Showing papers on "Data acquisition published in 2007"

TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry

Abstract: TanDEM-X (TerraSAR-X add-on for digital elevation measurements) is an innovative spaceborne radar interferometer that is based on two TerraSAR-X radar satellites flying in close formation. The primary objective of the TanDEM-X mission is the generation of a consistent global digital elevation model (DEM) with an unprecedented accuracy, which is equaling or surpassing the HRTI-3 specification. Beyond that, TanDEM-X provides a highly reconfigurable platform for the demonstration of new radar imaging techniques and applications. This paper gives a detailed overview of the TanDEM-X mission concept which is based on the systematic combination of several innovative technologies. The key elements are the bistatic data acquisition employing an innovative phase synchronization link, a novel satellite formation flying concept allowing for the collection of bistatic data with short along-track baselines, as well as the use of new interferometric modes for system verification and DEM calibration. The interferometric performance is analyzed in detail, taking into account the peculiarities of the bistatic operation. Based on this analysis, an optimized DEM data acquisition plan is derived which employs the combination of multiple data takes with different baselines. Finally, a collection of instructive examples illustrates the capabilities of TanDEM-X for the development and demonstration of new remote sensing applications.

Online analysis enhances use of NASA Earth science data

Abstract: Giovanni, the Goddard Earth Sciences Data and Information Services Center (GES DISC) Interactive Online Visualization and Analysis Infrastructure, has provided researchers with advanced capabilities to perform data exploration and analysis with observational data from NASA Earth observation satellites. In the past 5-10 years, examining geophysical events and processes with remote-sensing data required a multistep process of data discovery, data acquisition, data management, and ultimately data analysis. Giovanni accelerates this process by enabling basic visualization and analysis directly on the World Wide Web. In the last two years, Giovanni has added new data acquisition functions and expanded analysis options to increase its usefulness to the Earth science research community.

National Instruments LabVIEW: A Programming Environment for Laboratory Automation and Measurement

Abstract: National Instruments LabVIEW is a graphical programming language that has its roots in automation control and data acquisition. Its graphical representation, similar to a process flow diagram, was created to provide an intuitive programming environment for scientists and engineers. The language has matured over the last 20 years to become a general purpose programming environment. LabVIEW has several key features which make it a good choice in an automation environment. These include simple network communication, turnkey implementation of common communication protocols (RS232, GPIB, etc.), powerful toolsets for process control and data fitting, fast and easy user interface construction, and an efficient code execution environment. We discuss the merits of the language and provide an example application suite written in-house which is used in integrating and controlling automation platforms.

The data acquisition system for the ANTARES Neutrino Telescope

Abstract: The ANTARES neutrino telescope is being constructed in the Mediterranean Sea. It consists of a large three-dimensional array of photo-multiplier tubes. The data acquisition system of the detector takes care of the digitisation of the photo-multiplier tube signals, data transport, data filtering, and data storage. The detector is operated using a control program interfaced with all elements. The design and the implementation of the data acquisition system are described.

Automatic real-time optimization and intelligent control of electrical power distribution and transmission systems

Abstract: A system for real-time optimization of power resources on an electrical system is disclosed. The system includes a data acquisition component, an analytics server, a control element and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system.The analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a power flow optimization engine. The virtual system modeling engine is configured to generate predicted data output for the electrical system utilizing a virtual system model of the electrical system. The control element is interfaced with an electrical system component and communicatively connected to the analytics server. The client terminal is communicatively connected to the analytics server.

Data acquisition schemes for continuous two-particle time-of-flight coincidence experiments

Abstract: Three data acquisition schemes for two-particle coincidence experiments with a continuous source are discussed. The single-start/single-stop technique, implemented with a time-to-pulse-height converter, results in a complicated spectrum and breaks down severely at high count rates. The single-start/multiple-stop setup, based on a time-to-digital converter and the first choice in today’s similar coincidence experiments, performs significantly better at high count rates, but its performance is still hampered if the time-of-flight range is large, and the false coincidence background is variable if the event frequency and the collection efficiency of the starts are both high. A straightforward, multistart/multistop setup is proposed for coincidence experiments. By collecting all detector data, it ensures the highest signal-to-noise ratio, constant background, and fast data acquisition and can now be easily constructed with commercially available time-to-digital converters. Analytical and numerically evaluated formulas are derived to characterize the performance of each setup in a variety of environments. Computer simulated spectra are presented to illustrate the analytically predicted features of the various raw time-of-flight distributions obtained with each technique.

