bluuebunny/crossref_metadata_2025 huggingface dataset:
Confucian Exemplars and Catholic Saints as Models for Women in Nineteenth-Century Korea
Authors: Torrey Deberniere
Published: March 2020

Women in Joseon Korea (1392–1910) were held to high standards of virtue, which were propagated through didactic texts such as the “Chaste and Obedient Biographies” volume of Lienü Zhuan, the Chinese classic featuring biographies of exemplary women. Joseon women who converted to Catholicism were also educated in standards of Catholic virtue, often through the biographies of saints, which shared with the Confucian exemplar stories an emphasis on faithfulness and self-sacrifice. Yet, the differences between Confucian and Catholic standards of virtue were great enough to elicit persecution of Catholics throughout the nineteenth century. Therefore conversion would have involved evaluating one set of standards against the other and determining that Catholicism was worth the price of social marginalization and persecution. Through a comparison of the Confucian exemplar stories and Catholic saints’ stories, this paper explores how Catholic standards of virtue might have motivated conversion of Joseon women to Catholicism. This comparison highlights aspects of the saints’ stories that offered lifestyle choices unavailable to women in traditional Joseon society and suggests that portrayals of the saints’ confidence in the face of human and natural oppressors could also have provided inspiration to ease the price of conversion.
Nonlocal CNN SAR Image Despeckling
Authors: Cozzolino Davide , Verdoliva Luisa , Scarpa Giuseppe , Poggi Giovanni
Published: March 2020

We propose a new method for SAR image despeckling, which performs nonlocal filtering with a deep learning engine. Nonlocal filtering has proven very effective for SAR despeckling. The key idea is to exploit image self-similarities to estimate the hidden signal. In its simplest form, pixel-wise nonlocal means, the target pixel is estimated through a weighted average of neighbors, with weights chosen on the basis of a patch-wise measure of similarity. Here, we keep the very same structure of plain nonlocal means, to ensure interpretability of results, but use a convolutional neural network to assign weights to estimators. Suitable nonlocal layers are used in the network to take into account information in a large analysis window. Experiments on both simulated and real-world SAR images show that the proposed method exhibits state-of-the-art performance. In addition, the comparison of weights generated by conventional and deep learning-based nonlocal means provides new insight into the potential and limits of nonlocal information for SAR despeckling.
An Improved Convolution Neural Network-Based Model for Classifying Foliage and Woody Components from Terrestrial Laser Scanning Data
Authors: Wu Bingxiao , Zheng Guang , Chen Yang
Published: March 2020

Separating foliage and woody components can effectively improve the accuracy of simulating the forest eco-hydrological processes. It is still challenging to use deep learning models to classify canopy components from the point cloud data collected in forests by terrestrial laser scanning (TLS). In this study, we developed a convolution neural network (CNN)-based model to separate foliage and woody components (FWCNN) by combing the geometrical and laser return intensity (LRI) information of local point sets in TLS datasets. Meanwhile, we corrected the LRI information and proposed a contribution score evaluation method to objectively determine hyper-parameters (learning rate, batch size, and validation split rate) in the FWCNN model. Our results show that: (1) Correcting the LRI information could improve the overall classification accuracy (OA) of foliage and woody points in tested broadleaf (from 95.05% to 96.20%) and coniferous (from 93.46% to 94.98%) TLS datasets (Kappa ≥ 0.86). (2) Optimizing hyper-parameters was essential to enhance the running efficiency of the FWCNN model, and the determined hyper-parameter set was suitable to classify all tested TLS data. (3) The FWCNN model has great potential to classify TLS data in mixed forests with OA > 84.26% (Kappa ≥ 0.67). This work provides a foundation for retrieving the structural features of woody materials within the forest canopy.
Obtaining Urban Waterlogging Depths from Video Images Using Synthetic Image Data
Authors: Jiang Jingchao , Qin Cheng-Zhi , Yu Juan , Cheng Changxiu , Liu Junzhi , Huang Jingzhou
Published: March 2020

