FPGA-based Data Acquisition System Research Articles

Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak.

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. In this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.

Systematic approach to measure the performance of microchannel-plate photomultipliers

In this paper, we present our approach to systematically measure numerous performance parameters of microchannel plate photomultiplier tubes. The experimental setups, the analyses and selected results are discussed. Although the techniques used may be different in other locations, the document is intended as a guide for comparable measurements with other types of microchannel plate photomultiplier tubes. Measurements are shown for the following performance parameters: spectral and spatial quantum efficiency, collection efficiency, gain as a function of voltage, position and magnetic field, time resolution, rate capability and lifetime. By using a dedicated 3-axis stepper and an FPGA-based data acquisition system, also inner PMT parameters are measured as a function of the active area, such as relative detection efficiency, dark count rate, time resolution, recoil electron and afterpulse distributions, as well as charge sharing and electronic crosstalk. In addition, some of the parameters are investigated inside a strong magnetic field. For many of these measurements, the change of most setup parameters and the subsequent analysis can be controlled semi-automatically by software scripts.

Design of an FPGA-Based High-Speed Data Acquisition System for Frequency Scanning Interferometry Long-Range Measurement

Design of an FPGA-Based High-Speed Data Acquisition System for Frequency Scanning Interferometry Long-Range Measurement

Compact FPGA-Based Data Acquisition System for a High-Channel, High-Count-Rate TOF-PET Insert for Brain PET/MRI

Compact FPGA-Based Data Acquisition System for a High-Channel, High-Count-Rate TOF-PET Insert for Brain PET/MRI

FPGA-Based Implementation of SCADA System for Fuel Management

With the emergence of Digital Processors and Logic Design technologies, like FPGAs, the trend of using IC-based plug-and-play modules has been minimized. The focus is commercial off-shelf designing and developing modules and sub-modules on new FPGA technology. Some of the critical factors that make FPGA more suitable include enormous computational power, capabilities to perform logic operations, high-speed clocks, fast memory, and various built-in primitives for computation-intensive arithmetic operations. FPGA becomes suitable for implementing data capturing and processing systems with these characteristics. This paper is about designing and developing an FPGA-based Supervisory Control and Data Acquisition (SCADA) System to monitor and control the fuel level transition between two tanks that generally are designed using PLC. PLC-based SCADA systems are easy to implement, but when discussing performance, PLC cannot replace FPGA. The FPGA chosen is Spartan-3 due to its low cost and meeting the industrial temperature needs. At the same time, it may be translated into any advanced FPGA, producing fewer sources and power consumption benefits. The simulator is Xilinx 14.7 ISE, along with SDK. The implemented system provides the inherent amenities of SCADA along with the benefits of high speed, more accuracy, reduced and predictable delay, and a purely digitized system facilitated by the FPGA.

FPGA based Data Acquisition System for Machine Tool Spindle

FPGA based Data Acquisition System for Machine Tool Spindle

Multichannel FPGA-based Data Acquisition System for Fan-beam Dual Energy X-ray Absorptiometry

This study aimed to develop a multichannel field programmable gate array (FPGA) based data acquisition (DAQ) system that could process 8 analog signals from a 1 × 8 LYSO array coupled with a Geiger-mode avalanche photodiode (GAPD) for fan-beam dual-energy X-ray absorptiometry (DEXA) system. Each analog input signal was digitized using an 8-channel analog-to-digital converter (ADC) with a 125 MHz sampling rate and an input range of -1.0 to 1.0 V. The 14-bit digital signals were then fed into a Xilinx Virtex-6 FPGA-based evaluation board that implemented digital signal processing logic. The collected low- and high-energy events of each pixel for each input channel were stored in an on-chip block ram-based first-in-first-out (FIFO) module, and then further transmitted to a personal computer (PC) via the USB port. The intrinsic characteristics, spectral responses, and image acquisition were performed. The estimated coefficient of determination (R²) was 0.999 in voltage linearity and no considerable alterations in voltage resolution were observed. The energy resolutions of the peak 59.5 keV (Am-241) were ~28.2 %. Dual-energy peaks were isolated in the X-ray energy spectra. The synchronization between the movement part of the DEXA system and the timing control block inside the FPGA chip was well controlled in various conditions. The DEXA phantom images for each pixel were clearly resolved. This study demonstrated that FPGA with only on-chip memory resources can be used to build a multichannel DAQ system for fan-beam DEXA systems.

