Showing papers on "Data acquisition published in 2015"

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

Abstract: Advanced materials characterization techniques with ever-growing data acquisition speed and storage capabilities represent a challenge in modern materials science, and new procedures to quickly assess and analyze the data are needed. Machine learning approaches are effective in reducing the complexity of data and rapidly homing in on the underlying trend in multi-dimensional data. Here, we show that by employing an algorithm called the mean shift theory to a large amount of diffraction data in high-throughput experimentation, one can streamline the process of delineating the structural evolution across compositional variations mapped on combinatorial libraries with minimal computational cost. Data collected at a synchrotron beamline are analyzed on the fly, and by integrating experimental data with the inorganic crystal structure database (ICSD), we can substantially enhance the accuracy in classifying the structural phases across ternary phase spaces. We have used this approach to identify a novel magnetic phase with enhanced magnetic anisotropy which is a candidate for rare-earth free permanent magnet.

ClearVolume: open-source live 3D visualization for light-sheet microscopy

Abstract: To the editor: Current state-of-the-art light sheet microscopes rely on sophisticated control software to perform the acquisition of gigabytes of image data per second over the course of hours or even days. Typically the microscopes acquire data in a first step, and only in a second step this data is processed and visualized offline. The delay between data acquisition and data assessment wastes time and storage space. Technology that makes it possible to view and assess the data during imaging would offer significant advantages. However, even the most advanced microscopes only display the latest image plane acquired or projection while the raw volumetric data is saved to disk [1–4].

Intelligent Agriculture Greenhouse Environment Monitoring System Based on IOT Technology

Abstract: In recent years, greenhouse technology in agriculture is to automation, information technology direction with the IOT (Internet of Things) technology rapid development and wide application. This paper takes CC2530 chip as the core, presents the design and implementation of agriculture Greenhouse Environment monitoring system based on ZigBee technology, the wireless sensor and control nodes takes CC2530F256 as core to control the environment data. This system is made up of front-end data acquisition, data processing, data transmission and data reception. The ambient temperature is real-time processed by the temperature sensor of data terminal node. Processed data is send to the intermediate node through a wireless network. Intermediate node aggregates all data, and then sends the data to the PC through a serial port, at the same time, staff may view, analysis and storage the data by the PC that provide real-time data for agricultural greenhouse, fans and other temperature control equipment, and achieve automatic temperature control.

Zigbee-based data acquisition system for online monitoring of grid-connected photovoltaic system

Abstract: Design of a wireless monitoring system for remote application.Design of an algorithm for wireless data processing system to store data in server.Development of a PC-based control application for the wireless monitoring system.Development of a web-based application to view and monitor the system online. For grid-connected photovoltaic (PV) system, monitoring is considered as a crucial aspect for observing the stability and performance of the system. The simplest method is to have the data collected and transmitted across data cables. Due to the cost and technical limitations of the data cable, the monitoring station needs to be located reasonably close to the monitored plant. Apart being inconvenient, the use of data cable often adds capital and maintenance cost to the system. In this research project, a Zigbee-based wireless monitoring system is developed for online monitoring of a grid-connected photovoltaic system. Parameters like temperature, irradiation, PV power output and grid inverter power output are monitored. The implementation process, including design and development of the hardware and software, is explained in detail. A user-friendly web-application is also developed, such that the monitored data is easily accessible via internet. To validate the performance, the system has been implemented on 1.25kWp grid-connected photovoltaic system.

Development and Evaluation of a UAV-Photogrammetry System for Precise 3D Environmental Modeling

Abstract: The specific requirements of UAV-photogrammetry necessitate particular solutions for system development, which have mostly been ignored or not assessed adequately in recent studies. Accordingly, this paper presents the methodological and experimental aspects of correctly implementing a UAV-photogrammetry system. The hardware of the system consists of an electric-powered helicopter, a high-resolution digital camera and an inertial navigation system. The software of the system includes the in-house programs specifically designed for camera calibration, platform calibration, system integration, on-board data acquisition, flight planning and on-the-job self-calibration. The detailed features of the system are discussed, and solutions are proposed in order to enhance the system and its photogrammetric outputs. The developed system is extensively tested for precise modeling of the challenging environment of an open-pit gravel mine. The accuracy of the results is evaluated under various mapping conditions, including direct georeferencing and indirect georeferencing with different numbers, distributions and types of ground control points. Additionally, the effects of imaging configuration and network stability on modeling accuracy are assessed. The experiments demonstrated that 1.55 m horizontal and 3.16 m vertical absolute modeling accuracy could be achieved via direct geo-referencing, which was improved to 0.4 cm and 1.7 cm after indirect geo-referencing.

