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Here, 0.3 wt.%Zr was introduced in an Al-4 wt.%Cu-0.5 wt.%Mn-0.1 wt.%Fe alloy to investigate its influence on the microstructure and mechanical properties of the alloy. The microstructures of both as-cast and T6-treated Al–Cu–Mn–Fe (ACMF) and Al–Cu–Mn–Fe–Zr (ACMFZ) alloys were analyzed. The intermetallic compounds formed through the casting procedure include Al2Cu and Al7Cu2Fe, and the Al2Cu phase dissolves into the matrix and re-precipitates as θ′ phase during the T6 process. The introduction of Zr results in the precipitation of L12-Al3Zr nanometric precipitates after T6, while the θ′ precipitates in ACMFZ alloy are much finer than those in ACMF alloy. The L12-Al3Zr precipitates were found coherently located with θ′, which was assumed beneficial for stabilizing the θ′ precipitates during the high-temperature tensile process. The tensile properties of ACMF and ACMFZ alloys at room temperature and elevated temperatures (200, 300, and 400 °C) were tested. Especially, the yield strength of ACMFZ alloys can reach 128 MPa and 65 MPa at 300 °C and 400 °C, respectively, which are 31% and 33% higher than those of ACMF alloys. The strengthening mechanisms of grain size, L12-Al3Zr, and θ′ precipitates on the tensile properties were discussed. This work may be referred to for designing Al–Cu alloys for application in high-temperature fields. [ABSTRACT FROM AUTHOR]

Continuous and reliable positioning anywhere anytime is one of the critical technologies for autonomous driving, whereas it is challenging yet, especially in urban environments. Due to the presence of buildings, Global Navigation Satellite System (GNSS) is not reliable for degraded positioning accuracy and availability. With the simultaneous localization and mapping (SLAM) approach, Light detection and ranging (LiDAR) has been widely used by autonomous driving vehicles for localization and environmental perception. However, LiDAR odometry (LO) or SLAM is not accurate enough for autonomous driving as it suffers from accumulative errors. This study proposes a GNSS/LiDAR/Map integrated positioning solution to provide ubiquitous positioning for autonomous driving, especially in urban areas, including urban canyons and underground, where GNSS is blocked partly or even fully. Matching LiDAR scans with a 3D map, named 3D map-based global localization (MGL), can provide an absolute positioning solution with respect to the map. A drift error model of LiDAR odometry is proposed to improve the accuracy of LiDAR odometry, and its model parameters are estimated online using either GNSS or MGL absolute locations. Using the graph optimization approach, GNSS and/or MGL precise absolute positioning are integrated with LO relative positioning to provide continuous and reliable positioning even when GNSS is degraded. The proposed solution is validated using the NCLT public dataset, which contains LiDAR data for building 3D maps and 3D map-based global localization, respectively. Based on the NCLT dataset, we simulate three typical urban scenarios to verify the performance of ubiquitous positioning. The experiments show that the proposed drift error correction method decreases accumulated error of LiDAR odometry by 35.5%, and the proposed GNSS/LiDAR/Map solution can provide continuous positioning with the availability of 100%, and improves the positioning accuracy by 49.8%, compared to the state-of-the-art approach of GNSS/LO fusion. [ABSTRACT FROM AUTHOR]

The flow and cavitation in the flow passage of the control valve are calculated, and the effects of operating conditions (including injection pulse width, rail pressure and ball valve lift) on the flow and cavitation characteristics are analyzed under dynamic boundary conditions. The simulation results show that the cavitation in the outflowing control-orifice (OA) and the guide-hole are almost unaffected by these operating conditions. In contrast, the cavitation process in the ball valve chamber has two distinct stages, which can be classified as violent and relatively smooth. The shorter the pulse width, the more severe the average degree of cavitation in the ball valve chamber; however, the risk of cavitation erosion on the ball valve and the ball valve seat surface does not increase too much. The increase of rail pressure and the increase of ball valve lift will aggravate the cavitation, and the cavitation position will move forward closer to the sealing annular surface. [ABSTRACT FROM AUTHOR]

Due to the complexities of indoor WiFi signal propagations, it is challenging to improve the performance of indoor fingerprint-based positioning techniques which is the main hot research in Internet of Things. Most existing methods have limited positioning accuracy, since they do not take the full advantage of the information available, i.e. timing information attached to the Received Signal Strength Indicator (RSSI) vector, and adopt the inappropriate training methods. This paper proposes an indoor localization method based on Convolutional Neural Network (CNN) by using time-series RSSI, termed CTSLoc, by taking into account the correlation among RSSI in time and space. A CNN model is used to extract the temporal fluctuation patterns of RSSI and learn the nonlinear mappings from the signal features with time and space to position coordinates. Finally the trained model is used to predict the user's location. An extensive experiment has been carried out in a space with the size of nearly 1000 squared meters, and a comprehensive comparison with several existing methods indicates that CTSLoc attains a lower average localization error (i.e. 4.23 m) and more stable performance than those methods. The CTSLoc method performs relatively less dependent on the amount of data which also eliminates spatial ambiguity and reduces the effect of noise on localization. [ABSTRACT FROM AUTHOR]

Fusing LiDAR and high definition (HD) maps is a feasible way to achieve global localization in GNSS-denied environments, which is necessary in driverless vehicle and robotic technologies. This paper proposes a single-shot global localization solution that uses only a single current scan of a rotating multiple-beam LiDAR sensor to locate its own location and pose with respect to an HD map in the form of georeferenced point clouds. This single-shot global localization solution estimates the state of the current moment without the previous moment state and thus avoids the nonconvergence problems that plague filter-based methods. The proposed solution allows HD maps from diverse LiDAR sensors to be used for global localization and is more robust than existing methods. The proposed solution consists of two procedures: offline preprocessing and online global localization. In the offline procedure, diverse HD maps are preprocessed to construct a global prior map for the localization process. The online global localization procedure includes two elements: place recognition, location and pose estimation. A novel Cross-Section Shape Context (CSSC) descriptor that is highly descriptive and rotation-invariant is proposed for subsequent processes. Two strategies, two-stage similarity estimation and Nearest Cluster Distance Ratio (NCDR), based on the CSSC descriptor are proposed to improve place recognition precision. A Selective Generalized Iterative Closest Point (SGICP) algorithm is proposed to calculate location and pose accurately using the CSSC descriptor. Comprehensive experiments were performed to evaluate this solution. A comparison of the precision-recall curve of multiple scenes, particularly under changed viewpoint scenes, shows that the CSSC descriptor is more robust than existing descriptors. Experimental analysis also confirms that the proposed strategies, two-stage similarity estimation and NCDR, improve place recognition precision. Also, compared to the generalized iterative closest point algorithm, the SGICP algorithm achieved better accuracy by 31% and efficiency by 60%. The proposed solution achieves a mean relative translation error (RTE) improvement of 27% over the OneShot algorithm on the KITTI dataset. The proposed solution had an average 77% improvement over 1σ RTE relative to the benchmark in tests with the long-term localization NCLT dataset. The mean RTE of the proposed solution was 0.13 m using HD maps from different LiDAR sensors. Our code is available at: https://github.com/Dongxu05/CSSC. [ABSTRACT FROM AUTHOR]

