Off-line Data Processing Research Articles

Development of High-Speed Image Acquisition and Processing System for Real-Time Plasma Control on EAST

In order to realize advanced plasma control based on visible cameras, a high-speed image acquisition and processing system has been developed recently on Experimental Advanced Superconducting Tokamak (EAST). This system is optimized in many ways to achieve high-speed, real-time, low-latency performance, and to load multiple acquisition cards simultaneously. The acquisition rate of this system can be close to 10 000 FPS when the frame size is set as 320 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times240$ </tex-math></inline-formula> and the pixel depth is set as 8 bits. DMA is used for high-speed data transmission; the memory copy function is optimized for reducing the time cost on data memory reading and writing. Besides, the visualization subsystem based on the Python web can communicate with the acquisition machines and also can synthesize data of multiple acquisition machines in real time to perform image fusion and access display. In addition, a thermal event recognition function based on visible imaging is also included in this system. The convolutional neural network (CNN) model of hot spots and multifaceted asymmetric radiation from the edge (MARFE) detection for EAST plasma discharge has been developed so far. The detection results can be visualized in quasi-real time. In terms of data storage, a new data storage format is designed; a GUI for off-line data analysis and processing based on MATLAB is provided.

Development of Information Systems for Online and Offline Data Processing in the NICA Experiments

Development of Information Systems for Online and Offline Data Processing in the NICA Experiments

IoT-Based Sensor Data Fusion for Determining Optimality Degrees of Microclimate Parameters in Commercial Greenhouse Production of Tomato.

Optimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants’ comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season.

FPGA implementation of neural network accelerator for pulse information extraction in high energy physics

Extracting the amplitude and time information from the shaped pulse is an important step in nuclear physics experiments. For this purpose, a neural network can be an alternative in off-line data processing. For processing the data in real time and reducing the off-line data storage required in a trigger event, we designed a customized neural network accelerator on a field programmable gate array platform to implement specific layers in a convolutional neural network. The latter is then used in the front-end electronics of the detector. With fully reconfigurable hardware, a tested neural network structure was used for accurate timing of shaped pulses common in front-end electronics. This design can handle up to four channels of pulse signals at once. The peak performance of each channel is 1.665 Giga operations per second at a working frequency of 25 MHz.

First SETI Observations with China’s Five-hundred-meter Aperture Spherical Radio Telescope (FAST)

Abstract The Search for Extraterrestrial Intelligence (SETI) attempts to address the possibility of the presence of technological civilizations beyond the Earth. Benefiting from high sensitivity, large sky coverage, and an innovative feed cabin for China’s Five-hundred-meter Aperture Spherical radio Telescope (FAST), we performed SETI’s first observations with FAST’s newly commissioned 19 beam receiver; we report preliminary results in this paper. Using the data stream produced by the SERENDIP VI real-time multibeam SETI spectrometer installed at FAST, as well as its off-line data processing pipelines, we identify and remove four kinds of radio frequency interference (RFI): zone, broadband, multibeam, and drifting, utilizing the Nebula SETI software pipeline combined with machine-learning algorithms. After RFI mitigation, the Nebula pipeline identifies and ranks interesting narrowband candidate ET signals, scoring candidates by the number of times candidate signals have been seen at roughly the same sky position and same frequency, signal strength, proximity to a nearby star or object of interest, along with several other scoring criteria. We show four example candidate groups that demonstrate this RFI mitigation and candidate selection. This preliminary testing on FAST data helps to validate our SETI instrumentation techniques as well as our data processing pipeline.

A neural network for online spike classification that improves decoding accuracy.

Separating neural signals from noise can improve brain-computer interface performance and stability. However, most algorithms for separating neural action potentials from noise are not suitable for use in real time and have shown mixed effects on decoding performance. With the goal of removing noise that impedes online decoding, we sought to automate the intuition of human spike-sorters to operate in real time with an easily tunable parameter governing the stringency with which spike waveforms are classified. We trained an artificial neural network with one hidden layer on neural waveforms that were hand-labeled as either spikes or noise. The network output was a likelihood metric for each waveform it classified, and we tuned the network's stringency by varying the minimum likelihood value for a waveform to be considered a spike. Using the network's labels to exclude noise waveforms, we decoded remembered target location during a memory-guided saccade task from electrode arrays implanted in prefrontal cortex of rhesus macaque monkeys. The network classified waveforms in real time, and its classifications were qualitatively similar to those of a human spike-sorter. Compared with decoding with threshold crossings, in most sessions we improved decoding performance by removing waveforms with low spike likelihood values. Furthermore, decoding with our network's classifications became more beneficial as time since array implantation increased. Our classifier serves as a feasible preprocessing step, with little risk of harm, that could be applied to both off-line neural data analyses and online decoding.NEW & NOTEWORTHY Although there are many spike-sorting methods that isolate well-defined single units, these methods typically involve human intervention and have inconsistent effects on decoding. We used human classified neural waveforms as training data to create an artificial neural network that could be tuned to separate spikes from noise that impaired decoding. We found that this network operated in real time and was suitable for both off-line data processing and online decoding.

