Parallel data processing in GA.SP data acquisition system

The ghost imaging is a new field in the research of the quantum nature of light. The long-distance reflective ghost imaging experiment with thermal light is suit for remote sensing with a high practicability. A real-time data acquisition (DAQ) system is presented for the experiment. In the setup, the light from source is separated into signal beam and reference beam. The signal beam which illuminates the target is collected by a bucket detector. The reference beam is directly captured by an area detector. The DAQ system employs a photo-multiplier tube as the bucket detector and a charge-coupled device camera as the area detector. The two detectors separately detect intensity fluctuations of two light beams. Meanwhile, the DAQ system implements high-speed and high-precision data acquisition and data processing to reconstruct the image of the target. The results of electronic tests and experiments show that the DAQ system satisfies the requirements of the experiment.

Service-oriented architecture of adaptive, intelligent data acquisition and processing systems for long-pulse fusion experiments

The problem of mapping parallel programs on to multiprocessor systems is a fundamental problem of great significance in parallel processing. In this paper we propose a fast heuristic algorithm to solve this problem on transputer networks. Our mapping algorithm mainly contains three modules: grouping, placement and routeing, where grouping puts processes in the program into tasks which can be one-to-one placed on to processors in the transputer network, placement sets the grouped tasks on to the processors and routeing produces edge-disjoint physical communication paths for logical communication requirements. The algorithm works by combining three modules under a self-adjusting scheme towards a successful mapping result. For mapping n processes in an arbitrary parallel program on to m processors in a transputer network of grid structure, our algorithm has a worst-case time complexity O(max {n 1 , m 5 }) under full adjusting, O(max {n 2 , m 4 }) under semi-adjusting and O(max {n 2 , m 2 }) under no adjusting, where the last holds only for the transputer networks providing message routeing and multiplexing. The algorithm has been implemented in Occam on the Hathi-2 transputer system.

A Data Acquisition (DAQ) system has become to be used in the field of large-scale scientific experiments with the advance of information technology. Especially, in DAQ System for a high energy physics experiment such as ATLAS experiment at CERN, parallel processing using a large number of network switches enhances the efficiency of acquiring and analyzing experimental data. Additionally, a DAQ system has many-to-one simultaneous communication that gathers data generated by a large number of detectors into one computer for analysis. Properties of such communication can cause network traffic congestions and packet losses, thereby decreasing throughput. One solution for such problem is Data Center Bridging (DCB) technology used to establish lossless Ethernet networks. In addition, since event fragments in a large-scale DAQ system are transmitted using many network switches, the buffer size of a network switch is critical. This study focuses on the queue of the network switch of a DAQ system using DCB and the effects on system performance. The fairness and throughput of each flow in many-to-one simultaneous communication system were measured. We confirmed maximum performance was achieved when an average queue length in the network switch was small.

Transputer networks for the on-line analysis of fine-grained electromagnetic calorimeter data

Cloud computing is a technology in which the Cloud Service Providers (CSP) provide many virtual servers to the users to store their information in the cloud. The faults occurring on the assignment and dismission of the virtual machines, the processing cost in the allocation of resources must also be considered. The parallel processing of the information on the virtual machines must be done effectively and in an efficient manner. A variety of systems were developed to facilitate Many Task Computing (MTC). These systems aim to hide the issues of parallelism and fault tolerant and they are used in many applications. In this paper, we introduced Nephele, a data processing framework to exploit dynamic resource provisioning offered by IaaS clouds. The performance evaluation of the virtual machines has been evaluated and the allocation and deallocation of job tasks to the specific virtual machines has also been considered. A performance comparison with the well known data processing framework hadoop has been done. Thus this paper tells about the effective and efficient manner of processing the data by parallel processing and allocating the correct resources for the desired task. It also helps to reduce the cost of resource utilization by exploiting the dynamic resource utilization.

A data acquisition system is required to interpret the electrical signals produced by radiation detectors when a gamma ray, neutron or other radiation type interacts with it. The data acquisition system can take on many forms to match the wide variety of radiation detector types available and the different ways in which the signals from these detectors can be processed to give information on the detected radiation. First presented is a detailed overview of a variety of analog data acquisition topologies ranging from simple radiation event counters through to pulse shape discrimination. A comparison is then made with the digital counterparts of these data acquisition topologies, highlighting the relative merits and shortcomings of both analog and digital implementations. Next, the important factors relating to how data is processed, stored and transferred through different stages of a data acquisition chain is discussed and suggestions given on how to avoid bottle-necks and ensure balanced data flow throughout the system. Also discussed are the key challenges associated with the design and configuration of data acquisition systems for active interrogation (AI) environments such as dealing with the high event rates, capturing the time development of active radiation signatures, and techniques for working with pulsed interrogating sources.

