Affective computing is an emerging research area which provides insights on human's mental state through human-machine interaction. During the interaction process, bio-signal analysis is essential to detect human affective changes. Currently, machine learning methods to analyse bio-signals are the state of the art to detect the affective states, but most empirical works mainly deploy traditional machine learning methods rather than deep learning models due to the need for explainability. In this paper, we propose a deep learning model to process multimodal-multisensory bio-signals for affect recognition. It supports batch training for different sampling rate signals at the same time, and our results show significant improvement compared to the state of the art. Furthermore, the results are interpreted at the sensor- and signal- level to improve the explainaibility of our deep learning model.

Wireless mobile networks, comprising a hierarchical layer of cells, use the cell layering to provide alternate routing for calls. This paper proposes a model for multilayered wireless mobile communications networks that provides a unified treatment of overflow to account for intra- and interlayer overflow. This takes account of the inherent overlap between cells in the same layer and between layers, and hence the prospect of controlling the overflow to achieve a tradeoff between new call and handover performance. The mobility of users within the layers is included in the model, and this allows handover performance to be estimated. Network performance is evaluated in terms of caller-perceived performance by calculating the probability of call failure using a Markov chain model of call progress. An example is presented that provides a comparison of the network performance for a range of overflow policies. The main contribution of this paper is the development of an analytic method that treats overflow in a unified manner. This allows the approximate performance of overflow strategies that balance the need to maintain calls in progress with the desire to accept more new calls to be evaluated for large networks.