Measurement of the neutron total cross section of carbon at the Back-n white neutron beam of CSNS

To verify the performance of the neutron total cross-sectional spectrometer, the neutron total cross section of carbon is initially measured in the energy range of 1 eV to 20 MeV using the time-of-flight method. The measurement is performed at the Back-n white neutron source with a 76-m time-of-flight path using the China Spallation Neutron Source. A multilayer fast fission chamber with 235U and 238U is employed as the neutron detector. The diameter and thickness of the natural graphite sample are 70 mm and 40 mm, respectively. Signal waveforms are collected using a data acquisition system. Off-line data processing was used to obtain the neutron time-of-flight spectra and transmissions. The uncertainty of the counting statistics is generally approximately 3% for each bin in the energy range of 1–20 MeV. It is determined that the results for the neutron total cross section of carbon obtained using 235U cells are in good agreement with the results obtained using 238U cells within limits of statistical uncertainty. Moreover, the measured total cross sections show good agreement with the broadening evaluated data.

Drone detection and radar‐cross‐section measurements by RAD‐DAR

RAD-DAR is a frequency-modulated-continuous-wave radar demonstrator, working at X-band, completely designed and constructed by the authors' research group following the ubiquitous-radar concept. After introducing its main hardware and software blocks, with special emphasis to its off-line signal processing and data processing, this study presents the results of a drone detection test, where RAD-DAR achieved a DJI-Phantom 4 detection and tracking at a range up to 3 km. The system performance is discussed with different flights including an attack manoeuvre, and a free flight, which helps to highlight their advantages in surveillance tasks due to the RAD-DAR staring nature. Furthermore, range, speed and azimuth accuracies are discussed, considering the drone global positioning system data. Finally, this study shows a statistical radar cross-section study based on the processed data of the drone and attempts to classify this target by means of Swerling models.

ATLAS Distributed Computing: Its Central Services core

The ATLAS Distributed Computing (ADC) Project is responsible for the off-line processing of data produced by the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. It facilitates data and workload management for ATLAS computing on the Worldwide LHC Computing Grid (WLCG). ADC Central Services operations (CSOPS) is a vital part of ADC, responsible for the deployment and configuration of services needed by ATLAS computing and operation of those services on CERN IT infrastructure, providing knowledge of CERN IT services to ATLAS service managers and developers, and supporting them in case of issues. Currently this entails the management of 43 different OpenStack projects, with more than 5000 cores allocated for these virtual machines, as well as overseeing the distribution of 29 petabytes of storage space in EOS for ATLAS. As the LHC begins to get ready for the next long shut-down, which will bring in many new upgrades to allow for more data to be captured by the on-line systems, CSOPS must not only continue to support the existing services, but plan ahead for the expected increase in data, users, and services that will be required. This paper attempts to explain the current state of CSOPS as well as the strategies in place to maintain the service functionality in the long term.

Online & Offline data storage and data processing at the European XFEL facility

For the upcoming experiments at the European XFEL light source facility, a new online and offline data processing and storage infrastructure is currently being built and verified. Based on the experience of the system being developed for the Petra III light source at DESY, presented at the last CHEP conference, we further develop the system to cope with the much higher volumes and rates ( 50GB/sec) together with a more complex data analysis and infrastructure conditions (i.e. long range InfiniBand connections). This work will be carried out in collaboration of DESY/IT, European XFEL and technology support from IBM/Research. This presentation will shortly wrap up the experience of 1 year runtime of the PetraIII ([3]) system, continue with a short description of the challenges for the European XFEL ([2]) experiments and the main section, showing the proposed system for online and offline with initial result from real implementation (HW & SW). This will cover the selected cluster filesystem GPFS ([5]) including Quality of Service (QOS), extensive use of flash based subsystems and other new and unique features this architecture will benefit from.