Reference objects in video images can be used to indicate urban waterlogging depths. The detection of reference objects is the key step to obtain waterlogging depths from video images. Object detection models with convolutional neural networks (CNNs) have been utilized to detect reference objects. These models require a large number of labeled images as the training data to ensure the applicability at a city scale. However, it is hard to collect a sufficient number of urban flooding images containing valuable reference objects, and manually labeling images is time-consuming and expensive. To solve the problem, we present a method to synthesize image data as the training data. Firstly, original images containing reference objects and original images with water surfaces are collected from open data sources, and reference objects and water surfaces are cropped from these original images. Secondly, the reference objects and water surfaces are further enriched via data augmentation techniques to ensure the diversity. Finally, the enriched reference objects and water surfaces are combined to generate a synthetic image dataset with annotations. The synthetic image dataset is further used for training an object detection model with CNN. The waterlogging depths are calculated based on the reference objects detected by the trained model. A real video dataset and an artificial image dataset are used to evaluate the effectiveness of the proposed method. The results show that the detection model trained using the synthetic image dataset can effectively detect reference objects from images, and it can achieve acceptable accuracies of waterlogging depths based on the detected reference objects. The proposed method has the potential to monitor waterlogging depths at a city scale.
Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service
Authors: Fuster Beatriz , Sánchez-Zapero Jorge , Camacho Fernando , García-Santos Vicente , Verger Aleixandre , Lacaze Roselyne , Weiss Marie , Baret Frederic , Smets Bruno
Published: March 2020

The Copernicus Global Land Service (CGLS) provides global time series of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) and fraction of vegetation cover (fCOVER) data at a resolution of 300 m and a frequency of 10 days. We performed a quality assessment and validation of Version 1 Collection 300 m products that were consistent with the guidelines of the Land Product Validation (LPV) subgroup of the Committee on Earth Observation System (CEOS) Working Group on Calibration and Validation (WGCV). The spatiotemporal patterns of Collection 300 m V1 LAI, fAPAR and fCOVER products are consistent with CGLS Collection 1 km V1, Collection 1 km V2 and Moderate Resolution Imagery Spectroradiometer Collection 6 (MODIS C6) products. The Collection 300 m V1 products have good precision and smooth temporal profiles, and the interannual variations are consistent with similar satellite products. The accuracy assessment using ground measurements mainly over crops shows an overall root mean square deviation of 1.01 (44.3%) for LAI, 0.12 (22.2%) for fAPAR and 0.21 (42.6%) for fCOVER, with positive mean biases of 0.36 (15.5%), 0.05 (10.3%) and 0.16 (32.2%), respectively. The products meet the CGLS user accuracy requirements in 69.1%, 62.5% and 29.7% of the cases for LAI, fAPAR and fCOVER, respectively. The CGLS will continue the production of Collection 300 m V1 LAI, fAPAR and fCOVER beyond the end of the PROBA-V mission by using Sentinel-3 OLCI as input data.
Pen Culture Detection Using Filter Tensor Analysis with Multi-Temporal Landsat Imagery
Authors: Xi Yanxin , Ji Luyan , Geng Xiurui
Published: March 2020

Aquaculture plays an important role in China’s total fisheries production nowadays, and it leads to a few problems, for example water quality degradation, which has damaging effect on the sustainable development of environment. Among the many forms of aquaculture that deteriorate the water quality, disorderly pen culture is especially severe. Pen culture began very early in Yangchenghu Lake and Taihu Lake in China and part of the pen culture still exists. Thus, it is of great significance to evaluate the distribution and area of the pen culture in the two lakes. However, the traditional method for pen culture detection is based on the factual measurement, which is labor and time consuming. At present, with the development of remote sensing technologies, some target detection algorithms for multi/hyper-spectral data have been used in the pen culture detection, but most of them are intended for the single-temporal remote sensing data. Recently, a target detection algorithm called filter tensor analysis (FTA), which is specially designed for multi-temporal remote sensing data, has been reported and has achieved better detection results compared to the traditional single-temporal methods in many cases. This paper mainly aims to investigate the pen culture in Yangchenghu Lake and Taihu Lake with FTA implemented on the multi-temporal Landsat imagery, by determining the optimal time phases combination of the Landsat data in advance. Furthermore, the suitability and superiority of FTA over Constrained Energy Minimization (CEM) in the process of pen culture detection were tested. It was observed in the experiments on the data of those two lakes that FTA can detect the pen culture much more accurately than CEM with Landsat data of selected bands and of limited number of time phases.
Measuring Winds and Currents with Ka-Band Doppler Scatterometry: An Airborne Implementation and Progress towards a Spaceborne Mission
Authors: Wineteer Alexander , Perkovic-Martin Dragana , Monje Raquel , Rodríguez Ernesto , Gál Tamás , Niamsuwan Noppasin , Nicaise Fabien , Srinivasan Karthik , Baldi Chad , Majurec Ninoslav , Stiles Bryan
Published: March 2020

