Ultrafast switching liquid crystals for electro-optic transmissive and reflective displays and microscopic lasers

We have used novel liquid crystals with extremely large flexoelectric coefficients in a range of ultra-fast photonic/display modes, namely 1) the uniform lying helix, that leads to in-plain switching, birefringence based displays with 100 μs switching times at low fields, i.e.2-5 V/μm, wide viewing angle and analogue or grey scale capability, 2) the uniform standing helix, using planar surface alignment and in-plane fields, with sub ms response times and optical contrasts in excess of 5000:1 with a perfect black "off state", 3) the wide temperature range blue phase that leads to field controlled reflective color and 4) high slope efficiency, wide wavelength range tunable narrow linewidth microscopic liquid crystal lasers.

Ultrafast switching liquid crystals for electro-optic transmissive and reflective displays and microscopic lasers

We report on novel liquid crystals with extremely large flexoelectric coefficients in a range of ultra-fast photonic modes, namely 1) the uniform lying helix, that leads to in-plain switching, birefringence phase devices with 100 μs switching times at low fields, i.e.2-5 V/μm, and analogue or grey scale capability, 2) the uniform standing helix, using planar surface alignment and in-plane fields, with sub ms response times and optical contrasts in excess of 5000:1 with a perfect optically isotropic or black "off state", 3) the wide temperature range blue phase that leads to field controlled reflective color, 4) chiral nematic optical reflectors electric field tunable over a wide wavelength range and 5) high slope efficiency, wide wavelength range tunable narrow linewidth microscopic liquid crystal lasers. © 2011 Materials Research Society.

A Bayesian wavelet-based multidimensional deconvolution with sub-band emphasis

This paper proposes a new algorithm for waveletbased multidimensional image deconvolution which employs subband-dependent minimization and the dual-tree complex wavelet transform in an iterative Bayesian framework. In addition, this algorithm employs a new prior instead of the popular ℓ1 norm, and is thus able to embed a learning scheme during the iteration which helps it to achieve better deconvolution results and faster convergence. © 2008 IEEE.