bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Forward Model, Data Type, and Prior Information in Probabilistic Inversion of Crosshole GPR Data
Authors: Qin Hui , Wang Zhengzheng , Tang Yu , Geng Tiesuo
Published: January 2021

The crosshole ground penetrating radar (GPR) is a widely used tool to map subsurface properties, and inversion methods are used to derive electrical parameters from crosshole GPR data. In this paper, a probabilistic inversion algorithm that uses Markov chain Monte Carlo (MCMC) simulations within the Bayesian framework is implemented to infer the posterior distribution of the relative permittivity of the subsurface medium. Close attention is paid to the critical elements of this method, including the forward model, data type and prior information, and their influence on the inversion results are investigated. First, a uniform prior distribution is used to reflect the lack of prior knowledge of model parameters, and inversions are performed using the straight-ray model with first-arrival traveltime data, the finite-difference time-domain (FDTD) model with first-arrival traveltime data, and the FDTD model with waveform data, respectively. The cases using first-arrival traveltime data require an unreasonable number of model evaluations to converge, yet are not able to recover the real relative permittivity field. In contrast, the inversion using the FDTD model with waveform data successfully infers the correct model parameters. Then, the smooth constraint of model parameters is employed as the prior distribution. The inversion results demonstrate that the prior information barely affects the inversion results using the FDTD model with waveform data, but significantly improves the inversion results using first-arrival traveltime data by decreasing the computing time and reducing uncertainties of the posterior distribution of model parameters.
Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform
Authors: Seydi Seyd Teymoor , Akhoondzadeh Mehdi , Amani Meisam , Mahdavi Sahel
Published: January 2021

Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products.
Satellite-Based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia
Authors: Peng Jiabin , Liu Tie , Huang Yue , Ling Yunan , Li Zhengyang , Bao Anming , Chen Xi , Kurban Alishir , De Maeyer Philippe
Published: January 2021

Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: 1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. 2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. 3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. 4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. 5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone.
Characterizing the Development Pattern of a Colluvial Landslide Based on Long-Term Monitoring in the Three Gorges Reservoir
Authors: Liang Xin , Gui Lei , Wang Wei , Du Juan , Ma Fei , Yin Kunlong
Published: January 2021

Since the impoundment of the Three Gorges Reservoir (TGR) in June 2003, the fluctuation of the reservoir water level coupled with rainfall has resulted in more than 2500 landslides in this region. Among these instability problems, most colluvial landslides exhibit slow-moving patterns and pose a significant threat to local people and channel navigation. Advanced monitoring techniques are therefore implemented to investigate landslide deformation and provide insights for the subsequent countermeasures. In this study, the development pattern of a large colluvial landslide, locally named the Ganjingzi landslide, is analyzed on the basis of long-term monitoring. To understand the kinematic characteristics of the landslide, an integrated analysis based on real-time and multi-source monitoring, including the global navigation satellite system (GNSS), crackmeters, inclinometers, and piezometers, was conducted. The results indicate that the Ganjingzi landslide exhibits a time-variable response to the reservoir water fluctuation and rainfall. According to the supplement of community-based monitoring, the evolution of the landslide consists of three stages, namely the stable stage before reservoir impoundment, the initial movement stage of retrogressive failure, and the shallow movement stage with stepwise acceleration. The latter two stages are sensitive to the drawdown of reservoir water level and rainfall infiltration, respectively. All of the monitoring approaches used in this study are significant for understanding the time-variable pattern of colluvial landslides and are essential for landslide mechanism analysis and early warning for risk mitigation.
Estimating Daily Actual Evapotranspiration at a Landsat-Like Scale Utilizing Simulated and Remote Sensing Surface Temperature
Authors: Wang Dakang , Yu Tao , Liu Yan , Gu Xingfa , Mi Xiaofei , Shi Shuaiyi , Ma Meihong , Chen Xinran , Zhang Yin , Liu Qixin , Mumtaz Faisal , Zhan Yulin
Published: January 2021

