Abstract
The aim of this work is to develop an application for Android able to classifying urban sounds in a real life context. It also enables the collection and classification of new sounds. To train our classifier we use the UrbanSound8K data set available online. We have used a hybrid approach to obtain features, by combining SAX-based multiresolution motif discovery with Mel-Frequency Cepstral Coefficients (MFCC). We also describe different configurations of motif discovery for defining attributes and compare the use of Random Forest and SVM algorithms on this kind of data. Copyright 2016 ACM.

Abstract
Cyber-Physical Systems (CPS) is an emergent approach that focuses on the integration of computational applications with physical devices, being designed as a network of interacting cyber and physical elements. CPS control and monitor real-world physical infrastructures and thus is starting having a high impact in industrial automation. As such design, implementation and operation of CPS and management of the resulting automation infrastructure is of key importance for the industry. In this work, an overview of key aspects of industrial CPS, their technologies and emerging directions, as well as challenges for their implementation is presented. Based on the hands-on experiences gathered from four European, innovation projects over the last decade (i.e. SOCRADES, IMC-AESOP, GRACE and ARUM), a key challenges have been identified and a prioritization and timeline are pointed out with the aim to increase Technology Readiness Levels and lead to their usage in industrial automation environments.

Abstract
Our work proposes a hardware architecture for a Long Short-Term Memory (LSTM) Neural Network, aiming to outperform software implementations, by exploiting its inherent parallelism. The main design decisions are presented, along with the proposed network architecture. A description of the main building blocks of the network is also presented. The network is synthesized for various sizes and platforms, and the performance results are presented and analyzed. Our synthesized network achieves a 251 times speed-up over a custom-built software network, running on an i7-3770k Desktop computer, proving the benefits of parallel computation for this kind of network.

Abstract
This study presents a scheme to detect and isolate faults in over-actuated electric vehicles. Although this research work is still emerging, it already provides a view of the main challenges on the problem and discusses some possible approaches that can be useful to overcome the key difficulties. This paper intends to present a fault detection algorithm based on Unknown Input Observer (UIO). The residuals are built through the difference of signals between the measured outputs and the output estimations from the observer. The main idea is to detect fault in the electric motors and steering wheel actuator. The algorithm is presented and tested with some fault scenarios using the co-simulation tool between Simulink/MATLAB and the high-fidelity model from Carsim software.

Abstract
Robots are being increasingly used in safety-critical contexts, such as transportation and health. The need for flexible behavior in these contexts, due to human interaction factors or unstructured operating environments, led to a transition from hardware-to software-based safety mechanisms in robotic systems, whose reliability and quality is imperative to guarantee. Source code static analysis is a key component in formal software verification. It consists on inspecting code, often using automated tools, to determine a set of relevant properties that are known to influence the occurrence of defects in the final product. This paper presents HAROS, a generic, plug-in-driven, framework to evaluate code quality, through static analysis, in the context of the Robot Operating System (ROS), one of the most widely used robotic middleware. This tool (equipped with plug-ins for computing metrics and conformance to coding standards) was applied to several publicly available ROS repositories, whose results are also reported in the paper, thus providing a first overview of the internal quality of the software being developed in this community.

Abstract
In this work, spiral phase lenses fabricated on the tip of single mode optical fibers are reported. This allows tailoring the fundamental guided mode, a Gaussian beam, into a Laguerre - Gaussian profile without using additional optical elements. The lenses are fabricated using Focused Ion Beam milling, enabling high resolution in the manufacturing process. The phase profiles are evaluated and validated using an implementation of the Finite Differences Time Domain. The output optical intensity profiles matching the numerical simulations are presented and analyzed. Finally, results on cell trapping and manipulation are briefly described.