Digital Analyzer of Diamond Detector Signals for ITER Vertical Neutron Camera

2014 ◽  
Vol 9 (3) ◽  
pp. 11-19
Author(s):  
Petr Zubarev ◽  
Svetlana Ivanenko ◽  
Alina Ivanova ◽  
Andrey Kvashnin ◽  
Aleksandr Kotelnikov ◽  
...  
Keyword(s):  
Signal Processing ◽  
Energy Spectrum ◽  
Digital Signal Processing ◽  
Digital Signal ◽  
Processing Unit ◽  
Diamond Detector ◽  
Pile Up ◽  
Digital Analyzer ◽  
Signal Processing Unit

In this paper, digital analyzer of diamond detector signals of ITER Vertical Neutron Camera (ITER VNC) are described, which uses digital signal processing. Digital analyzer of pulse signals is based on ADC12500PXIe (two channels, 12 bit, 500 MHz, PXI Express), which satisfies the ITER VNC requirements. In this paper, the architecture of digital signal processing unit is given. Trapezoidal digital shaper for pile-up separation and energy spectrum unit are described. In addition, structure of digital analyzer software levels are considered

Transputer based digital signal processing unit for A 3-D vision system

1989 ◽  
Vol 27 (1-5) ◽  
pp. 143-146 ◽  
Author(s):  
P. Jokitalo ◽  
E. Honkanen ◽  
I. Moring ◽  
H. Palo ◽  
K. Rautiola
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Vision System ◽  
Digital Signal ◽  
Processing Unit ◽  
Signal Processing Unit

A diamond-detector-based fast-particle spectrometer with digital signal processing

2008 ◽  
Vol 51 (4) ◽  
pp. 529-534 ◽  
Author(s):  
V. A. Krasilnikov ◽  
D. A. Skopintsev ◽  
V. N. Amosov ◽  
Yu. A. Kashchuk ◽  
A. V. Krasilnikov
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital Signal ◽  
Fast Particle ◽  
Diamond Detector

scholarly journals THE USE OF DIGITAL SIGNAL PROCESSING AND A NEURAL NETWORK WHEN GENERATING A FORECAST OF TIME SERIES OF DATA FOR THE PURPOSE OF DETECTING ANOMALIES IN THE IN THE AUTOMATED CONTROL OF TECHNOLOGICAL PROCESSES

2020 ◽  
Vol 20 (1) ◽  
pp. 24-34
Author(s):  
A. N. Ragozin ◽  
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Input Signal ◽  
Forecast Error ◽  
Digital Signal ◽  
Processing Unit ◽  
Automated Control ◽  
Prediction Result ◽  
Low Pass

n order to detect anomalies and improve the quality of forecasting dynamic data flows observed from sensors in Industrial Control System (ACS)., it is proposed to use a predictive mod-ule consisting of a series-connected digital signal processing unit (DSP) and a predictive unit using a neural network (predictive autoencoder ( Auto Encoder), predictive Autoencoder (PAE)). The study showed that the preliminary DSP block of the predicted input signal, consisting of a parallel set (comb) of digital low-pass filters with finite impulse responses (FIR-LPF), leads to a non-equilibrium account of the correlation relationships of the time samples of the input signal and to increase the accuracy of the final prediction result. The predicted autoencoder (PAE) pro-posed and considered in the work, in addition to restoring the input signal or part of the input signal at the PAE output, also generates the predicted samples of the input signal for the speci-fied number of «forward» time steps at the output, which increases the accuracy of the predic-tion result. The reduction of the forecast error occurs due to the imposition of restrictions in the formation of the forecast, that is, an additional requirement to restore the input samples of the samples – «stabilizers» at the NS output. The introduction of «stabilizers» increases the accuracy of the prediction result.

A Multi-Point Radio Displacement Measurement Method for Testing Quake-Proof Structure

2002 ◽  
Author(s):  
Atsushi Okamura ◽  
Tadashi Mikoshiba ◽  
Wataru Yosizaki ◽  
Hideki Nagai ◽  
Atsushi Mogi ◽  
...  
Keyword(s):  
Signal Processing ◽  
Measurement Method ◽  
Experimental System ◽  
Digital Signal ◽  
Reference Points ◽  
Displacement Measurement ◽  
Shaking Table ◽  
Processing Unit ◽  
Signal Processing Unit ◽  
Phase Differences

In this paper, we propose a remote 3-D displacement measurement method using the radio wave for indoor shaking table facilities. It also shows the effectiveness of the proposed method by some radio experiments using a shaking table. The proposed measurement system consists of simple transmitters attached to each reference point and several receiving antennas at different places connected with a digital signal processing unit. The locations of the transmitters are simultaneously estimated by observing the phase differences between signals at receiving antennas. This prototype radio experimental system consists of 3 transmitters of frequency 2.45GHz, 4 receiving antennas and a microcomputer. We made the demonstration for the reconstructions of the 0.1Hz and 2Hz sinusoidal excitations at 3 reference points of the testing structure on the shaking table. The accuracy of estimated displacement was several centi-meters to several mili-meters.

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