Showing papers on "Data acquisition published in 2014"

Data Preprocessing in Data Mining

Abstract: Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given. Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Empatica E3 — A wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition

Abstract: The Empatica E3 is a wearable wireless multisensor device for real-time computerized biofeedback and data acquisition. The E3 has four embedded sensors: photoplethysmograph (PPG), electrodermal activity (EDA), 3-axis accelerometer, and temperature. It is small, light and comfortable and it is suitable for almost all real-life applications. The E3 operates both in streaming mode for real-time data processing using a Bluetooth low energy interface and in recording mode using its internal flash memory. With E3, it is possible to conduct research outside of the lab by acquiring continuous data for ambulatory situations in a comfortable and non-distracting way.

The Toast++ software suite for forward and inverse modeling in optical tomography.

Abstract: We present the Toast++ open-source software environment for solving the forward and inverse problems in diffuse optical tomography (DOT). The software suite consists of a set of libraries to simulate near-infrared light propagation in highly scattering media with complex boundaries and heterogeneous internal parameter distribution, based on a finite-element solver. Steady-state, time- and frequency-domain data acquisition systems can be modeled. The forward solver is implemented in C++ and supports performance acceleration with parallelization for shared and distributed memory architectures, as well as graphics processing computation. Building on the numerical forward solver, Toast++ contains model-based iterative inverse solvers for reconstructing the volume distribution of absorption and scattering parameters from boundary measurements of light transmission. A range of regularization methods are provided, including the possibility of incorporating prior knowledge of internal structure. The user can link to the Toast++ libraries either directly to compile application programs for DOT, or make use of the included MATLAB and PYTHON bindings to generate script-based solutions. This approach allows rapid prototyping and provides a rich toolset in both environments for debugging, testing, and visualization.

Operational Modal Analysis of Civil Engineering Structures: An Introduction and Guide for Applications

Abstract: Introduction.- Mathematical Tools for Random Data Analysis.- Data Acquisition.- Data Processing.- Applications.- Automated OMA.

Effect of dwell time on single particle inductively coupled plasma mass spectrometry data acquisition quality

Abstract: The characterization, sizing, and quantification of metal-based nanoparticles (NP) in a variety of matrices using single particle-inductively coupled plasma-mass spectrometry (SP-ICP-MS) is becoming increasingly popular due to the sensitive nature of the technique. Nanoparticle events in the plasma are less than 0.5 ms in duration; however current quadrupole-based ICP-MS instruments are limited to instrument dwell times in the millisecond range and have data acquisition overhead that adversely affects data quality. Novel instrument settings and data processing techniques can be used to explore the benefits of continuous data acquisition rates as fast as 105 Hz (or 10 μs dwell times). This paper provides data on the different effects data acquisition rate has on the quality of data that can be obtained by SP-ICP-MS. The effect of varying the dwell time and its influence on particle integration, particle counting, particle sizing, and background signal is discussed. This paper provides data on identifying the significant instrument settings and their implications on nanoparticle characterization.

Generation of a Precise Roadway Map for Autonomous Cars

Abstract: This paper proposes a map generation algorithm for a precise roadway map designed for autonomous cars. The roadway map generation algorithm is composed of three steps, namely, data acquisition, data processing, and road modeling. In the data acquisition step, raw trajectory and motion data for map generation are acquired through exploration using a probe vehicle equipped with GPS and on-board sensors. The data processing step then processes the acquired trajectory and motion data into roadway geometry data. GPS trajectory data are unsuitable for direct roadway map use by autonomous cars due to signal interruptions and multipath; therefore, motion information from the on-board sensors is applied to refine the GPS trajectory data. A fixed-interval optimal smoothing theory is used for a refinement algorithm that can improve the accuracy, continuity, and reliability of road geometry data. Refined road geometry data are represented into the B-spline road model. A gradual correction algorithm is proposed to accurately represent road geometry with a reduced amount of control parameters. The developed map generation algorithm is verified and evaluated through experimental studies under various road geometry conditions. The results show that the generated roadway map is sufficiently accurate and reliable to utilize for autonomous driving.

Use of commercial off-the-shelf digital cameras for scientific data acquisition and scene-specific color calibration

Abstract: Commercial off-the-shelf digital cameras are inexpensive and easy-to-use instruments that can be used for quantitative scientific data acquisition if images are captured in raw format and processed so that they maintain a linear relationship with scene radiance. Here we describe the image-processing steps required for consistent data acquisition with color cameras. In addition, we present a method for scene-specific color calibration that increases the accuracy of color capture when a scene contains colors that are not well represented in the gamut of a standard color-calibration target. We demonstrate applications of the proposed methodology in the fields of biomedical engineering, artwork photography, perception science, marine biology, and underwater imaging.

