Asymmetric Gaussian Function Research Articles

Lifetime of Long-Lived Sunspot Groups

Studies of active region (AR) lifetimes are mostly restricted to short-lived ARs. The aim of this paper is to include recurrent ARs, which should be identified unambiguously. The first step is the algorithmic listing of possible returns; then, the candidates are visually checked using the unique HTML-feature of the Debrecen sunspot database. The final step is application of an asymmetric Gaussian function, introduced in previous articles, for short-lived ARs. This function has a surprisingly good fit to the data on correctly identified recurrent sunspot groups over several rotations enabling the reconstruction of the development on the far side of the sun. The Gnevyshev–Waldmeier rule for the area–lifetime relationship is not applicable for recurrent ARs; however, as a novel approach, a linear regression analysis extended to long lifetimes made it possible to recognize two populations of sizes for which two different area–lifetime relationships can be obtained. The lifetimes exhibit weak dependencies on the heliographic latitude and solar cycle phase. If an asymmetric Gaussian cannot be fit to the data, then they presumably belong to consecutive members of an active nest.

Improving extraction phenology accuracy using SIF coupled with the vegetation index and mapping the spatiotemporal pattern of bamboo forest phenology

Monitoring plant phenology is vital to maintaining the global carbon balance and management under climate change. Bamboo forest is an essential forest type in subtropical China with a strong carbon sequestration capacity. In recent years, vegetation indices (VIs), which characterize canopy structural parameters, and solar-induced chlorophyll fluorescence (SIF), indicating the photosynthetic activity of vegetation, have provided new perspectives on plant phenology at regional and global scales. However, the best data sources and methods for extracting the phenology of bamboo forests remain to be explored. In this study, new vegetation indices were innovatively constructed by normalizing the VIs (enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), and near-infrared reflectance of vegetation (NIRv)) based on Moderate Resolution Imaging Spectroradiometer (MODIS) products and SIF products (GOSIF) based on OCO-2 satellites and then taking the mean values of the normalized VIs (EVI, EVI2 and NIRv) and SIF. We called the new indices SVs (SIF and VIs combined indices, including Se (SIF and EVI combined index), Se2 (SIF and EVI2 combined index), or Sn (SIF and NIRv combined index)). Two time series reconstruction methods (asymmetric Gaussian (AG) function fitting and double logistic (DL) function) and two extractive phenology parameter methods (dynamic threshold method (DT) and comparative threshold method (CT)) were employed to extract phenological information. The advantages of SVs for extracting bamboo forest phenology (BFP) were verified by comparing the extraction performance of VIs, SIF, and SVs on the SOS and EOS of bamboo forests. Thus, the best way to extract BFP was explored, and the spatial distribution and spatial-temporal variation characteristics of BFP in China from 2011 to 2020 were analyzed. The results are described as follows: (1) SVs are better able to extract BFP parameters compared with VIs and SIF, especially in bamboo forest-specific off-years and on-years; (2) SIF has better accuracy than VIs in extracting BFP, where both SOS and EOS values obtained from VIs are overestimated, and SIF can reflect BFP information earlier; and (3) the best data sources for extracting SOS and EOS in bamboo forests are Sn and Se, respectively, and the optimal methods are AG_CT and DL_DT, respectively. Compared with SIF, the R2 values of Sn and Se extracted SOS and EOS are improved by 40.7% and 7.7%, and the RMSE values are reduced by 24.7% and 0.7%, respectively; and (4) the SOS for bamboo forests in China from 2011 to 2020 was mainly concentrated in 80–100 days, with an overall advancing trend; the EOS was mainly concentrated in 300–320 days, with an overall delay. The results show that the SVs obtained by coupling VIs and SIF can better track the BFP information, providing a practical reference for macroscopic monitoring of BFP based on medium-resolution time series data.

