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New system trades hardware for software to reduce cost. Composite train of interspersed blade-detection pulses produced by gates at four probe ports on data-acquisition board. Pulses latch count from high-speed wraparound clock counter and initiate writing of count onto current address of memory. Result: time corresponding to each blade passage stamped into memory.
Skilled movement is an integral part of the human existence. A better understanding of motor skills and their development is a prerequisite to the construction of truly flexible intelligent agents. We present MAEANDER, a computational model of human motor behavior, that uniformly addresses both the acquisition of skills through observation and the improvement of skills through practice. MAEANDER consists of a sensory-effector interface, a memory of movements, and a set of performance and learning mechanisms that let it recognize and generate motor skills. The system initially acquires such skills by observing movements performed by another agent and constructing a concept hierarchy. Given a stored motor skill in memory, MAEANDER will cause an effector to behave appropriately. All learning involves changing the hierarchical memory of skill concepts to more closely correspond to either observed experience or to desired behaviors. We evaluated MAEANDER empirically with respect to how well it acquires and improves both artificial movement types and handwritten script letters from the alphabet. We also evaluate MAEANDER as a psychological model by comparing its behavior to robust phenomena in humans and by considering the richness of the predictions it makes.
This research explores the ability of machine learning to perform signal separation of an Ethernet style encoded, full-duplex communication. Typical signal separation currently requires an active tap of the communication line, followed by a recombination and retransmission of the data. The purpose of this research is to study a passive approach to data acquisition from a full-duplex signal. The machine learning model used in this research is a long-short-term memory recurrent neural network (LSTM-RNN). The results show that the LSTM was largely successful in recreating the transmission signal from the measured data points, though the separated signals have not yet been tested using a decoding method.
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Novel electronic devices can often be operated in a plethoraof ways, which makes testing circuits comprised of them difficult.Often, no single tool can simultaneously analyze the operatingmargins, maximum speed, and failure modes of a circuit, particularlywhen the intended behavior of subcomponents of the circuit is notstandardized. This work demonstrates a cost-effective time-domaindata acquisition system for electronic circuits that enables moreintricate verification techniques than are practical withconventional experimental setups. We use high-speeddigital-to-analog converters and real-timemulti-gigasample-per-second waveform processing to push experimentalcircuits beyond their maximum operating speed. Our customtime-tagging data capture firmware reduces memory requirements andcan be used to determine when errors occur. The firmware iscombined with a thermal-noise-limited analog frontend with50 dB of dynamic range. Compared to currentlyavailable commercial test equipment that is seven times moreexpensive, this data acquisition system was able to operate asuperconducting shift register at a nearly three-times-higher clockfrequency (200 MHz vs. 80 MHz).
The possibility to use an electrotactile stimulation in teleoperation and to observe the interpretation of such information as a feedback to the operator was investigated. It is proposed that visual feedback is more informative than an electrotactile one; and that complex electrotactile feedback slows down both the motor decision and motor response processes, is processed as an all or nothing signal, and bypasses the receptive structure and accesses directly in a working memory where information is sequentially processed and where memory is limited in treatment capacity. The electrotactile stimulation is used as an alerting signal. It is suggested that the visual dominance effect is the result of the advantage of both a transfer function and a sensory memory register where information is pretreated and memorized for a short time. It is found that dividing attention has an effect on the acquisition of the information but not on the subsequent decision processes.
Coarsely digitized maximum levels recorded in blown fuses. Circuit feeds power to accelerometer and makes nonvolatile record of maximum level to which output of accelerometer rises during measurement interval. In comparison with inertia-type single-preset-trip-point mechanical maximum-acceleration-recording devices, circuit weighs less, occupies less space, and records accelerations within narrower bands of uncertainty. In comparison with prior electronic data-acquisition systems designed for same purpose, circuit simpler, less bulky, consumes less power, costs and analysis of data recorded in magnetic or electronic memory devices. Circuit used, for example, to record accelerations to which commodities subjected during transportation on trucks.