Ocean surface winds and currents are tightly coupled, essential climate variables, synoptic measurements of which require a remote sensing approach. Global measurements of ocean vector winds have been provided by scatterometers for decades, but a synoptic approach to measuring total vector surface currents has remained elusive. Doppler scatterometry is a coherent burst-scatterometry technique that builds on the long heritage of spinning pencil beam scatterometers to enable the wide-swath, simultaneous measurement of ocean surface vector winds and currents. To prove the measurement concept, NASA funded the DopplerScatt airborne Doppler scatterometer through the Instrument Incubator Program (IIP) and Airborne Instrument Technology Transition (AITT) program. DopplerScatt has successfully shown that pencil beam Doppler scatterometry can be used to form wide swath measurements of ocean winds and currents, and has increased the technology readiness level of key instrument components, including: Ka-band pulsed radar hardware, optimized scatterometer burst-mode operation, calibration techniques, geophysical model functions, and processing algorithms. With the promise and progress shown by DopplerScatt, and the importance of air-sea interactions in mind, the National Academy’s Decadal Survey has targeted simultaneous measurements of winds and currents from a Doppler scatterometer for an Earth Explorer class spaceborne mission. Besides DopplerScatt’s place as a technology stepping stone towards a satellite mission, DopplerScatt provides scientifically important measurements of ocean currents and winds (400 m resolution) and their derivatives (1 km resolution) over a 25 km swath. These measurements are enabling studies of the submesoscales and air-sea interactions that were previously impossible, and are central to the upcoming NASA Earth Ventures Suborbital-3 Submesoscale Ocean Dynamics Experiment (S-MODE). This paper summarizes the development of DopplerScatt hardware, systems, calibration, and operations, and how advances in each relate to progress towards a spaceborne Doppler scatterometer mission.
Evaluation of Land Surface Temperature Retrieval from Landsat 8/TIRS Images before and after Stray Light Correction Using the SURFRAD Dataset
Authors: Guo Jinxin , Ren Huazhong , Zheng Yitong , Lu Shangzong , Dong Jiaji
Published: March 2020

Landsat 8/thermal infrared sensor (TIRS) is suffering from the problem of stray light that makes an inaccurate radiance for two thermal infrared (TIR) bands and the latest correction was conducted in 2017. This paper focused on evaluation of land surface temperature (LST) retrieval from Landsat 8 before and after the correction using ground-measured LST from five surface radiation budget network (SURFRAD) sites. Results indicated that the correction increased the band radiance at the top of the atmosphere for low temperature but decreased such radiance for high temperature. The root-mean-square error (RMSE) of LST retrieval decreased by 0.27 K for Band 10 and 0.78 K for Band 11 using the single-channel algorithm. For the site with high temperature, the LST retrieval RMSE of the single-channel algorithm for Band 11 even reduced by 1.4 K. However, the accuracy of two of three split-window algorithms adopted in this paper decreased. After correction, the single-channel algorithm for Band 10 and the linear split-window algorithm had the least RMSE (approximately 2.5 K) among five adopted algorithms. Moreover, besides SURFRAD sites, it is necessary to validate using more robust and homogeneous ground-measured datasets to help solely clarify the effect of the correction on LST retrieval.
PolSAR Image Classification Based on Statistical Distribution and MRF
Authors: Yin Junjun , Liu Xiyun , Yang Jian , Chu Chih-Yuan , Chang Yang-Lang
Published: March 2020

Classification is an important topic in synthetic aperture radar (SAR) image processing and interpretation. Because of speckle and imaging geometrical distortions, land cover mapping is always a challenging task especially in complex landscapes. In this study, we aim to find a robust and efficient method for polarimetric SAR (PolSAR) image classification. The Markov random field (MRF) has been widely used for capturing the spatial-contextual information of the image. In this paper, we firstly introduce two ways to construct the Wishart mixture model and compare their performances using real PolSAR data. Then, the better mixture model and two other classical statistically distributions are combined with MRF to construct the MRF models. In order to improve the robustness of the models, the constant false alarm rate (CFAR)-based edge penalty term and an adaptive neighborhood system are embedded into the MRF energy functional. Classification is implemented in two schemes, i.e., pixel-based and region-based classifications. Finally, agriculture fields are used as the test scenario to evaluate the robustness and applicability of these algorithms.
A Combined IR-GPS Satellite Analysis for Potential Applications in Detecting and Predicting Lightning Activity
Authors: D’Adderio Leo Pio , Pazienza Luigi , Mascitelli Alessandra , Tiberia Alessandra , Dietrich Stefano
Published: March 2020

