We report a mixed-signal data acquisition (DAQ) system for optically detected magnetic resonance (ODMR) of solid-state spins. This work provides a uniform DAQ solution for NV center quantum control system and could be easily extended to other spin-based systems. With this system, we performed general-purpose ODMR and advanced Lock-in detection experiments of nitrogen-vacancy (NV) centers, and the reported DAQ system shows excellent performance in both single and ensemble spin cases. The system consist of 4 analog channels (2 inputs and 2 outputs) and 16 optional digital channels works at up to 125 MHz clock rate. To this end, we realized a mixed-signal DAQ system which can acquire both analog and digital signals with precise hardware synchronization. The ODMR experiments often require high-speed mixed-signal data acquisition and processing for general and specific tasks. This system is designed and implemented based on a Field-Programmable-Gate-Array (FPGA) chip assisted with high-speed peripherals.
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This paper provides an update on the progress being made toward the aforementioned research project, and its primary focus is on developing a method to put a dollar amount on the benefits of information sharing among the many parties involved in the SOS. We propose using machine learning techniques to forecast SOS capabilities through the sharing of relevant data sets, and we prescribe the information linkages across systems to make this possible. In this research, we have applied different machine learning models to the IBM HR analytics data set to determine the corresponding analytics by SOS stakeholders that can improve SOS capacity. This article serves as an intermediate analysis of the above research work and aims to estimate the benefit of information sharing among the SOS stakeholders. Predicting SOS capabilities by exchanging relevant data sets and prescribing information connections between systems, we propose to use machine learning techniques.
The goal of this research is to determine how the exchange of data sets and the corresponding analytics by SOS stakeholders can improve SOS capacity. Data analytics and decision-making regarding the independent system is rarely shared across SOS stakeholders, even though the systems contribute to and benefit from the larger SOS. Every organisation that acquires a sophisticated system employs some type of data analytics to evaluate the system’s independent objectives, which is universally accepted. In the last few decades, machine learning and data analytics have been widely used in system design and acquisitions. An SOS’s systems serve two purposes: first, to accomplish their own specific aims, and second, to provide resources to the SOS as a whole.
The scalable architecture allows us to increase the throughput of the system and achieve a true triggerless mode of operation.Ī system of system’s ability to function is derived from the integration of systems from different sources. The timeslice builder combines streaming data by their time and consists of the data switch and the spillbuffer build. The main component which allows us to achieve these goals is a high-performance and cost-effective hardware timeslice builder. By routing data based on the timeslices we can average data rates and easily achieve scalability. We define a unit of detector data as image and combine images from different detectors within a time window to timesliceses. The data selection and data assembly require a time structure of the data streams with different granularity for different detectors. The system includes front-end cards, fully-digital hardware filter, data multiplexers, a timeslice builder, and a high-level trigger farm. 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 is designed to have a maximum throughput of 5 GB/s. 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.
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