Various methods have been proposed to reconstruct admixture histories by analyzing the length of ancestral chromosomal tracts, such as estimating the admixture time and number of admixture events. However, available methods do not explicitly consider the complex admixture structure, which characterizes the joining and mixing patterns of different ancestral populations during the admixture process, and instead assume a simplified one-by-one sequential admixture model. In this study, we proposed a novel approach that considers the non-sequential admixture structure to reconstruct admixture histories. Specifically, we introduced a hierarchical admixture model that incorporated four ancestral populations and developed a new method, called HierarchyMix, which uses the length of ancestral tracts and the number of ancestry switches along genomes to reconstruct the four-way admixture history. By automatically selecting the optimal admixture model using the Bayesian information criterion principles, HierarchyMix effectively estimates the corresponding admixture parameters. Simulation studies confirmed the effectiveness and robustness of HierarchyMix. We also applied HierarchyMix to Uyghurs and Kazakhs, enabling us to reconstruct the admixture histories of Central Asians. Our results highlight the importance of considering complex admixture structures and demonstrate that HierarchyMix is a useful tool for analyzing complex admixture events.

Fast and continuous data acquisition (DAQ) with well resolved spectral information is essential for high-speed and high-fidelity measurement of thermophysical parameters of industrial processes using laser absorption spectroscopy tomography (LAST). However, the state-of-the-art DAQ systems suffer: inability to collect raw spectral data in real-time due to the very high data throughput; degradation of spectral integrity when excessive on-chip down-sampling is implemented to reduce data throughput. In this article, we designed a star-networked and reconfigurable DAQ system for real-time LAST imaging at kilo-Hz frame rate. The DAQ system is embedded with a new field programmable gate array (FPGA)-accelerated digital lock-in technique, whereby a cascaded integrator-comb (CIC) filter is implemented for down-sampling of the raw signal with well-maintained spectral information. Furthermore, a customized data-encapsulation protocol is developed to enable continuity of real-time data communication between the front-end DAQ hubs and back-end processor. Performance of the developed DAQ system is experimentally validated by flame temperature imaging at 1 kHz, providing the necessary temporal resolution to penetrate turbulent flow and related industrial processes such as reaction propagation.

Tunable diode laser absorption spectroscopy tomography (TDLAST) has been widely applied for imaging two-dimensional distributions of industrial flow-field parameters, e.g., temperature and species concentration. Two main interested imaging objectives in TDLAST are the local combustion and its radiation in the entire sensing region. State-of-the-art algorithms were developed to retrieve either of the two objectives. In this article, we address both by developing a novel multioutput imaging neural network, named as spatially progressive neural network (SpaProNet). This network consists of locally and globally prioritized reconstruction stages. The former enables hierarchical imaging of the finely resolved and highly accurate local combustion, but coarsely resolved background. The latter retrieves a fine-resolved image for the entire sensing region, at the cost of slightly trading off the reconstruction accuracy in the combustion zone. Furthermore, the proposed network is driven by the hydrodynamics of the real reactive flows, in which the training dataset is obtained from large eddy simulation. The proposed SpaProNet is validated by both simulation and lab-scale experiment. In all test cases, the visual and quantitative metric comparisons show that the proposed SpaProNet outperforms the existing methods from the following two perspectives: 1) the locally prioritized stage provides ever-better accuracy in the combustion zone; and 2) the globally prioritized stage shows turbulence-indicative accuracy in the entire sensing region for diagnosis of heat radiation from the flame and flame-air interactions.

Chemical species tomography (CST) has been widely used for in situ imaging of critical parameters, e.g., species concentration and temperature, in reactive flows. However, even with state-of-the-art computational algorithms, the method is limited due to the inherently ill-posed and rank-deficient tomographic data inversion and by high computational cost. These issues hinder its application for real-time flow diagnosis. To address them, we present here a novel convolutional neural network, namely CSTNet, for high-fidelity, rapid, and simultaneous imaging of species concentration and temperature using CST. CSTNet introduces a shared feature extractor that incorporates the CST measurements and sensor layout into the learning network. In addition, a dual-branch decoder with internal crosstalk, which automatically learns the naturally correlated distributions of species concentration and temperature, is proposed for image reconstructions. The proposed CSTNet is validated both with simulated datasets and with measured data from real flames in experiments using an industry-oriented sensor. Superior performance is found relative to previous approaches in terms of reconstruction accuracy and robustness to measurement noise. This is the first time, to the best of our knowledge, that a deep learning-based method for CST has been experimentally validated for simultaneous imaging of multiple critical parameters in reactive flows using a low-complexity optical sensor with a severely limited number of laser beams.

