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.

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.

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.

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.

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.

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

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.

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.

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.

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

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.

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.

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

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.

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.

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.

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.

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.

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.

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

Abstract: This article presents an embedded multilane traffic data acquisition system based on an asynchronous temporal contrast vision sensor, and algorithms for vehicle speed estimation developed to make efficient use of the asynchronous high-precision timing information delivered by this sensor. The vision sensor features high temporal resolution with a latency of less than 100 µs, wide dynamic range of 120 dB of illumination, and zero-redundancy, asynchronous data output. For data collection, processing and interfacing, a low-cost digital signal processor is used. The speed of the detected vehicles is calculated from the vision sensor's asynchronous temporal contrast event data. We present three different algorithms for velocity estimation and evaluate their accuracy by means of calibrated reference measurements. The error of the speed estimation of all algorithms is near zero mean and has a standard deviation better than 3% for both traffic flow directions. The results and the accuracy limitations as well as the combined use of the algorithms in the system are discussed.

Abstract: Traditionally, flow cytometry (FCM) has been a tube-based technique limited to small-scale laboratory and clinical studies. High throughput methods for FCM have recently been developed for drug discovery and advanced research methods (1-4). As an example, flow cytometry high content screening (FC-HCS) can process up to a thousand samples daily at a single workstation, and the results have been equivalent or superior to traditional manual multiparameter staining and analysis techniques. The amount of information generated by high throughput technologies, such as FC-HCS, need to be transformed into executive summaries, which are brief enough for creative studies by a human researcher (5). Quality control and quality assessment are crucial steps in the development, and use of new high throughput technologies and their associated information services (5-7). Quality control in clinical cell analysis by FCM has been considered (8,9). As an example, Edwards et al. (9) proposed some quality scores for monitoring the quality of immunophenotyping process (e.g., blood acquisition, cell preparation, lymphocyte staining). They showed that a low degree of temporal parameter variation exits within individual whereas significant variations can exist between donors with respect to the parameter monitored. However little has been done with high throughput FCM. For example, quality control of FCM experiments should include the assessment of instrument parameters that affect the accuracy and precision of data. In that respect, Gratama et al. (10) have proposed some guidelines such as monitoring the fluorescence measurements by computing calibration plots for each fluorescent parameter. However, such procedures are not yet systematically applied, and data quality assessment is often needed to overcome a lack of data quality control. The aim of data quality assessment is to detect whether any measurements of any samples are substantially different from the others, in ways that were not likely to be biologically motivated. The rationale is that such samples should be identified, investigated, and potentially removed from any downstream analyses. Quality control, on the other hand, measures such quantities during the assaying procedure and can alert the user to problems at a time where they can be corrected. Data quality assessment in high throughput FCM experiment is complicated by the volume of data involved and by the many processing steps required to produce those data. Each instrument manufacturer has created software to drive the data acquisition process of the cytometer (e.g., CellQuest Pro by BD Biosciences, San Jose, CA; Summit by DakoCytomation, Fort Collins, CO; or Expo32 by Beckman Coulter, Fullerton, CA). These tools are primarily designed for their proprietary instrument interface and offer few, or no, data quality assessment functions. Third party analysis and management tools, such as FlowJo (Tree Star, Ashland, OR), WinList (Verity Software House, Topsham, ME) or FCSExpress (Denovo Software, Thornhill, Canada) provide researchers with more capable “offline” analysis tools but remain limited in term of data quality assessment. We propose a number of one- and two-dimensional graphical methods for exploring the data in the hope that they would be of some use to the investigators. The basis of our approach is that, given a cell line, or a single sample, divided in several aliquots, the distribution of the same physical or chemical characteristics (e.g., side light scatter - SSC-or forward light scatter -FSC-) should be similar between aliquots. To test this hypothesis, we made use of graphical exploratory data analysis (EDA). Five distinct visualization methods were implemented to explore the distributions and densities of ungated FCM data: Empirical Cumulative Distribution Function (ECDF) plots, histograms, boxplots, and two types of bivariate plots. These different graphical methods should provide investigators with different views of the data. ECDF plots have been widely used in the analysis of microarray data where they help to detect defective print tips, or plates of reagents that have not been well handled (11). These plots can quickly reveal differences in the distributions, but are not particularly useful for understanding the shape of a distribution. Histograms help to visualize the shape of the distribution and can reveal structure, such as the mode. Boxplots summarize the location of the distribution and can reveal asymmetry but are mainly applicable to unimodal distributions. Boxplots are also commonly used in the processing of microarray data where they help to identify hybridization artifacts and assess the need for between-array normalization to deal with scale differences among different arrays (11). Finally, we use bivariate plots representation in two different ways. In fact in some cases, when comparing two samples, we found two-dimensional displays more informative, i.e., two-dimensional summaries can show differences in samples, while the one-dimensional summaries, mentioned earlier, are similar. One common use of bivariate plots in FCM experiments is to display the joint distribution of two continuous variables as dot plots (e.g., FSC versus SSC). However the analysis of such dot plots might be a challenge as the high density of plotted data points (an average of 10,000 data points per sample) might form a blot and the frequency of the observations might not be easily appreciated. To overcome this issue we propose to use contour plots where contour lines might be interpreted as the frequency of observations with respect to the x–y plane. The second use of bivariate plots, for high throughput FCM data, is to render per well summary statistics for a particular plate in the format of a scatterplot. In this view each point represents a single well and the x and y values are chosen to be various summary statistics. We illustrate the need and usefulness of those visualization tools to assess FCM data quality through examination of two FC-HCS datasets. Our results demonstrate that the application of these graphical analysis methods to ungated FCM data provides a systematic and efficient method of data quality assessment, preventing time-consuming gating and further analysis of unreliable samples. Although the methods we propose are primarily aimed at the discovery of data quality problems, they may detect differences that are biologically motivated. Hence, we discourage the automatic removal of aberrant samples and emphasize the need to check whether such underlying biological causes are present.