Early-Age Cement Paste Temperature Development Monitoring Using Infrared Thermography and Thermo-Sensors

Infrared thermography is an advanced technique usually applied for the assessment of thermal losses through different elements of the building envelope, or as a method for detection of damage (cracks) in reinforced concrete elements, such as bridges. Use of this method for the investigation of temperature development during early cement hydration is still an evolving area of research. For the purpose of verifying the reliability of the method, two types of cubic samples of different heights were prepared using a cement-based paste, with 20% of cement (by mass) replaced with fly ash. Temperature development was measured in two ways: using infrared thermography and thermo-sensors embedded in the samples. Additionally, the obtained results were modeled using the asymmetric Gaussian function. Peak temperatures in the middle of each sample were higher than the peak temperatures measured on the sample surface, with differences ranging between 2 °C and 4 °C. Differences between the temperature measurements of the thermo-sensors placed on the surface of the sample and thermal camera were lower than 2 °C. Very good compliance of the results was obtained for both the camera and the surface sensors measurements, as well as for the modeling coefficients.

A Field Theory-Based Novel Algorithm for Navigational Hazard Index

Collision risk assessment is crucial for autonomous ships to identify risks and make decisions for collision avoidance. In this paper, a field theory-based navel algorithm is proposed to define and describe the risk of ship collision. Based on the field theory and the asymmetric Gaussian function, the ship collision risk field is constructed, and the field potential is used to characterize the navigational hazard index. The effectiveness and superiority of the algorithm are verified through study cases. The results show that the proposed algorithm can obtain the index continuously, reflect the risk level accurately, and provide reliable basis for the navigation of autonomous ships. Therefore, it has important application potential in the development of intelligence navigation.

A novel multi-model fusion framework diagnoses the complex variation characteristics of ecological indicators and quantitatively reveals their driving mechanism.

Systematic analysis of the change law and driving mechanism of ecological indicators (GPP, ET, WUE), as well as the study of maximum threshold of water resources benefit changing with ecological benefit, are important prerequisites for realizing the scientific allocation and efficient utilization of water resources in desert riparian forests. However, previous studies have defects in the detailed description of the change characteristics of ecological indicators. How to accurately diagnose the characteristics of a site, mutation year, pattern (linear, exponential, logarithmic, etc.), duration of change, future change trends of ecological indicators in a desert riparian environment, as well as quantitatively revealing their driving mechanisms, are major scientific problems that need to be solved urgently. In this regard, an ensemble function coupling a logistic function and an asymmetric Gaussian function was creatively adopted, a novel framework was created to integrate the time-series trajectory fitting method and the sensitivity analysis method, and the arid and ecologically fragile Tarim River Basin was taken as a typical area. The results showed that with enhanced water resource management in the Tarim River Basin, GPP, ET, and WUE all showed patterns of increasing change and could be expected to continue to rise or to remain at a high-level stable state. The longest continuous period of GPP change was 15 years, showing that ecological restoration is a long-term process. The years of GPP mutation were consistent with the implementation periods of major measures in the Tarim River Basin (1990, 2001, and 2011), indicating the reliability of this framework. More importantly, when GPP increased to 216.44 gCm-2, the maximum WUE threshold of 0.93 gCm-2mm-1 occurred. This threshold can be used as a reference criterion for efficient utilization of ecological water in the basin. Among the ecological indicators studied, GPP was the most sensitive to environmental change, but GPP, with 80.60% of pixel area, showed a weak memory effect（α<0.4). Besides, GPP was the most sensitive to the leaf area index (LAI) and had the strongest correlation with it (p<0.001). Therefore, LAI can be used as the main control factor for judging plant growth. This research can provide important scientific guidance and reference for the analysis of ecological indicator changes and the sustainable utilization of water resources in arid areas.

