A Servo-Clock Model for Chains of Transparent Clocks Affected by Synchronization Period Jitter

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Enhancing Safety on Construction Sites: A UWB-Based Proximity Warning System Ensuring GDPR Compliance to Prevent Collision Hazards

Construction is known as one of the most dangerous industries in terms of worker safety. Collisions due the excessive proximity of workers to moving construction vehicles are one of the leading causes of fatal and non-fatal accidents on construction sites internationally. Proximity warning systems (PWS) have been proposed in the literature as a solution to detect the risk for collision and to alert workers and equipment operators in time to prevent collisions. Although the role of sensing technologies for situational awareness has been recognised in previous studies, several factors still need to be considered. This paper describes the design of a prototype sensor-based PWS, aimed mainly at small and medium-sized construction companies, to collect real-time data directly from construction sites and to warn workers of a potential risk of collision accidents. It considers, in an integrated manner, factors such as cost of deployment, the actual nature of a construction site as an operating environment and data protection. A low-cost, ultra-wideband (UWB)-based proximity detection system has been developed that can operate with or without fixed anchors. In addition, the PWS is compliant with the General Data Protection Regulation (GDPR) of the European Union. A privacy-by-design approach has been adopted and privacy mechanisms have been used for data protection. Future work could evaluate the PWS in real operational conditions and incorporate additional factors for its further development, such as studies on the timely interpretation of data

AUDIO CLASSIFICATION IN SPEECH AND MUSIC: A COMPARISON OF DIFFERENT APPROACHES

This paper presents a comparison between different techniques for audio classification into homogeneous segments of speech and music. The first method is based on Zero Crossing Rate and Bayesian Classification (ZB), and it is very simple from a computational point of view. The second approach uses a Multi Layer Perceptron network (MLP) and requires therefore more computations. The performance of the proposed algorithms has been evaluated in terms of misclassification errors and precision in music-speech change detection. Both the proposed algorithms give good results, even if the MLP shows the best performance

Application of an ANFIS Algorithm to Sensor Data Processing

Some sensors require frequent recalibrations; therefore, calibration cost must be limited. In this paper, a new calibration technique is presented. It is a two-phase method which is based on adaptive neuro-fuzzy networks, and it shows superior performances with respect to traditional algorithms, requiring fewer calibration points and less computational power in the recalibration phase. Feasibility has been demonstrated with a pyroelectric biaxial positioning system, reaching performance to the limit of the adopted test bench, on the order of 20 um with respect to a whole sensible area of 7 mm x 7 mm