Cosmic rays, mostly composed of high energy muons, continuously hit the Earth's surface (at sea level the rate is about 10 000 m−2 min−1). Various technologies are adopted for their detection and are widespread in the field of particle and nuclear physics. In this paper, cosmic ray muon detection techniques are assessed for measurement applications in engineering, where these methods could be suitable for several applications, with specific reference to situations where environmental conditions are weakly controlled and/or where the parts to be measured are hardly accessible. Since cosmic ray showering phenomena show statistical nature, the Monte Carlo technique has been adopted to numerically simulate a particular application, where a set of muon detectors are employed for alignment measurements on an industrial press. An analysis has been performed to estimate the expected measurement uncertainty and system resolution, which result to be strongly dependent on the dimensions and geometry of the set-up, on the presence of materials interposed between detectors and, ultimately, on the elapsed time available for the data taking.

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This paper presents a new method for the automated processing of surface electromyography (SEMG) signals, particularly suited for the detection of muscle activation timing. The method has an intermediate level of complexity between simpler (but less performing) and more complex (but in general slower) methods, and is successfully used in the development of biomedical devices for rehabilitation carried out by our group.\ud The method proposed here is based on a statistical approach for threshold computation that is implemented without the need of maximum voluntary contraction or relaxed state, usually required to overcome the difficulty in obtaining the threshold value. The method is compared to 10 popular automated standard methods using different types of simulated signals that approximate the behavior of real SEMG signals. Both the number of activations detected and the onset time measured are analyzed. The algorithm is then applied to real SEMG signals, acquired from healthy subjects. The results are finally compared with literature values.\ud The results show that the proposed algorithm is the best performing method when both the number of activations and the activation timing are considered. In real applications, the algorithm gives results compatible with well-agreed literature data