Actual evapotranspiration (ET) with high spatiotemporal resolution is very important for the research on agricultural water resource management and the water cycle processes, and it is helpful to realize precision agriculture and smart agriculture, and provides critical references for agricultural layout planning. Due to the impact of the clouds, weather environment, and the orbital period of optical satellite, there are difficulties in providing daily remote sensing data that are not contaminated by clouds for estimating daily ET with high spatial-temporal resolution. By improving the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), this manuscript proposes the method to fuse high temporal and low spatial resolution Weather Research and Forecasting (WRF) model surface skin temperature (TSK) with the low temporal and high spatial resolution remote sensing surface temperature for obtaining high spatiotemporal resolution daily surface temperature to be used in the estimation of the high spatial resolution daily ET (ET_WRFHR). The distinction of this study from the previous literatures can be summarized as the novel application of the fusion of WRF-simulated TSK and remote sensing surface temperature, giving full play to the availability of model surface skin temperature data at any time and region, making up for the shortcomings of the remote sensing data, and combining the high spatial resolution of remote sensing data to obtain ET with high spatial (Landsat-like scale) and temporal (daily) resolution. The ET_WRFHR were cross-validated and quantitatively verified with MODIS ET products (MOD16) and observations (ET_Obs) from eddy covariance system. Results showed that ET_WRFHR not only better reflects the difference and dynamic evolution process of ET for different land types but also better identifies the details of various fine geographical objects. It also represented a high correlation with the ET_Obs by the R2 amount reaching 0.9186. Besides, the RMSE and BIAS between ET_WRFHR and the ET_Obs are obtained as 0.77 mm/d and −0.08 mm/d respectively. High R2, as well as the small RMSE and BIAS amounts, indicate that ET_WRFHR has achieved a very good performance.
Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products
Authors: Kang Jian , Jin Rui , Li Xin , Zhang Yang
Published: January 2021

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.
Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau
Authors: Costa Jessica da Silva , Liesenberg Veraldo , Schimalski Marcos Benedito , Sousa Raquel Valério de , Biffi Leonardo Josoé , Gomes Alessandra Rodrigues , Neto Sílvio Luís Rafaeli , Mitishita Edson , Bispo Polyanna da Conceição
Published: January 2021

The Santa Catarina Southern Plateau is located in Southern Brazil and is a region that has gained considerable attention due to the rapid conversion of the typical landscape of natural grasslands and wetlands into agriculture, reforestation, pasture, and more recently, wind farms. This study’s main goal was to characterize the polarimetric attributes of the experimental quad-polarization acquisition mode of the Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR-2) for mapping seven land cover classes. The polarimetric attributes were evaluated alone and combined with SENTINEL-2A using a supervised classification method based on the Support Vector Machine (SVM) algorithm. The results showed that the intensity backscattering alone reached an overall classification accuracy of 37.48% and a Kappa index of 0.26. Interestingly, the addition of polarimetric features increased to 71.35% and 0.66, respectively. It shows that the use of polarimetric decomposition features was relatively efficient in discriminating land cover classes. SENTINEL-2A data alone performed better and achieved a weighted overall accuracy and Kappa index of 85.56% and 0.82. This increase was also significant for the Z-test. However, the addition of ALOS/PALSAR-2 derived features to SENTINEL-2A slightly improved accuracy and was marginally significant at a 95% confidence level only when all features were considered. Possible implications for that performance are the accumulated precipitation prior to SAR data acquisition, which coincides with the rainy season period. The experimental quad-polarization mode of ALOS/PALSAR- 2 shall be evaluated in the near future over different seasonal conditions to confirm results. Alternatively, further studies are then suggested by focusing on additional features derived from SAR data such as texture and interferometric coherence to increase classification accuracy. These measures would be an interesting data source for monitoring specific land cover classes such as the threatened grasslands and wetlands during periods of frequent cloud coverage. Future investigations could also address multitemporal approaches employing either single or multifrequency SAR.
Real-Time Detection and Spatial Localization of Insulators for UAV Inspection Based on Binocular Stereo Vision
Authors: Ma Yunpeng , Li Qingwu , Chu Lulu , Zhou Yaqin , Xu Chang
Published: January 2021

Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line components. In this paper, an automatic transmission line inspection system incorporating UAV remote sensing with binocular visual perception technology is developed to accurately detect and locate power equipment in real time. The system consists of a UAV module, embedded industrial computer, binocular visual perception module, and control and observation module. Insulators, which are key components in power transmission lines as well as fault-prone components, are selected as the detection targets. Insulator detection and spatial localization in aerial images with cluttered backgrounds are interesting but challenging tasks for an automatic transmission line inspection system. A two-stage strategy is proposed to achieve precise identification of insulators. First, candidate insulator regions are obtained based on RGB-D saliency detection. Then, the skeleton structure of candidate insulator regions is extracted. We implement a structure search to realize the final accurate detection of insulators. On the basis of insulator detection results, we further propose a real-time object spatial localization method that combines binocular stereo vision and a global positioning system (GPS). The longitude, latitude, and height of insulators are obtained through coordinate conversion based on the UAV’s real-time flight data and equipment parameters. Experiment results in the actual inspection environment (220 kV power transmission line) show that the presented system meets the requirement of robustness and accuracy of insulator detection and spatial localization in practical engineering.
The Effect of Miss and Tuck Stitches on a Weft Knit Strain Sensor
Authors: Ayodele Emmanuel , Zaidi Syed Ali Raza , Scott Jane , Zhang Zhiqiang , Hafeez Maryam , McLernon Des
Published: January 2021

Weft knitted conductive fabrics can act as excellent textile strain sensors for human motion capture. The loop architecture dictates the overall electrical properties of weft knit strain sensors. Therefore, research into loop architecture is relevant for comprehensively investigating the design space of e-textile sensors. There are three main types of knit stitches, Knitted loop stitch, Miss stitch, and Tuck stitch. Nevertheless, most of the research into weft knit strain sensors has largely focused on fabrics with only knitted loop stitches. Miss and tuck stitches will affect the contact points in the sensor and, consequently, its piezoresistivity. Therefore, this paper investigates the impact of incorporating miss and tuck stitches on the piezoresistivity of a weft knit sensor. Particularly, the electromechanical models of a miss stitch and a tuck stitch in a weft knit sensor are proposed. These models were used in order to develop loop configurations of sensors that consist of various percentages of miss or tuck stitches. Subsequently, the developed loop configurations were simulated while using LTspice and MATLAB software; and, verified experimentally through a tensile test. The experimental results closely agree with the simulated results. Furthermore, the results reveal that increases in the percentage of tuck or miss stitches in weft knit sensor decrease the initial and average resistance of the sensor. In addition, it was observed that, although the piezoresistivity of a sensor with tuck or miss stitches is best characterised as a quadratic polynomial, increases in the percentage of tuck stitches in the sensor increase the linearity of the sensor’s piezoresistivity.
A Comparison of Various Algorithms for Classification of Food Scents Measured with an Ion Mobility Spectrometry
Authors: Minaev Georgy , Müller Philipp , Salminen Katri , Rantala Jussi , Surakka Veikko , Visa Ari
Published: January 2021

The present aim was to compare the accuracy of several algorithms in classifying data collected from food scent samples. Measurements using an electronic nose (eNose) can be used for classification of different scents. An eNose was used to measure scent samples from seven food scent sources, both from an open plate and a sealed jar. The k-Nearest Neighbour (k-NN) classifier provides reasonable accuracy under certain conditions and uses traditionally the Euclidean distance for measuring the similarity of samples. Therefore, it was used as a baseline distance metric for the k-NN in this paper. Its classification accuracy was compared with the accuracies of the k-NN with 66 alternative distance metrics. In addition, 18 other classifiers were tested with raw eNose data. For each classifier various parameter settings were tried and compared. Overall, 304 different classifier variations were tested, which differed from each other in at least one parameter value. The results showed that Quadratic Discriminant Analysis, MLPClassifier, C-Support Vector Classification (SVC), and several different single hidden layer Neural Networks yielded lower misclassification rates applied to the raw data than k-NN with Euclidean distance. Both MLP Classifiers and SVC yielded misclassification rates of less than 3% when applied to raw data. Furthermore, when applied both to the raw data and the data preprocessed by principal component analysis that explained at least 95% or 99% of the total variance in the raw data, Quadratic Discriminant Analysis outperformed the other classifiers. The findings of this study can be used for further algorithm development. They can also be used, for example, to improve the estimation of storage times of fruit.
Development of Integrative Methodologies for Effective Excavation Progress Monitoring
Authors: Rasul Abdullah , Seo Jaho , Khajepour Amir
Published: January 2021