Computational methods for large-scale 3D acoustic finite-difference modeling: A tutorial

Abstract: We present a set of methods for modeling wavefields in three dimensions with the acoustic-wave equation. The primary applications of these modeling methods are the study of acquisition design, multiple suppression, and subsalt imaging for surface-streamer and ocean-bottom recording geometries. We show how to model the acoustic wave equation in three dimensions using limited computer memory, typically using a single workstation, leading to run times on the order of a few CPU hours to a CPU day. The structure of the out-of-core method presented is also used to improve the efficiency of in-core modeling, where memory-to-cache-to-memory data flow is essentially the same as the data flow for an out-of-core method. Starting from the elastic-wave equation, we develop a vector-acoustic algorithm capable of efficiently modeling multicomponent data in an acoustic medium. We show that data from this vector-acoustic algorithm can be used to test upgoing/downgoing separation of P-waves recorded by ocean-bottom seismic...

Systems and methods for real-time system monitoring and predictive analysis

Abstract: A system for providing real-time modeling of an electrical system under management is disclosed. The system includes a data acquisition component, a virtual system modeling engine, and an analytics engine. The data acquisition component is communicatively connected to a sensor configured to provide real-time measurements of data output from an element of the system. The virtual system modeling engine is configured to generate a predicted data output for the element. The analytics engine is communicatively connected to the data acquisition system and the virtual system modeling engine and is configured to monitor and analyze a difference between the real-time data output and the predicted data output.

Coloring electroanatomical maps to indicate ultrasound data acquisition

Abstract: During acquisition of ultrasound data in a medical imaging procedure, three-dimensional model of a structure being imaged, eg, an electroanatomical map, is co-displayed and visually marked, to indicate progress of data acquisition The plane of intersection successive two-dimensional images are marked on the as a line or colored region on the three-dimensional model This display enables the operator to determine regions where sufficient data have been captured, and guides the operator to areas where additional data collection is still needed Various color schemes are used to indicate the relative sufficiency of data collection

Web based fault detection architecture

Abstract: A remote analysis system for equipment condition monitoring and the like, using a data acquisition device operable at the remote site of monitored equipment, a wide area network for communication of data to an analysis server, and an empirical model for analyzing operational performance based on data from the device. An information processor such as a personal computer (PC) or an embedded processor application is coupled to the data acquisition device for collecting signals indicative of the monitored machine or process. A communications network, such as a wireless or telephony network, or a wide area network application such as an intranet or the Internet, facilitates communications to an analysis server for conveying the collected signals to an application service provider (ASP) for analysis of the remotely monitored site. A communications server may also be used for facilitating communications via a number of different communications networks. A notification server is provided responsive to the analysis server for completing a notification procedure for a customer subscribing to the ASP services for remote analysis with the data acquisition device at the process-monitoring site. The customer may be notified through a variety of electronic or telephonic communication methods, including, e-mail, facsimile, telephone calls, or subscriber dial-up and the like.

Systems and methods for real-time dynamic simulation of uninterruptible power supply solutions and their control logic systems

Abstract: A system for real-time modeling of uninterruptible power supply (UPS) control elements protecting an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine and a UPS transient stability simulation engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The UPS transient stability simulation engine stores and processes patterns observed from the real-time data output and utilizes a user-defined UPS control logic model to forecast an aspect of the interaction between UPS control elements and the electrical system subjected to a simulated contingency event.

