Abstract
Wireless communication plays a critical role in determining the lifetime of Internet-of-Things (IoT) systems. Data aggregation approaches have been widely used to enhance the performance of IoT applications. Such approaches reduce the number of packets that are transmitted by combining multiple packets into one transmission unit, thereby minimising energy consumption, collisions and congestion. However, current data aggregation schemes restrict developers to a specific network structure or cannot handle multi-hop data aggregation. In this paper, we propose Hitch Hiker 2.0, a component binding model that provides support for multi-hop data aggregation. Hitch Hiker uses component meta-data to discover remote component bindings and to construct a multi-hop overlay network within the free payload space of existing traffic flows. Hitch Hiker 2.0 provides end-to-end routing of low-priority traffic while using only a small fraction of the energy of standard communication. This paper extends upon our previous work by incorporating new mechanisms for decentralised route discovery and providing additional application case studies and evaluation. We have developed a prototype implementation of Hitch Hiker for the LooCI component model. Our evaluation shows that Hitch Hiker consumes minimal resources and that using Hitch Hiker to deliver low-priority traffic reduces energy consumption by up to 32 %.

Abstract
Participation of consumers in Demand Response (DR) programs improves system stability and reliability as well as market efficiency. Retailers and distributors purchase DR to advance business and system reliability, respectively. Meanwhile, large consumers, Distribution System Operators (DSOs), Load Service Entities (LSEs), and DR aggregators sell DR to increase their own profits. In this context, DR aggregators are key elements of power systems that enhance the participation of consumers in electricity markets. These market participants can negotiate their aggregated DR with other market players in Demand Response eXchange (DRX) markets, and participate in the energy and ancillary service markets. Hence, this paper proposes a stochastic model to optimize the performance of a DR aggregator to take part in the day-ahead energy, ancillary services and intraday DRX markets. In order to mitigate the negative impacts of uncertainties, Conditional Value at Risk (CVaR) is also incorporated to the proposed model. Numerical studies indicate that the proposed model for DR aggregator can arrange its offering/bidding strategies to participate in the mentioned markets simultaneously. © 2016 IEEE.

Abstract
This paper presents a new concept for a deep sea lander system combining both sea bottom permanence characteristics with autonomous repositioning functionalities and efficient ascent/descent motion in the water column. The TURTLE hybrid lander is a particular type of autonomous underwater vehicle designed to act as sea bottom fixed observation node or in operations of transport equipment to the deep sea. The paper discusses the general concept of operation and applications and also presents the developed prototype. This system was developed under a dual use EDA (European Defense Agency) project and with national and European funds. Considered as one of the dual use (civil and military) success stories, the demonstrator was equipped to sensors allowing both seismographic data gathering and acoustic monitoring applications.

Abstract
A general concept on the use of computer vision in a way in which objects in the scene act as visual markers is presented, with special focus on its ability to increase the contextual awareness of a blind user with relation to its surroundings. A demonstration of the concept has been developed and is presented in this work. Having a rough estimation of user location and previous knowledge, from memory, of which visual features should be present in an environment, the recognition of visual elements in a scene validates and provides a trust indicator about the estimated spatial context of a blind user of an assistive orientation device, reducing the gap naturally imposed by his impairment.

Abstract

Abstract
Background: Poincare delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincare (MSP) plots. Methods: Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincare plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. Results: We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. Conclusions: This generalized multiscale approach to Poincare plots may be useful in visualizing other types of time series.