Investigations on growth, structure, optical properties and laser damage threshold of organic nonlinear optical crystals of Guanidinium L-Ascorbate

Single crystals of Guanidinium L-Ascorbate (GuLA) were grown and crystal structure was determined by direct methods. GuLA crystallizes in orthorhombic, non-centrosymmetric space group P2(1)2(1)2(1). The UV-cutoff was determined as 325 nm. The morphology was generated and the interplanar angles estimated and compared with experimental values. Second harmonic generation conversion efficiency was measured and compared with other salts of L-Ascorbic acid. Surface laser damage threshold was calculated as 11.3GW/cm(2) for a single shot of laser of 1064 nm wavelength.

Study on effect of optical wavelength on photo induced strain sensitivity in carbon nanotubes using fiber Bragg grating

In this work, the role of optical wavelength on the photo induced strain in carbon nanotubes (CNT) is probed using a Fiber Bragg Grating (FBG), upon exposure to infrared (IR) (21 mu epsilon mW(-1)) and visible (9 mu epsilon mW(-1)) radiations. The strain sensitivity in CNT is monitored over a smaller range (10(-3) to 10(-9) epsilon) by exposing to a low optical power varying in the range 10(-3) to 10(-6) W. In addition, the wavelength dependent response and recovery periods of CNT under IR (tau(rise) = 150 ms, tau(fall) = 280 ms) and visible (tau(rise) = 1.07 s, tau(fall) = 1.18 s) radiations are evaluated in detail. This study can be further extended to measure the sensitivity of nano-scale photo induced strains in nano materials and opens avenues to control mechanical actuation using various optical wavelengths.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.