Generalized Asymmetric Correntropy for Robust Adaptive Filtering: A Theoretical and Simulation Study

Correntropy has been proved to be effective in eliminating the adverse effects of impulsive noises in adaptive filtering. However, correntropy is not desirable when the error between the two random variables is asymmetrically distributed around zero. To address this problem, asymmetric correntropy using an asymmetric Gaussian function as the kernel function was proposed. However, an asymmetric Gaussian function is not always the best choice and can be further expanded. In this paper, we propose a robust adaptive filtering based on a more flexible definition of asymmetric correntropy, which is called generalized asymmetric correntropy that adopts a generalized asymmetric Gaussian density (GAGD) function as the kernel. With the shape parameter properly selected, the generalized asymmetric correntropy may get better performance than the original asymmetric correntropy. The steady-state performance of the adaptive filter based on the generalized maximum asymmetric correntropy criterion (GMACC) is theoretically studied and verified by simulation experiments. The asymmetric characteristics of queue delay in satellite networks is analyzed and described, and the proposed algorithm is used to predict network delay, which is essential in space telemetry. Simulation results demonstrate the desirable performance of the new algorithm.

A New Approach for Including Social Conventions into Social Robots Navigation by Using Polygonal Triangulation and Group Asymmetric Gaussian Functions.

Many authors have been working on approaches that can be applied to social robots to allow a more realistic/comfortable relationship between humans and robots in the same space. This paper proposes a new navigation strategy for social environments by recognizing and considering the social conventions of people and groups. To achieve that, we proposed the application of Delaunay triangulation for connecting people as vertices of a triangle network. Then, we defined a complete asymmetric Gaussian function (for individuals and groups) to decide zones where the robot must avoid passing. Furthermore, a feature generalization scheme called socialization feature was proposed to incorporate perception information that can be used to change the variance of the Gaussian function. Simulation results have been presented to demonstrate that the proposed approach can modify the path according to the perception of the robot compared to a standard A* algorithm.

Detecting winter canola (Brassica napus) phenological stages using an improved shape-model method based on time-series UAV spectral data

Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous studies in canola phenology monitoring focused mainly on the flowering stage, using its apparent structure features and colors. Additional phenological stages have been largely overlooked. The objective of this study was to improve a shape-model method (SMM) for extracting winter canola phenological stages from time-series top-of-canopy reflectance images collected by an unmanned aerial vehicle (UAV). The transformation equation of the SMM was refined to account for the multi-peak features of the temporal dynamics of three vegetation indices (VIs) (NDVI, EVI, and CIred-edge). An experiment with various seeding scenarios was conducted, including four different seeding dates and three seeding densities. Three mathematical functions: asymmetric Gaussian function (AGF), Fourier function, and double logistic function, were employed to fit time-series vegetation indices to extract information about phenological stages. The refined SMM effectively estimated the phenological stages of canola, with a minimum root mean square error (RMSE) of 3.7 days for all phenological stages. The AGF function provided the best fitting performance, as it captured multiple peaks in the growth dynamics characteristics for all seeding date scenarios using four scaling parameters. For the three selected VIs, CIred-edge achieved the greatest accuracy in estimating the phenological stage dates. This study demonstrates the high potential of the refined SMM for estimating winter canola phenology.

Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation

The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes.

Robustness and Accuracy of the Particle Swarm Optimisation Method in the Solution of Inverse Heat Transfer Problems with Phase Change

Some engineering heat transfer problems cannot be solved directly as certain parameters influencing heat transfer are unknown. This is where the solution of inverse heat transfer problems might be utilized. The paper explores application of the Particle Swarm Optimisation (PSO) method to heat transfer problems with phase change. In order to assess the robustness and accuracy of the method, the study was conducted on simulation results. The explored case is an air-PCM heat exchanger. In the first step, the simulations with various values of parameters influencing heat transfer were performed. All cases were simulated as transient with time-dependent boundary conditions. In the second step, some of these parameters (phase change temperature, density, thermal conductivity, etc.) were assumed to be unknown and they were obtained from the simulation results with the use of the PSO method. The cost function was defined in the form of a root mean square error between the simulation results (the outlet air temperature from the heat exchanger) and the results of the optimized scenario. The number of unknown parameters varied from one to seven (in case of the description of the relationship of effective heat capacity on temperature, which was parameterized in the form of an asymmetric Gaussian function). The results have shown that the PSO is a robust and accurate tool with only up to 2 % difference in the enthalpy of fusion and no significant discrepancies during the higher dimension optimisation.

