Abstract Computed Tomography (CT) is fundamentally an inverse problem combining linear operators, regularization and discrete inference. Artificial intelligence has improved reconstruction, denoising and segmentation. Quantum Computing (QC) is typically discussed in terms of computational speed. For CT, the more relevant question is structural compatibility. Several CT subproblems admit formulations that are aligned with quantum-native primitives for structured linear algebra and quadratic optimization. This short paper identifies three directions: reconstruction, denoising and segmentation. It briefly formalizes each component and details a QUBO-based formulation for the segmentation stage implemented in a prototype demonstrator, QUBOSegment . We also report ongoing development of a production-oriented system, NextGenSegment (NGS) , built on our Modular Adaptive Processing Infrastructure (MAPI). This work is intentionally scoped as a formulation- and workflow-oriented proof of concept rather than a benchmarking study claiming quantum performance advantage. The aim is to expose the CT community to technically grounded QC formulations and to encourage systematic benchmarking in realistic synchrotron and laboratory settings.

A compact physics-guided neural network for real-time pile-up rejection (PUR) in nuclear radiation spectroscopy is presented. The proposed dual-head architecture processes raw digitized waveforms and jointly predicts the global pile-up (PU) probability and the presence of a resolvable second peak, enabling reliable discrimination among single events, unresolvable PU, and resolvable PU. Fixed-point quantization and structured channel-wise pruning reduce parameters and arithmetic operations by 4.7 x with minimal loss in classification performance relative to the floating-point baseline. The model is synthesized with hls4mland deployed on a Xilinx Zynq-7000 SoC-field-programmable gate array (FPGA), achieving fully deterministic inference at 50 MHz with approximately 87% resource utilization. Measurements with Na-22, Cs-137, and Co-60 sources demonstrate stable real-time discrimination and robust generalization, including correct identification of saturated or out-of-range pulses. These results establish compressed physics-guided neural networks as an efficient and interpretable solution for embedded PUR in high-count-rate spectrometric front-end electronics.

In recent years, the limitations of von Neumann-based supercomputers in terms of energy efficiency and performance scalability have stressed the need for alternative computing paradigms. This paper introduces the HyperFPGA, an open SoC-FPGA cluster designed for experimental research on novel supercomputing architectures and paradigms. Unlike existing platforms, the HyperFPGA offers flexibility across a wide range of configurations, from electrical standards for data transmission to distributed computing models. Additionally, it is equipped with multiple power consumption monitors to independently measure different computational and hardware aspects, separating data movement from computation. The paper details the design and implementation of the HyperFPGA and demonstrates its basic functionality and scalability through a custom computational solution for the non-attacking n-queens problem, a well-known distributed computing problem. These preliminary tests demonstrate the ability of the HyperFPGA to efficiently handle computationally intensive and distributed tasks, showcasing its potential for fine-grained reconfigurable supercomputing solutions addressing a broad spectrum of scientific and engineering problems. The platform is available for open collaborative projects focused on energy-efficient supercomputing and computational physics.

Recent advancements in data acquisition hardware and online digital pulse processing facilitate real-time detection and timestamping of single photons with nanosecond-level resolution. The integration of this technology with a precision movement system facilitates conditions for developing innovative image reconstruction algorithms, thereby enhancing traditional methods. Moreover, the active system control enables stable and safe scanning by maintaining a constant distance from the surface of the object while ensuring uniformity in the photon collection. This paper outlines the design and development of an MA-XRF scanner for cultural heritage studies, along with the presentation of an elemental map reconstruction algorithm based on single-photon mapping, highlighting its advantages and limitations. The scanner is based on a three-axis system covering an inspection range of 510 mm in X and Y axis and 50 mm in the Z axis, with 10 mu m effective step resolution. Additionally, it incorporates a laser proximity sensor, allowing for dynamic distance-to-surface adjustment with a precision of 10 mu m. The X-ray beam can be collimated to a spot size of 600 or to 185 mu m by using a polycapillary lens positioned 1 mm from the surface. The mechanical system is synchronized with the photon collection performed by a silicon drift detector with an energy range from characteristic XRF lines of Sulphur (2.308 keV) to Barium (32.19 keV), and a multichannel analyzer operating in time-list mode (TLIST), with a timestamp resolution of 40 ns. The resulting elemental map allows for image reconstruction with different pixel sizes selected by the user. The system is controlled by an Application Programming Interface (API) developed in Python, allowing for seamless integration with a Jupyter Notebook server for remote operation. This collaborative effort between the Multidisciplinary Laboratory (MLAB) of the Abdus Salam International Centre of Theoretical Physics (ICTP) and the Nuclear Science and Instrumentation Laboratory (NSIL) of the International Atomic Energy Agency (IAEA) aims to advance research capabilities in cultural heritage studies.