Continuous estimates of the vertical integrated precipitable water vapor content from the tropospheric delay of the signal received by the antennas of the global positioning system (GPS) are used in this paper, in conjunction with the measurements of the Meteosat Second Generation (MSG) spinning enhanced visible and infrared imager (SEVIRI) radiometer and with the lightning activity, collected here by the ground-based lightning detection network (LINET), in order to identify links and recurrent patterns useful for improving nowcasting applications. The analysis of a couple of events is shown here as an example of more general behavior. Clear signs appear before the peak of lightning activity on a timescale from 2 to 3 h. In particular, the lightning activity is generally preceded by a period in which the difference between SEVIRI brightness temperature (TB) at channel 5 and channel 6 (i.e., ∆TB) presents quite constant values around 0 K. This trend is accompanied by an increase in precipitable water vapor (PWV) values, reaching a maximum in conjunction with the major flash activity. The results shown in this paper evidence good potentials of using radiometer and GPS measurements together for predicting the abrupt intensification of lightning activity in nowcasting systems.
A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions
Authors: Xu Shengyu , Qing Linbo , Han Longmei , Liu Mei , Peng Yonghong , Shen Lifang
Published: March 2020

For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on single remote sensing image data or social sensing data. The multi-dimensional information which was attained from various data source and could reflect the attribute or function about the urban functional regions that could be lost in some extent. To sense urban functional regions comprehensively and accurately, we developed a multi-mode framework through the integration of spatial geographic characteristics of remote sensing images and the functional distribution characteristics of social sensing data of Point-of-Interest (POI). In this proposed framework, a deep multi-scale neural network was developed first for the functional recognition of remote sensing images in urban areas, which explored the geographic feature information implicated in remote sensing. Second, the POI function distribution was analyzed in different functional areas of the city, then the potential relationship between POI data categories and urban region functions was explored based on the distance metric. A new RPF module is further deployed to fuse the two characteristics in different dimensions and improve the identification performance of urban region functions. The experimental results demonstrated that the proposed method can efficiently achieve the accuracy of 82.14% in the recognition of functional regions. It showed the great usability of the proposed framework in the identification of urban functional regions and the potential to be applied in a wide range of areas.
Editorial for the Special Issue “Remote Sensing of the Terrestrial Hydrologic Cycle”
Authors: Tang Qiuhong , Qi Youcun , Wang Zhihui , Pan Yun
Published: March 2020

To address global water security issues, it is important to understand the evolving global water system and its natural and anthropogenic influencing factors [...]
Influence of the Sun Position and Platform Orientation on the Quality of Imagery Obtained from Unmanned Aerial Vehicles
Authors: Sekrecka Aleksandra , Wierzbicki Damian , Kedzierski Michal
Published: March 2020

Images acquired at a low altitude can be the source of accurate information about various environmental phenomena. Often, however, this information is distorted by various factors, so a correction of the images needs to be performed to recreate the actual reflective properties of the imaged area. Due to the low flight altitude, the correction of images from UAVs (unmanned aerial vehicles) is usually limited to noise reduction and detector errors. The article shows the influence of the Sun position and platform deviation angles on the quality of images obtained by UAVs. Tilting the camera placed on an unmanned platform leads to incorrect exposures of imagery, and the order of this distortion depends on the position of the Sun during imaging. An image can be considered in three-dimensional space, where the x and y coordinates determine the position of the pixel and the third dimension determines its exposure. This assumption is the basis for the proposed method of image exposure compensation. A three-dimensional transformation by rotation is used to determine the adjustment matrix to correct the image quality. The adjustments depend on the angles of the platform and the difference between the direction of flight and the position of the Sun. An additional factor regulates the value of the adjustment depending on the ratio of the pitch and roll angles. The experiments were carried out for two sets of data obtained with different unmanned systems. The correction method used can improve the block exposure by up to 60%. The method gives the best results for simple systems, not equipped with lighting compensation systems.
Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China
Authors: Yang Xiaoying , Lu Yang , Tan Mou Leong , Li Xiaogang , Wang Guoqing , He Ruimin
Published: March 2020

