An Optimization of 16-Point Discrete Cosine Transform Implemented into a FPGA as a Design for a Spectral First Level Surface Detector Trigger in Extensive Air Shower Experiments

Rozdział książki: Applications of Digital Signal Processing Edited by Christian Cuadrado-LabordeThis work was funded by the Polish Committee of Science under KBN Grant No. N N202 200239

FPGA Based Serial and Single-Clock Cycle Pipelined Fast Fourier Transforms in a Radio Detection of Cosmic Rays

Rozdział książki - Design and Architectures for Digital Signal ProcessingThis chapter has been supported by the National Center of Researches and Development (Poland) under the Grant No. ERA-NET-ASPERA/02/11

Quarks Mixing in Chiral Symmetries

We discuss a subject of the quarks mixing in SU4∗SU4 and SU6∗SU6 symmetries trying to calculate the quarks mixing angles and the complex phase responsible for the CP non-conservation on the basis of the Gell-Mann Oakes Renner model. Assuming symmetry breaking in a limit of exact sub-symmetries for simultaneous quarks rotations in both electric charge sub-spaces we can estimate all mention above parameters. A perfect agreement of the experimental value of the Cabibbo angles with a sum of simultaneous quarks mixing angles in doublets (u,c) and (d,s) in the SU4∗SU4 symmetry suggests that a quarks mixing is realized in a maximal allowed range. The same assumption used for the SU6∗SU6 and a simultaneous maximal allowed quarks mixing in both electric charge triplets (u,c,t) and (d,s,b) gives a perfect agreement with the experimental value of the Cabibbo angle and estimation on the angles Θ2 and Θ3 as well as a bond for the complex phase δ

Artificial Neural Network as a FPGA Trigger for a Detection of Neutrino-Induced Air Showers

Neutrinos play a fundamental role in the understanding of the origin of ultrahigh-energy cosmic rays (UHECR). They interact through charged and neutral currents in the atmosphere generating extensive air showers. However, the very low rate of events potentially generated by neutrinos is a significant challenge for detection techniques and requires both sophisticated algorithms and high-resolution hardware. We developed the FPGA trigger which is generated by a neural network. The algorithm can recognize various waveform types. It has been developed and tested on ADC traces of the Pierre Auger surface detectors. We developed the algorithm of artificial neural network on a MATLAB platform. Trained network that we implemented into the largest Cyclone V E FPGA was used for the prototype of the front-end board for the AugerPrime. We tested several variants, and the Levenberg–Marquardt algorithm (trainlm) was the most efficient. The network was trained: (a) to recognize ‘old’ very inclined showers (real Auger data were used as patterns for both positive and negative markers: for reconstructed inclined showers and for triggered by time over threshold (ToT), respectively, (b) to recognize ‘neutrino-induced showers’. Here, we used simulated data for positive markers and vertical real showers for negative ones.This work is supported by the National Science Centre (Poland) under NCN Grant No. 2013/08/ M/ST9/00322. The authors would like to thank the Pierre Auger Collaboration for an opportunity of using the CORSIKA and offline simulation packages