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Heart failure is a chronic disease that alternates intense and weak phases and requires repeated and frequent hospital treatments. The use of automatic instruments for a remote and ubiquitous monitoring of biological parameters relevant to heart failure pathophysiology offers new perspectives to improve the patients' quality of life and the efficacy of the treatment.

Object-oriented approaches to still and moving picture coding, thanks to the extensive use of efficient algorithms for image segmentation and region coding, are expected to improve significantly the rate-distortion performances of conventional block-based methodologies. Recent results show that most of the chances to win this challenge depend on the efficiency in image segmentation: the precise aim of our investigation and the purpose of this paper is just to show the concrete possibility of employing fast adaptive algorithms for segmenting images and video frames and allowing high quality coding with very high compression. The segmentation algorithm we propose relies on the recursive computation of the Spatial Gray Level Dependence Matrix (SGLDM) and on a fast adaptive procedure for the statistics evaluation. Subsequent clustering of the image pixels in the 5-dimensional statistical-spatial space MVCXY, composed of local mean (M), local variance (V), local correlation (C) and spatial coordinates (XY) is encharged of providing final image segmentation. Target applications for videophone coding of color sequences at very low bitrate are foreseen.

An innovative algorithm is presented for the design and implementation of a self-adaptive vector quantizer for videophone coding. The codebook is organized in a binary tree structure capable to modify its topology and content in order to adaptively track the input varying statistics. The resulting tree structure is unbalanced, in the sense that some branches are extended more than others, depending on the input vector distribution, and therefore the suboptimality of tree structured VQ with respect to the full-search approach is greatly reduced. As it is known a priori that the class of input video data consists of a human head slowly moving against a static background, knowledge based segmentation of incoming frames can be efficiently performed. Exhaustive simulations have shown the effectiveness of the algorithm in standard CIF videophone sequences. Some procedures have been parallelized and implemented on a AT&T Pixel Machine for frame-rate coding. Results and comments on both the algorithm and the parallel implementation are presented.

The exponential increase in the number and types of mobile devices, along with their ever-growing sets of capabilities, have enabled the development of new architectures that aim to harness such heterogeneity. Transient clouds are examples of mobile clouds, which are created on-the-fly by the devices present in an environment to share their physical resources (e.g. CPU, memory and network) and would disappear as the nodes leave the network. They enable a device to go beyond its own physical limitations through utilising the capabilities offered by nearby devices over an ad hoc network. This idea exploits the device-to-device (D2D) communications paradigm, which allows two nearby devices to communicate with each other in the licenced cellular bandwidth without a base station involved. In this paper, we present a transient context-aware cloud (TCAC) paradigm based on the assumption that the nodes of the network care more about providing/learning higher level functionalities rather than lower level capabilities in D2D scenario. The proposed architecture, realised by using a WiFi Direct, can be portable through any paradigm, which exploits the D2D communications, so opening the doors to forthcoming 5G scenarios. We present a prototype implementation of our architecture over Android smartphones connected via WiFi Direct along with the performance metrics (power/energy consumption and accuracy) to show the benefits of TCAC. A theoretical and analytical model for the energy consumption related to a device within the TCAC is provided as well. Copyright © 2015 John Wiley & Sons, Ltd.