Data collection and analysis is vital for evaluating the performance of any equipment or system. Though the methodology of collection, transmission, storage and retrieval varies in different scenarios, the basic components of the set-up remain the same. The Data Acquisition and Monitoring System evolved from the need for remote monitoring of facilities that did not have an independent reliable infrastructure to implement a full-fledged SCADA system. Back when IT infrastructure was prioritized for business communications within the limited bandwidth allocated, collection and transmission of process data through the available network infrastructure was a challenge. More so when there are concerns regarding the confidentiality of data being collected and transmitted. Individual facilities were provided with local instrumentation and controls for reliable operations. With the growing production rates, the focus generally goes for improving the processing facilities rather than reading, reporting or recording. Instead of an independent dedicated network for Supervisory Control and Data Acquisition system that mandate a huge infrastructural cost and dedicated skilled personnel, a pilot project was initiated with a minimum budget to test data transfer through existing communication network. Field transmitters were installed on Wellheads, Risers and Manifolds and Remote Terminal Units were provided to collect data from two platforms and to facilitate data transfer to the servers through existing microwave communication network with minimum field instrumentation. Considering bandwidth limitations and priority for business communications, servers in redundant configuration were placed strategically to ensure continuous data gathering. Pilot project was successful in providing reliable data and trends with minimum disruptions. Augmentation of DAMS network was envisioned to implement Digital Oil Field for monitoring multiple assets in real time. Implementation of Digital Oil Field continued with the addition of new field instrumentation and RTUs followed by server upgrades and integration of PLC based platform control systems to add four major producing platforms to the Data Acquisition and Monitoring System through existing microwave communication network. Upon successful commissioning, it was possible to gather data from various wellheads, process equipment and other independent meters for remote real time monitoring and analysis through user friendly HMI screens and customized dashboards to manage multiple assets effectively, improving performance evaluation, safety and reliability of operations.

The potential advantage of optics is the capability of high-speed parallel transmission and processing of structured data. It appears that investigations directed toward the development of digital optical computers is one of the most promising and ambitious areas in optical computing. We have studied digital optical computers applying the global interconnection of light and image coding technique.

In view of the phenomenon of repeated development in data acquisition system, a universal data acquisition system was designed by using LabVIEW software based on NI-6358 data acquisition card. Users could customize the conversion of each channel and store the configuration information through files. A high-speed data acquisition and processing framework was proposed by combining state machine and production/consumer system, and it realized synchronous collection of 32 channels and display, convert, stored in real time. The test and operation results showed that the system had high sampling rate, stable and reliable operation, and could meet the requirement of generality.

Multifrequency Spin Wave Device for Parallel Data Processing using Micro Structured Yttrium Iron Garnet Thin Films

Cyber-physical applications need to process a lot of sensor data, for example, to analyze traffic patterns and structural soundness of critical infrastructures. Although the amount of sensor data to process is increasing fast, system support to efficiently store and analyze an extensive amount of sensor data largely lags behind. To efficiently store, retrieve, and process massive sensor data, we are developing a sensor data center (SDC) that supports spatio-temporal sensor data structures and parallel sensor data processing using clustered computational nodes composed of commodity hardware. The SDC sharply contrasts to most existing data centers that do not support spatio-temporal sensor data storage, retrieval, and processing. In this paper, we especially focus on the problem of potential load imbalance due to data access skews that adversely affects the timeliness of parallel sensor data processing. Specifically, we present an adaptive data replication method to address access skews in a SDC. In our performance evaluation performed in a preliminary version of a SDC, our adaptive approach substantially outperforms a baseline that does not support adaptive data replication.

In this paper a data acquisition system which is a combination of hardware and software modules provide solution for high frequency data acquisition and processing for the upcoming satellites. The data acquisition system consists of FPGA based hardware for high frequency satellite data reception, preprocessing along with software modules for few elements of higher level processing thus providing near real time processed output products. The demodulator which receives the Intermediate Frequency data demodulates it, can also archive data in the raw form without any preprocessing done on it. But with the in house developed hardware presented in this paper, it enables us to receive and process the demodulated signal from the demodulator on this hardware and transfer to the system housing the hardware, thereby enabling real time data processing of data and structured data archival on system for further processing in near real time. Thus the FPGA provides the service of data acquisition and processing for all the missions in the ground station before it is archived for permanent and structured data. A new high frequency data acquisition system designed to receive data at 2.88 GHz coming from satellites are encoded, encrypted and compressed which are decoded, decrypted and decompressed in the hardware using the data acquisition system. The FPGA hardware, software and server architecture enable us to receive and process high frequency satellite data, such as those transmitted by Cartosat-2S, Cartosat-3 and the upcoming NISAR.

In this paper, a dual high purity germanium (HPGe) gamma spectrometer detection system with an increased solid angle was developed. The detection system consists of a pair of Broad Energy Germanium (BE-5030p) detectors and an XIA LLC digital gamma finder/Pixie-4 data-acquisition system. A data file processor was developed containing five modules that parses Pixie-4 list-mode data output files and classifies detections into anticoincident/coincident events and their specific coincidence types (double/triple/quadruple) for further analysis. A novel cosmic veto system was installed in the detection system. It was designed to be easy to install around an existing system while still providing sufficient cosmic veto shielding comparable to other designs. This paper describes the coverage and efficiency of this cosmic veto and the data processing system. It has been demonstrated that the cosmic veto system can provide a mean background reduction of 66.1%, which results in a mean MDA improvement of 58.3%. The counting time to meet the required MDA for specific radionuclide can be reduced by a factor of 2-3 compared to those using a conventional HPGe system. This paper also provides an initial overview of coincidence timing distributions between an incoming event from a cosmic veto plate and HPGe detector.

With the development of the Internet of Things (IoT), the data acquisition system is more and more important for the follow-up of experimental data. In this paper, two data collecting platforms based on IoT technology are built for collecting and processing experimental data, so that the processing of experimental data can get rid of local restrictions more conveniently. One of the data platforms in this paper is based on AL Cloud IoT Platform, and the other one is a self-built cloud server architecture. The WeChat Mini program and the PC webpage are designed to monitor the state of the device and display the data visually. MySQL is used to store experimental data.KeywordsInternet of ThingsData acquisitionWeChat Mini ProgramMySQL