Signal Model and Parameter Estimation for Colocated Mimo Radar

In this paper, we consider the signal model and parameter estimation for multiple-input multiple-output (MIMO) radar with colocated antennas on stationary platforms. Considering internal clutter motion, a closed form of the covariance matrix of the clutter signal is derived. Based on the proposed closed form and low rank property of the clutter covariance matrix and by using the singular value decomposition, we have proposed a subspace model for the clutter signal. Following the proposed signal model, we have provided maximum likelihood (ML) estimation for its unknown parameters. Finally, the application of the proposed ML estimation in space time adaptive processing (STAP) is investigated in simulation results. Our ML estimation needs no secondary training data and it can be used in scenarios with nonhomogeneous clutter in range.

Improved log-Gaussian approximation for over-dispersed Poisson regression: Application to spatial analysis of COVID-19

In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiability and computational efficiency. Especially, due to an identification problem, Poisson regression can be unstable for small samples with many zeros. Provided this, we develop a closed-form inference for an over-dispersed Poisson regression including Poisson additive mixed models. The approach is derived via mode-based log-Gaussian approximation. The resulting method is fast, practical, and free from the identification problem. Monte Carlo experiments demonstrate that the estimation error of the proposed method is a considerably smaller estimation error than the closed-form alternatives and as small as the usual Poisson regressions. For counts with many zeros, our approximation has better estimation accuracy than conventional Poisson regression. We obtained similar results in the case of Poisson additive mixed modeling considering spatial or group effects. The developed method was applied for analyzing COVID-19 data in Japan. This result suggests that influences of pedestrian density, age, and other factors on the number of cases change over periods.

Closed Form Solution of Plane-Parallel Turbulent Flow Along an Unbounded Plane Surface

In this paper, a century-old problem is solved; namely, to find a unified analytic description of the non-uniform distribution of mean velocity across the entire domain of turbulent flow for all Reynolds numbers within the framework of the Prandtl mixing length theory. This study obtains a closed form solution of the mean velocity profile of plane turbulent flow for the Prandtl theory, and as well an approximate analytical solution for the van Driest mixing length theory. The profiles of several useful quantities are given based the closed form solution, such as turbulent viscosity, Reynolds turbulent stress, Kolmogorov's scaling law, and energy dissipation density. The investigation shows that the energy dissipation density at the surface is finite, whereas Landau's energy dissipation density is infinite. Strictly speaking, the closed form solution reveals that the universality of the turbulent velocity logarithmic profile no longer holds, but the von K\'arm\'an constant is still universal. Furthermore, a new formulation of the resistance coefficient of turbulent flow in pipes is formulated in implicit form.

A Mixed FSO/RF Integrated Satellite-High Altitude Platform Relaying Networks for Multiple Terrestrial Users with Presence of Eavesdropper: A Secrecy Performance

In this paper, the secrecy performance of a mixed free space optical (FSO)/radio frequency (RF) integrated satellite-high altitude platform (HAP) relaying networks for terrestrial multiusers with the existence of an eavesdropper is investigated. In this network, FSO is adopted to establish the link between the satellite and HAP for which it experiences Gamma-Gamma distributions under different detection schemes (i.e., heterodyne and intensity modulation direct detection). The transmission between the amplify-and-forward (AF) relaying HAP and terrestrial multiusers is through the RF and is modeled as shadowed-Rician fading distribution. Owning to broadcasting nature of RF link, it is assumed that an eavesdropper attempts to intercept the users’ confidential message, and the eavesdropper link is subjected to Rician distributions. Specifically, the closed-form expression for the system equivalent end-to-end cumulative distribution function is derived by exploiting the Meijer’s G and Fox’s H functions. Based on this expression, the exact closed-form expressions of the system connection outage probability, secrecy outage probability, and strictly positive secrecy capacity are obtained under the different detection schemes at HAP. Moreover, the asymptotic analyze of the system secrecy outage probability is provided to obtain more physical insights. Furthermore, the accuracy of all the derived analytical closed-form expressions is verified through the Monte-Carlo simulations. In addition, the impact of atmospheric turbulence, pointing errors, shadowing severity parameters, and Rician factor are thoroughly evaluated. Under the same system conditions, the results depict that heterodyne detection outperforms the intensity modulation direct detection.