Systems and methods for real- time protective device evaluation in an electrical power distribution system

Abstract: A system for providing real-time modeling of protective device in an electrical system under management is disclosed. The system includes a data acquisition component, a virtual system modeling engine, and an analytics engine. The data acquisition component is communicatively connected to a sensor configured to provide real-time measurements of data output from protective devices within the system under management. The virtual system modeling engine is configured to update a virtual mode of the system based on the status of the protective devices and to generate predicted data for the system using the updated virtual model. The analytics engine is communicatively connected to the data acquisition system and the virtual system modeling engine and is configured to monitor and analyze a difference between the real-time data output and the predicted data output. The analytics engine is also configured to determine the bracing capabilities for the protective devices.

Integration of Ground-Penetrating Radar and Laser Position Sensors for Real-Time 3-D Data Fusion

Abstract: Full-resolution 3-D ground-penetrating radar (GPR) imaging of the near surface should be simple and efficient. Geoscientists, archeologists, and engineers need a tool capable of generating interpretable subsurface views at centimeter-to-meter resolution of field sites ranging from smooth parking lots to rugged terrain. The authors have integrated novel rotary laser positioning technology with GPR into such a 3-D imaging system. The laser positioning enables acquisition of centimeter accurate x, y, and z coordinates from multiple small detectors attached to moving GPR antennas. Positions streaming with 20 updates/s from each detector are fused in real time with the GPR data. The authors developed software for automated data acquisition and real time 3-D GPR data quality control on slices at selected depths. Industry standard (SEGY) format data cubes and animations are generated within an hour after the last trace has been acquired. Such instant 3-D GPR can be used as an on-site imaging tool supporting field work, hypothesis testing, and optimized excavation and sample collection in the exploration of the static and dynamic nature of the shallow subsurface

Systems and methods for alarm filtering and management within a real-time data acquisition and monitoring environment

Abstract: A system for filtering and interpreting real-time sensory data from an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, and a decision engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The decision engine compares the real-time data output against the predicted data output to filter out and interpret indicia of electrical system health and performance. The client terminal is communicatively connected to the power analytics server and configured to display the filtered and interpreted indicia.

Behavior Measurement, Analysis, and Regime Classification in Car Following

Abstract: This paper first reports a data acquisition method that the authors used in a project on modeling driver behavior for microscopic traffic simulations. An advanced instrumented vehicle was employed to collect driver-behavior data, mainly car-following and lane-changing patterns, on Swedish roads. To eliminate the measurement noise in acquired car-following patterns, the Kalman smoothing algorithm was applied to the state-space model of the physical states (acceleration, speed, and position) of both instrumented and tracked vehicles. The denoised driving patterns were used in the analysis of driver properties in the car-following stage. For further modeling of car-following behavior, we developed and implemented a consolidated fuzzy clustering algorithm to classify different car-following regimes from the preprocessed data. The algorithm considers time continuity of collected driver-behavior patterns and can be more reliably applied in the classification of continuous car-following regimes when the classical fuzzy C-means algorithm gives unclear results

A single-chip signal processing and telemetry engine for an implantable 96-channel neural data acquisition system.

Abstract: A fully implantable neural data acquisition system is a key component of a clinically viable cortical brain–machine interface. We present the design and implementation of a single-chip device that serves the processing needs of such a system. Our device processes 96 channels of multi-unit neural data and performs all digital processing necessary for bidirectional wireless communication. The implementation utilizes a single programmable logic device that is responsible for performing data reduction on the 96 channels of neural data, providing a bidirectional telemetry interface to a transceiver and performing command interpretation and system supervision. The device takes as input neural data sampled at 31.25 kHz and outputs a line-encoded serial bitstream containing the information to be transmitted by the transceiver. Data can be output in one of the following four modes: (1) streaming uncompressed data from a single channel, (2) extracted spike waveforms from any subset of the 96 channels, (3) 1 ms bincounts for each channel or (4) streaming data along with extracted spikes from a single channel. The device can output up to 2000 extracted spikes per second with latencies suitable for a brain–machine interface application. This device provides all of the digital processing components required by a fully implantable system.