The HyperFPGA cluster is an experimental platform that aims to provide access to high-performance computing education. Developed at the ICTP's Multidisciplinary laboratory, it features a robust MPSoC-FPGA based remote cluster that can be tailored for students in resource-limited regions. Through an intuitive JupyterHub interface, HyperFPGA provides a rich, hands-on experience in heterogeneous computing, allowing seamless remote interaction with CPU-FPGA systems. By eliminating physical barriers and reducing logistical costs, HyperFPGA democratizes access to advanced computational resources, fostering scalable, collaborative, and impactful learning. Planned future upgrades, such as GPU integration, promise to broaden its reach and reshape the global educational landscape in scientific computing.

Cutting-edge integrated circuits (ICs) are increasingly vulnerable to hardware faults, which can jeopardize the overall reliability of the system. Hence, it is crucial to evaluate the impact of faults to identify hardware vulnerabilities that later designers can use to devise fault-mitigation solutions during the circuit design stages. Fault injection (FI) through in-circuit emulation tackles the inherent complexity of fault evaluations by adopting FPGA emulation strategies. Although various works report frameworks using this FI approach, most of them are technology-dependent and hardly scalable when dealing with the increasing complexity of modern IC architectures. In addition, none of these frameworks is disclosed, which limits the adoption of fault-emulation strategies due to the inherent complexity and the required time to set up a functional FI environment. This work introduces SHADOWFI, a generic, open-source, and netlist-based fault-emulation framework that leverages the computational capabilities of hyperscale infrastructures for fault characterization and reliability estimation of complex IC designs. SHADOWFI offers two different functional workflows: i) simulation, which enables the parallelization of FI tasks on high-performance computing systems, and ii) emulation, which leverages the flexibility of FPGA cluster implementations. The framework automates saboteur insertion, FI campaign execution, and report generation, requiring minimal user configuration. Each SHADOWFI workflow was evaluated on a set of IC design benchmarks, demonstrating practical usability and significant speedup in fault injection. SHADOWFI is publicly available at https://github.com/divadnauj-GB/SHADOWFI.git

Time-resolved ultrafast phenomena with hard X-ray radiation are key research areas for applications like pump-and-probe spectroscopy. The demand for higher performance drives advancements in detector technologies and multichannel acquisition techniques. This motivates our proposal for an innovative fully digital 3D (x-y-time) imager for hard X-rays. Key challenges in detector design include improving time resolution, spatial resolution (limited by the multipixel approach), and quantum efficiency, which is low for silicon detectors in the hard X-ray range. We propose using a Separate Absorption and Multiplication Avalanche PhotoDiode (SAM-APD) based on III-V semiconductors. GaAs-based alloys, with higher atomic number and mobility, offer significantly better efficiency and speed for hard X-ray absorption compared to silicon. Regarding acquisition systems, the shift towards multichannel methods and the need to minimize power and area per channel has led to the transition from traditional pixelated voltage-mode electronics to time-based acquisition systems, where both spatial (x and y) and timing information are linked to the detection event's time. By coupling a large-area GaAs SAM-APD (several mm in diameter) to two Cross Delay Lines (CDLs) and a 4 -channel 15 -ps precision FPGA-based Time-to-Digital Converter (TDC), we aim to achieve temporal and spatial resolutions of tens of picoseconds and hundreds of micrometers. This approach offers a powerful alternative to pixelated systems, requiring neither aggressive lithography nor one readout channel per pixel, using only four channels.