Development of a hybrid single-photon imaging detector with embedded CMOS pixelated anode

The development of a single-photon detector based on a vacuum tube, transmission photocathode, microchannel plate and CMOS pixelated read-out anode is presented. This imager will be capable of detecting up to 1 billion photons per second over an area of 7 cm2, with simultaneous measurement of position and time with resolutions of about 5 microns and few tens of picosecond, respectively. The detector has embedded pulse-processing electronics with data-driven architecture, based on the Timepix4 ASIC, producing up to 160 Gb/s data that will be handled by a high-throughput FPGA-based external electronics and data acquisition system. These performances will enable significant advances in particle physics, life sciences, quantum optics or other emerging fields where the detection of single photons with excellent timing and position resolutions are simultaneously required.

Control and Diagnostics System Generator for Complex FPGA-Based Measurement Systems.

FPGA-based data acquisition and processing systems play an important role in modern high-speed, multichannel measurement systems, especially in High-Energy and Plasma Physics. Such FPGA-based systems require an extended control and diagnostics part corresponding to the complexity of the controlled system. Managing the complex structure of registers while keeping the tight coupling between hardware and software is a tedious and potentially error-prone process. Various existing solutions aimed at helping that task do not perfectly match all specific requirements of that application area. The paper presents a new solution based on the XML system description, facilitating the automated generation of the control system’s HDL code and software components and enabling easy integration with the control software. The emphasis is put on reusability, ease of maintenance in the case of system modification, easy detection of mistakes, and the possibility of use in modern FPGAs. The presented system has been successfully used in data acquisition and preprocessing projects in high-energy physics experiments. It enables easy creation and modification of the control system definition and convenient access to the control and diagnostic blocks. The presented system is an open-source solution and may be adopted by the user for particular needs.

FPGA-Based Data Acquisition System With Preprocessing of Plasma Signal

Laser diagnostics, particularly the Thomson scattering technique, is one of the methods used to measure the electron temperature of plasma, which is highly contaminated by noise. In this work, novel preprocessing methods with adaptive filters have been proposed to suppress unwanted noise from the plasma signal. Simulations have been carried out on the real data captured in the laser diagnostic laboratory. The hardware architectures of novel preprocessing methods and adaptive filter-based noise cancellation systems to acquire filtered signals to a remote computer for signal monitoring are presented. Moreover, field-programmable gate array (FPGA)-based hardware simulation result for real-time noise cancellation is demonstrated with a developed graphical user interface (GUI). The proposed noise cancellation system occupies 4.09% lookup tables (LUTs) when implemented on the KC705 FPGA board and consumes 2.86-W power when operated at 250-MHz frequency.

FPGA-based multi-channel data acquisition system for Superheated Emulsion Detectors

A 10-channel Field Programmable Gate Array (FPGA)-based Data Acquisition (DAQ) system is developed for Superheated Emulsion Detectors (SEDs). The key features of the acquisition system are 12-bit resolution, a sampling rate of 500 KS/s/ch, triggered acquisition, user-programmable threshold, simultaneous acquisition and pre-post trigger adjustment. The indigenously developed compact DAQ unit consists of Analog Signal Processing modules, Analog-to-Digital Converter (ADC) boards and an FPGA board. The system is standalone, portable and interfaced to PC through UART. A data acquisition software is developed using LabVIEW for data logging and online representation. The DAQ unit is tested with a superheated emulsion detector. A capacitive diaphragm sensor is used to convert the acoustic pulses into electrical signals. The manuscript describes the design, hardware and software architecture, implementation and test results of the DAQ system.

An FPGA-based reconfigurable data acquisition system for LIDAR signal detection

An FPGA-based reconfigurable data acquisition system for LIDAR signal detection

An FPGA-based reconfigurable data acquisition system for LIDAR signal detection

An FPGA-based reconfigurable data acquisition system for LIDAR signal detection

Development of the optimal gamma-ray imaging system design

This article covers solutions in the field of the gamma-ray imaging systems, as well as proposes an optimal solution for design of such system.Gamma-ray imaging systems are proving their utility as a useful tool for radio-ecological research, investigation of isotope mishandling, nuclear incidents liquidation and so on. They allow viewing gamma-ray fields as if they were visible. For example, this kind of system is capable of pointing to radiation sources on top of video stream, which allows localization of radiation sources without the need of getting close to them with a dosimeter.Overview of the existing solutions covers directional imagers, collimator-based imagers, coded aperture imagers and rotation modulation systems. Each section of this paragraph outlines main advantages and disadvantages of the given solution.Based on a solution overview, a proposition of an optimal solution for this kind of system is made. Proposition covers a creation of the one-dimensional coded aperture system with modified uniformly-redundant array mask, scintillation detectors and FPGA-based data acquisition system. This kind of system uses a thick screen, also called mask, with holes. This screen casts a gamma-ray shadow onto an array of detectors. Then a data acquisition system is used to run a correlation algorithm, which finds a shadow pattern in detector’s readouts. This allows determining from what angle this shadow has been cast. The mask is formed by a modified uniformly-redundant array pattern to minimize the chance of incorrect correlation algorithm result and to use it’s ability to turn coded aperture into an inverse one. Performing measurements with and inverse mask allows compensation for irregular background radiation and irregularity between detector sensitivity in the array of sensors. It should also be noted, that using of such ability requires to physically turn the mask around it’s vertical axis.This paragraph outlines a number of design principles and requirements, such as avoidance of irregularities in radiation transparency of the mechanical construction, benefits of making the whole system remotely operated, overall requirements for supply and readout electronics and a proposal to use programmable logic integrated circuits as a data acquisition measure. This section also covers possible steps to improve system performance, via turning it around vertical axle, rotation of the viewing frame to create 2-dimensional images and performing correlation algorithm on the spectral data to distinguish different nuclides amongst gamma-ray sources.Overall conclusions of this article suggests an implementation of the test device to verify and field-test proposed technical solutions and to promote further research and development in this field.