Approximate Physical World Reconstruction Algorithms in Sensor Networks

Abstract: To observe the complicated physical world, the sensors in a network sense and sample the data from the physical world. Currently, most existing works use the Equi-Frequency Sampling (EFS) methods or EFS based methods for data acquisition. However, the accuracy of EFS and EFS based methods cannot be guaranteed in practice since the physical world keeps changing continuously, and these methods do not effectively support reconstruction of the monitored physical world. To overcome the shortages of EFS and EFS based methods, this paper focuses on designing physical-world-aware data acquisition algorithms to support O(?)-approximation to the physical world for any ? 0. Two physical-world-aware data acquisition algorithms are proposed. Both algorithms can adjust the sensing frequency automatically based on the changing trend of the physical world and the given ?. The thorough analysis on the performances of the algorithms are also provided. It is proven that the error bounds of the algorithms are O(?) and the complexities of the algorithms are O( 1 ?1=4 ). Based on the new data acquisition algorithms, an algorithm for reconstructing the physical world is proposed and analyzed. The theoretical analysis and experimental results show that the proposed algorithms have high performances on the aspects of accuracy and energy consumption

Information system integrated operation and maintenance monitoring service early warning platform and realization method thereof

Abstract: The invention relates to the technical field of an IT system, and particularly discloses an information system integrated operation and maintenance monitoring service early warning platform and a realization method thereof. The platform comprises a data acquisition layer which is used for acquiring basic data of the monitored device; a data processing layer which is used for processing the basic data collected by the data acquisition layer; a data presentation layer which is used for displaying the data after being processed by users; and an information system integrated operation and maintenance monitoring service early warning platform management system which is used for managing the platform. The system penetrates through three layers of the data acquisition layer, the data processing layer and the data presentation layer, and configures and manages the three layers of the data acquisition layer, the data processing layer and the data presentation layer in a unified way. The platform has refined, automatic, intelligent and integrated IT operation and maintenance monitoring functions so that information technology operation and maintenance capability of large-scale enterprises is enhanced.

A Distributed Fault Detection System Based on IWSN for Machine Condition Monitoring

Abstract: This paper introduces a novel framework for industrial wireless sensor networks (IWSNs) used for machine condition monitoring (MCM). Our approach enables the use of state-of-the-art computationally intensive classifiers in computationally weak sensor network nodes. The key idea is to split data acquisition, classifier building and training, and the operation phase, between different units. Computationally demanding processing is carried out in the central unit, while other tasks are distributed to the sensor nodes using over-the-air programming. The system is autonomously trained on the healthy state of a machine and then monitors a change in behavior which indicates a faulty state. Thanks to one-class classification, there is no need to introduce the faulty state of the machine in the training phase. We extend the diagnostic capability of the system using dynamic changes in the data acquisition and classification parts of the program in the sensor nodes. This enables the system to react to ambiguous machine states by temporarily changing the diagnostic focus. Compressing the information in the individual sensor nodes provided by in-node classification allows us to transmit only the classification result, instead of full signal waveforms. This enables the MCM system to be deployed with a large number of nodes, even with high sampling rates. The proposed concept was evaluated in IRIS IWSN by means of a rotary machine simulator.

Towards higher accuracy and better frequency response with standard multi-hole probes in turbulence measurement with remotely piloted aircraft (RPA)

Abstract: . This study deals with the problem of turbulence measurement with small remotely piloted aircraft (RPA). It shows how multi-hole probes (MHPs) can be used to measure fluctuating parts of the airflow in flight up to 20 Hz. Accurate measurement of the transient wind in the outdoor environment is needed for the estimation of the 3-D wind vector as well as turbulent fluxes of heat, momentum, water vapour, etc. In comparison to an established MHP system, experiments were done to show how developments of the system setup can improve data quality. The study includes a re-evaluation of the pneumatic tubing setup, the conversion from pressures to airspeed, the pressure transducers, and the data acquisition system. In each of these fields, the steps that were taken lead to significant improvements. A spectral analysis of airspeed data obtained in flight tests shows the capability of the system to measure atmospheric turbulence up to the desired frequency range.