Advancements in modern electronics have enabled the sampling of signals with higher resolution, facilitating the application of new techniques for the determination of pulse arrival times at detectors. In this article, we introduce a method for accurate and precise pulse arrival time estimation. This method is immune to offset and slow background variations and pulse pile-up effects, requiring a single parameter. The validation is performed through simulations and systematic comparisons with traditional methods using synthetic pulses and experimental data collected from a particle physics detector. The presented results demonstrated superior accuracy and precision of the proposed method compared to widely used constant fraction discrimination and leading-edge discrimination methods. Moreover, this method is suitable for hardware implementation and can be applied to a wide range of pulse types across various experimental contexts, making it a versatile tool for arrival time estimation in diverse applications.

An embedded system (ES) for gamma and neutron discrimination in mixed radiation environments is proposed, validated with an off-the-shelf detector consisting of a Cs2LiYCl6:Ce (CLYC) crystal coupled to a silicon photomultiplier (SiPM) cell array. This solution employs a machine learning classification model based on a multilayer perceptron (MLP) running on a commercial field-programmable gate array (FPGA), providing online single-event identification with 98.2% overall accuracy at rates higher than 200 kilocounts/s. Thermal neutrons and fast neutrons up to 5 MeV can be detected and discriminated from gamma events, even under pile-up scenarios with a dead-time lower than 2.5 mu s. The system exhibits excellent size, weight, and power consumption (SWaP) characteristics, packed in a volume smaller than 0.6 l and weighing less than 0.5 kg, while ensuring continuous operation with only 1.5 W. These features render our proposal suitable for embedded applications where low SWaP is critical and radiation levels manifest large count rates variability, such as space exploration, portable dosimeters, radiation surveillance on uncrewed aerial vehicles (UAVs), and soil moisture monitoring.

Field programmable gate arrays (FPGAs) have not only enhanced traditional sensing methods, such as pixel detection (CCD and CMOS), but also enabled the development of innovative approaches with significant potential for particle detection. This is particularly relevant in terahertz (THz) ray detection, where microbolometer-based focal plane arrays (FPAs) using microelectromechanical (MEMS) resonators are among the most promising solutions. Designing high-performance, high-pixel-density sensors is challenging without FPGAs, which are crucial for deterministic parallel processing, fast ADC/DAC control, and handling large data throughput. This paper presents a MEMS-resonator detector, fully managed via an FPGA, capable of controlling pixel excitation and tracking resonance-frequency shifts due to radiation using parallel digital lock-in amplifiers. The innovative FPGA architecture, based on a lock-in matrix, enhances the open-loop readout technique by a factor of 32. Measurements were performed on a frequency-multiplexed, 256-pixel sensor designed for imaging applications.

Atomic-scale imaging using scanning probe microscopy is a pivotal method for investigating the morphology and physico-chemical properties of nanostructured surfaces. Time resolution represents a significant limitation of this technique, as typical image acquisition times are on the order of several seconds or even a few minutes, while dynamic processes—such as surface restructuring or particle sintering, to be observed upon external stimuli such as changes in gas atmosphere or electrochemical potential—often occur within timescales shorter than a second. In this article, we present a fully redesigned field programmable gate array (FPGA)-based instrument that can be integrated into most commercially available standard scanning probe microscopes. This instrument not only significantly accelerates the acquisition of atomic-scale images by orders of magnitude but also enables the tracking of moving features such as adatoms, vacancies, or clusters across the surface (“atom tracking”) due to the parallel execution of sophisticated control and acquisition algorithms and the fast exchange of data with an external processor. Each of these measurement modes requires a complex series of operations within the FPGA that are explained in detail.