Excavation is one of the primary projects in the construction industry. Introducing various technologies for full automation of the excavation can be a solution to improve sensing and productivity that are the ongoing issues in this area. This paper covers three aspects of effective excavation progress monitoring that include excavation volume estimation, occlusion area detection, and 5D mapping. The excavation volume estimation component enables estimating the bucket volume and ground excavation volume. To achieve mapping of the hidden or occluded ground areas, integration of proprioceptive and exteroceptive sensing data was adopted. Finally, we proposed the idea of 5D mapping that provides the info of the excavated ground in terms of geometric space and material type/properties using a 3D ground map with LiDAR intensity and a ground resistive index. Through experimental validations with a mini excavator, the accuracy of the two different volume estimation methods was compared. Finally, a reconstructed map for occlusion areas and a 5D map were created using the bucket tip’s trajectory and multiple sensory data with convolutional neural network techniques, respectively. The created 5D map would allow for the provision of extended ground information beyond a normal 3D ground map, which is indispensable to progress monitoring and control of autonomous excavation.
Deep Reinforcement Learning-Empowered Resource Allocation for Mobile Edge Computing in Cellular V2X Networks
Authors: Li Dongji , Xu Shaoyi , Li Pengyu
Published: January 2021

With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, the dynamic environment of vehicles, and the long distances between the cloud servers and VUE induce some potential issues, such as extra communication delay and energy consumption. Fortunately, mobile edge computing (MEC), a promising computing paradigm, can ameliorate the above problems by enhancing the computing abilities of VUE through allocating the computational resources to VUE. In this paper, we propose a joint optimization algorithm based on a deep reinforcement learning algorithm named the double deep Q network (double DQN) to minimize the cost constituted of energy consumption, the latency of computation, and communication with the proper policy. The proposed algorithm is more suitable for dynamic scenarios and requires low-latency vehicular scenarios in the real world. Compared with other reinforcement learning algorithms, the algorithm we proposed algorithm improve the performance in terms of convergence, defined cost, and speed by around 30%, 15%, and 17%.
Equimetrix Device: Criteria Based Validation and Reliability Analysis of the Center of Mass and Base of Support of a Human Postural Assessment System
Authors: Fonseca Pedro , Sousa Manoela , Sebastião Ricardo , Goethel Márcio , Barralon Pierre , Idigoras Igone , Sousa Filipa , Machado Leandro , Vilas-Boas João Paulo
Published: January 2021

Human postural control is a fundamental ability for static and dynamic tasks, especially in hiper- and hipo-functional populations, such as the elderly. The Equimetrix is a clinical device developed to assess both the base of support (BoS) and the center of mass (CoM) dynamics, thus allowing their use as new evaluation and training tools. This study aims to perform a criteria based validation of Equimetrix by comparing the BoS and CoM data with gold-standard equipment. A motion capture system, force platform, and pressure mat were used to calculate the CoM, center of pressure (CoP) and BoS during bipedal, unipedal, feet together and full tandem stances. Results demonstrate an excellent reliability of Equimetrix in terms of spatial accuracy of the CoM, although over-estimating the CoM height. Differences were found when comparing Mean velocity Path with the CoM, but not with the CoP, indicating a lower reliability in time-based parameters. The Equimetrix presents a tendency to overestimate the BoS, with mixed reliability values, which may be related to the different size of sensing elements between the Equimetrix and the pressure sensing mat. These are encouraging results that should be further explored during dynamic tasks.
Gradient Waveguide Thickness Guided-Mode Resonance Biosensor
Authors: Yang Jia-Ming , Yang Nien-Zu , Chen Cheng-Hao , Huang Cheng-Sheng
Published: January 2021

