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An electronic circuit for controlling the focus of facsimile cameras is described. The circuit consists of balanced a.c. amplifiers, two square law function generators, and a differential amplifier and power drive. The invention includes a method for maintaining the imaging sensor at the expected location of the focal plane as the facsimile camera scans a scene or terrain. A block diagram of the electronic circuitry is provided.
An electronic system adjusts the frequency of a tunable laser, eventually locking the frequency to a peak in the optical absorption spectrum of a gas (or of a Fabry-Perot cavity that has an absorption peak like that of a gas). This system was developed to enable precise locking of the frequency of a laser used in differential absorption LIDAR measurements of trace atmospheric gases. This system also has great commercial potential as a prototype of means for precise control of frequencies of lasers in future dense wavelength-division-multiplexing optical communications systems. The operation of this system is completely automatic: Unlike in the operation of some prior laser-frequency-locking systems, there is ordinarily no need for a human operator to adjust the frequency manually to an initial value close enough to the peak to enable automatic locking to take over. Instead, this system also automatically performs the initial adjustment. The system (see Figure 1) is based on a concept of (1) initially modulating the laser frequency to sweep it through a spectral range that includes the desired absorption peak, (2) determining the derivative of the absorption peak with respect to the laser frequency for use as an error signal, (3) identifying the desired frequency [at the very top (which is also the middle) of the peak] as the frequency where the derivative goes to zero, and (4) thereafter keeping the frequency within a locking range and adjusting the frequency as needed to keep the derivative (the error signal) as close as possible to zero. More specifically, the system utilizes the fact that in addition to a zero crossing at the top of the absorption peak, the error signal also closely approximates a straight line in the vicinity of the zero crossing (see Figure 2). This vicinity is the locking range because the linearity of the error signal in this range makes it useful as a source of feedback for a proportional + integral + derivative control scheme that constantly adjusts the frequency in an effort to drive the error to zero. When the laser frequency deviates from the midpeak value but remains within the locking range, the magnitude and sign of the error signal indicate the amount of detuning and the control circuitry adjusts the frequency by what it estimates to be the negative of this amount in an effort to bring the error to zero.
Calvert-type, twin liquid metal solution calorimeter determines the heats of formation of transition metal alloys at high temperatures. The twin differential calorimeter measures the small heat effects generated over extended periods of time, has maximum operating temperature of 1073 degrees K and an automatic data recording system.
In applications involving elevated temperatures, the derivation of mathematical expressions (constitutive equations) describing the material behavior can be quite time consuming, involved and error-prone. Therefore intelligent application of symbolic systems to faciliate this tedious process can be of significant benefit. Presented here is a problem oriented, self contained symbolic expert system, named SDICE, which is capable of efficiently deriving potential based constitutive models in analytical form. This package, running under DOE MACSYMA, has the following features: (1) potential differentiation (chain rule), (2) tensor computations (utilizing index notation) including both algebraic and calculus; (3) efficient solution of sparse systems of equations; (4) automatic expression substitution and simplification; (5) back substitution of invariant and tensorial relations; (6) the ability to form the Jacobian and Hessian matrix; and (7) a relational data base. Limited aspects of invariant theory were also incorporated into SDICE due to the utilization of potentials as a starting point and the desire for these potentials to be frame invariant (objective). The uniqueness of SDICE resides in its ability to manipulate expressions in a general yet pre-defined order and simplify expressions so as to limit expression growth. Results are displayed, when applicable, utilizing index notation. SDICE was designed to aid and complement the human constitutive model developer. A number of examples are utilized to illustrate the various features contained within SDICE. It is expected that this symbolic package can and will provide a significant incentive to the development of new constitutive theories.
An algorithm designed to detect fronts automatically in satellite-derived sea-surface temperature (SST) fields is presented. The algorithm is operated at different levels to detect and differentiate between false and true edges. For purposes of comparison, the algorithm is applied to a test set of 98 SST images to detect the northern edge of the Gulf Stream. The algorithm successfully detected valid temperature fronts and ignored false edges, and also produced statistics about the temperature fronts that are useful in the subsequent analysis of these fronts. It is assumed that the algorithm performs equally well on other SST fronts such as those associated with rings, the subtropical convergence, or the shelf/slope fronts.