Place recognition is widely used for global localization technology. However, the existing place recognition solutions are limited by the requirement for the same type of sensors to be used in both the localization process and the mapping process. Therefore, the existing heterogeneous 3D map cannot be used for place recognition directly with the existing methods, leading to underutilization of information. In addition, most of the existing global feature descriptors used in place recognition solutions are still not highly descriptive and perform poorly under changed viewpoint scenes. To resolve these challenges, this paper presents a place recognition solution using virtual light detection and ranging (LiDAR) and polar grid height coding image (PGHCI) descriptors in the existing heterogeneous 3D map. First, virtual LiDAR is proposed to generate a series of virtual scans that are similar to the real scan of the localization sensor from the existing map, overcoming the limitation of the existing place recognition methods. Next, a novel PGHCI descriptor for place recognition is generated, and a method that overcomes the recognition difficulty of changed viewpoints in the same scene is presented. Two weighted distances for similarity estimation are analyzed, and the performance of the PGHCI descriptor with different parameters is evaluated. Finally, the performance of the PGHCI descriptor and the solution proposed in the paper is evaluated on several popular benchmark datasets and our own dataset. Comprehensive experiments demonstrated that the PGHCI descriptor has higher descriptiveness and is robust with respect to the changed viewpoint scene, as shown by the comparison of precision-recall (PR) curves using datasets with multiple scenes. The proposed place recognition solution has 100% success rates in the evaluation exclude under occluded conditions, showing that it is feasible to achieve robust place recognition using a heterogeneous 3D map. [ABSTRACT FROM AUTHOR]

Sports involve commonly players and equipment of high dynamics. Their location and motion data are essential for sports digitalization-related applications, such as from monitoring player skill level to even media presentation of sports. Location and motion data of players and equipment are important elements for precision sport analytics. With the popularization of the Internet of things, a variety of sports location and motion tracking technologies have advanced in terms of sensors and tracking methodologies to achieve ubiquitous coverage from outdoors to indoors, higher precision, and applicability for increasing sports scenarios. This paper presents a systematic survey on location and motion tracking technologies that have been applied in sports to drive various sports-related analytics and applications. First, several wearable sensor-based technologies are introduced with their principles, advantages and shortages in sports, and the location and motion tracking methods of each technology. Then, vison-based technology and its methodologies that detect, track, and recognize objects including human and equipment in sports are illustrated. Furthermore, sports-related data analytics and applications are presented using location and motion data of human and equipment. Data privacy and ethics are also focused based on the location and motion tracking technologies. Finally, challenges and further development of location and motion tracking in precision sports are discussed. The comprehensive survey provides a survey about state-of-the-art of location and motion sensors and methodologies, as well future trends of these technologies, which are beneficial for developing forthcoming precision sports. [ABSTRACT FROM AUTHOR]

Wetlands have received intensive interdisciplinary attention as unique ecosystems with valuable resources. As a new technology, the airborne LiDAR system has recently been applied in wetland research. However, most of the studies have focused on one or two LiDAR observations to extract either terrain or vegetation information from wetlands. This research aims at evaluating the combination of LiDAR's spatial, spectral, and derived information to improve the mapping and classification of wetlands. Six LiDAR data derived attributes (DSM, DTM, off-ground features, slope map, multiple pulse returns, and normalized intensity) have been utilized for wetland classification based on an object-oriented classification method. The overall classify accuracy of the proposed LiDAR's multi-attribute approach reaches 91.2%. [ABSTRACT FROM AUTHOR]

Ice-shelf thickness changes are of critical importance for understanding the stability of the Antarctic ice-sheet because they restrain the seaward flow of grounded glaciers. In this study, we find that neither backscatter nor leading-edge width contained in the least-squares fitting model can improve the accuracy of ice-shelf thickness change estimations from CryoSat-2, which is validated by comparing the CryoSat-2-derived elevation changes from least-squares fitting models with different combinations of waveform parameters against the Operation IceBridge ATM L4 data. Using the model without backscatter and leading-edge width to infer the thickness changes in Antarctic ice shelves from CryoSat-2, we find that the most significant thinning signals are mainly concentrated on the ice shelves along the Amundsen Sea coast, such as Getz, whose thickness variations are dominated by ocean-driven basal melting. This phenomenon has also been confirmed by the ICESat results. Overall, the Antarctic ice shelves volume changed on average by –0.34 ± 66.36 km3 yr−1 during the period from July 2010 to December 2016. [ABSTRACT FROM AUTHOR]

In terms of resource transportation, pipelines play a very important role, however, it’s leakage is almost inevitable, so leakage monitoring has always been a research hotspot. The leakage identification of water supply pipeline is studied in this paper. The vibration caused by turbulence and cavitation at the leak point has chaotic characteristics, and the spectral distribution of which has its own special area. Chaos can be judged by the Lyapunov exponent, this means that the occurrence of the leak is judged. By this method, the leakage can be effectively identified. The above theory is verified by experiments in this paper. [ABSTRACT FROM AUTHOR]

A comprehensive evaluation of Global Positioning System (GPS) Klobuchar, Galileo broadcast NeQuick2, international reference ionosphere (IRI) and global ionospheric map (GIM) models in estimating ionospheric total electron content (TEC) is performed using GPS-derived, constellation observing system for meteorology, ionosphere, and climate and JASON-2 TECs under various solar conditions in the Arctic and Antarctic. The performances of the four ionospheric models are first analysed for the overall polar regions. In addition, according to the temporal and spatial characteristics of the polar regions, the accuracies of the four models are evaluated for the polar days and nights, the Antarctic ice sheet (AIS) and the Arctic Ocean (AO), the Weddell Sea Anomaly (WSA) as well as for ionospheric storm. The results show that Klobuchar, NeQuick2, IRI2016 and GIM can mitigate the ionospheric delay by 28.69-29.41%, 44.57-56.09%, 43.38-55.99% and 67.17-77.56%, respectively. The performances of the four models during the polar days are obviously worse than those during the polar nights. In the AIS and AO, the Galileo broadcast NeQuick outperforms the GPS broadcast Klobuchar, and the root-mean-square error of IRI2016 performs almost the same as NeQuick2, but IRI2016 has a greater deviation. In addition, the GIM model can only mitigate the ionospheric delay by 46.81-56.72%, which is lower than other regions due to the lack of GPS ground station observations. Under the WSA conditions, the four models underestimate the real TEC to varying degrees, and the night-time deviations of the Klobuchar, NeQuick2 and IRI2016 models are significantly greater than the daytime deviations. The relative accuracy of four ionospheric models is lower during the ionospheric storm period than that in the ionospheric quiet period, especially the NeQuick2 and IRI2016 over the Antarctic. [ABSTRACT FROM AUTHOR]

