Reconstruction of a single-active-electron potential from electron momentum distribution produced by strong-field ionization using optimization technique

We present a method for retrieving of single-active electron potential in an atom or molecule from a given momentum distribution of photoelectrons ionized by a strong laser field. In this method the potential varying within certain limits is found as the result of the optimization procedure aimed at reproducing the given momentum distribution. The optimization using numerical solution of the time-dependent Schrodinger equation for ionization of a model one-dimensional atom shows the good accuracy of the potential reconstruction method. This applies to different ways used for representing of the potential under reconstruction, including a parametrization and determination of the potential by specifying its values on a spatial grid.Comment: 18 pages, 6 figure

Change detection in combination with spatial models and its effectiveness on underwater scenarios

This thesis proposes a novel change detection approach for underwater scenarios and combines it with different especially developed spatial models, this allows accurate and spatially coherent detection of any moving objects with a static camera in arbitrary environments. To deal with the special problems of underwater imaging pre-segmentations based on the optical flow and other special adaptions were added to the change detection algorithm so that it can better handle typical underwater scenarios like a scene crowded by a whole fish swarm

Doctor of Philosophy

dissertationMore than a century ago, in his labs in Colorado Springs and New York, Nikola Tesla started experimenting with wireless power transfer (WPT). His ideas were ahead of his time, but they fell into obscurity shortly after his death. Nowadays, WPT is no longer thought of as science fiction: neural prostheses, wearables, cellphones, and even electric vehicles can be powered through WPT. In its most common implementation, WPT leverages the magnetic coupling between resonant transmitter and receiver coils to exchange energy. Considerable work is devoted to the design and optimization of WPT antennas; efficiently transmitting the required amount of power can only be accomplished when the coil coupling is in the right range. In this work, we explore the use of spatial filters in WPT systems. Spatial filters are capable of controlling the harmonic content of an incident wave: subwavelength focal spots, perfect lensing, and diffractionless beams are some of their uses. In the first part of this dissertation, the focus is on the analysis and design of a compact negative permeability metamaterial slab. Compared to other works in the literature, this slab is an extremely small fraction of the wavelength, and works at a low operating frequency. Analysis and experimental validation demonstrate that the resulting metamaterial sample can be used in a 2-coil WPT system to achieve large range and efficiency enhancements. In the second part of this dissertation, the analysis and implementation of holographic screens is presented. A method to reduce the fabrication complexity of the desired holographic screen while maintaining the fidelity of the prescribed field distribution is presented. We demonstrate our method through the analysis, design, and experimental validation of a nondiffractive beam launcher with a Bessel field distribution. Finally, we utilize the analysis and methods presented in this work to design an antenna capable of prescribing a uniform field distribution. Because of this property, this WPT antenna is capable of transmitting near constant power with near constant efficiency to the target receiver load, without the need of an adaptive compensation system

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin