Abstract
Monte Carlo (MC) techniques are widely applied to check a design on its robustness and for estimating the production yield of integrated circuits. Using standard random MC and the sample yield for estimation, a very large number of samples is required for accurate verification, especially if a high yield is desired. This can make MC extremely time consuming, but if the data follows a normal Gaussian distribution a much faster yield prediction is possible by using the well-known C-PK method. We extended this specification-distance-based scheme for the far more difficult general non-normal case by three different means, ending up in a new generalized process capability index named C-GPK. First, we apply parametric modeling only to the specification-sided distribution part. This way any difficulties in distribution parts that actually have little yield impact do not degrade the model fit anymore. Second, to improve the parametric model we introduce a new tail parameter t. Third, to allow modeling of difficult asymmetrical, multimodal or flat distributions we also introduce a new reference location parameter instead of using the mean. An advantage of improving MC this way is that-in opposite to many other MC enhancements (like importance sampling)-the performance of the C-GPK is not negatively impacted by design complexity. We described the formulation of the C-GPK and derived confidence intervals using an advanced bootstrap scheme. We verified the performance against the sample yield and C-PK for a representative set of distributions, including real production data and MC data from the design of a CMOS operational amplifier and other circuits.

Abstract
This paper presents the analysis of switching-current (SC) and switching-resistance (SR) modes of operation in digital power amplifiers (DPAs). Large output power back-off (PBO) generally shifts the DPA from SR to SC operation. Hence, an analytical study is performed to characterize these regimes. A current-mode class-D architecture has been used to implement a DPA in which both operation conditions were examined. Two mechanisms were addressed to study the AM-AM performance of the DPA, namely the knee voltage and the output resistance of the transistor. To evaluate the impact of these parameters in the AM-AM profile, we proposed a simplified transistor model in which both parameters can be defined independently without affecting each other. This allowed us to isolate and determine the effects in the AM-AM distortion, helping to conclude that the output resistance turns out to be the most dominant parameter. The study has been validated using simulations in Spectre RF with three different CMOS process nodes (130, 65, and 45nm).

Abstract
The massification of mobile access and services has increased the demand for faster, reliable and ubiquitous networks, which has been leading to additional pressure on satellite service providers to provide larger throughput. This inherently raises the challenge of bandwidth management. Regulatory activities have led to frequency allocation charts that are growing more complex and harder to manage. Such problem needs therefore to be addressed in order to achieve a more efficient use of resources and cope with the escalating traffic in satellite communications in the sub-5GHz bands. The H2020 SCREEN project is addressing this challenge by resorting to cognitive radio (CR) technology at S-band. SCREEN is working towards maturing several CR enabling technologies up to TRL4/5, considering two reference scenarios: Satcom-enabled UAV constellations and Inter-Satellite Links for satellite networks. This paper focuses on the design, development, simulation and implementation of the proposed cognitive radio algorithms in SCREEN, namely spectrum sensing, dynamic spectrum manager (DSM), and learning techniques, presenting the most promising results achieved thus far. In CR environments, communication conditions may show a considerable variability, and therefore, adaptable and reconfigurable spectrum sensing architectures can bring valuable benefits. In this paper, we describe a multi-resolution spectrum sensing architecture, compatible with the proposed approach for dynamic spectrum management, which considers a local and a global DSM and how to combine both methods to offer a higher level of performance. Regarding learning techniques, SCREEN defined two principal strategies: Centralized learning and de-centralized learning that lend themselves to different protocol architectures, namely in terms of medium-access control. Additionally, a novel simulation framework for evaluating cognitive radio for Satcom applications is presented, which is based on the open source network simulator (ns-3). The simulator considers realistic satellite orbits, propagation loss and propagation delay models and supports the placement of interferer nodes. The simulation results are output in the open KMZ format, allowing visualization in Google Earth and other GIS. The integrated simulation tool is one of the major novelties of SCREEN. Simulation results and implications are presented on the comparison of both centralized and decentralized MAC approaches with different learning and channel assignment strategies, e.g. based on greedy or reinforcement learning. Finally, early implementation results of these algorithms in an off-the-shelf space Software-Defined Radio platform will be discussed, as a pioneer step into showing the true applicability of cognitive radio for a new generation of flexible and versatile space-bound transceivers. Copyright

Abstract
This work presents a novel dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. This database was used in the 1st Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed 2016). This competition aimed at recording recent advances in cross-spectrum iris and periocular recognition. Six submissions were evaluated for crossspectrum periocular recognition, and three for iris recognition. The submitted algorithms are briefly introduced. Detailed results are reported in this paper, and comparison of the results is discussed.

Abstract
The measurement and analysis of the arterial pulse waveform (APW) are the means for cardiovascular risk assessment. Optical sensors represent an attractive instrumental solution to APW assessment due to their truly non-contact nature that makes the measurement of the skin surface displacement possible, especially at the carotid artery site. In this work, an automatic method to extract and classify the acquired data of APW signals and noise segments was proposed. Two classifiers were implemented: k-nearest neighbours and support vector machine (SVM), and a comparative study was made, considering widely used performance metrics. This work represents a wide study in feature creation for APW. A pool of 37 features was extracted and split in different subsets: amplitude features, time domain statistics, wavelet features, cross-correlation features and frequency domain statistics. The support vector machine recursive feature elimination was implemented for feature selection in order to identify the most relevant feature. The best result (0.952 accuracy) in discrimination between signals and noise was obtained for the SVM classifier with an optimal feature subset .

Abstract
Body skin temperature is a useful parameter for diagnosing diseases and infrared thermography can, be a powerful tool in providing important information to detect body temperature changes in a noninvasive way. The aim of this work was to study the pattern of skin temperature during pregnancy, to establish skin temperature reference values and to find correlations between these and the pregnant population characteristics. Sixty-one healthy pregnant women (mean age 30.6 +/- 5.1 years) in the 8th-40th gestational week with normal pregnancies were examined in 31 regions of interest (ROI). The ROIs were defined all over the body in order to determine the most influenced by factors such as age or body mass index (BMI). The results obtained in this work highlight that in normal pregnant women the skin temperature is symmetrically distributed, with the symmetrical areas differing less than 0.5 degrees C, with a mean value of 0.25 +/- 0.23 degrees C. This study identified a significant negative correlation between the BMI and temperature. Age has been shown to have great influence on the skin temperature, with a significant increase of temperature observed with age. This work explores a novel medical application of infrared thermography and provides a characterization of thermal skin profile in human pregnancy for a large set of ROIs while also evaluating the effects of age and BMI.