The Continuously Running iFDAQ of the COMPASS Experiment

Recently, a stability of Data Acquisition System (DAQ) has become a vital precondition for a successful data taking in high energy physics experiments. The intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN is designed to be able to readout data at the maximum rate of the experiment and runsin a mode without any stops. DAQ systems fulfilling such requirements reach the efficiency up to 99%. The newly introduced continuously running mode enables to collect data without a necessity of any other user intervention.Such mode affects all processes of the iFDAQ with high emphasis on timing and precise synchronization. However, every undesirable interruption of data taking can potentially result in a possible loss of physics data. Running24/7 puts stress on reliability and robustness of the system. Therefore, the improvement of the iFDAQ stability had to come first. The continuously running mode and the improved iFDAQ stability helped to collect more physicsdata in the Run 2017. In the paper, we present the continuously running mode in more detail and discuss the overall iFDAQ stability.

The Online Monitoring API for the DIALOG Library of the COMPASS Experiment

Modern experiments demand a powerful and efficient Data Acquisition System (DAQ). The intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN is composed of many processes communicating between each other. The DIALOG library covers a communication mechanism between processes and establishes a communication layer to each of them. It has been introduced to the iFDAQ in the Run 2016 and improved significantly the stability of the system. The paper presents the online monitoring API for the DIALOG library. Communication between processes is challenging from a synchronization, reliability and robustness point of view. Online monitoring tools of the communication between processes are capable to reveal communication problems to be fixed in future. The debugging purpose has been crucial during introduction period to the iFDAQ. On the other hand, based on the measurement of communication between processes, the proper load balancing of processes among machines can improve the stability of the system. The online monitoring API offers a general approach for the implementation of many monitoring tools with different purposes. In the paper, it is discussed its fundamental concept, integration to a new monitoring tool and a few examples of monitoring tools are given.

Design of FPGA-based data acquisition system running near space

Design of FPGA-based data acquisition system running near space

Evaluation of digital methods for energy calculation and timing pick-off in positron emission tomography

Traditionally, pulse processing in Positron Emission Tomography (PET) has been based on analog or discrete circuits forming a decentralized processing system. However, there is a convergence for digital and integrated implementations due to the characteristics of the modern electronic devices which are real-time processing capable, such as Application-Specific Integrated Circuit (ASIC) and Field Programmable Gate Array (FPGA) with fast Analog to Digital Converters (ADC). However, FPGA can provide fast implementation at relatively low cost and also enables the development of sophisticated digital pulse processing algorithms to improve energy, position and time resolutions in PET systems. Our group has developed and evaluated one energy calculation and three timing pick-off methods for implementation onto an FPGA-based system. For a typical PET detector setup, our charge integration method presents energy resolution similar to previously designed PET detectors. The best performance for timing pick-off was achieved by the Initial Rise Interpolation (IRI) method, where a coincidence time resolution of around 440 ps is suitable for Time of Flight (TOF) PET. Future works include embedding the proposed algorithms in a FPGA-based data acquisition system under development by our group which will be employed in a PET prototype.

Track identification and reconstruction in fast neutron detection by MPGD

Micro pattern gaseous detectors have been widely used in position measurements of particle detection in the last two decades. In this work a novel method of track identification and reconstruction was developed for fast neutron detection. In this method the γ background could be eliminated as their signal duration is shorter. According to the analysis based on simulation data, with an incident neutron flux of 109s−1 and a 9-to-1 γ-neutron ratio, this method achieved a detection efficiency of 0.4% for fast neutron, with a position resolution of 0.3 mm. To demonstrate this method, a measurement of 241Am was also done and the detail of the data acquisition system used in the measurement was presented. Based on this method, an on-line tracking system can be built in an FPGA-based data acquisition system to significantly improve both the capability of counting rate and the spatial resolution.

Fast inference of deep neural networks in FPGAs for particle physics

Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. A companion compiler package for this work is developed based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.