ВИКОРИСТАННЯ ФУНКЦІЙ ГАУСА ДЛЯ АПРОКСИМАЦІЇ БІОЛОГІЧНИХ СИГНАЛІВ

Purpose. Currently, the method of mathematical analysis of various biological signals is increasingly used in medicine. Most of them are random and quasi-periodic processes, which greatly complicates the possibility of using an automated data processing process. Signals of this type include respiratory noises, phonocardiographic and electrocardiographic signals. The shape of the waves of the ECG signal is quite accurately described by asymmetric Gaussian pulses. It is possible to achieve the required approximation accuracy by introducing additional pulses to describe the diagnostically important inhomogeneities of the signal. In the previous work of the authors the simple algorithm of coefficients selection of asymmetric functions in the form of the Labview virtual device is realized. But for the cases of superposition of waves of different polarity and non-standard pulse shapes, the result of applying this algorithm gives a significant error. Methodology. In the analysis of waves of negative polarity, the presence of a positive burst, even a small value, leads to a complete disregard for the negative wave. To eliminate this shortcoming, it is necessary to determine the maximum and minimum values of the amplitudes of the samples in this area and compare their absolute values. A larger value will determine the polarity of the wave coming to the virtual instrument for analysis. The next complication is the presence of a wide flat wave top with an offset maximum value by extreme samples. This is a significant shift in the symmetry of the wave and, as a consequence, incorrect determination of parameters for a wide part of the pulse. To eliminate this shortcoming, it is proposed to determine all the influential extremes, and shift the center of symmetry to the middle of the flat top of the wave. In the case where the wave has a significant violation of symmetry, it is possible that the virtual device determines the midpoints and amplitudes of the left to right parts with a significant deviation from the actual peak value. To prevent the vertices of the Gaussian functions from shifting relative to the real maximum it was proposed to mirror the data sets corresponding to the left and right parts of the wave. Results. In this article, the algorithm of the virtual device operation for automatic determination of the parameters of the approximate asymmetric Gaussian curves in the Labview software environment is improved. It is planned to use approximation by Gaussian impulses to synthesize standard leads from reduced sets of ECG signal leads. According to preliminary studies, a surface of approximation coefficients has been created, which allows synthesizing a cardiac signal for an arbitrary derivation. Originality. Algorithm for approximating biological signals by asymmetric Gaussian functions is improved. In this case, a more confident approximation of waves was obtained in the presence of: bursts of opposite polarity; wide flat top of the wave when the maximum value is shifted to extreme samples; significant violation of the symmetry of the waves; narrow fragments of waves. Practical value. A virtual device was created to approximate signals (including ECG signal leads, respiratory noises and phonocardiosignals) using asymmetric Gaussian functions. The analysis of certain coefficients will make it possible to use them as average criteria for the diagnosis of the corresponding diseases, which requires further research. References 10, figures 11.

Robust tracking control for permanent magnet linear servo system using intelligent fractional-order backstepping control

In this paper, an intelligent fractional-order backstepping control (IFOBC) method combining fractional-order backstepping control (FOBC) and Takagi–Sugeno–Kang-type fuzzy neural network (TSKFNN) applicable to the permanent magnet linear synchronous motor (PMLSM) is adopted to achieve high-performance servo control fields. First, the global regulation and position tracking of the system are realized by backstepping control (BC). In order to improve the convergence speed and control accuracy of the system, a FOBC which has extra degree of freedom in the control parameters is designed. Second, since the boundary of the uncertainties in the system is difficult to estimate, which makes the switching control gain hard to select, the TSKFNN is introduced to estimate the uncertainties online. By using asymmetric Gaussian function as the membership function, the convergence speed and the accurate approximation capability of TSKFNN are improved greatly. Additionally, a robust compensator is developed to confront the uncertainties such as approximation error, optimal parameter vector and higher Taylor series. Therefore, the robustness of the system is further improved. Finally, the adaptive learning algorithms for the online training of the TSKFNN are derived using the Lyapunov theorem to guarantee the asymptotical stability of the system. The experiments implemented on a digital signal processor (DSP), TMS320F28335, demonstrate that the proposed control method provides a high-performance dynamic characteristics and that is robust with respect to parameter variations, external disturbances and nonlinear friction forces.