Rethinking Data-Intensive Science Using Scalable Analytics Systems

Abstract: "Next generation" data acquisition technologies are allowing scientists to collect exponentially more data at a lower cost. These trends are broadly impacting many scientific fields, including genomics, astronomy, and neuroscience. We can attack the problem caused by exponential data growth by applying horizontally scalable techniques from current analytics systems to accelerate scientific processing pipelines. In this paper, we describe ADAM, an example genomics pipeline that leverages the open-source Apache Spark and Parquet systems to achieve a 28x speedup over current genomics pipelines, while reducing cost by 63%. From building this system, we were able to distill a set of techniques for implementing scientific analyses efficiently using commodity "big data" systems. To demonstrate the generality of our architecture, we then implement a scalable astronomy image processing system which achieves a 2.8--8.9x improvement over the state-of-the-art MPI-based system.

A fast histogram-based similarity measure for detecting loop closures in 3-D LIDAR data

Abstract: We present a fast method of detecting loop closure opportunities through the use of similarity measures on histograms extracted from 3-D LIDAR data. We avoid computationally expensive features and compute histograms over simple global statistics of the LIDAR scans. The resulting histograms encode sufficient information to detect spatially close scans with high precision and recall and can be computed at rates faster than data acquisition on modest consumer-grade hardware. Our approach is able to match previously established results in LIDAR loop closure detection with less computational overhead.

Live Scan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors

Abstract: Live Scan3D is a free, open source system for live, 3D data acquisition using multiple Kinect v2 sensors. It allows the user to place any number of sensors in any physical configuration and start gathering data at real time speed. The freedom of placing the sensors in any configuration allows for many possible acquisition scenarios such as: capturing a single object from many viewpoints or creating 3D panoramas with multiple devices located close to each other. Thanks to the off-the-shelf Kinect v2 sensor the system is both accurate and inexpensive, opening 3D acquisition up to more recipients. In the paper we describe our system with the algorithms it is using and show its effectiveness in multiple scenarios including head shape reconstruction and 3D reconstruction of dynamic scenes.

The GBT-SCA, a radiation tolerant ASIC for detector control and monitoring applications in HEP experiments

Abstract: The future upgrades of the LHC experiments will increase the beam luminosity leading to a corresponding growth of the amounts of data to be treated by the data acquisition systems. To address these needs, the GBT (Giga-Bit Transceiver optical link [1,2]) architecture was developed to provide the simultaneous transfer of readout data, timing and trigger signals as well as slow control and monitoring data. The GBT-SCA ASIC, part of the GBT chip-set, has the purpose to distribute control and monitoring signals to the on-detector front-end electronics and perform monitoring operations of detector environmental parameters. In order to meet the requirements of different front-end ASICs used in the experiments, it provides various user-configurable interfaces capable to perform simultaneous operations. It is designed employing radiation tolerant design techniques to ensure robustness against SEUs and TID radiation effects and is implemented in a commercial 130 nm CMOS technology. This work presents the GBT-SCA architecture, the ASIC interfaces, the data transfer protocol, and its integration with the GBT optical link.

Wireless sensor networks in agriculture

Abstract: In the paper it is described the multilevel sensor network for data acquisition and processing in precision farming and ecological monitoring. The main aim of the multilevel sensor network is real-time data acquisition of state of plants on large territories, on-line data processing and timely decision making according to specified criteria. Wireless sensor network, which described in article, serves as primary data acquisition system in proposed multilevel sensor network.

Condition Monitoring and Damage Location of Wind Turbine Blades by Frequency Response Transmissibility Analysis

Abstract: Incipient defects occurring in long wind turbine (WT) blades are difficult to detect using the existing condition monitoring (CM) techniques. To tackle this issue, a new WT blade CM method is studied in this paper with the aid of the concept of transmissibility of frequency response functions. Different from the existing CM techniques that judge the health condition of a blade by interpreting individual CM signals, the proposed method jointly utilizes the CM signals measured by a number of neighboring sensors. This offers the proposed technique a unique capability of both damage detection and location. The proposed technique has been experimentally verified by using the real CM data collected during the fatigue and static tests of a full-scale WT blade. The experiment has shown that the new technique is effective not only in damage detection but also in damage location when either fiber Bragg grating strain gauges or accelerometers are used for data acquisition.

Real time capable infrared thermography for ASDEX Upgrade

Abstract: Infrared (IR) thermography is widely used in fusion research to study power exhaust and incident heat load onto the plasma facing components. Due to the short pulse duration of today’s fusion experiments, IR systems have mostly been designed for off-line data analysis. For future long pulse devices (e.g., Wendelstein 7-X, ITER), a real time evaluation of the target temperature and heat flux is mandatory. This paper shows the development of a real time capable IR system for ASDEX Upgrade. A compact IR camera has been designed incorporating the necessary magnetic and electric shielding for the detector, cooler assembly. The camera communication is based on the Camera Link industry standard. The data acquisition hardware is based on National Instruments hardware, consisting of a PXIe chassis inside and a fibre optical connected industry computer outside the torus hall. Image processing and data evaluation are performed using real time LabVIEW.