Air-to-air combat is modeled as a discrete differential game, and by constraining the game to searching for the best guidance laws from the sets of those considered for each opponent, feedback and outcome charts are obtained which can be used to turn one of the automatic opponents into an intelligent opponent against a human pilot. A one-on-one two-dimensional fully automatic, or manned versus automatic, air-to-air combat game has been designed which includes both attack and evasion alternatives for both aircraft. Guidance law selection occurs by flooding the initial-condition space with four simulated fights for each initial condition, depicting the various attack/evasion strategies for the two opponents, and recording the outcomes. For each initial condition, the minimax method from differential games is employed to determine the best choice from the available strategies.
Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging. This has led to wide-spread adoption of AI-powered tools, in pursuit of improving accuracy and efficiency of this process. While the unique challenges presented by each modality and clinical task demand customized tools, the cumbersome process of making problem-specific choices has triggered the critical need for a generic solution to enable rapid development of models in practice. In this spirit, we develop DDxNet, a deep architecture for time-varying clinical data, which we demonstrate to be well-suited for diagnostic tasks involving different modalities (ECG/EEG/EHR), required level of characterization (abnormality detection/phenotyping) and data fidelity (single-lead ECG/22-channel EEG). Using multiple benchmark problems, we show that DDxNet produces high-fidelity predictive models, and sometimes even provides significant performance gains over problem-specific solutions.
The Thermal Analysis Variant of the COMOC (computational continuum mechanics) computer system solves problems involving transient heat conduction and convection in stationary continua spanning arbitrarily irregular two-dimensional and axisymmetric solution domains. COMOC is based upon a finite element solution algorithm for the energy equation, and solves for the transient nodal temperature distribution using a highly stable and automatic explicit integration procedure. COMOC is extensively user-oriented, requires minimal input, and no a priori knowledge concerning the stability character of the differential equation system. It can readily output computed data in user-specified format fields, that geometrically resemble the solution domain discretization (for rapid engineering evaluation). Complete information is provided for applying COMOC to a specific problem.
An all-electronic crossbar switch consisting of FET series-shunt switches inside the feedback loops of operational amplifiers was designed and constructed. Time-shared solution of five different differential equations was obtained by using four integrators on ASTRAC 2, with a new solution being generated 250 times per second.
The difference in dynamic pressure in the propeller slipstreams as measured by sensors is divided by the freestream dynamic pressure generating a quantity proportional to the differential thrust coefficient. This quantity is used to command an electric trim motor to change the position of trim tab thereby retrimming the airplane to the new asymmetric power condition. The change in position of the trim tab produced by the electric trim motor is summed with the pilot's input to produce the actual trim tab position.
A substantial foundation of tools for model-based reasoning with incomplete knowledge was developed: QSIM (a qualitative simulation program) and its extensions for qualitative simulation; Q2, Q3 and their successors for quantitative reasoning on a qualitative framework; and the CC (component-connection) and QPC (Qualitative Process Theory) model compilers for building QSIM QDE (qualitative differential equation) models starting from different ontological assumptions. Other model-compilers for QDE's, e.g., using bond graphs or compartmental models, have been developed elsewhere. These model-building tools will support automatic construction of qualitative models from physical specifications, and further research into selection of appropriate modeling viewpoints. For monitoring and diagnosis, plausible hypotheses are unified against observations to strengthen or refute the predicted behaviors. In MIMIC (Model Integration via Mesh Interpolation Coefficients), multiple hypothesized models of the system are tracked in parallel in order to reduce the 'missing model' problem. Each model begins as a qualitative model, and is unified with a priori quantitative knowledge and with the stream of incoming observational data. When the model/data unification yields a contradiction, the model is refuted. When there is no contradiction, the predictions of the model are progressively strengthened, for use in procedure planning and differential diagnosis. Only under a qualitative level of description can a finite set of models guarantee the complete coverage necessary for this performance. The results of this research are presented in several publications. Abstracts of these published papers are presented along with abtracts of papers representing work that was synergistic with the NASA grant but funded otherwise. These 28 papers include but are not limited to: 'Combined qualitative and numerical simulation with Q3'; 'Comparative analysis and qualitative integral representations'; 'Model-based monitoring of dynamic systems'; 'Numerical behavior envelopes for qualitative models'; 'Higher-order derivative constraints in qualitative simulation'; and 'Non-intersection of trajectories in qualitative phase space: a global constraint for qualitative simulation.'
