A New Triple Filtering Algorithm and Its Application for Aerial GNSS/INS-Integrated Direct Georeferencing System

A global navigation satellite system and inertial navigation system- (GNSS/INS-) integrated system is employed to provide direct georeferencing (DG) in aerial photogrammetry. However, GNSS/INS suffers from stochastic error, strong nonlinearity, and weak observability problems in high dynamic or less maneuver scenarios. In this paper, we proposed a new triple filtering algorithm for aerial GNSS/INS integration. The new algorithm implements filtering in the sequence of forward, backward, and forward directions. Each filter is initialized by a previous filter to get a quick convergence, and the final result is combination of the last two filtering to smooth error. The proposed triple filtering strategy avoids inaccuracy in the 1st forward filtering when the system has not reached convergence. Moreover, it facilitates engineering implementation because backward filtering can employ the same equations with forward filtering. To assess stochastic error of the inertial measurement unit, the Allan variance method is used and abbreviated stochastic model is built. A real aerial testing is conducted, and the result indicates that DG can achieve horizontal accuracy of 5 cm by the proposed algorithm, which has 63% improvement compared to standard extended Kalman filter.

Lagrangian particle tracking with new weighted fraction Monte Carlo method for studying the soot particle size distributions in premixed flames

Purpose The purpose of this paper is to study the soot formation and evolution by using this newly developed Lagrangian particle tracking with weighted fraction Monte Carlo (LPT-WFMC) method. Design/methodology/approach The weighted soot particles are used in this MC framework and is tracked using Lagrangian approach. A detailed soot model based on the LPT-WFMC method is used to study the soot formation and evolution in ethylene laminar premixed flames. Findings The LPT-WFMC method is validated by both experimental and numerical results of the direct simulation Monte Carlo (DSMC) and Multi-Monte Carlo (MMC) methods. Compared with DSMC and MMC methods, the stochastic error analysis shows this new LPT-WFMC method could further extend the particle size distributions (PSDs) and improve the accuracy for predicting soot PSDs at larger particle size regime. Originality/value Compared with conventional weighted particle schemes, the weight distributions in LPT-WFMC method are adjustable by adopting different fraction functions. As a result, the number of numerical soot particles in each size interval could be also adjustable. The stochastic error of PSDs in larger particle size regime can also be minimized by increasing the number of numerical soot particles at larger size interval.

A tail of two pandas— whole genome k-mer signature analysis of the red panda (Ailurus fulgens) and the Giant panda (Ailuropoda melanoleuca)

Background The red panda (Ailurus fulgens) is a riddle of morphology, making it hard to tell whether it is an ursid, a procyonid, a mustelid, or a member of its own family. Previous genetic studies have given quite contradictory results as to its phylogenetic placement. Results A recently developed whole genome-based algorithm, the Whole Genome K-mer Signature algorithm was used to analyze the genomes of 28 species of Carnivora, including A. fulgens and several felid, ursid, mustelid, one mephitid species. This algorithm has the advantage of holistically using all the information in the genomes of these species. Being a genomics-based algorithm, it also reduces stochastic error to a minimum. Besides the whole genome, the mitochondrial DNA from 52 mustelids, mephitids, ursids, procyonids and A. fulgens were aligned to draw further phylogenetic inferences. The results from the whole genome study suggested that A. fulgens is a member of the mustelid clade (p = 9·10− 97). A. fulgens also separates from the mephitid Spilogala gracilis. The giant panda, Ailuropoda melanoleuca also clusters away from A. fulgens, together with other ursids (p = 1.2·10− 62). This could be due to the geographic isolation of A. fulgens from other mustelid species. However, results from the mitochondrial study as well as neighbor-joining methods based on the sequence identity matrix suggests that A. fulgens forms a monophyletic group. A Maximum Likelihood tree suggests that A. fulgens and Ursidae form a monophyletic group, although the bootstrap value is weak. Conclusions The main conclusion that we can draw from this study is that on a whole genome level A. fulgens possibly belongs to the mustelid clade, and not an ursid or a mephitid. This despite the fact that previously some researchers classified A. fulgens and A. melanoleuca as relatives. Since the genotype determines the phenotype, molecular-based classification takes precedence over morphological classifications. This affirms the results of some previous studies, which studied smaller portions of the genome. However, mitochondrial analyses based on neighbor-joining and maximum likelihood methods suggest otherwise.