A Framework for Model-Driven Acquisition and Analytics of Visual Data Using UAVs for Automated Construction Progress Monitoring

Abstract: Automated assessment of work-in-progress using large collections of site images and four-dimensional (4D) building information modelling (BIM) has potential to significantly improve the efficiency of construction project controls. Nevertheless, today’s manual procedures for taking site photos do not support the desired frequency or completeness for automated progress monitoring. While the usage of Unmanned Aerial Vehicles for acquisition of site images has gained popularity, their application for addressing issues associated with image-based progress monitoring and particularly leveraging 4D BIM for steering the data collection process has not been investigated before. By presenting examples from two case studies conducted on real-world construction projects, this paper suggests a framework for model-driven acquisition and analytics of progress images. In particular, the potential of spatial (geometry, appearance, and interconnectivity) and temporal information in 4D BIM for autonomous data acquisition and analytics that guarantees completeness and accuracy for both as-built modeling and monitoring work-in-progress at the schedule task-level is discussed.

FPGA-Based Voltage and Current Dual Drive System for High Frame Rate Electrical Impedance Tomography

Abstract: Electrical impedance tomography (EIT) is used to image the electrical property distribution of a tissue under test. An EIT system comprises complex hardware and software modules, which are typically designed for a specific application. Upgrading these modules is a time-consuming process, and requires rigorous testing to ensure proper functioning of new modules with the existing ones. To this end, we developed a modular and reconfigurable data acquisition (DAQ) system using National Instruments' (NI) hardware and software modules, which offer inherent compatibility over generations of hardware and software revisions. The system can be configured to use up to 32-channels. This EIT system can be used to interchangeably apply current or voltage signal, and measure the tissue response in a semi-parallel fashion. A novel signal averaging algorithm, and 512-point fast Fourier transform (FFT) computation block was implemented on the FPGA. FFT output bins were classified as signal or noise. Signal bins constitute a tissue's response to a pure or mixed tone signal. Signal bins' data can be used for traditional applications, as well as synchronous frequency-difference imaging. Noise bins were used to compute noise power on the FPGA. Noise power represents a metric of signal quality, and can be used to ensure proper tissue-electrode contact. Allocation of these computationally expensive tasks to the FPGA reduced the required bandwidth between PC, and the FPGA for high frame rate EIT. In 16-channel configuration, with a signal-averaging factor of 8, the DAQ frame rate at 100 kHz exceeded 110 frames ${\rm s} ^{-1}$ , and signal-to-noise ratio exceeded 90 dB across the spectrum. Reciprocity error was found to be $ for frequencies up to 1 MHz. Static imaging experiments were performed on a high-conductivity inclusion placed in a saline filled tank; the inclusion was clearly localized in the reconstructions obtained for both absolute current and voltage mode data.

fastSIM: a practical implementation of fast structured illumination microscopy

Abstract: A significant improvement in acquisition speed of structured illumination microscopy (SIM) opens a new field of applications to this already well-established super-resolution method towards 3D scanning real-time imaging of living cells. We demonstrate a method of increased acquisition speed on a two-beam SIM fluorescence microscope with a lateral resolution of ~100 nm at a maximum raw data acquisition rate of 162 frames per second (fps) with a region of interest of 16.5 × 16.5 µm2, free of mechanically moving components. We use a programmable spatial light modulator (ferroelectric LCOS) which promises precise and rapid control of the excitation pattern in the sample plane. A passive Fourier filter and a segmented azimuthally patterned polarizer are used to perform structured illumination with maximum contrast. Furthermore, the free running mode in a modern sCMOS camera helps to achieve faster data acquisition.

Optical data compression in time stretch imaging.

Abstract: Time stretch imaging offers real-time image acquisition at millions of frames per second and subnanosecond shutter speed, and has enabled detection of rare cancer cells in blood with record throughput and specificity. An unintended consequence of high throughput image acquisition is the massive amount of digital data generated by the instrument. Here we report the first experimental demonstration of real-time optical image compression applied to time stretch imaging. By exploiting the sparsity of the image, we reduce the number of samples and the amount of data generated by the time stretch camera in our proof-of-concept experiments by about three times. Optical data compression addresses the big data predicament in such systems.