One of the biggest challenges in forestry research is the effective and accurate measuring and monitoring of forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. This paper presented a novel computational method of low-cost forest inventory using global navigation satellite system (GNSS) signals in a crowdsourcing approach. Statistical features of GNSS signals were extracted from widely available GNSS devices and were used for predicting forest attributes, including tree height, diameter at breast height, basal area, stem volume, and above-ground biomass, in boreal forest conditions. The basic evidence of the predictions is the physical correlations between forest variables and the responses of GNSS signals penetrating through the forest. The random forest algorithm was applied to the predictions. GNSS-derived prediction accuracies were comparable with those of the most accurate 2-D remote sensing techniques, and the predictions can be improved further by integration with other publicly available data sources without additional cost. This type of crowdsourcing technique enables the collection of up-to-date forest data at low cost, and it significantly contributes to the development of new reference data collection techniques for forest inventory. Currently, field reference can account for half of the total costs of forest inventory. [ABSTRACT FROM PUBLISHER]

By utilizing the numerical technique of principal component analysis (PCA), this work analyses temporal and spatial variations of the ionosphere under various solar conditions during the period 1999-2013. Applying the PCA technique to the time series of the global ionospheric total electron content (TEC) maps provides an efficient method for analyzing the main ionospheric variability on a global scale that is able to decompose periodic variations (e.g., annual and semiannual oscillations) while retaining the asymmetry in the temporal and spatial domains (e.g., seasonal and equator anomalies). The TEC series of different local times are processed separately at two time scales: (1) the whole 15 years of the period of study and (2) the individual years. In contrast with previous studies, the analysis of the dataset of the 15 years shows that dawn (e.g., LT4-6) and late morning (LT10-12) are the more remarkable characteristic times for ionospheric variability. This study also reveals a cyclic trend of the variability with respect to local times. The first two modes, which contain 80-90% of the total variance, represent spatial distributions and temporal variations with respect to the different stages of the solar cycle and local times. Annual and semiannual variations are demodulated from the first two modes, and the results show that these variations evidently have distinct trends for daytime and nighttime. An exception is that, under active solar conditions, extremely strong solar irradiance during the daytime has a residual effect on the variability of the nighttime. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning has been widely used to analyze the 3D structure of a forest in detail and to generate data at the level of a reference plot for forest inventories without destructive measurements. Multi-scan terrestrial laser scanning is more commonly applied to collect plot-level data so that all of the stems can be detected and analyzed. However, it is necessary to match the point clouds of multiple scans to yield a point cloud with automated processing. Mismatches between datasets will lead to errors during the processing of multi-scan data. Classic registration methods based on flat surfaces cannot be directly applied in forest environments; therefore, artificial reference objects have conventionally been used to assist with scan matching. The use of artificial references requires additional labor and expertise, as well as greatly increasing the cost. In this study, we present an automated processing method for plot-level stem mapping that matches multiple scans without artificial references. In contrast to previous studies, the registration method developed in this study exploits the natural geometric characteristics among a set of tree stems in a plot and combines the point clouds of multiple scans into a unified coordinate system. Integrating multiple scans improves the overall performance of stem mapping in terms of the correctness of tree detection, as well as the bias and the root-mean-square errors of forest attributes such as diameter at breast height and tree height. In addition, the automated processing method makes stem mapping more reliable and consistent among plots, reduces the costs associated with plot-based stem mapping, and enhances the efficiency. [ABSTRACT FROM AUTHOR]

Forests have important impacts on the global carbon cycle and climate, and they are also related to a wide range of industrial sectors. Currently, one of the biggest challenges in forestry research is effectively and accurately measuring and monitoring forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. A low-cost crowdsourcing solution is needed for forest inventory to collect forest variables. Here, we propose global navigation satellite system (GNSS) signals as a novel type of observables for predicting forest attributes and show the feasibility of utilizing GNSS signals for estimating important attributes of forest plots, including mean tree height, mean diameter at breast height, basal area, stem volume and tree biomass. The prediction accuracies of the proposed technique were better in boreal forest conditions than those of the conventional techniques of 2D remote sensing. More importantly, this technique provides a novel, cost-effective way of collecting large-scale forest measurements in the crowdsourcing context. This technique can be applied by, for example, harvesters or persons hiking or working in forests because GNSS devices are widely used, and the field operation of this technique is simple and does not require professional forestry skills. [ABSTRACT FROM AUTHOR]

• Visualization experiment of equal proportion of the ball valve in injector control valve. • Interaction of cavitation between different parts of control valve. • Development law of cavitation in control valve. The generation and development of cavitation in the flow channel inside the injector ball valve had been characterized using a high-speed visualization system. The flow field information was obtained by numerical calculation of the dynamic boundary conditions. The generation mechanism and distribution law of cavitation in the ball valve were analyzed. The results show that cavitation first occurs at the gap of ball valve and then the diversion hole and the outflowing control-orifice (OA); the low-pressure area created by the throttling effect is the main factor in causing cavitation; and the flow line and vortex have a significant impact on cavitation distribution. The flow state of the fluid in the diversion hole and OA has a direct and obvious effect on the cavitation in the ball valve chamber. In the case of inlet pressure 35 MPa, when cavitation is stable, the vapor volume fraction of 0.7–1.0 accounts for 9% of the ball valve chamber volume, 89% of the diversion hole and 13% of the OA, and in case of 15 MPa they are <1%, 87% and 14%. [ABSTRACT FROM AUTHOR]

Knowledge of the polar ionospheric total electron content (TEC) and its future variations is of scientific and engineering relevance. In this study, a new method is developed to predict Arctic mean TEC on the scale of a solar cycle using previous data covering 14 years. The Arctic TEC is derived from global positioning system measurements using the spherical cap harmonic analysis mapping method. The study indicates that the variability of the Arctic TEC results in highly time-varying periodograms, which are utilized for prediction in the proposed method. The TEC time series is divided into two components of periodic oscillations and the average TEC. The newly developed method of TEC prediction is based on an extrapolation method that requires no input of physical observations of the time interval of prediction, and it is performed in both temporally backward and forward directions by summing the extrapolation of the two components. The backward prediction indicates that the Arctic TEC variability includes a 9 years period for the study duration, in addition to the well-established periods. The long-term prediction has an uncertainty of 4.8–5.6 TECU for different period sets. [ABSTRACT FROM AUTHOR]

Smartphone positioning is an enabling technology used to create new business in the navigation and mobile location-based services (LBS) industries. This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS. HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals. The smartphone sensors are used to measure the user's motion dynamics information (MDI), which represent the spatial correlation of various locations. Two algorithms based on hidden Markov model (HMM) problems, the grid-based filter and the Viterbi algorithm, are used in this paper as the central processor for data fusion to resolve the position estimates, and these algorithms are applicable for different applications, e.g., real-time navigation and location tracking, respectively. HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models, which have been commonly used in previous works. The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations. HIPE is a cost-efficient solution, and it can work flexibly with different smartphone platforms, which may have different types of sensors available for the measurement of MDI data. The reliability of the positioning solution was found to increase with increasing precision of the MDI data. [ABSTRACT FROM AUTHOR]

