Recurrent input transformations for Hidden Markov models

This paper presents a new architecture which integrates recurrent input transformations (RIT) and continuous density HMMs. The basic HMM structure is extended to accommodate recurrent neural networks which transform the input observations before they enter the Gaussian output distributions associated with the states of the HMM. During training the parameters of both HMM and RIT are simultaneously optimized according to the Maximum Mutual Information (MMI) criterion. Results are presented for the E-set recognition task which demonstrate the ability of recurrent input transformations to exploit longer term correlations in the speech signal and to give improved discrimination.

Revealing lithium-silicide phase transformations in nano-structured silicon-based lithium ion batteries via in situ NMR spectroscopy.

Nano-structured silicon anodes are attractive alternatives to graphitic carbons in rechargeable Li-ion batteries, owing to their extremely high capacities. Despite their advantages, numerous issues remain to be addressed, the most basic being to understand the complex kinetics and thermodynamics that control the reactions and structural rearrangements. Elucidating this necessitates real-time in situ metrologies, which are highly challenging, if the whole electrode structure is studied at an atomistic level for multiple cycles under realistic cycling conditions. Here we report that Si nanowires grown on a conducting carbon-fibre support provide a robust model battery system that can be studied by (7)Li in situ NMR spectroscopy. The method allows the (de)alloying reactions of the amorphous silicides to be followed in the 2nd cycle and beyond. In combination with density-functional theory calculations, the results provide insight into the amorphous and amorphous-to-crystalline lithium-silicide transformations, particularly those at low voltages, which are highly relevant to practical cycling strategies.

Image-based localization and pose recovery using scale invariant features

In this paper, we propose a vision based mobile robot localization strategy. Local scale-invariant features are used as natural landmarks in unstructured and unmodified environment. The local characteristics of the features we use prove to be robust to occlusion and outliers. In addition, the invariance of the features to viewpoint change makes them suitable landmarks for mobile robot localization. Scale-invariant features detected in the first exploration are indexed into a location database. Indexing and voting allow efficient recognition of global localization. The localization result is verified by epipolar geometry between the representative view in database and the view to be localized, thus the probability of false localization will be decreased. The localization system can recover the pose of the camera mounted on the robot by essential matrix decomposition. Then the position of the robot can be computed easily. Both calibrated and un-calibrated cases are discussed and relative position estimation based on calibrated camera turns out to be the better choice. Experimental results show that our approach is effective and reliable in the case of illumination changes, similarity transformations and extraneous features. © 2004 IEEE.

Representations of uncertainty in sensorimotor control.

Uncertainty is ubiquitous in our sensorimotor interactions, arising from factors such as sensory and motor noise and ambiguity about the environment. Setting it apart from previous theories, a quintessential property of the Bayesian framework for making inference about the state of world so as to select actions, is the requirement to represent the uncertainty associated with inferences in the form of probability distributions. In the context of sensorimotor control and learning, the Bayesian framework suggests that to respond optimally to environmental stimuli the central nervous system needs to construct estimates of the sensorimotor transformations, in the form of internal models, as well as represent the structure of the uncertainty in the inputs, outputs and in the transformations themselves. Here we review Bayesian inference and learning models that have been successful in demonstrating the sensitivity of the sensorimotor system to different forms of uncertainty as well as recent studies aimed at characterizing the representation of the uncertainty at different computational levels.

Inferring visuomotor priors for sensorimotor learning.

Sensorimotor learning has been shown to depend on both prior expectations and sensory evidence in a way that is consistent with Bayesian integration. Thus, prior beliefs play a key role during the learning process, especially when only ambiguous sensory information is available. Here we develop a novel technique to estimate the covariance structure of the prior over visuomotor transformations--the mapping between actual and visual location of the hand--during a learning task. Subjects performed reaching movements under multiple visuomotor transformations in which they received visual feedback of their hand position only at the end of the movement. After experiencing a particular transformation for one reach, subjects have insufficient information to determine the exact transformation, and so their second reach reflects a combination of their prior over visuomotor transformations and the sensory evidence from the first reach. We developed a Bayesian observer model in order to infer the covariance structure of the subjects' prior, which was found to give high probability to parameter settings consistent with visuomotor rotations. Therefore, although the set of visuomotor transformations experienced had little structure, the subjects had a strong tendency to interpret ambiguous sensory evidence as arising from rotation-like transformations. We then exposed the same subjects to a highly-structured set of visuomotor transformations, designed to be very different from the set of visuomotor rotations. During this exposure the prior was found to have changed significantly to have a covariance structure that no longer favored rotation-like transformations. In summary, we have developed a technique which can estimate the full covariance structure of a prior in a sensorimotor task and have shown that the prior over visuomotor transformations favor a rotation-like structure. Moreover, through experience of a novel task structure, participants can appropriately alter the covariance structure of their prior.

Derivation of a vector model for a Brushless Doubly-Fed Machine with multiple loops per nest

The paper presents a vector model for a Brushless Doubly-Fed Machine (BDFM). The BDFM has 4 and 8 pole stator windings and a nested-loop rotor cage. The rotor cage has six nests equally spaced around the circumference and each nest comprises three loops. All the rotor loops are short circuited via a common end-ring at one end. The vector model is derived based on the electrical equations of the machine and appropriate vector transformations. In contrast to the stator, there is no three phase circuit in the rotor. Therefore, the vector transformations suitable for three phase circuits can not be utilised for the rotor circuit. A new vector transformation is employed for the rotor circuit quantities. The approach presented in this paper can be extended for a BDFM with any stator poles combination and any number of loops per nest. Simulation results from the model implemented in Simulink are presented. © 2008 IEEE.