Marked reductions in pressure were measured when using the inflatable raft and the vacuum mattress. The results of this study will provide a basis for a larger study and, through that, the formulation of recommendations for standardized practice along a national care pathway.

There is increasing demand in modern day business applications for communication networks to be robust and reliable due to the complexity and critical nature of such applications. As such, data delivery is expected to be reliable and secure even in the harshest of environments. Software-Defined Networking (SDN) is gaining traction as a promising approach for designing network architectures which are robust and flexible. One reason for this is that separating the data plane from the control plane, increases the controller’s ability to configure the network rapidly. When network failure events occur, the network manager may trade-off the optimality of the achieved network reconfiguration with the responsivenss of the reconfiguration process. Responsiveness may be favoured when the network resources are under stress and the failure rate is high. We contribute SDN recovery methods that leverage information about the structure of the network to expedite network restoration when a link failure occurs. They operate by detecting community-like structures in the network topology and then they find alternative paths which have low operation and installation costs using this information. Extensive simulations are conducted to evaluate the proposed SDN recovery methods using open-source simulation tools. They provide evidence that the proposed approaches lead to performance gains when an alternative path is required among a set of candidate paths.

While many existing formal concept analysis algorithms are efficient, they are typically unsuitable for distributed implementation. Taking the MapReduce (MR) framework as our inspiration we introduce a distributed approach for performing formal concept mining. Our method has its novelty in that we use a light-weight MapReduce runtime called Twister which is better suited to iterative algorithms than recent distributed approaches. First, we describe the theoretical foundations underpinning our distributed formal concept analysis approach. Second, we provide a representative exemplar of how a classic centralized algorithm can be implemented in a distributed fashion using our methodology: we modify Ganter's classic algorithm by introducing a family of MR ⋆ algorithms, namely MRGanter and MRGanter+ where the prefix denotes the algorithm's lineage. To evaluate the factors that impact distributed algorithm performance, we compare our MR * algorithms with the state-of-the-art. Experiments conducted on real datasets demonstrate that MRGanter+ is efficient, scalable and an appealing algorithm for distributed problems.

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Power Weighted estimators have recently been proposed for relative attenuation and delay estimation in Blind Source Separation. Their provenance lies in the observation that speech is approximately Windowed-Disjoint Orthogonal (WDO) in the Time-Frequency (TF) domain; it has been reported that using WDO, derived from TF representations of speech, improves mixing parameter estimation. We show that Power Weighted relative attenuation and delay estimators can be derived from a particular case of a Weighted Bregman Divergence. We then propose a wider class of estimators, which we tune to give better parameter estimates for speech.

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We study the design of energy-efficient stochastic leader-selection algorithms in environments which are sensed by mobile handsets. This work optimizes the energy usage of the interaction between mobile handsets and an application server. The energy-efficiency of mobile handset communications is of increasing importance given the convergence of mobile computing and cloud computing. In some scenarios the lack of WiFi forces mobile devices to expend additional energy transmitting application specific sensed data over cellular networks. This paper focuses on (1) fairly allocating the overhead of transmitting sensed data to a cellular base station amongst the mobile devices by selecting a cluster-head (Fairness is measured in terms of the relative energy expended); (2) successively improving the location of the selected leader such that the efficiency of the entire system is improved; and finally, (3) allowing the mobile devices to gradually forget what they have learned so that the lifetime of the mobile devices is increased (without significantly affecting energy savings). We present two algorithms in this paper and demonstrate that the battery lifetime associated with sensor data transmissions of the mobile handsets may be increased by 20-40% without incurring a sensing accuracy penalty.