In smartphone indoor positioning, owing to the strong complementarity between pedestrian dead reckoning (PDR) and WiFi, a hybrid fusion scheme of them is drawing more and more attention. However, the outlier of WiFi will easily degrade the performance of the scheme, to remove them, many researches have been proposed such as: improving the WiFi individually or enhancing the scheme. Nevertheless, due to the inherent received signal strength (RSS) variation, there still exist some unremoved outliers. To solve this problem, this paper proposes the first outlier detection and removal strategy with the aid of Machine Learning (ML), so called WiFi-AGNES (Agglomerative Nesting), based on the extracted positioning characteristics of WiFi when the pedestrian is static. Then, the paper proposes the second outlier detection and removal strategy, so called WiFi-Chain, based on the extracted positioning characteristics of WiFi, PDR, and their complementary characteristics when the pedestrian is walking. Finally, a hybrid fusion scheme is proposed, which integrates the two proposed strategies, WiFi, PDR with an inertial-navigation-system-based (INS-based) attitude heading reference system (AHRS) via Extended Kalman Filter (EKF), and an Unscented Kalman Filter (UKF). The experiment results show that the two proposed strategies are effective and robust. With WiFi-AGNES, the minimum percentage of the maximum error (MaxE) is reduced by 66.5%; with WiFi-Chain, the MaxE of WiFi is less than 4.3 m; further the proposed scheme achieves the best performance, where the root mean square error (RMSE) is 1.43 m. Moreover, since characteristics are universal, the proposed scheme integrated the two characteristic-based strategies also possesses strong robustness. [ABSTRACT FROM AUTHOR]

Among the current indoor positioning technologies, Bluetooth low energy (BLE) has gained increasing attention. In particular, the traditional distance estimation derived from aggregate RSS and signal-attenuation models is generally unstable because of the complicated interference in indoor environments. To improve the adaptability and robustness of the BLE positioning system, we propose making full use of the three separate channels of BLE instead of their combination, which has generally been used before. In the first step, three signal-attenuation models are separately established for each BLE advertising channel in the offline phase, and a more stable distance in the online phase can be acquired by assembling measurements from all three channels with the distance decision strategy. Subsequently, a weighted trilateration method with uncertainties related to the distances derived in the first step is proposed to determine the user's optimal position. The test results demonstrate that our proposed algorithm for determining the distance error achieves a value of less than 2.2 m at 90%, while for the positioning error, it achieves a value of less than 2.4 m at 90%. Compared with the traditional methods, the positioning error of our method is reduced by 33% to 38% for different smartphones and scenarios. [ABSTRACT FROM AUTHOR]

Generally, performing multiple scans is necessary to cover entire scanning area, and multiple point clouds are thus obtained. These point clouds need to be registered together, which is the focus of our work. However, influenced by the noise, point density variation, partial overlap and so on, the success rate of registration is still low. For this reason, a pairwise registration method is first proposed. It includes two main components: the triple local coordinate image (TriLCI) descriptor and a new 3D transformation estimation technique. The descriptor has high descriptiveness and strong robustness, by which more right correspondences can be achieved. The 3D transformation estimation technique is employed to calculate the rotation matrix and translation vector under the condition that most of correspondences are false. In this technique, a new geometric distance constraint is developed, and is then applied to eliminate incorrect correspondences. The robust estimation based on the median is introduced to calculate the rotation matrix and translation vector. For multi-view registration, the connected graph algorithm and shape growing based method are applied. A comparative study is presented. The experimental results well demonstrate that the proposed pairwise registration method has high success rate. The comparative study gives the merits and demerits of the two multi-view registration methods. [ABSTRACT FROM AUTHOR]

Diet plays a crucial role in shaping the gut microbiota and overall health of animals. Traditionally, silkworms are fed fresh mulberry leaves, and artificial diets do not support good health. The aim of this study was to explore the relationship between the dietary transition from artificial diets to mulberry leaves and the effects on the gut microbiota and physiological changes in silkworms as a model organism. With the transition from artificial diets to mulberry leaves, the diversity of the silkworm gut microbiota increased, and the proportion of Enterococcus and Weissella, the dominant gut bacterial species in silkworms reared on artificial diets, decreased, whereas the abundance of Achromobacter and Rhodococcus increased. Dietary transition at different times, including the third or fifth instar larval stages, resulted in significant differences in the growth and development, immune resistance, and silk production capacity of silkworms. These changes might have been associated with the rapid adaptation of the intestinal microbiota of silkworms to dietary transition. This study preliminarily established a dietary transition–gut microbial model in silkworms based on the conversion from artificial diets to mulberry leaves, thus providing an important reference for future studies on the mechanisms through which habitual dietary changes affect host physiology through the gut microbiome. [ABSTRACT FROM AUTHOR]

Laser rangefinders (LRFs) are widely used in autonomous systems for indoor positioning and mobile mapping through the simultaneous localization and mapping (SLAM) approach. The extrinsic parameters of multiple LRFs need to be determined, and they are one of the key factors impacting system performance. This study presents an extrinsic calibration method of multiple LRFs that requires neither extra calibration sensors nor special artificial reference landmarks. Instead, it uses a naturally existing cuboid-shaped corridor as the calibration reference, and it hence needs no additional cost. The present method takes advantage of two types of geometric constraints for the calibration, which can be found in a common cuboid-shaped corridor. First, the corresponding point cloud is scanned by the set of LRFs. Second, the lines that are scanned on the corridor surfaces are extracted from the point cloud. Then, the lines within the same surface and the lines within two adjacent surfaces satisfy the coplanarity constraint and the orthogonality constraint, respectively. As such, the calibration problem is converted into a nonlinear optimization problem with the constraints. Simulation experiments and experiments based on real data verified the feasibility and stability of the proposed method. [ABSTRACT FROM AUTHOR]

Indoor navigation and location-based services increasingly show promising marketing prospects. Indoor positioning based on Wi-Fi radio signal has been studied for more than a decade because Wi-Fi, a signal of opportunity without extra cost, is extensively deployed for internet connections. Bayesian fingerprinting positioning, a classical Wi-Fi-based indoor positioning method, consists of two phases: radio map learning and position inference. Thus far, the application of Bayesian fingerprinting positioning is limited due to its poor usability; radio map learning requires an adequate number of received signal strength indication (RSSI) observables at each reference point, long-term fieldwork, and high development and maintenance costs. In this paper, based on a statistical analysis of actual RSSI observables, a Weibull–Bayesian density model is proposed to represent the probability density of Wi-Fi RSSI observables. The Weibull model, which is parameterized with three parameters that can be calculated with fewer samples, can calculate the probability density with a higher accuracy than the traditional histogram method. Furthermore, the parameterized Weibull model can simplify the radio map by storing only three parameters that can restore the whole probability density, i.e., it is not necessary to store the probability distribution based on traditionally separated RSSI bins. Bayesian positioning inference is performed in the positioning phase using probability density rather than the traditional probability distribution of predefined RSSI bins. The proposed method was implemented on an Android smartphone, and the performance was evaluated in different indoor environments. Results revealed that the proposed method enhanced the usability of Wi-Fi Bayesian fingerprinting positioning by requiring fewer RSSI observables and improved the positioning accuracy by 19–32% in different building environments compared with the classic histogram-based method, even when more samples were used. [ABSTRACT FROM AUTHOR]

