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Data acquisition system translates and processes graphical information recorded on high speed photographic film. It automatically scans the film and stores the information with a minimal use of the computer memory.
Nielsen Engineering & Research (NEAR) recently developed an ultrathin data acquisition system for use in turbomachinery testing at NASA Glenn Research Center. This system integrates a microelectromechanical- systems- (MEMS-) based absolute pressure sensor [0 to 50 psia (0 to 345 kPa)], temperature sensor, signal-conditioning application-specific integrated circuit (ASIC), microprocessor, and digital memory into a package which is roughly 2.8 in. (7.1 cm) long by 0.75 in. (1.9 cm) wide. Each of these components is flip-chip attached to a thin, flexible circuit board and subsequently ground and polished to achieve a total system thickness of 0.006 in. (0.15 mm). Because this instrument is so thin, it can be quickly adhered to any surface of interest where data can be collected without disrupting the flow being investigated. One issue in the development of the ultrathin data acquisition system was how to attach the MEMS pressure sensor to the circuit board in a manner which allowed the sensor s diaphragm to communicate with the ambient fluid while providing enough support for the chip to survive the grinding and polishing operations. The technique, developed by NEAR and Jabil Technology Services Group (San Jose, CA), is described below. In the approach developed, the sensor is attached to the specially designed circuit board, see Figure 1, using a modified flip-chip technique. The circular diaphragm on the left side of the sensor is used to actively measure the ambient pressure, while the diaphragm on the right is used to compensate for changes in output due to temperature variations. The circuit board is fabricated with an access hole through it so that when the completed system is installed onto a wind tunnel model (chip side down), the active diaphragm is exposed to the environment. After the sensor is flip-chip attached to the circuit board, the die is underfilled to support the chip during the subsequent grinding and polishing operations. To prevent this underfill material from getting onto the sensor s diaphragms, the circuit board is fabricated with two 25- micrometer-tall polymer rings, sized so that the diaphragms fit inside the rings once the chip is attached.
Our study used 1-year old C57BL/6NJ male and female mice (astronaut-relevant age) that underwent exposure to 0.5 gray of gamma radiation and were euthanized 12 weeks after. In this study, we used an MCAT mouse model for mitochondrial ROS quenching, which overexpress human catalase. MCAT mice were shown to live longer and age better. Hence, in this study we determined whether quenching ROS in the mitochondria will mitigate the adverse effects of ionizing radiation exposure on spaceflight-relevant tissues. As part of the analysis, we have completed 5 different behavioral tests which focus on memory, physical stance, stress, anxiety, and other mission relevant behaviors. In the Neuro-score battery, performed after both 1and 8 weeks post IR we saw that all female groups had significantly higher scores compared to males. When comparing the baseline vs 8 weeks of radiation, we saw that all the male groups (including the sham) had lower neuro-score, pointing out to aging effect in addition to IR. In the female groups only the female IR group had lower neuro-score and the MCAT group was protected from this effect. In the Nestlet building test we saw similarly that only females were affected by radiation, having lower scores and this effect was mitigated in the MCAT animals as well. In the Catwalk test we saw that females were faster, had higher swing speed and stride length in all four paws. Males had higher stand, step cycle and max contact area. Aging is associated with slowing of gait speed, swing speed and shortening of stride length which we see in males, this is consistent with physical appearance where males look markedly older. In the Light-Dark Box test we saw that females were more frequently present in the light side and altered zones more frequently, pointing out to a more exploratory and less anxious pattern of behavior. Similarly, to what was detected in the Nest building and Neuro-score test, in the Barnes maze test, during the acquisition phase (learning) we saw that IR affected more the females who did not do better in the maze after 4 days. On the other hand, during the probe phase of the test (spatial memory) the females visited the target hole and the box quadrant more often, but also had more errors vs males, which points out to possible serial escape vs spatial escape strategy. Overall, we see that older females look physically better are faster and perform better almost in all behavioral tests compared to their male counterparts. On the other hand, they are more sensitive to low dose radiation in many cases, in some cases this effect was mitigated in the MCAT model pointing out to the importance of ROS in these stressors. In the near future we will focus on corelating these behavioral tests with molecular findings such as for example brain IHC, plasma and hippocampal cytokines in order to find specific biomarkers for behavioral deficits.
Work on the High Resolution X-ray (HRX) Detector Program is described. In the laboratory and flight programs, multiple copies of a general purpose set of electronics which control the camera, signal processing and data acquisition, were constructed. A typical system consists of a phosphor convertor, image intensifier, a fiber optics coupler, a charge coupled device (CCD) readout, and a set of camera, signal processing and memory electronics. An initial rocket detector prototype camera was tested in flight and performed perfectly. An advanced prototype detector system was incorporated on another rocket flight, in which a high resolution heterojunction vidicon tube was used as the readout device for the H(alpha) telescope. The camera electronics for this tube were built in-house and included in the flight electronics. Performance of this detector system was 100 percent satisfactory. The laboratory X-ray system for operation on the ground is also described.
