Aberration correction for free space optical communications using rectangular zernike modal wavefront sensing

A time multiplexed rectangular Zernike modal wavefront sensor based on a nematic phase-only liquid crystal spatial light modulator and specially designed for a high power two-electrode tapered laser diode which is a compact and novel free space optical communication source is used in an adaptive beam steering free space optical communication system, enabling the system to have 1.25 GHz modulation bandwidth, 4.6° angular coverage and the capability of sensing aberrations within the system and caused by atmosphere turbulence up to absolute value of 0.15 waves amplitude and correcting them in one correction cycle. Closed-loop aberration correction algorithm can be implemented to provide convergence for larger and time varying aberrations. Improvement of the system signal-to-noise-ratio performance is achieved by aberration correction. To our knowledge, it is first time to use rectangular orthonormal Zernike polynomials to represent balanced aberrations for high power rectangular laser beam in practice. © 2014 IEEE.

Radio over free space optical link using a directly modulated two-electrode high power tapered laser

The analog modulation performance of a high-power two-electrode tapered laser is investigated. A 25dB dynamic range for 2.4GHz 802.11g signals is achieved with a 26dB loss budget, showing a >1km free space range is possible. © 2010 Optical Society of America.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.

Holographic wavefront sensing and correction for free space optical communications

Using holographic techniques and a tapered laser, an optical wireless communication system with 4.6 degree angular coverage, 1.25GHz modulation bandwidth and the capability of sensing and correcting aberrations within system and from atmosphere is reported. © OSA 2013.

Free space adaptive optical interconnect, using a ferroelectric liquid crystal SLM for beam steering

A free-space, board-to-board, adaptive optical interconnect demonstrator has been developed. Binary phase gratings displayed on a Ferroelectric Liquid Crystal Spatial Light Modulator are used to maintain data transfer at 1.25Gbps, given varying optical misalignment.© 2005 Optical Society of America.

Free-space locomotion with thread formation

The paper presents a new concept of locomotion for wheeled or legged robots through an object-free space. The concept is inspired by the behaviour of spiders forming silk threads to move in 3D space. The approach provides the possibility of variation in thread diameter by deforming source material, therefore it is useful for a wider coverage of payload by mobile robots. As a case study, we propose a technology for descending locomotion through a free space with inverted formation of threads in variable diameters. Inverted thread formation is enabled with source material thermoplastic adhesive (TPA) through thermally-induced phase transition. To demonstrate the feasibility of the technology, we have designed and prototyped a 300-gram wheeled robot that can supply and deform TPA into a thread and descend with the thread from an existing hanging structure. Experiment results suggest repeatable inverted thread formation with a diameter range of 1.1-4.5 mm, and a locomotion speed of 0.73 cm per minute with a power consumption of 2.5 W. © 2013 IEEE.

Linear dimensionality reduction: Survey, insights, and generalizations

© 2015 John P. Cunningham and Zoubin Ghahramani. Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data features of interest, such as covariance, dynamical structure, correlation between data sets, input-output relationships, and margin between data classes. Methods have been developed with a variety of names and motivations in many fields, and perhaps as a result the connections between all these methods have not been highlighted. Here we survey methods from this disparate literature as optimization programs over matrix manifolds. We discuss principal component analysis, factor analysis, linear multidimensional scaling, Fisher's linear discriminant analysis, canonical correlations analysis, maximum autocorrelation factors, slow feature analysis, sufficient dimensionality reduction, undercomplete independent component analysis, linear regression, distance metric learning, and more. This optimization framework gives insight to some rarely discussed shortcomings of well-known methods, such as the suboptimality of certain eigenvector solutions. Modern techniques for optimization over matrix manifolds enable a generic linear dimensionality reduction solver, which accepts as input data and an objective to be optimized, and returns, as output, an optimal low-dimensional projection of the data. This simple optimization framework further allows straightforward generalizations and novel variants of classical methods, which we demonstrate here by creating an orthogonal-projection canonical correlations analysis. More broadly, this survey and generic solver suggest that linear dimensionality reduction can move toward becoming a blackbox, objective-agnostic numerical technology.

Manopt, a matlab toolbox for optimization on manifolds

Optimization on manifolds is a rapidly developing branch of nonlinear optimization. Its focus is on problems where the smooth geometry of the search space can be leveraged to design effcient numerical algorithms. In particular, optimization on manifolds is well-suited to deal with rank and orthogonality constraints. Such structured constraints appear pervasively in machine learning applications, including low-rank matrix completion, sensor network localization, camera network registration, independent component analysis, metric learning, dimensionality reduction and so on. The Manopt toolbox, available at www.manopt.org, is a user-friendly, documented piece of software dedicated to simplify experimenting with state of the art Riemannian optimization algorithms. By dealing internally with most of the differential geometry, the package aims particularly at lowering the entrance barrier. © 2014 Nicolas Boumal.