Owing to their advantages of wide coverage and high spatiotemporal resolution, satellite precipitation products (SPPs) have been increasingly used as surrogates for traditional ground observations. In this study, we have evaluated the accuracy of the latest five GPM IMERG V6 and TRMM 3B42 V7 precipitation products across the monthly, daily, and hourly scale in the hilly Shuaishui River Basin in East-Central China. For evaluation, a total of four continuous and three categorical metrics have been calculated based on SPP estimates and historical rainfall records at 13 stations over a period of 9 years from 2009 to 2017. One-way analysis of variance (ANOVA) and multiple posterior comparison tests are used to assess the significance of the difference in SPP rainfall estimates. Our evaluation results have revealed a wide-ranging performance among the SPPs in estimating rainfall at different time scales. Firstly, two post-time SPPs (IMERG_F and 3B42) perform considerably better in estimating monthly rainfall. Secondly, with IMERG_F performing the best, the GPM products generally produce better daily rainfall estimates than the TRMM products. Thirdly, with their correlation coefficients all falling below 0.6, neither GPM nor TRMM products could estimate hourly rainfall satisfactorily. In addition, topography tends to impose similar impact on the performance of SPPs across different time scales, with more estimation deviations at high altitude. In general, the post-time IMERG_F product may be considered as a reliable data source of monthly or daily rainfall in the study region. Effective bias-correction algorithms incorporating ground rainfall observations, however, are needed to further improve the hourly rainfall estimates of the SPPs to ensure the validity of their usage in real-world applications.
An Empirical Model to Estimate Abundance of Nanophase Metallic Iron (npFe0) in Lunar Soils
Authors: Liu Dawei , Zhang Yuanzhi , Zhang Guangliang , Liu Bin , Ren Xin , Xu Rui , Li Chunlai
Published: March 2020

Lunar soils gradually become mature when they are exposed to a space environment, and nanophase metallic iron (npFe0) generates within them. npFe0 significantly changes the optical properties of lunar soils and affects the interpretation of the remotely sensed data of the lunar surface. In this study, a correlation analysis was conducted between npFe0 abundance and reflectance spectra at short wavelengths for lunar soil samples in four size groups based on their spectral and compositional data, collected by the Lunar Soil Characterization Consortium (LSCC). Results show that 540 nm single scattering albedo (SSA) of lunar soils correlates well with their corresponding npFe0 abundance for each size group of lunar soil samples. However, it is poorly correlated with npFe0 abundance when all size groups were considered because of the strong interference from grain size variation of lunar soils. To minimize the effect of grain size, the correlation of npFe0 abundance with the spectral ratio of 540 nm/810 nm SSA of all size groups for LSCC samples was calculated and results show that a higher correlation existed between them (R2 = 0.91). This ratio can serve as a simple empirical model for estimating npFe0 abundance in lunar soils. However, bias could be introduced to the estimation result when lunar soils possess a high content of agglutinitic glass and ilmenite. Our future work will focus on improving the model’s performance for these lunar soils.
Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature
Authors: Merchant Christopher J. , Block Thomas , Corlett Gary K. , Embury Owen , Mittaz Jonathan P. D. , Mollard James D. P.
Published: March 2020

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing.
BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images
Authors: Shao Zhenfeng , Tang Penghao , Wang Zhongyuan , Saleem Nayyer , Yam Sarath , Sommai Chatpong
Published: March 2020

Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints.
A FRET Based Two-Photon Fluorescent Probe for Visualizing Mitochondrial Thiols of Living Cells and Tissues
Authors: Liu Zhengkun , Wang Qianqian , Wang Hao , Su Wenting , Dong Shouliang
Published: March 2020

Glutathione (GSH) is the main component of the mitochondrial thiol pool and plays key roles in the biological processes. Many evidences have suggested that cysteine and homocysteine also exist in mitochondria and are interrelated with GSH in biological systems. The fluctuation of the levels of mitochondrial thiols has been linked to many diseases and cells’ dysfunction. Therefore, the monitoring of mitochondrial thiol status is of great significance for clinical studies. We report here a novel fluorescence resonance energy transfer based two-photon probe MT-1 for mitochondrial thiols detection. MT-1 was constructed by integrating the naphthalimide moiety (donor) and rhodamine B (accepter and targeting group) through a newly designed linker. MT-1 shows a fast response, high selectivity, and sensitivity to thiols, as well as a low limit of detection. The two-photon property of MT-1 allows the direct visualization of thiols in live cells and tissues by two-photon microscopy. MT-1 can serve as an effective tool to unravel the diverse biological functions of mitochondrial thiols in living systems.
Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions
Authors: El-Khatib Hassan , Popescu Dan , Ichim Loretta
Published: March 2020