Nonlinear systems modelling based on self-organizing fuzzy neural network with hierarchical pruning scheme

In this paper, a self-organizing fuzzy neural network with hierarchical pruning scheme (SOFNN-HPS) is proposed for nonlinear systems modelling in industrial processes. In SOFNN-HPS, to strike the optimal balance between system accuracy and network complexity, an online self-organizing scheme for identifying the structure and parameters of the network simultaneously is developed. First, to enhance the characterization ability of the fuzzy rules for nonlinear systems, the asymmetric Gaussian functions that can partition the input space more flexibly are introduced as membership functions. Second, a hierarchical pruning scheme, which is designed by rule density and rule significance, is used to delete the redundant fuzzy rules while using the geometric growing criteria to generate fuzzy rules automatically, which can avoid the requirement of pre-setting the pruning threshold and prevent the mistaken deletion of significant rules. Third, an adaptive allocation strategy is adopted to set the antecedent parameters of the fuzzy rules in the learning process, which can not only adjust the region of generalized ellipsoidal basis functions for better local approximation, but also balance the accuracy of the system and the interpretability of the rule base obtained. Finally, to speed up the convergence of the estimation error, a modified recursive least square algorithm is used to update the consequent parameters of the resulting fuzzy rules online. In addition, the convergence proofs of the estimation error and the network linear parameters of SOFNN-HPS are given, and they are helpful in successfully applying the SOFNN-HPS in practical engineering. To verify the effectiveness of SOFNN-HPS, two benchmark test problems and a key water quality parameter prediction experiment in the wastewater treatment process are examined. The simulation results demonstrate that the proposed SOFNN-HPS algorithm can obtain a self-organizing fuzzy neural network with compact structure and powerful generalization performance. The source codes of SOFNN-HPS and other competitors can be downloaded from https://github.com/hyitzhb/SOFNN-HPS.

Vegetation Phenology Influenced by Rapid Urbanization of The Yangtze Delta Region

Impacts of urbanization and climate change on ecosystems are widely studied, but these drivers of change are often difficult to isolate from each other and interactions are complicated. Ecosystem responses to each of these drivers are perhaps most clearly seen in phenology changes due to global climate change (warming climate) and urbanization (heat island effect). The phenology of vegetation can influence many important ecological processes, including primary production, evapotranspiration, and plant fitness. Therefore, evaluating the interacting effects of urbanization and climate change on vegetation phenology has the potential to provide information about the long-term impact of global change. Using remotely sensed time series of vegetation on the Yangtze River Delta in China, this study evaluated the impacts of rapid urbanization and climate change on vegetation phenology along an urban to rural gradient over time. Phenology markers were extracted annually from an 18-year time series by fitting the asymmetric Gaussian function model. Thermal remote sensing acquired at daytime and nighttime was used to explore the relationship between land surface temperature and vegetation phenology. On average, the spring phenology marker was 9.6 days earlier and the autumn marker was 6.63 days later in urban areas compared with rural areas. The spring phenology of urban areas advanced and the autumn phenology delayed over time. Across space and time, warmer spring daytime and nighttime land surface temperatures were related to earlier spring, while autumn daytime and nighttime land surface temperatures were related to later autumn phenology. These results suggest that urbanization, through surface warming, compounds the effect of climate change on vegetation phenology.