The background material and operational concepts of a computer-based system for an operating wind tunnel are described. An on-line real-time computer system was installed in a wind tunnel facility to gather static and dynamic data. The computer system monitored aerodynamic forces and moments of periodic and quasi-periodic functions, and displayed and plotted computed results in real time. The total system is comprised of several off-the-shelf, interconnected subsystems that are linked to a large data processing center. The system includes a central processor unit with 32,000 24-bit words of core memory, a number of standard peripherals, and several special processors; namely, a dynamic analysis subsystem, a 256-channel PCM-data subsystem and ground station, a 60-channel high-speed data acquisition subsystem, a communication link, and static force and pressure subsystems. The role of the test engineer as a vital link in the system is also described.
Satellite-borne Synthetic Aperture Radars (SAR) sense areas of several thousand square kilometers in seconds and transmit phase history signal data several tens of megabits per second. The Shuttle Imaging Radar-B (SIR-B) has a variable swath of 20 to 50 km and acquired data over 100 kms along track in about 13 seconds. With the simplification of separability of the reference function, the processing still requires considerable resources; high speed I/O, large memory and fast computation. Processing systems with regular hardware take hours to process one Seasat image and about one hour for a SIR-B image. Bringing this processing time closer to acquisition times requires an end-to-end system solution. For the purpose of demonstration, software was implemented on the present Massively Parallel Processor (MPP) configuration for processing Seasat and SIR-B data. The software takes advantage of the high processing speed offered by the MPP, the large Staging Buffer, and the high speed I/O between the MPP array unit and the Staging Buffer. It was found that with unoptimized Parallel Pascal code, the processing time on the MPP for a 4096 x 4096 sample subset of signal data ranges between 18 and 30.2 seconds depending on options.
Diffusion MRI (dMRI) non-invasively maps brain white matter yet necessitates denoising due to low signal-to-noise ratios. Patch2Self (P2S) employing self-supervised techniques and regression on a Casorati matrix effectively denoises dMRI images and has become the new de-facto standard in this field. P2S however is resource intensive both in terms of running time and memory usage as it uses all voxels (n) from all-but-one held-in volumes (d-1) to learn a linear mapping Phi : \mathbb R ^ n x(d-1) \mapsto \mathbb R ^ n for denoising the held-out volume. The increasing size and dimensionality of higher resolution dMRI acquisitions can make P2S infeasible for large-scale analyses. This work exploits the redundancy imposed by P2S to alleviate its performance issues and inspect regions that influence the noise disproportionately. Specifically this study makes a three-fold contribution: (1) We present Patch2Self2 (P2S2) a method that uses matrix sketching to perform self-supervised denoising. By solving a sub-problem on a smaller sub-space so called coreset we show how P2S2 can yield a significant speedup in training time while using less memory. (2) We present a theoretical analysis of P2S2 focusing on determining the optimal sketch size through rank estimation a key step in achieving a balance between denoising accuracy and computational efficiency. (3) We show how the so-called statistical leverage scores can be used to interpret the denoising of dMRI data a process that was traditionally treated as a black-box. Experimental results on both simulated and real data affirm that P2S2 maintains denoising quality while significantly enhancing speed and memory efficiency achieved by training on a reduced data subset.
The HCOR will be used onboard the ISS to record digital data during Ku-band Loss of Signal (LOS) periods. This recorded data will be played back to the ground during Ku-band Acquisition of Signal (AOS) periods. The Data Management (DM) Team at the Payload Operations and Integration Center is the primary operator of this complex recorder. The record and playback capabilities - along with the memory management functions - are presented in this paper. To illustrate how the DM Team plans to manage the record, playback and memory management tasks of operating the HCOR, an operational scenario for a ninety-minute orbit is presented.
Accurate energy forecasting is becoming critical due to many reasons: i ) optimal distributed energy resources operations and dispatch, ii) fault detection and diagnostics, and iii) meeting operational energy efficiency targets. Wattile uses deep learning (DL) for the building's short-term load forecasting application. Two specific types of neural networks called, Long Short Term Memory (LSTM) and Sequence-to-Sequence (S2S) models are used to make predictions. Forecasting models are trained using online historical weather and occupancy indicator data streams from the Intelligent Campus Program's data acquisition systems at the National Renewable Energy Laboratory (NREL) for main meters and sub-meters of multiple building types. These models use probabilistic methods to provide quantile-based forecasts in addition to nominal conditional median predictions of electricity consumption.
