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Inexpensive computer-to-computer link facilitates data communication in electrically noisy environments. Link can connect process-control computers while reducing errors from electrical noise of manufacturing operations.
High-quality tomographic reconstruction is not possible without the accurate localization of the center of rotation. Poor localization leads to artifacts in the data and can even cause reconstructions to fail. There are many approaches to solving this problem, some of which involve the collection of full sinograms, or even provisional tomographic reconstructions, in order to determine the center of rotation. Here, a simple method based on the expected symmetry of the Fourier transform of summed projections approximately 180° apart is presented; unlike cross-correlation methods, it requires only a single Fourier transform to compute, and uses mainly low spatial frequency information which is less susceptible to noise. This approach is shown to be fast, and robust against poor signal-to-noise as well as to projection images acquired at angles that are not exactly 180° apart. This rapid method can be useful as a first step in the processing of tomographic data.
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Algorithms for failure detection and identification for redundant noncolinear arrays of single degree of freedom gyros and accelerometers are described. These algorithms are optimum in the sense that detection occurs as soon as it is no longer possible to account for the instrument outputs as the outputs of good instruments operating within their noise tolerances, and identification occurs as soon as it is true that only a particular instrument failure could account for the actual instrument outputs within the noise tolerance of good instruments. An estimation algorithm is described which minimizes the maximum possible estimation error magnitude for the given set of instrument outputs. Monte Carlo simulation results are presented for the application of the algorithms to an inertial reference unit consisting of six gyros and six accelerometers in two alternate configurations.
An algorithm for failure detection and identification for redundant non-orthogonal arrays of single degree of freedom gyros and accelerometers is described which is optimum in the sense that detection occurs as soon as it is no longer possible to account for the instrument outputs as the outputs of good instruments operating within their noise tolerances, and identification occurs as soon as it is true that only a particular instrument failure could account for the actual instrument outputs within the noise tolerance of good instruments. An estimation algorithm is described which minimizes the maximum possible error magnitude for the given set of instrument outputs. Monte Carlo simulation results are presented for the application of the algorithms to an inertial reference unit consisting of six gyros and six accelerometers in two alternate configurations.
Minimax algorithms for failure detection and identification for redundant noncolinear arrays of single-degree-of-freedom gyros and accelerometers are described. These algorithms are optimum in the sense that detection occurs as soon as it is no longer possible to account for the instrument outputs as the outputs of good instruments operating within their noise tolerances, and identification occurs as soon as it is true that only a particular instrument failure could account for the actual instrument outputs within the noise tolerance of good instruments. An estimation algorithm is described which minimizes the maximum possible estimation error magnitude for the given set of instrument outputs. Monte Carlo simulation results are presented for the application of the algorithms to an inertial reference unit consisting of six gyros and six accelerometers in two alternate configurations.
Noise pollution caused by the presence of airfields adjacent to residential areas is studied. Noise effects on the sleep of residents near airports and the degree of the residents noise tolerance are evaluated. What aircraft noises are annoying and to what extent the annoyance varies with sound level are discussed.
The Electron Ion Collider (EIC) presently under construction at Brookhaven National Laboratory will collide polarized high energy electron beams with hadron beams with luminosities up to 10^34 cm?^2 s^?1 in center mass energy range of 20-140 GeV. Crab cavities are used to compensate the geometric luminosity due to a large crossing angle 25 mrad in the EIC. It was found that the phase noise in crab cavities will generate a significant emittance growth for the hadron beams. The phase noise tolerance from the analytical calculation is very small for the Hadron Storage Ring (HSR) of the EIC. In this article, we present our preliminary numerical simulation results with a 6-d symplectic particle tracking to determine the proton emittance growth rate with crab cavity phase noise and beam-beam interaction for the HSR.
To meet changing research demands, new scanning tunneling microscope (STM) features must constantly evolve. We describe the design, development, and performance of a modular plug-in STM, which is compact and stable. The STM head is equipped with a quick-connect socket that is matched to a universal connector plug, enabling it to be transferred between systems. This head can be introduced into a vacuum system via a load-lock and transferred to various sites equipped with the connector plug, permitting multi-site STM operation. Its design allows for reliable operation in a variety of experimental conditions, including a broad temperature range, ultra-high vacuum, high magnetic fields, and closed-cycle pulse-tube cooling. The STM’s compact size is achieved by a novel nested piezoelectric coarse walker design, which allows for large orthogonal travel in the X, Y, and Z directions, ideal for studying both bulk and thin film samples ranging in size from mm to μm. Its stability and noise tolerance are demonstrated by achieving atomic resolution under ambient conditions on a laboratory desktop with no vibrational or acoustic isolation. The operation of the nested coarse walkers is demonstrated by successful navigation to a μm-sized 2D sample.
One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand–maximize–compress (EMC) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.