Portable systems for detecting biomolecules have attracted considerable attention, owing to the demand for point-of-care testing applications. This has led to the development of lab-on-a-chip (LOC) devices. However, most LOCs are developed with a focus on automation and preprocessing of samples; fluorescence measurement, which requires additional off-chip detection instruments, remains the main detection method in conventional assays. By incorporating optical biosensors into LOCs, the biosensing system can be simplified and miniaturized. However, many optical sensors require an additional coupling device, such as a grating or prism, which complicates the optical path design of the system. In this study, we propose a new type of biosensor based on gradient waveguide thickness guided-mode resonance (GWT-GMR), which allows for the conversion of spectral information into spatial information such that the output signal can be recorded on a charge-coupled device for further analysis without any additional dispersive elements. A two-channel microfluidic chip with embedded GWT-GMRs was developed to detect two model assays in a buffer solution: albumin and creatinine. The results indicated that the limit of detection for albumin was 2.92 μg/mL for the concentration range of 0.8–500 μg/mL investigated in this study, and that for creatinine it was 12.05 μg/mL for the concentration range of 1–10,000 μg/mL. These results indicated that the proposed GWT-GMR sensor is suitable for use in clinical applications. Owing to its simple readout and optical path design, the GWT-GMR is considered ideal for integration with smartphones or as miniaturized displays in handheld devices, which could prove beneficial for future point-of-care applications.
Carbon Dioxide Gas Sensor Based on Polyhexamethylene Biguanide Polymer Deposited on Silicon Nano-Cylinders Metasurface
Authors: Kazanskiy Nikolay Lvovich , Butt Muhammad Ali , Khonina Svetlana Nikolaevna
Published: January 2021

In this paper, we have numerically investigated a metasurface based perfect absorber design, established on the impedance matching phenomena. The paper comprises of two parts. In the first part, the device performance of the perfect absorber—which is composed of silicon nano-cylindrical meta-atoms, periodically arranged on a thin gold layer—is studied. The device design is unique and works for both x-oriented and y-oriented polarized light, in addition to being independent of the angle of incidence. In the second part of the paper, a CO2 gas sensing application is explored by depositing a thin layer of functional host material—a polyhexamethylene biguanide polymer—on the metasurface. The refractive index of the host material decreases due to the absorption of the CO2 gas. As a result, the resonance wavelength of the perfect absorber performs a prominent blueshift. With the help of the proposed sensor design, based on metasurface, the CO2 gas concentration range of 0–524 ppm was detected. A maximum sensitivity of 17.3 pm/ppm was acquired for a gas concentration of 434 ppm. The study presented in this work explores the opportunity of utilizing the metasurface perfect absorber for gas sensing applications by employing functional host materials.
Hermeticity Analysis on SiC Cavity Structure for All-SiC Piezoresistive Pressure Sensor
Authors: Tian Baohua , Shang Haiping , Zhao Lihuan , Wang Dahai , Liu Yang , Wang Weibing
Published: January 2021

The hermeticity performance of the cavity structure has an impact on the long-term stability of absolute pressure sensors for high temperature applications. In this paper, a bare silicon carbide (SiC) wafer was bonded to a patterned SiC substrate with shallow grooves based on a room temperature direct bonding process to achieve a sealed cavity structure. Then the hermeticity analysis on the SiC cavity structure was performed. The microstructure observation demonstrates that the SiC wafers are tightly bonded and the cavities remain intact. Moreover, the tensile testing indicates that the tensile strength of bonding interface is ~8.01 MPa. Moreover, the quantitative analysis on the airtightness of cavity structure through leakage detection shows a helium leak rate of ~1.3 × 10−10 Pa⋅m3/s, which satisfies the requirement of the specification in the MIL-STD-883H. The cavity structure can also avoid an undesirable deep etching process and the problem caused by the mismatch of thermal expansion coefficients, which can be potentially further developed into an all-SiC piezoresistive pressure sensor employable for high temperature applications.
Precise Orbit Determination of MEX Flyby Phobos Using Simulated Radiometric and Image Data
Authors: Zhu Xinbo , Liu Lu , Liu Suyan , Xie Pan , Gao Wutong , Yan Jianguo
Published: January 2021

A navigation camera or topography camera is a standard payload for deep space missions and the image data are normally used for auto-navigation. In this work, we study the potential contribution of image data in precise orbit determination for deep space spacecraft. The Mars Express (MEX) spacecraft has generated extensive Phobos image data during flybys of Phobos, but these data have not been used in precise orbit determination because of the difficulty in employing these image data. Therefore, we did an experiment using simulated image data as the first step for exploring how to use real image data in precise orbit determination of spacecraft. Our results demonstrate that image data can provide stronger constraints on orbit in the tangential and normal directions than Doppler data. When the image data were used in the MEX orbit determination during the MEX Phobos flyby, the orbit determination accuracies in the tangential and normal directions were significantly improved. This work will provide a reference for real image data processing during MEX Phobos flyby to improve MEX orbit accuracy as well as Phobos ephemeris accuracy.
RF-Powered Low-Energy Sensor Nodes for Predictive Maintenance in Electromagnetically Harsh Industrial Environments
Authors: Paolini Giacomo , Guermandi Marco , Masotti Diego , Shanawani Mazen , Benassi Francesca , Benini Luca , Costanzo Alessandra
Published: January 2021