ACQ4: an open-source software platform for data acquisition and analysis in neurophysiology research

Abstract: The complexity of modern neurophysiology experiments requires specialized software to coordinate multiple acquisition devices and analyze the collected data. We have developed ACQ4, an open-source software platform for performing data acquisition and analysis in experimental neurophysiology. This software integrates the tasks of acquiring, managing, and analyzing experimental data. ACQ4 has been used primarily for standard patch-clamp electrophysiology, laser scanning photostimulation, multiphoton microscopy, intrinsic imaging, and calcium imaging. The system is highly modular, which facilitates the addition of new devices and functionality. The modules included with ACQ4 provide for rapid construction of acquisition protocols, live video display, and customizable analysis tools. Position-aware data collection allows automated construction of image mosaics and registration of images with 3-dimensional anatomical atlases. ACQ4 uses free and open-source tools including Python, NumPy/SciPy for numerical computation, PyQt for the user interface, and PyQtGraph for scientific graphics. Supported hardware includes cameras, patch clamp amplifiers, scanning mirrors, lasers, shutters, Pockels cells, motorized stages, and more. ACQ4 is available for download at http://www.acq4.org.

Anomaly detection in large-scale data stream networks

Abstract: This paper addresses the anomaly detection problem in large-scale data mining applications using residual subspace analysis. We are specifically concerned with situations where the full data cannot be practically obtained due to physical limitations such as low bandwidth, limited memory, storage, or computing power. Motivated by the recent compressed sensing (CS) theory, we suggest a framework wherein random projection can be used to obtained compressed data, addressing the scalability challenge. Our theoretical contribution shows that the spectral property of the CS data is approximately preserved under a such a projection and thus the performance of spectral-based methods for anomaly detection is almost equivalent to the case in which the raw data is completely available. Our second contribution is the construction of the framework to use this result and detect anomalies in the compressed data directly, thus circumventing the problems of data acquisition in large sensor networks. We have conducted extensive experiments to detect anomalies in network and surveillance applications on large datasets, including the benchmark PETS 2007 and 83 GB of real footage from three public train stations. Our results show that our proposed method is scalable, and importantly, its performance is comparable to conventional methods for anomaly detection when the complete data is available.

High frame-rate multichannel beam-scanning microscopy based on Lissajous trajectories

Abstract: A simple beam-scanning optical design based on Lissajous trajectory imaging is described for achieving up to kHz frame-rate optical imaging on multiple simultaneous data acquisition channels. In brief, two fast-scan resonant mirrors direct the optical beam on a circuitous trajectory through the field of view, with the trajectory repeat-time given by the least common multiplier of the mirror periods. Dicing the raw time-domain data into sub-trajectories combined with model-based image reconstruction (MBIR) 3D in-painting algorithms allows for effective frame-rates much higher than the repeat time of the Lissajous trajectory. Since sub-trajectory and full-trajectory imaging are simply different methods of analyzing the same data, both high-frame rate images with relatively low resolution and low frame rate images with high resolution are simultaneously acquired. The optical hardware required to perform Lissajous imaging represents only a minor modification to established beam-scanning hardware, combined with additional control and data acquisition electronics. Preliminary studies based on laser transmittance imaging and polarization-dependent second harmonic generation microscopy support the viability of the approach both for detection of subtle changes in large signals and for trace-light detection of transient fluctuations.

Two-dimensional electronic spectroscopy based on conventional optics and fast dual chopper data acquisition.

Abstract: We report an improved experimental scheme for two-dimensional electronic spectroscopy (2D-ES) based solely on conventional optical components and fast data acquisition. This is accomplished by working with two choppers synchronized to a 10 kHz repetition rate amplified laser system. We demonstrate how scattering and pump-probe contributions can be removed during 2D measurements and how the pump probe and local oscillator spectra can be generated and saved simultaneously with each population time measurement. As an example the 2D-ES spectra for cresyl violet were obtained. The resulting 2D spectra show a significant oscillating signal during population evolution time which can be assigned to an intramolecular vibrational mode.

TomoPy: A framework for the analysis of synchrotron tomographic data

Abstract: Analysis of large tomographic datasets at synchrotron light sources is becoming progressively more challenging due to the increasing data acquisition rates that new technologies in X-ray sources and detectors enable. The next generation of synchrotron facilities that are currently under design or construction throughout the world will provide diffraction limited X-ray sources and is expected to boost the current data rates by several orders of magnitude and stressing the need for the development and integration of efficient analysis tools more than ever. Here we describe in detail an attempt to provide such a collaborative framework for the analysis of synchrotron tomographic data that has the potential to unify the effort of different facilities and beamlines performing similar tasks. The proposed Python/C++ based framework is open-source, OS and data format independent, parallelizable and supports functional programming that many researchers prefer. This collaborative platform will affect all major synchrotron facilities where new effort is now dedicated into developing new tools that can be deployed at the facility for real time processing as well as distributed to users for off site data processing.

