Sensorless commutation of a two-phase linear machine for marine renewable power generation

A sensorless scheme is presented for a two-phase permanent-magnet linear machine targeted for use in marine wave-power generation. This is a field where system reliability is a key concern. The scheme is able to extract the effective inductance and back-emf of the machine's phases simultaneously from measurements of the current ripple present on the power electronic converter. These measurements can then be used to estimate position. An enhancement to the scheme in the presence of spatially-varying mutual inductance between phases allows more accurate and reliable tracking from indutance-based measurements than would otherwise be expected. This scheme is able to operate at any speed including, critically, when stationary. Experimental results show promise for the scheme, although some work to reduce the level of noise would be desirable. © 2013 IEEE.

Making the deployment of pico-PV more sustainable along the value chain

Pico-PV is an excellent technology for bringing electric light to rural areas in the developing world and replacing kerosene lanterns and candles. However, as pico-PV is a comparatively new technology, relatively little is known about appropriate methods for sustainable product development and deployment. For this reason current dissemination methods are often ineffective and unsustainable. This research aims to help project developers deploy pico-PV technologies successfully and in a sustainable manner. To achieve this, a conceptual framework of key sustainability criteria along the value chain was developed and tested. The analysis revealed that the most important criteria for the sustainable deployment of pico-PV systems are: (a) easy and safe operation of the product; (b) that a system for product return is established; (c) the retailer understands the target market and (d) the end-user is aware of the product's existence and its benefits. This research reveals that criteria (b) and (c) are of greatest concern. In light of these findings, the authors propose to focus on the following five factors; namely: (a) raising awareness for certification and creating market reassurance; (b) introducing support mechanisms to facilitate local repair; (c) using existing supply channels and establishing in-country (dis)assembly; (d) introducing financial support mechanisms at product supply stages and; (e) undertaking marketing campaigns. © 2013 Elsevier Ltd. All rights reserved.

Calibrating the Gaussian multi-target tracking model

We present novel batch and online (sequential) versions of the expectation-maximisation (EM) algorithm for inferring the static parameters of a multiple target tracking (MTT) model. Online EM is of particular interest as it is a more practical method for long data sets since in batch EM, or a full Bayesian approach, a complete browse of the data is required between successive parameter updates. Online EM is also suited to MTT applications that demand real-time processing of the data. Performance is assessed in numerical examples using simulated data for various scenarios. For batch estimation our method significantly outperforms an existing gradient based maximum likelihood technique, which we show to be significantly biased. © 2014 Springer Science+Business Media New York.

Correlations strike back (again): The case of associative memory retrieval

It has long been recognised that statistical dependencies in neuronal activity need to be taken into account when decoding stimuli encoded in a neural population. Less studied, though equally pernicious, is the need to take account of dependencies between synaptic weights when decoding patterns previously encoded in an auto-associative memory. We show that activity-dependent learning generically produces such correlations, and failing to take them into account in the dynamics of memory retrieval leads to catastrophically poor recall. We derive optimal network dynamics for recall in the face of synaptic correlations caused by a range of synaptic plasticity rules. These dynamics involve well-studied circuit motifs, such as forms of feedback inhibition and experimentally observed dendritic nonlinearities. We therefore show how addressing the problem of synaptic correlations leads to a novel functional account of key biophysical features of the neural substrate.