Abstract

Abstract
The list of Adaptive Optics (AO) simulators in the community has constantly been growing, guided by different needs and purposes (Compass, HCIPY, OOMAO, SOAPY, YAO. . .). In this paper, we present OOPAO (Object Oriented Python Adaptive Optics), a simulation tool based on the Matlab distribution OOMAO to adapt its philosophy to the Python language. This code was initially intended for internal use but the choice was made to make it public as it can benefit the community since it is fully developed in Python. The OOPAO repository is available in free access on GitHub (https://github.com/cheritier/OOPAO) with several tutorials. The tool consists of a full end-to-end simulator designed for AO analysis purposes. The principle is that the light from a given light source can be propagated through multiple objects (Atmosphere, Telescope, Deformable Mirror, Wave-Front Sensors. . .) among which experimental features can be input, in the spirit of OOMAO. This paper provides an overview of the main capabilities of the code and can be used as a user manual for interested users.

Abstract

Abstract

Abstract
Echocardiography is the most frequently used imaging modality in cardiology. However, its acquisition is affected by inter-observer variability and largely dependent on the operator's experience. In this context, artificial intelligence techniques could reduce these variabilities and provide a user independent system. In recent years, machine learning (ML) algorithms have been used in echocardiography to automate echocardiographic acquisition. This review focuses on the state-of-the-art studies that use ML to automate tasks regarding the acquisition of echocardiograms, including quality assessment (QA), recognition of cardiac views and assisted probe guidance during the scanning process. The results indicate that performance of automated acquisition was overall good, but most studies lack variability in their datasets. From our comprehensive review, we believe automated acquisition has the potential not only to improve accuracy of diagnosis, but also help novice operators build expertise and facilitate point of care healthcare in medically underserved areas.

Abstract
The segmented nature of the 10-m Keck telescopes combined with facility-class AO systems and science instruments, and a history of science-driven upgrades to these systems, offers a uniquely powerful pathfinder for future AO science facilities on the segmented ELTs. Keck’s 2035 Strategic Vision includes visible, high contrast and ground layer AO facilities all of which could support ELT AO pathfinding. Keck’s pathfinder strength is not just demonstrating new techniques or technologies but developing them into operational science capabilities. For example, since first Keck AO science in 1999, Keck has successfully implemented three generations of sodium-wavelength lasers and is currently implementing its third generation of real-time controller (this time GPU-based). Current pathfinder-related developments include laser tomography, near-infrared low order wavefront sensing and PSF-reconstruction for high Strehl ratio and high sky coverage on the Keck I AO system. Current AO-based primary mirror phasing techniques under development include the use of Zernike, pyramid and phase diversity techniques. High-contrast AO developments include near-infrared pyramid wavefront sensing, on-sky phase diversity, speckle nulling and predictive wavefront control. Another pathfinder development is the NASA Goddard-led ORCAS satellite to provide a bright artificial point source for AO-correction. A fast, visible science camera has been implemented in support of ORCAS, demonstrating 15 mas FWHM, and, in a further move toward the visible, ALPAO is developing a 2.5 mm spacing, 60x60 actuator deformable mirror for Keck. In addition, three new AO science instruments are planned: Liger as a prototype of TMT’s IRIS, HISPEC which is the same as TMT’s MODHIS (based on KPIC’s science success), and SCALES.