The Cherenkov Telescope Array production system for Monte Carlo simulations and analysis

The Cherenkov Telescope Array (CTA), an array of many tens of Imaging Atmospheric Cherenkov Telescopes deployed on an unprecedented scale, is the next-generation instrument in the field of very high energy gamma-ray astronomy. An average data stream of about 0.9 GB/s for about 1300 hours of observation per year is expected, therefore resulting in 4 PB of raw data per year and a total of 27 PB/year, including archive and data processing. The start of CTA operation is foreseen in 2018 and it will last about 30 years. The installation of the first telescopes in the two selected locations (Paranal, Chile and La Palma, Spain) will start in 2017. In order to select the best site candidate to host CTA telescopes (in the Northern and in the Southern hemispheres), massive Monte Carlo simulations have been performed since 2012. Once the two sites have been selected, we have started new Monte Carlo simulations to determine the optimal array layout with respect to the obtained sensitivity. Taking into account that CTA may be finally composed of 7 different telescope types coming in 3 different sizes, many different combinations of telescope position and multiplicity as a function of the telescope type have been proposed. This last Monte Carlo campaign represented a huge computational effort, since several hundreds of telescope positions have been simulated, while for future instrument response function simulations, only the operating telescopes will be considered. In particular, during the last 18 months, about 2 PB of Monte Carlo data have been produced and processed with different analysis chains, with a corresponding overall CPU consumption of about 125 M HS06 hours. In these proceedings, we describe the employed computing model, based on the use of grid resources, as well as the production system setup, which relies on the DIRAC interware. Finally, we present the envisaged evolutions of the CTA production system for the off-line data processing during CTA operations and the instrument response function simulations.

Multi-objective parameter optimization for a single-shaft series-parallel plug-in hybrid electric bus using genetic algorithm

Recently, the single-shaft series-parallel powertrain of Plug-in Hybrid Electric Bus (PHEB) has become one of the most popular powertrains due to its alterable operating modes, excellent fuel economy and strong adaptability for driving cycles. Nevertheless, for configuring the PHEB with single-shaft series-parallel powertrain in the development stage, it still faces greater challenge than other configurations when choosing and matching the main component parameters. Motivated by this issue, a comprehensive multi-objectives optimization strategy based on Genetic Algorithm (GA) is developed for the PHEB with the typical powertrain. First, considering repeatability and regularity of bus route, the methods of off-line data processing and mathematical statistics are adopted, to obtain a representative driving cycle, which could well reflect the general characteristic of the real-world bus route. Then, the economical optimization objective is defined, which is consist of manufacturing costs of the key components and energy consumption, and combined with the dynamical optimization objective, a multi-objective optimization function is put forward. Meanwhile, GA algorithm is used to optimize the parameters, for the optimal components combination of the novel series-parallel powertrain. Finally, a comparison with the prototype is carried out to verify the performance of the optimized powertrain along driving cycles. Simulation results indicate that the parameters of powertrain components obtained by the proposed comprehensive multi-objectives optimization strategy might get better fuel economy, meanwhile ensure the dynamic performance of PHEB. In contrast to the original, the costs declined by 18%. Hence, the strategy would provide a theoretical guidance on parameter selection for PHEB manufacturers.

光学非球面三坐标测量中的像散补偿

When a three Coordinate Measurement Machine is used to test the surface shape of an aspheric element in grinding and polishing, obtained result often shows astigmatism errors due to an imperfect compensation programming. This paper analyzes these measurement data, and points out that the astigmatism errors comes from the imprecise compensation programming for the radius of three-dimensional probe in the CMM. Then, It presents an off-line data processing method to compensate the measurement data and to eliminate the astigmatism. By calculation of the tangent vectors of row and column at the meshed central point, the normal vector of each point on the surface is obtained. By calculation of the offsets between the centers of probe and the contact points, the radius of the tree-dimensional probe in the CMM is compensated. The experiment of a standard sphere show that this method decreases the astigmatism PV(Peak-to-Valley) of the measurement result from 4.921 9 μm to 0.065 2 μm, eliminating basically the astigmatism errors and improving the accuracy of the coordinate measurement. Obtained results verify the effectiveness of the proposed compensation programming for the radius of tree-dimensional probe in the CMM. ? 2016, Science Press. All right reserved.

Computerization of experiments on interaction of microwave electromagnetic radiation with heterogeneous liquids

A method for computerization of experiments on the interaction of microwave electromagnetic radiation and heterogeneous liquids is implemented as a measurement software complex that is integrated in the laboratory setup. The complex allows computerized measurements of various parameters of interaction of electromagnetic waves and material substances, control of the setup, and off-line processing of experimental data. The proposed computerized experimental procedures are superior to conventional manual procedures owing to faster and simpler measurements and analysis. The advantages of the proposed complex are demonstrated in experiments with known substances and artificial colloids and emulsions that contain magnetite nanoparticles.