A Deep Learning Approach to Design and Discover Sustainable Cementitious Binders: Strategies to Learn From Small Databases and Develop Closed-form Analytical Models

To reduce the energy-intensity and carbon footprint of Portland cement (PC), the prevailing practice embraced by concrete technologists is to partially replace the PC in concrete with supplementary cementitious materials [SCMs: geological materials (e.g., limestone); industrial by-products (e.g., fly ash); and processed materials (e.g., calcined clay)]. Chemistry and content of the SCM profoundly affect PC hydration kinetics; which, in turn, dictates the evolutions of microstructure and properties of the [PC + SCM] binder. Owing to the substantial diversity in SCMs’ compositions–plus the massive combinatorial spaces, and the highly nonlinear and mutually-interacting processes that arise from SCM-PC interactions–state-of-the-art computational models are unable to produce a priori predictions of hydration kinetics or properties of [PC + SCM] binders. In the past 2 decades, the combination of Big data and machine learning (ML)—commonly referred to as the fourth paradigm of science–has emerged as a promising approach to learn composition-property correlations in materials (e.g., concrete), and capitalize on such learnings to produce a priori predictions of properties of materials with new compositions. Notwithstanding these merits, widespread use of ML models is hindered because they: 1) Require Big data to learn composition-property correlations, and, in general, large databases for concrete are not publicly available; and 2) Function as black-boxes, thus providing little-to-no insights into the materials laws like theory-based analytical models do. This study presents a deep learning (DL) model capable of producing a priori, high-fidelity predictions of composition- and time-dependent hydration kinetics and phase assemblage development in [PC + SCM] pastes. The DL is coupled with: 1) A fast Fourier transformation algorithm that reduces the dimensionality of training datasets (e.g., kinetic datasets), thus allowing the model to learn intrinsic composition-property correlations from a small database; and 2) A thermodynamic model that constrains the model, thus ensuring that predictions do not violate fundamental materials laws. The training and outcomes of the DL are ultimately leveraged to develop a simple, easy-to-use, closed-form analytical model capable of predicting hydration kinetics and phase assemblage development in [PC + SCM] pastes, using their initial composition and mixture design as inputs.

Closed-Form Formulation of the Thermodynamically Consistent Electrochemical Model Considering Electrochemical Co-Oxidation of CO and H2 for Simulating Solid Oxide Fuel Cells

Achieving efficient solid oxide fuel cell operation and simultaneous prevention of degradation effects calls for the development of precise on-line monitoring and control tools based on predictive, computationally fast models. The originality of the proposed modelling approach originates from the hypothesis that the innovative derivation procedure enables the development of a thermodynamically consistent multi-species electrochemical model that considers the electrochemical co-oxidation of carbon monoxide and hydrogen in a closed-form. The latter is achieved by coupling the equations for anodic reaction rates with the equation for anodic potential. Furthermore, the newly derived model is capable of accommodating the diffusive transport of gaseous species through the gas diffusion layer, yielding a computationally efficient quasi-one-dimensional model. This resolves a persistent knowledge gap, as the proposed modelling approach enables the modelling of multi-species fuels in a closed form, resulting in very high computational efficiency, and thus enable the model’s real-time capability. Multiple validation steps against polarisation curves with different fuel mixtures confirm the capability of the newly developed model to replicate experimental data. Furthermore, the presented results confirm the capability of the model to accurately simulate outside the calibrated variation space under different operating conditions and reformate mixtures. These functionalities position the proposed model as a beyond state-of-the-art tool for model supported development and control applications.