Adaptive rule engine based IoT enabled remote health care data acquisition and smart transmission system

Abstract: In the remote health care monitoring applications, the collected medical data from bio-medical sensors should be transmitted to the nearest gateway for further processing. Transmission of data contributes to a significant amount of power consumption by the transmitter and increase in the network traffic. In this paper we propose a low complex rule engine based health care data acquisition and smart transmission system architecture, which uses IEEE 802.15.4 standard for transferring data to the gateway. The power consumed and the network traffic generated by the device can be reduced by event based transmission rather than continuous transmission of data. We developed two different rule engines: static rule engine and adaptive rule engine, which decides whether to transmit the collected data based on the important features extracted from the data, thereby achieving power saving. In this paper, ECG data acquisition and transmission architecture is considered. The metrics used for performance analysis are the amount of power saving and reduction in network traffic. It is shown that the proposed rule engine gives a significant reduction in energy consumption and network traffic generated.

Accelerated MRI with CIRcular Cartesian UnderSampling (CIRCUS): a variable density Cartesian sampling strategy for compressed sensing and parallel imaging.

Abstract: Purpose: This study proposes and evaluates a novel method for generating efficient undersampling patterns for 3D Cartesian acquisition with compressed sensing (CS) and parallel imaging (PI). Methods: Image quality achieved with schemes that accelerate data acquisition, including CS and PI, are sensitive to the design of the specific undersampling scheme used. Ideally random sampling is required to recover MR images from undersampled data with CS. In practice, pseudo-random sampling schemes are usually applied. Radial or spiral sampling either for Cartesian or non-Cartesian acquisitions has been using because of its favorable features such as interleaving flexibility. In this study, we propose to undersample data on the ky-kz plane of the 3D Cartesian acquisition by circularly selecting sampling points in a way that maintains the features of both random and radial or spiral sampling. Results: The proposed sampling scheme is shown to outperform conventional random and radial or spiral samplings for 3D Cartesian acquisition and is found to be comparable to advanced variable-density Poisson- Disk sampling (vPDS) while retaining interleaving flexibility for dynamic imaging, based on the results with retrospective undersampling. Our preliminary results with the prospective implementation of the proposed undersampling strategy demonstrated its favorable features. Conclusions: The proposed undersampling patterns for 3D Cartesian acquisition possess the desirable properties of randomization and radial or spiral trajectories. It provides easy implementation, flexible sampling, and high accuracy of image reconstruction with CS and PI.

Research on the stress-magnetism effect of ferromagnetic materials based on three-dimensional magnetic flux leakage testing

Abstract: Metal magnetic memory is a non-destructive testing technique in which the stress-magnetism effect of ferromagnetic materials is applied to evaluate the stress-concentration zone. A test platform was developed to measure the three-dimensional magnetic flux, based on leakage flux theory, in order to realize real-time display, processing, and storage of magnetic signals by using LabVIEW programs. The distribution of the two-dimensional spectrum entropy of detection signals is intuitively displayed by Fourier transform and support vector machines model. Our results demonstrate that data acquisition can be realized accurately using magnetic flux leakage inspection technology based on LabVIEW and that the distribution of the spectrum entropy can provide a method for monitoring crack growth through diagnosis of internal stress concentrations in materials.

System and method for real-time noise reduction in mri data acquisition

Abstract: A method and system for real-time reduction of noise during MRI data acquisition is disclosed. At least one antenna is placed in proximity to, or within the cavity of, a standard MRI apparatus, and in connection with a standard data acquisition setup. The data acquisition by the antenna is synchronized to the MRI pulses. Any residual signal is defined as noise; if a particular data subset is deemed to be noisy, the noise can be reduced or eliminated by, for example, remeasuring the data subset or by direct subtraction of the noise from the measured signal. By placing one antenna within and one in proximity to the MRI apparatus, the system and method can also be used to determine the actual level of RF shielding in the MRI apparatus.

Sliding Mode Control for Active Suspension System with Data Acquisition Delay

Abstract: This paper addresses the problem of control of an active suspension system accomplished using a computer. Delay in the states due to the acquisition and transmission of data from sensors to the controller is taken into account. The proposed control strategy uses state predictors along with sliding mode control technique. Two approaches are made: a continuous-time and a discrete-time control. The proposed designs, continuous-time and discrete-time, are applied to the active suspension module simulator from Quanser. Results from computer simulations and experimental tests are analyzed to show the effectiveness of the proposed control strategy.