Method and system of ct data correction

Abstract: An adaptive CT data acquisition system and technique is presented whereby radiation emitted for CT data acquisition is dynamically controlled to limit exposure to those detectors of a CT detector assembly that may be particularly susceptible to saturation during a given data acquisition. The data acquisition technique recognizes that for a given subject size and position that pre-subject filtering and collimating of a radiation beam may be insufficient to completely prevent detector saturation. Therefore, the present invention includes implementation of a number of CT data correction techniques for correcting otherwise unusable data of a saturated CT detector. These data correction techniques include a nearest neighbor correction, off-centered phantom correction, off-centered synthetic data correction, scout data correction, planar radiogram correction, and a number of others. The invention is applicable with energy discriminating CT systems as well as with conventional CT systems and other multi-energy CT systems, such as dual kVp-based systems.

Compressive Sensing for GPR Imaging

Abstract: The theory of compressive sensing (CS) enables the reconstruction of sparse signals from a small set of non-adaptive linear measurements by solving a convex lscr1 minimization problem This paper presents a novel data acquisition and imaging algorithm for ground penetrating radars (GPR) based on CS by exploiting sparseness in the target space, ie, a small number of point-like targets Instead of measuring conventional radar returns and sampling at the Nyquist rate, linear projections of the returned signal with random vectors are taken as measurements Using simulated and experimental GPR data, it is shown that sparser and sharper target space images can be obtained compared to standard backprojection methods using only a small number of CS measurements Furthermore, the target region can even be sampled at random aperture points

Real-time high accuracy position and orientation system

Abstract: A real-time high accuracy position and orientation system (RT-HAPOS) system for a vehicle, such as an aircraft, comprises a global navigation satellite system (GNSS) receiver disposed on the vehicle and an integrated inertial navigation (IIN) module disposed on the vehicle. The GNSS receiver generates GNSS position data indicating approximate positions of the vehicle during a data acquisition period in which the vehicle is moving. The IIN module executes a real-time kinematic (RTK) algorithm during the data acquisition period to generate output position data indicating positions of the vehicle at a greater precision than the GNSS position data, based on the GNSS position data, inertial measurement data acquired on the vehicle during the data acquisition period, and a set of virtual reference station (VRS) observables received during the data acquisition period from a remote source external to the vehicle, where the VRS observables are based on the GNSS position data.

Noise Cancellation Signal Processing Method and Computer System for Improved Real-Time Electrocardiogram Artifact Correction During MRI Data Acquisition

Abstract: A system was developed for real-time electrocardiogram (ECG) analysis and artifact correction during magnetic resonance (MR) scanning, to improve patient monitoring and triggering of MR data acquisitions. Based on the assumption that artifact production by magnetic field gradient switching represents a linear time invariant process, a noise cancellation (NC) method is applied to ECG artifact linear prediction. This linear prediction is performed using a digital finite impulse response (FIR) matrix, that is computed employing ECG and gradient waveforms recorded during a training scan. The FIR filters are used during further scanning to predict artifacts by convolution of the gradient waveforms. Subtracting the artifacts from the raw ECG signal produces the correction with minimal delay. Validation of the system was performed both off-line, using prerecorded signals, and under actual examination conditions. The method is implemented using a specially designed Signal Analyzer and Event Controller (SAEC) computer and electronics. Real-time operation was demonstrated at 1 kHz with a delay of only 1 ms introduced by the processing. The system opens the possibility of automatic monitoring algorithms for electrophysiological signals in the MR environment

A compact multiphoton 3D imaging system for recording fast neuronal activity.

