Algorithms for Efficient Intelligence Collection

DTIC Science & Technology

2013-09-01

2006. Cortical substrates for exploratory decisions in humans. Nature 441(7095) 876–879. Deitchman, S. J. 1962. A lanchester model of guerrilla...Monterey, CA. Pearl, J. 1986. Fusion, propagation and structuring in belief networks. Artificial Intelligence 29 241–288. Schaffer, M. B. 1968. Lanchester

Learning topic models by belief propagation.

PubMed

Zeng, Jia; Cheung, William K; Liu, Jiming

2013-05-01

Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interest and touches on many important applications in text mining, computer vision and computational biology. This paper represents the collapsed LDA as a factor graph, which enables the classic loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Although two commonly used approximate inference methods, such as variational Bayes (VB) and collapsed Gibbs sampling (GS), have gained great success in learning LDA, the proposed BP is competitive in both speed and accuracy, as validated by encouraging experimental results on four large-scale document datasets. Furthermore, the BP algorithm has the potential to become a generic scheme for learning variants of LDA-based topic models in the collapsed space. To this end, we show how to learn two typical variants of LDA-based topic models, such as author-topic models (ATM) and relational topic models (RTM), using BP based on the factor graph representations.

Bayesian inversion analysis of nonlinear dynamics in surface heterogeneous reactions.

PubMed

Omori, Toshiaki; Kuwatani, Tatsu; Okamoto, Atsushi; Hukushima, Koji

2016-09-01

It is essential to extract nonlinear dynamics from time-series data as an inverse problem in natural sciences. We propose a Bayesian statistical framework for extracting nonlinear dynamics of surface heterogeneous reactions from sparse and noisy observable data. Surface heterogeneous reactions are chemical reactions with conjugation of multiple phases, and they have the intrinsic nonlinearity of their dynamics caused by the effect of surface-area between different phases. We adapt a belief propagation method and an expectation-maximization (EM) algorithm to partial observation problem, in order to simultaneously estimate the time course of hidden variables and the kinetic parameters underlying dynamics. The proposed belief propagation method is performed by using sequential Monte Carlo algorithm in order to estimate nonlinear dynamical system. Using our proposed method, we show that the rate constants of dissolution and precipitation reactions, which are typical examples of surface heterogeneous reactions, as well as the temporal changes of solid reactants and products, were successfully estimated only from the observable temporal changes in the concentration of the dissolved intermediate product.

A Comparison of JPDA and Belief Propagation for Data Association in SSA

NASA Astrophysics Data System (ADS)

Rutten, M.; Williams, J.; Gordon, N.; Jah, M.; Baldwin, J.; Stauch, J.

2014-09-01

The process of initial orbit determination, or catalogue maintenance, using a set of unlabeled observations requires a method of choosing which observation was due to which object. Realities of imperfect sensors mean that the association must be made in the presence of both missed detections and false alarms. Data association is not only essential to processing observations it can also be one of the most significant computational bottlenecks. The constrained admissible region multiple hypothesis filter (CAR-MHF) is an algorithm for initial orbit determination using short-arc observations of space objects. CAR-MHF has used joint probabilistic data association (JPDA), a well-established approach to multi-target data association. A recent development in the target tracking literature is the use of graphical models to formulate data association problems. Using an approximate inference algorithm, belief propagation (BP), on the graphical model results in an algorithm this is both computationally efficient and accurate. This paper compares CAR-MHF using JPDA and CAR-MHF using BP for the problem of initial orbit determination on a set of deep-space objects. The results of the analysis will show that by using the BP algorithm there are significant gains in computational load without any statistically significant loss in overall performance of the orbit determination.

Weighted community detection and data clustering using message passing

NASA Astrophysics Data System (ADS)

Shi, Cheng; Liu, Yanchen; Zhang, Pan

2018-03-01

Grouping objects into clusters based on the similarities or weights between them is one of the most important problems in science and engineering. In this work, by extending message-passing algorithms and spectral algorithms proposed for an unweighted community detection problem, we develop a non-parametric method based on statistical physics, by mapping the problem to the Potts model at the critical temperature of spin-glass transition and applying belief propagation to solve the marginals corresponding to the Boltzmann distribution. Our algorithm is robust to over-fitting and gives a principled way to determine whether there are significant clusters in the data and how many clusters there are. We apply our method to different clustering tasks. In the community detection problem in weighted and directed networks, we show that our algorithm significantly outperforms existing algorithms. In the clustering problem, where the data were generated by mixture models in the sparse regime, we show that our method works all the way down to the theoretical limit of detectability and gives accuracy very close to that of the optimal Bayesian inference. In the semi-supervised clustering problem, our method only needs several labels to work perfectly in classic datasets. Finally, we further develop Thouless-Anderson-Palmer equations which heavily reduce the computation complexity in dense networks but give almost the same performance as belief propagation.

LDPC decoder with a limited-precision FPGA-based floating-point multiplication coprocessor

NASA Astrophysics Data System (ADS)

Moberly, Raymond; O'Sullivan, Michael; Waheed, Khurram

2007-09-01

Implementing the sum-product algorithm, in an FPGA with an embedded processor, invites us to consider a tradeoff between computational precision and computational speed. The algorithm, known outside of the signal processing community as Pearl's belief propagation, is used for iterative soft-decision decoding of LDPC codes. We determined the feasibility of a coprocessor that will perform product computations. Our FPGA-based coprocessor (design) performs computer algebra with significantly less precision than the standard (e.g. integer, floating-point) operations of general purpose processors. Using synthesis, targeting a 3,168 LUT Xilinx FPGA, we show that key components of a decoder are feasible and that the full single-precision decoder could be constructed using a larger part. Soft-decision decoding by the iterative belief propagation algorithm is impacted both positively and negatively by a reduction in the precision of the computation. Reducing precision reduces the coding gain, but the limited-precision computation can operate faster. A proposed solution offers custom logic to perform computations with less precision, yet uses the floating-point format to interface with the software. Simulation results show the achievable coding gain. Synthesis results help theorize the the full capacity and performance of an FPGA-based coprocessor.

Multi-exemplar affinity propagation.

PubMed

Wang, Chang-Dong; Lai, Jian-Huang; Suen, Ching Y; Zhu, Jun-Yong

2013-09-01

The affinity propagation (AP) clustering algorithm has received much attention in the past few years. AP is appealing because it is efficient, insensitive to initialization, and it produces clusters at a lower error rate than other exemplar-based methods. However, its single-exemplar model becomes inadequate when applied to model multisubclasses in some situations such as scene analysis and character recognition. To remedy this deficiency, we have extended the single-exemplar model to a multi-exemplar one to create a new multi-exemplar affinity propagation (MEAP) algorithm. This new model automatically determines the number of exemplars in each cluster associated with a super exemplar to approximate the subclasses in the category. Solving the model is NP-hard and we tackle it with the max-sum belief propagation to produce neighborhood maximum clusters, with no need to specify beforehand the number of clusters, multi-exemplars, and superexemplars. Also, utilizing the sparsity in the data, we are able to reduce substantially the computational time and storage. Experimental studies have shown MEAP's significant improvements over other algorithms on unsupervised image categorization and the clustering of handwritten digits.

DNA motif elucidation using belief propagation.

PubMed

Wong, Ka-Chun; Chan, Tak-Ming; Peng, Chengbin; Li, Yue; Zhang, Zhaolei

2013-09-01

Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k=8∼10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors' websites: e.g. http://www.cs.toronto.edu/∼wkc/kmerHMM.

Theoretic derivation of directed acyclic subgraph algorithm and comparisons with message passing algorithm

NASA Astrophysics Data System (ADS)

Ha, Jeongmok; Jeong, Hong

2016-07-01

This study investigates the directed acyclic subgraph (DAS) algorithm, which is used to solve discrete labeling problems much more rapidly than other Markov-random-field-based inference methods but at a competitive accuracy. However, the mechanism by which the DAS algorithm simultaneously achieves competitive accuracy and fast execution speed, has not been elucidated by a theoretical derivation. We analyze the DAS algorithm by comparing it with a message passing algorithm. Graphical models, inference methods, and energy-minimization frameworks are compared between DAS and message passing algorithms. Moreover, the performances of DAS and other message passing methods [sum-product belief propagation (BP), max-product BP, and tree-reweighted message passing] are experimentally compared.

Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks

NASA Astrophysics Data System (ADS)

Ghasemian, Amir; Zhang, Pan; Clauset, Aaron; Moore, Cristopher; Peel, Leto

2016-07-01

The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information.

Graph rigidity, cyclic belief propagation, and point pattern matching.

PubMed

McAuley, Julian J; Caetano, Tibério S; Barbosa, Marconi S

2008-11-01

A recent paper [1] proposed a provably optimal polynomial time method for performing near-isometric point pattern matching by means of exact probabilistic inference in a chordal graphical model. Its fundamental result is that the chordal graph in question is shown to be globally rigid, implying that exact inference provides the same matching solution as exact inference in a complete graphical model. This implies that the algorithm is optimal when there is no noise in the point patterns. In this paper, we present a new graph that is also globally rigid but has an advantage over the graph proposed in [1]: Its maximal clique size is smaller, rendering inference significantly more efficient. However, this graph is not chordal, and thus, standard Junction Tree algorithms cannot be directly applied. Nevertheless, we show that loopy belief propagation in such a graph converges to the optimal solution. This allows us to retain the optimality guarantee in the noiseless case, while substantially reducing both memory requirements and processing time. Our experimental results show that the accuracy of the proposed solution is indistinguishable from that in [1] when there is noise in the point patterns.

Neighbourhood-consensus message passing and its potentials in image processing applications

NASA Astrophysics Data System (ADS)

Ružic, Tijana; Pižurica, Aleksandra; Philips, Wilfried

2011-03-01

In this paper, a novel algorithm for inference in Markov Random Fields (MRFs) is presented. Its goal is to find approximate maximum a posteriori estimates in a simple manner by combining neighbourhood influence of iterated conditional modes (ICM) and message passing of loopy belief propagation (LBP). We call the proposed method neighbourhood-consensus message passing because a single joint message is sent from the specified neighbourhood to the central node. The message, as a function of beliefs, represents the agreement of all nodes within the neighbourhood regarding the labels of the central node. This way we are able to overcome the disadvantages of reference algorithms, ICM and LBP. On one hand, more information is propagated in comparison with ICM, while on the other hand, the huge amount of pairwise interactions is avoided in comparison with LBP by working with neighbourhoods. The idea is related to the previously developed iterated conditional expectations algorithm. Here we revisit it and redefine it in a message passing framework in a more general form. The results on three different benchmarks demonstrate that the proposed technique can perform well both for binary and multi-label MRFs without any limitations on the model definition. Furthermore, it manifests improved performance over related techniques either in terms of quality and/or speed.

Algorithmic detectability threshold of the stochastic block model

NASA Astrophysics Data System (ADS)

Kawamoto, Tatsuro

2018-03-01

The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation-maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

Low Density Parity Check Codes Based on Finite Geometries: A Rediscovery and More

NASA Technical Reports Server (NTRS)

Kou, Yu; Lin, Shu; Fossorier, Marc

1999-01-01

Low density parity check (LDPC) codes with iterative decoding based on belief propagation achieve astonishing error performance close to Shannon limit. No algebraic or geometric method for constructing these codes has been reported and they are largely generated by computer search. As a result, encoding of long LDPC codes is in general very complex. This paper presents two classes of high rate LDPC codes whose constructions are based on finite Euclidean and projective geometries, respectively. These classes of codes a.re cyclic and have good constraint parameters and minimum distances. Cyclic structure adows the use of linear feedback shift registers for encoding. These finite geometry LDPC codes achieve very good error performance with either soft-decision iterative decoding based on belief propagation or Gallager's hard-decision bit flipping algorithm. These codes can be punctured or extended to obtain other good LDPC codes. A generalization of these codes is also presented.

Parallel and Distributed Systems for Probabilistic Reasoning

DTIC Science & Technology

2012-12-01

work at CMU I had the opportunity to work with Andreas Krause on Gaussian process models for signal quality estimation in wireless sensor networks ...we reviewed the natural parallelization of the belief propagation algorithm using the synchronous schedule and demonstrated both theoretically and...problem is that the power-law sparsity structure, commonly found in graphs derived from natural phenomena (e.g., social networks and the web

Metric Ranking of Invariant Networks with Belief Propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tao, Changxia; Ge, Yong; Song, Qinbao

The management of large-scale distributed information systems relies on the effective use and modeling of monitoring data collected at various points in the distributed information systems. A promising approach is to discover invariant relationships among the monitoring data and generate invariant networks, where a node is a monitoring data source (metric) and a link indicates an invariant relationship between two monitoring data. Such an invariant network representation can help system experts to localize and diagnose the system faults by examining those broken invariant relationships and their related metrics, because system faults usually propagate among the monitoring data and eventually leadmore » to some broken invariant relationships. However, at one time, there are usually a lot of broken links (invariant relationships) within an invariant network. Without proper guidance, it is difficult for system experts to manually inspect this large number of broken links. Thus, a critical challenge is how to effectively and efficiently rank metrics (nodes) of invariant networks according to the anomaly levels of metrics. The ranked list of metrics will provide system experts with useful guidance for them to localize and diagnose the system faults. To this end, we propose to model the nodes and the broken links as a Markov Random Field (MRF), and develop an iteration algorithm to infer the anomaly of each node based on belief propagation (BP). Finally, we validate the proposed algorithm on both realworld and synthetic data sets to illustrate its effectiveness.« less

A weighted belief-propagation algorithm for estimating volume-related properties of random polytopes

NASA Astrophysics Data System (ADS)

Font-Clos, Francesc; Massucci, Francesco Alessandro; Pérez Castillo, Isaac

2012-11-01

In this work we introduce a novel weighted message-passing algorithm based on the cavity method for estimating volume-related properties of random polytopes, properties which are relevant in various research fields ranging from metabolic networks, to neural networks, to compressed sensing. We propose, as opposed to adopting the usual approach consisting in approximating the real-valued cavity marginal distributions by a few parameters, using an algorithm to faithfully represent the entire marginal distribution. We explain various alternatives for implementing the algorithm and benchmarking the theoretical findings by showing concrete applications to random polytopes. The results obtained with our approach are found to be in very good agreement with the estimates produced by the Hit-and-Run algorithm, known to produce uniform sampling.

Prediction of properties of wheat dough using intelligent deep belief networks

NASA Astrophysics Data System (ADS)

Guha, Paramita; Bhatnagar, Taru; Pal, Ishan; Kamboj, Uma; Mishra, Sunita

2017-11-01

In this paper, the rheological and chemical properties of wheat dough are predicted using deep belief networks. Wheat grains are stored at controlled environmental conditions. The internal parameters of grains viz., protein, fat, carbohydrates, moisture, ash are determined using standard chemical analysis and viscosity of the dough is measured using Rheometer. Here, fat, carbohydrates, moisture, ash and temperature are considered as inputs whereas protein and viscosity are chosen as outputs. The prediction algorithm is developed using deep neural network where each layer is trained greedily using restricted Boltzmann machine (RBM) networks. The overall network is finally fine-tuned using standard neural network technique. In most literature, it has been found that fine-tuning is done using back-propagation technique. In this paper, a new algorithm is proposed in which each layer is tuned using RBM and the final network is fine-tuned using deep neural network (DNN). It has been observed that with the proposed algorithm, errors between the actual and predicted outputs are less compared to the conventional algorithm. Hence, the given network can be considered as beneficial as it predicts the outputs more accurately. Numerical results along with discussions are presented.

Residue-Specific Side-Chain Polymorphisms via Particle Belief Propagation.

PubMed

Ghoraie, Laleh Soltan; Burkowski, Forbes; Li, Shuai Cheng; Zhu, Mu

2014-01-01

Protein side chains populate diverse conformational ensembles in crystals. Despite much evidence that there is widespread conformational polymorphism in protein side chains, most of the X-ray crystallography data are modeled by single conformations in the Protein Data Bank. The ability to extract or to predict these conformational polymorphisms is of crucial importance, as it facilitates deeper understanding of protein dynamics and functionality. In this paper, we describe a computational strategy capable of predicting side-chain polymorphisms. Our approach extends a particular class of algorithms for side-chain prediction by modeling the side-chain dihedral angles more appropriately as continuous rather than discrete variables. Employing a new inferential technique known as particle belief propagation, we predict residue-specific distributions that encode information about side-chain polymorphisms. Our predicted polymorphisms are in relatively close agreement with results from a state-of-the-art approach based on X-ray crystallography data, which characterizes the conformational polymorphisms of side chains using electron density information, and has successfully discovered previously unmodeled conformations.

Fast H.264/AVC FRExt intra coding using belief propagation.

PubMed

Milani, Simone

2011-01-01

In the H.264/AVC FRExt coder, the coding performance of Intra coding significantly overcomes the previous still image coding standards, like JPEG2000, thanks to a massive use of spatial prediction. Unfortunately, the adoption of an extensive set of predictors induces a significant increase of the computational complexity required by the rate-distortion optimization routine. The paper presents a complexity reduction strategy that aims at reducing the computational load of the Intra coding with a small loss in the compression performance. The proposed algorithm relies on selecting a reduced set of prediction modes according to their probabilities, which are estimated adopting a belief-propagation procedure. Experimental results show that the proposed method permits saving up to 60 % of the coding time required by an exhaustive rate-distortion optimization method with a negligible loss in performance. Moreover, it permits an accurate control of the computational complexity unlike other methods where the computational complexity depends upon the coded sequence.

Typical performance of approximation algorithms for NP-hard problems

NASA Astrophysics Data System (ADS)

Takabe, Satoshi; Hukushima, Koji

2016-11-01

Typical performance of approximation algorithms is studied for randomized minimum vertex cover problems. A wide class of random graph ensembles characterized by an arbitrary degree distribution is discussed with the presentation of a theoretical framework. Herein, three approximation algorithms are examined: linear-programming relaxation, loopy-belief propagation, and the leaf-removal algorithm. The former two algorithms are analyzed using a statistical-mechanical technique, whereas the average-case analysis of the last one is conducted using the generating function method. These algorithms have a threshold in the typical performance with increasing average degree of the random graph, below which they find true optimal solutions with high probability. Our study reveals that there exist only three cases, determined by the order of the typical performance thresholds. In addition, we provide some conditions for classification of the graph ensembles and demonstrate explicitly some examples for the difference in thresholds.

Finding undetected protein associations in cell signaling by belief propagation.

PubMed

Bailly-Bechet, M; Borgs, C; Braunstein, A; Chayes, J; Dagkessamanskaia, A; François, J-M; Zecchina, R

2011-01-11

External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High-throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field.

Memory-efficient decoding of LDPC codes

NASA Technical Reports Server (NTRS)

Kwok-San Lee, Jason; Thorpe, Jeremy; Hawkins, Jon

2005-01-01

We present a low-complexity quantization scheme for the implementation of regular (3,6) LDPC codes. The quantization parameters are optimized to maximize the mutual information between the source and the quantized messages. Using this non-uniform quantized belief propagation algorithm, we have simulated that an optimized 3-bit quantizer operates with 0.2dB implementation loss relative to a floating point decoder, and an optimized 4-bit quantizer operates less than 0.1dB quantization loss.

Approximate inference on planar graphs using loop calculus and belief progagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chertkov, Michael; Gomez, Vicenc; Kappen, Hilbert

We introduce novel results for approximate inference on planar graphical models using the loop calculus framework. The loop calculus (Chertkov and Chernyak, 2006b) allows to express the exact partition function Z of a graphical model as a finite sum of terms that can be evaluated once the belief propagation (BP) solution is known. In general, full summation over all correction terms is intractable. We develop an algorithm for the approach presented in Chertkov et al. (2008) which represents an efficient truncation scheme on planar graphs and a new representation of the series in terms of Pfaffians of matrices. We analyzemore » in detail both the loop series and the Pfaffian series for models with binary variables and pairwise interactions, and show that the first term of the Pfaffian series can provide very accurate approximations. The algorithm outperforms previous truncation schemes of the loop series and is competitive with other state-of-the-art methods for approximate inference.« less

Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering

NASA Astrophysics Data System (ADS)

Kataoka, Shun; Kobayashi, Takuto; Yasuda, Muneki; Tanaka, Kazuyuki

2016-11-01

We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data. Suppose we have the network structure together with the vertex attribute data, that is, the information assigned to each vertex associated with the community to which it belongs. The problem addressed this paper is the detection of the community structure from the information of both the network structure and the vertex attribute data. Our approach is based on the Bayesian approach that models the posterior probability distribution of the community labels. The detection of the community structure in our method is achieved by using belief propagation and an EM algorithm. We numerically verified the performance of our method using computer-generated networks and real-world networks.

A Message Passing Approach to Side Chain Positioning with Applications in Protein Docking Refinement *

PubMed Central

Moghadasi, Mohammad; Kozakov, Dima; Mamonov, Artem B.; Vakili, Pirooz; Vajda, Sandor; Paschalidis, Ioannis Ch.

2013-01-01

We introduce a message-passing algorithm to solve the Side Chain Positioning (SCP) problem. SCP is a crucial component of protein docking refinement, which is a key step of an important class of problems in computational structural biology called protein docking. We model SCP as a combinatorial optimization problem and formulate it as a Maximum Weighted Independent Set (MWIS) problem. We then employ a modified and convergent belief-propagation algorithm to solve a relaxation of MWIS and develop randomized estimation heuristics that use the relaxed solution to obtain an effective MWIS feasible solution. Using a benchmark set of protein complexes we demonstrate that our approach leads to more accurate docking predictions compared to a baseline algorithm that does not solve the SCP. PMID:23515575

Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

NASA Astrophysics Data System (ADS)

Jha, Devesh K.; Chattopadhyay, Pritthi; Sarkar, Soumik; Ray, Asok

2016-05-01

This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot.

Causal inference in biology networks with integrated belief propagation.

PubMed

Chang, Rui; Karr, Jonathan R; Schadt, Eric E

2015-01-01

Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.

Sythesis of MCMC and Belief Propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ahn, Sungsoo; Chertkov, Michael; Shin, Jinwoo

Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most popular algorithms for computational inference in Graphical Models (GM). In principle, MCMC is an exact probabilistic method which, however, often suffers from exponentially slow mixing. In contrast, BP is a deterministic method, which is typically fast, empirically very successful, however in general lacking control of accuracy over loopy graphs. In this paper, we introduce MCMC algorithms correcting the approximation error of BP, i.e., we provide a way to compensate for BP errors via a consecutive BP-aware MCMC. Our framework is based on the Loop Calculus (LC) approach whichmore » allows to express the BP error as a sum of weighted generalized loops. Although the full series is computationally intractable, it is known that a truncated series, summing up all 2-regular loops, is computable in polynomial-time for planar pair-wise binary GMs and it also provides a highly accurate approximation empirically. Motivated by this, we first propose a polynomial-time approximation MCMC scheme for the truncated series of general (non-planar) pair-wise binary models. Our main idea here is to use the Worm algorithm, known to provide fast mixing in other (related) problems, and then design an appropriate rejection scheme to sample 2-regular loops. Furthermore, we also design an efficient rejection-free MCMC scheme for approximating the full series. The main novelty underlying our design is in utilizing the concept of cycle basis, which provides an efficient decomposition of the generalized loops. In essence, the proposed MCMC schemes run on transformed GM built upon the non-trivial BP solution, and our experiments show that this synthesis of BP and MCMC outperforms both direct MCMC and bare BP schemes.« less

Information flow on social networks: from empirical data to situation understanding

NASA Astrophysics Data System (ADS)

Roy, Heather; Abdelzaher, Tarek; Bowman, Elizabeth K.; Al Amin, Md. Tanvir

2017-05-01

This paper describes characteristics of information flow on social channels, as a function of content type and relations among individual sources, distilled from analysis of Twitter data as well as human subject survey results. The working hypothesis is that individuals who propagate content on social media act (e.g., decide whether to relay information or not) in accordance with their understanding of the content, as well as their own beliefs and trust relations. Hence, the resulting aggregate content propagation pattern encodes the collective content interpretation of the underlying group, as well as their relations. Analysis algorithms are described to recover such relations from the observed propagation patterns as well as improve our understanding of the content itself in a language agnostic manner simply from its propagation characteristics. An example is to measure the degree of community polarization around contentious topics, identify the factions involved, and recognize their individual views on issues. The analysis is independent of the language of discourse itself, making it valuable for multilingual media, where the number of languages used may render language-specific analysis less scalable.

Superpixel-based graph cuts for accurate stereo matching

NASA Astrophysics Data System (ADS)

Feng, Liting; Qin, Kaihuai

2017-06-01

Estimating the surface normal vector and disparity of a pixel simultaneously, also known as three-dimensional label method, has been widely used in recent continuous stereo matching problem to achieve sub-pixel accuracy. However, due to the infinite label space, it’s extremely hard to assign each pixel an appropriate label. In this paper, we present an accurate and efficient algorithm, integrating patchmatch with graph cuts, to approach this critical computational problem. Besides, to get robust and precise matching cost, we use a convolutional neural network to learn a similarity measure on small image patches. Compared with other MRF related methods, our method has several advantages: its sub-modular property ensures a sub-problem optimality which is easy to perform in parallel; graph cuts can simultaneously update multiple pixels, avoiding local minima caused by sequential optimizers like belief propagation; it uses segmentation results for better local expansion move; local propagation and randomization can easily generate the initial solution without using external methods. Middlebury experiments show that our method can get higher accuracy than other MRF-based algorithms.

Systematic Testing of Belief-Propagation Estimates for Absolute Free Energies in Atomistic Peptides and Proteins.

PubMed

Donovan-Maiye, Rory M; Langmead, Christopher J; Zuckerman, Daniel M

2018-01-09

Motivated by the extremely high computing costs associated with estimates of free energies for biological systems using molecular simulations, we further the exploration of existing "belief propagation" (BP) algorithms for fixed-backbone peptide and protein systems. The precalculation of pairwise interactions among discretized libraries of side-chain conformations, along with representation of protein side chains as nodes in a graphical model, enables direct application of the BP approach, which requires only ∼1 s of single-processor run time after the precalculation stage. We use a "loopy BP" algorithm, which can be seen as an approximate generalization of the transfer-matrix approach to highly connected (i.e., loopy) graphs, and it has previously been applied to protein calculations. We examine the application of loopy BP to several peptides as well as the binding site of the T4 lysozyme L99A mutant. The present study reports on (i) the comparison of the approximate BP results with estimates from unbiased estimators based on the Amber99SB force field; (ii) investigation of the effects of varying library size on BP predictions; and (iii) a theoretical discussion of the discretization effects that can arise in BP calculations. The data suggest that, despite their approximate nature, BP free-energy estimates are highly accurate-indeed, they never fall outside confidence intervals from unbiased estimators for the systems where independent results could be obtained. Furthermore, we find that libraries of sufficiently fine discretization (which diminish library-size sensitivity) can be obtained with standard computing resources in most cases. Altogether, the extremely low computing times and accurate results suggest the BP approach warrants further study.

Inferring evolution of gene duplicates using probabilistic models and nonparametric belief propagation.

PubMed

Zeng, Jia; Hannenhalli, Sridhar

2013-01-01

Gene duplication, followed by functional evolution of duplicate genes, is a primary engine of evolutionary innovation. In turn, gene expression evolution is a critical component of overall functional evolution of paralogs. Inferring evolutionary history of gene expression among paralogs is therefore a problem of considerable interest. It also represents significant challenges. The standard approaches of evolutionary reconstruction assume that at an internal node of the duplication tree, the two duplicates evolve independently. However, because of various selection pressures functional evolution of the two paralogs may be coupled. The coupling of paralog evolution corresponds to three major fates of gene duplicates: subfunctionalization (SF), conserved function (CF) or neofunctionalization (NF). Quantitative analysis of these fates is of great interest and clearly influences evolutionary inference of expression. These two interrelated problems of inferring gene expression and evolutionary fates of gene duplicates have not been studied together previously and motivate the present study. Here we propose a novel probabilistic framework and algorithm to simultaneously infer (i) ancestral gene expression and (ii) the likely fate (SF, NF, CF) at each duplication event during the evolution of gene family. Using tissue-specific gene expression data, we develop a nonparametric belief propagation (NBP) algorithm to predict the ancestral expression level as a proxy for function, and describe a novel probabilistic model that relates the predicted and known expression levels to the possible evolutionary fates. We validate our model using simulation and then apply it to a genome-wide set of gene duplicates in human. Our results suggest that SF tends to be more frequent at the earlier stage of gene family expansion, while NF occurs more frequently later on.

A good performance watermarking LDPC code used in high-speed optical fiber communication system

NASA Astrophysics Data System (ADS)

Zhang, Wenbo; Li, Chao; Zhang, Xiaoguang; Xi, Lixia; Tang, Xianfeng; He, Wenxue

2015-07-01

A watermarking LDPC code, which is a strategy designed to improve the performance of the traditional LDPC code, was introduced. By inserting some pre-defined watermarking bits into original LDPC code, we can obtain a more correct estimation about the noise level in the fiber channel. Then we use them to modify the probability distribution function (PDF) used in the initial process of belief propagation (BP) decoding algorithm. This algorithm was tested in a 128 Gb/s PDM-DQPSK optical communication system and results showed that the watermarking LDPC code had a better tolerances to polarization mode dispersion (PMD) and nonlinearity than that of traditional LDPC code. Also, by losing about 2.4% of redundancy for watermarking bits, the decoding efficiency of the watermarking LDPC code is about twice of the traditional one.

Scalable detection of statistically significant communities and hierarchies, using message passing for modularity

PubMed Central

Zhang, Pan; Moore, Cristopher

2014-01-01

Modularity is a popular measure of community structure. However, maximizing the modularity can lead to many competing partitions, with almost the same modularity, that are poorly correlated with each other. It can also produce illusory ‘‘communities’’ in random graphs where none exist. We address this problem by using the modularity as a Hamiltonian at finite temperature and using an efficient belief propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it. We show analytically and numerically that the proposed algorithm works all of the way down to the detectability transition in networks generated by the stochastic block model. It also performs well on real-world networks, revealing large communities in some networks where previous work has claimed no communities exist. Finally we show that by applying our algorithm recursively, subdividing communities until no statistically significant subcommunities can be found, we can detect hierarchical structure in real-world networks more efficiently than previous methods. PMID:25489096

Estimation of multiple sound sources with data and model uncertainties using the EM and evidential EM algorithms

NASA Astrophysics Data System (ADS)

Wang, Xun; Quost, Benjamin; Chazot, Jean-Daniel; Antoni, Jérôme

2016-01-01

This paper considers the problem of identifying multiple sound sources from acoustical measurements obtained by an array of microphones. The problem is solved via maximum likelihood. In particular, an expectation-maximization (EM) approach is used to estimate the sound source locations and strengths, the pressure measured by a microphone being interpreted as a mixture of latent signals emitted by the sources. This work also considers two kinds of uncertainties pervading the sound propagation and measurement process: uncertain microphone locations and uncertain wavenumber. These uncertainties are transposed to the data in the belief functions framework. Then, the source locations and strengths can be estimated using a variant of the EM algorithm, known as the Evidential EM (E2M) algorithm. Eventually, both simulation and real experiments are shown to illustrate the advantage of using the EM in the case without uncertainty and the E2M in the case of uncertain measurement.

Neural methods based on modified reputation rules for detection and identification of intrusion attacks in wireless ad hoc sensor networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

2010-04-01

Determining methods to secure the process of data fusion against attacks by compromised nodes in wireless sensor networks (WSNs) and to quantify the uncertainty that may exist in the aggregation results is a critical issue in mitigating the effects of intrusion attacks. Published research has introduced the concept of the trustworthiness (reputation) of a single sensor node. Reputation is evaluated using an information-theoretic concept, the Kullback- Leibler (KL) distance. Reputation is added to the set of security features. In data aggregation, an opinion, a metric of the degree of belief, is generated to represent the uncertainty in the aggregation result. As aggregate information is disseminated along routes to the sink node(s), its corresponding opinion is propagated and regulated by Josang's belief model. By applying subjective logic on the opinion to manage trust propagation, the uncertainty inherent in aggregation results can be quantified for use in decision making. The concepts of reputation and opinion are modified to allow their application to a class of dynamic WSNs. Using reputation as a factor in determining interim aggregate information is equivalent to implementation of a reputation-based security filter at each processing stage of data fusion, thereby improving the intrusion detection and identification results based on unsupervised techniques. In particular, the reputation-based version of the probabilistic neural network (PNN) learns the signature of normal network traffic with the random probability weights normally used in the PNN replaced by the trust-based quantified reputations of sensor data or subsequent aggregation results generated by the sequential implementation of a version of Josang's belief model. A two-stage, intrusion detection and identification algorithm is implemented to overcome the problems of large sensor data loads and resource restrictions in WSNs. Performance of the twostage algorithm is assessed in simulations of WSN scenarios with multiple sensors at edge nodes for known intrusion attacks. Simulation results show improved robustness of the two-stage design based on reputation-based NNs to intrusion anomalies from compromised nodes and external intrusion attacks.

Statistical mechanics of broadcast channels using low-density parity-check codes.

PubMed

Nakamura, Kazutaka; Kabashima, Yoshiyuki; Morelos-Zaragoza, Robert; Saad, David

2003-03-01

We investigate the use of Gallager's low-density parity-check (LDPC) codes in a degraded broadcast channel, one of the fundamental models in network information theory. Combining linear codes is a standard technique in practical network communication schemes and is known to provide better performance than simple time sharing methods when algebraic codes are used. The statistical physics based analysis shows that the practical performance of the suggested method, achieved by employing the belief propagation algorithm, is superior to that of LDPC based time sharing codes while the best performance, when received transmissions are optimally decoded, is bounded by the time sharing limit.

LPA-CBD an improved label propagation algorithm based on community belonging degree for community detection

NASA Astrophysics Data System (ADS)

Gui, Chun; Zhang, Ruisheng; Zhao, Zhili; Wei, Jiaxuan; Hu, Rongjing

In order to deal with stochasticity in center node selection and instability in community detection of label propagation algorithm, this paper proposes an improved label propagation algorithm named label propagation algorithm based on community belonging degree (LPA-CBD) that employs community belonging degree to determine the number and the center of community. The general process of LPA-CBD is that the initial community is identified by the nodes with the maximum degree, and then it is optimized or expanded by community belonging degree. After getting the rough structure of network community, the remaining nodes are labeled by using label propagation algorithm. The experimental results on 10 real-world networks and three synthetic networks show that LPA-CBD achieves reasonable community number, better algorithm accuracy and higher modularity compared with other four prominent algorithms. Moreover, the proposed algorithm not only has lower algorithm complexity and higher community detection quality, but also improves the stability of the original label propagation algorithm.

WS-BP: An efficient wolf search based back-propagation algorithm

NASA Astrophysics Data System (ADS)

Nawi, Nazri Mohd; Rehman, M. Z.; Khan, Abdullah

2015-05-01

Wolf Search (WS) is a heuristic based optimization algorithm. Inspired by the preying and survival capabilities of the wolves, this algorithm is highly capable to search large spaces in the candidate solutions. This paper investigates the use of WS algorithm in combination with back-propagation neural network (BPNN) algorithm to overcome the local minima problem and to improve convergence in gradient descent. The performance of the proposed Wolf Search based Back-Propagation (WS-BP) algorithm is compared with Artificial Bee Colony Back-Propagation (ABC-BP), Bat Based Back-Propagation (Bat-BP), and conventional BPNN algorithms. Specifically, OR and XOR datasets are used for training the network. The simulation results show that the WS-BP algorithm effectively avoids the local minima and converge to global minima.

Accuracy and robustness evaluation in stereo matching

NASA Astrophysics Data System (ADS)

Nguyen, Duc M.; Hanca, Jan; Lu, Shao-Ping; Schelkens, Peter; Munteanu, Adrian

2016-09-01

Stereo matching has received a lot of attention from the computer vision community, thanks to its wide range of applications. Despite of the large variety of algorithms that have been proposed so far, it is not trivial to select suitable algorithms for the construction of practical systems. One of the main problems is that many algorithms lack sufficient robustness when employed in various operational conditions. This problem is due to the fact that most of the proposed methods in the literature are usually tested and tuned to perform well on one specific dataset. To alleviate this problem, an extensive evaluation in terms of accuracy and robustness of state-of-the-art stereo matching algorithms is presented. Three datasets (Middlebury, KITTI, and MPEG FTV) representing different operational conditions are employed. Based on the analysis, improvements over existing algorithms have been proposed. The experimental results show that our improved versions of cross-based and cost volume filtering algorithms outperform the original versions with large margins on Middlebury and KITTI datasets. In addition, the latter of the two proposed algorithms ranks itself among the best local stereo matching approaches on the KITTI benchmark. Under evaluations using specific settings for depth-image-based-rendering applications, our improved belief propagation algorithm is less complex than MPEG's FTV depth estimation reference software (DERS), while yielding similar depth estimation performance. Finally, several conclusions on stereo matching algorithms are also presented.

Plausible inference: A multi-valued logic for problem solving

NASA Technical Reports Server (NTRS)

Friedman, L.

1979-01-01

A new logic is developed which permits continuously variable strength of belief in the truth of assertions. Four inference rules result, with formal logic as a limiting case. Quantification of belief is defined. Propagation of belief to linked assertions results from dependency-based techniques of truth maintenance so that local consistency is achieved or contradiction discovered in problem solving. Rules for combining, confirming, or disconfirming beliefs are given, and several heuristics are suggested that apply to revising already formed beliefs in the light of new evidence. The strength of belief that results in such revisions based on conflicting evidence are a highly subjective phenomenon. Certain quantification rules appear to reflect an orderliness in the subjectivity. Several examples of reasoning by plausible inference are given, including a legal example and one from robot learning. Propagation of belief takes place in directions forbidden in formal logic and this results in conclusions becoming possible for a given set of assertions that are not reachable by formal logic.

Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition

PubMed Central

Shen, Sheng; Yao, Xiaohui; Sheng, Meiping; Wang, Chen

2018-01-01

Underwater acoustic target recognition based on ship-radiated noise belongs to the small-sample-size recognition problems. A competitive deep-belief network is proposed to learn features with more discriminative information from labeled and unlabeled samples. The proposed model consists of four stages: (1) A standard restricted Boltzmann machine is pretrained using a large number of unlabeled data to initialize its parameters; (2) the hidden units are grouped according to categories, which provides an initial clustering model for competitive learning; (3) competitive training and back-propagation algorithms are used to update the parameters to accomplish the task of clustering; (4) by applying layer-wise training and supervised fine-tuning, a deep neural network is built to obtain features. Experimental results show that the proposed method can achieve classification accuracy of 90.89%, which is 8.95% higher than the accuracy obtained by the compared methods. In addition, the highest accuracy of our method is obtained with fewer features than other methods. PMID:29570642

Sequential Monte Carlo for Maximum Weight Subgraphs with Application to Solving Image Jigsaw Puzzles.

PubMed

Adluru, Nagesh; Yang, Xingwei; Latecki, Longin Jan

2015-05-01

We consider a problem of finding maximum weight subgraphs (MWS) that satisfy hard constraints in a weighted graph. The constraints specify the graph nodes that must belong to the solution as well as mutual exclusions of graph nodes, i.e., pairs of nodes that cannot belong to the same solution. Our main contribution is a novel inference approach for solving this problem in a sequential monte carlo (SMC) sampling framework. Usually in an SMC framework there is a natural ordering of the states of the samples. The order typically depends on observations about the states or on the annealing setup used. In many applications (e.g., image jigsaw puzzle problems), all observations (e.g., puzzle pieces) are given at once and it is hard to define a natural ordering. Therefore, we relax the assumption of having ordered observations about states and propose a novel SMC algorithm for obtaining maximum a posteriori estimate of a high-dimensional posterior distribution. This is achieved by exploring different orders of states and selecting the most informative permutations in each step of the sampling. Our experimental results demonstrate that the proposed inference framework significantly outperforms loopy belief propagation in solving the image jigsaw puzzle problem. In particular, our inference quadruples the accuracy of the puzzle assembly compared to that of loopy belief propagation.

Sequential Monte Carlo for Maximum Weight Subgraphs with Application to Solving Image Jigsaw Puzzles

PubMed Central

Adluru, Nagesh; Yang, Xingwei; Latecki, Longin Jan

2015-01-01

We consider a problem of finding maximum weight subgraphs (MWS) that satisfy hard constraints in a weighted graph. The constraints specify the graph nodes that must belong to the solution as well as mutual exclusions of graph nodes, i.e., pairs of nodes that cannot belong to the same solution. Our main contribution is a novel inference approach for solving this problem in a sequential monte carlo (SMC) sampling framework. Usually in an SMC framework there is a natural ordering of the states of the samples. The order typically depends on observations about the states or on the annealing setup used. In many applications (e.g., image jigsaw puzzle problems), all observations (e.g., puzzle pieces) are given at once and it is hard to define a natural ordering. Therefore, we relax the assumption of having ordered observations about states and propose a novel SMC algorithm for obtaining maximum a posteriori estimate of a high-dimensional posterior distribution. This is achieved by exploring different orders of states and selecting the most informative permutations in each step of the sampling. Our experimental results demonstrate that the proposed inference framework significantly outperforms loopy belief propagation in solving the image jigsaw puzzle problem. In particular, our inference quadruples the accuracy of the puzzle assembly compared to that of loopy belief propagation. PMID:26052182

The Acquisition of New Student Affairs Professionals' Beliefs and Perceptions about Faculty

ERIC Educational Resources Information Center

Myers, Michele Ann

2013-01-01

The purpose of this study is to better understand how new student affairs professionals acquire their beliefs and perceptions about faculty. This study originates from a conceptual framework that includes: (1) a call for collaboration, (2) the link between organizational culture, beliefs, and perceptions, and (3) the propagation of beliefs through…

Accumulate-Repeat-Accumulate-Accumulate Codes

NASA Technical Reports Server (NTRS)

Divsalar, Dariush; Dolinar, Samuel; Thorpe, Jeremy

2007-01-01

Accumulate-repeat-accumulate-accumulate (ARAA) codes have been proposed, inspired by the recently proposed accumulate-repeat-accumulate (ARA) codes. These are error-correcting codes suitable for use in a variety of wireless data-communication systems that include noisy channels. ARAA codes can be regarded as serial turbolike codes or as a subclass of low-density parity-check (LDPC) codes, and, like ARA codes they have projected graph or protograph representations; these characteristics make it possible to design high-speed iterative decoders that utilize belief-propagation algorithms. The objective in proposing ARAA codes as a subclass of ARA codes was to enhance the error-floor performance of ARA codes while maintaining simple encoding structures and low maximum variable node degree.

FPGA implementation of low complexity LDPC iterative decoder

NASA Astrophysics Data System (ADS)

Verma, Shivani; Sharma, Sanjay

2016-07-01

Low-density parity-check (LDPC) codes, proposed by Gallager, emerged as a class of codes which can yield very good performance on the additive white Gaussian noise channel as well as on the binary symmetric channel. LDPC codes have gained lots of importance due to their capacity achieving property and excellent performance in the noisy channel. Belief propagation (BP) algorithm and its approximations, most notably min-sum, are popular iterative decoding algorithms used for LDPC and turbo codes. The trade-off between the hardware complexity and the decoding throughput is a critical factor in the implementation of the practical decoder. This article presents introduction to LDPC codes and its various decoding algorithms followed by realisation of LDPC decoder by using simplified message passing algorithm and partially parallel decoder architecture. Simplified message passing algorithm has been proposed for trade-off between low decoding complexity and decoder performance. It greatly reduces the routing and check node complexity of the decoder. Partially parallel decoder architecture possesses high speed and reduced complexity. The improved design of the decoder possesses a maximum symbol throughput of 92.95 Mbps and a maximum of 18 decoding iterations. The article presents implementation of 9216 bits, rate-1/2, (3, 6) LDPC decoder on Xilinx XC3D3400A device from Spartan-3A DSP family.

An algorithm for U-Pb isotope dilution data reduction and uncertainty propagation

NASA Astrophysics Data System (ADS)

McLean, N. M.; Bowring, J. F.; Bowring, S. A.

2011-06-01

High-precision U-Pb geochronology by isotope dilution-thermal ionization mass spectrometry is integral to a variety of Earth science disciplines, but its ultimate resolving power is quantified by the uncertainties of calculated U-Pb dates. As analytical techniques have advanced, formerly small sources of uncertainty are increasingly important, and thus previous simplifications for data reduction and uncertainty propagation are no longer valid. Although notable previous efforts have treated propagation of correlated uncertainties for the U-Pb system, the equations, uncertainties, and correlations have been limited in number and subject to simplification during propagation through intermediary calculations. We derive and present a transparent U-Pb data reduction algorithm that transforms raw isotopic data and measured or assumed laboratory parameters into the isotopic ratios and dates geochronologists interpret without making assumptions about the relative size of sample components. To propagate uncertainties and their correlations, we describe, in detail, a linear algebraic algorithm that incorporates all input uncertainties and correlations without limiting or simplifying covariance terms to propagate them though intermediate calculations. Finally, a weighted mean algorithm is presented that utilizes matrix elements from the uncertainty propagation algorithm to propagate random and systematic uncertainties for data comparison between other U-Pb labs and other geochronometers. The linear uncertainty propagation algorithms are verified with Monte Carlo simulations of several typical analyses. We propose that our algorithms be considered by the community for implementation to improve the collaborative science envisioned by the EARTHTIME initiative.

Real-time handling of existing content sources on a multi-layer display

NASA Astrophysics Data System (ADS)

Singh, Darryl S. K.; Shin, Jung

2013-03-01

A Multi-Layer Display (MLD) consists of two or more imaging planes separated by physical depth where the depth is a key component in creating a glasses-free 3D effect. Its core benefits include being viewable from multiple angles, having full panel resolution for 3D effects with no side effects of nausea or eye-strain. However, typically content must be designed for its optical configuration in foreground and background image pairs. A process was designed to give a consistent 3D effect in a 2-layer MLD from existing stereo video content in real-time. Optimizations to stereo matching algorithms that generate depth maps in real-time were specifically tailored for the optical characteristics and image processing algorithms of a MLD. The end-to-end process included improvements to the Hierarchical Belief Propagation (HBP) stereo matching algorithm, improvements to optical flow and temporal consistency. Imaging algorithms designed for the optical characteristics of a MLD provided some visual compensation for depth map inaccuracies. The result can be demonstrated in a PC environment, displayed on a 22" MLD, used in the casino slot market, with 8mm of panel seperation. Prior to this development, stereo content had not been used to achieve a depth-based 3D effect on a MLD in real-time

Event-Based Stereo Depth Estimation Using Belief Propagation.

PubMed

Xie, Zhen; Chen, Shengyong; Orchard, Garrick

2017-01-01

Compared to standard frame-based cameras, biologically-inspired event-based sensors capture visual information with low latency and minimal redundancy. These event-based sensors are also far less prone to motion blur than traditional cameras, and still operate effectively in high dynamic range scenes. However, classical framed-based algorithms are not typically suitable for these event-based data and new processing algorithms are required. This paper focuses on the problem of depth estimation from a stereo pair of event-based sensors. A fully event-based stereo depth estimation algorithm which relies on message passing is proposed. The algorithm not only considers the properties of a single event but also uses a Markov Random Field (MRF) to consider the constraints between the nearby events, such as disparity uniqueness and depth continuity. The method is tested on five different scenes and compared to other state-of-art event-based stereo matching methods. The results show that the method detects more stereo matches than other methods, with each match having a higher accuracy. The method can operate in an event-driven manner where depths are reported for individual events as they are received, or the network can be queried at any time to generate a sparse depth frame which represents the current state of the network.

A comparison of algorithms for inference and learning in probabilistic graphical models.

PubMed

Frey, Brendan J; Jojic, Nebojsa

2005-09-01

Research into methods for reasoning under uncertainty is currently one of the most exciting areas of artificial intelligence, largely because it has recently become possible to record, store, and process large amounts of data. While impressive achievements have been made in pattern classification problems such as handwritten character recognition, face detection, speaker identification, and prediction of gene function, it is even more exciting that researchers are on the verge of introducing systems that can perform large-scale combinatorial analyses of data, decomposing the data into interacting components. For example, computational methods for automatic scene analysis are now emerging in the computer vision community. These methods decompose an input image into its constituent objects, lighting conditions, motion patterns, etc. Two of the main challenges are finding effective representations and models in specific applications and finding efficient algorithms for inference and learning in these models. In this paper, we advocate the use of graph-based probability models and their associated inference and learning algorithms. We review exact techniques and various approximate, computationally efficient techniques, including iterated conditional modes, the expectation maximization (EM) algorithm, Gibbs sampling, the mean field method, variational techniques, structured variational techniques and the sum-product algorithm ("loopy" belief propagation). We describe how each technique can be applied in a vision model of multiple, occluding objects and contrast the behaviors and performances of the techniques using a unifying cost function, free energy.

ViBe: a universal background subtraction algorithm for video sequences.

PubMed

Barnich, Olivier; Van Droogenbroeck, Marc

2011-06-01

This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques.

An algorithm for propagating the square-root covariance matrix in triangular form

NASA Technical Reports Server (NTRS)

Tapley, B. D.; Choe, C. Y.

1976-01-01

A method for propagating the square root of the state error covariance matrix in lower triangular form is described. The algorithm can be combined with any triangular square-root measurement update algorithm to obtain a triangular square-root sequential estimation algorithm. The triangular square-root algorithm compares favorably with the conventional sequential estimation algorithm with regard to computation time.

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks.

PubMed

Movahedi, Faezeh; Coyle, James L; Sejdic, Ervin

2018-05-01

Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this paper, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state-of-the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications. We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms could be modified to better suit the tasks desired. This review is intended to provide researchers with a broad overview of the currently existing deep belief network methodology for electroencephalography signals, as well as to highlight potential challenges for future research.

Automated Stitching of Microtubule Centerlines across Serial Electron Tomograms

PubMed Central

Weber, Britta; Tranfield, Erin M.; Höög, Johanna L.; Baum, Daniel; Antony, Claude; Hyman, Tony; Verbavatz, Jean-Marc; Prohaska, Steffen

2014-01-01

Tracing microtubule centerlines in serial section electron tomography requires microtubules to be stitched across sections, that is lines from different sections need to be aligned, endpoints need to be matched at section boundaries to establish a correspondence between neighboring sections, and corresponding lines need to be connected across multiple sections. We present computational methods for these tasks: 1) An initial alignment is computed using a distance compatibility graph. 2) A fine alignment is then computed with a probabilistic variant of the iterative closest points algorithm, which we extended to handle the orientation of lines by introducing a periodic random variable to the probabilistic formulation. 3) Endpoint correspondence is established by formulating a matching problem in terms of a Markov random field and computing the best matching with belief propagation. Belief propagation is not generally guaranteed to converge to a minimum. We show how convergence can be achieved, nonetheless, with minimal manual input. In addition to stitching microtubule centerlines, the correspondence is also applied to transform and merge the electron tomograms. We applied the proposed methods to samples from the mitotic spindle in C. elegans, the meiotic spindle in X. laevis, and sub-pellicular microtubule arrays in T. brucei. The methods were able to stitch microtubules across section boundaries in good agreement with experts' opinions for the spindle samples. Results, however, were not satisfactory for the microtubule arrays. For certain experiments, such as an analysis of the spindle, the proposed methods can replace manual expert tracing and thus enable the analysis of microtubules over long distances with reasonable manual effort. PMID:25438148

Automated stitching of microtubule centerlines across serial electron tomograms.

PubMed

Weber, Britta; Tranfield, Erin M; Höög, Johanna L; Baum, Daniel; Antony, Claude; Hyman, Tony; Verbavatz, Jean-Marc; Prohaska, Steffen

2014-01-01

Tracing microtubule centerlines in serial section electron tomography requires microtubules to be stitched across sections, that is lines from different sections need to be aligned, endpoints need to be matched at section boundaries to establish a correspondence between neighboring sections, and corresponding lines need to be connected across multiple sections. We present computational methods for these tasks: 1) An initial alignment is computed using a distance compatibility graph. 2) A fine alignment is then computed with a probabilistic variant of the iterative closest points algorithm, which we extended to handle the orientation of lines by introducing a periodic random variable to the probabilistic formulation. 3) Endpoint correspondence is established by formulating a matching problem in terms of a Markov random field and computing the best matching with belief propagation. Belief propagation is not generally guaranteed to converge to a minimum. We show how convergence can be achieved, nonetheless, with minimal manual input. In addition to stitching microtubule centerlines, the correspondence is also applied to transform and merge the electron tomograms. We applied the proposed methods to samples from the mitotic spindle in C. elegans, the meiotic spindle in X. laevis, and sub-pellicular microtubule arrays in T. brucei. The methods were able to stitch microtubules across section boundaries in good agreement with experts' opinions for the spindle samples. Results, however, were not satisfactory for the microtubule arrays. For certain experiments, such as an analysis of the spindle, the proposed methods can replace manual expert tracing and thus enable the analysis of microtubules over long distances with reasonable manual effort.

Parallel Clustering Algorithm for Large-Scale Biological Data Sets

PubMed Central

Wang, Minchao; Zhang, Wu; Ding, Wang; Dai, Dongbo; Zhang, Huiran; Xie, Hao; Chen, Luonan; Guo, Yike; Xie, Jiang

2014-01-01

Backgrounds Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs. Methods Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes. Result A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies. PMID:24705246

Efficient Geometric Sound Propagation Using Visibility Culling

NASA Astrophysics Data System (ADS)

Chandak, Anish

2011-07-01

Simulating propagation of sound can improve the sense of realism in interactive applications such as video games and can lead to better designs in engineering applications such as architectural acoustics. In this thesis, we present geometric sound propagation techniques which are faster than prior methods and map well to upcoming parallel multi-core CPUs. We model specular reflections by using the image-source method and model finite-edge diffraction by using the well-known Biot-Tolstoy-Medwin (BTM) model. We accelerate the computation of specular reflections by applying novel visibility algorithms, FastV and AD-Frustum, which compute visibility from a point. We accelerate finite-edge diffraction modeling by applying a novel visibility algorithm which computes visibility from a region. Our visibility algorithms are based on frustum tracing and exploit recent advances in fast ray-hierarchy intersections, data-parallel computations, and scalable, multi-core algorithms. The AD-Frustum algorithm adapts its computation to the scene complexity and allows small errors in computing specular reflection paths for higher computational efficiency. FastV and our visibility algorithm from a region are general, object-space, conservative visibility algorithms that together significantly reduce the number of image sources compared to other techniques while preserving the same accuracy. Our geometric propagation algorithms are an order of magnitude faster than prior approaches for modeling specular reflections and two to ten times faster for modeling finite-edge diffraction. Our algorithms are interactive, scale almost linearly on multi-core CPUs, and can handle large, complex, and dynamic scenes. We also compare the accuracy of our sound propagation algorithms with other methods. Once sound propagation is performed, it is desirable to listen to the propagated sound in interactive and engineering applications. We can generate smooth, artifact-free output audio signals by applying efficient audio-processing algorithms. We also present the first efficient audio-processing algorithm for scenarios with simultaneously moving source and moving receiver (MS-MR) which incurs less than 25% overhead compared to static source and moving receiver (SS-MR) or moving source and static receiver (MS-SR) scenario.

Registration of segmented histological images using thin plate splines and belief propagation

NASA Astrophysics Data System (ADS)

Kybic, Jan

2014-03-01

We register images based on their multiclass segmentations, for cases when correspondence of local features cannot be established. A discrete mutual information is used as a similarity criterion. It is evaluated at a sparse set of location on the interfaces between classes. A thin-plate spline regularization is approximated by pairwise interactions. The problem is cast into a discrete setting and solved efficiently by belief propagation. Further speedup and robustness is provided by a multiresolution framework. Preliminary experiments suggest that our method can provide similar registration quality to standard methods at a fraction of the computational cost.

An enhanced DWBA algorithm in hybrid WDM/TDM EPON networks with heterogeneous propagation delays

NASA Astrophysics Data System (ADS)

Li, Chengjun; Guo, Wei; Jin, Yaohui; Sun, Weiqiang; Hu, Weisheng

2011-12-01

An enhanced dynamic wavelength and bandwidth allocation (DWBA) algorithm in hybrid WDM/TDM PON is proposed and experimentally demonstrated. In addition to the fairness of bandwidth allocation, this algorithm also considers the varying propagation delays between ONUs and OLT. The simulation based on MATLAB indicates that the improved algorithm has a better performance compared with some other algorithms.

A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.

PubMed

Hajri, S; Liouane, N; Hammadi, S; Borne, P

2000-01-01

Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.

Single-image super-resolution based on Markov random field and contourlet transform

NASA Astrophysics Data System (ADS)

Wu, Wei; Liu, Zheng; Gueaieb, Wail; He, Xiaohai

2011-04-01

Learning-based methods are well adopted in image super-resolution. In this paper, we propose a new learning-based approach using contourlet transform and Markov random field. The proposed algorithm employs contourlet transform rather than the conventional wavelet to represent image features and takes into account the correlation between adjacent pixels or image patches through the Markov random field (MRF) model. The input low-resolution (LR) image is decomposed with the contourlet transform and fed to the MRF model together with the contourlet transform coefficients from the low- and high-resolution image pairs in the training set. The unknown high-frequency components/coefficients for the input low-resolution image are inferred by a belief propagation algorithm. Finally, the inverse contourlet transform converts the LR input and the inferred high-frequency coefficients into the super-resolved image. The effectiveness of the proposed method is demonstrated with the experiments on facial, vehicle plate, and real scene images. A better visual quality is achieved in terms of peak signal to noise ratio and the image structural similarity measurement.

Energy minimization in medical image analysis: Methodologies and applications.

PubMed

Zhao, Feng; Xie, Xianghua

2016-02-01

Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree-reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, for example, image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well. Copyright © 2015 John Wiley & Sons, Ltd.

Sampling-based real-time motion planning under state uncertainty for autonomous micro-aerial vehicles in GPS-denied environments.

PubMed

Li, Dachuan; Li, Qing; Cheng, Nong; Song, Jingyan

2014-11-18

This paper presents a real-time motion planning approach for autonomous vehicles with complex dynamics and state uncertainty. The approach is motivated by the motion planning problem for autonomous vehicles navigating in GPS-denied dynamic environments, which involves non-linear and/or non-holonomic vehicle dynamics, incomplete state estimates, and constraints imposed by uncertain and cluttered environments. To address the above motion planning problem, we propose an extension of the closed-loop rapid belief trees, the closed-loop random belief trees (CL-RBT), which incorporates predictions of the position estimation uncertainty, using a factored form of the covariance provided by the Kalman filter-based estimator. The proposed motion planner operates by incrementally constructing a tree of dynamically feasible trajectories using the closed-loop prediction, while selecting candidate paths with low uncertainty using efficient covariance update and propagation. The algorithm can operate in real-time, continuously providing the controller with feasible paths for execution, enabling the vehicle to account for dynamic and uncertain environments. Simulation results demonstrate that the proposed approach can generate feasible trajectories that reduce the state estimation uncertainty, while handling complex vehicle dynamics and environment constraints.

Sampling-Based Real-Time Motion Planning under State Uncertainty for Autonomous Micro-Aerial Vehicles in GPS-Denied Environments

PubMed Central

Li, Dachuan; Li, Qing; Cheng, Nong; Song, Jingyan

2014-01-01

This paper presents a real-time motion planning approach for autonomous vehicles with complex dynamics and state uncertainty. The approach is motivated by the motion planning problem for autonomous vehicles navigating in GPS-denied dynamic environments, which involves non-linear and/or non-holonomic vehicle dynamics, incomplete state estimates, and constraints imposed by uncertain and cluttered environments. To address the above motion planning problem, we propose an extension of the closed-loop rapid belief trees, the closed-loop random belief trees (CL-RBT), which incorporates predictions of the position estimation uncertainty, using a factored form of the covariance provided by the Kalman filter-based estimator. The proposed motion planner operates by incrementally constructing a tree of dynamically feasible trajectories using the closed-loop prediction, while selecting candidate paths with low uncertainty using efficient covariance update and propagation. The algorithm can operate in real-time, continuously providing the controller with feasible paths for execution, enabling the vehicle to account for dynamic and uncertain environments. Simulation results demonstrate that the proposed approach can generate feasible trajectories that reduce the state estimation uncertainty, while handling complex vehicle dynamics and environment constraints. PMID:25412217

Adaptive step-size algorithm for Fourier beam-propagation method with absorbing boundary layer of auto-determined width.