A new weighted fraction Monte Carlo method for particle coagulation

Purpose The purpose of this study is to investigate the aerosol dynamics of the particle coagulation process using a newly developed weighted fraction Monte Carlo (WFMC) method. Design/methodology/approach The weighted numerical particles are adopted in a similar manner to the multi-Monte Carlo (MMC) method, with the addition of a new fraction function (α). Probabilistic removal is also introduced to maintain a constant number scheme. Findings Three typical cases with constant kernel, free-molecular coagulation kernel and different initial distributions for particle coagulation are simulated and validated. The results show an excellent agreement between the Monte Carlo (MC) method and the corresponding analytical solutions or sectional method results. Further numerical results show that the critical stochastic error in the newly proposed WFMC method is significantly reduced when compared with the traditional MMC method for higher-order moments with only a slight increase in computational cost. The particle size distribution is also found to extend for the larger size regime with the WFMC method, which is traditionally insufficient in the classical direct simulation MC and MMC methods. The effects of different fraction functions on the weight function are also investigated. Originality Value Stochastic error is inevitable in MC simulations of aerosol dynamics. To minimize this critical stochastic error, many algorithms, such as MMC method, have been proposed. However, the weight of the numerical particles is not adjustable. This newly developed algorithm with an adjustable weight of the numerical particles can provide improved stochastic error reduction.

A tail of two pandas— Whole Genome K-mer Signature Analysis of the Red Panda (Ailurus fulgens) and the Giant Panda (Ailuropoda melanoleuca)

Background: The red panda (Ailurus fulgens) is a riddle of morphology, making it hard to tell whether it is an ursid, a procyonid, a mustelid, or a member of its own family. Previous genetic studies have given quite contradictory results as to its phylogenetic placement. Results: A recently developed whole genome-based algorithm, the Whole Genome K-mer Signature algorithm was used to analyze the genomes of 28 species of Carnivora, including A. fulgens and several felid, ursid, mustelid, one mephitid species. This algorithm has the advantage of holistically using all the information in the genomes of these species. Being a genomics-based algorithm, it also reduces stochastic error to a minimum. Besides the whole genome, the mitochondrial DNA from 52 mustelids, mephitids, ursids, procyonids as well as A. fulgens were also aligned to draw further phylogenetic inferences. The results from the whole genome study show that A. fulgens is a member of the mustelid clade (p = 9·10-97). A. fulgens also separates from the mephitid Spilogala gracilis. The giant panda, Ailuropoda melanoleuca also clusters away from A. fulgens, together with other ursids (p = 1.2·10-62). This could be due to the geographic isolation of A. fulgens from other mustelid species. However, results from the mitochondrial study based on the sequence identity matrix seem to place A. fulgens into its own group.Conclusions: The main conclusion that we can draw from this study is that on a whole genome level A. fulgens belongs to the mustelid clade, and not an ursid or a mephitid. This despite the fact that previously some researchers classified A. fulgens and A. melanoleuca as relatives. Since the genotype determines the phenotype, molecular-based classification takes precedence over morphological classifications. This affirms the results of some previous studies, which studied smaller portions of the genome. The mitochondrial results could be due to differing mutational pressures compared to the nucleus. It cannot be said for sure, but it is likely that A. fulgens belongs to the mustelid clade.

EIS: Measurement Model Program

The measurement model is used to analyze electrochemical impedance spectroscopy (EIS) data. The measurement model installation file works with the MS Windows operating system. With this program, you will be able to identify the stochastic error structure of your measurements, used to weight further regressions. You will be able to determine what part of your measurement is inconsistent with the Kramers-Kronig relations. You will be able to estimate capacitance and ohmic resistance, from which you can identify the characteristic frequency above which the geometry of the electrode may cause frequency dispersion. You will also be able to fit custom models to your data. The reference manual, reached from the Help Tab, also provides links to sample data, custom models, and Python code.Copyright ©2020, University of Florida Research Foundation, Inc., All Rights Reserved.

Quantization error in stereo imaging system with noise distributions

The problem of estimating quantization error in 2D images is an inherent problem in computer vision. The outcome of this problem is directly related to the error in reconstructed 3D position coordinates of an object. Thus estimation of quantization error has its own importance in stereo vision. Although the quantization error cannot be controlled fully, still statistical error analysis helps us to measure the performance of stereo systems that relies on the imaging parameters. Generally, it is assumed that the quantization error in 2D images is distributed uniformly that need not to be true from a practical aspect. In this paper, we have incorporated noise distributions (Triangular and Trapezoidal) for the stochastic error analysis of the quantization error in stereo imaging systems. For the validation of the theoretical analysis, the detailed simulation study is carried out by considering different cases.