The increasing accessibility of cutting-edge photon sources, such as the latest generation of FreeElectron Lasers (FELs) and synchrotron facilities, has substantially broadened research horizons, particularly in the investigation of chemical and physical dynamics like time-of-fligth mass spectrometry. Consequently, there is a need for a new generation of precise and flexible timeresolved acquisition systems. Two-dimensional particle detectors play a crucial role in this paradigm shift, evolving from mere pixelated imaging sensors to time-based devices capable of encoding the spatial coordinates of detected events into time delays, in addition to associating temporal information with each received event. In this sense, Cross Delay-Lines (CDLs) detectors play crucial roles. In addition to the information provided by the CDL, which can be obtained with a simple acquisition system based on 4-channel Time-to-Digital Converter (TDC), it becomes increasingly necessary to provide auxiliary measurement channels in order to acquire additional information to correlate with the CDL data. The acquisition electronics of these setups rely on TDCs based on Application-Specific Integrated Circuits (ASICs), followed by Field Programmable Gate Arrays (FPGAs) for data processing. However, the lack of adaptability and flexibility inherent in ASICs prompted us to explore a fully FPGA-based approach aimed at achieving outstanding precision at high measurement rate retaining the unparalleled flexibility that only an FPGA can offer. In the following work, we propose a system based on a 16-channel TDC with a precision of 12 ps r.m.s., 5 ns of dead-time, and 4 ps of integral non-linearity. In this regard, 4 channels of the TDC are connected to a CDL, achieving a spatial resolution up to $30 / 40 \ \mu \mathrm{m}$ FWHM, while the remaining 12 channels are used for detecting auxiliary events to be correlated with the spatial information.

One of the most pressing issues in the field of mobile robotics is power delivery. In the past, we have proposed a powered floor based solution. In this article, we propose a system which combines a powered floor with a robot pose estimation system. The floor on which the mobile robots stand is composed of an array of interdigitated conductors that provide DC power supply; the stripes of conductors are interwoven similarly to what happens in a carpet, thus creating a sort of checkerboard of positive and negative pads. The robots are powered through sliding contacts. The power supply voltage is modulated with a binary encoding that uniquely identifies each conductor stripe power line; in this way, each robot is able to self-localize, by exploiting the information coming from the contacting pins. We describe the theoretical framework that allows concurrent power delivery and localization (with error boundaries). Then, we present the experimental evaluation that we performed using a prototype realization of the proposed powered floor system.

Computational methods are driving high impact microscopy techniques such as ptychography. However, the design and implementation of new algorithms is often a laborious process, as many parts of the code are written in close-to-the-hardware programming constructs to speed up the reconstruction. In this article, we present SciComPty, a new ptychography software framework aiming at simulating ptychography datasets and testing state-of-the-art and new reconstruction algorithms. Despite its simplicity, the software leverages GPU accelerated processing through the PyTorch CUDA interface. This is essential for designing new methods that can readily be employed. As an example, we present an improved position refinement method based on Adam and a new version of the rPIE algorithm, adapted for partial coherence setups. Results are shown on both synthetic and real datasets. The software is released as open-source.

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.

In hard X-ray applications that require high detection efficiency and short response times, such as synchrotron radiation-based Mössbauer absorption spectroscopy and time-resolved fluorescence or photon beam position monitoring, III–V-compound semiconductors, and dedicated alloys offer some advantages over the Si-based technologies traditionally used in solid-state photodetectors. Amongst them, gallium arsenide (GaAs) is one of the most valuable materials thanks to its unique characteristics. At the same time, implementing charge-multiplication mechanisms within the sensor may become of critical importance in cases where the photogenerated signal needs an intrinsic amplification before being acquired by the front-end electronics, such as in the case of a very weak photon flux or when single-photon detection is required. Some GaAs-based avalanche photodiodes (APDs) were grown by a molecular beam epitaxy to fulfill these needs; by means of band gap engineering, we realised devices with separate absorption and multiplication region(s) (SAM), the latter featuring a so-called staircase structure to reduce the multiplication noise. This work reports on the experimental characterisations of gain, noise, and charge collection efficiencies of three series of GaAs APDs featuring different thicknesses of the absorption regions. These devices have been developed to investigate the role of such thicknesses and the presence of traps or defects at the metal–semiconductor interfaces responsible for charge loss, in order to lay the groundwork for the future development of very thick GaAs devices (thicker than 100 μm) for hard X-rays. Several measurements were carried out on such devices with both lasers and synchrotron light sources, inducing photon absorption with X-ray microbeams at variable and controlled depths. In this way, we verified both the role of the thickness of the absorption region in the collection efficiency and the possibility of using the APDs without reaching the punch-through voltage, thus preventing the noise induced by charge multiplication in the absorption region. These devices, with thicknesses suitable for soft X-ray detection, have also shown good characteristics in terms of internal amplification and reduction of multiplication noise, in line with numerical simulations.

