MacLeod Pittman (robingrouse56)

This article presents a non-classical imaging mechanism that produces a diffraction-limited and magnified ghost image of the internal structure of an object through the measurement of intensity fluctuation correlation formed by two-photon interference. In principle, the lensless X-ray ghost imaging mechanism may achieve a spatial resolution determined by the wavelength and the angular diameter of the X-ray source, ∼λ/Δθs, with possible reduction caused by additional optics. In addition, it has the ability to image select "slices" deep within an object, which can be used for constructing 3D view of its internal structure.Resonant biosensors are attractive for diagnostics because they can detect clinically relevant biomarkers with high sensitivity and in a label-free fashion. Most of the current solutions determine their detection limits in a highly stabilised laboratory environment, which does, however, not apply to real point-of-care applications. Here, we consider the more realistic scenario of low-cost components and an unstabilised environment and consider the related design implications. We find that sensors with lower quality-factor resonances are more fault tolerant, that a filtered LED lightsource is advantageous compared to a diode laser, and that a CMOS camera is preferable to a CCD camera for detection. We exemplify these findings with a guided mode resonance sensor and experimentally determine a limit of detection of 5.8 ± 1.7×10-5 refractive index units (RIU), which is backed up by a model identifying the various noise sources. Our findings will inform the design of high performance, low cost biosensors capable of operating in a real-world environment.We present simulations suggesting that it is possible to minimize the systematic errors of differential absorption lidar (DIAL) measurements caused by the Rayleigh-Doppler effect by selecting an online frequency close to one of the inflection points on either side of the absorption line. PIK-90 purchase Thus, it seems advantageous to select an absorption line of suitable cross section at these points on the line slopes rather than at the peak. First, we extend the classical simulation study of Ansmann (1985) for another water vapor absorption line but again with the online frequency at the line peak. As expected, we also found large systematic errors of more than 40% at the edges of aerosol layers and clouds. Second, we simulate the systematic errors for other online frequencies away from the peak for the same input profile. The results demonstrate that the errors vanish close to the inflection points. Since both the shape of the absorption lines and the width of the broadened backscatter signal depend on the atmospheric conditions, these optimum frequencies vary slightly with height and climatology. Third, we calculate the errors for a typical aerosol profile of the planetary boundary layer obtained from lidar measurements. With this case, we discuss how to select practically the online frequency so that the errors are minimized for all heights of interest. We found that the error reduces from 20 to less then 1% at the top of the planetary boundary layer while, at the same time, the error reduces from 6 to 2% in 5 km.The design of complex freeform imaging systems with advanced system specification is often a tedious task that requires extensive human effort. In addition, the lack of design experience or expertise that result from the complex and uncertain nature of freeform optics, in addition to the limited history of usage, also contributes to the design difficulty. In this paper, we propose a design framework of freeform imaging systems using reinforcement learning. A trial-and-error method employing different design routes that use a successive optimization process is applied in different episodes under an ε-greedy policy. An "exploitation-exploration, evaluation and back-up" approach is used to interact with the environment and discover optimal policies. Des