Abstract: We constructed a simple and compact imaging system designed specifically for the recording of fast neuronal activity in a 3D volume. The system uses an Yb:KYW femtosecond laser we designed for use with acousto-optic deflection. An integrated two-axis acousto-optic deflector, driven by digitally synthesized signals, can target locations in three dimensions. Data acquisition and the control of scanning are performed by a LeCroy digital oscilloscope. The total cost of construction was one order of magnitude lower than that of a typical Ti:sapphire system. The entire imaging apparatus, including the laser, fits comfortably onto a small rig for electrophysiology. Despite the low cost and simplicity, the convergence of several new technologies allowed us to achieve the following capabilities: i) full-frame acquisition at video rates suitable for patch clamping; ii) random access in under ten microseconds with dwelling ability in the nominal focal plane; iii) three-dimensional random access with the ability to perform fast volume sweeps at kilohertz rates; and iv) fluorescence lifetime imaging. We demonstrate the ability to record action potentials with high temporal resolution using intracellularly loaded potentiometric dye di-2-ANEPEQ. Our design proffers easy integration with electrophysiology and promises a more widespread adoption of functional two-photon imaging as a tool for the study of neuronal activity. The software and firmware we developed is available for download at http://neurospy.org/ under an open source license.

PULSAR: A MATLAB Toolbox for Parallel Magnetic Resonance Imaging Using Array Coils and Multiple Channel Receivers

Abstract: Partial parallel imaging (PPI) techniques using array coils and multichannel receivers have become an effective approach to achieving fast magnetic resonance imaging (MRI). This article presents a Matlab toolbox called PULSAR (Parallel imaging Utilizing Localized Surface-coil Acquisition and Reconstruction) that can simulate the data acquisi- tion and image reconstruction, and analyze performance of five common PPI techniques. PULSAR can simulate sensitivity functions of rectangular loop coils using a quasi-static model based on Biot-Savart's Law and undersampled multichannel data acquisition on a rectilinear k-space grid. In addition, PULSAR provides performance evaluation of the techniques based on artifact power (AP), signal-to-noise ratio (SNR), and computational complexity. In this article, the structure and functionality of the PULSAR toolbox are described. Examples using both the simulated and real four-channel and eight-channel data were used to demonstrate the utilities of the toolbox and to show that PULSAR is a convenient and effective means to study the PPI under different coil geometries, acquisition strategies, and reconstruction methods. © 2007 Wiley Periodicals, Inc. Concepts Magn

Systems and methods for real-time advanced visualization for predicting the health, reliability and performance of an electrical power system

Abstract: A system for real-time three-dimensional (3D) visualization of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, a machine learning engine and a 3D visualization engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system. The 3D visualization engine renders the virtual system model and the forecasted aspect into a 3D visual model.

Systems and methods for a real-time synchronized electrical power system simulator for "what-if" analysis and prediction over electrical power networks

Abstract: A system for real-time modeling of electrical system performance is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The power analytics server is comprised of a virtual system modeling engine, an analytics engine and a power system simulation engine. The virtual system modeling engine is configured to generate predicted data output utilizing a first virtual system model. The analytics engine is configured to synchronize the first virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold. The power system simulation engine is configured to store and process patterns and facilitate modification of parameters on the first virtual system model to create a second virtual system model; and forecast an aspect of the electrical system operating under parameters of the second virtual system model. The client terminal displays the forecasted aspects.

Systems and methods for performing automatic real-time harmonics analyses for use in real-time power analytics of an electrical power distribution system

Abstract: A system for conducting performing real-time harmonics analysis of an electrical power distribution and transmission system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire realtime data output from the electrical system. The power analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a machine learning engine. The machine learning engine is configured to store and process patterns observed from the real-time data output and the predicted data output, forecasting harmonic distortions in the electrical system subjected to a simulated contingency event.

Systems and methods for predictive monitoring including real-time strength and security analysis in an electrical power distribution system

Abstract: A system for performing real-time failure mode analysis of a monitored system is disclosed. The system includes a data acquisition component, an analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the monitored system. The analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a machine learning engine.

Systems and methods for automatic real-time capacity assessment for use in real-time power analytics of an electrical power distribution system

Abstract: A system for conducting a real-time power capacity assessment of an electrical system is disclosed The system includes a data acquisition component, a power analytics server and a client terminal The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system The power analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a machine learning engine The machine learning engine is configured to store and process patterns observed from the real-time data output and the predicted data output, forecasting power capacity of the electrical system subjected to a simulated contingency event