Validation of a spatially variant resolution model for small animal brain PET studies

In small animal positron emission tomography (PET) studies, given the spatial resolution of preclinical PET scanners, quantification in small regions can be challenging. Moreover, in scans where animals are placed away from the center of the field of view (CFOV), e.g. in simultaneous scans of multiple animals, quantification accuracy can be compromised due to the loss of spatial resolution towards the edge of the FOV. Here, we implemented a spatially variant resolution model to improve quantification in small regions and to allow simultaneous scanning of multiple animals without compromising quantification accuracy. The scanner’s point spread function (PSF) was characterized across the FOV and modelled using a spatially variant and asymmetric Gaussian function. The spatially variant PSF (SVPSF) was then used for resolution modelling in the iterative reconstruction. To assess the image quality, a line source phantom in a cold and warm background, as well as mouse brain [18F]FDG scans, were performed. The SVPSF and the vendor’s maximum a posteriori (MAP3D) reconstructions produced uniform spatial resolution across the scanner FOV, but MAP3D resulted in lower spatial resolution. The line sources recovery coefficient using SVPSF was similar at the CFOV and at the edge of the FOV. In contrast, the other tested reconstructions produced lower recovery coefficient at the edge of the FOV. In mouse brain reconstructions, less spill-over from hot regions to cold regions, as well as more symmetric regional brain uptake was observed using SVPSF. The contrast in brain images was the highest using SVPSF, in mice scanned at the CFOV and off-center. Incorporation of a spatially variant resolution model for small animal brain PET improves quantification accuracy in small regions and produces consistent image spatial resolution across the FOV. Therefore, simultaneous scanning of multiple animals can benefit by using spatially variant resolution modelling.

Socially Acceptable Navigation of People with Multi-robot Teams

Socially acceptable navigation is a subject that involves developments regarding Human-Robot Interaction (HRI) and autonomous mobile robot navigation. In this context, there is little research considering people interacting with a robot team, and even fewer considering multi-robot teams with people. In this paper, a study on socially acceptable navigation involving a human and a robot team is presented. Four navigation strategies that consider social aspects are presented and compared in simulated environment by terms as the average number of robots invading the personal space and the number of robots to the person’s side, with two of them using Asymmetric Gaussian Functions (AGFs) as the person’s social zone model; a navigation perception comparison is made involving people to investigate their view on navigating with three robots in contrast to a single robot. Simulated and real-world experiments were performed showing that the proposed methods have advantages over each other on different aspects. The follow-up techniques for interaction between humans and a team of robots suggest that people’s perception may not be affected significantly when interacting with more than one robot.

Temperature-induced molecule assembly effects on the near-edge X-ray absorption fine-structure spectra of Zn-phthalocyanine layers on Si substrates.

The molecular arrangement of vacuum thermally deposited polycrystalline Zn-phthalocyanine (ZnPc) layers on Si substrates is investigated using near-edge X-ray absorption fine-structure (NEXAFS) spectroscopy in the proximity of the carbon edge at E0 = 287.33 eV. The data were collected as a function of the deposition substrate temperature TS (30, 90, 150°C) and the incidence angle θ (20°, 45°, 70°, 90°) of the synchrotron beam with respect to the sample plane. Each spectrum was analysed by mathematical simulation applying an error function for the carbon edge and a set of Voigt and (asymmetric) Gaussian functions for C1s → π* and C1s → σ* transitions of ZnPc, respectively. It turned out that part of the organic layer consists of adventitious carbon, which does not contribute to the molecular transitions of ZnPc, whereas all molecular features exhibit polarization-dependent peak areas pointing to a reasonable fraction of well-assembled molecules at any TS. The highest adventitious carbon fraction was found at TS = 30°C, whereas the highest polarization dependence was found at TS = 90°C. The calculated average molecular tilt angles for the three temperatures (30, 90, 150°C) were γ = 60.6°, 68.7° and 66.7°, respectively. If only the polarization-dependent fractions are considered, then the three samples can be mathematically described using a shared molecular tilt angle of γ= 68.7°, which corresponds to the average tilt angle of the TS = 90°C sample.