PubMed

Learn, R; Feigenbaum, E

2016-06-01

Two algorithms that enhance the utility of the absorbing boundary layer are presented, mainly in the framework of the Fourier beam-propagation method. One is an automated boundary layer width selector that chooses a near-optimal boundary size based on the initial beam shape. The second algorithm adjusts the propagation step sizes based on the beam shape at the beginning of each step in order to reduce aliasing artifacts.

Adaptive step-size algorithm for Fourier beam-propagation method with absorbing boundary layer of auto-determined width

DOE Office of Scientific and Technical Information (OSTI.GOV)

Learn, R.; Feigenbaum, E.

Two algorithms that enhance the utility of the absorbing boundary layer are presented, mainly in the framework of the Fourier beam-propagation method. One is an automated boundary layer width selector that chooses a near-optimal boundary size based on the initial beam shape. Furthermore, the second algorithm adjusts the propagation step sizes based on the beam shape at the beginning of each step in order to reduce aliasing artifacts.

Adaptive step-size algorithm for Fourier beam-propagation method with absorbing boundary layer of auto-determined width

DOE PAGES

Learn, R.; Feigenbaum, E.

2016-05-27

Two algorithms that enhance the utility of the absorbing boundary layer are presented, mainly in the framework of the Fourier beam-propagation method. One is an automated boundary layer width selector that chooses a near-optimal boundary size based on the initial beam shape. Furthermore, the second algorithm adjusts the propagation step sizes based on the beam shape at the beginning of each step in order to reduce aliasing artifacts.

Rethinking the learning of belief network probabilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Musick, R.

Belief networks are a powerful tool for knowledge discovery that provide concise, understandable probabilistic models of data. There are methods grounded in probability theory to incrementally update the relationships described by the belief network when new information is seen, to perform complex inferences over any set of variables in the data, to incorporate domain expertise and prior knowledge into the model, and to automatically learn the model from data. This paper concentrates on part of the belief network induction problem, that of learning the quantitative structure (the conditional probabilities), given the qualitative structure. In particular, the current practice of rotemore » learning the probabilities in belief networks can be significantly improved upon. We advance the idea of applying any learning algorithm to the task of conditional probability learning in belief networks, discuss potential benefits, and show results of applying neutral networks and other algorithms to a medium sized car insurance belief network. The results demonstrate from 10 to 100% improvements in model error rates over the current approaches.« less

Orbit-product representation and correction of Gaussian belief propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Jason K; Chertkov, Michael; Chernyak, Vladimir

We present a new interpretation of Gaussian belief propagation (GaBP) based on the 'zeta function' representation of the determinant as a product over orbits of a graph. We show that GaBP captures back-tracking orbits of the graph and consider how to correct this estimate by accounting for non-backtracking orbits. We show that the product over non-backtracking orbits may be interpreted as the determinant of the non-backtracking adjacency matrix of the graph with edge weights based on the solution of GaBP. An efficient method is proposed to compute a truncated correction factor including all non-backtracking orbits up to a specified length.

Evaluation of generalized degrees of freedom for sparse estimation by replica method

NASA Astrophysics Data System (ADS)

Sakata, A.

2016-12-01

We develop a method to evaluate the generalized degrees of freedom (GDF) for linear regression with sparse regularization. The GDF is a key factor in model selection, and thus its evaluation is useful in many modelling applications. An analytical expression for the GDF is derived using the replica method in the large-system-size limit with random Gaussian predictors. The resulting formula has a universal form that is independent of the type of regularization, providing us with a simple interpretation. Within the framework of replica symmetric (RS) analysis, GDF has a physical meaning as the effective fraction of non-zero components. The validity of our method in the RS phase is supported by the consistency of our results with previous mathematical results. The analytical results in the RS phase are calculated numerically using the belief propagation algorithm.

LinguisticBelief: a java application for linguistic evaluation using belief, fuzzy sets, and approximate reasoning.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Darby, John L.

LinguisticBelief is a Java computer code that evaluates combinations of linguistic variables using an approximate reasoning rule base. Each variable is comprised of fuzzy sets, and a rule base describes the reasoning on combinations of variables fuzzy sets. Uncertainty is considered and propagated through the rule base using the belief/plausibility measure. The mathematics of fuzzy sets, approximate reasoning, and belief/ plausibility are complex. Without an automated tool, this complexity precludes their application to all but the simplest of problems. LinguisticBelief automates the use of these techniques, allowing complex problems to be evaluated easily. LinguisticBelief can be used free of chargemore » on any Windows XP machine. This report documents the use and structure of the LinguisticBelief code, and the deployment package for installation client machines.« less

TU-AB-BRA-12: Impact of Image Registration Algorithms On the Prediction of Pathological Response with Radiomic Textures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yip, S; Coroller, T; Niu, N

2015-06-15

Purpose: Tumor regions-of-interest (ROI) can be propagated from the pre-onto the post-treatment PET/CT images using image registration of their CT counterparts, providing an automatic way to compute texture features on longitudinal scans. This exploratory study assessed the impact of image registration algorithms on textures to predict pathological response. Methods: Forty-six esophageal cancer patients (1 tumor/patient) underwent PET/CT scans before and after chemoradiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumor ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. One co-occurrence, two run-length and size zone matrix texturesmore » were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs and texture quantification resulting from different algorithms were compared using overlap volume (OV) and coefficient of variation (CoV), respectively. Results: Tumor volumes were better captured by ROIs propagated by deformable rather than the rigid registration. The OV between rigidly and deformably propagated ROIs were 69%. The deformably propagated ROIs were found to be similar (OV∼80%) except for fast-demons (OV∼60%). Rigidly propagated ROIs with run-length matrix textures failed to significantly differentiate between responders and non-responders (AUC=0.65, p=0.07), while the differentiation was significant with other textures (AUC=0.69–0.72, p<0.03). Among the deformable algorithms, fast-demons was the least predictive (AUC=0.68–0.71, p<0.04). ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC=0.71–0.78, p<0.01) despite substantial variation in texture quantification (CoV>70%). Conclusion: Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, rigid and fast-demons deformable algorithms are not recommended due to their inferior performance compared to other algorithms. The project was supported in part by a Kaye Scholar Award.« less

Discrete geometric analysis of message passing algorithm on graphs

NASA Astrophysics Data System (ADS)

Watanabe, Yusuke

2010-04-01

We often encounter probability distributions given as unnormalized products of non-negative functions. The factorization structures are represented by hypergraphs called factor graphs. Such distributions appear in various fields, including statistics, artificial intelligence, statistical physics, error correcting codes, etc. Given such a distribution, computations of marginal distributions and the normalization constant are often required. However, they are computationally intractable because of their computational costs. One successful approximation method is Loopy Belief Propagation (LBP) algorithm. The focus of this thesis is an analysis of the LBP algorithm. If the factor graph is a tree, i.e. having no cycle, the algorithm gives the exact quantities. If the factor graph has cycles, however, the LBP algorithm does not give exact results and possibly exhibits oscillatory and non-convergent behaviors. The thematic question of this thesis is "How the behaviors of the LBP algorithm are affected by the discrete geometry of the factor graph?" The primary contribution of this thesis is the discovery of a formula that establishes the relation between the LBP, the Bethe free energy and the graph zeta function. This formula provides new techniques for analysis of the LBP algorithm, connecting properties of the graph and of the LBP and the Bethe free energy. We demonstrate applications of the techniques to several problems including (non) convexity of the Bethe free energy, the uniqueness and stability of the LBP fixed point. We also discuss the loop series initiated by Chertkov and Chernyak. The loop series is a subgraph expansion of the normalization constant, or partition function, and reflects the graph geometry. We investigate theoretical natures of the series. Moreover, we show a partial connection between the loop series and the graph zeta function.

Accurate Finite Difference Algorithms

NASA Technical Reports Server (NTRS)

Goodrich, John W.

1996-01-01

Two families of finite difference algorithms for computational aeroacoustics are presented and compared. All of the algorithms are single step explicit methods, they have the same order of accuracy in both space and time, with examples up to eleventh order, and they have multidimensional extensions. One of the algorithm families has spectral like high resolution. Propagation with high order and high resolution algorithms can produce accurate results after O(10(exp 6)) periods of propagation with eight grid points per wavelength.

Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction

NASA Astrophysics Data System (ADS)

Yip, Stephen S. F.; Coroller, Thibaud P.; Sanford, Nina N.; Huynh, Elizabeth; Mamon, Harvey; Aerts, Hugo J. W. L.; Berbeco, Ross I.

2016-01-01

Change in PET-based textural features has shown promise in predicting cancer response to treatment. However, contouring tumour volumes on longitudinal scans is time-consuming. This study investigated the usefulness of contour propagation in texture analysis for the purpose of pathologic response prediction in esophageal cancer. Forty-five esophageal cancer patients underwent PET/CT scans before and after chemo-radiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumour ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. PET images were converted into 256 discrete values. Co-occurrence, run-length, and size zone matrix textures were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs from different algorithms were compared using Dice similarity index (DSI). Contours propagated by the fast-demons, fast-free-form and rigid algorithms did not fully capture the high FDG uptake regions of tumours. Fast-demons propagated ROIs had the least agreement with other contours (DSI  =  58%). Moderate to substantial overlap were found in the ROIs propagated by all other algorithms (DSI  =  69%-79%). Rigidly propagated ROIs with co-occurrence texture failed to significantly differentiate between responders and non-responders (AUC  =  0.58, q-value  =  0.33), while the differentiation was significant with other textures (AUC  =  0.71‒0.73, p  <  0.009). Among the deformable algorithms, fast-demons (AUC  =  0.68‒0.70, q-value  <  0.03) and fast-free-form (AUC  =  0.69‒0.74, q-value  <  0.04) were the least predictive. ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC  =  0.72‒0.78, q-value  <  0.01). Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, fast-demons, fast-free-form, and rigid algorithms should be applied with care due to their inferior performance compared to other algorithms.

Label propagation algorithm for community detection based on node importance and label influence

NASA Astrophysics Data System (ADS)

Zhang, Xian-Kun; Ren, Jing; Song, Chen; Jia, Jia; Zhang, Qian

2017-09-01

Recently, the detection of high-quality community has become a hot spot in the research of social network. Label propagation algorithm (LPA) has been widely concerned since it has the advantages of linear time complexity and is unnecessary to define objective function and the number of community in advance. However, LPA has the shortcomings of uncertainty and randomness in the label propagation process, which affects the accuracy and stability of the community. For large-scale social network, this paper proposes a novel label propagation algorithm for community detection based on node importance and label influence (LPA_NI). The experiments with comparative algorithms on real-world networks and synthetic networks have shown that LPA_NI can significantly improve the quality of community detection and shorten the iteration period. Also, it has better accuracy and stability in the case of similar complexity.

Modified multiblock partial least squares path modeling algorithm with backpropagation neural networks approach

NASA Astrophysics Data System (ADS)

Yuniarto, Budi; Kurniawan, Robert

2017-03-01

PLS Path Modeling (PLS-PM) is different from covariance based SEM, where PLS-PM use an approach based on variance or component, therefore, PLS-PM is also known as a component based SEM. Multiblock Partial Least Squares (MBPLS) is a method in PLS regression which can be used in PLS Path Modeling which known as Multiblock PLS Path Modeling (MBPLS-PM). This method uses an iterative procedure in its algorithm. This research aims to modify MBPLS-PM with Back Propagation Neural Network approach. The result is MBPLS-PM algorithm can be modified using the Back Propagation Neural Network approach to replace the iterative process in backward and forward step to get the matrix t and the matrix u in the algorithm. By modifying the MBPLS-PM algorithm using Back Propagation Neural Network approach, the model parameters obtained are relatively not significantly different compared to model parameters obtained by original MBPLS-PM algorithm.

Dynamics and Stability of Acoustic Wavefronts in the Ocean

DTIC Science & Technology

2014-09-30

processes on underwater acoustic fields. The 3-D HWT algorithm was also applied to investigate long- range propagation of infrasound in the atmosphere...oceanographic processes on underwater sound propagation and also has been demonstrated to be an efficient and robust technique for modeling infrasound ...algorithm by modeling propagation of infrasound generated by Eyjafjallajökull volcano in southern Iceland. Eruptions of this volcano were recorded by

Fast Back-Propagation Learning Using Steep Activation Functions and Automatic Weight

Treesearch

Tai-Hoon Cho; Richard W. Conners; Philip A. Araman

1992-01-01

In this paper, several back-propagation (BP) learning speed-up algorithms that employ the ãgainä parameter, i.e., steepness of the activation function, are examined. Simulations will show that increasing the gain seemingly increases the speed of convergence and that these algorithms can converge faster than the standard BP learning algorithm on some problems. However,...

Scene Text Recognition using Similarity and a Lexicon with Sparse Belief Propagation

PubMed Central

Weinman, Jerod J.; Learned-Miller, Erik; Hanson, Allen R.

2010-01-01

Scene text recognition (STR) is the recognition of text anywhere in the environment, such as signs and store fronts. Relative to document recognition, it is challenging because of font variability, minimal language context, and uncontrolled conditions. Much information available to solve this problem is frequently ignored or used sequentially. Similarity between character images is often overlooked as useful information. Because of language priors, a recognizer may assign different labels to identical characters. Directly comparing characters to each other, rather than only a model, helps ensure that similar instances receive the same label. Lexicons improve recognition accuracy but are used post hoc. We introduce a probabilistic model for STR that integrates similarity, language properties, and lexical decision. Inference is accelerated with sparse belief propagation, a bottom-up method for shortening messages by reducing the dependency between weakly supported hypotheses. By fusing information sources in one model, we eliminate unrecoverable errors that result from sequential processing, improving accuracy. In experimental results recognizing text from images of signs in outdoor scenes, incorporating similarity reduces character recognition error by 19%, the lexicon reduces word recognition error by 35%, and sparse belief propagation reduces the lexicon words considered by 99.9% with a 12X speedup and no loss in accuracy. PMID:19696446

A total variation diminishing finite difference algorithm for sonic boom propagation models

NASA Technical Reports Server (NTRS)

Sparrow, Victor W.

1993-01-01

It is difficult to accurately model the rise phases of sonic boom waveforms with traditional finite difference algorithms because of finite difference phase dispersion. This paper introduces the concept of a total variation diminishing (TVD) finite difference method as a tool for accurately modeling the rise phases of sonic booms. A standard second order finite difference algorithm and its TVD modified counterpart are both applied to the one-way propagation of a square pulse. The TVD method clearly outperforms the non-TVD method, showing great potential as a new computational tool in the analysis of sonic boom propagation.

Imaging tilted transversely isotropic media with a generalised screen propagator

NASA Astrophysics Data System (ADS)

Shin, Sung-Il; Byun, Joongmoo; Seol, Soon Jee

2015-01-01

One-way wave equation migration is computationally efficient compared with reverse time migration, and it provides a better subsurface image than ray-based migration algorithms when imaging complex structures. Among many one-way wave-based migration algorithms, we adopted the generalised screen propagator (GSP) to build the migration algorithm. When the wavefield propagates through the large velocity variation in lateral or steeply dipping structures, GSP increases the accuracy of the wavefield in wide angle by adopting higher-order terms induced from expansion of the vertical slowness in Taylor series with each perturbation term. To apply the migration algorithm to a more realistic geological structure, we considered tilted transversely isotropic (TTI) media. The new GSP, which contains the tilting angle as a symmetric axis of the anisotropic media, was derived by modifying the GSP designed for vertical transversely isotropic (VTI) media. To verify the developed TTI-GSP, we analysed the accuracy of wave propagation, especially for the new perturbation parameters and the tilting angle; the results clearly showed that the perturbation term of the tilting angle in TTI media has considerable effects on proper propagation. In addition, through numerical tests, we demonstrated that the developed TTI-GS migration algorithm could successfully image a steeply dipping salt flank with high velocity variation around anisotropic layers.

Thermal-depth matching in dynamic scene based on affine projection and feature registration

NASA Astrophysics Data System (ADS)

Wang, Hongyu; Jia, Tong; Wu, Chengdong; Li, Yongqiang

2018-03-01

This paper aims to study the construction of 3D temperature distribution reconstruction system based on depth and thermal infrared information. Initially, a traditional calibration method cannot be directly used, because the depth and thermal infrared camera is not sensitive to the color calibration board. Therefore, this paper aims to design a depth and thermal infrared camera calibration board to complete the calibration of the depth and thermal infrared camera. Meanwhile a local feature descriptors in thermal and depth images is proposed. The belief propagation matching algorithm is also investigated based on the space affine transformation matching and local feature matching. The 3D temperature distribution model is built based on the matching of 3D point cloud and 2D thermal infrared information. Experimental results show that the method can accurately construct the 3D temperature distribution model, and has strong robustness.

Optimal and adaptive methods of processing hydroacoustic signals (review)

NASA Astrophysics Data System (ADS)

Malyshkin, G. S.; Sidel'nikov, G. B.

2014-09-01

Different methods of optimal and adaptive processing of hydroacoustic signals for multipath propagation and scattering are considered. Advantages and drawbacks of the classical adaptive (Capon, MUSIC, and Johnson) algorithms and "fast" projection algorithms are analyzed for the case of multipath propagation and scattering of strong signals. The classical optimal approaches to detecting multipath signals are presented. A mechanism of controlled normalization of strong signals is proposed to automatically detect weak signals. The results of simulating the operation of different detection algorithms for a linear equidistant array under multipath propagation and scattering are presented. An automatic detector is analyzed, which is based on classical or fast projection algorithms, which estimates the background proceeding from median filtering or the method of bilateral spatial contrast.

A Novel Handwritten Letter Recognizer Using Enhanced Evolutionary Neural Network

NASA Astrophysics Data System (ADS)

Mahmoudi, Fariborz; Mirzashaeri, Mohsen; Shahamatnia, Ehsan; Faridnia, Saed

This paper introduces a novel design for handwritten letter recognition by employing a hybrid back-propagation neural network with an enhanced evolutionary algorithm. Feeding the neural network consists of a new approach which is invariant to translation, rotation, and scaling of input letters. Evolutionary algorithm is used for the global search of the search space and the back-propagation algorithm is used for the local search. The results have been computed by implementing this approach for recognizing 26 English capital letters in the handwritings of different people. The computational results show that the neural network reaches very satisfying results with relatively scarce input data and a promising performance improvement in convergence of the hybrid evolutionary back-propagation algorithms is exhibited.

Phase transitions in semisupervised clustering of sparse networks

NASA Astrophysics Data System (ADS)

Zhang, Pan; Moore, Cristopher; Zdeborová, Lenka

2014-11-01

Predicting labels of nodes in a network, such as community memberships or demographic variables, is an important problem with applications in social and biological networks. A recently discovered phase transition puts fundamental limits on the accuracy of these predictions if we have access only to the network topology. However, if we know the correct labels of some fraction α of the nodes, we can do better. We study the phase diagram of this semisupervised learning problem for networks generated by the stochastic block model. We use the cavity method and the associated belief propagation algorithm to study what accuracy can be achieved as a function of α . For k =2 groups, we find that the detectability transition disappears for any α >0 , in agreement with previous work. For larger k where a hard but detectable regime exists, we find that the easy/hard transition (the point at which efficient algorithms can do better than chance) becomes a line of transitions where the accuracy jumps discontinuously at a critical value of α . This line ends in a critical point with a second-order transition, beyond which the accuracy is a continuous function of α . We demonstrate qualitatively similar transitions in two real-world networks.

Analog Delta-Back-Propagation Neural-Network Circuitry

NASA Technical Reports Server (NTRS)

Eberhart, Silvio

1990-01-01

Changes in synapse weights due to circuit drifts suppressed. Proposed fully parallel analog version of electronic neural-network processor based on delta-back-propagation algorithm. Processor able to "learn" when provided with suitable combinations of inputs and enforced outputs. Includes programmable resistive memory elements (corresponding to synapses), conductances (synapse weights) adjusted during learning. Buffer amplifiers, summing circuits, and sample-and-hold circuits arranged in layers of electronic neurons in accordance with delta-back-propagation algorithm.

Students' Mathematical Reasoning and Beliefs in Non-Routine Task Solving

ERIC Educational Resources Information Center

Jäder, Jonas; Sidenvall, Johan; Sumpter, Lovisa

2017-01-01

Beliefs and problem solving are connected and have been studied in different contexts. One of the common results of previous research is that students tend to prefer algorithmic approaches to mathematical tasks. This study explores Swedish upper secondary school students' beliefs and reasoning when solving non-routine tasks. The results regarding…

Propagating Qualitative Values Through Quantitative Equations

NASA Technical Reports Server (NTRS)

Kulkarni, Deepak

1992-01-01

In most practical problems where traditional numeric simulation is not adequate, one need to reason about a system with both qualitative and quantitative equations. In this paper, we address the problem of propagating qualitative values represented as interval values through quantitative equations. Previous research has produced exponential-time algorithms for approximate solution of the problem. These may not meet the stringent requirements of many real time applications. This paper advances the state of art by producing a linear-time algorithm that can propagate a qualitative value through a class of complex quantitative equations exactly and through arbitrary algebraic expressions approximately. The algorithm was found applicable to Space Shuttle Reaction Control System model.

The graphical brain: Belief propagation and active inference

PubMed Central

Friston, Karl J.; Parr, Thomas; de Vries, Bert

2018-01-01

This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium. Author Summary This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models that can entertain both discrete and continuous states. This leads to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to characterize the requisite message passing, and link this formal characterization to canonical microcircuits and extrinsic connectivity in the brain. PMID:29417960

Gram-Schmidt algorithms for covariance propagation

NASA Technical Reports Server (NTRS)

Thornton, C. L.; Bierman, G. J.

1977-01-01

This paper addresses the time propagation of triangular covariance factors. Attention is focused on the square-root free factorization, P = UD(transpose of U), where U is unit upper triangular and D is diagonal. An efficient and reliable algorithm for U-D propagation is derived which employs Gram-Schmidt orthogonalization. Partitioning the state vector to distinguish bias and coloured process noise parameters increase mapping efficiency. Cost comparisons of the U-D, Schmidt square-root covariance and conventional covariance propagation methods are made using weighted arithmetic operation counts. The U-D time update is shown to be less costly than the Schmidt method; and, except in unusual circumstances, it is within 20% of the cost of conventional propagation.

Gram-Schmidt algorithms for covariance propagation

NASA Technical Reports Server (NTRS)

Thornton, C. L.; Bierman, G. J.

1975-01-01

This paper addresses the time propagation of triangular covariance factors. Attention is focused on the square-root free factorization, P = UDU/T/, where U is unit upper triangular and D is diagonal. An efficient and reliable algorithm for U-D propagation is derived which employs Gram-Schmidt orthogonalization. Partitioning the state vector to distinguish bias and colored process noise parameters increases mapping efficiency. Cost comparisons of the U-D, Schmidt square-root covariance and conventional covariance propagation methods are made using weighted arithmetic operation counts. The U-D time update is shown to be less costly than the Schmidt method; and, except in unusual circumstances, it is within 20% of the cost of conventional propagation.

Network propagation in the cytoscape cyberinfrastructure.

PubMed

Carlin, Daniel E; Demchak, Barry; Pratt, Dexter; Sage, Eric; Ideker, Trey

2017-10-01

Network propagation is an important and widely used algorithm in systems biology, with applications in protein function prediction, disease gene prioritization, and patient stratification. However, up to this point it has required significant expertise to run. Here we extend the popular network analysis program Cytoscape to perform network propagation as an integrated function. Such integration greatly increases the access to network propagation by putting it in the hands of biologists and linking it to the many other types of network analysis and visualization available through Cytoscape. We demonstrate the power and utility of the algorithm by identifying mutations conferring resistance to Vemurafenib.

Program Helps Simulate Neural Networks

NASA Technical Reports Server (NTRS)

Villarreal, James; Mcintire, Gary

1993-01-01

Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

Multi-agent based control of large-scale complex systems employing distributed dynamic inference engine

NASA Astrophysics Data System (ADS)

Zhang, Daili

Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult to get accurate and complete information. Second, the system may be physically highly distributed. Third, the system evolves very quickly. Fourth, emergent global behaviors of the system can be caused by small disturbances at the component level. The Multi-Agent Based Control (MABC) method as an implementation of distributed intelligent control has been the focus of research since the 1970s, in an effort to solve the above-mentioned problems in controlling large-scale complex systems. However, to the author's best knowledge, all MABC systems for large-scale complex systems with significant uncertainties are problem-specific and thus difficult to extend to other domains or larger systems. This situation is partly due to the control architecture of multiple agents being determined by agent to agent coupling and interaction mechanisms. Therefore, the research objective of this dissertation is to develop a comprehensive, generalized framework for the control system design of general large-scale complex systems with significant uncertainties, with the focus on distributed control architecture design and distributed inference engine design. A Hybrid Multi-Agent Based Control (HyMABC) architecture is proposed by combining hierarchical control architecture and module control architecture with logical replication rings. First, it decomposes a complex system hierarchically; second, it combines the components in the same level as a module, and then designs common interfaces for all of the components in the same module; third, replications are made for critical agents and are organized into logical rings. This architecture maintains clear guidelines for complexity decomposition and also increases the robustness of the whole system. Multiple Sectioned Dynamic Bayesian Networks (MSDBNs) as a distributed dynamic probabilistic inference engine, can be embedded into the control architecture to handle uncertainties of general large-scale complex systems. MSDBNs decomposes a large knowledge-based system into many agents. Each agent holds its partial perspective of a large problem domain by representing its knowledge as a Dynamic Bayesian Network (DBN). Each agent accesses local evidence from its corresponding local sensors and communicates with other agents through finite message passing. If the distributed agents can be organized into a tree structure, satisfying the running intersection property and d-sep set requirements, globally consistent inferences are achievable in a distributed way. By using different frequencies for local DBN agent belief updating and global system belief updating, it balances the communication cost with the global consistency of inferences. In this dissertation, a fully factorized Boyen-Koller (BK) approximation algorithm is used for local DBN agent belief updating, and the static Junction Forest Linkage Tree (JFLT) algorithm is used for global system belief updating. MSDBNs assume a static structure and a stable communication network for the whole system. However, for a real system, sub-Bayesian networks as nodes could be lost, and the communication network could be shut down due to partial damage in the system. Therefore, on-line and automatic MSDBNs structure formation is necessary for making robust state estimations and increasing survivability of the whole system. A Distributed Spanning Tree Optimization (DSTO) algorithm, a Distributed D-Sep Set Satisfaction (DDSSS) algorithm, and a Distributed Running Intersection Satisfaction (DRIS) algorithm are proposed in this dissertation. Combining these three distributed algorithms and a Distributed Belief Propagation (DBP) algorithm in MSDBNs makes state estimations robust to partial damage in the whole system. Combining the distributed control architecture design and the distributed inference engine design leads to a process of control system design for a general large-scale complex system. As applications of the proposed methodology, the control system design of a simplified ship chilled water system and a notional ship chilled water system have been demonstrated step by step. Simulation results not only show that the proposed methodology gives a clear guideline for control system design for general large-scale complex systems with dynamic and uncertain environment, but also indicate that the combination of MSDBNs and HyMABC can provide excellent performance for controlling general large-scale complex systems.

Back to the Future: Consistency-Based Trajectory Tracking

NASA Technical Reports Server (NTRS)

Kurien, James; Nayak, P. Pandurand; Norvig, Peter (Technical Monitor)

2000-01-01

Given a model of a physical process and a sequence of commands and observations received over time, the task of an autonomous controller is to determine the likely states of the process and the actions required to move the process to a desired configuration. We introduce a representation and algorithms for incrementally generating approximate belief states for a restricted but relevant class of partially observable Markov decision processes with very large state spaces. The algorithm presented incrementally generates, rather than revises, an approximate belief state at any point by abstracting and summarizing segments of the likely trajectories of the process. This enables applications to efficiently maintain a partial belief state when it remains consistent with observations and revisit past assumptions about the process' evolution when the belief state is ruled out. The system presented has been implemented and results on examples from the domain of spacecraft control are presented.

Numerical simulations of detonation propagation in gaseous fuel-air mixtures

NASA Astrophysics Data System (ADS)

Honhar, Praveen; Kaplan, Carolyn; Houim, Ryan; Oran, Elaine

2017-11-01

Unsteady multidimensional numerical simulations of detonation propagation and survival in mixtures of fuel (hydrogen or methane) diluted with air were carried out with a fully compressible Navier-Stokes solver using a simplified chemical-diffusive model (CDM). The CDM was derived using a genetic algorithm combined with the Nelder-Mead optimization algorithm and reproduces physically correct laminar flame and detonation properties. Cases studied are overdriven detonations propagating through confined mediums, with or without gradients in composition. Results from simulations confirm that the survival of the detonation depends on the channel heights. In addition, the simulations show that the propagation of the detonation waves depends on the steepness in composition gradients.

AUV Positioning Method Based on Tightly Coupled SINS/LBL for Underwater Acoustic Multipath Propagation.

PubMed

Zhang, Tao; Shi, Hongfei; Chen, Liping; Li, Yao; Tong, Jinwu

2016-03-11

This paper researches an AUV (Autonomous Underwater Vehicle) positioning method based on SINS (Strapdown Inertial Navigation System)/LBL (Long Base Line) tightly coupled algorithm. This algorithm mainly includes SINS-assisted searching method of optimum slant-range of underwater acoustic propagation multipath, SINS/LBL tightly coupled model and multi-sensor information fusion algorithm. Fuzzy correlation peak problem of underwater LBL acoustic propagation multipath could be solved based on SINS positional information, thus improving LBL positional accuracy. Moreover, introduction of SINS-centered LBL locating information could compensate accumulative AUV position error effectively and regularly. Compared to loosely coupled algorithm, this tightly coupled algorithm can still provide accurate location information when there are fewer than four available hydrophones (or within the signal receiving range). Therefore, effective positional calibration area of tightly coupled system based on LBL array is wider and has higher reliability and fault tolerance than loosely coupled. It is more applicable to AUV positioning based on SINS/LBL.

