Abstract
We present MusikVerb, a novel digital reverberation capable of adapting its output to the harmonic context of a live music performance. The proposed reverberation is aware of the harmonic content of an audio input signal and ‘tunes’ the reverberation output to its harmonic content using a spectral filtering technique. The dynamic behavior of MusikVerb avoids the sonic clutter of traditional reverberation, and most importantly, fosters creative endeavor by providing new expressive and musically-aware uses of reverberation. Despite its applicability to any input audio signal, the proposed effect has been designed primarily as a guitar pedal effect and a standalone software application. Copyright

Abstract

Abstract
The use of wireless sensor network nodes to support reliable communication exposes some challenging issues. For instance, the reduced available bandwidth combined with an error-prone communication medium impairs the provision of reliable communication services. Network coding techniques can be useful to mitigate some of these issues, where multiple message groups can be combined into single messages and retransmitted to their destinations, improving the network reliability and reducing the bandwidth consumption. However, an effective use of network coding requires the availability of wireless sensor network nodes able to encode/decode messages within the required timing constraints. This paper reports an experimental assessment of commercial off-the-shelf wireless sensor network nodes, running a set of network coding encoding/decoding tasks. The assessed nodes range from the high-performance ARM Cortex-M7 to the low capability Arduino Uno platforms, including some of the most popular ARM Cortex and ATMEL AVR processors. The performed experimental assessment demonstrates that highly complex network coding techniques (with fields as large as F28) can be efficiently implemented on a wide range of wireless sensor network nodes, including ARM Cortex, ATMEL AVR, and Arduino Uno platforms, smoothing some relevant reliable communication implementation issues.

Abstract
The study explores the technical optimization of an athlete through the use of intelligent system performance metrics that produce information obtained from inertial sensors associated to the coach's technical qualifications in real time, using Mixed Methods and Machine Learning. The purpose of this study is to illustrate, from the confusion matrices, the different performance metrics that provide information of high pertinence for the sports training in context. 2000 technical fencing actions with two levels of complexity were performed, captured through a single sensor applied in the armed hand and, simultaneously, the gesture’s qualification through a dichotomous way by the coach. The signals were divided into segments through Dynamic Time Warping, with the resulting extracted characteristics and qualitative assessments being fed to a Neural Network to learn the patterns inherent to a good or poor execution. The performance analysis of the resulting models returned a prediction accuracy of 76.6% and 72.7% for each exercise, but other metrics indicate the existence of high bias in the data. The study demonstrates the potential of intelligent algorithms to uncover trends not captured by other statistical methods. © Edições Desafio Singular.

Abstract
In the presence of accelerated fault rates, which are projected to be the norm on future exascale systems, it will become increasingly difficult for high-performance computing (HPC) applications to accomplish useful computation. Due to the fault-oblivious nature of current HPC programming paradigms and execution environments, HPC applications are insufficiently equipped to deal with errors. We believe that HPC applications should be enabled with capabilities to actively search for and correct errors in their computations. The redundant multithreading (RMT) approach offers lightweight replicated execution streams of program instructions within the context of a single application process. However, the use of complete redundancy incurs significant overhead to the application performance. In this paper we present RedThreads, an interface that provides application-level fault detection and correction based on RMT, but applies the thread-level redundancy adaptively. We describe the RedThreads syntax and semantics, and the supporting compiler infrastructure and runtime system. Our approach enables application programmers to scope the extent of redundant computation. Additionally, the runtime system permits the use of RMT to be dynamically enabled, or disabled, based on the resiliency needs of the application and the state of the system. Our experimental results demonstrate how adaptive RMT exploits programmer insight and runtime inference to dynamically navigate the trade-off space between an application’s resilience coverage and the associated performance overhead of redundant computation. © 2017, Springer Science+Business Media New York.

Abstract
Building automation systems (BAS) have promising potential to support power and energy management applications, in addition to the current and conventional intention of use for improving comfort. Integration of BAS with smart meters to provide remote monitoring and control over user-friendly interfaces can foster consumer awareness, proactiveness towards more effective use of energy and it is one of the keystones of the future's smart cities. This paper proposes development and field implementation of a remote monitoring, control and data processing system for residential customers. The system is designed to comprise a widely used BAS protocol (KNX) and smart meters in the market, adding on a central database and a web application. The developed solution is implemented in 3 houses with real residents in Istanbul, Turkey. The study provides developments for hardware-software integration in building energy management and solutions for problems encountered during field implementation. Energy management potential of three houses from the field is also discussed.