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Poincaré gauge theory of gravity: Friedman cosmology with even and odd parity modes: Analytic part
(2011)
This paper describes the modeling, optimization, mechanical design, and experimental characterization of a high power density wound field synchronous machine (WFSM) for electric vehicle traction applications. The WFSM is designed for brushless rotor field excitation using an axial flux hydrodynamic capacitive power coupler (CPC). The flexible design environment, which was used for large-scale multiobjective optimization of the WFSM, is described. A prototype WFSM, spray cooled with automatic transmission fluid, with an 80-kW output at a base speed of 4000 r/min has been tested. The prototyped WFSM achieves volumetric torque and power densities of 17.22 N·m/L and 7.19 kW/L referred to the envelope cylindrical volume of the active materials plus spray cooling rings. The specific torque and power densities are 4.69 N·m/kg and 1.95 kW/kg referred to the mass of the active materials and the shaft. The prototyped CPC provided up to 1-kW excitation power with a mass 600 g and volume of 0.271 L.
Image restoration is a process used to remove blur (from different sources like object motion or aberrations) from images by either non-blind or blind-deconvolution. The metrics commonly used to quantify the restoration process are peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Often only a small sample of test images are used (like Lena or the camera guy). In optical design research PSNR and SSIM are not normally used, here image quality metrics based on linear system theory (e.g. modulation transfer function, MTF) are used to quantify optical errors like spherical or chromatic aberration. In this article we investigate how different image restoration algorithms can be quantified by applying image quality metrics. We start with synthetic image data that is used in camera test stands (e.g. Siemens star etc.), apply two different spatially variant degradation algorithms, and restore the original image by a direct method (Wiener filtering within sub-images), and by an iterative method (alternating direction method of multipliers, ADMM). Afterwards we compare the quality metrics (like MTF curves) for the original, the degraded and the restored image. As a first result we show that restoration algorithms sometimes fail in dealing with non-natural scenes, e.g. slanted-edge targets. Further, these first results indicate a correlation between degradation and restoration, i.e. the restoration algorithms are not capable of removing the optically relevant errors introduced by the degradation, a fact neither visible nor available from the PSNR values. We discuss the relevance in the context of the automotive industry, where image restoration may yield distinct advantages for camera-based applications, but testing methods rely on the used image quality metrics.
We had already demonstrated a numerical model for the point spread function (PSF) of an optical system that can efficiently model both the experimental measurements and the lens design simulations of the PSF. The novelty lies in the portability and the parameterization of this model, which allow for completely new ways to validate optical systems, which is especially interesting not only for mass production optics such as in the automotive industry but also for ophthalmology. The numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network (ANN). After briefly describing both the principle and the applications of the model, we then discuss two optically important aspects: the spatial resolution and the accuracy of the model. Using mean squared error (MSE) as a metric, we vary the topology of the neural network, both in the number of neurons and in the number of hidden layers. Measurement and simulation of a PSF can have a much higher spatial resolution than the typical pixel size used in current camera sensors. We discuss the influence this has on the topology of the ANN. The relative accuracy of the averaged pixel MSE is below 10 − 4, thus giving confidence that the regression does indeed model the measurement data with good accuracy. This article is only the starting point, and we propose several research avenues for future work.
Training autonomous vehicles requires lots of driving sequences in all situations. Collecting and labeling these drive scenes is a very time-consuming and expensive process. Currently, it is not possible to reuse these drive scenes with different optical properties, because there exists no numerically efficient model for the transfer function of the optical system. We present a numerical model for the point spread function (PSF) of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network. The novelty lies in the portability and the parameterization of this model. We present a lens measurement series, yielding a numerical function for the PSF that depends only on the parameters defocus, field, and azimuth. By convolving existing images and videos with this PSF, we generate images as if seen through the measured lens. The methodology applies to any optical scenario, but we focus on the context of autonomous driving, where the quality of the detection algorithms depends directly on the optical quality of the used camera system. With this model, it is possible to reuse existing recordings, with the potential to avoid millions of test drive miles. The parameterization of the optical model allows for a method to validate the functional and safety limits of camera-based advanced driver assistance systems based on the real, measured lens actually used in the product.
In a previous work we have demonstrated a novel numerical model for the point spread function (PSF) of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The novelty lies in the portability and the parameterization of this model, which allows for completely new ways to validate optical systems, which is especially interesting for mass production optics like in the automotive industry, but also for ophtalmology. The numerical basis for this model is a non-linear regression of the PSF with an artificial neural network (ANN). In this work we examine two important aspects of this model: the spatial resolution and the accuracy of the model. Measurement and simulation of a PSF can have a much higher resolution then the typical pixel size used in current camera sensors, especially those for the automotive industry. We discuss the influence this has on on the topology of the ANN and the final application where the modeled PSF is actually used. Another important influence on the accuracy of the trained ANN is the error metric which is used during training. The PSF is a distinctly non-linear function, which varies strongly over field and defocus, but nonetheless exhibits strong symmetries and spatial relations. Therefore we examine different distance and similarity measures and discuss its influence on the modeling performance of the ANN.