A brief description of the morphology of lunar surface material returned by Luna 16 automatic lunar station is given. Adhesiveness of the surface material and its ability to be electrified is noted. Two main genetic groups of regolith particles are differentiated: primary, represented mainly by fragments of magmatic rocks of the basalt and gabbro types, as well as mineralized grains of their constituent minerals, and secondary, particles subjected to appreciable exogenic transformation on the surface of the Moon. The second group, representing more than 70 percent of the material in coarse fractions, includes particles of breccias, sintered aggregates of complex dendritic form, and glass and vitrified particles of varied composition.
Transformations of nonlinear systems were used to design automatic flight controllers for vertical and short takeoff aircraft. Under the assumption that a nonlinear system can be mapped to a controllable linear system, a method using partial differential equations was constructed to approximate transformations in cases where exact ones cannot be found. An application of the design theory to a rotorcraft, the UH-1H helicopter, was presented.
An algorithm for generating computational grids about arbitrary three-dimensional bodies is developed. The elliptic partial differential equation (PDE) approach developed by Steger and Sorenson and used in the NASA computer program GRAPE is extended from two to three dimensions. Forcing functions which are found automatically by the algorithm give the user the ability to control mesh cell size and skewness at boundary surfaces. This algorithm, as is typical of PDE grid generators, gives smooth grid lines and spacing in the interior of the grid. The method is applied to a rectilinear wind-tunnel case and to two body shapes in spherical coordinates.
Modern network devices collect a large amount of data that can be analyzed to identify bottlenecks, anomalies, cyber-attacks, etc. Therefore, there is often a need to analyze such collections of network data quite often by an external expert or by the research community. However, these collections of data contain sensitive, proprietary information. In order for the network data to be shared, it must first be anonymized. The overall objective of this project is to develop an innovative privacy management tool to anonymize network data and achieve sufficient privacy, acceptable data utility, and efficient data analysis at the same time. No existing anonymization methods can achieve all of these at the same time. The core of this technology is a differential private clustering algorithm that provides strong privacy protection, preserves data properties important for subsequent analysis, and allows the party receiving the anonymized data to conduct analysis directly on anonymized data without the need of decryption or any extra processing. The research carried out was to design, implement and verify a solution to this problem by completing the following tasks: 1) developing the core technology; 2) developing a context based method that automatically recommends fields that must be anonymized; 3) conducted experiments showing superior results using our approach compared to existing tools, and 4) developed an intuitive but basic user interface. The research that was conducted generated novel algorithmic techniques that utilize state-of-the-art methods such as condensation, differential privacy preservation, clustering, automated tuning based on contextual awareness, and recommendation techniques to specify columns to users for anonymization leading to optimal privacy that allows research analysis on the dataset. Experiments were conducted to evaluate the efficacy of these novel algorithmic techniques by performing analysis on original non-anonymized datasets, then conducting analysis on the same yet anonymized datasets and comparing the results of the analyses. Overall, the anonymized analysis results were within 1% of the original results, verifying that the generated technology not only guarantees a high level of privacy but also enables research analysis as if it were conducted on the original dataset. Potential applications of this technology include anonymization of any type of structured network datasets that contain sensitive identifiers, such as IP addresses, that can be used in multiple applications. For example, to create an AI or machine learning model for cyber security, e.g., to detect attacks, or for performance analysis, e.g., identify bottlenecks or predict performance. In addition, a market analysis that was conducted for potential applications of this technology identified a broader range of applications of our anonymization technology beyond the network sector that includes healthcare, banking, insurance, securities, finance (FISB), data brokering, cloud services, ad sales, and government.