This work describes the design, implementation, and validation of a wireless sensor network for predictive maintenance and remote monitoring in metal-rich, electromagnetically harsh environments. Energy is provided wirelessly at 2.45 GHz employing a system of three co-located active antennas designed with a conformal shape such that it can power, on-demand, sensor nodes located in non-line-of-sight (NLOS) and difficult-to-reach positions. This allows for eliminating the periodic battery replacement of the customized sensor nodes, which are designed to be compact, low-power, and robust. A measurement campaign has been conducted in a real scenario, i.e., the engine compartment of a car, assuming the exploitation of the system in the automotive field. Our work demonstrates that a one radio-frequency (RF) source (illuminator) with a maximum effective isotropic radiated power (EIRP) of 27 dBm is capable of transferring the energy of 4.8 mJ required to fully charge the sensor node in less than 170 s, in the worst case of 112-cm distance between illuminator and node (NLOS). We also show how, in the worst case, the transferred power allows the node to operate every 60 s, where operation includes sampling accelerometer data for 1 s, extracting statistical information, transmitting a 20-byte payload, and receiving a 3-byte acknowledgment using the extremely robust Long Range (LoRa) communication technology. The energy requirement for an active cycle is between 1.45 and 1.65 mJ, while sleep mode current consumption is less than 150 nA, allowing for achieving the targeted battery-free operation with duty cycles as high as 1.7%.
Antifouling Strategies for Sensors Used in Water Monitoring: Review and Future Perspectives
Authors: Delgado Adrián , Briciu-Burghina Ciprian , Regan Fiona
Published: January 2021

Water monitoring sensors in industrial, municipal and environmental monitoring are advancing our understanding of science, aid developments in process automatization and control and support real-time decisions in emergency situations. Sensors are becoming smaller, smarter, increasingly specialized and diversified and cheaper. Advanced deployment platforms now exist to support various monitoring needs together with state-of-the-art power and communication capabilities. For a large percentage of submersed instrumentation, biofouling is the single biggest factor affecting the operation, maintenance and data quality. This increases the cost of ownership to the extent that it is prohibitive to maintain operational sensor networks and infrastructures. In this context, the paper provides a brief overview of biofouling, including the development and properties of biofilms. The state-of-the-art established and emerging antifouling strategies are reviewed and discussed. A summary of the currently implemented solutions in commercially available sensors is provided and current trends are discussed. Finally, the limitations of the currently used solutions are reviewed, and future research and development directions are highlighted.
FAR-Net: Feature-Wise Attention-Based Relation Network for Multilabel Jujube Defect Classification
Authors: Xu Xiaohang , Zheng Hong , You Changhui , Guo Zhongyuan , Wu Xiongbin
Published: January 2021

In production, due to natural conditions or process peculiarities, a single product often may exhibit more than one type of defect. The accurate identification of all defects has an important guiding significance and practical value to improve the planting and production processes. Concerning the surface defect classification task, convolutional neural networks can be implemented as a powerful instrument. However, a typical convolutional neural network tends to consider an image as an inseparable entity and a single instance when extracting features; moreover, it may overlook semantic correlations between different labels. To address these limitations, in the present paper, we proposed a feature-wise attention-based relation network (FAR-Net) for multilabel jujube defect classification. The network included four different modules designed for (1) image feature extraction, (2) label-wise feature aggregation, (3) feature activation and deactivation, and (4) correlation learning among labels. To evaluate the proposed method, a unique multilabel jujube defect dataset was constructed as a benchmark for the multilabel classification task of the jujube defect images. The results of experiments show that owing to the relation learning mechanism, the average precision of the three main composite defects in the dataset increases by 5.77%, 4.07%, and 3.50%, respectively, compared to the backbone of our network, namely Inception v3, which indicated that the proposed FAR-Net effectively facilitated the learning of correlation between labels and eventually, improved the multilabel classification accuracy.
Rapid and Sensitive Quantification of the Pesticide Lindane by Polymer Modified Electrochemical Sensor
Authors: Noori Jafar Safaa , Mortensen John , Geto Alemnew
Published: January 2021

