Abstract
Chord progressions are widely used in music. The automatic generation of chord progressions can be challenging because it depends on many factors, such as the musical context, personal preference, and aesthetic choices. In this work, we propose a penalty function that encodes musical rules to automatically generate chord progressions. Then we use an artificial immune system (AIS) to minimize the penalty function when proposing candidates for the next chord in a sequence. The AIS is capable of finding multiple optima in parallel, resulting in several different chords as appropriate candidates. We performed a listening test to evaluate the chords subjectively and validate the penalty function. We found that chords with a low penalty value were considered better candidates than chords with higher penalty values.

Abstract
In multi-agent systems, local interactions among system components following relatively simple rules often result in complex overall systemic behavior. Complex behavioral and morphological patterns have been used to generate and organize audiovisual systems with artistic purposes. In this work, we propose to use the Actor model of social interactions to drive a concatenative synthesis engine called earGram in real time. The Actor model was originally developed to explore the emergence of complex visual patterns. In turn, earGram was originally developed to facilitate the creative exploration of concatenative sound synthesis. The integrated audiovisual system allows a human performer to interact with the system dynamics while receiving visual and auditory feedback. The interaction happens indirectly by disturbing the rules governing the social relationships amongst the actors, which results in a wide range of dynamic spatiotemporal patterns. A user-performer thus improvises within the behavioral scope of the system while evaluating the apparent connections between parameter values and actual complexity of the system output.

Abstract
ENDURE and BLUECOM+ are two EEA Grants projects that aim at enabling the monitoring of large and remote ocean areas by providing wireless communications and energy to humans and systems, namely unmanned vehicles, which are crucial for making large scale ocean monitoring cost-effective. ENDURE targets enabling autonomous underwater vehicles (AUVs) to remain in operation for longer periods of time than what is practical today, thus increasing the possibility of covering larger areas at lower costs. BLUECOM+ aims at connecting systems and humans in remote ocean areas by providing cost-effective, broadband, and reliable communications in alternative to satellite communications. Together these projects will bring up the enablers for data collection under the environmental monitoring programme, as well as to the achievement and sustainability of a GES in marine waters. © 2016 Taylor & Francis Group, London.

Abstract
Excessive dead time in complementary switches causes significant energy losses in DC-DC power conversion. The optimization of dead time prevents the degradation of overall efficiency by minimizing the body diode conduction of power switches and, as a consequence, also reduces reverse recovery losses. The present work aims at analyzing the influence of one of the most important characteristics of a digital controller, the timer resolution, in the context of dead-time optimization for synchronous buck converters. In specific, the analysis quantifies the efficiency dependency on the timer resolution, in a parameter set that comprises duty-cycle and dead-time, and also converter frequency and analog-to-digital converter accuracy. Based on a sensorless optimization strategy, the relationship between all these limiting factors is described, such as the number of bits of timer and analog-to-digital converter. To validate our approach experimental results are provided using a 12-to-1.8V DC-DC converter, controlled by low- and high-resolution pulse-width modulation signals generated with an XMC4200 microcontroller from Infineon Technologies. The measured results are consistent with our analysis, which predicts the power efficiency improvements not only with a fixed dead time approach, but also with the increment of timer resolution.

Abstract
Fingerprint segmentation is a crucial step of an automatic fingerprint identification system, since an accurate segmentation promote both the elimination of spurious minutiae close to the foreground boundaries and the reduction of the computation time of the following steps. In this paper, a new, and more robust fingerprint segmentation algorithm is proposed. The main novelty is the introduction of a more robust binarization process in the framework, mainly based on the fuzzy C-means clustering algorithm. Experimental results demonstrate significant benchmark progress on three existing FVC datasets.

Abstract