In the fields of autonomous vehicles, virtual reality and three-dimensional (3D) reconstruction, 2D laser rangefinders have been widely employed for different purposes, such as localization, mapping, and simultaneous location and mapping. However, the extrinsic calibration of multiple 2D laser rangefinders is a fundamental prerequisite for guaranteeing their performance. In contrast to existing calibration methods that rely on manual procedures or suffer from low accuracy, an automatic and high-accuracy solution is proposed in this paper for the extrinsic calibration of 2D laser rangefinders. In the proposed method, a mobile sphere is used as a calibration target, thereby allowing the automatic extrapolation of a spherical center and the automatic matching of corresponding points. Based on the error analysis, a matching machine of corresponding points with a low error is established with the restriction constraint of the scan circle radius, thereby achieving the goal of high-accuracy calibration. Experiments using the Hokuyo UTM-30LX sensor show that the method can increase the extrinsic orientation accuracy to a sensor intrinsic accuracy of 10 mm without requiring manual measurements or manual correspondence among sensor data. Therefore, the calibration method in this paper is automatic, highly accurate, and highly effective, and it meets the requirements of practical applications. [ABSTRACT FROM AUTHOR]

Interest point detection plays an important role in many computer vision applications. This work is motivated by the light detection and ranging odometry task in autonomous driving. Existing methods are not capable of detecting enough interest points in unstructured scenarios where there are little constructions or trees around, and correspondingly light detection and ranging odometry will fail to continuous localisation. An interest point detector is proposed for detecting interest points from multi‐beam light detection and ranging point cloud using unsupervised convolutional neural network. The point cloud is projected into a two‐dimensional structured data according to the scanning geometry. Then the convolutional neural network filters trained in an unsupervised manner are used to generate a local feature map with the two‐dimensional structured data as input. Finally, interest points are obtained by extracting the grids that have significant differences with their neighbour grids. Based on an odometry benchmark, the experiments show that the proposed interest point detector can capture more local details, which contributes to more than 16% error decrease in point cloud registration in highway scenes. [ABSTRACT FROM AUTHOR]

Indoor positioning is in high demand in a variety of applications, and indoor environment is a challenging scene for visual positioning. This paper proposes an accurate visual positioning method for smartphones. The proposed method includes three procedures. First, an indoor high-precision 3D photorealistic map is produced using a mobile mapping system, and the intrinsic and extrinsic parameters of the images are obtained from the mapping result. A point cloud is calculated using feature matching and multi-view forward intersection. Second, top-K similar images are queried using hamming embedding with SIFT feature description. Feature matching and pose voting are used to select correctly matched image, and the relationship between image points and 3D points is obtained. Finally, outlier points are removed using P3P with the coarse focal length. Perspective-four-point with unknown focal length and random sample consensus are used to calculate the intrinsic and extrinsic parameters of the query image and then to obtain the positioning of the smartphone. Compared with established baseline methods, the proposed method is more accurate and reliable. The experiment results show that 70 percent of the images achieve location error smaller than 0.9 m in a 10 m × 15.8 m room, and the prospect of improvement is discussed. [ABSTRACT FROM AUTHOR]

The largest earthquake (Mw 6.4) in northwestern Croatia ruptured the faults near the city of Petrinja on 29 December 2020, at 11:19 UTC. The epicenter was located ~3 km southwest of Petrinja, ~40 km southeast of Zagreb, the capital of the Republic of Croatia. Here we investigated the geometric and kinematic properties of the 2020 Mw 6.4 Petrinja earthquake using a joint inversion of ascending and descending interferograms from three tracks of Sentinel-1 Single-Look Complex (SLC) images. The coseismic and early postseismic surface displacements associated with the Petrinja earthquake were imaged using standard DInSAR and SBAS time-series InSAR methods, respectively. The distributed slip model was inverted based on the ground surface displacements with maximum slip patch in 5 km depth. The early postseismic deformation occurred on the northwestern extent of coseismic slip, and it cannot be well modeled by the coseismic model. We thus suggested that the postseismic deformation was caused by a combined effect of the postseismic afterslips and aftershocks occurring in this area. Based on the inverted slip model, we calculated the Coulomb stress change in the region, and found a good correlation between positive Coulomb failure stress ∆CFS and the distribution of aftershocks. Based on these results, we identified which faults are more active, and then better estimated the seismic hazards in the region. [ABSTRACT FROM AUTHOR]

As an attractive technique for measuring water vapor, the Global Navigation Satellite System (GNSS) faces additional challenges in dynamic applications such as in the open sea. We present a new method of retrieving precipitable water vapor (PWV) based on GNSS multi‐antenna precise point positioning (PPP), which uses GNSS data from multiple antennas and incorporates the constraints of known baseline vector and common tropospheric delay. The 4‐day shipborne dynamic experiment along the China coast demonstrates that the baseline vector constraint shortens the convergence time of positioning and atmospheric parameters, and also slightly improves their accuracies. The common tropospheric delay constraint helps to provide compromised, more robust, and sometimes more accurate PWV estimates. An evaluation with radiosonde‐derived PWVs shows that the combination of the two constraints achieves the best accuracy reaching 4.2 mm. This method helps to expand GNSS meteorology to the vast ocean and benefits satellite altimetry and weather forecasting. Plain Language Summary: Water vapor plays an important role in atmospheric processes ranging from global climate change to mesoscale and micro‐scale weather systems. The Global Navigation Satellite System (GNSS) is an attractive technique to measure water vapor, which has been extensively investigated for ground‐based static stations. In dynamic applications such as in the open sea, it is faced with additional challenges which may potentially degrade the performance. This study presents a new method for retrieving precipitable water vapor (PWV) based on GNSS multi‐antenna precise point positioning (PPP). It uses GNSS data from multiple antennas closely spaced and mounted on the same platform, and incorporates additional constraints of known baseline vector and common tropospheric delay. We have conducted a 4‐day shipborne dynamic experiment along the China coast and demonstrated that this method can shorten the convergence time and provide more robust and sometimes more accurate PWVs compared to conventional PPP. An accuracy level of 4.2 mm is achieved with this method for the ocean‐based dynamic experiment. This study helps to expand GNSS meteorology to challenging environments such as in the open sea, which will benefit weather forecasting and nowcasting. Key Points: A new method of retrieving precipitable water vapor (PWV) is presented based on Global Navigation Satellite System (GNSS) multi‐antenna precise point positioning (PPP)This method can shorten the convergence time and provide more robust and more accurate GNSS‐based PWVThe improved performance is helpful to expand GNSS meteorology to the vast ocean and benefits satellite altimetry and weather forecasting [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions (GHG) and nutrient loss are considered to be critical environmental issues facing rice field ecosystems. Biochars (BCs), as carbon-rich materials with porous structures, large specific surface areas, and enriched surface functional groups, have shown great potential for improving environmental problems in rice fields and increasing rice yields. However, thus far, we still lack an adequate summary and review of the performance characteristics of BCs and their environmental behavior in paddy soils. In this paper, we critically review the positive effects of BC application on the growth and yield of rice, nutrient loss reduction, and GHG reduction. Biomass type, pyrolysis temperature, and modification method are considered to be the key factors that determine the performance characteristics of BCs. The application of BCs could promote rice yield and mitigate CO2, N2O, and CH4 emissions by improving soil physicochemical properties and microbial communities, providing nutrient sources, and reducing nutrient losses. Finally, this paper illustrates the potential ecological risks of BC application on paddy fields, including the risks of inconclusive research results and secondary pollution. These shortcomings need to be addressed in future research to ensure the sustainability of BC application. [ABSTRACT FROM AUTHOR]