AUV Positioning Method Based on Tightly Coupled SINS/LBL for Underwater Acoustic Multipath Propagation

PubMed Central

Zhang, Tao; Shi, Hongfei; Chen, Liping; Li, Yao; Tong, Jinwu

2016-01-01

This paper researches an AUV (Autonomous Underwater Vehicle) positioning method based on SINS (Strapdown Inertial Navigation System)/LBL (Long Base Line) tightly coupled algorithm. This algorithm mainly includes SINS-assisted searching method of optimum slant-range of underwater acoustic propagation multipath, SINS/LBL tightly coupled model and multi-sensor information fusion algorithm. Fuzzy correlation peak problem of underwater LBL acoustic propagation multipath could be solved based on SINS positional information, thus improving LBL positional accuracy. Moreover, introduction of SINS-centered LBL locating information could compensate accumulative AUV position error effectively and regularly. Compared to loosely coupled algorithm, this tightly coupled algorithm can still provide accurate location information when there are fewer than four available hydrophones (or within the signal receiving range). Therefore, effective positional calibration area of tightly coupled system based on LBL array is wider and has higher reliability and fault tolerance than loosely coupled. It is more applicable to AUV positioning based on SINS/LBL. PMID:26978361

LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks

NASA Astrophysics Data System (ADS)

Berahmand, Kamal; Bouyer, Asgarali

2018-03-01

Community detection is an essential approach for analyzing the structural and functional properties of complex networks. Although many community detection algorithms have been recently presented, most of them are weak and limited in different ways. Label Propagation Algorithm (LPA) is a well-known and efficient community detection technique which is characterized by the merits of nearly-linear running time and easy implementation. However, LPA has some significant problems such as instability, randomness, and monster community detection. In this paper, an algorithm, namely node’s label influence policy for label propagation algorithm (LP-LPA) was proposed for detecting efficient community structures. LP-LPA measures link strength value for edges and nodes’ label influence value for nodes in a new label propagation strategy with preference on link strength and for initial nodes selection, avoid of random behavior in tiebreak states, and efficient updating order and rule update. These procedures can sort out the randomness issue in an original LPA and stabilize the discovered communities in all runs of the same network. Experiments on synthetic networks and a wide range of real-world social networks indicated that the proposed method achieves significant accuracy and high stability. Indeed, it can obviously solve monster community problem with regard to detecting communities in networks.

Shock Detector for SURF model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Menikoff, Ralph

2016-01-11

SURF and its extension SURFplus are reactive burn models aimed at shock initiation and propagation of detonation waves in high explosives. A distinctive feature of these models is that the burn rate depends on the lead shock pressure. A key part of the models is an algorithm to detect the lead shock. Typically, shock capturing hydro algorithms have small oscillations behind a shock. Here we investigate how well the shock detection algorithm works for a nearly steady propagating detonation wave in one-dimension using the Eulerian xRage code.

Variational and symplectic integrators for satellite relative orbit propagation including drag

NASA Astrophysics Data System (ADS)

Palacios, Leonel; Gurfil, Pini

2018-04-01

Orbit propagation algorithms for satellite relative motion relying on Runge-Kutta integrators are non-symplectic—a situation that leads to incorrect global behavior and degraded accuracy. Thus, attempts have been made to apply symplectic methods to integrate satellite relative motion. However, so far all these symplectic propagation schemes have not taken into account the effect of atmospheric drag. In this paper, drag-generalized symplectic and variational algorithms for satellite relative orbit propagation are developed in different reference frames, and numerical simulations with and without the effect of atmospheric drag are presented. It is also shown that high-order versions of the newly-developed variational and symplectic propagators are more accurate and are significantly faster than Runge-Kutta-based integrators, even in the presence of atmospheric drag.

A wide-angle high Mach number modal expansion for infrasound propagation.

PubMed

Assink, Jelle; Waxler, Roger; Velea, Doru

2017-03-01

The use of modal expansions to solve the problem of atmospheric infrasound propagation is revisited. A different form of the associated modal equation is introduced, valid for wide-angle propagation in atmospheres with high Mach number flow. The modal equation can be formulated as a quadratic eigenvalue problem for which there are simple and efficient numerical implementations. A perturbation expansion for the treatment of attenuation, valid for stratified media with background flow, is derived as well. Comparisons are carried out between the proposed algorithm and a modal algorithm assuming an effective sound speed, including a real data case study. The comparisons show that the effective sound speed approximation overestimates the effect of horizontal wind on sound propagation, leading to errors in traveltime, propagation path, trace velocity, and absorption. The error is found to be dependent on propagation angle and Mach number.

Recognition physical activities with optimal number of wearable sensors using data mining algorithms and deep belief network.

PubMed

Al-Fatlawi, Ali H; Fatlawi, Hayder K; Sai Ho Ling

2017-07-01

Daily physical activities monitoring is benefiting the health care field in several ways, in particular with the development of the wearable sensors. This paper adopts effective ways to calculate the optimal number of the necessary sensors and to build a reliable and a high accuracy monitoring system. Three data mining algorithms, namely Decision Tree, Random Forest and PART Algorithm, have been applied for the sensors selection process. Furthermore, the deep belief network (DBN) has been investigated to recognise 33 physical activities effectively. The results indicated that the proposed method is reliable with an overall accuracy of 96.52% and the number of sensors is minimised from nine to six sensors.

Three-dimensional propagation in near-field tomographic X-ray phase retrieval

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ruhlandt, Aike, E-mail: aruhlan@gwdg.de; Salditt, Tim

An extension of phase retrieval algorithms for near-field X-ray (propagation) imaging to three dimensions is presented, enhancing the quality of the reconstruction by exploiting previously unused three-dimensional consistency constraints. This paper presents an extension of phase retrieval algorithms for near-field X-ray (propagation) imaging to three dimensions, enhancing the quality of the reconstruction by exploiting previously unused three-dimensional consistency constraints. The approach is based on a novel three-dimensional propagator and is derived for the case of optically weak objects. It can be easily implemented in current phase retrieval architectures, is computationally efficient and reduces the need for restrictive prior assumptions, resultingmore » in superior reconstruction quality.« less

Short-Block Protograph-Based LDPC Codes

NASA Technical Reports Server (NTRS)

Divsalar, Dariush; Dolinar, Samuel; Jones, Christopher

2010-01-01

Short-block low-density parity-check (LDPC) codes of a special type are intended to be especially well suited for potential applications that include transmission of command and control data, cellular telephony, data communications in wireless local area networks, and satellite data communications. [In general, LDPC codes belong to a class of error-correcting codes suitable for use in a variety of wireless data-communication systems that include noisy channels.] The codes of the present special type exhibit low error floors, low bit and frame error rates, and low latency (in comparison with related prior codes). These codes also achieve low maximum rate of undetected errors over all signal-to-noise ratios, without requiring the use of cyclic redundancy checks, which would significantly increase the overhead for short blocks. These codes have protograph representations; this is advantageous in that, for reasons that exceed the scope of this article, the applicability of protograph representations makes it possible to design highspeed iterative decoders that utilize belief- propagation algorithms.

Gauging Variational Inference

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chertkov, Michael; Ahn, Sungsoo; Shin, Jinwoo

Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM). Since it is computationally intractable, approximate methods have been used to resolve the issue in practice, where meanfield (MF) and belief propagation (BP) are arguably the most popular and successful approaches of a variational type. In this paper, we propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP (G-BP), improving MF and BP, respectively. Both provide lower bounds for the partition function by utilizing the so-called gauge transformation which modifies factors of GM while keeping the partition function invariant. Moreover, we provemore » that both G-MF and G-BP are exact for GMs with a single loop of a special structure, even though the bare MF and BP perform badly in this case. Our extensive experiments, on complete GMs of relatively small size and on large GM (up-to 300 variables) confirm that the newly proposed algorithms outperform and generalize MF and BP.« less

Cooperative Scheduling of Imaging Observation Tasks for High-Altitude Airships Based on Propagation Algorithm

PubMed Central

Chuan, He; Dishan, Qiu; Jin, Liu

2012-01-01

The cooperative scheduling problem on high-altitude airships for imaging observation tasks is discussed. A constraint programming model is established by analyzing the main constraints, which takes the maximum task benefit and the minimum cruising distance as two optimization objectives. The cooperative scheduling problem of high-altitude airships is converted into a main problem and a subproblem by adopting hierarchy architecture. The solution to the main problem can construct the preliminary matching between tasks and observation resource in order to reduce the search space of the original problem. Furthermore, the solution to the sub-problem can detect the key nodes that each airship needs to fly through in sequence, so as to get the cruising path. Firstly, the task set is divided by using k-core neighborhood growth cluster algorithm (K-NGCA). Then, a novel swarm intelligence algorithm named propagation algorithm (PA) is combined with the key node search algorithm (KNSA) to optimize the cruising path of each airship and determine the execution time interval of each task. Meanwhile, this paper also provides the realization approach of the above algorithm and especially makes a detailed introduction on the encoding rules, search models, and propagation mechanism of the PA. Finally, the application results and comparison analysis show the proposed models and algorithms are effective and feasible. PMID:23365522

Coherent field propagation between tilted planes.

PubMed

Stock, Johannes; Worku, Norman Girma; Gross, Herbert

2017-10-01

Propagating electromagnetic light fields between nonparallel planes is of special importance, e.g., within the design of novel computer-generated holograms or the simulation of optical systems. In contrast to the extensively discussed evaluation between parallel planes, the diffraction-based propagation of light onto a tilted plane is more burdensome, since discrete fast Fourier transforms cannot be applied directly. In this work, we propose a quasi-fast algorithm (O(N 3  log N)) that deals with this problem. Based on a proper decomposition into three rotations, the vectorial field distribution is calculated on a tilted plane using the spectrum of plane waves. The algorithm works on equidistant grids, so neither nonuniform Fourier transforms nor an explicit complex interpolation is necessary. The proposed algorithm is discussed in detail and applied to several examples of practical interest.

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE PAGES

Oyen, Diane; Anderson, Blake; Sentz, Kari; ...

2017-09-15

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oyen, Diane; Anderson, Blake; Sentz, Kari

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

JIGSAW: Joint Inhomogeneity estimation via Global Segment Assembly for Water-fat separation.

PubMed

Lu, Wenmiao; Lu, Yi

2011-07-01

Water-fat separation in magnetic resonance imaging (MRI) is of great clinical importance, and the key to uniform water-fat separation lies in field map estimation. This work deals with three-point field map estimation, in which water and fat are modelled as two single-peak spectral lines, and field inhomogeneities shift the spectrum by an unknown amount. Due to the simplified spectrum modelling, there exists inherent ambiguity in forming field maps from multiple locally feasible field map values at each pixel. To resolve such ambiguity, spatial smoothness of field maps has been incorporated as a constraint of an optimization problem. However, there are two issues: the optimization problem is computationally intractable and even when it is solved exactly, it does not always separate water and fat images. Hence, robust field map estimation remains challenging in many clinically important imaging scenarios. This paper proposes a novel field map estimation technique called JIGSAW. It extends a loopy belief propagation (BP) algorithm to obtain an approximate solution to the optimization problem. The solution produces locally smooth segments and avoids error propagation associated with greedy methods. The locally smooth segments are then assembled into a globally consistent field map by exploiting the periodicity of the feasible field map values. In vivo results demonstrate that JIGSAW outperforms existing techniques and produces correct water-fat separation in challenging imaging scenarios.

Model Checking Degrees of Belief in a System of Agents

NASA Technical Reports Server (NTRS)

Raimondi, Franco; Primero, Giuseppe; Rungta, Neha

2014-01-01

Reasoning about degrees of belief has been investigated in the past by a number of authors and has a number of practical applications in real life. In this paper we present a unified framework to model and verify degrees of belief in a system of agents. In particular, we describe an extension of the temporal-epistemic logic CTLK and we introduce a semantics based on interpreted systems for this extension. In this way, degrees of beliefs do not need to be provided externally, but can be derived automatically from the possible executions of the system, thereby providing a computationally grounded formalism. We leverage the semantics to (a) construct a model checking algorithm, (b) investigate its complexity, (c) provide a Java implementation of the model checking algorithm, and (d) evaluate our approach using the standard benchmark of the dining cryptographers. Finally, we provide a detailed case study: using our framework and our implementation, we assess and verify the situational awareness of the pilot of Air France 447 flying in off-nominal conditions.

An automated workflow for patient-specific quality control of contour propagation

NASA Astrophysics Data System (ADS)

Beasley, William J.; McWilliam, Alan; Slevin, Nicholas J.; Mackay, Ranald I.; van Herk, Marcel

2016-12-01

Contour propagation is an essential component of adaptive radiotherapy, but current contour propagation algorithms are not yet sufficiently accurate to be used without manual supervision. Manual review of propagated contours is time-consuming, making routine implementation of real-time adaptive radiotherapy unrealistic. Automated methods of monitoring the performance of contour propagation algorithms are therefore required. We have developed an automated workflow for patient-specific quality control of contour propagation and validated it on a cohort of head and neck patients, on which parotids were outlined by two observers. Two types of error were simulated—mislabelling of contours and introducing noise in the scans before propagation. The ability of the workflow to correctly predict the occurrence of errors was tested, taking both sets of observer contours as ground truth, using receiver operator characteristic analysis. The area under the curve was 0.90 and 0.85 for the observers, indicating good ability to predict the occurrence of errors. This tool could potentially be used to identify propagated contours that are likely to be incorrect, acting as a flag for manual review of these contours. This would make contour propagation more efficient, facilitating the routine implementation of adaptive radiotherapy.

Modeling Delamination in Postbuckled Composite Structures Under Static and Fatigue Loads

NASA Technical Reports Server (NTRS)

Bisagni, Chiara; Brambilla, Pietro; Bavila, Carlos G.

2013-01-01

The ability of the Abaqus progressive Virtual Crack Closure Technique (VCCT) to model delamination in composite structures was investigated for static, postbuckling, and fatigue loads. Preliminary evaluations were performed using simple Double Cantilever Beam (DCB) and Mixed-Mode Bending (MMB) specimens. The nodal release sequences that describe the propagation of the delamination front were investigated. The effect of using a sudden or a gradual nodal release was evaluated by considering meshes aligned with the crack front as well as misaligned meshes. Fatigue simulations were then performed using the Direct Cyclic Fatigue (DCF) algorithm. It was found that in specimens such as the DCB, which are characterized by a nearly linear response and a pure fracture mode, the algorithm correctly predicts the Paris Law rate of propagation. However, the Abaqus DCF algorithm does not consider different fatigue propagation laws in different fracture modes. Finally, skin/stiffener debonding was studied in an aircraft fuselage subcomponent in which debonding occurs deep into post-buckling deformation. VCCT was shown to be a robust tool for estimating the onset propagation. However, difficulties were found with the ability of the current implementation of the Abaqus progressive VCCT to predict delamination propagation within structures subjected to postbuckling deformations or fatigue loads.

Data classification using metaheuristic Cuckoo Search technique for Levenberg Marquardt back propagation (CSLM) algorithm

NASA Astrophysics Data System (ADS)

Nawi, Nazri Mohd.; Khan, Abdullah; Rehman, M. Z.

2015-05-01

A nature inspired behavior metaheuristic techniques which provide derivative-free solutions to solve complex problems. One of the latest additions to the group of nature inspired optimization procedure is Cuckoo Search (CS) algorithm. Artificial Neural Network (ANN) training is an optimization task since it is desired to find optimal weight set of a neural network in training process. Traditional training algorithms have some limitation such as getting trapped in local minima and slow convergence rate. This study proposed a new technique CSLM by combining the best features of two known algorithms back-propagation (BP) and Levenberg Marquardt algorithm (LM) for improving the convergence speed of ANN training and avoiding local minima problem by training this network. Some selected benchmark classification datasets are used for simulation. The experiment result show that the proposed cuckoo search with Levenberg Marquardt algorithm has better performance than other algorithm used in this study.

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation

PubMed Central

Scellier, Benjamin; Bengio, Yoshua

2017-01-01

We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function. Because the objective function is defined in terms of local perturbations, the second phase of Equilibrium Propagation corresponds to only nudging the prediction (fixed point or stationary distribution) toward a configuration that reduces prediction error. In the case of a recurrent multi-layer supervised network, the output units are slightly nudged toward their target in the second phase, and the perturbation introduced at the output layer propagates backward in the hidden layers. We show that the signal “back-propagated” during this second phase corresponds to the propagation of error derivatives and encodes the gradient of the objective function, when the synaptic update corresponds to a standard form of spike-timing dependent plasticity. This work makes it more plausible that a mechanism similar to Backpropagation could be implemented by brains, since leaky integrator neural computation performs both inference and error back-propagation in our model. The only local difference between the two phases is whether synaptic changes are allowed or not. We also show experimentally that multi-layer recurrently connected networks with 1, 2, and 3 hidden layers can be trained by Equilibrium Propagation on the permutation-invariant MNIST task. PMID:28522969

Recognition of genetically modified product based on affinity propagation clustering and terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Liu, Jianjun; Kan, Jianquan

2018-04-01

In this paper, based on the terahertz spectrum, a new identification method of genetically modified material by support vector machine (SVM) based on affinity propagation clustering is proposed. This algorithm mainly uses affinity propagation clustering algorithm to make cluster analysis and labeling on unlabeled training samples, and in the iterative process, the existing SVM training data are continuously updated, when establishing the identification model, it does not need to manually label the training samples, thus, the error caused by the human labeled samples is reduced, and the identification accuracy of the model is greatly improved.

Statistical analysis of loopy belief propagation in random fields

NASA Astrophysics Data System (ADS)

Yasuda, Muneki; Kataoka, Shun; Tanaka, Kazuyuki

2015-10-01

Loopy belief propagation (LBP), which is equivalent to the Bethe approximation in statistical mechanics, is a message-passing-type inference method that is widely used to analyze systems based on Markov random fields (MRFs). In this paper, we propose a message-passing-type method to analytically evaluate the quenched average of LBP in random fields by using the replica cluster variation method. The proposed analytical method is applicable to general pairwise MRFs with random fields whose distributions differ from each other and can give the quenched averages of the Bethe free energies over random fields, which are consistent with numerical results. The order of its computational cost is equivalent to that of standard LBP. In the latter part of this paper, we describe the application of the proposed method to Bayesian image restoration, in which we observed that our theoretical results are in good agreement with the numerical results for natural images.

Decision-level fusion of SAR and IR sensor information for automatic target detection

NASA Astrophysics Data System (ADS)

Cho, Young-Rae; Yim, Sung-Hyuk; Cho, Hyun-Woong; Won, Jin-Ju; Song, Woo-Jin; Kim, So-Hyeon

2017-05-01

We propose a decision-level architecture that combines synthetic aperture radar (SAR) and an infrared (IR) sensor for automatic target detection. We present a new size-based feature, called target-silhouette to reduce the number of false alarms produced by the conventional target-detection algorithm. Boolean Map Visual Theory is used to combine a pair of SAR and IR images to generate the target-enhanced map. Then basic belief assignment is used to transform this map into a belief map. The detection results of sensors are combined to build the target-silhouette map. We integrate the fusion mass and the target-silhouette map on the decision level to exclude false alarms. The proposed algorithm is evaluated using a SAR and IR synthetic database generated by SE-WORKBENCH simulator, and compared with conventional algorithms. The proposed fusion scheme achieves higher detection rate and lower false alarm rate than the conventional algorithms.

Particle Filter with State Permutations for Solving Image Jigsaw Puzzles

PubMed Central

Yang, Xingwei; Adluru, Nagesh; Latecki, Longin Jan

2016-01-01

We deal with an image jigsaw puzzle problem, which is defined as reconstructing an image from a set of square and non-overlapping image patches. It is known that a general instance of this problem is NP-complete, and it is also challenging for humans, since in the considered setting the original image is not given. Recently a graphical model has been proposed to solve this and related problems. The target label probability function is then maximized using loopy belief propagation. We also formulate the problem as maximizing a label probability function and use exactly the same pairwise potentials. Our main contribution is a novel inference approach in the sampling framework of Particle Filter (PF). Usually in the PF framework it is assumed that the observations arrive sequentially, e.g., the observations are naturally ordered by their time stamps in the tracking scenario. Based on this assumption, the posterior density over the corresponding hidden states is estimated. In the jigsaw puzzle problem all observations (puzzle pieces) are given at once without any particular order. Therefore, we relax the assumption of having ordered observations and extend the PF framework to estimate the posterior density by exploring different orders of observations and selecting the most informative permutations of observations. This significantly broadens the scope of applications of the PF inference. Our experimental results demonstrate that the proposed inference framework significantly outperforms the loopy belief propagation in solving the image jigsaw puzzle problem. In particular, the extended PF inference triples the accuracy of the label assignment compared to that using loopy belief propagation. PMID:27795660

A modified adaptive algorithm of dealing with the high chirp when chirped pulses propagating in optical fiber

NASA Astrophysics Data System (ADS)

Wu, Lianglong; Fu, Xiquan; Guo, Xing

2013-03-01

In this paper, we propose a modified adaptive algorithm (MAA) of dealing with the high chirp to efficiently simulate the propagation of chirped pulses along an optical fiber for the propagation distance shorter than the "temporal focal length". The basis of the MAA is that the chirp term of initial pulse is treated as the rapidly varying part by means of the idea of the slowly varying envelope approximation (SVEA). Numerical simulations show that the performance of the MAA is validated, and that the proposed method can decrease the number of sampling points by orders of magnitude. In addition, the computational efficiency of the MAA compared with the time-domain beam propagation method (BPM) can be enhanced with the increase of the chirp of initial pulse.

Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations

NASA Technical Reports Server (NTRS)

Osher, Stanley; Sethian, James A.

1987-01-01

New numerical algorithms are devised (PSC algorithms) for following fronts propagating with curvature-dependent speed. The speed may be an arbitrary function of curvature, and the front can also be passively advected by an underlying flow. These algorithms approximate the equations of motion, which resemble Hamilton-Jacobi equations with parabolic right-hand-sides, by using techniques from the hyperbolic conservation laws. Non-oscillatory schemes of various orders of accuracy are used to solve the equations, providing methods that accurately capture the formation of sharp gradients and cusps in the moving fronts. The algorithms handle topological merging and breaking naturally, work in any number of space dimensions, and do not require that the moving surface be written as a function. The methods can be used also for more general Hamilton-Jacobi-type problems. The algorithms are demonstrated by computing the solution to a variety of surface motion problems.

A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network

PubMed Central

Ahmed, Afaz Uddin; Tariqul Islam, Mohammad; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation. PMID:25133214

A novel user classification method for femtocell network by using affinity propagation algorithm and artificial neural network.

PubMed

Ahmed, Afaz Uddin; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.

Dynamic belief state representations.

PubMed

Lee, Daniel D; Ortega, Pedro A; Stocker, Alan A

2014-04-01

Perceptual and control systems are tasked with the challenge of accurately and efficiently estimating the dynamic states of objects in the environment. To properly account for uncertainty, it is necessary to maintain a dynamical belief state representation rather than a single state vector. In this review, canonical algorithms for computing and updating belief states in robotic applications are delineated, and connections to biological systems are highlighted. A navigation example is used to illustrate the importance of properly accounting for correlations between belief state components, and to motivate the need for further investigations in psychophysics and neurobiology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Enhancing data locality by using terminal propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hendrickson, B.; Leland, R.; Van Driessche, R.

1995-12-31

Terminal propagation is a method developed in the circuit placement community for adding constraints to graph partitioning problems. This paper adapts and expands this idea, and applies it to the problem of partitioning data structures among the processors of a parallel computer. We show how the constraints in terminal propagation can be used to encourage partitions in which messages are communicated only between architecturally near processors. We then show how these constraints can be handled in two important partitioning algorithms, spectral bisection and multilevel-KL. We compare the quality of partitions generated by these algorithms to each other and to Partitionsmore » generated by more familiar techniques.« less

Technique for handling wave propagation specific effects in biological tissue: mapping of the photon transport equation to Maxwell's equations.

PubMed

Handapangoda, Chintha C; Premaratne, Malin; Paganin, David M; Hendahewa, Priyantha R D S

2008-10-27

A novel algorithm for mapping the photon transport equation (PTE) to Maxwell's equations is presented. Owing to its accuracy, wave propagation through biological tissue is modeled using the PTE. The mapping of the PTE to Maxwell's equations is required to model wave propagation through foreign structures implanted in biological tissue for sensing and characterization of tissue properties. The PTE solves for only the magnitude of the intensity but Maxwell's equations require the phase information as well. However, it is possible to construct the phase information approximately by solving the transport of intensity equation (TIE) using the full multigrid algorithm.

Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network

PubMed Central

He, Jun; Yang, Shixi; Gan, Chunbiao

2017-01-01

Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods. PMID:28677638

Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network.

PubMed

He, Jun; Yang, Shixi; Gan, Chunbiao

2017-07-04

Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

Back-propagation learning of infinite-dimensional dynamical systems.

PubMed

Tokuda, Isao; Tokunaga, Ryuji; Aihara, Kazuyuki

2003-10-01

This paper presents numerical studies of applying back-propagation learning to a delayed recurrent neural network (DRNN). The DRNN is a continuous-time recurrent neural network having time delayed feedbacks and the back-propagation learning is to teach spatio-temporal dynamics to the DRNN. Since the time-delays make the dynamics of the DRNN infinite-dimensional, the learning algorithm and the learning capability of the DRNN are different from those of the ordinary recurrent neural network (ORNN) having no time-delays. First, two types of learning algorithms are developed for a class of DRNNs. Then, using chaotic signals generated from the Mackey-Glass equation and the Rössler equations, learning capability of the DRNN is examined. Comparing the learning algorithms, learning capability, and robustness against noise of the DRNN with those of the ORNN and time delay neural network, advantages as well as disadvantages of the DRNN are investigated.

Deformable image registration based automatic CT-to-CT contour propagation for head and neck adaptive radiotherapy in the routine clinical setting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kumarasiri, Akila, E-mail: akumara1@hfhs.org; Siddiqui, Farzan; Liu, Chang

2014-12-15

Purpose: To evaluate the clinical potential of deformable image registration (DIR)-based automatic propagation of physician-drawn contours from a planning CT to midtreatment CT images for head and neck (H and N) adaptive radiotherapy. Methods: Ten H and N patients, each with a planning CT (CT1) and a subsequent CT (CT2) taken approximately 3–4 week into treatment, were considered retrospectively. Clinically relevant organs and targets were manually delineated by a radiation oncologist on both sets of images. Four commercial DIR algorithms, two B-spline-based and two Demons-based, were used to deform CT1 and the relevant contour sets onto corresponding CT2 images. Agreementmore » of the propagated contours with manually drawn contours on CT2 was visually rated by four radiation oncologists in a scale from 1 to 5, the volume overlap was quantified using Dice coefficients, and a distance analysis was done using center of mass (CoM) displacements and Hausdorff distances (HDs). Performance of these four commercial algorithms was validated using a parameter-optimized Elastix DIR algorithm. Results: All algorithms attained Dice coefficients of >0.85 for organs with clear boundaries and those with volumes >9 cm{sup 3}. Organs with volumes <3 cm{sup 3} and/or those with poorly defined boundaries showed Dice coefficients of ∼0.5–0.6. For the propagation of small organs (<3 cm{sup 3}), the B-spline-based algorithms showed higher mean Dice values (Dice = 0.60) than the Demons-based algorithms (Dice = 0.54). For the gross and planning target volumes, the respective mean Dice coefficients were 0.8 and 0.9. There was no statistically significant difference in the Dice coefficients, CoM, or HD among investigated DIR algorithms. The mean radiation oncologist visual scores of the four algorithms ranged from 3.2 to 3.8, which indicated that the quality of transferred contours was “clinically acceptable with minor modification or major modification in a small number of contours.” Conclusions: Use of DIR-based contour propagation in the routine clinical setting is expected to increase the efficiency of H and N replanning, reducing the amount of time needed for manual target and organ delineations.« less

A Seed-Based Plant Propagation Algorithm: The Feeding Station Model

PubMed Central

Salhi, Abdellah

2015-01-01

The seasonal production of fruit and seeds is akin to opening a feeding station, such as a restaurant. Agents coming to feed on the fruit are like customers attending the restaurant; they arrive at a certain rate and get served at a certain rate following some appropriate processes. The same applies to birds and animals visiting and feeding on ripe fruit produced by plants such as the strawberry plant. This phenomenon underpins the seed dispersion of the plants. Modelling it as a queuing process results in a seed-based search/optimisation algorithm. This variant of the Plant Propagation Algorithm is described, analysed, tested on nontrivial problems, and compared with well established algorithms. The results are included. PMID:25821858

Human activity recognition based on feature selection in smart home using back-propagation algorithm.

PubMed

Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

2014-09-01

In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

A Novel Latin Hypercube Algorithm via Translational Propagation

PubMed Central

Pan, Guang; Ye, Pengcheng

2014-01-01

Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is directly related to the experimental designs used. Optimal Latin hypercube designs are frequently used and have been shown to have good space-filling and projective properties. However, the high cost in constructing them limits their use. In this paper, a methodology for creating novel Latin hypercube designs via translational propagation and successive local enumeration algorithm (TPSLE) is developed without using formal optimization. TPSLE algorithm is based on the inspiration that a near optimal Latin Hypercube design can be constructed by a simple initial block with a few points generated by algorithm SLE as a building block. In fact, TPSLE algorithm offers a balanced trade-off between the efficiency and sampling performance. The proposed algorithm is compared to two existing algorithms and is found to be much more efficient in terms of the computation time and has acceptable space-filling and projective properties. PMID:25276844

Multi-label spacecraft electrical signal classification method based on DBN and random forest

PubMed Central

Li, Ke; Yu, Nan; Li, Pengfei; Song, Shimin; Wu, Yalei; Li, Yang; Liu, Meng

2017-01-01

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data. PMID:28486479

Multi-label spacecraft electrical signal classification method based on DBN and random forest.