Determination of object data by template-based UAV control

Abstract: Method for providing information about an object (5) using an unmanned aerial vehicle with a data acquisition unit, comprising determining positional data with reference to the object (5), the positional data being referenced to a measuring coordinate system, providing a digital template (11) regarding the object (5), the template (11) at least partly representing the object (5) in coarse manner, and referencing the template (11) with the positional data so that the template (11) corresponds as to its spatial parameters to the object in the measuring coordinate system. Moreover, a spatial position of at least one data acquisition point (12) or section is related to the object (5) is derived based on at least the positional data and/or the template (11), the unmanned aerial vehicle is controlled in a manner such that it approaches the at least one data acquisition point (12) or section and object information is acquired as to at least a part of the object (5) according to the at least one data acquisition point (12) or section by triggering the data acquisition unit depending on fulfilling a defined distance criterion, the distance criterion defining a spatial relation between an actual position of the unmanned aerial vehicle and the at least one data acquisition point (12) or section.

Method for acquiring and storing big data of power information

Abstract: The present invention, belonging to the field of acquisition and storage of big data of power information, relates to a method for acquiring and storing the big data of power information, and solves the problems in the acquisition and storage process of the big data of power information. The method comprises three steps: data acquisition, data storage and management, and data analysis. The method achieves the beneficial effects that the present invention provides the method for acquiring and storing the power information big data; and the problems of low response speed of a service system and long consumed time for waiting of a user, which are generated due to a large cardinal number of power users and a large quantity of information, can be well solved. Meanwhile, for massive information generated by a trading system on the basis of power data, the method can better complete data extension and analysis functions than a traditional database. Moreover, according to the method, massive Internet media data can be acquired, stored and processed so as to better learn about the trade trend; and during the operation process, the storage method has an efficient inquiring function so that retrieval can be rapidly completed when the data volume is increased sharply.

A Novel Compressed Sensing Scheme for Photoacoustic Tomography

Abstract: Speeding up the data acquisition is one of the central aims for advancing tomographic imaging. On the one hand, this reduces motion artifacts due to undesired movements, and on the other hand this decreases the examination time for the patient. In this article, we propose a new scheme for speeding up the data collection process in photoacoustic tomography. Our proposal is based on compressed sensing and reduces acquisition time and system costs while maintaining image quality. As measurement data we use random combinations of pressure values that we use to recover a complete set of pressure data prior to the actual image reconstruction. We obtain theoretical recovery guarantees for our compressed sensing scheme and support the theory by reconstruction results on simulated data as well as on experimental data.

Intelligent remote data acquisition system based on Internet of Things

Abstract: The invention provides an intelligent remote data acquisition system based on Internet of Things. The intelligent remote data acquisition system comprises a multichannel asynchronous data acquisition module, a multiprotocol intermediate module, a parameter management module, a data transmitting module, an emergency management module and a self-diagnosis and power outage processing module, wherein the multichannel asynchronous data acquisition module comprises a parameter reading module, a protocol packet generating module, a data packet transmitting module and a data packet receiving module; the multiprotocol intermediate module comprises a protocol algorithm converting module and a data packet generating module; the parameter management module comprises an acquisition and allocation management module and a parameter automatic update management module; the data transmitting module comprises a network protocol management module, a SOCKET connection module, a data encapsulation module and a data transmitting module; the emergency management module comprises an event configuration management module, an event processing module and an automatic alarming module; the self-diagnosis and power outage processing module comprises a system detection module and a network detection module.

Big Data Analysis of Manufacturing Processes

Abstract: The high complexity of manufacturing processes and the continuously growing amount of data lead to excessive demands on the users with respect to process monitoring, data analysis and fault detection. For these reasons, problems and faults are often detected too late, maintenance intervals are chosen too short and optimization potential for higher output and increased energy efficiency is not sufficiently used. A possibility to cope with these challenges is the development of self-learning assistance systems, which identify relevant relationships by observation of complex manufacturing processes so that failures, anomalies and need for optimization are automatically detected. The assistance system developed in the present work accomplishes data acquisition, process monitoring and anomaly detection in industrial and agricultural processes. The assistance system is evaluated in three application cases: Large distillation columns, agricultural harvesting processes and large-scale sorting plants. In this paper, the developed infrastructures for data acquisition in these application cases are described as well as the developed algorithms and initial evaluation results.

Laboratory-based X-ray phase-contrast imaging with misaligned optical elements

Abstract: We report on a laboratory X-ray phase-contrast imaging technique based on the edge illumination principle that substantially relaxes the existing limitations on system set up and data acquisition, allowing an increase in tolerance of misalignments by at least two orders of magnitude. The robustness of this approach was systematically studied by means of experiments with our prototype. Numerical simulations were also performed in order to assess the dependence of the image quality on the data acquisition scheme. The results show that errors in the positioning of the masks within a 1° range for all the angles, and within 1 mm range of translation, do not noticeably affect image quality. We also show that, if the misalignment does not exceed few tens of micrometers, three intensity projections are sufficient to effectively retrieve the three representations of the sample, allowing for optimization of dose or time efficiency even with a non-ideal system.