Abstract
Cardiovascular diseases are a growing epidemiological burden in today's society. A great deal of effort has been made to find solutions able to perform non-invasive monitoring and early diagnosis of such pathologies. The pulse wave velocity and certain waveform characteristics constitute some of the most important cardiovascular risk indicators. Optical sensors are an attractive instrumental solution in this kind of time assessment applications due to their truly non-contact operation capability and better resolution than commercial devices. This study consisted on the experimental validation and a clinical feasibility for a non-invasive and multi-parametric optical system for evaluation of the cardiovascular condition. Two prototypes, based on two different types of photodetectors (planar and avalanche photodiode) were tested in a small group of volunteers, and the main hemodynamic parameters were measured, such as pulse wave velocity and indexes of pulse waveform analysis: the Augmentation Index, Subendocardial Viability Ratio and Ejection Time Index. The probes under study proved to be able to measure the pulse pressure wave in a reliable manner at the carotid site, and demonstrated the consistency of the parameters determined using dedicated algorithms. This study represents a preliminary evaluation of an optical system devoted to the clinical evaluation environment. Further development to take this system to a higher level of clinical significance, by incorporating it in a multicenter study, is currently underway. © 2013 Biomedical Engineering Society.

Abstract
Cardiovascular diseases (CVDs) are the leading cause of death in the world. The pulse wave analysis provides a new insight in the analysis of these pathologies, while data mining techniques can contribute for an efficient diagnostic method. Amongst the various available techniques, artificial neural networks (ANNs) are well established in biomedical applications and have numerous successful classification applications. Also, clustering procedures have proven to be very useful in assessing different risk groups in terms of cardiovascular function in healthy populations. In this paper, a robust data mining approach was performed for cardiac risk patterns identification. Eight classifiers were tested: C4.5, Random Forest, RIPPER, Naive Bayes, Bayesian Network, Multy-layer perceptron (MLP) (1 and 2-hidden layers) and radial basis function (RBF). As for clustering procedures, k-means clustering (using Euclidean distance) and expectation-maximization (EM) were the chosen algorithms. Two datasets were used as case studies to perform classification and clustering analysis. The accuracy values are good with intervals between 88.05% and 97.15%. The clustering techniques were essential in the analysis of a dataset where little information was available, allowing the identification of different clusters that represent different risk group in terms cardiovascular function. The three cluster analysis has allowed the characterization of distinctive features for each of the clusters. Reflected wave time (T_RP) and systolic wave time (T_SP) were the selected features for clusters visualization. Data mining methodologies have proven their usefulness in screening studies due to its descriptive and predictive power.

Abstract
Presently the interest in non-invasive devices for monitoring the cardiovascular system has increased in importance, especially in the diagnosis of some pathologies. The proposed optical device reveals an attractive instrumental solution for local pulse wave velocity (PWV) assessment and other hemodynamic parameters analysis, such as Augmentation Index (AIx), Subendocardial Viability Ratio (SEVR), Maximum Rate of Pressure Change (dP/dtmax) and Ejection Time Index (ETI). These parameters allow a better knowledge on the cardiovascular condition and management of many disease states. Two studies were performed in order to validate this technology. Firstly, a comparative test between the optical system and a gold-standard in PWV assessment was carried out. Afterwards, a large study was performed in 131 young subjects to establish carotid PWV reference values as well as other hemodynamic parameters and to find correlations between these and the population characteristics. The results allowed the use of this new technique as a reliable method to determine these parameters. For the total of subjects values for carotid PWV vary between 3-7.69 m s-1 a clear correlation with age and smoking status was found out. The Aix varies between -6.15% and 11.46% and exhibit a negative correlation with heart, and dP/dtmax parameter shows a significant decrease with age.

Abstract
The Arterial Pressure Waveform (APW) can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1) a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2) the acquired position and amplitude of onset, Systolic Peak (SP), Point of Inflection (Pi) and Dicrotic Wave (DW) were used for the computation of some morphological attributes; (3) pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4) classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic), J48 (decision tree) and RIPPER (rule-based induction); and (5) we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx). Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95%) and high area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve (0.961). Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

Abstract
Reflective semiconductor optical amplifier (RSOA) is considered a strong candidate to play an important role in realizing the next generation wavelength division multiplexing passive optical network, based on the wavelength reuse concept. Therefore, an accurate and efficient modeling of RSOA is of significant importance. We present a time-domain wideband model for simulation of spatial and temporal distribution of photons and carriers in a bulk RSOA. We provide a novel approach for efficient amplified spontaneous emission modeling, considering a tradeoff between the accuracy and the computational efficiency. The multiobjective genetic algorithm is utilized for parameter extraction. Experimental validation has been performed for continuous wave input, non-return to zero (NRZ) on-off keying, and quadrature phase-shift keying (QPSK) signaling pulses up to 40 Gb/s of bit rate, in both amplification and remodulation regimes. We further present systematic performance evaluation under remodulation scenario. Saturation, noise, chirp, and signal broadening are successfully predicted, while reducing the computational time compared to other wideband models.

Abstract
As the size and cost of embedded devices continue to decrease, it becomes economically feasible to densely deploy networks with very large quantities of such nodes, and thus enabling the implementation of networks with increasingly larger number of nodes becomes a relevant problem. In this paper we describe a novel algorithm to obtain the number of live nodes with a very low time-complexity. In particular, we develop a mechanism to estimate the number of nodes or the number of proposed values (COUNT), with a time complexity that increases sublinearly with the number of nodes. The approach we propose is based on the wise exploitation of dominance-based protocols and offers excellent scalability properties for emerging applications in dense Cyber Physical Systems.