A low cost optical data acquisition system was designed to measure deflection of vibrating rotor blade tips. The basic principle of the new design is to record raw data, which is a set of blade arrival times, in memory and to perform all processing by software following a run. This approach yields a simple and inexpensive system with the least possible hardware. Functional elements of the system were breadboarded and operated satisfactorily during rotor simulations on the bench, and during a data collection run with a two-bladed rotor in the Lewis Research Center Spin Rig. Software was written to demonstrate the sorting and processing of data stored in the system control computer, after retrieval from the data acquisition system. The demonstration produced an accurate graphical display of deflection versus time.
A low cost optical data acquisition system was designed to measure deflection of vibrating rotor blade tips. The basic principle of the new design is to record raw data, which is a set of blade arrival times, in memory and to perform all processing by software following a run. This approach yields a simple and inexpensive system with the least possible hardware. Functional elements of the system were breadboarded and operated satisfactorily during rotor simulations on the bench, and during a data collection run with a two-bladed rotor in the Lewis Research Center Spin Rig. Software was written to demonstrate the sorting and processing of data stored in the system control computer, after retrieval from the data acquisition system. The demonstration produced an accurate graphical display of deflection versus time.
Carbon capture and storage (CCS) has emerged as a key solution in the fight against climate change. However, for CCS to succeed, it is crucial to ensure that the sequestered CO2 stays safely trapped underground. The U.S. Department of Energy (DOE) has emphasized the need for advancements in subsurface monitoring, measurement, reporting, and verification. Aside from caprock integrity failure, the other primary failure points usually involve defective cement in the casing annulus of wellbores or plugged and abandoned wells. In addition, many energy producers (e.g., oil and gas, geothermal) and storage and disposal operators (e.g., H2 and water) must deal with the same issue. Poorly placed or degraded cement can create pathways for gas or fluid to escape from casing annuli and in plugged and abandoned or orphan wells, posing environmental risks. Yet, a reliable and cost-effective way to monitor cement and well integrity over multiple decades is still unavailable. Traditional geophysical methods like 4D seismic imaging and surface-based electromagnetic monitoring lack the resolution and accuracy for detecting these types of failures (Vasco et al., 2022; Fawad and Mondol, 2021). Wireline logging is expensive to run continuously and is obtrusive to the operation. While fiber optics can potentially be a solution, its bulkiness can significantly compromise the cement's integrity. To address these challenges, the Advanced Energy Consortium (AEC) at The University of Texas at Austin’s Bureau of Economic Geology (the Bureau) has been pioneering research in subsurface monitoring using its portfolio of distributed autonomous microfabricated sensors for harsh subsurface environments since 2008. A class of these microsensors [System on a Chip (SoC)] can be mixed in cement and permanently placed without compromising the cement column; the sensors would then communicate with each other or a data acquisition (DAQ) master node. Another class of the AEC microsensors can be fully autonomous, with rechargeable micro-batteries capable of exceeding 100°C, flash memory, and, currently, a pressure and temperature sensor. They are designed to circulate in mud, geothermal fluids, U-loops, or pipelines. They can log data into memory and are unobtrusive to operations. Our team has been working on a multi-year DOE-funded project (DE-FE0031856)—supported by $2.95M in federal funding and $0.75M in cost-matching from the AEC—to demonstrate SoC sensor utility for CO2 leakage monitoring in CCS applications. This multi-institutional collaboration developed a novel sensing architecture utilizing radiofrequency (RF) microsensors embedded within the cement sheath. These sensors detect CO2 migration and are interrogated via a Smart Casing Collar (SCC).