Smart hvac manifold system

Abstract: The smart HVAC manifold system for servicing air conditioning systems is designed to dynamically manage the data acquisition process and to measure and calculate the performance indicators and output as the load conditions and or equipment operation change taking into account variables in the installation that can impact performance. Both visually and by a very specific data set the performance of the equipment and the installation can quickly be assessed and specific problems identified along with suggestions of typical faults or problems that may need addressed by the technician.

Process observation in fiber laser-based selective laser melting

Abstract: The process observation in selective laser melting (SLM) focuses on observing the interaction point where the powder is processed. To provide process relevant information, signals have to be acquired that are resolved in both time and space. Especially in high-power SLM, where more than 1 kW of laser power is used, processing speeds of several meters per second are required for a high-quality processing results. Therefore, an implementation of a suitable process observation system has to acquire a large amount of spatially resolved data at low sampling speeds or it has to restrict the acquisition to a predefined area at a high sampling speed. In any case, it is vitally important to synchronously record the laser beam position and the acquired signal. This is a prerequisite that allows the recorded data become information. Today, most SLM systems employ f-theta lenses to focus the processing laser beam onto the powder bed. This report describes the drawbacks that result for process observation and suggests a variable retro-focus system which solves these issues. The beam quality of fiber lasers delivers the processing laser beam to the powder bed at relevant focus diameters, which is a key prerequisite for this solution to be viable. The optical train we present here couples the processing laser beam and the process observation coaxially, ensuring consistent alignment of interaction zone and observed area. With respect to signal processing, we have developed a solution that synchronously acquires signals from a pyrometer and the position of the laser beam by sampling the data with a field programmable gate array. The relevance of the acquired signals has been validated by the scanning of a sample filament. Experiments with grooved samples show a correlation between different powder thicknesses and the acquired signals at relevant processing parameters. This basic work takes a first step toward self-optimization of the manufacturing process in SLM. It enables the addition of cognitive functions to the manufacturing system to the extent that the system could track its own process. The results are based on analyzing and redesigning the optical train, in combination with a real-time signal acquisition system which provides a solution to certain technological barriers.

Large-scale distributed network safety data acquisition method and system

Abstract: The invention relates to a large-scale distributed network safety data acquisition method and system The method comprises the steps of multimode data acquisition, data analysis and standardization and data distribution and transmission The system comprises an acquisition agent module, a data acquisition module, a data analysis module and a data distribution and transmission module With respect to data acquisition, multiple modes such as an active mode, a passive mode and a data stream mirror image mode are adopted, and comprehensive acquisition of various types of data is realized; with respect to data analysis, a data analysis and standardization mechanism based on strategies is adopted, original data are extracted, mapped, replaced, supplemented and the like by means of writing analysis strategies, and therefore quick analysis of a newly added data format and data standardization oriented to multiple application systems are realized; with respect to transmission, the multi-stage connection technology and the multi-path distribution technology are adopted, elastic combination, cascading deployment and multi-path distribution between acquisition systems are realized, and the requirements for vertical and horizontal expansion of a network environment and acquisition of mass data information are met

A Versatile Readout System for Small to Medium Scale Gaseous and Silicon Detectors

Abstract: This paper presents the design and performance of a readout system for gaseous and silicon detectors built for the Minos nuclear physics experiment. A major constraint was to provide a multi-thousand channel, high performance readout system with low manpower effort and tight cost. This was achieved by the re-use of some earlier ASIC and front-end card (FEC) developments, the design of a new digital readout card, called the Feminos, and the use of commercial off-the-shelf components. The proposed system fully exploits the capability of the existing 72-channel AFTER chip designed for the T2K experiment and allows seamless migration to the 64-channel AGET chip, a pin-compatible evolution under production by the GET collaboration. The Feminos is a low complexity card designed to read out a FEC equipped with four AFTER chips (T2K model) or a newly assembled FEC populated with four AGET chips. The trigger clock module (TCM) is a synchronization board that allows system scaling up to 6912 channels with 24 Feminos and FECs, a commercial Gigabit Ethernet switch, and a data acquisition PC. The design of the Feminos hardware, firmware and embedded software are detailed and it is explained how high performance, rapid development and low cost were reached. System operation and data acquisition throughput scaling with multiple Feminos are investigated.