Transition-edge sensors (TES) are detectors able to count single photons from x-ray to infrared, generating pulses with amplitudes proportional to the absorbed photon energy. The TES performance depends on the sensor parameters and also on the noise level. The evaluation of the energy resolution is thus dependent on the type of signal analysis applied. In this work we report the results of an off-line analysis applied to pulses measured with a TiAu TES for the optical region. The pulses were acquired with a digital oscilloscope and further elaborated with numerical methods. Different kinds of digital filters were applied for improving the TES energy resolution, starting from simple Savitzky-Golay filters to more complex Wiener filters. Particular attention was paid both to time-domain and frequency-domain analyses. The first aims to extract features of interest as the photon pulse amplitude, arrival time and time jitter. The second can help for better energy resolution, aiming to identify and enhance only the photon pulse frequency components and reducing the noisy ones
Convolutive blind source separation (CBSS) is one of the main branches in the field of intelligent signal processing. Inspired by the thought of sliding discrete Fourier transform (DFT), an idea of the sliding Z-transform is introduced in the present study. Based on the sliding Z-transform, a method called the sliding Z-transform for CBSS (ZCBSS) is applied to CBSS for communication signals with the same or different frequency. It is found that the deduced algorithm can accomplish the separation task without setting any preset parameter and directly recover time-domain sources from the convolutive mixtures with the help of robust instantaneous blind source separation algorithms. Simulations are carried out accordingly, and it is concluded that the proposed approach is effective in terms of both separation performance and signal de-noising.
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