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Sem PDF conforme despacho. This work was supported by North Portugal Regional Operational Programme (NORTE 2020), Portugal 2020 and the European Regional Development Fund (ERDF) from European Union through the project Symbiotic technology for societal efficiency gains: Deus ex Machina (DEM) [NORTE-01-0145-FEDER-000026]. ; When a comparison between time series is required, measurement functions provide meaningful scores to characterize similarity between sequences. Quite often, time series appear warped in time, i.e, although they may exhibit amplitude and shape similarity, they appear dephased in time. The most common algorithm to overcome this challenge is the Dynamic Time Warping, which aligns each sequence prior establishing distance measurements. However, Dynamic Time Warping takes only into account amplitude similarity. A distance which characterizes the degree of time warping between two sequences can deliver new insights for applications where the timing factor is essential, such well-defined movements during sports or rehabilitation exercises. We propose a novel measurement called Time Alignment Measurement, which delivers similarity information on the temporal domain. We demonstrate the potential of our approach in measuring performance of time series alignment methodologies and in the characterization of synthetic and real time series data acquired during human movement. ; publishersversion ; published

Industry 4.0, allied with the growth and democratization of Artificial Intelligence (AI) and the advent of IoT, is paving the way for the complete digitization and automation of industrial processes. Maintenance is one of these processes, where the introduction of a predictive approach, as opposed to the traditional techniques, is expected to considerably improve the industry maintenance strategies with gains such as reduced downtime, improved equipment effectiveness, lower maintenance costs, increased return on assets, risk mitigation, and, ultimately, profitable growth. With predictive maintenance, dedicated sensors monitor the critical points of assets. The sensor data then feed into machine learning algorithms that can infer the asset health status and inform operators and decision-makers. With this in mind, in this paper, we present TIP4.0, a platform for predictive maintenance based on a modular software solution for edge computing gateways. TIP4.0 is built around Yocto, which makes it readily available and compliant with Commercial Off-the-Shelf (COTS) or proprietary hardware. TIP4.0 was conceived with an industry mindset with communication interfaces that allow it to serve sensor networks in the shop floor and modular software architecture that allows it to be easily adjusted to new deployment scenarios. To showcase its potential, the TIP4.0 platform was validated over COTS hardware, and we considered a public data-set for the simulation of predictive maintenance scenarios. We used a Convolution Neural Network (CNN) architecture, which provided competitive performance over the state-of-the-art approaches, while being approximately four-times and two-times faster than the uncompressed model inference on the Central Processing Unit (CPU) and Graphical Processing Unit, respectively. These results highlight the capabilities of distributed large-scale edge computing over industrial scenarios.