Physiological modelling for improved reliability in silhouette-driven gradient-based hand tracking

We present a gradient-based motion capture system that robustly tracks a human hand, based on abstracted visual information - silhouettes. Despite the ambiguity in the visual data and despite the vulnerability of gradient-based methods in the face of such ambiguity, we minimise problems related to misfit by using a model of the hand's physiology, which is entirely non-visual, subject-invariant, and assumed to be known a priori. By modelling seven distinct aspects of the hand's physiology we derive prior densities which are incorporated into the tracking system within a Bayesian framework. We demonstrate how the posterior is formed, and how our formulation leads to the extraction of the maximum a posteriori estimate using a gradient-based search. Our results demonstrate an enormous improvement in tracking precision and reliability, while also achieving near real-time performance. © 2009 IEEE.

Comparison of Image-Based and Time-of-Flight-Based Technologies for 3D Reconstruction of Infrastructure

Image-based (i.e., photo/videogrammetry) and time-of-flight-based (i.e., laser scanning) technologies are typically used to collect spatial data of infrastructure. In order to help architecture, engineering, and construction (AEC) industries make cost-effective decisions in selecting between these two technologies with respect to their settings, this paper makes an attempt to measure the accuracy, quality, time efficiency, and cost of applying image-based and time-of-flight-based technologies to conduct as-built 3D reconstruction of infrastructure. In this paper, a novel comparison method is proposed, and preliminary experiments are conducted. The results reveal that if the accuracy and quality level desired for a particular application is not high (i.e., error < 10 cm, and completeness rate > 80%), image-based technologies constitute a good alternative for time-of-flight-based technologies and significantly reduce the time and cost needed for collecting the data on site.

Comparison of Image-Based and Time-of-Flight-Based Technologies for Three-Dimensional Reconstruction of Infrastructure

Image-based (i.e., photo/videogrammetry) and time-of-flight-based (i.e., laser scanning) technologies are typically used to collect spatial data of infrastructure. In order to help architecture, engineering, and construction (AEC) industries make cost-effective decisions in selecting between these two technologies with respect to their settings, this paper makes an attempt to measure the accuracy, quality, time efficiency, and cost of applying image-based and time-of-flight-based technologies to conduct as-built 3D reconstruction of infrastructure. In this paper, a novel comparison method is proposed, and preliminary experiments are conducted. The results reveal that if the accuracy and quality level desired for a particular application is not high (i.e., error < 10 cm, and completeness rate > 80%), image-based technologies constitute a good alternative for time-of-flight-based technologies and significantly reduce the time and cost needed for collecting the data on site.

A printable optical time-temperature integrator based on shape memory in a chiral nematic polymer network

An optical and irreversible temperature sensor (e.g., a time-temperature integrator) is reported based on a mechanically embossed chiral-nematic polymer network. The polymer consists of a chemical and a physical (hydrogen-bonded) network and has a reflection band in the visible wavelength range. The sensors are produced by mechanical embossing at elevated temperatures. A relative large compressive deformation (up to 10%) is obtained inducing a shift to shorter wavelength of the reflection band (>30 nm). After embossing, a temperature sensor is obtained that exhibits an irreversible optical response. A permanent color shift to longer wavelengths (red) is observed upon heating of the polymer material to temperatures above the glass transition temperature. It is illustrated that the observed permanent color shift is related to shape memory in the polymer material. The films can be printed on a foil, thus showing that these sensors are potentially interesting as time-temperature integrators for applications in food and pharmaceutical products. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Bayesian model selection for time series using Markov chain Monte Carlo

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms.

Reflex contributions to the directional tuning of arm stiffness

It has been shown that during arm movement, humans selectively change the endpoint stiffness of their arm to compensate for the instability in an unstable environment. When the direction of the instability is rotated with respect to the direction of movement, it was found that humans modify the antisymmetric component of their endpoint stiffness. The antisymmetric component of stiffness arises due to reflex responses suggesting that the subjects may have tuned their reflex responses as part of the feedforward adaptive control. The goal of this study was to examine whether the CNS modulates the gain of the reflex response for selective tuning of endpoint impedance. Subjects performed reaching movements in three unstable force fields produced by a robotic manipulandum, each field differing only in the rotational component. After subjects had learned to compensate for the field, allowing them to make unperturbed movements to the target, the endpoint stiffness of the arm was estimated in the middle of the movements. At the same time electromyographic activity (EMG) of six arm muscles was recorded. Analysis of the EMG revealed differences across force fields in the reflex gain of these muscles consistent with stiffness changes. This study suggests that the CNS modulates the reflex gain as part of the adaptive feedforward command in which the endpoint impedance is selectively tuned to overcome environmental instability. © 2008 Springer-Verlag Berlin Heidelberg.

Gaussian process volatility model

The prediction of time-changing variances is an important task in the modeling of financial data. Standard econometric models are often limited as they assume rigid functional relationships for the evolution of the variance. Moreover, functional parameters are usually learned by maximum likelihood, which can lead to over-fitting. To address these problems we introduce GP-Vol, a novel non-parametric model for time-changing variances based on Gaussian Processes. This new model can capture highly flexible functional relationships for the variances. Furthermore, we introduce a new online algorithm for fast inference in GP-Vol. This method is much faster than current offline inference procedures and it avoids overfitting problems by following a fully Bayesian approach. Experiments with financial data show that GP-Vol performs significantly better than current standard alternatives.

An architecture design with data model for product recovery management systems

Due to concerns about environmental protection and resource utilization, product lifecycle management for end-of-life (EOL) has received increasing attention in many industrial sectors including manufacturing, maintenance/repair, and recycling/refurbishing of the product. To support these functions, crucial issues are studied to realize a product recovery management system (PRMS), including: (1) an architecture design for EOL services, such as remanufacturing and recycling; (2) a product data model required for EOL activity based on international standards; and (3) an infrastructure for information acquisition and mapping to product lifecycle information. The presented works are illustrated via a realistic scenario. © 2008 Elsevier B.V. All rights reserved.