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In the last few decades, increasing research effort has focused on the design of telecommunication payload systems with advanced features and lower costs in space applications. In this context, photonic solutions have already proven the potential to achieve additional functionalities, such as multiplexing or switching of RF or microwave signals, with consequent additional benefits in terms of size and mass reduction. In this paper, we report on the design of a 2 × 2 switching cell based on a thermo-optic interferometric configuration, whose key element is a sub-wavelength grating. We have theoretically demonstrated a broadband operation, with better performance in terms of operating wavelength range and compactness with respect to the existing interferometric cells. The switching cell shows a worst extinction ratio of about 13 dB, insertion loss of less than 2 dB, crosstalk of 12 dB, over a bandwidth of 150 nm, within a footprint as small as 240 µm × 9 µm. To demonstrate its potential use as a routing fabric in flexible telecommunication satellite payloads, as an example, the designed switching cell has been used as a building block of an 8 × 8 dilated Banyan matrix, where large bandwidth (150 nm), low crosstalk (−38 dB), small footprint (≈1620 µm × 576 µm) and relatively low power consumption (276 mW) have been achieved.

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The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.

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