Designing efficient and noise-tolerant quantum computation protocols generally begins with an understanding of quantum error-correcting codes and their native logical operations. The simplest class of native operations are transversal gates, which are naturally fault-tolerant. Here, in this paper, we aim to characterize the transversal gates of quantum Reed–Muller (RM) codes by exploiting the well-studied properties of their classical counterparts. We start our work by establishing a new geometric characterization of quantum RM codes via the Boolean hypercube and its associated subcube complex. More specifically, a set of stabilizer generators for a quantum RM code can be described via transversal X and Z operators acting on subcubes of particular dimensions. This characterization leads us to define subcube operators composed of single-qubit π/2 k Z -rotations that act on subcubes of given dimensions. We first characterize the action of subcube operators on the code space: depending on the dimension of the subcube, these operators either (1) act as a logical identity on the code space, (2) implement non-trivial logic, or (3) rotate a state away from the code space. Second, and more remarkably, we uncover that the logic implemented by these operators corresponds to circuits of multi-controlled-Z gates that have an explicit and simple combinatorial description. Overall, this suite of results yields a comprehensive understanding of a class of natural transversal operators for quantum RM codes.
The recent advent of quantum computing has the potential to overhaul security, communications, and scientific modeling. Superconducting qubits are a leading platform that is advancing noise-tolerant intermediate-scale quantum processors. The implementation requires scaling to large numbers of superconducting qubits, circuit depths, and gate speeds, wherein high-purity RF signal generation and effective cabling transport are desirable. Fiber photonic-enhanced RF signal generation has demonstrated the principle of addressing both signal generation and transport requirements, supporting intermediate qubit numbers and robust packaging efforts; however, fiber-based approaches to RF signal distribution are often bounded by their phase instability. Here, we present a silicon photonic integrated circuit-based version of a photonic-enhanced RF signal generator that demonstrates the requisite stability, as well as a path towards the necessary signal fidelity.
A model for stereo vision, which is based on the human-binocular vision system, is proposed. Data collected from studies of neurophysiology of the human binocular system are discussed. An algorithm for the implementation of this stereo vision model is derived. The algorithm is tested on computer-generated and real scene images. Examples of a computer-generated image and a grey-level image are presented. It is noted that the proposed method is computationally efficient for depth perception, and the results indicate accuracies that are noise tolerant.
A versatile neural-net model is explained in terms familiar to computer scientists and engineers. It is called the sparse distributed memory, and it is a random-access memory for very long words (for patterns with thousands of bits). Its potential utility is the result of several factors: (1) a large pattern representing an object or a scene or a moment can encode a large amount of information about what it represents; (2) this information can serve as an address to the memory, and it can also serve as data; (3) the memory is noise tolerant--the information need not be exact; (4) the memory can be made arbitrarily large and hence an arbitrary amount of information can be stored in it; and (5) the architecture is inherently parallel, allowing large memories to be fast. Such memories can become important components of future computers.
Fault isolation and sensor placement are vital for monitoring and diagnosis. A sensor conveys information about a system's state that guides troubleshooting if problems arise. We are using machine learning methods to uncover behavioral patterns over snapshots of system simulations that will aid fault isolation and sensor placement, with an eye towards minimality, fault coverage, and noise tolerance.
The authors proposed a novel Zernike moment-based generalized step edge detection method which can be used for segmenting range and intensity images. A generalized step edge detector is developed to identify different kinds of edges in range images. These edge maps are thinned and linked to provide final segmentation. A generalized edge is modeled in terms of five parameters: orientation, two slopes, one step jump at the location of the edge, and the background gray level. Two complex and two real Zernike moment-based masks are required to determine all these parameters of the edge model. Theoretical noise analysis is performed to show that these operators are quite noise tolerant. Experimental results are included to demonstrate edge-based segmentation technique.
Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper.
The first research area under this grant addresses the problem of classifying time series according to their morphological features in the time domain. A supervised learning system called CALCHAS, which induces a classification procedure for signatures from preclassified examples, was developed. For each of several signature classes, the system infers a model that captures the class's morphological features using Bayesian model induction and the minimum message length approach to assign priors. After induction, a time series (signature) is classified in one of the classes when there is enough evidence to support that decision. Time series with sufficiently novel features, belonging to classes not present in the training set, are recognized as such. A second area of research assumes two sources of information about a system: a model or domain theory that encodes aspects of the system under study and data from actual system operations over time. A model, when it exists, represents strong prior expectations about how a system will perform. Our work with a diagnostic model of the RCS (Reaction Control System) of the Space Shuttle motivated the development of SIG, a system which combines information from a model (or domain theory) and data. As it tracks RCS behavior, the model computes quantitative and qualitative values. Induction is then performed over the data represented by both the 'raw' features and the model-computed high-level features. Finally, work on clustering for operating mode discovery motivated some important extensions to the clustering strategy we had used. One modification appends an iterative optimization technique onto the clustering system; this optimization strategy appears to be novel in the clustering literature. A second modification improves the noise tolerance of the clustering system. In particular, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus making post-clustering analysis easier.