Source search campaigns involve measurements of background gamma-ray spectra with a mobile detector-spectrometer traveling along arbitrarily chosen trajectories over a wide screening area. Radiation counts are typically measured with a tellurium-doped sodium iodide [NaI(Tl)] scintillator detector-spectrometer in short acquisition intervals, usually 1 s. The objective is to detect orphan isotopes with half-lives shorter than those of the isotopes in the natural background. In principle, radioisotopes can be identified by their unique gamma emission spectrum. However, detecting orphan isotopes in search data is challenging because low counts measured in short acquisition intervals result in incomplete spectral lines. In this study, we investigate the performance of a Hopfield neural network (HNN) that implements an auto-associative memory for the detection of isotopes of interest in an urban search campaign. The HNN is trained on one example of gamma spectra with well-resolved spectral lines of each isotope of interest. During testing, the auto-associative memory implementation of the HNN processes low-count gamma spectra with partially complete isotopic lines by matching incoming measurements to the closest one of its memory-stored patterns. The testing database consisted of almost 10 000 1-s gamma spectra, including measurements of orphan isotopes 137 Cs, 241 Am, and 131 I, obtained during two urban search surveys with a NaI(Tl) detector. The performance of the HNN detection algorithm was evaluated using precision, recall, and F1 scores, and benchmarked with a multiple linear regression (MLR) identification algorithm. In conclusion, the test results demonstrate that HNN outperforms MLR in the detection of all the isotopes of interest.
Compact unit made of readily available solid-state components. Proposed system for acquisition of rapidly changing data self-contained and portable. Conceived for monitoring such aerodynamic effects as flutter, vibration, shock, sound, and pressure. Offers precise and fast acquisition of data and large data-storage capacity: has maximum sampling rate of 125 kHz, access time of 15 ns, and 1-million-bit memory. Measures time with "smart" (microprocessor-controlled) watch that maintains calendar time for more than 10 years without external power. Provides standby power from "smart" battery furnishing up to 1 ampere-hour of charge if power from main batteries lost.
A hardware/software interface designed to mate a Datametrics models 1085A electronic manometer to a Hewlett-Packard (HP) model 2100S computer is described. The software driver operates in the basic control system (BCS) and requires 173 words of memory; the hardware interface uses an HP data source interface (DSI) card which requires one computer input/output channel. Results obtained by incorporating this interface into a data acquisition system indicate that the interface operates successfully.
Acquisition of science in space applications is shifting from teleoperated gathering to an automated on-board analysis with improvements in the use of on-board memory, CPU, bandwidth and data quality. In this paper, we describe algorithms to autonomously detect dust devils and clouds from a rover and summarize the results. These algorithms meet high hit-to-miss ratios and satisfy strict requirements of CPU, memory usage and bandwidth. The detectors have been scheduled for upload to the Mars Exploration Rovers (MER) in 2006. These are the first autonomous science processes in the rovers.
Here, this work extends our previous research entitled "MemXCT: Memory-centric X-ray CT Reconstruction with Massive Parallelization" that was originally published at SC19 conference (Hidayetoglu et al., 2019) with reproducibility of the computational imaging performance. X-ray computed tomography (XCT) is regularly used at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be in the order of terabytes. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use an exception rather than the rule. We propose a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a memory-centric iterative reconstruction system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures involving KNL and GPU. MemXCT can reconstruct a large (11Kx11K) mouse brain tomogram in 10 seconds using 4096 KNL nodes (256K cores). The results presented in our original article at the SC19 were based on large-scale supercomputing resources. The MemXCT application was selected for the Student Cluster Competition (SCC) Reproducibility Challenge and evaluated on a variety of cloud computing resources by universities around the world in the SC20 conference. We summarize the results of the top-ranked SCC Reproducibility Challenge teams and identify the most pertinent measures for ensuring the reproducibility of our experiments in this article.
The NASA Langley Research Center's Wind Tunnel Reinvestment project plans to shrink the existing data acquisition electronics to fit inside a wind tunnel model. Space limitations within a model necessitate a distributed system of Application Specific Integrated Circuits (ASICs) rather than a centralized system based on PC boards. This thesis will focus on the design of the prototype of the communication Controller board. A portion of the communication Controller board is to be used as the basis of an ASIC design. The communication Controller board will communicate between the internal model modules and the external data acquisition computer. This board is based around an Field Programmable Gate Array (FPGA), to allow for reconfigurability. In addition to the FPGA, this board contains buffer Random Access Memory (RAM), configuration memory (EEPROM), drivers for the communications ports, and passive components.
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.