Optical tweezing beam control using liquid crystal adaptive optical elements

Liquid crystal (LC) adaptive optical elements are described, which provide an alternative to existing micropositioning technologies in optical tweezing. A full description of this work is given in [1]. An adaptive LC prism supplies tip/tilt to the phase profile of the trapping beam, giving rise to an available steering radius within the x-y plane of 10 μm. Additionally, a modally addressed adaptive LC lens provides defocus, offering a z-focal range for the trapping site of 100 μm. The result is full three-dimensional positional control of trapped particle(s) using a simple and wholly electronic control system. Compared to competing technologies, these devices provide a lower degree of controllability, but have the advantage of simplicity, cost and light efficiency. Furthermore, due to their birefringence, LC elements offer the opportunity of the creation of dual optical traps with controllable depth and separation.

High speed liquid crystal over silicon display based on the flexoelectro-optic effect.

One of the key technologies to evolve in the displays market in recent years is liquid crystal over silicon (LCOS) microdisplays. Traditional LCOS devices and applications such as rear projection televisions, have been based on intensity modulation electro-optical effects, however, recent developments have shown that multi-level phase modulation from these devices is extremely sought after for applications such as holographic projectors, optical correlators and adaptive optics. Here, we propose alternative device geometry based on the flexoelectric-optic effect in a chiral nematic liquid crystal. This device is capable of delivering a multilevel phase shift at response times less than 100 microsec which has been verified by phase shift interferometry using an LCOS test device. The flexoelectric on silicon device, due to its remarkable characteristics, enables the next generation of holographic devices to be realized.

Simulating dynamic covariance structures for testing the adaptive behavior of variable selection algorithms (Invited Paper)

Variable selection for regression is a classical statistical problem, motivated by concerns that too large a number of covariates may bring about overfitting and unnecessarily high measurement costs. Novel difficulties arise in streaming contexts, where the correlation structure of the process may be drifting, in which case it must be constantly tracked so that selections may be revised accordingly. A particularly interesting phenomenon is that non-selected covariates become missing variables, inducing bias on subsequent decisions. This raises an intricate exploration-exploitation tradeoff, whose dependence on the covariance tracking algorithm and the choice of variable selection scheme is too complex to be dealt with analytically. We hence capitalise on the strength of simulations to explore this problem, taking the opportunity to tackle the difficult task of simulating dynamic correlation structures. © 2008 IEEE.

Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme's effectiveness in both real and simulated streaming environments. © Springer-Verlag 2009.

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Reconfigurable free-space optical switching technologies for storage area networks

Reconfigurable shutter-based free-space optical switching technologies using fiber ribbon and multiple wavelengths per fiber for Storage Area Networks (SANs) application are presented and demonstrated. ©2009 SPIE-OSA-IEEE.

Reconfigurable free-space optical switching technologies for storage area networks

Reconfigurable shutter-based free-space optical switching technologies using fiber ribbon and multiple wavelengths per fiber for Storage Area Networks (SANs) application are presented and demonstrated. ©2009 Optical Society of America.