The generation of dense and accurate point clouds from airborne light detection and ranging (LiDAR) waveform data is crucial to forest inventory. This work proposes a deconvolution method with: 1) an automatic stopping criterion to differentiate near-adjacent targets and 2) an iterative false subwaveform removal algorithm to remove outliers caused by noise. Synthetic waveforms with different overlap rates were processed using the proposed method, the Gaussian decomposition (GD) method, and the Richardson Lucy (RL) deconvolution method. Results showed that: 1) the number of subwaveforms detected by the proposed method is 9% higher than that of the RL and 20% higher than that of the GD when the overlap rates are larger than 0.6 and 2) the proposed method is of the smallest ground and peak distance errors. Results of the indoor experiment also show that the proposed method is superior in finding near targets meanwhile leading to small ground and peak distance error. Furthermore, the proposed method was tested by airborne waveforms from the Dagujia forest farm. The point cloud density acquired by the proposed method is 3% and 35% higher than that by the RL and GD method. Fewer outliers are produced by the proposed method. The number of individual trees extracted from the proposed point clouds is 22%, 51%, and 57% greater than those extracted from the RL, the GD, and the reference point clouds using the canopy height model-based method. Best individual tree extraction result is produced by the proposed method, especially for an area with small trees.

BACKGROUND: Computer simulations have been widely applied in population genetics and evolutionary studies. A great deal of effort has been made over the past two decades in developing simulation tools. However, there are not many simulation tools suitable for studying population admixture. RESULTS: We here developed a forward-time simulator, AdmixSim 2, an individual-based tool that can flexibly and efficiently simulate population genomics data under complex evolutionary scenarios. Unlike its previous version, AdmixSim 2 is based on the extended Wright-Fisher model, and it implements many common evolutionary parameters to involve gene flow, natural selection, recombination, and mutation, which allow users to freely design and simulate any complex scenario involving population admixture. AdmixSim 2 can be used to simulate data of dioecious or monoecious populations, autosomes, or sex chromosomes. To our best knowledge, there are no similar tools available for the purpose of simulation of complex population admixture. Using empirical or previously simulated genomic data as input, AdmixSim 2 provides phased haplotype data for the convenience of further admixture-related analyses such as local ancestry inference, association studies, and other applications. We here evaluate the performance of AdmixSim 2 based on simulated data and validated functions via comparative analysis of simulated data and empirical data of African American, Mexican, and Uyghur populations. CONCLUSIONS: AdmixSim 2 is a flexible simulation tool expected to facilitate the study of complex population admixture in various situations.

Point clouds registration is an important research topic in the field of data fusion from camera and light detection and ranging (LiDAR). In this letter, a new registration method, fast multiscale registration (FMSR), takes the scale factor into account and is proposed for the registration of two point clouds obtained from camera and LiDAR. An adaptive-scale keypoint quality algorithm was used to detect and match keypoints, which were input to the coarse registration process to improve the coarse registration accuracy. A new heuristic criterion was also proposed for fine registration, which avoids falling into the local minima. Furthermore, to increase efficiency of fine registration, the k-nearest neighbors algorithm was selected to directly search the optimal matching from the raw point clouds without triangulating point clouds into mesh. The FMSR method is highly precise, insensitive to outliers, and relatively efficient. Experimental results showed that the root-mean-square error of the registration was approximately 0.2 m when the size of the object was about 20.3 m × 7.85 m × 26.56 m, the total number of matched points was 12 789, and the execution time was approximately 2.1 s, indicating that the proposed method resulted in improved accuracy and efficiency of registration.