PubMed

Li, Ke; Yu, Nan; Li, Pengfei; Song, Shimin; Wu, Yalei; Li, Yang; Liu, Meng

2017-01-01

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data.

Testing of Gyroless Estimation Algorithms for the Fuse Spacecraft

NASA Technical Reports Server (NTRS)

Harman, R.; Thienel, J.; Oshman, Yaakov

2004-01-01

This paper documents the testing and development of magnetometer-based gyroless attitude and rate estimation algorithms for the Far Ultraviolet Spectroscopic Explorer (FUSE). The results of two approaches are presented, one relies on a kinematic model for propagation, a method used in aircraft tracking, and the other is a pseudolinear Kalman filter that utilizes Euler's equations in the propagation of the estimated rate. Both algorithms are tested using flight data collected over a few months after the failure of two of the reaction wheels. The question of closed-loop stability is addressed. The ability of the controller to meet the science slew requirements, without the gyros, is analyzed.

Regional seismic wavefield computation on a 3-D heterogeneous Earth model by means of coupled traveling wave synthesis

USGS Publications Warehouse

Pollitz, F.F.

2002-01-01

I present a new algorithm for calculating seismic wave propagation through a three-dimensional heterogeneous medium using the framework of mode coupling theory originally developed to perform very low frequency (f < ???0.01-0.05 Hz) seismic wavefield computation. It is a Greens function approach for multiple scattering within a defined volume and employs a truncated traveling wave basis set using the locked mode approximation. Interactions between incident and scattered wavefields are prescribed by mode coupling theory and account for the coupling among surface waves, body waves, and evanescent waves. The described algorithm is, in principle, applicable to global and regional wave propagation problems, but I focus on higher frequency (typically f ??????0.25 Hz) applications at regional and local distances where the locked mode approximation is best utilized and which involve wavefields strongly shaped by propagation through a highly heterogeneous crust. Synthetic examples are shown for P-SV-wave propagation through a semi-ellipsoidal basin and SH-wave propagation through a fault zone.

Experimental study of stochastic noise propagation in SPECT images reconstructed using the conjugate gradient algorithm.

PubMed

Mariano-Goulart, D; Fourcade, M; Bernon, J L; Rossi, M; Zanca, M

2003-01-01

Thanks to an experimental study based on simulated and physical phantoms, the propagation of the stochastic noise in slices reconstructed using the conjugate gradient algorithm has been analysed versus iterations. After a first increase corresponding to the reconstruction of the signal, the noise stabilises before increasing linearly with iterations. The level of the plateau as well as the slope of the subsequent linear increase depends on the noise in the projection data.

An improved wavelet neural network medical image segmentation algorithm with combined maximum entropy

NASA Astrophysics Data System (ADS)

Hu, Xiaoqian; Tao, Jinxu; Ye, Zhongfu; Qiu, Bensheng; Xu, Jinzhang

2018-05-01

In order to solve the problem of medical image segmentation, a wavelet neural network medical image segmentation algorithm based on combined maximum entropy criterion is proposed. Firstly, we use bee colony algorithm to optimize the network parameters of wavelet neural network, get the parameters of network structure, initial weights and threshold values, and so on, we can quickly converge to higher precision when training, and avoid to falling into relative extremum; then the optimal number of iterations is obtained by calculating the maximum entropy of the segmented image, so as to achieve the automatic and accurate segmentation effect. Medical image segmentation experiments show that the proposed algorithm can reduce sample training time effectively and improve convergence precision, and segmentation effect is more accurate and effective than traditional BP neural network (back propagation neural network : a multilayer feed forward neural network which trained according to the error backward propagation algorithm.

Comparative Analysis of Soft Computing Models in Prediction of Bending Rigidity of Cotton Woven Fabrics

NASA Astrophysics Data System (ADS)

Guruprasad, R.; Behera, B. K.

2015-10-01

Quantitative prediction of fabric mechanical properties is an essential requirement for design engineering of textile and apparel products. In this work, the possibility of prediction of bending rigidity of cotton woven fabrics has been explored with the application of Artificial Neural Network (ANN) and two hybrid methodologies, namely Neuro-genetic modeling and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling. For this purpose, a set of cotton woven grey fabrics was desized, scoured and relaxed. The fabrics were then conditioned and tested for bending properties. With the database thus created, a neural network model was first developed using back propagation as the learning algorithm. The second model was developed by applying a hybrid learning strategy, in which genetic algorithm was first used as a learning algorithm to optimize the number of neurons and connection weights of the neural network. The Genetic algorithm optimized network structure was further allowed to learn using back propagation algorithm. In the third model, an ANFIS modeling approach was attempted to map the input-output data. The prediction performances of the models were compared and a sensitivity analysis was reported. The results show that the prediction by neuro-genetic and ANFIS models were better in comparison with that of back propagation neural network model.

The role of idiotypic interactions in the adaptive immune system: a belief-propagation approach

NASA Astrophysics Data System (ADS)

Bartolucci, Silvia; Mozeika, Alexander; Annibale, Alessia

2016-08-01

In this work we use belief-propagation techniques to study the equilibrium behaviour of a minimal model for the immune system comprising interacting T and B clones. We investigate the effect of the so-called idiotypic interactions among complementary B clones on the system’s activation. Our results show that B-B interactions increase the system’s resilience to noise, making clonal activation more stable, while increasing the cross-talk between different clones. We derive analytically the noise level at which a B clone gets activated, in the absence of cross-talk, and find that this increases with the strength of idiotypic interactions and with the number of T cells sending signals to the B clones. We also derive, analytically and numerically, via population dynamics, the critical line where clonal cross-talk arises. Our approach allows us to derive the B clone size distribution, which can be experimentally measured and gives important information about the adaptive immune system response to antigens and vaccination.

Migration of dispersive GPR data

USGS Publications Warehouse

Powers, M.H.; Oden, C.P.; ,

2004-01-01

Electrical conductivity and dielectric and magnetic relaxation phenomena cause electromagnetic propagation to be dispersive in earth materials. Both velocity and attenuation may vary with frequency, depending on the frequency content of the propagating energy and the nature of the relaxation phenomena. A minor amount of velocity dispersion is associated with high attenuation. For this reason, measuring effects of velocity dispersion in ground penetrating radar (GPR) data is difficult. With a dispersive forward model, GPR responses to propagation through materials with known frequency-dependent properties have been created. These responses are used as test data for migration algorithms that have been modified to handle specific aspects of dispersive media. When either Stolt or Gazdag migration methods are modified to correct for just velocity dispersion, the results are little changed from standard migration. For nondispersive propagating wavefield data, like deep seismic, ensuring correct phase summation in a migration algorithm is more important than correctly handling amplitude. However, the results of migrating model responses to dispersive media with modified algorithms indicate that, in this case, correcting for frequency-dependent amplitude loss has a much greater effect on the result than correcting for proper phase summation. A modified migration is only effective when it includes attenuation recovery, performing deconvolution and migration simultaneously.

Measuring contraction propagation and localizing pacemaker cells using high speed video microscopy

PubMed Central

Akl, Tony J.; Nepiyushchikh, Zhanna V.; Gashev, Anatoliy A.; Zawieja, David C.; Coté, Gerard L.

2011-01-01

Previous studies have shown the ability of many lymphatic vessels to contract phasically to pump lymph. Every lymphangion can act like a heart with pacemaker sites that initiate the phasic contractions. The contractile wave propagates along the vessel to synchronize the contraction. However, determining the location of the pacemaker sites within these vessels has proven to be very difficult. A high speed video microscopy system with an automated algorithm to detect pacemaker location and calculate the propagation velocity, speed, duration, and frequency of the contractions is presented in this paper. Previous methods for determining the contractile wave propagation velocity manually were time consuming and subject to errors and potential bias. The presented algorithm is semiautomated giving objective results based on predefined criteria with the option of user intervention. The system was first tested on simulation images and then on images acquired from isolated microlymphatic mesenteric vessels. We recorded contraction propagation velocities around 10 mm∕s with a shortening speed of 20.4 to 27.1 μm∕s on average and a contraction frequency of 7.4 to 21.6 contractions∕min. The simulation results showed that the algorithm has no systematic error when compared to manual tracking. The system was used to determine the pacemaker location with a precision of 28 μm when using a frame rate of 300 frames per second. PMID:21361700

Consistency functional map propagation for repetitive patterns

NASA Astrophysics Data System (ADS)

Wang, Hao

2017-09-01

Repetitive patterns appear frequently in both man-made and natural environments. Automatically and robustly detecting such patterns from an image is a challenging problem. We study repetitive pattern alignment by embedding segmentation cue with a functional map model. However, this model cannot tackle the repetitive patterns directly due to the large photometric and geometric variations. Thus, a consistency functional map propagation (CFMP) algorithm that extends the functional map with dynamic propagation is proposed to address this issue. This propagation model is acquired in two steps. The first one aligns the patterns from a local region, transferring segmentation functions among patterns. It can be cast as an L norm optimization problem. The latter step updates the template segmentation for the next round of pattern discovery by merging the transferred segmentation functions. Extensive experiments and comparative analyses have demonstrated an encouraging performance of the proposed algorithm in detection and segmentation of repetitive patterns.

Implementations of back propagation algorithm in ecosystems applications

NASA Astrophysics Data System (ADS)

Ali, Khalda F.; Sulaiman, Riza; Elamir, Amir Mohamed

2015-05-01

Artificial Neural Networks (ANNs) have been applied to an increasing number of real world problems of considerable complexity. Their most important advantage is in solving problems which are too complex for conventional technologies, that do not have an algorithmic solutions or their algorithmic Solutions is too complex to be found. In general, because of their abstraction from the biological brain, ANNs are developed from concept that evolved in the late twentieth century neuro-physiological experiments on the cells of the human brain to overcome the perceived inadequacies with conventional ecological data analysis methods. ANNs have gained increasing attention in ecosystems applications, because of ANN's capacity to detect patterns in data through non-linear relationships, this characteristic confers them a superior predictive ability. In this research, ANNs is applied in an ecological system analysis. The neural networks use the well known Back Propagation (BP) Algorithm with the Delta Rule for adaptation of the system. The Back Propagation (BP) training Algorithm is an effective analytical method for adaptation of the ecosystems applications, the main reason because of their capacity to detect patterns in data through non-linear relationships. This characteristic confers them a superior predicting ability. The BP algorithm uses supervised learning, which means that we provide the algorithm with examples of the inputs and outputs we want the network to compute, and then the error is calculated. The idea of the back propagation algorithm is to reduce this error, until the ANNs learns the training data. The training begins with random weights, and the goal is to adjust them so that the error will be minimal. This research evaluated the use of artificial neural networks (ANNs) techniques in an ecological system analysis and modeling. The experimental results from this research demonstrate that an artificial neural network system can be trained to act as an expert ecosystem analyzer for many applications in ecological fields. The pilot ecosystem analyzer shows promising ability for generalization and requires further tuning and refinement of the basis neural network system for optimal performance.

Validation of Ionosonde Electron Density Reconstruction Algorithms with IONOLAB-RAY in Central Europe

NASA Astrophysics Data System (ADS)

Gok, Gokhan; Mosna, Zbysek; Arikan, Feza; Arikan, Orhan; Erdem, Esra

2016-07-01

Ionospheric observation is essentially accomplished by specialized radar systems called ionosondes. The time delay between the transmitted and received signals versus frequency is measured by the ionosondes and the received signals are processed to generate ionogram plots, which show the time delay or reflection height of signals with respect to transmitted frequency. The critical frequencies of ionospheric layers and virtual heights, that provide useful information about ionospheric structurecan be extracted from ionograms . Ionograms also indicate the amount of variability or disturbances in the ionosphere. With special inversion algorithms and tomographical methods, electron density profiles can also be estimated from the ionograms. Although structural pictures of ionosphere in the vertical direction can be observed from ionosonde measurements, some errors may arise due to inaccuracies that arise from signal propagation, modeling, data processing and tomographic reconstruction algorithms. Recently IONOLAB group (www.ionolab.org) developed a new algorithm for effective and accurate extraction of ionospheric parameters and reconstruction of electron density profile from ionograms. The electron density reconstruction algorithm applies advanced optimization techniques to calculate parameters of any existing analytical function which defines electron density with respect to height using ionogram measurement data. The process of reconstructing electron density with respect to height is known as the ionogram scaling or true height analysis. IONOLAB-RAY algorithm is a tool to investigate the propagation path and parameters of HF wave in the ionosphere. The algorithm models the wave propagation using ray representation under geometrical optics approximation. In the algorithm , the structural ionospheric characteristics arerepresented as realistically as possible including anisotropicity, inhomogenity and time dependence in 3-D voxel structure. The algorithm is also used for various purposes including calculation of actual height and generation of ionograms. In this study, the performance of electron density reconstruction algorithm of IONOLAB group and standard electron density profile algorithms of ionosondes are compared with IONOLAB-RAY wave propagation simulation in near vertical incidence. The electron density reconstruction and parameter extraction algorithms of ionosondes are validated with the IONOLAB-RAY results both for quiet anddisturbed ionospheric states in Central Europe using ionosonde stations such as Pruhonice and Juliusruh . It is observed that IONOLAB ionosonde parameter extraction and electron density reconstruction algorithm performs significantly better compared to standard algorithms especially for disturbed ionospheric conditions. IONOLAB-RAY provides an efficient and reliable tool to investigate and validate ionosonde electron density reconstruction algorithms, especially in determination of reflection height (true height) of signals and critical parameters of ionosphere. This study is supported by TUBITAK 114E541, 115E915 and Joint TUBITAK 114E092 and AS CR 14/001 projects.

Assimilative model for ionospheric dynamics employing delay, Doppler, and direction of arrival measurements from multiple HF channels

NASA Astrophysics Data System (ADS)

Fridman, Sergey V.; Nickisch, L. J.; Hausman, Mark; Zunich, George

2016-03-01

We describe the development of new HF data assimilation capabilities for our ionospheric inversion algorithm called GPSII (GPS Ionospheric Inversion). Previously existing capabilities of this algorithm included assimilation of GPS total electron content data as well as assimilation of backscatter ionograms. In the present effort we concentrated on developing assimilation tools for data related to HF propagation channels. Measurements of propagation delay, angle of arrival, and the ionosphere-induced Doppler from any number of known propagation links can now be utilized by GPSII. The resulting ionospheric model is consistent with all assimilated measurements. This means that ray tracing simulations of the assimilated propagation links are guaranteed to be in agreement with measured data within the errors of measurement. The key theoretical element for assimilating HF data is the raypath response operator (RPRO) which describes response of raypath parameters to infinitesimal variations of electron density in the ionosphere. We construct the RPRO out of the fundamental solution of linearized ray tracing equations for a dynamic magnetoactive plasma. We demonstrate performance and internal consistency of the algorithm using propagation delay data from multiple oblique ionograms (courtesy of Defence Science and Technology Organisation, Australia) as well as with time series of near-vertical incidence sky wave data (courtesy of the Intelligence Advanced Research Projects Activity HFGeo Program Government team). In all cases GPSII produces electron density distributions which are smooth in space and in time. We simulate the assimilated propagation links by performing ray tracing through GPSII-produced ionosphere and observe that simulated data are indeed in agreement with assimilated measurements.

Efficient kinetic Monte Carlo method for reaction-diffusion problems with spatially varying annihilation rates

NASA Astrophysics Data System (ADS)

Schwarz, Karsten; Rieger, Heiko

2013-03-01

We present an efficient Monte Carlo method to simulate reaction-diffusion processes with spatially varying particle annihilation or transformation rates as it occurs for instance in the context of motor-driven intracellular transport. Like Green's function reaction dynamics and first-passage time methods, our algorithm avoids small diffusive hops by propagating sufficiently distant particles in large hops to the boundaries of protective domains. Since for spatially varying annihilation or transformation rates the single particle diffusion propagator is not known analytically, we present an algorithm that generates efficiently either particle displacements or annihilations with the correct statistics, as we prove rigorously. The numerical efficiency of the algorithm is demonstrated with an illustrative example.

Reconsidering Constructivism in Qualitative Research

ERIC Educational Resources Information Center

Lee, Cheu-Jey George

2012-01-01

This article examines constructivism, a paradigm in qualitative research that has been propagated by Egon Guba, Yvonna Lincoln, and Norman Denzin. A distinction is made between whether the basic presuppositions of constructivism are credible compared to those of a competing paradigm and whether constructivism's beliefs are internally consistent.…

Fast Transformation of Temporal Plans for Efficient Execution

NASA Technical Reports Server (NTRS)

Tsamardinos, Ioannis; Muscettola, Nicola; Morris, Paul

1998-01-01

Temporal plans permit significant flexibility in specifying the occurrence time of events. Plan execution can make good use of that flexibility. However, the advantage of execution flexibility is counterbalanced by the cost during execution of propagating the time of occurrence of events throughout the flexible plan. To minimize execution latency, this propagation needs to be very efficient. Previous work showed that every temporal plan can be reformulated as a dispatchable plan, i.e., one for which propagation to immediate neighbors is sufficient. A simple algorithm was given that finds a dispatchable plan with a minimum number of edges in cubic time and quadratic space. In this paper, we focus on the efficiency of the reformulation process, and improve on that result. A new algorithm is presented that uses linear space and has time complexity equivalent to Johnson s algorithm for all-pairs shortest-path problems. Experimental evidence confirms the practical effectiveness of the new algorithm. For example, on a large commercial application, the performance is improved by at least two orders of magnitude. We further show that the dispatchable plan, already minimal in the total number of edges, can also be made minimal in the maximum number of edges incoming or outgoing at any node.

A memory-efficient staining algorithm in 3D seismic modelling and imaging

NASA Astrophysics Data System (ADS)

Jia, Xiaofeng; Yang, Lu

2017-08-01

The staining algorithm has been proven to generate high signal-to-noise ratio (S/N) images in poorly illuminated areas in two-dimensional cases. In the staining algorithm, the stained wavefield relevant to the target area and the regular source wavefield forward propagate synchronously. Cross-correlating these two wavefields with the backward propagated receiver wavefield separately, we obtain two images: the local image of the target area and the conventional reverse time migration (RTM) image. This imaging process costs massive computer memory for wavefield storage, especially in large scale three-dimensional cases. To make the staining algorithm applicable to three-dimensional RTM, we develop a method to implement the staining algorithm in three-dimensional acoustic modelling in a standard staggered grid finite difference (FD) scheme. The implementation is adaptive to the order of spatial accuracy of the FD operator. The method can be applied to elastic, electromagnetic, and other wave equations. Taking the memory requirement into account, we adopt a random boundary condition (RBC) to backward extrapolate the receiver wavefield and reconstruct it by reverse propagation using the final wavefield snapshot only. Meanwhile, we forward simulate the stained wavefield and source wavefield simultaneously using the nearly perfectly matched layer (NPML) boundary condition. Experiments on a complex geologic model indicate that the RBC-NPML collaborative strategy not only minimizes the memory consumption but also guarantees high quality imaging results. We apply the staining algorithm to three-dimensional RTM via the proposed strategy. Numerical results show that our staining algorithm can produce high S/N images in the target areas with other structures effectively muted.

Calculation of the attenuation and phase displacement per unit of length due to rain composed of ellipsoidal drops

NASA Technical Reports Server (NTRS)

Maggiori, D.

1981-01-01

All of the phenomena which influence the propagation of radiowaves at frequencies above 10 GHz (attenuation, depolarization, scintillation) can by intensified by parameters directly derived from a solution of individual scatter, naturally in addition to be meteorological elements which characterize the physical medium. The diffusion caused by rainy precipitation was studied using Mie's algorithm for rain composed of spherical drops, and Oguchi's algorithm for rain composed of drops in an ellipsoidal form with axes of rotational symmetry arrange along the vertical line of a generic reference point. Specific phase displacement and attenuation along the principal planes, propagation of radiowaves in generic polarization, and propagation with inclined axes are also considered.

An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks

PubMed Central

Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

2016-01-01

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

An Adaptive Cultural Algorithm with Improved Quantum-behaved Particle Swarm Optimization for Sonar Image Detection.

PubMed

Wang, Xingmei; Hao, Wenqian; Li, Qiming

2017-12-18

This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of particles, iterative times and the fitness value of particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved particle swarm optimization algorithm (QPSO). The improved quantum-behaved particle swarm optimization algorithm (IQPSO) can make particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.

Dynamic calibration and analysis of crack tip propagation in energetic materials using real-time radiography

NASA Astrophysics Data System (ADS)

Butt, Ali

Crack propagation in a solid rocket motor environment is difficult to measure directly. This experimental and analytical study evaluated the viability of real-time radiography for detecting bore regression and propellant crack propagation speed. The scope included the quantitative interpretation of crack tip velocity from simulated radiographic images of a burning, center-perforated grain and actual real-time radiographs taken on a rapid-prototyped model that dynamically produced the surface movements modeled in the simulation. The simplified motor simulation portrayed a bore crack that propagated radially at a speed that was 10 times the burning rate of the bore. Comparing the experimental image interpretation with the calibrated surface inputs, measurement accuracies were quantified. The average measurements of the bore radius were within 3% of the calibrated values with a maximum error of 7%. The crack tip speed could be characterized with image processing algorithms, but not with the dynamic calibration data. The laboratory data revealed that noise in the transmitted X-Ray intensity makes sensing the crack tip propagation using changes in the centerline transmitted intensity level impractical using the algorithms employed.

The Gnosis of Media

ERIC Educational Resources Information Center

Jochum, Uwe

2004-01-01

Much contemporary library thinking and planning hinges on the belief that the true telos (or mission) of libraries is to merge into the new electronic environment, usually referred to metonymically as "the Internet." In this article, I argue that those who propagate the Internet as the coming information paradise, subsuming and superseding…

Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief.

PubMed

Douglas, P K; Harris, Sam; Yuille, Alan; Cohen, Mark S

2011-05-15

Machine learning (ML) has become a popular tool for mining functional neuroimaging data, and there are now hopes of performing such analyses efficiently in real-time. Towards this goal, we compared accuracy of six different ML algorithms applied to neuroimaging data of persons engaged in a bivariate task, asserting their belief or disbelief of a variety of propositional statements. We performed unsupervised dimension reduction and automated feature extraction using independent component (IC) analysis and extracted IC time courses. Optimization of classification hyperparameters across each classifier occurred prior to assessment. Maximum accuracy was achieved at 92% for Random Forest, followed by 91% for AdaBoost, 89% for Naïve Bayes, 87% for a J48 decision tree, 86% for K*, and 84% for support vector machine. For real-time decoding applications, finding a parsimonious subset of diagnostic ICs might be useful. We used a forward search technique to sequentially add ranked ICs to the feature subspace. For the current data set, we determined that approximately six ICs represented a meaningful basis set for classification. We then projected these six IC spatial maps forward onto a later scanning session within subject. We then applied the optimized ML algorithms to these new data instances, and found that classification accuracy results were reproducible. Additionally, we compared our classification method to our previously published general linear model results on this same data set. The highest ranked IC spatial maps show similarity to brain regions associated with contrasts for belief > disbelief, and disbelief < belief. Copyright © 2010 Elsevier Inc. All rights reserved.

Least-squares Legendre spectral element solutions to sound propagation problems.

PubMed

Lin, W H

2001-02-01

This paper presents a novel algorithm and numerical results of sound wave propagation. The method is based on a least-squares Legendre spectral element approach for spatial discretization and the Crank-Nicolson [Proc. Cambridge Philos. Soc. 43, 50-67 (1947)] and Adams-Bashforth [D. Gottlieb and S. A. Orszag, Numerical Analysis of Spectral Methods: Theory and Applications (CBMS-NSF Monograph, Siam 1977)] schemes for temporal discretization to solve the linearized acoustic field equations for sound propagation. Two types of NASA Computational Aeroacoustics (CAA) Workshop benchmark problems [ICASE/LaRC Workshop on Benchmark Problems in Computational Aeroacoustics, edited by J. C. Hardin, J. R. Ristorcelli, and C. K. W. Tam, NASA Conference Publication 3300, 1995a] are considered: a narrow Gaussian sound wave propagating in a one-dimensional space without flows, and the reflection of a two-dimensional acoustic pulse off a rigid wall in the presence of a uniform flow of Mach 0.5 in a semi-infinite space. The first problem was used to examine the numerical dispersion and dissipation characteristics of the proposed algorithm. The second problem was to demonstrate the capability of the algorithm in treating sound propagation in a flow. Comparisons were made of the computed results with analytical results and results obtained by other methods. It is shown that all results computed by the present method are in good agreement with the analytical solutions and results of the first problem agree very well with those predicted by other schemes.

New method for propagating the square root covariance matrix in triangular form. [using Kalman-Bucy filter

NASA Technical Reports Server (NTRS)

Choe, C. Y.; Tapley, B. D.

1975-01-01

A method proposed by Potter of applying the Kalman-Bucy filter to the problem of estimating the state of a dynamic system is described, in which the square root of the state error covariance matrix is used to process the observations. A new technique which propagates the covariance square root matrix in lower triangular form is given for the discrete observation case. The technique is faster than previously proposed algorithms and is well-adapted for use with the Carlson square root measurement algorithm.

Intelligent switching between different noise propagation algorithms: analysis and sensitivity

DOT National Transportation Integrated Search

2012-08-10

When modeling aircraft noise on a large scale (such as an analysis of annual aircraft : operations at an airport), it is important that the noise propagation model used for the : analysis be both efficient and accurate. In this analysis, three differ...

AWARE - The Automated EUV Wave Analysis and REduction algorithm

NASA Astrophysics Data System (ADS)

Ireland, J.; Inglis; A. R.; Shih, A. Y.; Christe, S.; Mumford, S.; Hayes, L. A.; Thompson, B. J.

2016-10-01

Extreme ultraviolet (EUV) waves are large-scale propagating disturbances observed in the solar corona, frequently associated with coronal mass ejections and flares. Since their discovery over two hundred papers discussing their properties, causes and physics have been published. However, their fundamental nature and the physics of their interactions with other solar phenomena are still not understood. To further the understanding of EUV waves, and their relation to other solar phenomena, we have constructed the Automated Wave Analysis and REduction (AWARE) algorithm for the detection of EUV waves over the full Sun. The AWARE algorithm is based on a novel image processing approach to isolating the bright wavefront of the EUV as it propagates across the corona. AWARE detects the presence of a wavefront, and measures the distance, velocity and acceleration of that wavefront across the Sun. Results from AWARE are compared to results from other algorithms for some well known EUV wave events. Suggestions are also give for further refinements to the basic algorithm presented here.

The power of neural nets

NASA Technical Reports Server (NTRS)

Ryan, J. P.; Shah, B. H.

1987-01-01

Implementation of the Hopfield net which is used in the image processing type of applications where only partial information about the image may be available is discussed. The image classification type of algorithm of Hopfield and other learning algorithms, such as the Boltzmann machine and the back-propagation training algorithm, have many vital applications in space.

Improvement in error propagation in the Shack-Hartmann-type zonal wavefront sensors.

PubMed

Pathak, Biswajit; Boruah, Bosanta R

2017-12-01

Estimation of the wavefront from measured slope values is an essential step in a Shack-Hartmann-type wavefront sensor. Using an appropriate estimation algorithm, these measured slopes are converted into wavefront phase values. Hence, accuracy in wavefront estimation lies in proper interpretation of these measured slope values using the chosen estimation algorithm. There are two important sources of errors associated with the wavefront estimation process, namely, the slope measurement error and the algorithm discretization error. The former type is due to the noise in the slope measurements or to the detector centroiding error, and the latter is a consequence of solving equations of a basic estimation algorithm adopted onto a discrete geometry. These errors deserve particular attention, because they decide the preference of a specific estimation algorithm for wavefront estimation. In this paper, we investigate these two important sources of errors associated with the wavefront estimation algorithms of Shack-Hartmann-type wavefront sensors. We consider the widely used Southwell algorithm and the recently proposed Pathak-Boruah algorithm [J. Opt.16, 055403 (2014)JOOPDB0150-536X10.1088/2040-8978/16/5/055403] and perform a comparative study between the two. We find that the latter algorithm is inherently superior to the Southwell algorithm in terms of the error propagation performance. We also conduct experiments that further establish the correctness of the comparative study between the said two estimation algorithms.

Stereo Image Dense Matching by Integrating Sift and Sgm Algorithm

NASA Astrophysics Data System (ADS)

Zhou, Y.; Song, Y.; Lu, J.

2018-05-01

Semi-global matching(SGM) performs the dynamic programming by treating the different path directions equally. It does not consider the impact of different path directions on cost aggregation, and with the expansion of the disparity search range, the accuracy and efficiency of the algorithm drastically decrease. This paper presents a dense matching algorithm by integrating SIFT and SGM. It takes the successful matching pairs matched by SIFT as control points to direct the path in dynamic programming with truncating error propagation. Besides, matching accuracy can be improved by using the gradient direction of the detected feature points to modify the weights of the paths in different directions. The experimental results based on Middlebury stereo data sets and CE-3 lunar data sets demonstrate that the proposed algorithm can effectively cut off the error propagation, reduce disparity search range and improve matching accuracy.

Learning from Bees: An Approach for Influence Maximization on Viral Campaigns

PubMed Central

Sankar, C. Prem; S., Asharaf

2016-01-01

Maximisation of influence propagation is a key ingredient to any viral marketing or socio-political campaigns. However, it is an NP-hard problem, and various approximate algorithms have been suggested to address the issue, though not largely successful. In this paper, we propose a bio-inspired approach to select the initial set of nodes which is significant in rapid convergence towards a sub-optimal solution in minimal runtime. The performance of the algorithm is evaluated using the re-tweet network of the hashtag #KissofLove on Twitter associated with the non-violent protest against the moral policing spread to many parts of India. Comparison with existing centrality based node ranking process the proposed method significant improvement on influence propagation. The proposed algorithm is one of the hardly few bio-inspired algorithms in network theory. We also report the results of the exploratory analysis of the network kiss of love campaign. PMID:27992472

Computer algorithms for automated detection and analysis of local Ca2+ releases in spontaneously beating cardiac pacemaker cells

PubMed Central

Kim, Mary S.; Tsutsui, Kenta; Stern, Michael D.; Lakatta, Edward G.; Maltsev, Victor A.

