2021
DOI: 10.1109/tns.2021.3093701
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Data Acquisition System for the COMPASS++/ AMBER Experiment

Abstract: We present a new data acquisition system for the COMPASS++/AMBER experiment designed as a further development of the Intelligent FPGA-based Data Acquisition framework. The system is designed to have a maximum throughput of 5 GB/s. We designed the system to provide free-running continuous readout which allows us to implement a sophisticated data filtering by delaying the decision until the hardware filter and high-level trigger stage which processes data. The system includes front-end cards, fully-digital hardw… Show more

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Cited by 5 publications
(3 citation statements)
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“…Six ALPIDE sensors per FlexPCB are connected to six of those transceivers and two transceivers connect to SFPs. An ethernet interface allows connecting the The data is then filtered and temporarily stored for aggregation on the output optical links, then the raw data is sent to the DHMux [11] using the FriDAQ [11] protocol. The DHMux aggregates multiple links and sends them to the spill-buffer card.…”
Section: Read-out Of the Spd Detectorsmentioning
confidence: 99%
“…Six ALPIDE sensors per FlexPCB are connected to six of those transceivers and two transceivers connect to SFPs. An ethernet interface allows connecting the The data is then filtered and temporarily stored for aggregation on the output optical links, then the raw data is sent to the DHMux [11] using the FriDAQ [11] protocol. The DHMux aggregates multiple links and sends them to the spill-buffer card.…”
Section: Read-out Of the Spd Detectorsmentioning
confidence: 99%
“…As for large spin ensembles, photodetectors (PD) are suitable for the detection of its strong fluorescence, which transfer the fluorescence into analog signals. For further handling of the information, data acquisition (DAQ) systems with signal sampling, pre-processing, and transmission functions are required in applications, which are widely used in scientific research areas [20][21][22][23][24][25].…”
Section: Introductionmentioning
confidence: 99%
“…Analytics 3.0 deployment presents a unique challenge for the acquisition and development of SOS capabilities, as each of the contributing organisations employs a different set of predictive and prescriptive analytics tools for their respective systems (the Literature Review of Machine Learning Techniques and Applications in the DOD provides details on predictive machine learning techniques as applied primarily in the DOD application space) [7]. All SOS stakeholders rarely have access to these analyses and the underlying data sets [14][15][16].…”
Section: Introductionmentioning
confidence: 99%