Abstract
Typical polar digital power amplifiers (DPAs) employ unit-cells operated in class-E or D-1, denoting a switched-resistance operation which degrades linearity. Besides introducing higher demand on digital predistortion (DPD), it also requires extra quantization bits, impacting the overall efficiency and system complexity. To address this, the present work makes use of an optimized constant-current cascode unit-cell which is combined with reduced conduction angle to achieve linear and efficient operation, while minimizing the effort on DPD and/or calibration. A design strategy is developed which focuses on the cascode bias voltage and transistor relative dimensions as design parameters, allowing cascode efficiency optimization without compromising linearity or reliability. A single-ended polar switched constant-current DPA is implemented in 180-nm standard CMOS. Continuous-wave measurements performed at 800 MHz demonstrate an output power of 24 dBm with a PAE of 47%. The DPA dynamic behavior was tested with a 64-QAM signal with 10 MS/s, achieving an average PAE of 20.9% with a peak-to-average power ratio (PAPR) of 8.7 dB and adjacent-channel leakage ratio (ACLR) = 40.34 dB. These results demonstrate comparable performance with the prior art while using only 6-bits clocked at 100 MHz baseband sampling frequency.

Abstract
A high production yield, Y = 1 - pfail, and thus a low failure rate, pfail, is a key requirement for successful chip design and the design of many other technical products and systems. We focus on IC design in the analog and mixedsignal domains, where Monte Carlo (MC) techniques have been a standard method for many years (see "Important Monte Carlo Rules Engineers Should Know"). Circuits have to be reliable under certain ranges of environmental parameters, such as supply voltage (V) and temperature (T). Furthermore, the set of semiconductor technology parameters (P) varies significantly, from die to die (global variations) to device to device (local variations, called mismatch). Many circuit tricks are known to minimize all of these influences (for example, using cascodes for a high power-supply rejection, differential pairs to cancel out threshold voltages, special layout techniques, and so on), but at some point problems become hard to anticipate, and further improvements are difficult to achieve. We must accept such variations and need to analyze their impact on production yield, which is a function of these parameters and the specifications (such as design topology and component sizes, among others). © 2009-2012 IEEE.

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Abstract
Pervasive and useR fOcused biomeTrics bordEr projeCT (PROTECT) is an EU project funded by the Horizon 2020 research and Innovation Programme. The main aim of PROTECT was to build an advanced biometric-based person identification system that works robustly across a range of border crossing types and that has strong user-centric features. This work presents the case study of the multibiometric verification system developed within PROTECT. The system has been developed to be suitable for different borders such as air, sea, and land borders. The system covers two use cases: the walk-through scenario, in which the traveller is on foot; the drive-through scenario, in which the traveller is in a vehicle. Each deployment includes a different set of biometric traits and this study illustrates how to evaluate such multibiometric system in accordance with international standards and, in particular, how to overcome practical problems that may be encountered when dealing with multibiometric evaluation, such as different score distributions and missing scores.

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Abstract
AbstractAtrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations—a technology known as photoplethysmography (PPG)—from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.