The main purpose of the study was to develop a high accuracy system able to diagnose skin lesions using deep learning–based methods. We propose a new decision system based on multiple classifiers like neural networks and feature–based methods. Each classifier (method) gives the final decision system a certain weight, depending on the calculated accuracy, helping the system make a better decision. First, we created a neural network (NN) that can differentiate melanoma from benign nevus. The NN architecture is analyzed by evaluating it during the training process. Some biostatistic parameters, such as accuracy, specificity, sensitivity, and Dice coefficient are calculated. Then, we developed three other methods based on convolutional neural networks (CNNs). The CNNs were pre-trained using large ImageNet and Places365 databases. GoogleNet, ResNet-101, and NasNet-Large, were used in the enumeration order. CNN architectures were fine-tuned in order to distinguish the different types of skin lesions using transfer learning. The accuracies of the classifications were determined. The last proposed method uses the classical method of image object detection, more precisely, the one in which some features are extracted from the images, followed by the classification step. In this case, the classification was done by using a support vector machine. Just as in the first method, the sensitivity, specificity, Dice similarity coefficient and accuracy are determined. A comparison of the obtained results from all the methods is then done. As mentioned above, the novelty of this paper is the integration of these methods in a global fusion-based decision system that uses the results obtained by each individual method to establish the fusion weights. The results obtained by carrying out the experiments on two different free databases shows that the proposed system offers higher accuracy results.
Deep Learning Classification for Diabetic Foot Thermograms
Authors: Cruz-Vega Israel , Hernandez-Contreras Daniel , Peregrina-Barreto Hayde , Rangel-Magdaleno Jose de Jesus , Ramirez-Cortes Juan Manuel
Published: March 2020

According to the World Health Organization (WHO), Diabetes Mellitus (DM) is one of the most prevalent diseases in the world. It is also associated with a high mortality index. Diabetic foot is one of its main complications, and it comprises the development of plantar ulcers that could result in an amputation. Several works report that thermography is useful to detect changes in the plantar temperature, which could give rise to a higher risk of ulceration. However, the plantar temperature distribution does not follow a particular pattern in diabetic patients, thereby making it difficult to measure the changes. Thus, there is an interest in improving the success of the analysis and classification methods that help to detect abnormal changes in the plantar temperature. All this leads to the use of computer-aided systems, such as those involved in artificial intelligence (AI), which operate with highly complex data structures. This paper compares machine learning-based techniques with Deep Learning (DL) structures. We tested common structures in the mode of transfer learning, including AlexNet and GoogleNet. Moreover, we designed a new DL-structure, which is trained from scratch and is able to reach higher values in terms of accuracy and other quality measures. The main goal of this work is to analyze the use of AI and DL for the classification of diabetic foot thermograms, highlighting their advantages and limitations. To the best of our knowledge, this is the first proposal of DL networks applied to the classification of diabetic foot thermograms. The experiments are conducted over thermograms of DM and control groups. After that, a multi-level classification is performed based on a previously reported thermal change index. The high accuracy obtained shows the usefulness of AI and DL as auxiliary tools to aid during the medical diagnosis.
Sensing and Modelling Mechanical Response in Large Deformation Indentation of Adherent Cell Using Atomic Force Microscopy
Authors: Shen Tianyao , Shirinzadeh Bijan , Zhong Yongmin , Smith Julian , Pinskier Joshua , Ghafarian Mohammadali
Published: March 2020

The mechanical behaviour of adherent cells when subjected to the local indentation can be modelled via various approaches. Specifically, the tensegrity structure has been widely used in describing the organization of discrete intracellular cytoskeletal components, including microtubules (MTs) and microfilaments. The establishment of a tensegrity model for adherent cells has generally been done empirically, without a mathematically demonstrated methodology. In this study, a rotationally symmetric prism-shaped tensegrity structure is introduced, and it forms the basis of the proposed multi-level tensegrity model. The modelling approach utilizes the force density method to mathematically assure self-equilibrium. The proposed multi-level tensegrity model was developed by densely distributing the fundamental tensegrity structure in the intracellular space. In order to characterize the mechanical behaviour of the adherent cell during the atomic force microscopy (AFM) indentation with large deformation, an integrated model coupling the multi-level tensegrity model with a hyperelastic model was also established and applied. The coefficient of determination between the computational force-distance (F-D) curve and the experimental F-D curve was found to be at 0.977 in the integrated model on average. In the simulation range, along with the increase in the overall deformation, the local stiffness contributed by the cytoskeletal components decreased from 75% to 45%, while the contribution from the hyperelastic components increased correspondingly.
Passive Detection of Ship-Radiated Acoustic Signal Using Coherent Integration of Cross-Power Spectrum with Doppler and Time Delay Compensations
Authors: Guo Wei , Piao Shengchun , Guo Junyuan , Lei Yahui , Iqbal Kashif
Published: March 2020

