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Presented for the first time is a machine learning enhanced optical distance sensor. The distance sensor is based on our previously demonstrated distance measurement technique that uses an Electronically Controlled Variable Focus Lens (ECVFL) with a laser source to illuminate a target plane with a controlled optical beam spot. This spot with varying spot sizes is viewed by an off-axis camera and the spot size data is processed to compute the distance. In particular, proposed and demonstrated in this paper is the use of a regularized polynomial regression based supervised machine learning algorithm to enhance the accuracy of the operational sensor. The algorithm uses the acquired features and corresponding labels that are the actual target distance values to train a machine learning model. The optimized training model is trained over a 1000 mm (or 1 m) experimental target distance range. Using the machine learning algorithm produces a training set and testing set distance measurement errors of

Presented is an active no moving parts Depth From Defocus (DFD) system for autofocus and three-dimensional (3-D) imaging applications. The proposed system uses highly coherent laser light and its beam conditioning optics to realize an active illumination system suited for long range and large field of view imaging, overcoming the limited field of view in previous active DFD systems. This is the first time an active DFD system is implemented and experimentally demonstrated using an Electronically Controlled Variable Focus Lens (ECVFL) as an imaging lens that electrically varies the focal length to acquire images with different degrees of defocus, necessary for DFD operations. An experimental demonstration of the proposed system acting as a distance sensor is compared to an incoherent illumination projector DFD system tested over a 1 m range. Apart from showing the effectiveness of the ECVFL as an imaging optic for cameras implementing DFD based autofocus algorithms, presented experimental results for the novel system indicate a depth measurement percentage error of