A programmable System-on-Chip based digital pulse processing for high resolution X-ray spectroscopy

Cicuttin, A.; Crespo, M.L.; Mannatunga, Kasun Sameera; Garcia, Victor Villaverde; Baldazzi, G.; Rignanese, L. P.; Ahangarianabhari, M.; Bertuccio, G.; Fabiani, Sergio; Rachevski, A.; Rashevskaya, I.; Vacchi, A.; Zampa, G.; Zampa, N.; Bellutti, P.; Picciotto, A.; Piemonte, C.; Zorzi, N.

doi:10.1109/icaees.2016.7888100

Cited by 12 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, more modern architectures (namely, Efficient-Net) are wider (mainly by having parallel convolution windows) as well as much deeper than older architectures (namely, VGG16). The final top layers of the network were fixed for all backbones and consisted of flattening the last convolutional layer, two fully-connected layers with 50% dropout 45 , and a final classification layer with sigmoid activation. The optimization step (training) was done using the Adam 46 implementation of the stochastic gradient descent method 47 and binary cross-entropy was used as the loss function.…”

Section: Methodsmentioning

confidence: 99%

Deep neural networks for active wave breaking classification

Stringari

Guimarães

Filipot

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Wave breaking is an important process for energy dissipation in the open ocean and coastal seas. It drives beach morphodynamics, controls air-sea interactions, determines when ship and offshore structure operations can occur safely, and influences on the retrieval of ocean properties from satellites. Still, wave breaking lacks a proper physical understanding mainly due to scarce observational field data. Consequently, new methods and data are required to improve our current understanding of this process. In this paper we present a novel machine learning method to detect active wave breaking, that is, waves that are actively generating visible bubble entrainment in video imagery data. The present method is based on classical machine learning and deep learning techniques and is made freely available to the community alongside this publication. The results indicate that our best performing model had a balanced classification accuracy score of $$\approx$$ ≈ 90% when classifying active wave breaking in the test dataset. An example of a direct application of the method includes a statistical description of geometrical and kinematic properties of breaking waves. We expect that the present method and the associated dataset will be crucial for future research related to wave breaking in several areas of research, which include but are not limited to: improving operational forecast models, developing risk assessment and coastal management tools, and refining the retrieval of remotely sensed ocean properties.

show abstract

Section: Methodsmentioning

confidence: 99%

Deep neural networks for active wave breaking classification

Stringari

Guimarães

Filipot

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…A simplified block diagram of a DPP [33] for high-resolution X-ray spectroscopy is shown in Figure 3. It consists of two separate data channels: one for the precise detection of the photon arrival and the other one for shaping the input pulse based on an FIR filter.…”

Section: Digital Pulse Shapingmentioning

confidence: 99%

Data Analysis and Filter Optimization for Pulse-Amplitude Measurement: A Case Study on High-Resolution X-ray Spectroscopy

Mannatunga

Valinoti

Samayoa

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

In this study, we present a procedure to optimize a set of finite impulse response filter (FIR) coefficients for digital pulse-amplitude measurement. Such an optimized filter is designed using an adapted digital penalized least mean square (DPLMS) method. The effectiveness of the procedure is demonstrated using a dataset from a case study on high-resolution X-ray spectroscopy based on single-photon detection and energy measurements. The energy resolutions of the Kα and Kβ lines of the Manganese energy spectrum have been improved by approximately 20%, compared to the reference values obtained by fitting individual photon pulses with the corresponding mathematical model.

show abstract

“…The purpose of this project was to develop a complete data acquisition and pulse processing system, based on the hardware setup described in Section 3.1.1. The system acquires and digitizes pulses coming from a detector, extracts their arrival times and amplitudes, and transmits the resulting data to a computer via Ethernet for offline energy spectrum analysis [44].…”

Section: Project 1: Digital Pulse Processor For X-ray and Nuclear Spectroscopymentioning

confidence: 99%

Remote Laboratory for E-Learning of Systems on Chip and Their Applications to Nuclear and Scientific Instrumentation

et al. 2021

Self Cite

View full text Add to dashboard Cite

Configuring and setting up a remote access laboratory for an advanced online school on fully programmable System-on-Chip (SoC) proved to be an outstanding challenge. The school, jointly organized by the International Centre for Theoretical Physics (ICTP) and the International Atomic Energy Agency (IAEA), focused on SoC and its applications to nuclear and scientific instrumentation and was mainly addressed to physicists, computer scientists and engineers from developing countries. The use of e-learning tools, which some of them adopted and others developed, allowed the school participants to directly access both integrated development environment software and programmable SoC platforms. This facilitated the follow-up of all proposed exercises and the final project. During the four weeks of the training activity, we faced and overcame different technology and communication challenges, whose solutions we describe in detail together with dedicated tools and design methodology. We finally present a summary of the gained experience and an assessment of the results we achieved, addressed to those who foresee to organize similar initiatives using e-learning for advanced training with remote access to SoC platforms.

show abstract

A programmable System-on-Chip based digital pulse processing for high resolution X-ray spectroscopy

Cited by 12 publications

References 18 publications

Deep neural networks for active wave breaking classification

Deep neural networks for active wave breaking classification

Data Analysis and Filter Optimization for Pulse-Amplitude Measurement: A Case Study on High-Resolution X-ray Spectroscopy

Remote Laboratory for E-Learning of Systems on Chip and Their Applications to Nuclear and Scientific Instrumentation

Contact Info

Product

Resources

About