Incentives, problems and solutions associated with interfacing laboratory equipment with multiuser, virtual memory computers are presented. The major difficulty concerns how to utilize these computers effectively in a medium sized research group. This entails optimization of hardware interconnections and software to facilitate multiple instrument control, data acquisition and processing. The architecture of the system that was devised, and associated programming and subroutines are described. An example program involving computer controlled hardware for ultrasonic scan imaging is provided to illustrate the operational features.
Incentives, problems and solutions associated with interfacing laboratory equipment with multiuser, virtual memory computers are presented. The major difficulty concerns how to utilize these computers effectively in a medium sized research group. This entails optimization of hardware interconnections and software to facilitate multiple instrument control, data acquisition and processing. The architecture of the system that was devised, and associated programming and subroutines are described. An example program involving computer controlled hardware for ultrasonic scan imaging is provided to illustrate the operational features.
An electronic unit denoted the Miniature Intelligent Sensor Module performs sensor-signal-conditioning functions and local processing of sensor data. The unit includes four channels of analog input/output circuitry, a processor, volatile and nonvolatile memory, and two Ethernet communication ports, all housed in a weathertight enclosure. The unit accepts AC or DC power. The analog inputs provide programmable gain, offset, and filtering as well as shunt calibration and auto-zeroing. Analog outputs include sine, square, and triangular waves having programmable frequencies and amplitudes, as well as programmable amplitude DC. One innovative aspect of the design of this unit is the integration of a relatively powerful processor and large amount of memory along with the sensor-signalconditioning circuitry so that sophisticated computer programs can be used to acquire and analyze sensor data and estimate and track the health of the overall sensor-data-acquisition system of which the unit is a part. The unit includes calibration, zeroing, and signalfeedback circuitry to facilitate health monitoring. The processor is also integrated with programmable logic circuitry in such a manner as to simplify and enhance acquisition of data and generation of analog outputs. A notable unique feature of the unit is a cold-junction compensation circuit in the back shell of a sensor connector. This circuit makes it possible to use Ktype thermocouples without compromising a housing seal. Replicas of this unit may prove useful in industrial and manufacturing settings - especially in such large outdoor facilities as refineries. Two features can be expected to simplify installation: the weathertight housings should make it possible to mount the units near sensors, and the Ethernet communication capability of the units should facilitate establishment of communication connections for the units.
The field of nuclear instrumentation covers a wide range of applications, including counting, spectrometry, pulse shape discrimination and multi-channel coincidence. These applications are the topic of many researches, new algorithms and implementations are constantly proposed thanks to advances in digital signal processing. However, these improvements are not yet implemented in instrumentation devices. This is especially true for neutron-gamma discrimination applications which traditionally use charge comparison method while literature proposes other algorithms based on frequency domain or wavelet theory which show better performances. Another example is pileups which are generally rejected while pileup correction algorithms also exist. These processes are traditionally performed offline due to two issues. The first is the Poissonian characteristic of the signal, composed of random arrival pulses which requires to current architectures to work in data flow. The second is the real-time requirement, which implies losing pulses when the pulse rate is too high. Despite the possibility of treating the pulses independently from each other, current architectures paralyze the acquisition of the signal during the processing of a pulse. This loss is called dead-time. These two issues have led current architectures to use dedicated solutions based on re-configurable components like Field Programmable Gate Arrays (FPGAs) to overcome the need of performance necessary to deal with dead-time. However, dedicated hardware algorithm implementations on re-configurable technologies are complex and time-consuming. For all these reasons, a programmable Digital pulse Processing (DPP) architecture in a high level language such as Cor C++ which can reduce dead-time would be worthwhile for nuclear instrumentation. This would reduce prototyping and test duration by reducing the level of hardware expertise to implement new algorithms. However, today's programmable solutions do not meet the need of performance to operate online and not allow scaling with the increase in the number of measurement channel. That is why an innovative DPP architecture is proposed in this paper. This architecture is able to overcome dead-time while being programmable and is flexible with the number of measurement channel. Proposed architecture is based on an innovative execution model for pulse processing applications which can be summarized as follow. The signal is not composed of pulses only, consequently, pulses processing does not have to operate on the entire signal. Therefore, the first step of our proposal is pulse extraction by the use of dedicated components named pulse extractors. The triggering step can be achieved after the analog-to-digital conversion without any signal shaping or filtering stages. Pileup detection and accurate pulse time stamping are done at this stage. Any application downstream this step can work on adaptive variable-sized array of samples simplifying pulse processing methods. Then, once the data flow is broken, it is possible to distribute pulses on Functional Units (FUs) which perform processing. As the date of each pulse is known, they can be processed individually out-of-order to provide the results. To manage the pulses distribution, a scheduler and an