Efficient and robust Pedestrian Detection using Deep Learning for Human-Aware Navigation

This paper addresses the problem of Human-Aware Navigation (HAN), using multi camera sensors to implement a vision-based person tracking system. The main contributions of this paper are as follows: a novel and efficient Deep Learning person detection and a standardization of human-aware constraints. In the first stage of the approach, we propose to cascade the Aggregate Channel Features (ACF) detector with a deep Convolutional Neural Network (CNN) to achieve fast and accurate Pedestrian Detection (PD). Regarding the human awareness (that can be defined as constraints associated with the robot’s motion), we use a mixture of asymmetric Gaussian functions, to define the cost functions associated to each constraint. Both methods proposed herein are evaluated individually to measure the impact of each of the components. The final solution (including both the proposed pedestrian detection and the human-aware constraints) is tested in a typical domestic indoor scenario, in four distinct experiments. The results show that the robot is able to cope with human-aware constraints, defined after common proxemics and social rules.

Impacts of snow cover duration on vegetation spring phenology over the Tibetan Plateau

Snow cover occupies large percentage of land surface in Tibetan Plateau. Snow cover duration (SCD) during non-growing seasons plays a critical role in regulating alpine vegetation’s phenology by affecting the energy budgets of land surface and soil moisture conditions. Different period’s snow cover during non-growing season may have distinct effect on the vegetation’s phenology. Start of season (SOS) has been observed advanced under the ongoing climate change in the plateau, but it still remains unclear how the SCD alters the SOS. This study attempts to answer the following questions: (i) What is the pattern of spatial and temporal variations for SCD and grassland SOS? (ii) Which period’s SCD plays a critical role in grassland’s SOS? The remote sensing datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS) were utilized to compute the SOS and SCD on the Tibetan Plateau over 2003–15. The Asymmetric Gaussian function was applied to extract SOS. We also explored the spatial pattern and temporal variation of SOS and SCD. Then, by using linear correlation coefficients, we investigated the driving effects of different period’s non-growing season SCD on SOS. The non-growing season SCD slightly decreased during 2003–15, while SOS exhibited an overall advancing trend. Advanced trends in SOS were observed in the eastern plateau, and the delayed trends were mainly founded in western plateau. Snow cover area exhibited two separate peaks during autumn and late winter over the plateau. Extended SCD regions mainly distributed in middle-east of the plateau, while shrunken SCD distributed in other regions of the plateau. SCD of different seasons caused distinct effects on vegetation SOS. Lengthened autumn SCD advanced SOS over the eastern plateau. The slightly lengthened SCD postponed SOS over the western plateau. In the wet meadow regions, advanced SOS was positively associated with SCD during the entire non-growing season, whereas for the dry steppe, SCD over the preseason played a more dominant role. The SCD of previous autumn and winter also showed lag effect on SOS over meadow regions to a certain extent. This study confirmed the importance of SCD to phenological processes at the beginning of growing season and further suggested that role of SCD should be discriminated for different periods and for different heat-water conditions. With the lag effects and SCD’s distinct effect of different seasons considered, predictions on the Tibetan Plateau’s spring phenology could be improved.

Software for analysis of vegetation indices dynamics

The article describes a software package designed for processing and analyzing of vegetation indices based on satellite data. A program is developed in the free-distributed environment Octave. The input data of the program are vegetation indices (MODIS NDVI and EVI). The data are pre-processed by removing the peak values using the Savitsky-Golay filter and a seasonal decomposition by a moving average. To exclude the influence of snow cover and the period without vegetation, data of the growing season are used in the calculation. The vegetation period is determined by phenological parameters of the beginning and end of the growth period. The program determines the phenological parameters by a double logistic function, an asymmetric Gaussian function, and a fourth-degree polynomial to approximate the original time series. After the preliminary processing, a linear trend of the series, its statistical significance, the minimum and maximum values, coefficients of variation, etc., are calculated. The correlation function between the vegetation indices and the time series set by the user is also implemented. The output data are exported in raster and text formats. In conclusion, an example of using the software for calculating the NDVI trends in the Uda River basin in Transbaikalia is given.