We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ‘learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.

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Abstract. We present a method for recovering the structure of a plant directly from a small set of widely-spaced images. Structure recovery is more complex than shape estimation, but the resulting structure estimate is more closely related to phenotype than is a 3D geometric model. The method we propose is applicable to a wide variety of plants, but is demonstrated on wheat. Wheat is made up of thin elements with few identifiable features, making it difficult to analyse using standard feature matching techniques. Our method instead analyses the structure of plants using only their silhouettes. We employ a generate-and-test method, using a database of manually modelled leaves and a model for their composition to synthesise plausible plant structures which are evaluated against the images. The method is capable of efficiently recovering accurate estimates of plant structure in a wide variety of imaging scenarios, with no manual intervention.

Optical lenses are only able to focus a single scene plane onto the sensor, leaving the remainder of the scene subject to varying levels of defocus. The apparent depth of field can be extended by capturing a sequence with varying focal planes that is merged by selecting, for each pixel in the target image, the most focused corresponding pixel from the stack. This process is heavily dependent on capturing a stabilised sequence-a requirement that is impractical for hand-held cameras. Here we have developed a novel method that can merge a focus stack captured by a hand-held camera despite changes in shooting position and focus. Our approach is able to register the sequence using affine transformation before fusing the focus stack. We have developed a merging process that is able to identify the focused pixels for each pixel in the stack and therefore select the most appropriate pixels for the synthetically focused image. We have proposed a novel approach for capturing qualified focus stack on mobile phone cameras. Furthermore, we test our approach on a mobile phone platform that can automatically capture a focus stack as easily as a photographer capturing a conventional image.