We demonstrate optical manipulation of structures and defects in liquid crystals (LCs). The effective refractive index depends on the LC molecular orientations and the laser beam's polarization. We use the orientation-mediated refractive index contrast for the laser trapping in LCs with a homogeneous composition, but with spatially-varying patterns of molecular orientations. Tightly-focused polarized beams allow for optical trapping of disclinations and their clusters, dislocations and oily streaks, cholesteric fingers and focal conic domains, etc. We calculate the optical gradient forces for typical structures and explain the trapping properties at low laser powers. We also show that when a high-power beam causes local molecular realignment, the laser trapping properties change for two reasons: (1) the refractive index pattern and optical gradient forces are modified; (2) additional elastic structural forces arise to minimize the elastic free energy.

A novel technique for the calibration of laser trapping systems that utilizes two-photon-excited fluorescence of commercial dye-stained microspheres has been demonstrated. The trapping forces as well as the trapping efficiency have been measured for various liquid environments and trapping depths. The trapping efficiency in water was found to decrease with an increase of trapping depths because of the enlargement of the trapping beam waist caused by aberrations of the optical system.

We describe laser-induced two-dimensional periodic photonic structures formed by localized particle-like excitations in an untwisted confined cholesteric liquid crystal. The individual particle-like excitations (dubbed "Torons") contain three-dimensional twist of the liquid crystal director matched to the uniform background director field by topological point defects. Using both single-beam-steering and holographic pattern generation approaches, the periodic crystal lattices are tailored by tuning their periodicity, reorienting their crystallographic axes, and introducing defects. Moreover, these lattices can be dynamically reconfigurable: generated, modified, erased and then recreated, depending on the needs of a particular photonic application. This robust control is performed by tightly focused laser beams of power 10-100 mW and by low-frequency electric fields at voltages ~10 V applied to the transparent electrodes.

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In remote sensing, the conversion of at-sensor radiance to surface reflectance for each pixel in a scene is an essential component of many analysis tasks. The empirical line method (ELM) is the most used technique among remote sensing practitioners due to its reliability and production of accurate reflectance measurements. However, the at-altitude radiance ratio (AARR), a more recently proposed methodology, is attractive as it allows reflectance conversion to be carried out in real time throughout data collection, does not require calibrated samples of pre-measured reflectance to be placed in scene, and can account for changes in illumination conditions. The benefits of AARR can substantially reduce the level of effort required for collection setup and subsequent data analysis, and provide a means for large-scale automation of remote sensing data collection, even in atypical flight conditions. In this study, an onboard, downwelling irradiance spectrometer integrated onto a small unmanned aircraft system (sUAS) is utilized to characterize the performance of AARR-generated reflectance from hyperspectral radiance data under a variety of challenging illumination conditions. The observed error introduced by AARR is often on par with ELM and acceptable depending on the application requirements and natural variation in the reflectance of the targets of interest. Additionally, a number of radiometric and atmospheric corrections are proposed that could increase the accuracy of the method in future trials, warranting further research.

A multiple-trap single-beam scanning laser tweezer system was developed and characterized. Different stationary and mobile multiple-trap modes were generated for polystyrene beads in a water environment. Trapping efficiency and stability were investigated for several dynamic parameters such as transition time between the sites, waiting time on a single site, number of trapping sites, and IR laser power. Optimal parameters for efficient generation of complex arrays and matrices were determined. We demonstrate an example of a single laser beam multiple-trap application by measuring the trap's stiffness in water for our laser tweezer setup.

One common computer vision task is to track an object as it moves from frame to frame within a video sequence. There are a myriad of applications for such capability and the underlying technologies to achieve this tracking are very well understood. More recently, deep convolutional neural networks have been employed to not only track, but also to classify objects as they are tracked from frame to frame. These models can be used in a tracking paradigm known as tracking by detection and can achieve very high tracking accuracy. The major drawback to these deep neural networks is the large amount of mathematical operations that must be performed for each inference which negatively impacts the number of tracked frames per second. For edge applications residing on size, weight, and power limited platforms, such as unmanned aerial vehicles, high frame rate and low latency real time tracking can be an elusive target. To overcome the limited power and computational resources of an edge compute device, various optimizations have been performed to trade off tracking speed, accuracy, power, and latency. Previous works on motion based interpolation with neural networks either do not take into account the latency accrued from camera image capture to tracking result or they compensate for this latency but are bottlenecked by the motion interpolation operation instead. The algorithm presented in this work gains the performance speedup used in previous motion based neural network inference papers and also performs a novel look back operation that is less cumbersome than other competing motion interpolation methods.INDEX TERMS CNN, classifier, detector, neural network, low latency, tracker, UAV, YOLO, look back, drone, image processing.