interconnection network are used. pulses are distributed on the first FU which is not busy without congesting the interconnection network. For this reason, the process duration does not result anymore in dead-time if there are enough FUs. FUs are designed to be standalone and to comprises at least a programmable general purpose processor (ARM, Microblaze) allowing the implementation of complex algorithms without any modification of the hardware. An acquisition chain is composed of a succession of algorithms which lead to organize our FUs as a software macro-pipeline, A simple approach consists in assigning one algorithm per FU. Consequently, the global latency becomes the worst latency of algorithms execution on FU. Moreover, as algorithms are executed locally - i.e. on a FU - this approach limits shared memory requirement. To handle multichannel, we propose FUs sharing, this approach maximize the chance to find a non-busy FU to process an incoming pulse. This is possible since each channel receive random event independently, the pulse extractors associated to them do not necessarily need to access simultaneously to all Computing resources at the same time to distribute their pulses. The major contribution of this paper is the proposition of an execution model and its associated hardware programmable architecture for digital pulse processing that can handle multiple acquisition channels while maintaining the scalability thanks to the use of shared resources. This execution model and associated architecture are validated by simulation of a cycle accurate architecture SystemC model. Proposed architecture shows promising results in terms of scalability while maintaining zero dead-time. This work also permit the sizing of hardware resources requirement required for a predefined set of applications. Future work will focus on the interconnection network and a scheduling policy that can exploit the variable-length of pulses. Then, the hardware implementation of this architecture will be performed and tested for a representative set of application.
A field spectrometer covering the range 0.4 to 2.5 microns was developed that acquires spectra in 2 seconds at 872 points within the spectrum. The Portable Instant Display and Analysis Spectrometer (PIDAS) can acquire spectra every 8 seconds and stores up to 288 spectra in bubble memory. A hand held display unit allows for display of the current spectrum acquired and superimposed on one of 128 permanently stored library spectra. PIDAS represents a major advance in the technology of field spectral data acquisition and for the first time makes possible the acquisition of enough spectra to characterize the mean and intraclass variance within a LANDSAT MSS or TM pixel.
Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques continue to improve. For example, simulations are tackling ever larger domains with higher fidelity, and high-throughput microscopy techniques generate larger data that are fundamental to gather biologically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a scientific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability. We present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical streaming data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Second, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime system automates mapping analysis algorithms to whatever computational hardware is available, reducing the complexity of developing scaling algorithms. Here, we demonstrate the usability and performance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.
A new Particle Imaging Velocimetry (PIV) data acquisition and analysis system, which is an order of magnitude faster than any previously proposed system has been constructed and tested. The new Particle Displacement Tracing (PDT) system is an all electronic technique employing a video camera and a large memory buffer frame-grabber board. Using a simple encoding scheme, a time sequence of single exposure images are time coded into a single image and then processed to track particle displacements and determine velocity vectors. Application of the PDT technique to a counter-rotating vortex flow produced over 1100 velocity vectors in 110 seconds when processed on an 80386 PC.
A new particle imaging velocimetry data acquisition and analysis system, which is an order of magnitude faster than any previously proposed system, has been constructed and tested. The new particle displacement tracking (PDT) system is an all electronic technique employing a video camera and a large memory buffer frame-grabber board. Using a simple encoding scheme, a time sequence of single exposure images is time-coded into a single image and then processed to track particle displacements and determine two-dimensional velocity vectors. Use of the PDT technique in a counterrotating vortex flow produced over 1100 velocity vectors in 110 s when processed on an 80386 PC.
A reconfigurable telemetry multiplexer is described which includes a monitor-terminal and a plurality of remote terminals. The remote terminals each include signal conditioning for a plurality of sensors for measuring parameters which are converted by an analog to digital converter. CPU's in the remote terminals store instructions for prompting system configuration and reconfiguration commands. The measurements, instructions, and the terminal's present configuration and status data are transmitted to the monitor-terminal and displayed. In response to menu-driven prompts generated and displayed at the monitor-terminal, data generation request commands, status and health commands, and the like are input at the monitor-terminal and transmitted to the remote terminals. The CPU in each remote terminal receives the various commands, stores them in electrically alterable memory, and reacts in accordance with the commands to reconfigure a plurality of aspects of the system. The CPU in each terminal also generates parameter measurements, status and health signals, and transmits these signals of the respective terminals to the monitor-terminal for low data rate operator viewing and to higher rate external transmission/monitor equipment. Reconfiguration may be in real time during the general period of parameter measurement acquisition, and may include alteration of the gain, automatic gain rescaling, bias, and or sampling rates associated with one or more of the parameter measurements made by the remote terminals.