A simplified correlation index is proposed to be used in real-time pulse shape recognition systems. This index is similar to the classic Pearson's correlation coefficient, but it can be efficiently implemented in FPGA devices with far fewer logic resources and excellent performance. Numerical simulations with synthetic data and comparisons with the Pearson's correlation show the suitability of the proposed index in applications such as the discrimination and counting of pulses with a predefined shape. Superior performance is evident in signal-to-noise ratio scenarios close to unity. FPGA implementation of Person's method and the proposed correlation index have been successfully tested and the main results are summarized.

III-V-compound semiconductors offer many advantages over silicon-based technolo-gies traditionally used in solid-state photodetectors, especially in hard X-ray applications that require high detection efficiency and short response times. Amongst them, gallium arsenide (GaAs) has very promising characteristics in terms of X-ray absorption and high carrier velocity. Furthermore, implementing charge-multiplication mechanisms within the sensor may become of critical importance in cases where the photogenerated signal needs an intrinsic amplification before being acquired by the front-end electronics. This work reports on the experimental characteriza-tion by means of lasers and synchrotron radiation of gain, noise, and charge collection efficiencies of GaAs avalanche photodiodes (APDs), realized by molecular beam epitaxy (MBE), featuring separate absorption and multiplication regions (SAM) and different absorption region thicknesses. These devices have been fabricated to investigate the role of the thickness of the absorption region and of possible traps or defects at the metal-semiconductor interfaces in the collection efficiency in order to lay the groundwork for the future development of thicker GaAs devices for detection of hard X-rays.

Phytosanitary treatment is one of the most critical operations in vineyard management. Ideally, the spraying system should treat only the canopy, avoiding drift, leakage and wasting of product where leaves are not present: variable rate distribution can be a successful approach, allowing the minimization of losses and improving economic as well as environmental performances. The target of this paper is to realize a smart control system to spray phytosanitary treatment just on the leaves, optimizing the overall costs/benefits ratio. Four different optical-based systems for leaf recognition are analyzed, and their performances are compared using a synthetic vineyard model. In the paper, we consider the usage of three well-established methods (infrared barriers, LIDAR 2-D and stereoscopic cameras), and we compare them with an innovative low-cost real-time solution based on a suitable computer vision algorithm that uses a simple monocular camera as input. The proposed algorithm, analyzing the sequence of input frames and exploiting the parallax property, estimates the depth map and eventually reconstructs the profile of the vineyard’s row to be treated. Finally, the performances obtained by the new method are evaluated and compared with those of the other methods on a well-controlled artificial environment resembling an actual vineyard setup while traveling at standard tractor forward speed.

The distinction of secondary particles in extensive air showers, specifically muons and electrons, is one of the requirements to perform a good measurement of the composition of primary cosmic rays. We describe two methods for pulse shape detection and discrimination of muons and electrons implemented on FPGA. One uses an artificial neural network (ANN) algorithm; the other exploits a correlation approach based on finite impulse response (FIR) filters. The novel hls4ml package is used to build the ANN inference model. Both methods were implemented and tested on Xilinx FPGA System on Chip (SoC) devices: ZU9EG Zynq UltraScale+ and ZC7Z020 Zynq. The data set used for the analysis was captured with a data acquisition system on an experimental site based on a water Cherenkov detector. A comparison of the accuracy of the detection, resources utilization and power consumption of both methods is presented. The results show an overall accuracy on particle discrimination of 96.62% for the ANN and 92.50% for the FIR-based correlation, with execution times of 848 ns and 752 ns, respectively.