The fuel injector is a critical component of the internal combustion engine. The diameters of the injector nozzle and the control chamber's oil inlet and outlet are generally between 0.2 and 0.5 mm, which are typical microchannel structures. During high-pressure injection, the cavitation phenomenon in the channel seriously affects the reliability of the internal combustion engine. The choice of turbulence and cavitation models is the key to investigate the cavitation in the microchannel by using numerical methods. Based on the Winklhofer microchannel fuel experiment, five representative turbulence models were used to construct a microchannel model, and the results were compared and analyzed with the experiment. The results show that the pressure gradient values obtained from the combination of RNG k-ε and ZGB models were similar to the experimental data, with an error of less than 6%. The cavitation distribution calculated from the combination of LES and ZGB models was most consistent with the experimental observation data. The outlet mass flow rate obtained from the LES and ZGB models matched the trend of the experimental data in the pressure difference range of 19 bar to 85 bar, with an error of less than 2%. For the cross-sectional flow rate calculation, the RNG k-ε and ZGB models had the smallest calculation errors, with errors below 11%. [ABSTRACT FROM AUTHOR]

Sr element is commonly used to modify eutectic Si in Al–Si alloys, while Al 2 Si 2 Sr phase is quite easy to precipitate when Sr content is high to tens of ppm level. In this study, the growth models of Al 2 Si 2 Sr crystal as well as its behavior in Al– x Si–2 Sr (x=5, 8, 12, 18, 25 and 40, wt%) alloys were investigated. Polyhedral, triangular pyramid and dendritic morphologies of Al 2 Si 2 Sr crystals were detected, and their growth models were discussed. Based on the crystallographic parameters, the disregistry between (0001)Al 2 Si 2 Sr and (111)Si is calculated to be 9.7%. Transmission electron microscopy analysis confirms that the Al 2 Si 2 Sr phase is not likely to be a good nucleation site for Si. Differential scanning calorimetry analysis indicates that Al 2 Si 2 Sr phase is firstly precipitated in Al–5, 8, 12, 18Si–2 Sr alloys, while its precipitating temperature is close to primary Si in Al–25Si–2 Sr alloy and it falls behind to primary Si in Al–40Si–2 Sr alloy. Interestingly, amounts of Al 2 Si 2 Sr dendrites were engulfed by primary Si in Al–40Si–2 Sr alloy. Based on the widely–known impurity–induced twinning mechanism in Sr modified Al–Si alloys, the Al 2 Si 2 Sr dendrites were deduced to be in–situ evolved from Sr–rich clusters, which are absorbed on {111}Si planes. This work may provide a new perspective for understanding the modification mechanisms of Sr element on Si crystal in Al–Si–Sr alloys. • The crystal morphologies and growth patterns of Al 2 Si 2 Sr were analyzed. • Al 2 Si 2 Sr phase is excluded to be nucleating site for Si crystal. • Sr-rich clusters may be absorbed on {111}Si planes when Si precipitates first. [ABSTRACT FROM AUTHOR]

This study investigates a prediction model for the cycle injection quantity in a high-pressure common rail injector under a transient thermal boundary. The results show that the transient temperature increase curve calculated by the mathematical model of the common rail injector under adiabatic flow is significantly different from the experimental data. A non-isothermal model of the injector coupled with heat transfer is established, which considers the actual heat transfer phenomenon. The excellent agreement between the new calculation results and the experimental data confirms that the fuel injection process of a common rail injector comprises the coupled phenomena of fuel heating and heat transfer. Based on the established simulation model, it is found that in the continuous injection process of the injector, owing to the thermal effect of injection, the cycle injection quantity decreases gradually with an increase in the injector working time and then stabilizes. Under the condition of an injection pulse width of 1.2 ms and frequency of 100 Hz, when the injection pressure increases from 140 MPa to 300 MPa, the reduction in the cycle injection quantity increases from 3.9% to 7.8%, because the higher injection pressure results in higher transient heat at the nozzle holes. In the work of common rail injector assemblies, to achieve more accurate control of the cycle injection quantity, it is necessary to include the correction of a decreasing cycle injection quantity caused by transient heat in the electronic control system. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS) is one of the most effective means for landslide monitoring. At present, most studies on GNSS-based landslide monitoring focus on the long-term landslide analysis, while short-term landslide displacement is not clear. The purpose of this article is to explore a short-term displacement detection method based on GNSS kinematic positioning for landslide monitoring. The significance and feasibility of short-term landslide monitoring are presented, and a short-term displacement detection method based on GNSS kinematic positioning time series segmentation is proposed. The coordinate time series is reconstructed by the Daubechies wavelet to extract the abrupt components. The detection window is formed by the current epoch coordinates and the previous epochs’ coordinates and segmented according to the segmentation index. The segmentation point obtained by segmenting the detection window is regarded as a possible change point (PCP), and a test is conducted to determine whether the segmentation point is a change point (CP). Simulation and field experiments were carried out to verify the proposed method. The results show the feasibility and effectiveness of the method for short-term landslide change detection. The influence of the detection window size and segmentation index on the proposed method is discussed, and suggestions for the selection of detection window size as well as segmentation index are given. [ABSTRACT FROM AUTHOR]

In the past few decades, global navigation satellite system (GNSS) technology has been widely used in structural health monitoring (SHM), and the monitoring mode has evolved from long-term deformation monitoring to dynamic monitoring. This paper gives an overview of GNSS-based dynamic monitoring technologies for SHM. The review is classified into three parts, which include GNSS-based dynamic monitoring technologies for SHM, the improvement of GNSS-based dynamic monitoring technologies for SHM, as well as denoising and detrending algorithms. The significance and progress of Real-Time Kinematic (RTK), Precise Point Position (PPP), and direct displacement measurement techniques, as well as single-frequency technology for dynamic monitoring, are summarized, and the comparison of these technologies is given. The improvement of GNSS-based dynamic monitoring technologies for SHM is given from the perspective of multi-GNSS, a high-rate GNSS receiver, and the integration between the GNSS and accelerometer. In addition, the denoising and detrending algorithms for GNSS-based observations for SHM and corresponding applications are summarized. Challenges of low-cost and widely covered GNSS-based technologies for SHM are discussed, and problems are posed for future research. [ABSTRACT FROM AUTHOR]

