Real-Time Data Acquisition and Processing System for MHz Repetition Rate Image Sensors

Mielczarek, Aleksander; Makowski, Dariusz; Gerth, Ch.; Steffen, B.; Caselle, M.; Rota, Lorenzo

doi:10.3390/en14217403

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…In [20], a real-time data acquisition and processing system for MHz repetition rate image sensors is presented. The system uses modern FPGA circuits to help in the efficient collection and processing of data.…”

Section: Related Workmentioning

confidence: 99%

A Low-Cost Hardware/Software Platform for Lossless Real-Time Data Acquisition from Imaging Spectrometers

Fernández-Conde

2023

Sensors

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In real-time data-intensive applications, achieving real-time data acquisition from sensors and simultaneous storage with the necessary performance is challenging, especially if “no-data-lost” requirements are present. Ad hoc solutions are generally expensive and suffer from a lack of modularity and scalability. In this work, we present a hardware/software platform built using commercial off-the-shelf elements, designed to acquire and store digitized signals captured from imaging spectrometers capable of supporting real-time data acquisition with stringent throughput requirements (sustained rates in the boundaries of 100 MBytes/s) and simultaneous information storage in a lossless fashion. The correct combination of commercial hardware components with a properly configured and optimized multithreaded software application has satisfied the requirements in determinism and capacity for processing and storing large amounts of information in real time, keeping the economic cost of the system low. This real-time data acquisition and storage system has been tested in different conditions and scenarios, being able to successfully capture 100,000 1 Mpx-sized images generated at a nominal speed of 23.5 MHz (input throughput of 94 Mbytes/s, 4 bytes acquired per pixel) and store the corresponding data (300 GBytes of data, 3 bytes stored per pixel) concurrently without any single byte of information lost or altered. The results indicate that, in terms of throughput and storage capacity, the proposed system delivers similar performance to data acquisition systems based on specialized hardware, but at a lower cost, and provides more flexibility and adaptation to changing requirements.

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Section: Related Workmentioning

confidence: 99%

A Low-Cost Hardware/Software Platform for Lossless Real-Time Data Acquisition from Imaging Spectrometers

Fernández-Conde

2023

Sensors

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“…A carrier board [14] for the KALYPSO detector has been developed that is used for the data acquisition, processing, and transmission to the FLASH front-end electronics in MTCA.4 standard [15] to be fully integrated into the accelerator control system. The clock and trigger signals of the FLASH timing system are used for the photon pulse synchronous recording of all pulses in a pulse train at 1 MHz readout rate.…”

Section: Kaylpso At Flashmentioning

confidence: 99%

Ultra-fast detector for wide range spectral measurements

Caselle

Bründermann

Düsterer³

et al. 2019

Real-Time Measurements, Rogue Phenomena, and Single-Shot Applications IV

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KALYPSO is a novel detector operating at line rates above 10 Mfps. The detector board holds a silicon or InGaAs linear array sensor with spectral sensitivity ranging from 400 nm to 2600 nm. The sensor is connected to a cutting-edge, custom designed, ASIC readout chip which is responsible for the remarkable frame rate. The FPGA readout architecture enables continuous data acquisition and processing in real time. This detector is currently employed in many synchrotron facilities for beam diagnostics and for the characterization of self-built Ytterbium-doped fiber laser emitting around 1050 nm with a bandwidth of 40 nm.

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“…In order to better process digital images, an image processing experimental system built on the Matlab GUI platform was designed for various levels of image processing knowledge modules after the literature [5] was evaluated and integrated the content of image analysis experiments. The literature [6] constructed a real-time data acquisition and processing system based on a repetition rate image sensor and introduced modern FPGA circuits into this system for more efficient data collection and processing. According to the literature [7], image analysis and classification of high-dimensional data on images are accomplished using the non-monotonic border propagation method on weighted graphs, using a new formulation of the level set equation on a weighted graph and taking into account time-varying and stationary versions of this equation in the existence of signed velocities.…”

Section: Introductionmentioning

confidence: 99%

Research on computer network information image data processing based on machine learning

Mei,

Sun

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, we aim to progress the efficiency and accuracy of information processing and security detection in computer networks by introducing convolutional neural networks in machine learning algorithms that are capable of multi-scaling from the channel attention module and spatial attention module in extracting image information. Global maximum pooling and global averaging are done for the feature maps generated by both modules to get the clearest feature maps by dimensionality reduction. The loss function is used to calculate the feature maps to reduce the data loss generated during data extraction and finally complete the image data processing. To verify the effectiveness of the proposed platform, network images containing different amounts of data are input into the platform, and the accuracy and loss of data extraction are obtained. The results show that the data extraction accuracy of the reduced platform is up to 100%, which is 6% higher than other platforms. The number of data losses in other platforms is more than twice of this paper, while the number of losses in this paper can be controlled within 5. It can be seen that convolutional neural network in machine learning improves the accuracy of data extraction from computer network information images and reduces the loss in data extraction.

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Real-Time Data Acquisition and Processing System for MHz Repetition Rate Image Sensors

Cited by 3 publications

References 10 publications

A Low-Cost Hardware/Software Platform for Lossless Real-Time Data Acquisition from Imaging Spectrometers

A Low-Cost Hardware/Software Platform for Lossless Real-Time Data Acquisition from Imaging Spectrometers

Ultra-fast detector for wide range spectral measurements

Research on computer network information image data processing based on machine learning

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