Photonic processing at NASA Ames Research Center

The Photonic Processing group is engaged in applied research on optical processors in support of the Ames vision to lead the development of autonomous intelligent systems. Optical processors, in conjunction with numeric and symbolic processors, are needed to provide the powerful processing capability that is required for many future agency missions. The research program emphasizes application of analog optical processing, where free-space propagation between components allows natural implementations of algorithms requiring a large degree of parallel computation. Special consideration is given in the Ames program to the integration of optical processors into larger, heterogeneous computational systems. Demonstration of the effective integration of optical processors within a broader knowledge-based system is essential to evaluate their potential for dependable operation in an autonomous environment such as space. The Ames Photonics program is currently addressing several areas of interest. One of the efforts is to develop an optical correlator system with two programmable spatial light modulators (SLMs) to perform distortion invariant pattern recognition. Another area of research is optical neural networks, also for use in distortion-invariant pattern recognition

Oil shocks, monetary policy, and economic activity

Various reasons have been given to explain downturns in U.S. economic activity since World War II. Romer and Romer (1989) argued that these recessions were primarily associated with monetary contractions, while Hamilton (1983) and others attributed them to oil price increases. We investigate these competing hypotheses and find that when measures of oil prices are included, the Romers’ measure of monetary policy does not significantly explain economic downturns. However, alternative measures of monetary policy, specifically the federal funds rate the spread between the ten-year Treasury rate and the federal funds rate, are significantly linked to economic activity. We also find that Hamilton’s result that oil prices significantly influence real activity are robust to the inclusion of these alternative indicators of monetary policy.Petroleum industry and trade ; Monetary policy ; Economic conditions

Higher-order neural network software for distortion invariant object recognition

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing

Optical Potential Field Mapping System

The present invention relates to an optical system for creating a potential field map of a bounded two dimensional region containing a goal location and an arbitrary number of obstacles. The potential field mapping system has an imaging device and a processor. Two image writing modes are used by the imaging device, electron deposition and electron depletion. Patterns written in electron deposition mode appear black and expand. Patterns written in electron depletion mode are sharp and appear white. The generated image represents a robot's workspace. The imaging device under processor control then writes a goal location in the work-space using the electron deposition mode. The black image of the goal expands in the workspace. The processor stores the generated images, and uses them to generate a feedback pattern. The feedback pattern is written in the workspace by the imaging device in the electron deposition mode to enhance the expansion of the original goal pattern. After the feedback pattern is written, an obstacle pattern is written by the imaging device in the electron depletion mode to represent the obstacles in the robot's workspace. The processor compares a stored image to a previously stored image to determine a change therebetween. When no change occurs, the processor averages the stored images to produce the potential field map

Method and system for pattern analysis using a coarse-coded neural network

A method and system for performing pattern analysis with a neural network coarse-coding a pattern to be analyzed so as to form a plurality of sub-patterns collectively defined by data. Each of the sub-patterns comprises sets of pattern data. The neural network includes a plurality fields, each field being associated with one of the sub-patterns so as to receive the sub-pattern data therefrom. Training and testing by the neural network then proceeds in the usual way, with one modification: the transfer function thresholds the value obtained from summing the weighted products of each field over all sub-patterns associated with each pattern being analyzed by the system

MEMS 411: Group B Prosthetic Arm Design

Design a custom prosthetic arm for a customer who is missing her arm two inches past her elbow. Introduce mobility so that she can utilize the arm to perform more everyday tasks than she was able to before with her immobile arm

Detection of the synthetic cathinone N,N-dimethylpentylone in seized samples from prisons

Drug use is prevalent in prisons with drugs associated with depressant effects found to be moreprevalent than stimulants. Synthetic cathinones (SCats; often sold as “bath salts”, “ecstasy”, “molly”,and “monkey dust”) are the second largest category of new psychoactive substances (NPS) currentlymonitored by the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) and arecommonly used as substitutes for regulated stimulants, such as amphetamine, cocaine, and MDMA.N,N-dimethylpentylone (also known as dimethylpentylone, dipentylone, and bk-DMBDP) was detectedfor the first time in the Scottish prisons in seven powder samples seized between January and July 2023.Samples were analyzed using gas chromatography-mass spectrometry (GC-MS), ultra-highperformance liquid chromatography-quadrupole time of flight mass spectrometry (UPLC-QToF-MS),and nuclear magnetic resonance imaging (NMR). Dimethylpentylone was detected alongside otherdrugs in four samples, including the novel benzodiazepine desalkylgidazepam (bromonordiazepam) andthe synthetic cannabinoid receptor agonists (SCRAs) MDMB-INACA and MDMB-4en-PINACA

Improving Feedback from Automated Reviews of Student Spreadsheets

Spreadsheets are one of the most widely used tools for end users. As a result, spreadsheets such as Excel are now included in many curricula. However, digital solutions for assessing spreadsheet assignments are still scarce in the teaching context. Therefore, we have developed an Intelligent Tutoring System (ITS) to review students' Excel submissions and provide individualized feedback automatically. Although the lecturer only needs to provide one reference solution, the students' submissions are analyzed automatically in several ways: value matching, detailed analysis of the formulas, and quality assessment of the solution. To take the students' learning level into account, we have developed feedback levels for an ITS that provide gradually more information about the error by using one of the different analyses. Feedback at a higher level has been shown to lead to a higher percentage of correct submissions and was also perceived as well understandable and helpful by the students

Variability of extragalactic X-ray jets on kiloparsec scales

Unexpectedly strong X-ray emission from extragalactic radio jets on kiloparsec scales has been one of the major discoveries of Chandra, the only X-ray observatory capable of sub-arcsecond-scale imaging. The origin of this X-ray emission, which appears as a second spectral component from that of the radio emission, has been debated for over two decades. The most commonly assumed mechanism is inverse Compton upscattering of the Cosmic Microwave Background (IC-CMB) by very low-energy electrons in a still highly relativistic jet. Under this mechanism, no variability in the X-ray emission is expected. Here we report the detection of X-ray variability in the large-scale jet population, using a novel statistical analysis of 53 jets with multiple Chandra observations. Taken as a population, we find that the distribution of p-values from a Poisson model is strongly inconsistent with steady emission, with a global p-value of 1.96e-4 under a Kolmogorov-Smirnov test against the expected Uniform (0,1) distribution. These results strongly imply that the dominant mechanism of X-ray production in kpc-scale jets is synchrotron emission by a second population of electrons reaching multi-TeV energies. X-ray variability on the time-scale of months to a few years implies extremely small emitting volumes much smaller than the cross-section of the jet.Comment: Published in Nature Astronomy 29 May 2023; Supplemental Information and Excel File include