Smartphone indoor localization has attracted considerable attention over the past decade because of the considerable business potential in terms of indoor navigation and location-based services. In particular, Wi-Fi RSS (received signal strength) fingerprinting for indoor localization has received significant attention in the industry, for its advantage of freely using off-the-shelf APs (access points). However, RSS measured by heterogeneous mobile devices is generally biased due to the variety of embedded hardware, leading to a systematical mismatch between online measures and the pre-established radio maps. Additionally, the fingerprinting method based on a single RSS measurement usually suffers from signal fluctuations due to environmental changes or human body blockage, leading to possible large localization errors. In this context, this study proposes a space-constrained pairwise signal strength differences (PSSD) strategy to improve Wi-Fi fingerprinting reliability, and mitigate the effect of hardware bias of different smartphone devices on positioning accuracy without requiring a calibration process. With the efforts of these two aspects, the proposed solution enhances the usability of Wi-Fi fingerprint positioning. The PSSD approach consists of two critical operations in constructing particular fingerprints. First, we construct the signal strength difference (SSD) radio map of the area of interest, which uses the RSS differences between APs to minimize the device-dependent effect. Then, the pairwise RSS fingerprints are constructed by leveraging the time-series RSS measurements and potential spatial topology of pedestrian locations of these measurement epochs, and consequently reducing possible large positioning errors. To verify the proposed PSSD method, we carry out extensive experiments with various Android smartphones in a campus building. In the case of heterogeneous devices, the experimental results demonstrate that PSSD fingerprinting achieves a mean error ∼20% less than conventional RSS fingerprinting. In addition, PSSD fingerprinting achieves a 90-percentile accuracy of no greater than 5.5 m across the tested heterogeneous smartphones [ABSTRACT FROM AUTHOR]

Cycle slip (CS) is a primary error source in Precise Point Positioning/Inertial Navigation System (PPP/INS) integrated systems. In this study, an INS-aided CS detection and repair method is presented. It utilizes high-precision INS information instead of a pseudorange to remove the satellite–receiver geometric range in the wide-lane (WL) and ionospheric-free (IF) phase combinations and creates an INS-aided WL (WL-INS) model and an INS-aided IF (IF-INS) model. Since INS information is superior to pseudorange, the INS-aided models have high detection accuracy. However, the effectiveness of INS-aided models cannot persist for a long time because of INS accumulation error. To overcome the disturbance of INS error, improved INS-aided models are proposed. This idea takes advantage of the long wavelength of WL combination and tries to fix WL CS. Once it succeeds, the INS error can be evaluated and removed. The proposed method was tested using land vehicle data, in which simulated cycle slips and signal interruption were introduced. The results show that this method can accurately detect and repair different cycle slips between the continuous Global Positioning System (GPS) epoch. When it comes to the cycle slip after a GPS interruption, the method can also accelerate PPP re-convergence, as it is not affected by the inertial accumulation error. [ABSTRACT FROM AUTHOR]

Human activities such as coal mining may cause rapid ground subsidence, which will damage severely on human-built structures such as coal transport railway or buildings, and even kill lives. In coal-mining areas, ground subsidence happens continuously, and its amplitude may be up to 10 cm per day. In order to assure the safety of coal-mining areas, it's necessary to monitor the ground subsidence timely and precisely. This paper presents a continuously operating real-time global navigation satellite system ground subsidence monitoring system, which consists of hardware, data processing algorithms and software, communication link and peripheral equipment. A particular architecture was designed for field operation. For the data processing, the Vondrak filter is proposed to process the monitoring data. We operated the proposed monitoring system on the coal-mining area and verified the performance during the mining period from early July to December 2017. The monitoring results show that the proposed system has an accuracy of 5 mm for ground subsidence monitoring on the basis of precise leveling data that were simultaneously observed. The proposed method can meet the accuracy requirement of ground subsidence monitoring, and it can provide continuous subsidence information in real time, which cannot be achieved by the traditional leveling surveying method. The monitoring system and data processing method can be applied to the monitoring of ground subsidence in subsidence area as well as geological disasters such as landslides. [ABSTRACT FROM AUTHOR]

Abstract Quantitative comparisons of tree height observations from different sources are scarce due to the difficulties in effective sampling. In this study, the reliability and robustness of tree height observations obtained via a conventional field inventory, airborne laser scanning (ALS) and terrestrial laser scanning (TLS) were investigated. A carefully designed non-destructive experiment was conducted that included 1174 individual trees in 18 sample plots (32 m × 32 m) in a Scandinavian boreal forest. The point density of the ALS data was approximately 450 points/m2. The TLS data were acquired with multi-scans from the center and the four quadrant directions of the sample plots. Both the ALS and TLS data represented the cutting edge point cloud products. Tree heights were manually measured from the ALS and TLS point clouds with the aid of existing tree maps. Therefore, the evaluation results revealed the capacities of the applied laser scanning (LS) data while excluding the influence of data processing approach such as the individual tree detection. The reliability and robustness of different tree height sources were evaluated through a cross-comparison of the ALS-, TLS-, and field- based tree heights. Compared to ALS and TLS, field measurements were more sensitive to stand complexity, crown classes, and species. Overall, field measurements tend to overestimate height of tall trees, especially tall trees in codominant crown class. In dense stands, high uncertainties also exist in the field measured heights for small trees in intermediate and suppressed crown class. The ALS-based tree height estimates were robust across all stand conditions. The taller the tree, the more reliable was the ALS-based tree height. The highest uncertainty in ALS-based tree heights came from trees in intermediate crown class, due to the difficulty of identifying treetops. When using TLS, reliable tree heights can be expected for trees lower than 15–20 m in height, depending on the complexity of forest stands. The advantage of LS systems was the robustness of the geometric accuracy of the data. The greatest challenges of the LS techniques in measuring individual tree heights lie in the occlusion effects, which lead to omissions of trees in intermediate and suppressed crown classes in ALS data and incomplete crowns of tall trees in TLS data. [ABSTRACT FROM AUTHOR]

This study explores an iterative method for simultaneously estimating the present-day glacial isostatic adjustment (GIA), ice mass change and elastic vertical crustal deformation of the Antarctic ice sheet (AIS) for the period October 2003–October 2009. The estimations are derived by combining mass measurements of the GRACE mission and surface height observations of the ICESat mission under the constraint of GPS vertical crustal deformation rates in the spatial domain. The influence of active subglacial lakes on GIA estimates are mitigated for the first time through additional processing of ICESat data. The inferred GIA shows that the strongest uplift is found in the Amundsen Sea Embayment (ASE) sector and subsidence mostly occurs in Adelie Terre and the East Antarctica inland. The total GIA-related mass change estimates for the entire AIS, West Antarctica Ice Sheet (WAIS), East Antarctica Ice Sheet (EAIS), and Antarctic Peninsula Ice Sheet (APIS) are 43 ± 38, 53 ± 24, −23 ± 29 and 13 ± 6 Gt a−1, respectively. The overall ice mass change of the AIS is −46 ± 43 Gt a−1 (WAIS: −104 ± 25, EAIS: 77 ± 35, APIS: −20 ± 6). The most significant ice mass loss and most significant elastic vertical crustal deformations are concentrated in the ASE and northern Antarctic Peninsula. [ABSTRACT FROM PUBLISHER]

Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data. [ABSTRACT FROM AUTHOR]

