Hygrothermal performance of wood-hemp insulation in timber frame wall panels with and without a vapour barrier

An in situ experiment on a full-scale timber frame test building was carried out to study the hygrothermal performance of wood-hemp composite insulation in timber frame wall panels with and without a vapour barrier. The heat transfer properties and the likelihood of mould growth and condensation in the panels were compared. Step changes in the internal relative humidity were performed to explore the effects of high, normal and low internal moisture loads on the wall panels. No significant difference in the average equivalent thermal transmittance (U-values) between the panels with and without a vapour barrier was observed. The average equivalent U-values of the panels were close to the U-values calculated from the manufacturers’ declared thermal conductivity values of the insulation. The likelihood of condensation was higher at the interface of the wood-hemp insulation and the oriented strand board (OSB) in the panel without a vapour barrier. In terms of the parametric assessment of the mould germination potential, the relative humidity, the temperature and the exposure conditions in the insulation-OSB interfaces of the panel without a vapour barrier were found to be more favourable to the germination of mould spores. Nonetheless, when the insulations were dismantled, no mould was visually detected.

A Spiking Neural Network Model of the Medial Superior Olive using Spike Timing Dependent Plasticity for Sound Localisation

Sound localization can be defined as the ability to identify the position of an input sound source and is considered a powerful aspect of mammalian perception. For low frequency sounds, i.e., in the range 270 Hz-1.5 KHz, the mammalian auditory pathway achieves this by extracting the Interaural Time Difference between sound signals being received by the left and right ear. This processing is performed in a region of the brain known as the Medial Superior Olive (MSO). This paper presents a Spiking Neural Network (SNN) based model of the MSO. The network model is trained using the Spike Timing Dependent Plasticity learning rule using experimentally observed Head Related Transfer Function data in an adult domestic cat. The results presented demonstrate how the proposed SNN model is able to perform sound localization with an accuracy of 91.82% when an error tolerance of +/-10 degrees is used. For angular resolutions down to 2.5 degrees , it will be demonstrated how software based simulations of the model incur significant computation times. The paper thus also addresses preliminary implementation on a Field Programmable Gate Array based hardware platform to accelerate system performance.

Spiking neural network model of sound localisation using the interaural intensity difference

In this paper, a spiking neural network (SNN) architecture to simulate the sound localization ability of the mammalian auditory pathways using the interaural intensity difference cue is presented. The lateral superior olive was the inspiration for the architecture, which required the integration of an auditory periphery (cochlea) model and a model of the medial nucleus of the trapezoid body. The SNN uses leaky integrateand-fire excitatory and inhibitory spiking neurons, facilitating synapses and receptive fields. Experimentally derived headrelated transfer function (HRTF) acoustical data from adult domestic cats were employed to train and validate the localization ability of the architecture, training used the supervised learning algorithm called the remote supervision method to determine the azimuthal angles. The experimental results demonstrate that the architecture performs best when it is localizing high-frequency sound data in agreement with the biology, and also shows a high degree of robustness when the HRTF acoustical data is corrupted by noise.