Abstract
We propose two methods to characterize the Non-Common Path Aberrations (NCPA) on the TMT/NFIRAOS system; these techniques are known as Phase Diversity and Focal Plane Sharpening. We demonstrate the feasibility of these techniques on an experimental bench. We also explore the operational effects of Phase Diversity and how it might be best applied to a NFIRAOS-like system. In particular we explore the technique of single image Phase Diversity along with the effects of i) estimating either Zernike modes or Disk Harmonics, ii) using multiple diverse images, and III) using diversities other than focus. These operational considerations are explored in a simulation of the NFIRAOS system and we aim to find the best estimation of the NFIRAOS NCPA in the presence of different levels of noise. We find a realistic estimation of NFIRAOS NCPA would be with multi-image Phase Diversity-with focus-diverse images sampled at asymmetric positions on either side of the focal plane (with no estimation of the object).

Abstract
In preparation of future multiobject spectrographs (MOS) whose one of the major role is to provide an extensive statistical studies of high redshifted galaxies surveyed, the demonstrator CANARY has been designed to tackle technical challenges related to open-loop adaptive optics (AO) control with jointed Natural Guide Star and Laser Guide Star tomography. We have developed a point spread function (PSF) reconstruction algorithm dedicated to multiobject adaptive optics systems using system telemetry to estimate the PSF potentially anywhere in the observed field, a prerequisite to postprocess AO-corrected observations in integral field spectroscopy. We show how to handle off-axis data to estimate the PSF using atmospheric tomography and compare it to a classical approach that uses on-axis residual phase from a truth sensor observing a natural bright source. We have reconstructed over 450 on-sky CANARY PSFs and we get bias/1-s standard-deviation (std) of 1.3/4.8 on the H-band Strehl ratio (SR) with 92.3% of correlation between reconstructed and sky SR. On the full-width at half-maximum, we get, respectively, 2.94 mas, 19.9 mas, and 88.3% for the bias, std, and correlation. The reference method achieves 0.4/3.5/95% on the SR and 2.71 mas/14.9 mas/92.5% on the FWHM for the bias/std/correlation.

Abstract
In tomographic adaptive-optics (AO) systems, errors due to tomographic wavefront reconstruction limit the performance and angular size of the scientific field of view (FoV), where AO correction is effective. We propose a multi time-step tomographic wavefront reconstruction method to reduce the tomographic error by using measurements from both the current and previous time steps simultaneously. We further outline the method to feed the reconstructor with both wind speed and direction of each turbulence layer. An end-to-end numerical simulation, assuming a multi-object AO (MOAO) system on a 30 m aperture telescope, shows that the multi timestep reconstruction increases the Strehl ratio (SR) over a scientific FoV of 10 arc min in diameter by a factor of 1.5-1.8 when compared to the classical tomographic reconstructor, depending on the guide star asterism and with perfect knowledge of wind speeds and directions. We also evaluate the multi time-step reconstruction method and the wind estimation method on the RAVEN demonstrator under laboratory setting conditions. The wind speeds and directions at multiple atmospheric layers are measured successfully in the laboratory experiment by our wind estimation method with errors below 2 ms-1. With these wind estimates, the multi time-step reconstructor increases the SR value by a factor of 1.2-1.5, which is consistent with a prediction from the end-to-end numerical simulation.

Abstract
Smartphones are everywhere, and they are a very attractive platform to perform unobtrusive monitoring of users. In this work, we use common features of modern smartphones to build a human activity recognition (HAR) system for elderly care. We have built a classifier that detects the transport mode of the user including whether an individual is inactive, walking, in bus, in car, in train or in metro. We evaluated our approach using over 24 hours of transportation data from a group of 15 individuals. Our tests show that our classifier can detect the transportation mode with over 90% accuracy.

Abstract
Cyber-Physical Systems (CPSs) represent systems where computations are tightly coupled with the physical world, meaning that physical data is the core component that drives computation. Industrial automation systems, wireless sensor networks, mobile robots and vehicular networks are just a sample of cyber-physical systems. Typically, CPSs have limited computation and storage capabilities due to their tiny size and being embedded into larger systems. With the emergence of cloud computing and the Internet-of-Things (IoT), there are several new opportunities for these CPSs to extend their capabilities by taking advantage of the cloud resources in different ways. In this survey paper, we present an overview of research efforts on the integration of cyber-physical systems with cloud computing and categorize them into three areas: (1) remote brain, (2) big data manipulation, (3) and virtualization. In particular, we focus on three major CPSs namely mobile robots, wireless sensor networks and vehicular networks. © 2016 Elsevier B.V.

Abstract
Smartphones are everywhere, and they are a very attractive platform to perform unobtrusive monitoring of users. In this work, we use common features of modern smartphones to build a human activity recognition (HAR) system for elderly care. We have built a classifier that detects the transport mode of the user including whether an individual is inactive, walking, in bus, in car, in train or in metro. We evaluated our approach using over 24 hours of transportation data from a group of 15 individuals. Our tests show that our classifier can detect the transportation mode with over 90% accuracy.