Passive sonar is widely used for target detection, identification and classification based on the target radiated acoustic signal. Under the influence of Doppler, generated by relative motion between the moving target and the sonar array, the received ship-radiated acoustic signals are non-stationary and time-varying, which has a negative effect on target detection and other fields. In order to reduce the influence of Doppler and improve the performance of target detection, a coherent integration method based on cross-power spectrum is proposed in this paper. It can be concluded that the frequency shift and phase change in the cross-power spectrum obtained by each pair of data segments can be corrected with the compensations of time scale (Doppler) factor and time delay. Moreover, the time scale factor and time delay can be estimated from the amplitude and phase of the original cross-power spectrum, respectively. Therefore, coherent integration can be implemented with the compensated cross-power spectra. Simulation and experimental data processing results show that the proposed method can provide sufficient processing gains and effectively extract the discrete spectra for the detection of moving targets.
Linear and Nonlinear Deformation Effects in the Permanent GNSS Network of Cyprus
Authors: Danezis Chris , Chatzinikos Miltiadis , Kotsakis Christopher
Published: March 2020

Permanent Global Navigation Satellite Systems (GNSS) reference stations are well established as a powerful tool for the estimation of deformation induced by man-made or physical processes. GNSS sensors are successfully used to determine positions and velocities over a specified time period, with unprecedented accuracy, promoting research in many safety-critical areas, such as geophysics and geo-tectonics, tackling problems that torment traditional equipment and providing deformation products with absolute accuracy. Cyprus, being located at the Mediterranean fault, exhibits a very interesting geodynamic regime, which has yet to be investigated thoroughly. Accordingly, this research revolves around the estimation of crustal deformation in Cyprus using GNSS receivers. CYPOS (CYprus POsitioning System), a network of seven permanent GNSS stations has been operating since 2008, under the responsibility of the Department of Lands and Surveys. The continuous flow of positioning data collected over this network, offers the required information to investigate the behavior of the crustal deformation field of Cyprus using GNSS sensors for the first time. This paper presents the results of a multi-year analysis (11/2011–01/2017) of daily GNSS data and provides inferences of linear and nonlinear deforming signals into the position time series of the network stations. Specifically, 3D station velocities and seasonal periodic displacements are jointly estimated and presented via a data stacking approach with respect to the IGb08 reference frame.
A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning
Authors: He Jun , Ouyang Ming , Yong Chen , Chen Danfeng , Guo Jing , Zhou Yan
Published: March 2020

Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used as the main architecture. To improve the robustness and stability, a novel weight strategy based on distance metric and standard deviation metric was employed to assign the weights of three sparse auto-encodes. Softmax classifier is used to classify the fault types of integrated features. The effectiveness of the proposed method is validated with extensive experiments, and comparisons with the related methods and researches on the widely-used motor bearing dataset verify the superiority of the proposed method. The results show that the testing accuracy and the standard deviation are 99.71% and 0.05%.
A Novel Smooth Variable Structure Smoother for Robust Estimation
Authors: Chen Yu , Xu Luping , Yan Bo , Li Cong
Published: March 2020

The smooth variable structure filter (SVSF) is a new-type filter based on the sliding-mode concepts and has good stability and robustness in overcoming the modeling uncertainties and errors. However, SVSF is insufficient to suppress Gaussian noise. A novel smooth variable structure smoother (SVSS) based on SVSF is presented here, which mainly focuses on this drawback and improves the SVSF estimation accuracy of the system. The estimation of the linear Gaussian system state based on SVSS is divided into two steps: Firstly, the SVSF state estimate and covariance are computed during the forward pass in time. Then, the smoothed state estimate is computed during the backward pass by using the innovation of the measured values and covariance estimate matrix. According to the simulation results with respect to the maneuvering target tracking, SVSS has a better performance compared with another smoother based on SVSF and the Kalman smoother in different tracking scenarios. Therefore, the SVSS proposed in this paper could be widely applied in the field of state estimation in dynamic system.
Optimization of Experimental Variables Influencing Apoptosome Biosensor in HEK293T Cells
Authors: Oladzad Azarakhsh , Nikkhah Maryam , Hosseinkhani Saman
Published: March 2020