Lindane is documented by the Environmental Protection Agency (EPA) as one of the most toxic registered pesticides. Conventional detection of lindane in the environment requires manual field sampling and complex, time-consuming analytical sample handling relying on skilled labor. In this study, an electrochemical sensing system based on a modified electrode is reported. The system is capable of detecting lindane in aqueous medium in only 20 s. The surface of a conventional carbon electrode is modified with a film of conductive polymer that enables detection of lindane down to 30 nanomolar. The electrode modification procedure is simple and results in a robust sensor that can withstand intensive use. The sensitivity of the sensor is 7.18 µA/µM and the performance was demonstrated in the determination of lindane in spiked ground water. This suggests that the sensor is potentially capable of providing useful readings for decision makers. The rapid and sensitive quantification of lindane in aqueous medium is one step forward to new opportunities for direct, autonomous control of the pesticide level in the environment.
Low-Asymmetry Interface for Multiuser VR Experiences with Both HMD and Non-HMD Users
Authors: Zhang Qimeng , Ban Ji-Su , Kim Mingyu , Byun Hae Won , Kim Chang-Hun
Published: January 2021

We propose a low-asymmetry interface to improve the presence of non-head-mounted-display (non-HMD) users in shared virtual reality (VR) experiences with HMD users. The low-asymmetry interface ensures that the HMD and non-HMD users’ perception of the VR environment is almost similar. That is, the point-of-view asymmetry and behavior asymmetry between HMD and non-HMD users are reduced. Our system comprises a portable mobile device as a visual display to provide a changing PoV for the non-HMD user and a walking simulator as an in-place walking detection sensor to enable the same level of realistic and unrestricted physical-walking-based locomotion for all users. Because this allows non-HMD users to experience the same level of visualization and free movement as HMD users, both of them can engage as the main actors in movement scenarios. Our user study revealed that the low-asymmetry interface enables non-HMD users to feel a presence similar to that of the HMD users when performing equivalent locomotion tasks in a virtual environment. Furthermore, our system can enable one HMD user and multiple non-HMD users to participate together in a virtual world; moreover, our experiments show that the non-HMD user satisfaction increases with the number of non-HMD participants owing to increased presence and enjoyment.
Evaluation of the Azure Kinect and Its Comparison to Kinect V1 and Kinect V2
Authors: Tölgyessy Michal , Dekan Martin , Chovanec Ľuboš , Hubinský Peter
Published: January 2021

The Azure Kinect is the successor of Kinect v1 and Kinect v2. In this paper we perform brief data analysis and comparison of all Kinect versions with focus on precision (repeatability) and various aspects of noise of these three sensors. Then we thoroughly evaluate the new Azure Kinect; namely its warm-up time, precision (and sources of its variability), accuracy (thoroughly, using a robotic arm), reflectivity (using 18 different materials), and the multipath and flying pixel phenomenon. Furthermore, we validate its performance in both indoor and outdoor environments, including direct and indirect sun conditions. We conclude with a discussion on its improvements in the context of the evolution of the Kinect sensor. It was shown that it is crucial to choose well designed experiments to measure accuracy, since the RGB and depth camera are not aligned. Our measurements confirm the officially stated values, namely standard deviation ≤17 mm, and distance error <11 mm in up to 3.5 m distance from the sensor in all four supported modes. The device, however, has to be warmed up for at least 40–50 min to give stable results. Due to the time-of-flight technology, the Azure Kinect cannot be reliably used in direct sunlight. Therefore, it is convenient mostly for indoor applications.
Development and Validation of 2D-LiDAR-Based Gait Analysis Instrument and Algorithm
Authors: Yoon Seongjun , Jung Hee-Won , Jung Heeyoune , Kim Keewon , Hong Suk-Koo , Roh Hyunchul , Oh Byung-Mo
Published: January 2021

