Abstract

Abstract
Acoustic sensing is nowadays a very demanding field which plays an important role in modern society, with applications spanning from structural health monitoring to medical imaging. Fiber-optics can bring many advantages to this field, and fiber-optic acoustic sensors show already performance levels capable of competing with the standard sensors based on piezoelectric transducers. This review presents the recent advances in the field of fiber-optic dynamic strain sensing, particularly for acoustic detection. Three dominant technologies are identified - fiber Bragg gratings, interferometric Mach-Zehnder, and Fabry-Pérot configurations - and their recent developments are summarized. © 2014 The Author(s).

Abstract
The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment.

Abstract
Traditionally, uorescence-based analytical methods were associated with high-cost bulky laboratory equipment. However, a diversity of technological advances taking place over the last 20 years concurred to change this picture. In the eld of optoelectronics, the advent of low-cost blue and UV lasers and LEDs, together with new miniature CCD spectrometers and high-sensitivity avalanche photodiodes (APDs), is enabling a new generation of spectroscopy tools to be developed and deployed in eld applications. On the other hand, chemistry and material sciences are delivering new types of uorescent materials with enhanced sensing properties, such as long-lived metallo-organic complexes or highly photostable quantum dots (QDs). Such developments are expanding the uses of uorescence and enabling new applications such as long-term multiwavelength imaging, ow cytometry analysis in microuidic chips, high-throughput DNA sequencing, and ber-optic-based diagnostic probes. © 2015 by Taylor and Francis Group, LLC.

Abstract
Deduplication of primary storage volumes in a cloud computing environment is increasingly desirable, as the resulting space savings contribute to the cost effectiveness of a large scale multi-tenant infrastructure. However, traditional archival and backup deduplication systems impose prohibitive overhead for latency-sensitive applications deployed at these infrastructures while, current primary deduplication systems rely on special cluster filesystems, centralized components, or restrictive workload assumptions. We present DEDIS, a fully-distributed and dependable system that performs exact and cluster-wide background deduplication of primary storage. DEDIS does not depend on data locality and works on top of any unsophisticated storage backend, centralized or distributed, that exports a basic shared block device interface. The evaluation of an open-source prototype shows that DEDIS scales out and adds negligible overhead

Abstract
This paper proposes a novel Energy Aggregator model responsible for managing the flexibility of low voltage customers in order to reduce electricity costs. The flexibility of the customers is represented by the availability of their controllable loads to reduce/ increase power consumption. The flexible loads are managed according to the customers' preferences and the technical limitations of the flexible loads. The Energy Aggregator model developed includes an algorithm designed to manage customers' flexibility in quasi-real-time, with the objective of minimizing the deviations from the energy bought by the aggregator in the market. A scenario with 30 households located in a semi-urban area is used to illustrate the application of the algorithm and validate the proposed approach.