The apoptotic protease-activating factor 1 (Apaf-1) split luciferase biosensor has been used as a biological tool for the detection of early stage of apoptosis. The effect of doxorubicin in a cell-based assay and the addition of cytochrome c and ATP in a cell-free system have been used to test the functionality of the reporter for the detection of apoptosome formation. Here, our data established a drug- and cytochrome c/ATP-independent way of apoptosis induction relying on the expression of the biosensor itself to induce formation of apoptosome. Overexpression of Apaf-1 constructs led to increased split luciferase activity and caspase-3 activity in the absence of any drug treatment. Caspase-3 activity was significantly inhibited when caspase-9DN was co-overexpressed, while the activity of the Apaf1 biosensor was significantly increased. Our results show that the Apaf-1 biosensor does not detect etoposide-induced apoptosis.
Gas Sensing Properties of Cobalt Titanate with Multiscale Pore Structure: Experiment and Simulation
Authors: Li Mingchun , Wang Baoting , Tao Aili , Li Shengfei
Published: March 2020

A diffusion-reaction coupled model was presented to investigate the effects of multiscale pore structure characteristics on gas sensing properties. A series of CoTiO3 powders with different pore size distributions were fabricated by sol-gel method. Experimental results on cobalt titanate thick films show that a well-defined multiscale pore structure is particularly desired for the improvement of sensing performance, instead of just increasing the specific surface area. The theoretical responses of sensing elements with different pore size distributions were derived and compared with experimental data on CoTiO3 sensors exposed to ethanol. The calculated sensitivities considering the influence of pore size changes were also found to be in agreement with the experimental results. A dimensionless Thiele modulus Th was introduced for assessing the critical point corresponding to the transformation from surface reaction-controlled sensitivity into diffusion-controlled sensitivity.
Double-Constraint Inpainting Model of a Single-Depth Image
Authors: Jin Wu , Zun Li , Yong Liu
Published: March 2020

In real applications, obtained depth images are incomplete; therefore, depth image inpainting is studied here. A novel model that is characterised by both a low-rank structure and nonlocal self-similarity is proposed. As a double constraint, the low-rank structure and nonlocal self-similarity can fully exploit the features of single-depth images to complete the inpainting task. First, according to the characteristics of pixel values, we divide the image into blocks, and similar block groups and three-dimensional arrangements are then formed. Then, the variable splitting technique is applied to effectively divide the inpainting problem into the sub-problems of the low-rank constraint and nonlocal self-similarity constraint. Finally, different strategies are used to solve different sub-problems, resulting in greater reliability. Experiments show that the proposed algorithm attains state-of-the-art performance.
Analysis and Compensation of Bias Drift for a Micromachined Spinning-Rotor Gyroscope with Electrostatic Suspension
Authors: Wang Shunyue , Han Fengtian
Published: March 2020

Bias stability is one of primary characteristics of precise gyroscopes for inertial navigation. Analysis of various sources of the bias drift in a micromachined electrostatically suspended gyroscope (MESG) indicates that the bias stability is dominated by the temperature-induced drift. The analytical results of temperature drift resulting from the rotor structure and capacitive position sensing electronics are modeled and analyzed to characterize the drift mechanism of the MESG. The experimental results indicate that the bias drift is mainly composed of two components, i.e., rapidly changing temperature drift and slowly changing time drift. Both the short-term and long-term bias drift of the MESG are tested and discussed to achieve online bias compensation. Finally, a neural network based-bias compensation scheme is presented and verified experimentally with improved bias stability of the MESG.
Production and Characterization of Hybrid Briquettes from Corncobs and Oil Palm Trunk Bark under a Low Pressure Densification Technique
Authors: Kpalo Sunday Yusuf , Zainuddin Mohamad Faiz , Manaf Latifah Abd , Roslan Ahmad Muhaimin
Published: March 2020

The objective of this research was to investigate the quality of hybrid briquettes developed from corncobs (CC) and oil palm trunk bark (OPTB) under a low-pressure densification technique. The materials were combined in varying ratios of CC to OPTB (100:0, 75:25, 50:50, 25:75, 0:100) and wastepaper pulp (10% by weight) was added to each mixture as a binder. The briquettes were produced using a manually operated 20-tonne hydraulic piston press at 28 °C temperature and ≤7 MPa compaction pressure. The mechanical strength of the briquettes was determined by the drop test and compression test methods, while a bomb calorimeter was used to determine the calorific values. The results showed that the physical properties of hybrid briquettes ranged from 9.24–10.00% moisture content, 0.38–0.40 g/cm3 density, and 87.60%–92.00% water resistance. Mechanical strength showed a 98.28%–99.08% shatter index and 18.47–21.75 MPa compressive strength, while calorific values ranged from 16.54–16.91 MJ/kg. The hybrid briquettes fared better than the CC briquettes. The significance of this study lies in the production of briquettes with suitable physical, mechanical and thermal properties by utilizing OPTB which have hitherto not been used, mixed with corncobs. This could bring substantial environmental and socio-economic benefits to rural communities of the developing countries.