Semantic transform: Weakly supervised semantic inference for relating visual attributes

Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets. © 2013 IEEE.

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.