Imaging an Event Horizon: Mitigation of Source Variability of Sagittarius A*

The black hole in the center of the Galaxy, associated with the compact source Sagittarius A* (Sgr A*), is predicted to cast a shadow upon the emission of the surrounding plasma flow, which encodes the influence of general relativity in the strong-field regime. The Event Horizon Telescope (EHT) is a Very Long Baseline Interferometry (VLBI) network with a goal of imaging nearby supermassive black holes (in particular Sgr A* and M87) with angular resolution sufficient to observe strong gravity effects near the event horizon. General relativistic magnetohydrodynamic (GRMHD) simulations show that radio emission from Sgr A* exhibits vari- ability on timescales of minutes, much shorter than the duration of a typical VLBI imaging experiment, which usually takes several hours. A changing source structure during the observations, however, violates one of the basic assumptions needed for aperture synthesis in radio interferometry imaging to work. By simulating realistic EHT observations of a model movie of Sgr A*, we demonstrate that an image of the average quiescent emission, featuring the characteristic black hole shadow and photon ring predicted by general relativity, can nonetheless be obtained by observing over multiple days and subsequent processing of the visibilities (scaling, averaging, and smoothing) before imaging. Moreover, it is shown that this procedure can be combined with an existing method to mitigate the effects of interstellar scattering. Taken together, these techniques allow the black hole shadow in the Galactic center to be recovered on the reconstructed image.Comment: 10 pages, 12figures, accepted for publication in Ap

INTELLIGENT VISION-BASED NAVIGATION SYSTEM

This thesis presents a complete vision-based navigation system that can plan and follow an obstacle-avoiding path to a desired destination on the basis of an internal map updated with information gathered from its visual sensor. For vision-based self-localization, the system uses new floor-edges-specific filters for detecting floor edges and their pose, a new algorithm for determining the orientation of the robot, and a new procedure for selecting the initial positions in the self-localization procedure. Self-localization is based on matching visually detected features with those stored in a prior map. For planning, the system demonstrates for the first time a real-world application of the neural-resistive grid method to robot navigation. The neural-resistive grid is modified with a new connectivity scheme that allows the representation of the collision-free space of a robot with finite dimensions via divergent connections between the spatial memory layer and the neuro-resistive grid layer. A new control system is proposed. It uses a Smith Predictor architecture that has been modified for navigation applications and for intermittent delayed feedback typical of artificial vision. A receding horizon control strategy is implemented using Normalised Radial Basis Function nets as path encoders, to ensure continuous motion during the delay between measurements. The system is tested in a simplified environment where an obstacle placed anywhere is detected visually and is integrated in the path planning process. The results show the validity of the control concept and the crucial importance of a robust vision-based self-localization process

Application of Artificial Fish Swarm Algorithm in Radial Basis Function Neural Network

Neural network is one of the branches with the most active research, development and application in computational intelligence and machine study. Radial basis function neural network (RBFNN) has achieved some success in more than one application field, especially in pattern recognition and functional approximation. Due to its simple structure, fast training speed and excellent generalization ability, it has been widely used. Artificial fish swarm algorithm (AFSA) is a new swarm intelligent optimization algorithm derived from the study on the preying behavior of fish swarm. This algorithm is not sensitive to the initial value and the parameter selection, but strong in robustness and simple and easy to realize and it also has parallel processing capability and global searching ability. This paper mainly researches the weight and threshold of AFSA in optimizing RBFNN. The simulation experiment proves that AFSA-RBFNN is significantly advantageous in global optimization capability and that it has outstanding global optimization ability and stability

Beyond Gr\"obner Bases: Basis Selection for Minimal Solvers

Many computer vision applications require robust estimation of the underlying geometry, in terms of camera motion and 3D structure of the scene. These robust methods often rely on running minimal solvers in a RANSAC framework. In this paper we show how we can make polynomial solvers based on the action matrix method faster, by careful selection of the monomial bases. These monomial bases have traditionally been based on a Gr\"obner basis for the polynomial ideal. Here we describe how we can enumerate all such bases in an efficient way. We also show that going beyond Gr\"obner bases leads to more efficient solvers in many cases. We present a novel basis sampling scheme that we evaluate on a number of problems

Review on electrical impedance tomography: Artificial intelligence methods and its applications

© 2019 by the authors. Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented

The Flagellar Motility of \u3cem\u3eChlamydomonas pf25\u3c/em\u3e Mutant Lacking an AKAP-binding Protein Is Overtly Sensitive to Medium Conditions

Radial spokes are a conserved axonemal structural complex postulated to regulate the motility of 9 + 2 cilia and flagella via a network of phosphoenzymes and regulatory proteins. Consistently, a Chlamydomonas radial spoke protein, RSP3, has been identified by RII overlays as an A-kinase anchoring protein (AKAP) that localizes the cAMP-dependent protein kinase (PKA) holoenzyme by binding to the RIIa domain of PKA RII subunit. However, the highly conserved docking domain of PKA is also found in the N termini of several AKAP-binding proteins unrelated to PKA as well as a 24-kDa novel spoke protein, RSP11. Here, we report that RSP11 binds to RSP3 directly in vitro and colocalizes with RSP3 toward the spoke base near outer doublets and dynein motors in axonemes. Importantly, RSP11 mutant pf25 displays a spectrum of motility, from paralysis with flaccid or twitching flagella as other spoke mutants to wild-typelike swimming. The wide range of motility changes reversibly depending on the condition of liquid media without replacing defective proteins. We postulate that radial spokes use the RIIa/AKAP module to regulate ciliary and flagellar beating; absence of the spoke RIIa protein exposes a medium-sensitive regulatory mechanism that is not obvious in wild-type Chlamydomonas