OBJECTIVE: A new computerized semi-automatic method for left ventricular (LV) chamber segmentation is presented. METHODS: The LV is imaged by real-time three-dimensional echocardiography (RT3DE). The surface detection model, based on level set techniques, is applied to RT3DE data for image analysis. The modified level set partial differential equation we use is solved by applying numerical methods for conservation laws. The initial conditions are manually established on some slices of the entire volume. The solution obtained for each slice is a contour line corresponding with the boundary between LV cavity and LV endocardium. RESULTS: The mathematical model has been applied to sequences of frames of human hearts (volume range: 34-109 ml) imaged by 2D and reconstructed off-line and RT3DE data. Volume estimation obtained by this new semi-automatic method shows an excellent correlation with those obtained by manual tracing (r = 0.992). Dynamic change of LV volume during the cardiac cycle is also obtained. CONCLUSION: The volume estimation method is accurate; edge based segmentation, image completion and volume reconstruction can be accomplished. The visualization technique also allows to navigate into the reconstructed volume and to display any section of the volume.
The NASA Langley Research Center is currently engaged in a stall/spin research program to provide the fundamental information and design guidelines required to predict the stall/spin characteristics of fighter aircraft. The prediction methods under study include theoretical spin prediction techniques and piloted simulation studies. The paper discusses the overall status of theoretical techniques including: (1) input data requirements, (2) math model requirements, and (3) correlation between theoretical and experimental results. The Langley Differential Maneuvering Simulator (DMS) facility has been used to evaluate the spin susceptibility of several current fighters during typical air combat maneuvers and to develop and evaluate the effectiveness of automatic departure/spin prevention concepts. The evaluation procedure is described and some of the more significant results of the studies are presented.
Purpose In clinical practice, invasiveness is an important reference indicator for differentiating the malignant degree of subsolid pulmonary nodules. These nodules can be classified as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). The automatic determination of a nodule's invasiveness based on chest CT scans can guide treatment planning. However, it is challenging, owing to the insufficiency of training data and their interclass similarity and intraclass variation. To address these challenges, we propose a two‐stage deep learning strategy for this task: prior‐feature learning followed by adaptive‐boost deep learning. Methods The adaptive‐boost deep learning is proposed to train a strong classifier for invasiveness classification of subsolid nodules in chest CT images, using multiple 3D convolutional neural network (CNN)‐based weak classifiers. Because ensembles of multiple deep 3D CNN models have a huge number of parameters and require large computing resources along with more training and testing time, the prior‐feature learning is proposed to reduce the computations by sharing the CNN layers between all weak classifiers. Using this strategy, all weak classifiers can be integrated into a single network. Results Tenfold cross validation of binary classification was conducted on a total of 1357 nodules, including 765 noninvasive (AAH and AIS) and 592 invasive nodules (MIA and IAC). Ablation experimental results indicated that the proposed binary classifier achieved an accuracy of with an AUC of 81.3 . These results are superior compared to those achieved by three experienced chest imaging specialists who achieved an accuracy of , , and , respectively. About 200 additional nodules were also collected. These nodules covered 50 cases for each category (AAH, AIS, MIA, and IAC, respectively). Both binary and multiple classifications were performed on these data and the results demonstrated that the proposed method definitely achieves better performance than the performance achieved by nonensemble deep learning methods. Conclusions It can be concluded that the proposed adaptive‐boost deep learning can significantly improve the performance of invasiveness classification of pulmonary subsolid nodules in CT images, while the prior‐feature learning can significantly reduce the total size of deep models. The promising results on clinical data show that the trained models can be used as an effective lung cancer screening tool in hospitals. Moreover, the proposed strategy can be easily extended to other similar classification tasks in 3D medical images.