Real-time Signal Reconstruction Research Articles

FPGA implementation of a deep learning algorithm for real-time signal reconstruction in particle detectors under high pile-up conditions

The analog signals generated in the read-out electronics of particle detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital filters, which provides information about the energy deposited in the detector. The classical digital filters have a good performance in ideal situations with Gaussian electronic noise and no pulse shape distortion. However, high-energy particle colliders, such as the Large Hadron Collider (LHC) at CERN, can produce multiple simultaneous events, which produce signal pileup. The performance of classical digital filters deteriorates in these conditions since the signal pulse shape gets distorted. In addition, this type of experiments produces a high rate of collisions, which requires high throughput data acquisitions systems. In order to cope with these harsh requirements, new read-out electronics systems are based on high-performance FPGAs, which permit the utilization of more advanced real-time signal reconstruction algorithms. In this paper, a deep learning method is proposed for real-time signal reconstruction in high pileup particle detectors. The performance of the new method has been studied using simulated data and the results are compared with a classical FIR filter method. In particular, the signals and FIR filter used in the ATLAS Tile Calorimeter are used as benchmark. The implementation, resources usage and performance of the proposed Neural Network algorithm in FPGA are also presented.

Toward High-Quality Real-Time Signal Reconstruction From STFT Magnitude

An efficient algorithm for real-time signal reconstruction from the magnitude of the short-time Fourier transform (STFT) is introduced. The proposed approach combines the strengths of two previously published algorithms: the real-time phase gradient heap integration and the Gnann and Spiertz's real-time iterative spectrogram inversion with look-ahead. An extensive comparison with the state-of-the-art algorithms in a reproducible manner is presented.

On the equivalence of nonlinear positive feedback circuits to constrained iterative signal reconstruction algorithms

Constrained nonlinear iterative algorithms for signal restoration and reconstruction from zero crossings or clipped signals have a common structure of iterations and can be written as an infinite nonlinear operator series. This operator series corresponds to an analog nonlinear positive feedback circuit: the signal transformation, after an infinite number of iterations, is equivalent to the signal obtained by passing through the nonlinear instantaneous positive-feedback analog circuit. The corresponding structures of the feedback circuits for two reconstruction algorithms are presented. This interpretation offers interesting possibilities for real-time signal reconstruction. >