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This study presents a demonstration of the surface morphology behavior of gallium antimonide (GaSb) layers deposited on gallium arsenide (GaAs) (100) substrates using three different methods: metamorphic, interfacial misfit (IMF) matrix, and a method based on a Polish patent application number P.443805. The first two methods are commonly used, while the third differs in the sequence of successive steps and the presence of Be doping at the initial growth stage. By comparing GaSb layers made by these methods for the same growth parameters, the most favorable procedure for forming a GaSb buffer layer is selected. Using GaAs substrates with a GaSb buffer layer is a cheaper alternative to using GaSb substrates in infrared detector structures based on II-type superlattices T2SL, such as InAs/GaSb. The quality of the GaSb buffer layer determines the quality of the subsequent layers that form the entire T2SL and affects factors such as dark current in terms of application, Comment: 8 pages, 11 figures

The performance of estimated models is often evaluated in terms of their predictive capability. In this study, we investigate another important aspect of estimated model evaluation: the disparity between the statistical and dynamical properties of estimated models and their source system. Specifically, we focus on estimated models obtained via the regression method, sparse identification of nonlinear dynamics (SINDy), one of the promising algorithms for determining equations of motion from time series of dynamical systems. We chose our data source dynamical system to be a higher-dimensional instance of the Lorenz 2005 type II model, an important meteorological toy model. We examine how the dynamical and statistical properties of the estimated models are affected by the standard deviation of white Gaussian noise added to the numerical data on which the estimated models were fitted. Our results show that the dynamical properties of the estimated models match those of the source system reasonably well within a range of data-added noise levels, where the estimated models do not generate divergent (unbounded) trajectories. Additionally, we find that the dynamics of the estimated models become increasingly less chaotic as the data-added noise level increases. We also perform a variance analysis of the (SINDy) estimated model's free parameters, revealing strong correlations between parameters belonging to the same component of the estimated model's ordinary differential equation., Comment: 13 pages + refs, 10 figures

Non-parametric system identification with Gaussian Processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle (AUV) dynamics with low amount of data. Multi-output Gaussian processes and its aptitude to model the dynamic system of an underactuated AUV without losing the relationships between tied outputs is used. The simulation of a first-principles model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom (DoF) is also shown in this paper. Multi-output Gaussian processes are compared with the popular technique of recurrent neural network show that Multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing a measurement of confidence.

Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method described and evaluated in this paper can be used as a nonlinear system identification method for gray-box modeling. It consists of two interlaced parts of modeling that are computer-aided. The first performs computer-aided identification of a model structure composed of elements selected from user-specified domain-specific modeling knowledge, while the second part performs parameter estimation. In this paper, recent developments of the equation discovery method called process-based modeling, suited for nonlinear system identification, are elaborated and illustrated on two continuous-time case studies. The first case study illustrates the use of the process-based modeling on synthetic data while the second case-study evaluates on measured data for a standard system-identification benchmark. The experimental results clearly demonstrate the ability of process-based modeling to reconstruct both model structure and parameters from measured data.

Despite the rise of deep learning in numerous areas of computer vision and image processing, iris recognition has not benefited considerably from these trends so far. Most of the existing research on deep iris recognition is focused on new models for generating discriminative and robust iris representations and relies on methodologies akin to traditional iris recognition pipelines. Hence, the proposed models do not approach iris recognition in an end-to-end manner, but rather use standard heuristic iris segmentation (and unwrapping) techniques to produce normalized inputs for the deep learning models. However, because deep learning is able to model very complex data distributions and nonlinear data changes, an obvious question arises. How important is the use of traditional segmentation methods in a deep learning setting? To answer this question, we present in this paper an empirical analysis of the impact of iris segmentation on the performance of deep learning models using a simple two stage pipeline consisting of a segmentation and a recognition step. We evaluate how the accuracy of segmentation influences recognition performance but also examine if segmentation is needed at all. We use the CASIA Thousand and SBVPI datasets for the experiments and report several interesting findings., Comment: 6 pages, 3 figures, 3 tables, submitted to IWBF 2019