2017-01-01

Local Ca2+ Releases (LCRs) are crucial events involved in cardiac pacemaker cell function. However, specific algorithms for automatic LCR detection and analysis have not been developed in live, spontaneously beating pacemaker cells. In the present study we measured LCRs using a high-speed 2D-camera in spontaneously contracting sinoatrial (SA) node cells isolated from rabbit and guinea pig and developed a new algorithm capable of detecting and analyzing the LCRs spatially in two-dimensions, and in time. Our algorithm tracks points along the midline of the contracting cell. It uses these points as a coordinate system for affine transform, producing a transformed image series where the cell does not contract. Action potential-induced Ca2+ transients and LCRs were thereafter isolated from recording noise by applying a series of spatial filters. The LCR birth and death events were detected by a differential (frame-to-frame) sensitivity algorithm applied to each pixel (cell location). An LCR was detected when its signal changes sufficiently quickly within a sufficiently large area. The LCR is considered to have died when its amplitude decays substantially, or when it merges into the rising whole cell Ca2+ transient. Ultimately, our algorithm provides major LCR parameters such as period, signal mass, duration, and propagation path area. As the LCRs propagate within live cells, the algorithm identifies splitting and merging behaviors, indicating the importance of locally propagating Ca2+-induced-Ca2+-release for the fate of LCRs and for generating a powerful ensemble Ca2+ signal. Thus, our new computer algorithms eliminate motion artifacts and detect 2D local spatiotemporal events from recording noise and global signals. While the algorithms were developed to detect LCRs in sinoatrial nodal cells, they have the potential to be used in other applications in biophysics and cell physiology, for example, to detect Ca2+ wavelets (abortive waves), sparks and embers in muscle cells and Ca2+ puffs and syntillas in neurons. PMID:28683095

Acoustical source reconstruction from non-synchronous sequential measurements by Fast Iterative Shrinkage Thresholding Algorithm

NASA Astrophysics Data System (ADS)

Yu, Liang; Antoni, Jerome; Leclere, Quentin; Jiang, Weikang

2017-11-01

Acoustical source reconstruction is a typical inverse problem, whose minimum frequency of reconstruction hinges on the size of the array and maximum frequency depends on the spacing distance between the microphones. For the sake of enlarging the frequency of reconstruction and reducing the cost of an acquisition system, Cyclic Projection (CP), a method of sequential measurements without reference, was recently investigated (JSV,2016,372:31-49). In this paper, the Propagation based Fast Iterative Shrinkage Thresholding Algorithm (Propagation-FISTA) is introduced, which improves CP in two aspects: (1) the number of acoustic sources is no longer needed and the only making assumption is that of a "weakly sparse" eigenvalue spectrum; (2) the construction of the spatial basis is much easier and adaptive to practical scenarios of acoustical measurements benefiting from the introduction of propagation based spatial basis. The proposed Propagation-FISTA is first investigated with different simulations and experimental setups and is next illustrated with an industrial case.

Nonlinear to Linear Elastic Code Coupling in 2-D Axisymmetric Media.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Preston, Leiph

Explosions within the earth nonlinearly deform the local media, but at typical seismological observation distances, the seismic waves can be considered linear. Although nonlinear algorithms can simulate explosions in the very near field well, these codes are computationally expensive and inaccurate at propagating these signals to great distances. A linearized wave propagation code, coupled to a nonlinear code, provides an efficient mechanism to both accurately simulate the explosion itself and to propagate these signals to distant receivers. To this end we have coupled Sandia's nonlinear simulation algorithm CTH to a linearized elastic wave propagation code for 2-D axisymmetric media (axiElasti)more » by passing information from the nonlinear to the linear code via time-varying boundary conditions. In this report, we first develop the 2-D axisymmetric elastic wave equations in cylindrical coordinates. Next we show how we design the time-varying boundary conditions passing information from CTH to axiElasti, and finally we demonstrate the coupling code via a simple study of the elastic radius.« less

Toward real-time diffuse optical tomography: accelerating light propagation modeling employing parallel computing on GPU and CPU

NASA Astrophysics Data System (ADS)

Doulgerakis, Matthaios; Eggebrecht, Adam; Wojtkiewicz, Stanislaw; Culver, Joseph; Dehghani, Hamid

2017-12-01

Parameter recovery in diffuse optical tomography is a computationally expensive algorithm, especially when used for large and complex volumes, as in the case of human brain functional imaging. The modeling of light propagation, also known as the forward problem, is the computational bottleneck of the recovery algorithm, whereby the lack of a real-time solution is impeding practical and clinical applications. The objective of this work is the acceleration of the forward model, within a diffusion approximation-based finite-element modeling framework, employing parallelization to expedite the calculation of light propagation in realistic adult head models. The proposed methodology is applicable for modeling both continuous wave and frequency-domain systems with the results demonstrating a 10-fold speed increase when GPU architectures are available, while maintaining high accuracy. It is shown that, for a very high-resolution finite-element model of the adult human head with ˜600,000 nodes, consisting of heterogeneous layers, light propagation can be calculated at ˜0.25 s/excitation source.

Tumor propagation model using generalized hidden Markov model

NASA Astrophysics Data System (ADS)

Park, Sun Young; Sargent, Dustin

2017-02-01

Tumor tracking and progression analysis using medical images is a crucial task for physicians to provide accurate and efficient treatment plans, and monitor treatment response. Tumor progression is tracked by manual measurement of tumor growth performed by radiologists. Several methods have been proposed to automate these measurements with segmentation, but many current algorithms are confounded by attached organs and vessels. To address this problem, we present a new generalized tumor propagation model considering time-series prior images and local anatomical features using a Hierarchical Hidden Markov model (HMM) for tumor tracking. First, we apply the multi-atlas segmentation technique to identify organs/sub-organs using pre-labeled atlases. Second, we apply a semi-automatic direct 3D segmentation method to label the initial boundary between the lesion and neighboring structures. Third, we detect vessels in the ROI surrounding the lesion. Finally, we apply the propagation model with the labeled organs and vessels to accurately segment and measure the target lesion. The algorithm has been designed in a general way to be applicable to various body parts and modalities. In this paper, we evaluate the proposed algorithm on lung and lung nodule segmentation and tracking. We report the algorithm's performance by comparing the longest diameter and nodule volumes using the FDA lung Phantom data and a clinical dataset.

Liouvillian propagators, Riccati equation and differential Galois theory

NASA Astrophysics Data System (ADS)

Acosta-Humánez, Primitivo; Suazo, Erwin

2013-11-01

In this paper a Galoisian approach to building propagators through Riccati equations is presented. The main result corresponds to the relationship between the Galois integrability of the linear Schrödinger equation and the virtual solvability of the differential Galois group of its associated characteristic equation. As the main application of this approach we solve Ince’s differential equation through the Hamiltonian algebrization procedure and the Kovacic algorithm to find the propagator for a generalized harmonic oscillator. This propagator has applications which describe the process of degenerate parametric amplification in quantum optics and light propagation in a nonlinear anisotropic waveguide. Toy models of propagators inspired by integrable Riccati equations and integrable characteristic equations are also presented.

Visual attitude propagation for small satellites

NASA Astrophysics Data System (ADS)

Rawashdeh, Samir A.

As electronics become smaller and more capable, it has become possible to conduct meaningful and sophisticated satellite missions in a small form factor. However, the capability of small satellites and the range of possible applications are limited by the capabilities of several technologies, including attitude determination and control systems. This dissertation evaluates the use of image-based visual attitude propagation as a compliment or alternative to other attitude determination technologies that are suitable for miniature satellites. The concept lies in using miniature cameras to track image features across frames and extracting the underlying rotation. The problem of visual attitude propagation as a small satellite attitude determination system is addressed from several aspects: related work, algorithm design, hardware and performance evaluation, possible applications, and on-orbit experimentation. These areas of consideration reflect the organization of this dissertation. A "stellar gyroscope" is developed, which is a visual star-based attitude propagator that uses relative motion of stars in an imager's field of view to infer the attitude changes. The device generates spacecraft relative attitude estimates in three degrees of freedom. Algorithms to perform the star detection, correspondence, and attitude propagation are presented. The Random Sample Consensus (RANSAC) approach is applied to the correspondence problem to successfully pair stars across frames while mitigating falsepositive and false-negative star detections. This approach provides tolerance to the noise levels expected in using miniature optics and no baffling, and the noise caused by radiation dose on orbit. The hardware design and algorithms are validated using test images of the night sky. The application of the stellar gyroscope as part of a CubeSat attitude determination and control system is described. The stellar gyroscope is used to augment a MEMS gyroscope attitude propagation algorithm to minimize drift in the absence of an absolute attitude sensor. The stellar gyroscope is a technology demonstration experiment on KySat-2, a 1-Unit CubeSat being developed in Kentucky that is in line to launch with the NASA ELaNa CubeSat Launch Initiative. It has also been adopted by industry as a sensor for CubeSat Attitude Determination and Control Systems (ADCS). KEYWORDS: Small Satellites, Attitude Determination, Egomotion Estimation, RANSAC, Image Processing.

Localization Algorithm with On-line Path Loss Estimation and Node Selection

PubMed Central

Bel, Albert; Vicario, José López; Seco-Granados, Gonzalo

2011-01-01

RSS-based localization is considered a low-complexity algorithm with respect to other range techniques such as TOA or AOA. The accuracy of RSS methods depends on the suitability of the propagation models used for the actual propagation conditions. In indoor environments, in particular, it is very difficult to obtain a good propagation model. For that reason, we present a cooperative localization algorithm that dynamically estimates the path loss exponent by using RSS measurements. Since the energy consumption is a key point in sensor networks, we propose a node selection mechanism to limit the number of neighbours of a given node that are used for positioning purposes. Moreover, the selection mechanism is also useful to discard bad links that could negatively affect the performance accuracy. As a result, we derive a practical solution tailored to the strict requirements of sensor networks in terms of complexity, size and cost. We present results based on both computer simulations and real experiments with the Crossbow MICA2 motes showing that the proposed scheme offers a good trade-off in terms of position accuracy and energy efficiency. PMID:22163992

Application of affinity propagation algorithm based on manifold distance for transformer PD pattern recognition

NASA Astrophysics Data System (ADS)

Wei, B. G.; Huo, K. X.; Yao, Z. F.; Lou, J.; Li, X. Y.

2018-03-01

It is one of the difficult problems encountered in the research of condition maintenance technology of transformers to recognize partial discharge (PD) pattern. According to the main physical characteristics of PD, three models of oil-paper insulation defects were set up in laboratory to study the PD of transformers, and phase resolved partial discharge (PRPD) was constructed. By using least square method, the grey-scale images of PRPD were constructed and features of each grey-scale image were 28 box dimensions and 28 information dimensions. Affinity propagation algorithm based on manifold distance (AP-MD) for transformers PD pattern recognition was established, and the data of box dimension and information dimension were clustered based on AP-MD. Study shows that clustering result of AP-MD is better than the results of affinity propagation (AP), k-means and fuzzy c-means algorithm (FCM). By choosing different k values of k-nearest neighbor, we find clustering accuracy of AP-MD falls when k value is larger or smaller, and the optimal k value depends on sample size.

PANDA: Protein function prediction using domain architecture and affinity propagation.

PubMed

Wang, Zheng; Zhao, Chenguang; Wang, Yiheng; Sun, Zheng; Wang, Nan

2018-02-22

We developed PANDA (Propagation of Affinity and Domain Architecture) to predict protein functions in the format of Gene Ontology (GO) terms. PANDA at first executes profile-profile alignment algorithm to search against PfamA, KOG, COG, and SwissProt databases, and then launches PSI-BLAST against UniProt for homologue search. PANDA integrates a domain architecture inference algorithm based on the Bayesian statistics that calculates the probability of having a GO term. All the candidate GO terms are pooled and filtered based on Z-score. After that, the remaining GO terms are clustered using an affinity propagation algorithm based on the GO directed acyclic graph, followed by a second round of filtering on the clusters of GO terms. We benchmarked the performance of all the baseline predictors PANDA integrates and also for every pooling and filtering step of PANDA. It can be found that PANDA achieves better performances in terms of area under the curve for precision and recall compared to the baseline predictors. PANDA can be accessed from http://dna.cs.miami.edu/PANDA/ .

A stable second order method for training back propagation networks

NASA Technical Reports Server (NTRS)

Nachtsheim, Philip R.

1993-01-01

A simple method for improving the learning rate of the back-propagation algorithm is described. The basis of the method is that approximate second order corrections can be incorporated in the output units. The extended method leads to significant improvements in the convergence rate.

Parabolic equation for nonlinear acoustic wave propagation in inhomogeneous moving media

NASA Astrophysics Data System (ADS)

Aver'yanov, M. V.; Khokhlova, V. A.; Sapozhnikov, O. A.; Blanc-Benon, Ph.; Cleveland, R. O.

2006-12-01

A new parabolic equation is derived to describe the propagation of nonlinear sound waves in inhomogeneous moving media. The equation accounts for diffraction, nonlinearity, absorption, scalar inhomogeneities (density and sound speed), and vectorial inhomogeneities (flow). A numerical algorithm employed earlier to solve the KZK equation is adapted to this more general case. A two-dimensional version of the algorithm is used to investigate the propagation of nonlinear periodic waves in media with random inhomogeneities. For the case of scalar inhomogeneities, including the case of a flow parallel to the wave propagation direction, a complex acoustic field structure with multiple caustics is obtained. Inclusion of the transverse component of vectorial random inhomogeneities has little effect on the acoustic field. However, when a uniform transverse flow is present, the field structure is shifted without changing its morphology. The impact of nonlinearity is twofold: it produces strong shock waves in focal regions, while, outside the caustics, it produces higher harmonics without any shocks. When the intensity is averaged across the beam propagating through a random medium, it evolves similarly to the intensity of a plane nonlinear wave, indicating that the transverse redistribution of acoustic energy gives no considerable contribution to nonlinear absorption.

Exemplar-Based Image Inpainting Using a Modified Priority Definition.

PubMed

Deng, Liang-Jian; Huang, Ting-Zhu; Zhao, Xi-Le

2015-01-01

Exemplar-based algorithms are a popular technique for image inpainting. They mainly have two important phases: deciding the filling-in order and selecting good exemplars. Traditional exemplar-based algorithms are to search suitable patches from source regions to fill in the missing parts, but they have to face a problem: improper selection of exemplars. To improve the problem, we introduce an independent strategy through investigating the process of patches propagation in this paper. We first define a new separated priority definition to propagate geometry and then synthesize image textures, aiming to well recover image geometry and textures. In addition, an automatic algorithm is designed to estimate steps for the new separated priority definition. Comparing with some competitive approaches, the new priority definition can recover image geometry and textures well.

Exemplar-Based Image Inpainting Using a Modified Priority Definition

PubMed Central

Deng, Liang-Jian; Huang, Ting-Zhu; Zhao, Xi-Le

2015-01-01

Exemplar-based algorithms are a popular technique for image inpainting. They mainly have two important phases: deciding the filling-in order and selecting good exemplars. Traditional exemplar-based algorithms are to search suitable patches from source regions to fill in the missing parts, but they have to face a problem: improper selection of exemplars. To improve the problem, we introduce an independent strategy through investigating the process of patches propagation in this paper. We first define a new separated priority definition to propagate geometry and then synthesize image textures, aiming to well recover image geometry and textures. In addition, an automatic algorithm is designed to estimate steps for the new separated priority definition. Comparing with some competitive approaches, the new priority definition can recover image geometry and textures well. PMID:26492491

ECS: efficient communication scheduling for underwater sensor networks.

PubMed

Hong, Lu; Hong, Feng; Guo, Zhongwen; Li, Zhengbao

2011-01-01

TDMA protocols have attracted a lot of attention for underwater acoustic sensor networks (UWSNs), because of the unique characteristics of acoustic signal propagation such as great energy consumption in transmission, long propagation delay and long communication range. Previous TDMA protocols all allocated transmission time to nodes based on discrete time slots. This paper proposes an efficient continuous time scheduling TDMA protocol (ECS) for UWSNs, including the continuous time based and sender oriented conflict analysis model, the transmission moment allocation algorithm and the distributed topology maintenance algorithm. Simulation results confirm that ECS improves network throughput by 20% on average, compared to existing MAC protocols.

Automated Development of Accurate Algorithms and Efficient Codes for Computational Aeroacoustics

NASA Technical Reports Server (NTRS)

Goodrich, John W.; Dyson, Rodger W.

1999-01-01

The simulation of sound generation and propagation in three space dimensions with realistic aircraft components is a very large time dependent computation with fine details. Simulations in open domains with embedded objects require accurate and robust algorithms for propagation, for artificial inflow and outflow boundaries, and for the definition of geometrically complex objects. The development, implementation, and validation of methods for solving these demanding problems is being done to support the NASA pillar goals for reducing aircraft noise levels. Our goal is to provide algorithms which are sufficiently accurate and efficient to produce usable results rapidly enough to allow design engineers to study the effects on sound levels of design changes in propulsion systems, and in the integration of propulsion systems with airframes. There is a lack of design tools for these purposes at this time. Our technical approach to this problem combines the development of new, algorithms with the use of Mathematica and Unix utilities to automate the algorithm development, code implementation, and validation. We use explicit methods to ensure effective implementation by domain decomposition for SPMD parallel computing. There are several orders of magnitude difference in the computational efficiencies of the algorithms which we have considered. We currently have new artificial inflow and outflow boundary conditions that are stable, accurate, and unobtrusive, with implementations that match the accuracy and efficiency of the propagation methods. The artificial numerical boundary treatments have been proven to have solutions which converge to the full open domain problems, so that the error from the boundary treatments can be driven as low as is required. The purpose of this paper is to briefly present a method for developing highly accurate algorithms for computational aeroacoustics, the use of computer automation in this process, and a brief survey of the algorithms that have resulted from this work. A review of computational aeroacoustics has recently been given by Lele.

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce.

PubMed

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.

Feature and Statistical Model Development in Structural Health Monitoring

NASA Astrophysics Data System (ADS)

Kim, Inho

All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development. The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally. Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering. The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns. Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.

An extended affinity propagation clustering method based on different data density types.

PubMed

Zhao, XiuLi; Xu, WeiXiang

2015-01-01

Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

Fast-kick-off monotonically convergent algorithm for searching optimal control fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liao, Sheng-Lun; Ho, Tak-San; Rabitz, Herschel

2011-09-15

This Rapid Communication presents a fast-kick-off search algorithm for quickly finding optimal control fields in the state-to-state transition probability control problems, especially those with poorly chosen initial control fields. The algorithm is based on a recently formulated monotonically convergent scheme [T.-S. Ho and H. Rabitz, Phys. Rev. E 82, 026703 (2010)]. Specifically, the local temporal refinement of the control field at each iteration is weighted by a fractional inverse power of the instantaneous overlap of the backward-propagating wave function, associated with the target state and the control field from the previous iteration, and the forward-propagating wave function, associated with themore » initial state and the concurrently refining control field. Extensive numerical simulations for controls of vibrational transitions and ultrafast electron tunneling show that the new algorithm not only greatly improves the search efficiency but also is able to attain good monotonic convergence quality when further frequency constraints are required. The algorithm is particularly effective when the corresponding control dynamics involves a large number of energy levels or ultrashort control pulses.« less

Efficient irregular wavefront propagation algorithms on Intel® Xeon Phi™

PubMed Central

Gomes, Jeremias M.; Teodoro, George; de Melo, Alba; Kong, Jun; Kurc, Tahsin; Saltz, Joel H.

2016-01-01

We investigate the execution of the Irregular Wavefront Propagation Pattern (IWPP), a fundamental computing structure used in several image analysis operations, on the Intel® Xeon Phi™ co-processor. An efficient implementation of IWPP on the Xeon Phi is a challenging problem because of IWPP’s irregularity and the use of atomic instructions in the original IWPP algorithm to resolve race conditions. On the Xeon Phi, the use of SIMD and vectorization instructions is critical to attain high performance. However, SIMD atomic instructions are not supported. Therefore, we propose a new IWPP algorithm that can take advantage of the supported SIMD instruction set. We also evaluate an alternate storage container (priority queue) to track active elements in the wavefront in an effort to improve the parallel algorithm efficiency. The new IWPP algorithm is evaluated with Morphological Reconstruction and Imfill operations as use cases. Our results show performance improvements of up to 5.63× on top of the original IWPP due to vectorization. Moreover, the new IWPP achieves speedups of 45.7× and 1.62×, respectively, as compared to efficient CPU and GPU implementations. PMID:27298591

Efficient irregular wavefront propagation algorithms on Intel® Xeon Phi™.

PubMed

Gomes, Jeremias M; Teodoro, George; de Melo, Alba; Kong, Jun; Kurc, Tahsin; Saltz, Joel H

2015-10-01

We investigate the execution of the Irregular Wavefront Propagation Pattern (IWPP), a fundamental computing structure used in several image analysis operations, on the Intel ® Xeon Phi ™ co-processor. An efficient implementation of IWPP on the Xeon Phi is a challenging problem because of IWPP's irregularity and the use of atomic instructions in the original IWPP algorithm to resolve race conditions. On the Xeon Phi, the use of SIMD and vectorization instructions is critical to attain high performance. However, SIMD atomic instructions are not supported. Therefore, we propose a new IWPP algorithm that can take advantage of the supported SIMD instruction set. We also evaluate an alternate storage container (priority queue) to track active elements in the wavefront in an effort to improve the parallel algorithm efficiency. The new IWPP algorithm is evaluated with Morphological Reconstruction and Imfill operations as use cases. Our results show performance improvements of up to 5.63 × on top of the original IWPP due to vectorization. Moreover, the new IWPP achieves speedups of 45.7 × and 1.62 × , respectively, as compared to efficient CPU and GPU implementations.

Combined distributed and concentrated transducer network for failure indication

NASA Astrophysics Data System (ADS)

Ostachowicz, Wieslaw; Wandowski, Tomasz; Malinowski, Pawel

2010-03-01

In this paper algorithm for discontinuities localisation in thin panels made of aluminium alloy is presented. Mentioned algorithm uses Lamb wave propagation methods for discontinuities localisation. Elastic waves were generated and received using piezoelectric transducers. They were arranged in concentrated arrays distributed on the specimen surface. In this way almost whole specimen could be monitored using this combined distributed-concentrated transducer network. Excited elastic waves propagate and reflect from panel boundaries and discontinuities existing in the panel. Wave reflection were registered through the piezoelectric transducers and used in signal processing algorithm. Proposed processing algorithm consists of two parts: signal filtering and extraction of obstacles location. The first part was used in order to enhance signals by removing noise from them. Second part allowed to extract features connected with wave reflections from discontinuities. Extracted features damage influence maps were a basis to create damage influence maps. Damage maps indicated intensity of elastic wave reflections which corresponds to obstacles coordinates. Described signal processing algorithms were implemented in the MATLAB environment. It should be underlined that in this work results based only on experimental signals were presented.

A Mixed Approach to Similarity Metric Selection in Affinity Propagation-Based WiFi Fingerprinting Indoor Positioning.

PubMed

Caso, Giuseppe; de Nardis, Luca; di Benedetto, Maria-Gabriella

2015-10-30

The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms.

A Mixed Approach to Similarity Metric Selection in Affinity Propagation-Based WiFi Fingerprinting Indoor Positioning

PubMed Central

Caso, Giuseppe; de Nardis, Luca; di Benedetto, Maria-Gabriella

2015-01-01

The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms. PMID:26528984

Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines

NASA Astrophysics Data System (ADS)

Qayyum, Abdul; Saad, Naufal M.; Kamel, Nidal; Malik, Aamir Saeed

2018-01-01

The monitoring of vegetation near high-voltage transmission power lines and poles is tedious. Blackouts present a huge challenge to power distribution companies and often occur due to tree growth in hilly and rural areas. There are numerous methods of monitoring hazardous overgrowth that are expensive and time-consuming. Accurate estimation of tree and vegetation heights near power poles can prevent the disruption of power transmission in vulnerable zones. This paper presents a cost-effective approach based on a convolutional neural network (CNN) algorithm to compute the height (depth maps) of objects proximal to power poles and transmission lines. The proposed CNN extracts and classifies features by employing convolutional pooling inputs to fully connected data layers that capture prominent features from stereo image patches. Unmanned aerial vehicle or satellite stereo image datasets can thus provide a feasible and cost-effective approach that identifies threat levels based on height and distance estimations of hazardous vegetation and other objects. Results were compared with extant disparity map estimation techniques, such as graph cut, dynamic programming, belief propagation, and area-based methods. The proposed method achieved an accuracy rate of 90%.

An approach to the development of numerical algorithms for first order linear hyperbolic systems in multiple space dimensions: The constant coefficient case

NASA Technical Reports Server (NTRS)

Goodrich, John W.

1995-01-01

Two methods for developing high order single step explicit algorithms on symmetric stencils with data on only one time level are presented. Examples are given for the convection and linearized Euler equations with up to the eighth order accuracy in both space and time in one space dimension, and up to the sixth in two space dimensions. The method of characteristics is generalized to nondiagonalizable hyperbolic systems by using exact local polynominal solutions of the system, and the resulting exact propagator methods automatically incorporate the correct multidimensional wave propagation dynamics. Multivariate Taylor or Cauchy-Kowaleskaya expansions are also used to develop algorithms. Both of these methods can be applied to obtain algorithms of arbitrarily high order for hyperbolic systems in multiple space dimensions. Cross derivatives are included in the local approximations used to develop the algorithms in this paper in order to obtain high order accuracy, and improved isotropy and stability. Efficiency in meeting global error bounds is an important criterion for evaluating algorithms, and the higher order algorithms are shown to be up to several orders of magnitude more efficient even though they are more complex. Stable high order boundary conditions for the linearized Euler equations are developed in one space dimension, and demonstrated in two space dimensions.

Quantum and electromagnetic propagation with the conjugate symmetric Lanczos method.

PubMed

Acevedo, Ramiro; Lombardini, Richard; Turner, Matthew A; Kinsey, James L; Johnson, Bruce R

2008-02-14

The conjugate symmetric Lanczos (CSL) method is introduced for the solution of the time-dependent Schrodinger equation. This remarkably simple and efficient time-domain algorithm is a low-order polynomial expansion of the quantum propagator for time-independent Hamiltonians and derives from the time-reversal symmetry of the Schrodinger equation. The CSL algorithm gives forward solutions by simply complex conjugating backward polynomial expansion coefficients. Interestingly, the expansion coefficients are the same for each uniform time step, a fact that is only spoiled by basis incompleteness and finite precision. This is true for the Krylov basis and, with further investigation, is also found to be true for the Lanczos basis, important for efficient orthogonal projection-based algorithms. The CSL method errors roughly track those of the short iterative Lanczos method while requiring fewer matrix-vector products than the Chebyshev method. With the CSL method, only a few vectors need to be stored at a time, there is no need to estimate the Hamiltonian spectral range, and only matrix-vector and vector-vector products are required. Applications using localized wavelet bases are made to harmonic oscillator and anharmonic Morse oscillator systems as well as electrodynamic pulse propagation using the Hamiltonian form of Maxwell's equations. For gold with a Drude dielectric function, the latter is non-Hermitian, requiring consideration of corrections to the CSL algorithm.

Forward Sound Propagation Around Seamounts: Application of Acoustic Models to the Kermit-Roosevelt and Elvis Seamounts

DTIC Science & Technology

2009-06-01

large number of range steps. Brooke et al. [73] developed a Canadian Parabolic Equation model ( PECan ). In the model, the split-step Padé algorithm... PECan : A Canadian parabolic equation model for underwater sound propagation. J. Computational Acoustics, 9(1):69-100, 2001 [74] Michael D

Toward real-time diffuse optical tomography: accelerating light propagation modeling employing parallel computing on GPU and CPU.

PubMed

Doulgerakis, Matthaios; Eggebrecht, Adam; Wojtkiewicz, Stanislaw; Culver, Joseph; Dehghani, Hamid

2017-12-01

Parameter recovery in diffuse optical tomography is a computationally expensive algorithm, especially when used for large and complex volumes, as in the case of human brain functional imaging. The modeling of light propagation, also known as the forward problem, is the computational bottleneck of the recovery algorithm, whereby the lack of a real-time solution is impeding practical and clinical applications. The objective of this work is the acceleration of the forward model, within a diffusion approximation-based finite-element modeling framework, employing parallelization to expedite the calculation of light propagation in realistic adult head models. The proposed methodology is applicable for modeling both continuous wave and frequency-domain systems with the results demonstrating a 10-fold speed increase when GPU architectures are available, while maintaining high accuracy. It is shown that, for a very high-resolution finite-element model of the adult human head with ∼600,000 nodes, consisting of heterogeneous layers, light propagation can be calculated at ∼0.25  s/excitation source. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Leaders and followers: quantifying consistency in spatio-temporal propagation patterns

NASA Astrophysics Data System (ADS)

Kreuz, Thomas; Satuvuori, Eero; Pofahl, Martin; Mulansky, Mario

2017-04-01

Repetitive spatio-temporal propagation patterns are encountered in fields as wide-ranging as climatology, social communication and network science. In neuroscience, perfectly consistent repetitions of the same global propagation pattern are called a synfire pattern. For any recording of sequences of discrete events (in neuroscience terminology: sets of spike trains) the questions arise how closely it resembles such a synfire pattern and which are the spike trains that lead/follow. Here we address these questions and introduce an algorithm built on two new indicators, termed SPIKE-order and spike train order, that define the synfire indicator value, which allows to sort multiple spike trains from leader to follower and to quantify the consistency of the temporal leader-follower relationships for both the original and the optimized sorting. We demonstrate our new approach using artificially generated datasets before we apply it to analyze the consistency of propagation patterns in two real datasets from neuroscience (giant depolarized potentials in mice slices) and climatology (El Niño sea surface temperature recordings). The new algorithm is distinguished by conceptual and practical simplicity, low computational cost, as well as flexibility and universality.