Stereo images have long been the main practical data source for the high-accuracy retrieval of 3-D information over large areas. However, stereoscopy has been surpassed by laser scanning (LS) techniques in recent years, particularly in forested areas, because the reflection of laser points from object surfaces directly provides 3-D geometric features and because the laser beam has good penetration capacity through forest canopies. In the last few years, image-based point clouds have become a more widely available data source because of advances in matching algorithms and computer hardware. This paper explores the possibility of using consumer cameras for forest field data collection and presents an application of terrestrial image-based point clouds derived from a handheld camera to forest plot inventories. In the experiment, the sample forest plot was photographed in a stop-and-go mode using different routes and camera settings. Five data sets were generated from photographs taken in the field, representing different photographic conditions. The stem detection accuracy ranged between 60% and 84%, and the root-mean-square errors of the estimated diameters at breast height were between 2.98 and 6.79 cm. The performance of image-based point clouds in forest data collection was compared with that of point clouds derived from two LS techniques, i.e., terrestrial LS (the professional level) and personal LS (an emerging technology). The study indicates that the construction of image-based point clouds of forest field data requires only low-cost, low-weight, and easy-to-use equipment and automated data processing. Photographic measurement is easy and relatively fast. The accuracy of tree attribute estimates is close to an acceptable level for forest field inventory but is lower than that achieved with the tested LS techniques. [ABSTRACT FROM PUBLISHER]

This paper evaluated the feasibility of a terrestrial point cloud generated utilizing an uncalibrated hand-held consumer camera at a plot level and measuring the plot at an individual-tree level. Individual tree stems in the plot were detected and modeled from the image-based point cloud, and the diameter-at-breast-height (DBH) of each tree was estimated. The detected-results were compared with field measurements and with those derived from the single-scan terrestrial laser scanning (TLS) data. The experiment showed that the mapping accuracy was 88% and the root mean squared error of DBH estimates of individual trees was 2.39 cm, which is acceptable for practical applications and was similar to the results achieved using TLS. The main advantages of the image-based point cloud data lie in the low cost of the equipment required for the data collection, the simple and fast field measurements and the automated data processing, which may be interesting and important for certain applications, such as field inventories by landowners who do not have supports from external experts. The disadvantages of the image-based point cloud data include the limited capability of mapping small trees and complex forest stands. [ABSTRACT FROM AUTHOR]

A new circuit model for designing and manufacturing an S-band low noise amplifier (LNA) with the software, Advanced Design System (ADS), is introduced in this paper. The proposed model involves shunted impedance at the grid to achieve a stable LNA without measuring the S-parameters of transistors at low temperatures. The LNA was measured over the operation band of 2.2-2.3 GHz, which has input and output standing wave ratios below 1.2. The noise figure of the manufactured LNA was about 0.2 dB and the gain was above 22 dB, which indicated that our LNA worked well at 77 K. [ABSTRACT FROM AUTHOR]

The elevation changes of ice sheets have been recognized as an essential climate variable. Long-term time series of these changes are an important parameter to understand climate change, and the longest time-series of ice sheet elevation changes can be derived from combining multiple Ku-band satellite altimetry missions. However, unresolved intermission biases obscure the record. Here, we revise the mathematical model commonly used in the literature to simultaneously correct for intermission bias and ascending–descending bias to ensure the self-consistency and cohesion of the elevation time series across missions. This updated approach is applied to combine Envisat and CryoSat-2 radar altimetry in the period of 2002–2019. We tested this approach by validating it against airborne and satellite laser altimetry. Combining the detailed temporal and spatial evolution of elevation changes with firn densification-modeled volume changes due to surface processes, we found that the Amundsen Sea sector accounts for most of the total volume loss of the Antarctic Ice Sheet (AIS), mainly from ice dynamics. However, surface processes dominate the volume changes in the key regions, such as the Totten Glacier sector, Dronning Maud Land, Princess Elizabeth Land, and the Bellingshausen Sea sector. Overall, accelerated volume loss in the West Antarctic continues to outpace the gains observed in the East Antarctic. The total volume change during 2002–2019 for the AIS was −68.7 ± 8.1 km3/y, with an acceleration of −5.5 ± 0.9 km3/y2. [ABSTRACT FROM AUTHOR]

Snow plays a critical role in hydrological monitoring and global climate change, especially in the Arctic region. As a novel remote sensing technique, global navigation satellite system interferometric reflectometry (GNSS-IR) has shown great potential for detecting reflector characteristics. In this study, a field experiment of snow depth sensing with GNSS-IR was conducted in Ny-Alesund, Svalbard, and snow depth variations over the 2014–2018 period were retrieved. First, an improved approach was proposed to estimate snow depth with GNSS observations by introducing wavelet decomposition before spectral analysis, and this approach was validated by in situ snow depths obtained from a meteorological station. The proposed approach can effectively separate the noise power from the signal power without changing the frequency composition of the original signal, particularly when the snow depth changes sharply. Second, snow depth variations were analyzed at three stages including snow accumulation, snow ablation and snow stabilization, which correspond to different snow-surface-reflection characteristics. For these three stages of snow depth variations, the mean absolute errors (MAE) were 4.77, 5.11 and 3.51 cm, respectively, and the root mean square errors (RMSE) were 6.00, 6.34 and 3.78 cm, respectively, which means that GNSS-IR can be affected by different snow surface characteristics. Finally, the impact of rainfall on snow depth estimation was analyzed for the first time. The results show that the MAE and RMSE were 2.19 and 2.08 cm, respectively, when there was no rainfall but 5.63 and 5.46 cm, respectively, when it was rainy, which indicates that rainfall reduces the accuracy of snow depth estimation by GNSS-IR. [ABSTRACT FROM AUTHOR]

In various dynamic scenes, there are moveable objects such as pedestrians, which may challenge simultaneous localization and mapping (SLAM) algorithms. Consequently, the localization accuracy may be degraded, and a moving object may negatively impact the constructed maps. Maps that contain semantic information of dynamic objects impart humans or robots with the ability to semantically understand the environment, and they are critical for various intelligent systems and location-based services. In this study, we developed a computationally efficient SLAM solution that is able to accomplish three tasks in real time: (1) complete localization without accuracy loss due to the existence of dynamic objects and generate a static map that does not contain moving objects, (2) extract semantic information of dynamic objects through a computionally efficient approach, and (3) eventually generate semantic maps, which overlay semantic objects on static maps. The proposed semantic SLAM solution was evaluated through four different experiments on two data sets, respectively verifying the tracking accuracy, computational efficiency, and the quality of the generated static maps and semantic maps. The results show that the proposed SLAM solution is computationally efficient by reducing the time consumption for building maps by 2/3; moreover, the relative localization accuracy is improved, with a translational error of only 0.028 m, and is not degraded by dynamic objects. Finally, the proposed solution generates static maps of a dynamic scene without moving objects and semantic maps with high-precision semantic information of specific objects. [ABSTRACT FROM AUTHOR]

Affected by the complexity of the indoor environment, accurate indoor positioning is challenging in many localization based services (LBS). Recently, it has been recognized that, visible light communication (VLC) is promising for indoor navigation and positioning, due to the low implementation cost with marginal modification to the existing infrastructure and the possibility to achieve high accurate positioning results. Provided that the positions of the light emitting diodes (LEDs) are known to the receiver, the angle of arrival (AOA) of the light signal is able to be estimated by a camera embedded in a smart phone, and thus the position of the smart phone can be derived based on the triangulation. In this paper, the performance of the positioning accuracy is analyzed based on indoor positioning with VLC, and the analytical upper bound of location error is derived. Extensive simulation results have verified the theoretical analysis on the VLC-based localization approach in different indoor scenarios. In order to obtain better location performance, the principles of choosing reference LED and localization LED are also given. [ABSTRACT FROM AUTHOR]