25th IEEE Real Time Conference - La Biodola, Elba, Italy

Session

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing - PS

27 May 2026, 11:05

Elena Room (Hotel Hermitage)

196. A 1D-Convolutional Autoencoder for Pulse Compression in Nuclear DAQ Systems

Jose Manuel Deltoro Berrio

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

High-speed digital Data Acquisition (DAQ) systems in modern nuclear physics face a common bottleneck: the massive data throughput generated by digitizing full waveforms at high sampling rates. This limitation restricts the effective counting rate and increases the beam time required to achieve statistical significance.

This work proposes a generic, deep learning-based pulse compression...

102. A High-performance DOI Encoding Algorithm for PET Detectors with High Coupling Ratio

Yonggang Wang (University of Science and Technology of China)

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

For the single-ended readout positron emission tomography (PET) detectors, the simplest method for determining depth-of-interaction (DOI) is based on the ratio of the photodetector signal of the corresponding crystal to the overall detector signal by placing a light guide on top of the detector. However, as the coupling ratio between the crystal and the photodetector increases in pursuit of...

131. A Maximum Log-Likelihood Regression Approach for Quantitative Mixture Prediction in PGNAA Spectroscopy

Helmand Shayan

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

Scrap recycling is a vital source of sustainable raw materials, yet real-time analysis of heterogeneous metal flows remains a significant challenge. While Prompt Gamma Neutron Activation Analysis (PGNAA) offers a non-destructive method for elemental analysis, traditional categorical classification models are limited by their inability to resolve intermediate material compositions. In this...

161. A Scalable Intelligent Agent System for Automated Monitoring and Debugging Support of the JUNO Electronics system

Yifan Yang (Universite Libre de Bruxelles (BE))

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

Following the start of stable data taking at the Jiangmen Underground Neutrino Observatory (JUNO), reliable link monitoring is required beyond standard data-quality checks. In particular, the Back-End Card subsystem is a key component of trigger links, and a recurring operational challenge is rapid and accurate root-cause localization when a link drops across multiple hardware and software...

75. An Initial Study of Neural-Network-Assisted Approaches for Drift Chamber Tracking

Mr Viet Nguyen (RIKEN Nishina Center)

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

Drift chambers are widely used for charged-particle tracking in nuclear and high-energy physics experiments. Track reconstruction commonly involves combinatorial hit association followed by fitting. In experiments, additional noise enlarges the search space, leading to increased and unstable tracking runtime, a problem for (near-) real-time analysis. Motivated by this behavior,...

119. Benchmarking Neural Network Inference on Versal ACAP AI Engines for Real-Time Detector Alignment

Akshay Malige (Brookhaven National Laboratory (US))

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

Accurate alignment of detector elements in real-time is essential to maintain the integrity of reconstructed particle trajectories, especially in high-rate environments like the ATLAS experiment at the Large Hadron Collider (LHC). Any misalignment in the detector geometry can introduce systematic biases and potentially affect the accuracy of precision physics measurements. Current calibration...

52. Design-Space Exploration and Integer Quantization of Graph Neural Networks for Real-Time FPGA Track Finding

Andrea Cardini (Universidad de Oviedo)

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

Real-time track finding for displaced-muon signatures in the CMS Level-1 trigger must operate under strict fixed-latency constraints (12.5~$\mu$s) while processing high-throughput detector data. Graph neural networks (GNNs) provide a natural representation of sparse, irregular detector geometries; however, mapping message-passing models to FPGAs requires careful co-optimization of numerical...

127. Event-Driven Spiking Neural Networks for Emergent Robotic Swarm Behavior

Dr Marcos Turqueti

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

Spiking Neural Networks (SNNs) offer inherent energy efficiency for edge robotics, but their dynamic, event-driven, and sparse nature complicates the provision of hard real-time guarantees. This paper presents a lightweight, custom event-driven scheduler implemented on a resource-constrained Cortex-M0+ microcontroller. The scheduler ensures predictable execution by isolating high-priority...