泰克频谱仪IQT文件的后台处理及在电子冷却实验中的应用

泰克频谱仪IQT文件的后台处理及在电子冷却实验中的应用

Wearable ECG Recorder with Acceleration Sensors for Monitoring Daily Stress

A small and light-weight wearable electrocardiograph (ECG) device with three accelerometers (x, y, and z axis) was developed for prolonged monitoring of everyday stress. It consists of an amplifier, a microcomputer with an analog/digital converter, a tri-axial accelerometer, and a memory card. Four parameters can be sampled at 1 kHz for a maximum of 27 h with the default battery and a 1 GB memory card. An algorithm for the reliable and clear detection of R wave peaks of ECG was also developed for accurate heart rate variability (HRV) analysis. The algorithm reduces ECG motion artifacts induced by body movements. Off-line data processing includes autonomic nervous system (ANS) activity bi-spectral analysis and the application of the tone-entropy method to HRV data. The availability of the system was tested through simulated office work and three-day monitoring. Both short-term and circadian rhythms of ANS activity were clearly observed. The experimental data verifies the functionality of the proposed system.

Proof of concept of an imaging system demonstrator for PET applications with SiPM

A PET imaging system demonstrator based on LYSO crystal arrays coupled to SiPM matrices is under construction at the University and INFN of Pisa. Two SiPM matrices, composed of 8×8 SiPM pixels, and 1,5mm pitch, have been coupled one to one to a LYSO crystals array and read out by a custom electronics system. front-end ASICs were used to read 8 channels of each matrix. Data from each front-end were multiplexed and sent to a DAQ board for the digital conversion; a motherboard collects the data and communicates with a host computer through a USB port for the storage and off-line data processing. In this paper we show the first preliminary tomographic image of a point-like radioactive source acquired with part of the two detection heads in time coincidence.

A digital wireless system for closed-loop inhibition of nociceptive signals

Neurostimulation of the spinal cord or brain has been used to inhibit nociceptive signals in pain management applications. Nevertheless, most of the current neurostimulation models are based on open-loop system designs. There is a lack of closed-loop systems for neurostimulation in research with small freely-moving animals and in future clinical applications. Based on our previously developed analog wireless system for closed-loop neurostimulation, a digital wireless system with real-time feedback between recorder and stimulator modules has been developed to achieve multi-channel communication. The wireless system includes a wearable recording module, a wearable stimulation module and a transceiver connected to a computer for real-time and off-line data processing, display and storage. To validate our system, wide dynamic range neurons in the spinal cord dorsal horn have been recorded from anesthetized rats in response to graded mechanical stimuli (brush, pressure and pinch) applied in the hind paw. The identified nociceptive signals were used to automatically trigger electrical stimulation at the periaqueductal gray in real time to inhibit their own activities by the closed-loop design. Our digital wireless closed-loop system has provided a simplified and efficient method for further study of pain processing in freely-moving animals and potential clinical application in patients.

Experimental Assessment of Different Receiver Structures for Underwater Acoustic Communications over Multipath Channels

Underwater communication channels are often complicated, and in particular multipath propagation may cause intersymbol interference (ISI). This paper addresses how to remove ISI, and evaluates the performance of three different receiver structures and their implementations. Using real data collected in a high-frequency (10–14 kHz) field experiment, the receiver structures are evaluated by off-line data processing. The three structures are multichannel decision feedback equalizer (DFE), passive time reversal receiver (passive-phase conjugation (PPC) with a single channel DFE), and the joint PPC with multichannel DFE. In sparse channels, dominant arrivals represent the channel information, and the matching pursuit (MP) algorithm which exploits the channel sparseness has been investigated for PPC processing. In the assessment, it is found that: (1) it is advantageous to obtain spatial gain using the adaptive multichannel combining scheme; and (2) the MP algorithm improves the performance of communications using PPC processing.

Noise Cancellation of Ocular and Muscular Artifacts from EEG Signals Based on Adaptive Filtering

The Electroencephalogram (EEG) is a useful tool for clinical diagnosis. Artifacts in EEG records are caused by various factors like line interference, Electro-oculogram (EOG), Electro-Cardiogram ECG, Electromyogram EMG. These noise sources increase the difficulty in analyzing the EEG and for obtaining proper clinical information. Regression based methods for removing various artifacts require various procedures for preprocessing and calibration that are inconvenient and time consuming. Independent Component Analysis (ICA) (1) method requires off-line processing of data collected from a sufficiently larger number of channels and its success depends on correct identification of noise components. When application requires real-time removal of artifacts or when calibration trials cannot be conducted owing to various constraints, this method becomes unsuitable. This paper describes a method of removing EOG and EMG artifacts from EEG based on adaptive filtering using RLS algorithm.