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
In this paper we present a 12-dimensional tonal space in the context of the Tonnetz, Chew's Spiral Array, and Harte's 6-dimensional Tonal Centroid Space. The proposed Tonal Interval Space is calculated as the weighted Discrete Fourier Transform of normalized 12-element chroma vectors, which we represent as six circles covering the set of all possible pitch intervals in the chroma space. By weighting the contribution of each circle (and hence pitch interval) independently, we can create a space in which angular and Euclidean distances among pitches, chords, and regions concur with music theory principles. Furthermore, the Euclidean distance of pitch configurations from the centre of the space acts as an indicator of consonance.

Abstract
We present Conchord, a system for real-time automatic generation of musical harmony through navigation in a novel 12-dimensional Tonal Interval Space. In this tonal space, angular and Euclidean distances among vectors representing multi-level pitch configurations equate with music theory principles, and vector norms acts as an indicator of consonance. Building upon these attributes, users can intuitively and dynamically define a collection of chords based on their relation to a tonal center (or key) and their consonance level. Furthermore, two algorithmic strategies grounded in principles from function and root-motion harmonic theories allow the generation of chord progressions characteristic of Western tonal music.

Abstract
We present D'accord, a generative music system for creating harmonically compatible accompaniments of symbolic and musical audio inputs with any number of voices, instrumentation, and complexity. The main novelty of our approach centers on offering multiple ranked solutions between a database of pitch configurations and a given musical input based on tonal pitch relatedness and consonance indicators computed in a perceptually motivated Tonal Interval Space. Furthermore, we detail a method to estimate the key of symbolic and musical audio inputs based on attributes of the space, which underpins the generation of key-related pitch configurations. The system is controlled via an adaptive interface implemented for Ableton Live, MAX, and Pure Data, which facilitates music creation for users regardless of music expertise and simultaneously serves as a performance, entertainment, and learning tool. We perform a threefold evaluation of D'accord, which assesses the level of accuracy of our key-finding algorithm, the user enjoyment of generated harmonic accompaniments, and the usability and learnability of the system.