Acquiring gait parameters from usual walking is important to predict clinical outcomes including life expectancy, risk of fall, and neurocognitive performance in older people. We developed a novel gait analysis tool that is small, less-intrusive and is based on two-dimensional light detection and ranging (2D-LiDAR) technology. Using an object-tracking algorithm, we conducted a validation study of the spatiotemporal tracking of ankle locations of young, healthy participants (n = 4) by comparing our tool and a stereo camera with the motion capture system as a gold standard modality. We also assessed parameters including step length, step width, cadence, and gait speed. The 2D-LiDAR system showed a much better accuracy than that of a stereo camera system, where mean absolute errors were 46.2 ± 17.8 mm and 116.3 ± 69.6 mm, respectively. Gait parameters from the 2D-LiDAR system were in good agreement with those from the motion capture system (r = 0.955 for step length, r = 0.911 for cadence). Simultaneous tracking of multiple targets by the 2D-LiDAR system was also demonstrated. The novel system might be useful in space and resource constrained clinical practice for older adults.
A Kinect-Based Interactive System for Home-Assisted Active Aging
Authors: Muñoz Gabriel Fuertes , Cardenas Ramón Alberto Mollineda , Pla Filiberto
Published: January 2021

Virtually every country in the world is facing an unprecedented challenge: society is aging. Assistive technologies are expected to play a key role in promoting healthy lifestyles in the elderly. This paper presents a Kinect-based interactive system for home-assisted healthy aging, which guides, supervises, and corrects older users when they perform scheduled physical exercises. Interactions take place in gamified environments with augmented reality. Many graphical user interface elements and workflows have been designed considering the sensory, physical and technological shortcomings of the elderly, adapting accordingly the interaction methods, graphics, exercises, tolerance margins, physical goals, and scoring criteria. Experiments involved 57 participants aged between 65 and 80 who performed the same physical routine six times during 15 days. After each session, participants completed a usability survey. Results provided significant evidence that support (1) the effectiveness of the system in assisting older users of different age ranges, (2) the accuracy of the system in measuring progress in physical achievement of the elderly, and (3) a progressive acceptance of the system as it was used. As a main conclusion, the experiments verified that despite their poor technological skills, older people can adapt positively to the use of an interactive assistance tool for active aging if they experience clear benefits.
Encapsulation of Capacitive Micromachined Ultrasonic Transducers (CMUTs) for the Acoustic Communication between Medical Implants
Authors: Oevermann Jorge , Weber Peter , Tretbar Steffen H.
Published: January 2021

The aim of this work was to extend conventional medical implants by the possibility of communication between them. For reasons of data security and transmitting distances, this communication should be realized using ultrasound, which is generated and detected by capacitive micromachined ultrasonic transducers (CMUTs). These offer the advantage of an inherent high bandwidth and a high integration capability. To protect the surrounding tissue, it has to be encapsulated. In contrast to previous results of other research groups dealing with the encapsulation of CMUTs, the goal here is to integrate the CMUT into the housing of a medical implant. In this work, CMUTs were designed and fabricated for a center frequency of 2 MHz in water and experimentally tested on their characteristics for operation behind layers of Polyether ether ketone (PEEK) and titanium, two typical materials for the housings of medical implants. It could be shown that with silicone as a coupling layer it is possible to operate a CMUT behind the housing of an implant. Although it changes the characteristics of the CMUT, the setup is found to be well suited for communication between two transducers over a distance of at least 8 cm.
Improving the Ambient Temperature Control Performance in Smart Homes and Buildings
Authors: Fontes Fernando , Antão Rómulo , Mota Alexandre , Pedreiras Paulo
Published: January 2021

Currently, it is becoming increasingly common to find numerous electronic devices installed in office and residential spaces as part of building automation solutions. These devices provide a rich set of data related to the inside and outside environment, such as indoor and outdoor temperature, humidity, and solar radiation. However, commercial of-the-shelf climatic control systems continue to rely on simple controllers like proportional-integral-derivative or even on-off, which do not take into account such variables. This work evaluates the potential performance gains of adopting more advanced controllers, in this case based on pole-placement, enhanced with additional variables, namely solar radiation and external temperature, obtained with dedicated low-cost sensors. This approach is evaluated both in simulated and real-world environments. The obtained results show that pole-placement controllers clearly outperform on-off controllers and that the use of the additional variables in pole-placement controllers allows relevant performance gains in key parameters such as error signal MSE (17%) and control signal variance (40%), when compared with simple PP controllers. The observed energy consumption savings obtained by using the additional variables are marginal (≈1%, but the reduction of the error signal MSE and control signal variance have a significant impact on energy consumption peaks and on equipment lifetime, thus largely compensating the increase in the system complexity.