proposed method drastically differs from the classical group key management schemes, where one compromised node completely breaks the system. The proposed method is mainly based on a hash chain with multiple outputs defined at the gateway and shared with the other nodes in the network.
A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm
Authors: He Feng , Ye Qing
Published: February 2022

Bearings are widely used in various electrical and mechanical equipment. As their core components, failures often have serious consequences. At present, most parameter adjustment methods are still manual adjustments of parameters. This adjustment method is easily affected by prior knowledge, easily falls into the local optimal solution, cannot obtain the global optimal solution, and requires a lot of resources. Therefore, this paper proposes a new method for bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by a simulated annealing algorithm. Firstly, the original bearing vibration signal is extracted by wavelet packet transform to obtain the spectrogram, and then the obtained spectrogram is sent to the convolutional neural network for parameter adjustment, and finally the simulated annealing algorithm is used to adjust the parameters. To verify the effectiveness of the method, the bearing database of Case Western Reserve University is used for testing, and the traditional intelligent bearing fault diagnosis methods are compared. The results show that the new method for bearing fault diagnosis proposed in this paper has a better and more reliable diagnosis effect than the existing machine learning and deep learning methods.
Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and Survey
Authors: Wong Lauren J. , Michaels Alan J.
Published: February 2022

Transfer learning is a pervasive technology in computer vision and natural language processing fields, yielding exponential performance improvements by leveraging prior knowledge gained from data with different distributions. However, while recent works seek to mature machine learning and deep learning techniques in applications related to wireless communications, a field loosely termed radio frequency machine learning, few have demonstrated the use of transfer learning techniques for yielding performance gains, improved generalization, or to address concerns of training data costs. With modifications to existing transfer learning taxonomies constructed to support transfer learning in other modalities, this paper presents a tailored taxonomy for radio frequency applications, yielding a consistent framework that can be used to compare and contrast existing and future works. This work offers such a taxonomy, discusses the small body of existing works in transfer learning for radio frequency machine learning, and outlines directions where future research is needed to mature the field.
Angle-Only Filtering of a Maneuvering Target in 3D
Authors: Mallick Mahendra , Tian Xiaoqing , Zhu Yun , Morelande Mark
Published: February 2022

We consider the state estimation of a maneuvering target in 3D using bearing and elevation measurements from a passive infrared search and track (IRST) sensor. Since the range is not observable, the sensor must perform a maneuver to observe the state of the target. The target moves with a nearly constant turn (NCT) in the XY-plane and nearly constant velocity (NCV) along the Z-axis. The natural choice for the NCT motion is to allow perturbations in speed and angular rate in the stochastic differential equation, as has been pointed out previously for a 2D scenario using range and bearing measurements. The NCT motion in the XY-plane cannot be discretized exactly, whereas the NCV motion along the Z-axis is discretized exactly. We discretize the continuous-time NCT model using the first and second-order Taylor approximations to obtain discrete-time NCT models, and we consider the polar velocity and Cartesian velocity-based states for the NCT model. The dynamic and measurement models are nonlinear in the target state. We use the cubature Kalman filter to estimate the target state. Accuracies of the first and second-order Taylor approximations are compared using the polar velocity-based and Cartesian velocity-based models using Monte Carlo simulations. Numerical results for realistic scenarios considered show that the second-order Taylor approximation provides the best accuracy using the polar velocity or Cartesian velocity-based models.
Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping
Authors: Lee Hyeon-Seung , Shin Beom-Soo , Thomasson J. Alex , Wang Tianyi , Zhang Zhao , Han Xiongzhe
Published: February 2022

Unmanned aerial vehicle-based remote sensing technology has recently been widely applied to crop monitoring due to the rapid development of unmanned aerial vehicles, and these technologies have considerable potential in smart agriculture applications. Field phenotyping using remote sensing is mostly performed using unmanned aerial vehicles equipped with RGB cameras or multispectral cameras. For accurate field phenotyping for precision agriculture, images taken from multiple perspectives need to be simultaneously collected, and phenotypic measurement errors may occur due to the movement of the drone and plants during flight. In this study, to minimize measurement error and improve the digital surface model, we proposed a collaborative driving system that allows multiple UAVs to simultaneously acquire images from different viewpoints. An integrated navigation system based on MAVSDK is configured for the attitude control and position control of unmanned aerial vehicles. Based on the leader–follower-based swarm driving algorithm and a long-range wireless network system, the follower drone cooperates with the leader drone to maintain a constant speed, direction, and image overlap ratio, and to maintain a rank to improve their phenotyping. A collision avoidance algorithm was developed because different UAVs can collide due to external disturbance (wind) when driving in groups while maintaining a rank. To verify and optimize the flight algorithm developed in this study in a virtual environment, a GAZEBO-based simulation environment was established. Based on the algorithm that has been verified and optimized in the previous simulation environment, some unmanned aerial vehicles were flown in the same flight path in a real field, and the simulation and the real field were compared. As a result of the comparative experiment, the simulated flight accuracy (RMSE) was 0.36 m and the actual field flight accuracy was 0.46 m, showing flight accuracy like that of a commercial program.
Steady-State Visual Evoked Potential-Based Brain–Computer Interface Using a Novel Visual Stimulus with Quick Response (QR) Code Pattern
Authors: Siribunyaphat Nannaphat , Punsawad Yunyong
Published: February 2022

Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems suffer from low SSVEP response intensity and visual fatigue, resulting in lower accuracy when operating the system for continuous commands, such as an electric wheelchair control. This study proposes two SSVEP improvements to create a practical BCI for communication and control in disabled people. The first is flicker pattern modification for increasing SSVEP response through mixing (1) fundamental and first harmonic frequencies, and (2) two fundamental frequencies for an additional number of commands. The second method utilizes a quick response (QR) code for visual stimulus patterns to increase the SSVEP response and reduce visual fatigue. Eight different stimulus patterns from three flickering frequencies (7, 13, and 17 Hz) were presented to twelve participants for the test and score levels of visual fatigue. Two popular SSVEP methods, i.e., power spectral density (PSD) with Welch periodogram and canonical correlation analysis (CCA) with overlapping sliding window, are used to detect SSVEP intensity and response, compared to the checkerboard pattern. The results suggest that the QR code patterns can yield higher accuracy than checkerboard patterns for both PSD and CCA methods. Moreover, a QR code pattern with low frequency can reduce visual fatigue; however, visual fatigue can be easily affected by high flickering frequency. The findings can be used in the future to implement a real-time, SSVEP-based BCI for verifying user and system performance in actual environments.
Canvas Painting Analysis Using Spectroscopic Analysis and Microcharacterisation Techniques
Authors: Borg Braeden , Dunn Michelle , Ang Andrew S. M. , Villis Carl
Published: February 2022

Raman spectroscopy is a well-recognised tool for the analysis of materials in canvas paintings. However, it can be difficult to interpret the peaks of the spectra without the additional context of the artwork such as the age, provenance, or colour. Reflectance spectrophotometry can be used to capture the colour of pigments, dyes, and lacquers, but is seldom used to complement Raman data. Additionally, reflectance spectrophotometry results can be influenced by the surface profile of the painting. To overcome these limitations, this work brings together three different analysis modalities to provide a singular, analytical map of the artwork. Raman spectroscopy was used to conduct the chemical identification of pigments, binding media, and varnish present in a synthetic painting sample. Reflectance spectrophotometry was applied to obtain colour information of the surface paint of the sample. Three-dimensional optical profilometry data was used to characterise the micro topology of the paint surface. These three data sets were spatially matched allowing the recorded spectroscopic data to be displayed with the corresponding colour and surface topography across the paint surface.
Doppler Measurement of Modulated Light for High Speed Vehicles
Authors: Sung Kookjin , Majji Manoranjan
Published: February 2022

Technical details associated with a novel relative motion sensor system are elaborated in the paper. By utilizing the Doppler effect, the optical sensor system estimates the relative motion rates between the sensor and the moving object equipped with modulating light sources and relatively inexpensive electrical components. A transimpedance amplifier (TIA) sensing circuit is employed to measure the Doppler shift exhibited by the amplitude modulated light sources on the moving platform. Implementation details associated with the amplitude modulation and photo-detection processes are discussed using representative hardware elements. A heterodyne mixing process with a reference signal is shown to improve the signal-to-noise ratios of the Doppler shift estimation processing pipeline. Benchtop prototype experiments are used to demonstrate the utility of the proposed technology for relative motion estimation applications.
Approach Developed According to Sustainable Development Goals and Challenges for Future Professionals in Social Intervention
Authors: Picado-Valverde Eva Maria , Yurrebaso Amaia , Guzmán-Ordaz Raquel , Nieto-Librero Ana Belén , Gonzalez-García Nerea
Published: February 2022

In 2015, the United Nations and various countries committed to achieving the Sustainable Development Goals. The 17 goals revolve around 3 main axes: eradicating poverty, protecting the planet, and ensuring peace and prosperity for all people by 2030. These goals are integrated so that interventions in one area inevitably affect the others. Undoubtedly, this application involves developing competencies related to Prejudice, conflict resolution, and empowerment. Our research aims to analyse the knowledge and competency of university students undergoing specific training to facilitate the application of UNESCO’s objectives in their work performance, while incorporating human rights as a basis for all future actions. A total of 241 students from the University of Salamanca participated. The average age of the sample was 21.13 years; 76.8% were female, and 23.2% were male (22.41 ± 7.17 years old). The data collection protocol included questions related to knowledge of the Sustainable Development Goals and involving SDGs in their personal life and future profession, which were assessed using the empowerment Scale, the Conflictalk Scale, and the Subtle and Overt Bias Scale. Significant differences were found between SDGs knowledge and involvement with academic courses. There was a direct relationship between this knowledge and involvement with the control, esteem, and activism dimensions of the Empowerment Scale, cooperative from the Conflictalk Scale, and positive emotions had inverse relationships with threat–rejection, and traditional values from the prejudice scale. Our study found that students who are more engaged with the SDGs resolve conflicts cooperatively, foster community activism, and experience positive emotions, whereas students with aggressive conflict resolution are more Prejudiced.