Tuneable planar integrated optical systems

Planar integrated free-space optical systems are well suited for a variety of applications, such as optical interconnects and security devices. Here, we demonstrate dynamic functionality of such microoptical systems by the integration of adaptive liquid-crystal-devices. © 2007 Optical Society of America.

Auto ID based control systems - An overview

This paper will provide a rationale for developing control systems based on the availability of automated identification (Auto ID) information provision. Much of the Auto-ID research has to date focussed on developing the essential infrastructure for dynamically extracting, networking and storing product data. These developments will help to revolutionise the accuracy, quality and timeliness of data acquired by Business Information Systems and should lead to major cost savings and performance improvements as a result. This paper introduces an additional phase of Auto ID research and development in which the nature of control system decisions is reconsidered in the light of the availability of ubiquitous, unique, item-level information. The paper will: (i) Indicate why the availability of ubiquitous, unique, item-level data can enable enhanced and fundamentally different control approaches and highlight potential benefits from control systems incorporating this Auto ID data (ii) Demonstrate what is required to develop control systems based around the availability of Auto ID data. (iii) Outline the research challenges in determining how such systems will be developed.

Sparsity-Inducing Optimization Algorithm for the Extraction of Planar Structures in Noisy Point-Cloud Data

Most of the manual labor needed to create the geometric building information model (BIM) of an existing facility is spent converting raw point cloud data (PCD) to a BIM description. Automating this process would drastically reduce the modeling cost. Surface extraction from PCD is a fundamental step in this process. Compact modeling of redundant points in PCD as a set of planes leads to smaller file size and fast interactive visualization on cheap hardware. Traditional approaches for smooth surface reconstruction do not explicitly model the sparse scene structure or significantly exploit the redundancy. This paper proposes a method based on sparsity-inducing optimization to address the planar surface extraction problem. Through sparse optimization, points in PCD are segmented according to their embedded linear subspaces. Within each segmented part, plane models can be estimated. Experimental results on a typical noisy PCD demonstrate the effectiveness of the algorithm.

Surface tension-induced global instability of planar jets and wakes

The effect of surface tension on global stability of co-flow jets and wakes at a moderate Reynolds number is studied. The linear temporal two-dimensional global modes are computed without approximations. All but one of the flow cases under study are globally stable without surface tension. It is found that surface tension can cause the flow to be globally unstable if the inlet shear (or equivalently, the inlet velocity ratio) is strong enough. For even stronger surface tension, the flow is re-stabilized. As long as there is no change of the most unstable mode, increasing surface tension decreases the oscillation frequency. Short waves appear in the high-shear region close to the nozzle, and their wavelength increases with increasing surface tension. The critical shear (the weakest inlet shear at which a global instability is found) gives rise to antisymmetric disturbances for the wakes and symmetric disturbances for the jets. However, at stronger shear, the opposite symmetry can be the most unstable one, in particular for wakes at high surface tension. The results show strong effects of surface tension that should be possible to reproduce experimentally as well as numerically.

Calibration of the spring constant of cantilevers of arbitrary shape using the phase signal in an atomic force microscope.

The measurement of cantilever parameters is an essential part of performing a calibrated measurement with an atomic force microscope (AFM). The thermal motion method is a widely used technique for calibrating the spring constant of an AFM cantilever, which can be applied to non-rectangular cantilevers. Given the trend towards high frequency scanning, calibration of non-rectangular cantilevers is of increasing importance. This paper presents two results relevant to cantilever calibration via the thermal motion method. We demonstrate the possibility of using the AFM's phase signal to acquire the thermal motion. This avoids the challenges associated with connecting the raw photodiode signal to a separate spectrum analyser. We also describe how numerical calculations may be used to calculate the parameters needed in a thermal motion calibration of a non-rectangular cantilever. Only accurate knowledge of the relative size of the in-plane dimensions of the cantilever is needed in this computation. We use this pair of results in the calibration of a variety of rectangular and non-rectangular cantilevers. We observe an average difference between the Sader and thermal motion values of cantilever stiffness of 10%.

Er3+ excitation and up-conversion processes in y 2-xErxSi2O7 films for planar optical amplifiers

Structural and optical properties of Y2-xErxSi 2O7 thin films have been studied. For higher Er content mechanisms related to Er-Er interactions increase optical efficiency. Moreover the influence of up-conversion has been estimated. ©2009 IEEE.

Model reductions for inference: generality of pairwise, binary, and planar factor graphs.

We offer a solution to the problem of efficiently translating algorithms between different types of discrete statistical model. We investigate the expressive power of three classes of model-those with binary variables, with pairwise factors, and with planar topology-as well as their four intersections. We formalize a notion of "simple reduction" for the problem of inferring marginal probabilities and consider whether it is possible to "simply reduce" marginal inference from general discrete factor graphs to factor graphs in each of these seven subclasses. We characterize the reducibility of each class, showing in particular that the class of binary pairwise factor graphs is able to simply reduce only positive models. We also exhibit a continuous "spectral reduction" based on polynomial interpolation, which overcomes this limitation. Experiments assess the performance of standard approximate inference algorithms on the outputs of our reductions.