Information recovery in propagation-based imaging with decoherence effects

NASA Astrophysics Data System (ADS)

Froese, Heinrich; Lötgering, Lars; Wilhein, Thomas

2017-05-01

During the past decades the optical imaging community witnessed a rapid emergence of novel imaging modalities such as coherent diffraction imaging (CDI), propagation-based imaging and ptychography. These methods have been demonstrated to recover complex-valued scalar wave fields from redundant data without the need for refractive or diffractive optical elements. This renders these techniques suitable for imaging experiments with EUV and x-ray radiation, where the use of lenses is complicated by fabrication, photon efficiency and cost. However, decoherence effects can have detrimental effects on the reconstruction quality of the numerical algorithms involved. Here we demonstrate propagation-based optical phase retrieval from multiple near-field intensities with decoherence effects such as partially coherent illumination, detector point spread, binning and position uncertainties of the detector. Methods for overcoming these systematic experimental errors - based on the decomposition of the data into mutually incoherent modes - are proposed and numerically tested. We believe that the results presented here open up novel algorithmic methods to accelerate detector readout rates and enable subpixel resolution in propagation-based phase retrieval. Further the techniques are straightforward to be extended to methods such as CDI, ptychography and holography.

Algorithm for lens calculations in the geometrized Maxwell theory

NASA Astrophysics Data System (ADS)

Kulyabov, Dmitry S.; Korolkova, Anna V.; Sevastianov, Leonid A.; Gevorkyan, Migran N.; Demidova, Anastasia V.

2018-04-01

Nowadays the geometric approach in optics is often used to find out media parameters based on propagation paths of the rays because in this case it is a direct problem. However inverse problem in the framework of geometrized optics is usually not given attention. The aim of this work is to demonstrate the work of the proposed the algorithm in the framework of geometrized approach to optics for solving the problem of finding the propagation path of the electromagnetic radiation depending on environmental parameters. The methods of differential geometry are used for effective metrics construction for isotropic and anisotropic media. For effective metric space ray trajectories are obtained in the form of geodesic curves. The introduced algorithm is applied to well-known objects, Maxwell and Luneburg lenses. The similarity of results obtained by classical and geometric approach is demonstrated.

Assessing performance of flaw characterization methods through uncertainty propagation

NASA Astrophysics Data System (ADS)

Miorelli, R.; Le Bourdais, F.; Artusi, X.

2018-04-01

In this work, we assess the inversion performance in terms of crack characterization and localization based on synthetic signals associated to ultrasonic and eddy current physics. More precisely, two different standard iterative inversion algorithms are used to minimize the discrepancy between measurements (i.e., the tested data) and simulations. Furthermore, in order to speed up the computational time and get rid of the computational burden often associated to iterative inversion algorithms, we replace the standard forward solver by a suitable metamodel fit on a database built offline. In a second step, we assess the inversion performance by adding uncertainties on a subset of the database parameters and then, through the metamodel, we propagate these uncertainties within the inversion procedure. The fast propagation of uncertainties enables efficiently evaluating the impact due to the lack of knowledge on some parameters employed to describe the inspection scenarios, which is a situation commonly encountered in the industrial NDE context.

Evaluation of a parallel implementation of the learning portion of the backward error propagation neural network: experiments in artifact identification.

PubMed Central

Sittig, D. F.; Orr, J. A.

1991-01-01

Various methods have been proposed in an attempt to solve problems in artifact and/or alarm identification including expert systems, statistical signal processing techniques, and artificial neural networks (ANN). ANNs consist of a large number of simple processing units connected by weighted links. To develop truly robust ANNs, investigators are required to train their networks on huge training data sets, requiring enormous computing power. We implemented a parallel version of the backward error propagation neural network training algorithm in the widely portable parallel programming language C-Linda. A maximum speedup of 4.06 was obtained with six processors. This speedup represents a reduction in total run-time from approximately 6.4 hours to 1.5 hours. We conclude that use of the master-worker model of parallel computation is an excellent method for obtaining speedups in the backward error propagation neural network training algorithm. PMID:1807607

A space-time discretization procedure for wave propagation problems

NASA Technical Reports Server (NTRS)

Davis, Sanford

1989-01-01

Higher order compact algorithms are developed for the numerical simulation of wave propagation by using the concept of a discrete dispersion relation. The dispersion relation is the imprint of any linear operator in space-time. The discrete dispersion relation is derived from the continuous dispersion relation by examining the process by which locally plane waves propagate through a chosen grid. The exponential structure of the discrete dispersion relation suggests an efficient splitting of convective and diffusive terms for dissipative waves. Fourth- and eighth-order convection schemes are examined that involve only three or five spatial grid points. These algorithms are subject to the same restrictions that govern the use of dispersion relations in the constructions of asymptotic expansions to nonlinear evolution equations. A new eighth-order scheme is developed that is exact for Courant numbers of 1, 2, 3, and 4. Examples are given of a pulse and step wave with a small amount of physical diffusion.

Individual muscle segmentation in MR images: A 3D propagation through 2D non-linear registration approaches.

PubMed

Ogier, Augustin; Sdika, Michael; Foure, Alexandre; Le Troter, Arnaud; Bendahan, David

2017-07-01

Manual and automated segmentation of individual muscles in magnetic resonance images have been recognized as challenging given the high variability of shapes between muscles and subjects and the discontinuity or lack of visible boundaries between muscles. In the present study, we proposed an original algorithm allowing a semi-automatic transversal propagation of manually-drawn masks. Our strategy was based on several ascending and descending non-linear registration approaches which is similar to the estimation of a Lagrangian trajectory applied to manual masks. Using several manually-segmented slices, we have evaluated our algorithm on the four muscles of the quadriceps femoris group. We mainly showed that our 3D propagated segmentation was very accurate with an averaged Dice similarity coefficient value higher than 0.91 for the minimal manual input of only two manually-segmented slices.

Research on particle swarm optimization algorithm based on optimal movement probability

NASA Astrophysics Data System (ADS)

Ma, Jianhong; Zhang, Han; He, Baofeng

2017-01-01

The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.

Elastic modelling in tilted transversely isotropic media with convolutional perfectly matched layer boundary conditions

NASA Astrophysics Data System (ADS)

Han, Byeongho; Seol, Soon Jee; Byun, Joongmoo

2012-04-01

To simulate wave propagation in a tilted transversely isotropic (TTI) medium with a tilting symmetry-axis of anisotropy, we develop a 2D elastic forward modelling algorithm. In this algorithm, we use the staggered-grid finite-difference method which has fourth-order accuracy in space and second-order accuracy in time. Since velocity-stress formulations are defined for staggered grids, we include auxiliary grid points in the z-direction to meet the free surface boundary conditions for shear stress. Through comparisons of displacements obtained from our algorithm, not only with analytical solutions but also with finite element solutions, we are able to validate that the free surface conditions operate appropriately and elastic waves propagate correctly. In order to handle the artificial boundary reflections efficiently, we also implement convolutional perfectly matched layer (CPML) absorbing boundaries in our algorithm. The CPML sufficiently attenuates energy at the grazing incidence by modifying the damping profile of the PML boundary. Numerical experiments indicate that the algorithm accurately expresses elastic wave propagation in the TTI medium. At the free surface, the numerical results show good agreement with analytical solutions not only for body waves but also for the Rayleigh wave which has strong amplitude along the surface. In addition, we demonstrate the efficiency of CPML for a homogeneous TI medium and a dipping layered model. Only using 10 grid points to the CPML regions, the artificial reflections are successfully suppressed and the energy of the boundary reflection back into the effective modelling area is significantly decayed.

SU-F-J-88: Comparison of Two Deformable Image Registration Algorithms for CT-To-CT Contour Propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gopal, A; Xu, H; Chen, S

Purpose: To compare the contour propagation accuracy of two deformable image registration (DIR) algorithms in the Raystation treatment planning system – the “Hybrid” algorithm based on image intensities and anatomical information; and the “Biomechanical” algorithm based on linear anatomical elasticity and finite element modeling. Methods: Both DIR algorithms were used for CT-to-CT deformation for 20 lung radiation therapy patients that underwent treatment plan revisions. Deformation accuracy was evaluated using landmark tracking to measure the target registration error (TRE) and inverse consistency error (ICE). The deformed contours were also evaluated against physician drawn contours using Dice similarity coefficients (DSC). Contour propagationmore » was qualitatively assessed using a visual quality score assigned by physicians, and a refinement quality score (0 0.9 for lungs, > 0.85 for heart, > 0.8 for liver) and similar qualitative assessments (VQS < 0.35, RQS > 0.75 for lungs). When anatomical structures were used to control the deformation, the DSC improved more significantly for the biomechanical DIR compared to the hybrid DIR, while the VQS and RQS improved only for the controlling structures. However, while the inclusion of controlling structures improved the TRE for the hybrid DIR, it increased the TRE for the biomechanical DIR. Conclusion: The hybrid DIR was found to perform slightly better than the biomechanical DIR based on lower TRE while the DSC, VQS, and RQS studies yielded comparable results for both. The use of controlling structures showed considerable improvement in the hybrid DIR results and is recommended for clinical use in contour propagation.« less

ECS: Efficient Communication Scheduling for Underwater Sensor Networks

PubMed Central

Hong, Lu; Hong, Feng; Guo, Zhongwen; Li, Zhengbao

2011-01-01

TDMA protocols have attracted a lot of attention for underwater acoustic sensor networks (UWSNs), because of the unique characteristics of acoustic signal propagation such as great energy consumption in transmission, long propagation delay and long communication range. Previous TDMA protocols all allocated transmission time to nodes based on discrete time slots. This paper proposes an efficient continuous time scheduling TDMA protocol (ECS) for UWSNs, including the continuous time based and sender oriented conflict analysis model, the transmission moment allocation algorithm and the distributed topology maintenance algorithm. Simulation results confirm that ECS improves network throughput by 20% on average, compared to existing MAC protocols. PMID:22163775

Multiple Time-Step Dual-Hamiltonian Hybrid Molecular Dynamics — Monte Carlo Canonical Propagation Algorithm

PubMed Central

Weare, Jonathan; Dinner, Aaron R.; Roux, Benoît

2016-01-01

A multiple time-step integrator based on a dual Hamiltonian and a hybrid method combining molecular dynamics (MD) and Monte Carlo (MC) is proposed to sample systems in the canonical ensemble. The Dual Hamiltonian Multiple Time-Step (DHMTS) algorithm is based on two similar Hamiltonians: a computationally expensive one that serves as a reference and a computationally inexpensive one to which the workload is shifted. The central assumption is that the difference between the two Hamiltonians is slowly varying. Earlier work has shown that such dual Hamiltonian multiple time-step schemes effectively precondition nonlinear differential equations for dynamics by reformulating them into a recursive root finding problem that can be solved by propagating a correction term through an internal loop, analogous to RESPA. Of special interest in the present context, a hybrid MD-MC version of the DHMTS algorithm is introduced to enforce detailed balance via a Metropolis acceptance criterion and ensure consistency with the Boltzmann distribution. The Metropolis criterion suppresses the discretization errors normally associated with the propagation according to the computationally inexpensive Hamiltonian, treating the discretization error as an external work. Illustrative tests are carried out to demonstrate the effectiveness of the method. PMID:26918826

Direct phase projection and transcranial focusing of ultrasound for brain therapy.

PubMed

Pinton, Gianmarco F; Aubry, Jean-Francois; Tanter, Mickaël

2012-06-01

Ultrasound can be used to noninvasively treat the human brain with hyperthermia by focusing through the skull. To obtain an accurate focus, especially at high frequencies (>500 kHz), the phase of the transmitted wave must be modified to correct the aberrations introduced by the patient's individual skull morphology. Currently, three-dimensional finite-difference time-domain simulations are used to model a point source at the target. The outward-propagating wave crosses the measured representation of the human skull and is recorded at the therapy array transducer locations. The signal is then time reversed and experimentally transmitted back to its origin. These simulations are resource intensive and add a significant delay to treatment planning. Ray propagation is computationally efficient because it neglects diffraction and only describes two propagation parameters: the wave's direction and the phase. We propose a minimal method that is based only on the phase. The phase information is projected from the external skull surface to the array locations. This replaces computationally expensive finite-difference computations with an almost instantaneous direct phase projection calculation. For the five human skull samples considered, the phase distribution outside of the skull is shown to vary by less than λ/20 as it propagates over a 5 cm distance and the validity of phase projection is established over these propagation distances. The phase aberration introduced by the skull is characterized and is shown to have a good correspondence with skull morphology. The shape of this aberration is shown to have little variation with propagation distance. The focusing quality with the proposed phase-projection algorithm is shown to be indistinguishable from the gold-standard full finite-difference simulation. In conclusion, a spherical wave that is aberrated by the skull has a phase propagation that can be accurately described as radial, even after it has been distorted. By combining finite-difference simulations with a phase-projection algorithm, the time required for treatment planning is significantly reduced. The correlation length of the phase is used to validate the algorithm and it can also be used to provide guiding parameters for clinical array transducer design in terms of transducer spacing and phase error.

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce

PubMed Central

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987

Updating Parameters for Volcanic Hazard Assessment Using Multi-parameter Monitoring Data Streams And Bayesian Belief Networks

NASA Astrophysics Data System (ADS)

Odbert, Henry; Aspinall, Willy

2014-05-01

Evidence-based hazard assessment at volcanoes assimilates knowledge about the physical processes of hazardous phenomena and observations that indicate the current state of a volcano. Incorporating both these lines of evidence can inform our belief about the likelihood (probability) and consequences (impact) of possible hazardous scenarios, forming a basis for formal quantitative hazard assessment. However, such evidence is often uncertain, indirect or incomplete. Approaches to volcano monitoring have advanced substantially in recent decades, increasing the variety and resolution of multi-parameter timeseries data recorded at volcanoes. Interpreting these multiple strands of parallel, partial evidence thus becomes increasingly complex. In practice, interpreting many timeseries requires an individual to be familiar with the idiosyncrasies of the volcano, monitoring techniques, configuration of recording instruments, observations from other datasets, and so on. In making such interpretations, an individual must consider how different volcanic processes may manifest as measureable observations, and then infer from the available data what can or cannot be deduced about those processes. We examine how parts of this process may be synthesised algorithmically using Bayesian inference. Bayesian Belief Networks (BBNs) use probability theory to treat and evaluate uncertainties in a rational and auditable scientific manner, but only to the extent warranted by the strength of the available evidence. The concept is a suitable framework for marshalling multiple strands of evidence (e.g. observations, model results and interpretations) and their associated uncertainties in a methodical manner. BBNs are usually implemented in graphical form and could be developed as a tool for near real-time, ongoing use in a volcano observatory, for example. We explore the application of BBNs in analysing volcanic data from the long-lived eruption at Soufriere Hills Volcano, Montserrat. We discuss the uncertainty of inferences, and how our method provides a route to formal propagation of uncertainties in hazard models. Such approaches provide an attractive route to developing an interface between volcano monitoring analyses and probabilistic hazard scenario analysis. We discuss the use of BBNs in hazard analysis as a tractable and traceable tool for fast, rational assimilation of complex, multi-parameter data sets in the context of timely volcanic crisis decision support.

Combining Volcano Monitoring Timeseries Analyses with Bayesian Belief Networks to Update Hazard Forecast Estimates

NASA Astrophysics Data System (ADS)

Odbert, Henry; Hincks, Thea; Aspinall, Willy

2015-04-01

Volcanic hazard assessments must combine information about the physical processes of hazardous phenomena with observations that indicate the current state of a volcano. Incorporating both these lines of evidence can inform our belief about the likelihood (probability) and consequences (impact) of possible hazardous scenarios, forming a basis for formal quantitative hazard assessment. However, such evidence is often uncertain, indirect or incomplete. Approaches to volcano monitoring have advanced substantially in recent decades, increasing the variety and resolution of multi-parameter timeseries data recorded at volcanoes. Interpreting these multiple strands of parallel, partial evidence thus becomes increasingly complex. In practice, interpreting many timeseries requires an individual to be familiar with the idiosyncrasies of the volcano, monitoring techniques, configuration of recording instruments, observations from other datasets, and so on. In making such interpretations, an individual must consider how different volcanic processes may manifest as measureable observations, and then infer from the available data what can or cannot be deduced about those processes. We examine how parts of this process may be synthesised algorithmically using Bayesian inference. Bayesian Belief Networks (BBNs) use probability theory to treat and evaluate uncertainties in a rational and auditable scientific manner, but only to the extent warranted by the strength of the available evidence. The concept is a suitable framework for marshalling multiple strands of evidence (e.g. observations, model results and interpretations) and their associated uncertainties in a methodical manner. BBNs are usually implemented in graphical form and could be developed as a tool for near real-time, ongoing use in a volcano observatory, for example. We explore the application of BBNs in analysing volcanic data from the long-lived eruption at Soufriere Hills Volcano, Montserrat. We show how our method provides a route to formal propagation of uncertainties in hazard models. Such approaches provide an attractive route to developing an interface between volcano monitoring analyses and probabilistic hazard scenario analysis. We discuss the use of BBNs in hazard analysis as a tractable and traceable tool for fast, rational assimilation of complex, multi-parameter data sets in the context of timely volcanic crisis decision support.

Weighted compactness function based label propagation algorithm for community detection

NASA Astrophysics Data System (ADS)

Zhang, Weitong; Zhang, Rui; Shang, Ronghua; Jiao, Licheng

2018-02-01

Community detection in complex networks, is to detect the community structure with the internal structure relatively compact and the external structure relatively sparse, according to the topological relationship among nodes in the network. In this paper, we propose a compactness function which combines the weight of nodes, and use it as the objective function to carry out the node label propagation. Firstly, according to the node degree, we find the sets of core nodes which have great influence on the network. The more the connections between the core nodes and the other nodes are, the larger the amount of the information these kernel nodes receive and transform. Then, according to the similarity of the nodes between the core nodes sets and the nodes degree, we assign weights to the nodes in the network. So the label of the nodes with great influence will be the priority in the label propagation process, which effectively improves the accuracy of the label propagation. The compactness function between nodes and communities in this paper is based on the nodes influence. It combines the connections between nodes and communities with the degree of the node belongs to its neighbor communities based on calculating the node weight. The function effectively uses the information of nodes and connections in the network. The experimental results show that the proposed algorithm can achieve good results in the artificial network and large-scale real networks compared with the 8 contrast algorithms.

Direct Position Determination of Unknown Signals in the Presence of Multipath Propagation

PubMed Central

Yu, Hongyi

2018-01-01

A novel geolocation architecture, termed “Multiple Transponders and Multiple Receivers for Multiple Emitters Positioning System (MTRE)” is proposed in this paper. Existing Direct Position Determination (DPD) methods take advantage of a rather simple channel assumption (line of sight channels with complex path attenuations) and a simplified MUltiple SIgnal Classification (MUSIC) algorithm cost function to avoid the high dimension searching. We point out that the simplified assumption and cost function reduce the positioning accuracy because of the singularity of the array manifold in a multi-path environment. We present a DPD model for unknown signals in the presence of Multi-path Propagation (MP-DPD) in this paper. MP-DPD adds non-negative real path attenuation constraints to avoid the mistake caused by the singularity of the array manifold. The Multi-path Propagation MUSIC (MP-MUSIC) method and the Active Set Algorithm (ASA) are designed to reduce the dimension of searching. A Multi-path Propagation Maximum Likelihood (MP-ML) method is proposed in addition to overcome the limitation of MP-MUSIC in the sense of a time-sensitive application. An iterative algorithm and an approach of initial value setting are given to make the MP-ML time consumption acceptable. Numerical results validate the performances improvement of MP-MUSIC and MP-ML. A closed form of the Cramér–Rao Lower Bound (CRLB) is derived as a benchmark to evaluate the performances of MP-MUSIC and MP-ML. PMID:29562601

Direct Position Determination of Unknown Signals in the Presence of Multipath Propagation.

PubMed

Du, Jianping; Wang, Ding; Yu, Wanting; Yu, Hongyi

2018-03-17

A novel geolocation architecture, termed "Multiple Transponders and Multiple Receivers for Multiple Emitters Positioning System (MTRE)" is proposed in this paper. Existing Direct Position Determination (DPD) methods take advantage of a rather simple channel assumption (line of sight channels with complex path attenuations) and a simplified MUltiple SIgnal Classification (MUSIC) algorithm cost function to avoid the high dimension searching. We point out that the simplified assumption and cost function reduce the positioning accuracy because of the singularity of the array manifold in a multi-path environment. We present a DPD model for unknown signals in the presence of Multi-path Propagation (MP-DPD) in this paper. MP-DPD adds non-negative real path attenuation constraints to avoid the mistake caused by the singularity of the array manifold. The Multi-path Propagation MUSIC (MP-MUSIC) method and the Active Set Algorithm (ASA) are designed to reduce the dimension of searching. A Multi-path Propagation Maximum Likelihood (MP-ML) method is proposed in addition to overcome the limitation of MP-MUSIC in the sense of a time-sensitive application. An iterative algorithm and an approach of initial value setting are given to make the MP-ML time consumption acceptable. Numerical results validate the performances improvement of MP-MUSIC and MP-ML. A closed form of the Cramér-Rao Lower Bound (CRLB) is derived as a benchmark to evaluate the performances of MP-MUSIC and MP-ML.

Statistical, Graphical, and Learning Methods for Sensing, Surveillance, and Navigation Systems

DTIC Science & Technology

2016-06-28

harsh propagation environments. Conventional filtering techniques fail to provide satisfactory performance in many important nonlinear or non...Gaussian scenarios. In addition, there is a lack of a unified methodology for the design and analysis of different filtering techniques. To address...these problems, we have proposed a new filtering methodology called belief condensation (BC) DISTRIBUTION A: Distribution approved for public release

Control Theoretic Modeling for Uncertain Cultural Attitudes and Unknown Adversarial Intent

DTIC Science & Technology

2009-02-01

Constructive computational tools. 15. SUBJECT TERMS social learning, social networks , multiagent systems, game theory 16. SECURITY CLASSIFICATION OF: a...over- reactionary behaviors; 3) analysis of rational social learning in networks : analysis of belief propagation in social networks in various...general methodology as a predictive device for social network formation and for communication network formation with constraints on the lengths of

Airborne Wireless Communication Modeling and Analysis with MATLAB

DTIC Science & Technology

2014-03-27

research develops a physical layer model that combines antenna modeling using computational electromagnetics and the two-ray propagation model to...predict the received signal strength. The antenna is modeled with triangular patches and analyzed by extending the antenna modeling algorithm by Sergey...7  2.7. Propagation Modeling : Statistical Models ............................................................8  2.8. Antenna Modeling

Exponential propagators for the Schrödinger equation with a time-dependent potential.

PubMed

Bader, Philipp; Blanes, Sergio; Kopylov, Nikita

2018-06-28

We consider the numerical integration of the Schrödinger equation with a time-dependent Hamiltonian given as the sum of the kinetic energy and a time-dependent potential. Commutator-free (CF) propagators are exponential propagators that have shown to be highly efficient for general time-dependent Hamiltonians. We propose new CF propagators that are tailored for Hamiltonians of the said structure, showing a considerably improved performance. We obtain new fourth- and sixth-order CF propagators as well as a novel sixth-order propagator that incorporates a double commutator that only depends on coordinates, so this term can be considered as cost-free. The algorithms require the computation of the action of exponentials on a vector similar to the well-known exponential midpoint propagator, and this is carried out using the Lanczos method. We illustrate the performance of the new methods on several numerical examples.

Analysis of Accuracy and Epoch on Back-propagation BFGS Quasi-Newton

NASA Astrophysics Data System (ADS)

Silaban, Herlan; Zarlis, Muhammad; Sawaluddin

2017-12-01

Back-propagation is one of the learning algorithms on artificial neural networks that have been widely used to solve various problems, such as pattern recognition, prediction and classification. The Back-propagation architecture will affect the outcome of learning processed. BFGS Quasi-Newton is one of the functions that can be used to change the weight of back-propagation. This research tested some back-propagation architectures using classical back-propagation and back-propagation with BFGS. There are 7 architectures that have been tested on glass dataset with various numbers of neurons, 6 architectures with 1 hidden layer and 1 architecture with 2 hidden layers. BP with BFGS improves the convergence of the learning process. The average improvement convergence is 98.34%. BP with BFGS is more optimal on architectures with smaller number of neurons with decreased epoch number is 94.37% with the increase of accuracy about 0.5%.

Deep greedy learning under thermal variability in full diurnal cycles

NASA Astrophysics Data System (ADS)

Rauss, Patrick; Rosario, Dalton

2017-08-01

We study the generalization and scalability behavior of a deep belief network (DBN) applied to a challenging long-wave infrared hyperspectral dataset, consisting of radiance from several manmade and natural materials within a fixed site located 500 m from an observation tower. The collections cover multiple full diurnal cycles and include different atmospheric conditions. Using complementary priors, a DBN uses a greedy algorithm that can learn deep, directed belief networks one layer at a time and has two layers form to provide undirected associative memory. The greedy algorithm initializes a slower learning procedure, which fine-tunes the weights, using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of spectral data and their labels, despite significant data variability between and within classes due to environmental and temperature variation occurring within and between full diurnal cycles. We argue, however, that more questions than answers are raised regarding the generalization capacity of these deep nets through experiments aimed at investigating their training and augmented learning behavior.

Comparisons of hybrid radiosity-diffusion model and diffusion equation for bioluminescence tomography in cavity cancer detection

NASA Astrophysics Data System (ADS)

Chen, Xueli; Yang, Defu; Qu, Xiaochao; Hu, Hao; Liang, Jimin; Gao, Xinbo; Tian, Jie

2012-06-01

Bioluminescence tomography (BLT) has been successfully applied to the detection and therapeutic evaluation of solid cancers. However, the existing BLT reconstruction algorithms are not accurate enough for cavity cancer detection because of neglecting the void problem. Motivated by the ability of the hybrid radiosity-diffusion model (HRDM) in describing the light propagation in cavity organs, an HRDM-based BLT reconstruction algorithm was provided for the specific problem of cavity cancer detection. HRDM has been applied to optical tomography but is limited to simple and regular geometries because of the complexity in coupling the boundary between the scattering and void region. In the provided algorithm, HRDM was first applied to three-dimensional complicated and irregular geometries and then employed as the forward light transport model to describe the bioluminescent light propagation in tissues. Combining HRDM with the sparse reconstruction strategy, the cavity cancer cells labeled with bioluminescent probes can be more accurately reconstructed. Compared with the diffusion equation based reconstruction algorithm, the essentiality and superiority of the HRDM-based algorithm were demonstrated with simulation, phantom and animal studies. An in vivo gastric cancer-bearing nude mouse experiment was conducted, whose results revealed the ability and feasibility of the HRDM-based algorithm in the biomedical application of gastric cancer detection.

Comparisons of hybrid radiosity-diffusion model and diffusion equation for bioluminescence tomography in cavity cancer detection.

PubMed

Chen, Xueli; Yang, Defu; Qu, Xiaochao; Hu, Hao; Liang, Jimin; Gao, Xinbo; Tian, Jie

2012-06-01

Bioluminescence tomography (BLT) has been successfully applied to the detection and therapeutic evaluation of solid cancers. However, the existing BLT reconstruction algorithms are not accurate enough for cavity cancer detection because of neglecting the void problem. Motivated by the ability of the hybrid radiosity-diffusion model (HRDM) in describing the light propagation in cavity organs, an HRDM-based BLT reconstruction algorithm was provided for the specific problem of cavity cancer detection. HRDM has been applied to optical tomography but is limited to simple and regular geometries because of the complexity in coupling the boundary between the scattering and void region. In the provided algorithm, HRDM was first applied to three-dimensional complicated and irregular geometries and then employed as the forward light transport model to describe the bioluminescent light propagation in tissues. Combining HRDM with the sparse reconstruction strategy, the cavity cancer cells labeled with bioluminescent probes can be more accurately reconstructed. Compared with the diffusion equation based reconstruction algorithm, the essentiality and superiority of the HRDM-based algorithm were demonstrated with simulation, phantom and animal studies. An in vivo gastric cancer-bearing nude mouse experiment was conducted, whose results revealed the ability and feasibility of the HRDM-based algorithm in the biomedical application of gastric cancer detection.

Symbolic Algebra Development for Higher-Order Electron Propagator Formulation and Implementation.

PubMed

Tamayo-Mendoza, Teresa; Flores-Moreno, Roberto

2014-06-10

Through the use of symbolic algebra, implemented in a program, the algebraic expression of the elements of the self-energy matrix for the electron propagator to different orders were obtained. In addition, a module for the software package Lowdin was automatically generated. Second- and third-order electron propagator results have been calculated to test the correct operation of the program. It was found that the Fortran 90 modules obtained automatically with our algorithm succeeded in calculating ionization energies with the second- and third-order electron propagator in the diagonal approximation. The strategy for the development of this symbolic algebra program is described in detail. This represents a solid starting point for the automatic derivation and implementation of higher-order electron propagator methods.

Optimal placement of multiple types of communicating sensors with availability and coverage redundancy constraints

NASA Astrophysics Data System (ADS)

Vecherin, Sergey N.; Wilson, D. Keith; Pettit, Chris L.

2010-04-01

Determination of an optimal configuration (numbers, types, and locations) of a sensor network is an important practical problem. In most applications, complex signal propagation effects and inhomogeneous coverage preferences lead to an optimal solution that is highly irregular and nonintuitive. The general optimization problem can be strictly formulated as a binary linear programming problem. Due to the combinatorial nature of this problem, however, its strict solution requires significant computational resources (NP-complete class of complexity) and is unobtainable for large spatial grids of candidate sensor locations. For this reason, a greedy algorithm for approximate solution was recently introduced [S. N. Vecherin, D. K. Wilson, and C. L. Pettit, "Optimal sensor placement with terrain-based constraints and signal propagation effects," Unattended Ground, Sea, and Air Sensor Technologies and Applications XI, SPIE Proc. Vol. 7333, paper 73330S (2009)]. Here further extensions to the developed algorithm are presented to include such practical needs and constraints as sensor availability, coverage by multiple sensors, and wireless communication of the sensor information. Both communication and detection are considered in a probabilistic framework. Communication signal and signature propagation effects are taken into account when calculating probabilities of communication and detection. Comparison of approximate and strict solutions on reduced-size problems suggests that the approximate algorithm yields quick and good solutions, which thus justifies using that algorithm for full-size problems. Examples of three-dimensional outdoor sensor placement are provided using a terrain-based software analysis tool.