Abstract: This paper presents a distributed system for remote monitoring of vehicle diagnostics and geographical position. This is achieved by using on-board microcomputer system, called on-board smart box (OBSB), general packet radio service (GPRS) and a remote server. The OBSB which is equipped with an integrated global positioning system (GPS) receiver is empowered by a software application that manages the processes of local data acquisition and transmission of the acquired data to the remote server via GPRS. When programmed with speed limits in a certain geographical region, the OBSB allows the traffic control authority to supervise violations of speed limits from inside vehicles rather than outside supervision via certain check points. Appropriate vocal and text warning messages are issued when a vehicle exceeds the permitted speed limit at a certain location. A prototype system is designed and implemented with a small number of sensors. On-road experiments have demonstrated the robustness, efficiency and applicability of the proposed system.

Abstract: A seismic spread has a plurality of seismic stations positioned over a terrain of interest and a controller programmed to automate the data acquisition activity In one aspect, the present disclosure provides a method for forming a seismic spread by developing a preliminary map of suggested locations for seismic devices and later forming a final map having in-field determined location data for the seismic devices Each suggested location is represented by a virtual flag used to navigate to each suggested location A seismic device is placed at each suggested location and the precise location of the each placed seismic devices is determined by a navigation device The determined locations are used to form a second map based on the determined location of the one or more of the placed seismic devices Using the virtual flag eliminates having to survey the terrain and place physical markers and later remove them

Abstract: A data acquisition system including a first sensor, a second sensor, and an electronic data acquisition device. The sensors can assume a variety of forms, such as analog, digital, bus, GPS, etc., and have disparate information formats. The data acquisition unit is electronically coupled to the sensors, and includes a processor, an aggregator module and a communication device. The processor processes information from the sensors. The aggregator module correlates signaled information from the first sensor with signaled information from the second sensor based on time. Finally, the communication device is adapted to transmit information generated by the aggregator module to a location remote of the housing.