Abstract
Surgery planing of breast cancer interventions is gaining importance among physicians, who recognize value in discussing the possible aesthetic outcomes of surgery with patients. Research is been propelled to create patient-specific breast models, but breast image registration algorithms are still limited, particularly for the purpose of matching pre- and post-surgical data of patient's breast surfaces. Yet, this is a fundamental task to learn prediction models of breast healing process after surgery. In this paper, a coarse-to-fine registration strategy is proposed to match breast surface data acquired before and after surgery. Methods are evaluated in their ability to register surfaces in an anatomical reliable way, and results suggest proper alignment adequated to be used as input to train deformable models.

Abstract
In this work, we present parameter regions for the existence of stable plain solitons of the cubic complex Ginzburg-Landau equation (CGLE) with higher-order terms associated with a fourth-order expansion. Using a perturbation approach around the nonlinear Schrdinger equation soliton and a full numerical analysis that solves an ordinary differential equation for the soliton profiles and using the Evans method in the search for unstable eigenvalues, we have found that the minimum equation allowing these stable solitons is the cubic CGLE plus a term known in optics as Raman-delayed response, which is responsible for the redshift of the spectrum. The other favorable term for the occurrence of stable solitons is a term that represents the increase of nonlinear gain with higher frequencies. At the stability boundary, a bifurcation occurs giving rise to stable oscillatory solitons for higher values of the nonlinear gain. These oscillations can have very high amplitudes, with the pulse energy changing more than two orders of magnitude in a period, and they can even exhibit more complex dynamics such as period-doubling.

Abstract
This paper describes an energy management system that is being developed in the AnyPLACE project to support new energy services, like demand response, in residential buildings. In the project end-user interfaces are designed and implemented to allow the input of preferences regarding the flexible use of shiftable and thermal appliances. Monitoring and self-learning algorithm are used to allow additional information to be collected and an automation platform is available for the management and control of appliances. An energy management algorithm is presented that processes end-user preferences and devices characteristics to produce an optimal dispatch considering demand response incentives. Results show the successful implementation of an optimized energy scheduling.

Abstract
Software developers have always found it difficult to adopt Field-Programmable Gate Arrays (FPGAs) as computing platforms. Recent advances in HLS tools aim to ease the mapping of computations to FPGAs by abstracting the hardware design effort via a standard OpenCL interface and execution model. However, OpenCL is a low-level programming language and requires that developers master the target architecture in order to achieve efficient results. Thus, efforts addressing the generation of OpenCL from high-level languages are of paramount importance to increase design productivity and to help software developers. Existing approaches bridge this by translating MATLAB/Octave code into C, or similar languages, in order to improve performance by efficiently compiling for the target hardware. One example is the MATISSE source-to-source compiler, which translates MATLAB code into standard-compliant C and/or OpenCL code. In this paper, we analyse the viability of combining both flows so that sections of MATLAB code can be translated to specialized hardware with a small amount of effort, and test a few code optimizations and their effect on performance. We present preliminary results relative to execution times, and resource and power consumption, for two OpenCL kernels generated by MATISSE, and manual optimizations of each kernel based on different coding techniques.

Abstract
Heart rate variability (HRV) analysis has been used as a quantitative marker of the autonomous nervous system activity to measure mental stress. Wearable sensors have been emerging as a solution to collect HRV data for stress assessment in a real context, however such studies raise additional requirements. The wearable system must be minimally obtrusive to allow the subjects to perform their tasks without interference, and inconspicuous to avoid the anxiety associated with wearing medical devices in public. The purpose of this study was to quantify the accuracy trade-off in the use of a chest band heart rate sensor that is less intrusive and less costly than a wearable electrocardiogram (ECG). The HRV metrics extracted from a chest band heart rate monitor, Zephyr HxM (TM) (Zph (TM)), were compared with those extracted from an ECG certified medical device, Vital Jacket (TM) (VJ (TM)). The two systems were worn simultaneously. under laboratory conditions by a population of 14 young and healthy subjects, aged 20 to 26 years, under the stress induced by the Trier Social Stress Test (TSST) procedure. The results showed a mean difference between RR intervals of 9 ms; a. root-mean square error. (RMSE) of less than 8% and. a Pearson's correlation higher than 0.946, considering all TSST phases. In the HRV analysis, the average of all normal intervals (AVNN) showed errors less than 2% between the two systems with a correlation higher than 0.99 for all TSST phases. We thus conclude that the used chest band sensor represents an alternative to the current wearable medical devices to monitor RR intervals, and could be used for mental stress monitoring similar to the TSST protocol.

Abstract
Background and Objectives: In spite of the existence of a multitude of techniques that allow the estimation of stress from physiological indexes, its fine-grained assessment is still a challenge for biomedical engineering. The short-term assessment of stress condition overcomes the limits to stress characterization with long blocks of time and allows to evaluate the behaviour change in real-world settings and also the stress level dynamics. The aim of the present study was to evaluate time and frequency domain and nonlinear heart rate variability (HRV) metrics for stress level assessment using a short-time window. Methods: The electrocardiogram (ECG) signal from 14 volunteers was monitored using the Vital Jacketml while they performed the Trier Social Stress Test (TSST) which is a standardized stress-inducing protocol. Window lengths from 220 s to 50 s for HRV analysis were tested in order to evaluate which metrics could be used to monitor stress levels in an almost continuous way. Results: A sub-set of HRV metrics (AVNN, rMSSD, SDNN and pNN20) showed consistent differences between stress and non-stress phases, and showed to be reliable parameters for the assessment of stress levels in short-term analysis. Conclusions: The AVNN metric, using 50 s of window length analysis, showed that it is the most reliable metric to recognize stress level across the four phases of TSST and allows a fine-grained analysis of stress effect as an index of psychological stress and provides an insight into the reaction of the autonomic nervous system to stress.