27. FPGA-Deployed Variational Autoencoder for Real-Time Soft X-Ray Electron Temperature Reconstruction in RFX-mod2

Luca Orlandi (Università di Padova - Consorzio RFX)

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

RFX-mod2 is the upgraded version of the RFX-mod Reversed-Field Pinch (RFP) device, operated at Consorzio RFX. Given the substantial phenomenological and engineering complexity of the device, control performance is expected to benefit significantly from the integration of a broader set of diagnostics, such as Soft X-Ray (SXR) measurements, that help characterize the internal plasma state. A...

61. Intelligent Pulse Timing on Digitized Nuclear Signals with Self-Weakly-Supervised Neural Regression Models

Pengcheng Ai (Central China Normal University)

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

Pulse timing is an important task for nuclear radiation detectors and widely applied in nuclear spectroscopy, radiation imaging, high-energy physics, etc. While neural networks emerge as high-performance alternatives for precision timing of detector signals, the requirement of abundant labelled data poses a challenge for traditional supervised learning and limits the application of such...

99. Real-time Autoregressive Evolution of Tokamak Plasma Magnetic Measurements

Dr Shuai Li (Institute of Energy, Hefei Comprehensive National Science Center（Anhui Energy Laboratory))

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

High-precision and rapid evolution prediction of magnetic measurement signals are essential for tokamak plasma configuration control and the safe operation of discharges. Traditional physics-based models typically suffer from high computational complexity and long execution times, making it difficult to satisfy real-time requirements. Meanwhile, existing data-driven methods often encounter...

124. Real-Time Machine-Learning Inference Workflows at FRIB using EJFAT and ESNet

Giordano Cerizza (Facility for Rare Isotope Beams @ Michigan State University)

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

The Facility for Rare Isotope Beams (FRIB) is a United States Department of Energy Office of Science user facility focused on studying problems of national interest in low-energy nuclear physics. Real-time or near real-time analysis methods are critical tools for enabling FRIB science as new detectors and data acquisition technologies which allow for higher data rates and volumes are...

165. Real-Time Plasma Density Prediction and Control Framework for Pellet Injection on EAST Tokamak

Yucheng Wang

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

Precise control of plasma density is critical for high-performance steady-state operation in fusion devices. The candidate fueling method for density control for future tokamak will be pellet injection. However, pellet injection introduces rapid, highly non-linear density perturbations that challenge the latency and accuracy limitations of traditional feedback systems. This study presents a...

219. Self-Supervised and Semi-Supervised Machine Learning for Waveform-Based Neutron/Gamma Discrimination in EJ-276 Plastic Scintillator

Hai Vo

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

Accurate neutron/gamma pulse shape discrimination (PSD) in plastic scintillators is strongly limited at low energies and further complicated by the scarcity and uncertainty of labeled training data, particularly for mixed neutron–gamma sources such as Cf-252. Conventional supervised deep learning approaches rely heavily on clean labels, which are often difficult to obtain in practical...

218. Single-Detector Cosmic-Ray Muon Identification in Plastic Scintillators Using Machine Learning

Hai Vo

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

Cosmic rays at ground level are dominated by high-energy muons and are traditionally identified using coincidence techniques that require multiple detectors. In this work, a machine-learning-based approach is proposed for single-detector cosmic-ray muon identification using a plastic scintillation detector. Waveform data were acquired from gamma-ray events obtained using standard...

145. Studies for a track finder algorithm based on Graph Neural Networks for the MEG II experiment

Antoine Venturini (INFN Pisa)

27/05/2026, 11:05

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

Poster presentation

The MEG II experiment at PSI searches for the charged lepton flavour violating decay $\mu^+ \to e^+\gamma$ with unprecedented sensitivity. Fast and efficient positron track reconstruction is a key challenge for online data processing, as the cylindrical drift chamber is a gaseous detector with intrinsically slow response and is therefore not used in the first-level trigger. Moreover, the...