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Applied Sciences
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Signal Processing and Machine Learning for Physics Applications
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Signal Processing and Machine Learning for Physics Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 May 2024 | Viewed by 2743

Special Issue Editors

Prof. Dr. Donato Cascio

E-Mail Website
Guest Editor
Dipartimento di Fisica e Chimica-“E. Segrè”, Università di Palermo, 90133 Palermo, Italy
Interests: artificial intelligence; applied physics; medical imaging; artificial vision
Special Issues, Collections and Topics in MDPI journals
Dr. Vincenzo Taormina

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Palermo, 90128 Palermo, Italy
Interests: pattern recognition; medical imaging; machine learning; image processing; deep learning

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the important roles of signal processing and machine learning in various fields of physics.

With the advent of advanced computing technologies, there has been an exponential growth in the amount of data generated in physics. To ensure effective use of this data and extract meaningful insights, it has become essential to develop advanced signal processing and machine learning techniques. The recent progress in machine learning techniques, particularly in deep learning, has revolutionized various fields of artificial vision, significantly pushing the state of the art of artificial vision systems into a wide range of high-level tasks. Such achievements can obviously help address problems in physics data analysis.

This Special Issue aims to showcase the recent advancements in signal processing and machine learning techniques and their applications in physics. It will cover a broad range of topics, including but not limited to particle physics, quantum computing, astrophysics, biophysics, materials science, environmental physics, econophysics, radiomics and complex systems.

The issue will also include case studies and examples of how signal processing and machine learning have contributed to solving real-world problems in these fields. Overall, this Special Issue will serve as a platform for researchers and practitioners to share their latest research and insights into the exciting and rapidly evolving fields of signal processing and machine learning in physics applications.

Prof. Dr. Donato Cascio
Dr. Vincenzo Taormina
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • signal processing
  • machine learning
  • applied physics
  • deep learning
  • big data analysis
  • pattern recognition
  • clustering
  • classification
  • physics-inspired ML algorithms

Published Papers (2 papers)

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Research

19 pages, 17515 KiB  
Article
Development of Fast Protection System with Xilinx ZYNQ SoC for RAON Heavy-Ion Accelerator
by Seung-Hee Nam, Changwook Son and Jungbae Bahng
Appl. Sci. 2023, 13(12), 7121; https://doi.org/10.3390/app13127121 - 14 Jun 2023
Viewed by 905
Abstract
The development of the fast protection system (FPS) was driven by the critical need to safeguard internal components of the accelerator from beam damage and minimize operational downtime. During accelerator operation, various faults can occur, posing a significant risk. The FPS [...] Read more.
The development of the fast protection system (FPS) was driven by the critical need to safeguard internal components of the accelerator from beam damage and minimize operational downtime. During accelerator operation, various faults can occur, posing a significant risk. The FPS acts as a rapid response system, initiating a shutdown signal to a reliable chopper system to prevent beam damage and ensure the operational availability of the accelerator. To meet the stringent shut off time requirements specific to critical faults, the FPS was designed to respond within 50 µs, while the total FPS time, including acquisition, redundancy, and processing, needed to be less than 20 µs. In order to achieve these goals, a customized FPS was developed for the RAON heavy-ion accelerator, utilizing the Xilinx ZYNQ system-on-chip (SoC). The FPS system comprised seven acquisition modules, one mitigation module with an embedded SoC, and employed optical fiber connections for efficient data transmission. This article provides a comprehensive account of the design, development, and testing of the FPS system. Experimental tests were conducted to validate its performance. These tests included verifying the accuracy of cyclic redundancy checks, acquiring interlock signals in short pulses, and measuring the delay time during abnormal signal occurrences. Of particular significance is the measurement of the total signal processing time for a 1 km optical cable in the RAON system, which was determined to be 9.8 µs. This result successfully met the stringent requirement of 20 µs for the FPS time. The ability of the FPS to operate within the desired time frame demonstrates its effectiveness in protecting the accelerator’s components from beam damage and minimizing downtime. Consequently, the FPS ensures the operational availability of the accelerator while maintaining the safety and integrity of its internal systems. By providing a detailed account of the FPS’s design, development, and testing, this article contributes valuable insights into the capabilities of the FPS in real-world accelerator scenarios. The successful implementation of the RAON-optimized FPS with the Xilinx ZYNQ SoC reaffirms its effectiveness as a fast and reliable protection system, thus enhancing the overall operational performance of the accelerator. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Physics Applications)
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16 pages, 1259 KiB  
Article
Multi-Site MRI Data Harmonization with an Adversarial Learning Approach: Implementation to the Study of Brain Connectivity in Autism Spectrum Disorders
by Federico Campo, Alessandra Retico, Sara Calderoni and Piernicola Oliva
Appl. Sci. 2023, 13(11), 6486; https://doi.org/10.3390/app13116486 - 25 May 2023
Viewed by 1310
Abstract
Magnetic resonance imaging (MRI) nowadays plays an important role in the identification of brain underpinnings in a wide range of neuropsychiatric disorders, including Autism Spectrum Disorders (ASD). Characterizing the hallmarks in these pathologies is not a straightforward task and machine learning (ML) is [...] Read more.
Magnetic resonance imaging (MRI) nowadays plays an important role in the identification of brain underpinnings in a wide range of neuropsychiatric disorders, including Autism Spectrum Disorders (ASD). Characterizing the hallmarks in these pathologies is not a straightforward task and machine learning (ML) is certainly one of the most promising tools for addressing complex and non-linear problems. ML algorithms and, in particular, deep neural networks (DNNs), need large datasets in order to be properly trained and thus ensure generalization capabilities on new data. Large datasets can be obtained by collecting images from different centers, thus bringing unavoidable biases in the analysis due to differences in hardware and scanning protocols between different centers. In this work, we dealt with the issue of multicenter MRI data harmonization by comparing two different approaches: the analytical ComBat-GAM procedure, whose effectiveness is already documented in the literature, and an originally developed site-adversarial deep neural network (ad-DNN). The latter aims to perform a classification task while simultaneously searching for site-relevant patterns in order to make predictions free from site-related biases. As a case study, we implemented DNN and ad-DNN classifiers to distinguish subjects with ASD with respect to typical developing controls based on functional connectivity measures derived from data of the multicenter ABIDE collection. The classification performance of the proposed ad-DNN, measured in terms of the area under the ROC curve (AUC), achieved the value of AUC = 0.70±0.03, which is comparable to that obtained by a DNN on data harmonized according to the analytical procedure (AUC = 0.71±0.01). The relevant functional connectivity alterations identified by both procedures showed an agreement between each other and with the patterns of neuroanatomical alterations previously detected in the same cohort of subjects. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Physics Applications)
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