Development of Fast Algorithms Using Recursion, Nesting and Iterations for Computational Electromagnetics

NASA Technical Reports Server (NTRS)

Chew, W. C.; Song, J. M.; Lu, C. C.; Weedon, W. H.

1995-01-01

In the first phase of our work, we have concentrated on laying the foundation to develop fast algorithms, including the use of recursive structure like the recursive aggregate interaction matrix algorithm (RAIMA), the nested equivalence principle algorithm (NEPAL), the ray-propagation fast multipole algorithm (RPFMA), and the multi-level fast multipole algorithm (MLFMA). We have also investigated the use of curvilinear patches to build a basic method of moments code where these acceleration techniques can be used later. In the second phase, which is mainly reported on here, we have concentrated on implementing three-dimensional NEPAL on a massively parallel machine, the Connection Machine CM-5, and have been able to obtain some 3D scattering results. In order to understand the parallelization of codes on the Connection Machine, we have also studied the parallelization of 3D finite-difference time-domain (FDTD) code with PML material absorbing boundary condition (ABC). We found that simple algorithms like the FDTD with material ABC can be parallelized very well allowing us to solve within a minute a problem of over a million nodes. In addition, we have studied the use of the fast multipole method and the ray-propagation fast multipole algorithm to expedite matrix-vector multiplication in a conjugate-gradient solution to integral equations of scattering. We find that these methods are faster than LU decomposition for one incident angle, but are slower than LU decomposition when many incident angles are needed as in the monostatic RCS calculations.

An improved label propagation algorithm based on node importance and random walk for community detection

NASA Astrophysics Data System (ADS)

Ma, Tianren; Xia, Zhengyou

2017-05-01

Currently, with the rapid development of information technology, the electronic media for social communication is becoming more and more popular. Discovery of communities is a very effective way to understand the properties of complex networks. However, traditional community detection algorithms consider the structural characteristics of a social organization only, with more information about nodes and edges wasted. In the meanwhile, these algorithms do not consider each node on its merits. Label propagation algorithm (LPA) is a near linear time algorithm which aims to find the community in the network. It attracts many scholars owing to its high efficiency. In recent years, there are more improved algorithms that were put forward based on LPA. In this paper, an improved LPA based on random walk and node importance (NILPA) is proposed. Firstly, a list of node importance is obtained through calculation. The nodes in the network are sorted in descending order of importance. On the basis of random walk, a matrix is constructed to measure the similarity of nodes and it avoids the random choice in the LPA. Secondly, a new metric IAS (importance and similarity) is calculated by node importance and similarity matrix, which we can use to avoid the random selection in the original LPA and improve the algorithm stability. Finally, a test in real-world and synthetic networks is given. The result shows that this algorithm has better performance than existing methods in finding community structure.

Multigrid Methods for the Computation of Propagators in Gauge Fields

NASA Astrophysics Data System (ADS)

Kalkreuter, Thomas

Multigrid methods were invented for the solution of discretized partial differential equations in order to overcome the slowness of traditional algorithms by updates on various length scales. In the present work generalizations of multigrid methods for propagators in gauge fields are investigated. Gauge fields are incorporated in algorithms in a covariant way. The kernel C of the restriction operator which averages from one grid to the next coarser grid is defined by projection on the ground-state of a local Hamiltonian. The idea behind this definition is that the appropriate notion of smoothness depends on the dynamics. The ground-state projection choice of C can be used in arbitrary dimension and for arbitrary gauge group. We discuss proper averaging operations for bosons and for staggered fermions. The kernels C can also be used in multigrid Monte Carlo simulations, and for the definition of block spins and blocked gauge fields in Monte Carlo renormalization group studies. Actual numerical computations are performed in four-dimensional SU(2) gauge fields. We prove that our proposals for block spins are “good”, using renormalization group arguments. A central result is that the multigrid method works in arbitrarily disordered gauge fields, in principle. It is proved that computations of propagators in gauge fields without critical slowing down are possible when one uses an ideal interpolation kernel. Unfortunately, the idealized algorithm is not practical, but it was important to answer questions of principle. Practical methods are able to outperform the conjugate gradient algorithm in case of bosons. The case of staggered fermions is harder. Multigrid methods give considerable speed-ups compared to conventional relaxation algorithms, but on lattices up to 184 conjugate gradient is superior.

A Collaborative Recommend Algorithm Based on Bipartite Community

PubMed Central

Fu, Yuchen; Liu, Quan; Cui, Zhiming

2014-01-01

The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which proves that considering the network topology of recommendation systems could help us to improve recommendation results. However, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an important role in collaborative recommend processing. Therefore, on account of data characteristics and application requirements of collaborative recommend systems, we proposed a link community partitioning algorithm based on the label propagation and a collaborative recommendation algorithm based on the bipartite community. Then we designed numerical experiments to verify the algorithm validity under benchmark and real database. PMID:24955393

Strategies to reduce the complexity of hydrologic data assimilation for high-dimensional models

NASA Astrophysics Data System (ADS)

Hernandez, F.; Liang, X.

2017-12-01

Probabilistic forecasts in the geosciences offer invaluable information by allowing to estimate the uncertainty of predicted conditions (including threats like floods and droughts). However, while forecast systems based on modern data assimilation algorithms are capable of producing multi-variate probability distributions of future conditions, the computational resources required to fully characterize the dependencies between the model's state variables render their applicability impractical for high-resolution cases. This occurs because of the quadratic space complexity of storing the covariance matrices that encode these dependencies and the cubic time complexity of performing inference operations with them. In this work we introduce two complementary strategies to reduce the size of the covariance matrices that are at the heart of Bayesian assimilation methods—like some variants of (ensemble) Kalman filters and of particle filters—and variational methods. The first strategy involves the optimized grouping of state variables by clustering individual cells of the model into "super-cells." A dynamic fuzzy clustering approach is used to take into account the states (e.g., soil moisture) and forcings (e.g., precipitation) of each cell at each time step. The second strategy consists in finding a compressed representation of the covariance matrix that still encodes the most relevant information but that can be more efficiently stored and processed. A learning and a belief-propagation inference algorithm are developed to take advantage of this modified low-rank representation. The two proposed strategies are incorporated into OPTIMISTS, a state-of-the-art hybrid Bayesian/variational data assimilation algorithm, and comparative streamflow forecasting tests are performed using two watersheds modeled with the Distributed Hydrology Soil Vegetation Model (DHSVM). Contrasts are made between the efficiency gains and forecast accuracy losses of each strategy used in isolation, and of those achieved through their coupling. We expect these developments to help catalyze improvements in the predictive accuracy of large-scale forecasting operations by lowering the costs of deploying advanced data assimilation techniques.

Adaptive laser link reconfiguration using constraint propagation

NASA Technical Reports Server (NTRS)

Crone, M. S.; Julich, P. M.; Cook, L. M.

1993-01-01

This paper describes Harris AI research performed on the Adaptive Link Reconfiguration (ALR) study for Rome Lab, and focuses on the application of constraint propagation to the problem of link reconfiguration for the proposed space based Strategic Defense System (SDS) Brilliant Pebbles (BP) communications system. According to the concept of operations at the time of the study, laser communications will exist between BP's and to ground entry points. Long-term links typical of RF transmission will not exist. This study addressed an initial implementation of BP's based on the Global Protection Against Limited Strikes (GPALS) SDI mission. The number of satellites and rings studied was representative of this problem. An orbital dynamics program was used to generate line-of-site data for the modeled architecture. This was input into a discrete event simulation implemented in the Harris developed COnstraint Propagation Expert System (COPES) Shell, developed initially on the Rome Lab BM/C3 study. Using a model of the network and several heuristics, the COPES shell was used to develop the Heuristic Adaptive Link Ordering (HALO) Algorithm to rank and order potential laser links according to probability of communication. A reduced set of links based on this ranking would then be used by a routing algorithm to select the next hop. This paper includes an overview of Constraint Propagation as an Artificial Intelligence technique and its embodiment in the COPES shell. It describes the design and implementation of both the simulation of the GPALS BP network and the HALO algorithm in COPES. This is described using a 59 Data Flow Diagram, State Transition Diagrams, and Structured English PDL. It describes a laser communications model and the heuristics involved in rank-ordering the potential communication links. The generation of simulation data is described along with its interface via COPES to the Harris developed View Net graphical tool for visual analysis of communications networks. Conclusions are presented, including a graphical analysis of results depicting the ordered set of links versus the set of all possible links based on the computed Bit Error Rate (BER). Finally, future research is discussed which includes enhancements to the HALO algorithm, network simulation, and the addition of an intelligent routing algorithm for BP.

Medication adherence as a learning process: insights from cognitive psychology.

PubMed

Rottman, Benjamin Margolin; Marcum, Zachary A; Thorpe, Carolyn T; Gellad, Walid F

2017-03-01

Non-adherence to medications is one of the largest contributors to sub-optimal health outcomes. Many theories of adherence include a 'value-expectancy' component in which a patient decides to take a medication partly based on expectations about whether it is effective, necessary, and tolerable. We propose reconceptualising this common theme as a kind of 'causal learning' - the patient learns whether a medication is effective, necessary, and tolerable, from experience with the medication. We apply cognitive psychology theories of how people learn cause-effect relations to elaborate this causal-learning challenge. First, expectations and impressions about a medication and beliefs about how a medication works, such as delay of onset, can shape a patient's perceived experience with the medication. Second, beliefs about medications propagate both 'top-down' and 'bottom-up', from experiences with specific medications to general beliefs about medications and vice versa. Third, non-adherence can interfere with learning about a medication, because beliefs, adherence, and experience with a medication are connected in a cyclic learning problem. We propose that by conceptualising non-adherence as a causal-learning process, clinicians can more effectively address a patient's misconceptions and biases, helping the patient develop more accurate impressions of the medication.

A Survey of Symplectic and Collocation Integration Methods for Orbit Propagation

NASA Technical Reports Server (NTRS)

Jones, Brandon A.; Anderson, Rodney L.

2012-01-01

Demands on numerical integration algorithms for astrodynamics applications continue to increase. Common methods, like explicit Runge-Kutta, meet the orbit propagation needs of most scenarios, but more specialized scenarios require new techniques to meet both computational efficiency and accuracy needs. This paper provides an extensive survey on the application of symplectic and collocation methods to astrodynamics. Both of these methods benefit from relatively recent theoretical developments, which improve their applicability to artificial satellite orbit propagation. This paper also details their implementation, with several tests demonstrating their advantages and disadvantages.

Shear wave speed recovery in transient elastography and supersonic imaging using propagating fronts

NASA Astrophysics Data System (ADS)

McLaughlin, Joyce; Renzi, Daniel

2006-04-01

Transient elastography and supersonic imaging are promising new techniques for characterizing the elasticity of soft tissues. Using this method, an 'ultrafast imaging' system (up to 10 000 frames s-1) follows in real time the propagation of a low frequency shear wave. The displacement of the propagating shear wave is measured as a function of time and space. The objective of this paper is to develop and test algorithms whose ultimate product is images of the shear wave speed of tissue mimicking phantoms. The data used in the algorithms are the front of the propagating shear wave. Here, we first develop techniques to find the arrival time surface given the displacement data from a transient elastography experiment. The arrival time surface satisfies the Eikonal equation. We then propose a family of methods, called distance methods, to solve the inverse Eikonal equation: given the arrival times of a propagating wave, find the wave speed. Lastly, we explain why simple inversion schemes for the inverse Eikonal equation lead to large outliers in the wave speed and numerically demonstrate that the new scheme presented here does not have any large outliers. We exhibit two recoveries using these methods: one is with synthetic data; the other is with laboratory data obtained by Mathias Fink's group (the Laboratoire Ondes et Acoustique, ESPCI, Université Paris VII).

Active semi-supervised learning method with hybrid deep belief networks.

PubMed

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

Numerical simulation and experimental validation of Lamb wave propagation behavior in composite plates

NASA Astrophysics Data System (ADS)

Kim, Sungwon; Uprety, Bibhisha; Mathews, V. John; Adams, Daniel O.

2015-03-01

Structural Health Monitoring (SHM) based on Acoustic Emission (AE) is dependent on both the sensors to detect an impact event as well as an algorithm to determine the impact location. The propagation of Lamb waves produced by an impact event in thin composite structures is affected by several unique aspects including material anisotropy, ply orientations, and geometric discontinuities within the structure. The development of accurate numerical models of Lamb wave propagation has important benefits towards the development of AE-based SHM systems for impact location estimation. Currently, many impact location algorithms utilize the time of arrival or velocities of Lamb waves. Therefore the numerical prediction of characteristic wave velocities is of great interest. Additionally, the propagation of the initial symmetric (S0) and asymmetric (A0) wave modes is important, as these wave modes are used for time of arrival estimation. In this investigation, finite element analyses were performed to investigate aspects of Lamb wave propagation in composite plates with active signal excitation. A comparative evaluation of two three-dimensional modeling approaches was performed, with emphasis placed on the propagation and velocity of both the S0 and A0 wave modes. Results from numerical simulations are compared to experimental results obtained from active AE testing. Of particular interest is the directional dependence of Lamb waves in quasi-isotropic carbon/epoxy composite plates. Numerical and experimental results suggest that although a quasi-isotropic composite plate may have the same effective elastic modulus in all in-plane directions, the Lamb wave velocity may have some directional dependence. Further numerical analyses were performed to investigate Lamb wave propagation associated with circular cutouts in composite plates.

Propagation based phase retrieval of simulated intensity measurements using artificial neural networks

NASA Astrophysics Data System (ADS)

Kemp, Z. D. C.

2018-04-01

Determining the phase of a wave from intensity measurements has many applications in fields such as electron microscopy, visible light optics, and medical imaging. Propagation based phase retrieval, where the phase is obtained from defocused images, has shown significant promise. There are, however, limitations in the accuracy of the retrieved phase arising from such methods. Sources of error include shot noise, image misalignment, and diffraction artifacts. We explore the use of artificial neural networks (ANNs) to improve the accuracy of propagation based phase retrieval algorithms applied to simulated intensity measurements. We employ a phase retrieval algorithm based on the transport-of-intensity equation to obtain the phase from simulated micrographs of procedurally generated specimens. We then train an ANN with pairs of retrieved and exact phases, and use the trained ANN to process a test set of retrieved phase maps. The total error in the phase is significantly reduced using this method. We also discuss a variety of potential extensions to this work.

A critical review of principal traffic noise models: Strategies and implications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garg, Naveen, E-mail: ngarg@mail.nplindia.ernet.in; Department of Mechanical, Production and Industrial Engineering, Delhi Technological University, Delhi 110042; Maji, Sagar

2014-04-01

The paper presents an exhaustive comparison of principal traffic noise models adopted in recent years in developed nations. The comparison is drawn on the basis of technical attributes including source modelling and sound propagation algorithms. Although the characterization of source in terms of rolling and propulsion noise in conjunction with advanced numerical methods for sound propagation has significantly reduced the uncertainty in traffic noise predictions, the approach followed is quite complex and requires specialized mathematical skills for predictions which is sometimes quite cumbersome for town planners. Also, it is sometimes difficult to follow the best approach when a variety ofmore » solutions have been proposed. This paper critically reviews all these aspects pertaining to the recent models developed and adapted in some countries and also discusses the strategies followed and implications of these models. - Highlights: • Principal traffic noise models developed are reviewed. • Sound propagation algorithms used in traffic noise models are compared. • Implications of models are discussed.« less

New approach for absolute fluence distribution calculations in Monte Carlo simulations of light propagation in turbid media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Böcklin, Christoph, E-mail: boecklic@ethz.ch; Baumann, Dirk; Fröhlich, Jürg

A novel way to attain three dimensional fluence rate maps from Monte-Carlo simulations of photon propagation is presented in this work. The propagation of light in a turbid medium is described by the radiative transfer equation and formulated in terms of radiance. For many applications, particularly in biomedical optics, the fluence rate is a more useful quantity and directly derived from the radiance by integrating over all directions. Contrary to the usual way which calculates the fluence rate from absorbed photon power, the fluence rate in this work is directly calculated from the photon packet trajectory. The voxel based algorithmmore » works in arbitrary geometries and material distributions. It is shown that the new algorithm is more efficient and also works in materials with a low or even zero absorption coefficient. The capabilities of the new algorithm are demonstrated on a curved layered structure, where a non-scattering, non-absorbing layer is sandwiched between two highly scattering layers.« less

CLUSTERING OF INTERICTAL SPIKES BY DYNAMIC TIME WARPING AND AFFINITY PROPAGATION

PubMed Central

Thomas, John; Jin, Jing; Dauwels, Justin; Cash, Sydney S.; Westover, M. Brandon

2018-01-01

Epilepsy is often associated with the presence of spikes in electroencephalograms (EEGs). The spike waveforms vary vastly among epilepsy patients, and also for the same patient across time. In order to develop semi-automated and automated methods for detecting spikes, it is crucial to obtain a better understanding of the various spike shapes. In this paper, we develop several approaches to extract exemplars of spikes. We generate spike exemplars by applying clustering algorithms to a database of spikes from 12 patients. As similarity measures for clustering, we consider the Euclidean distance and Dynamic Time Warping (DTW). We assess two clustering algorithms, namely, K-means clustering and affinity propagation. The clustering methods are compared based on the mean squared error, and the similarity measures are assessed based on the number of generated spike clusters. Affinity propagation with DTW is shown to be the best combination for clustering epileptic spikes, since it generates fewer spike templates and does not require to pre-specify the number of spike templates. PMID:29527130

Automatic Structural Parcellation of Mouse Brain MRI Using Multi-Atlas Label Fusion

PubMed Central

Ma, Da; Cardoso, Manuel J.; Modat, Marc; Powell, Nick; Wells, Jack; Holmes, Holly; Wiseman, Frances; Tybulewicz, Victor; Fisher, Elizabeth; Lythgoe, Mark F.; Ourselin, Sébastien

2014-01-01

Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework. PMID:24475148

Algorithms for Disconnected Diagrams in Lattice QCD

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gambhir, Arjun Singh; Stathopoulos, Andreas; Orginos, Konstantinos

2016-11-01

Computing disconnected diagrams in Lattice QCD (operator insertion in a quark loop) entails the computationally demanding problem of taking the trace of the all to all quark propagator. We first outline the basic algorithm used to compute a quark loop as well as improvements to this method. Then, we motivate and introduce an algorithm based on the synergy between hierarchical probing and singular value deflation. We present results for the chiral condensate using a 2+1-flavor clover ensemble and compare estimates of the nucleon charges with the basic algorithm.

PETOOL: MATLAB-based one-way and two-way split-step parabolic equation tool for radiowave propagation over variable terrain

NASA Astrophysics Data System (ADS)

Ozgun, Ozlem; Apaydin, Gökhan; Kuzuoglu, Mustafa; Sevgi, Levent

2011-12-01

A MATLAB-based one-way and two-way split-step parabolic equation software tool (PETOOL) has been developed with a user-friendly graphical user interface (GUI) for the analysis and visualization of radio-wave propagation over variable terrain and through homogeneous and inhomogeneous atmosphere. The tool has a unique feature over existing one-way parabolic equation (PE)-based codes, because it utilizes the two-way split-step parabolic equation (SSPE) approach with wide-angle propagator, which is a recursive forward-backward algorithm to incorporate both forward and backward waves into the solution in the presence of variable terrain. First, the formulation of the classical one-way SSPE and the relatively-novel two-way SSPE is presented, with particular emphasis on their capabilities and the limitations. Next, the structure and the GUI capabilities of the PETOOL software tool are discussed in detail. The calibration of PETOOL is performed and demonstrated via analytical comparisons and/or representative canonical tests performed against the Geometric Optic (GO) + Uniform Theory of Diffraction (UTD). The tool can be used for research and/or educational purposes to investigate the effects of a variety of user-defined terrain and range-dependent refractivity profiles in electromagnetic wave propagation. Program summaryProgram title: PETOOL (Parabolic Equation Toolbox) Catalogue identifier: AEJS_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJS_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 143 349 No. of bytes in distributed program, including test data, etc.: 23 280 251 Distribution format: tar.gz Programming language: MATLAB (MathWorks Inc.) 2010a. Partial Differential Toolbox and Curve Fitting Toolbox required Computer: PC Operating system: Windows XP and Vista Classification: 10 Nature of problem: Simulation of radio-wave propagation over variable terrain on the Earth's surface, and through homogeneous and inhomogeneous atmosphere. Solution method: The program implements one-way and two-way Split-Step Parabolic Equation (SSPE) algorithm, with wide-angle propagator. The SSPE is, in general, an initial-value problem starting from a reference range (typically from an antenna), and marching out in range by obtaining the field along the vertical direction at each range step, through the use of step-by-step Fourier transformations. The two-way algorithm incorporates the backward-propagating waves into the standard one-way SSPE by utilizing an iterative forward-backward scheme for modeling multipath effects over a staircase-approximated terrain. Unusual features: This is the first software package implementing a recursive forward-backward SSPE algorithm to account for the multipath effects during radio-wave propagation, and enabling the user to easily analyze and visualize the results of the two-way propagation with GUI capabilities. Running time: Problem dependent. Typically, it is about 1.5 ms (for conducting ground) and 4 ms (for lossy ground) per range step for a vertical field profile of vector length 1500, on Intel Core 2 Duo 1.6 GHz with 2 GB RAM under Windows Vista.

Development of a Nonlinear Probability of Collision Tool for the Earth Observing System

NASA Technical Reports Server (NTRS)

McKinley, David P.

2006-01-01

The Earth Observing System (EOS) spacecraft Terra, Aqua, and Aura fly in constellation with several other spacecraft in 705-kilometer mean altitude sun-synchronous orbits. All three spacecraft are operated by the Earth Science Mission Operations (ESMO) Project at Goddard Space Flight Center (GSFC). In 2004, the ESMO project began assessing the probability of collision of the EOS spacecraft with other space objects. In addition to conjunctions with high relative velocities, the collision assessment method for the EOS spacecraft must address conjunctions with low relative velocities during potential collisions between constellation members. Probability of Collision algorithms that are based on assumptions of high relative velocities and linear relative trajectories are not suitable for these situations; therefore an algorithm for handling the nonlinear relative trajectories was developed. This paper describes this algorithm and presents results from its validation for operational use. The probability of collision is typically calculated by integrating a Gaussian probability distribution over the volume swept out by a sphere representing the size of the space objects involved in the conjunction. This sphere is defined as the Hard Body Radius. With the assumption of linear relative trajectories, this volume is a cylinder, which translates into simple limits of integration for the probability calculation. For the case of nonlinear relative trajectories, the volume becomes a complex geometry. However, with an appropriate choice of coordinate systems, the new algorithm breaks down the complex geometry into a series of simple cylinders that have simple limits of integration. This nonlinear algorithm will be discussed in detail in the paper. The nonlinear Probability of Collision algorithm was first verified by showing that, when used in high relative velocity cases, it yields similar answers to existing high relative velocity linear relative trajectory algorithms. The comparison with the existing high velocity/linear theory will also be used to determine at what relative velocity the analysis should use the new nonlinear theory in place of the existing linear theory. The nonlinear algorithm was also compared to a known exact solution for the probability of collision between two objects when the relative motion is strictly circular and the error covariance is spherically symmetric. Figure I shows preliminary results from this comparison by plotting the probabilities calculated from the new algorithm and those from the exact solution versus the Hard Body Radius to Covariance ratio. These results show about 5% error when the Hard Body Radius is equal to one half the spherical covariance magnitude. The algorithm was then combined with a high fidelity orbit state and error covariance propagator into a useful tool for analyzing low relative velocity nonlinear relative trajectories. The high fidelity propagator is capable of using atmospheric drag, central body gravitational, solar radiation, and third body forces to provide accurate prediction of the relative trajectories and covariance evolution. The covariance propagator also includes a process noise model to ensure realistic evolutions of the error covariance. This paper will describe the integration of the nonlinear probability algorithm and the propagators into a useful collision assessment tool. Finally, a hypothetical case study involving a low relative velocity conjunction between members of the Earth Observation System constellation will be presented.

Gaussian-Beam Laser-Resonator Program

NASA Technical Reports Server (NTRS)

Cross, Patricia L.; Bair, Clayton H.; Barnes, Norman

1989-01-01

Gaussian Beam Laser Resonator Program models laser resonators by use of Gaussian-beam-propagation techniques. Used to determine radii of beams as functions of position in laser resonators. Algorithm used in program has three major components. First, ray-transfer matrix for laser resonator must be calculated. Next, initial parameters of beam calculated. Finally, propagation of beam through optical elements computed. Written in Microsoft FORTRAN (Version 4.01).

THREAT ANTICIPATION AND DECEPTIVE REASONING USING BAYESIAN BELIEF NETWORKS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allgood, Glenn O; Olama, Mohammed M; Lake, Joe E

Recent events highlight the need for tools to anticipate threats posed by terrorists. Assessing these threats requires combining information from disparate data sources such as analytic models, simulations, historical data, sensor networks, and user judgments. These disparate data can be combined in a coherent, analytically defensible, and understandable manner using a Bayesian belief network (BBN). In this paper, we develop a BBN threat anticipatory model based on a deceptive reasoning algorithm using a network engineering process that treats the probability distributions of the BBN nodes within the broader context of the system development process.

Elaboration of a semi-automated algorithm for brain arteriovenous malformation segmentation: initial results.

PubMed

Clarençon, Frédéric; Maizeroi-Eugène, Franck; Bresson, Damien; Maingreaud, Flavien; Sourour, Nader; Couquet, Claude; Ayoub, David; Chiras, Jacques; Yardin, Catherine; Mounayer, Charbel

2015-02-01

The purpose of our study was to distinguish the different components of a brain arteriovenous malformation (bAVM) on 3D rotational angiography (3D-RA) using a semi-automated segmentation algorithm. Data from 3D-RA of 15 patients (8 males, 7 females; 14 supratentorial bAVMs, 1 infratentorial) were used to test the algorithm. Segmentation was performed in two steps: (1) nidus segmentation from propagation (vertical then horizontal) of tagging on the reference slice (i.e., the slice on which the nidus had the biggest surface); (2) contiguity propagation (based on density and variance) from tagging of arteries and veins distant from the nidus. Segmentation quality was evaluated by comparison with six frame/s DSA by two independent reviewers. Analysis of supraselective microcatheterisation was performed to dispel discrepancy. Mean duration for bAVM segmentation was 64 ± 26 min. Quality of segmentation was evaluated as good or fair in 93% of cases. Segmentation had better results than six frame/s DSA for the depiction of a focal ectasia on the main draining vein and for the evaluation of the venous drainage pattern. This segmentation algorithm is a promising tool that may help improve the understanding of bAVM angio-architecture, especially the venous drainage. • The segmentation algorithm allows for the distinction of the AVM's components • This algorithm helps to see the venous drainage of bAVMs more precisely • This algorithm may help to reduce the treatment-related complication rate.

Semiclassical propagation of Wigner functions.

PubMed

Dittrich, T; Gómez, E A; Pachón, L A

2010-06-07

We present a comprehensive study of semiclassical phase-space propagation in the Wigner representation, emphasizing numerical applications, in particular as an initial-value representation. Two semiclassical approximation schemes are discussed. The propagator of the Wigner function based on van Vleck's approximation replaces the Liouville propagator by a quantum spot with an oscillatory pattern reflecting the interference between pairs of classical trajectories. Employing phase-space path integration instead, caustics in the quantum spot are resolved in terms of Airy functions. We apply both to two benchmark models of nonlinear molecular potentials, the Morse oscillator and the quartic double well, to test them in standard tasks such as computing autocorrelation functions and propagating coherent states. The performance of semiclassical Wigner propagation is very good even in the presence of marked quantum effects, e.g., in coherent tunneling and in propagating Schrodinger cat states, and of classical chaos in four-dimensional phase space. We suggest options for an effective numerical implementation of our method and for integrating it in Monte-Carlo-Metropolis algorithms suitable for high-dimensional systems.

Neural network for image compression

NASA Astrophysics Data System (ADS)

Panchanathan, Sethuraman; Yeap, Tet H.; Pilache, B.

1992-09-01

In this paper, we propose a new scheme for image compression using neural networks. Image data compression deals with minimization of the amount of data required to represent an image while maintaining an acceptable quality. Several image compression techniques have been developed in recent years. We note that the coding performance of these techniques may be improved by employing adaptivity. Over the last few years neural network has emerged as an effective tool for solving a wide range of problems involving adaptivity and learning. A multilayer feed-forward neural network trained using the backward error propagation algorithm is used in many applications. However, this model is not suitable for image compression because of its poor coding performance. Recently, a self-organizing feature map (SOFM) algorithm has been proposed which yields a good coding performance. However, this algorithm requires a long training time because the network starts with random initial weights. In this paper we have used the backward error propagation algorithm (BEP) to quickly obtain the initial weights which are then used to speedup the training time required by the SOFM algorithm. The proposed approach (BEP-SOFM) combines the advantages of the two techniques and, hence, achieves a good coding performance in a shorter training time. Our simulation results demonstrate the potential gains using the proposed technique.

Perturbation Biology: Inferring Signaling Networks in Cellular Systems

PubMed Central

Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

2013-01-01

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

Human wound photogrammetry with low-cost hardware based on automatic calibration of geometry and color

NASA Astrophysics Data System (ADS)

Jose, Abin; Haak, Daniel; Jonas, Stephan; Brandenburg, Vincent; Deserno, Thomas M.

2015-03-01

Photographic documentation and image-based wound assessment is frequently performed in medical diagnostics, patient care, and clinical research. To support quantitative assessment, photographic imaging is based on expensive and high-quality hardware and still needs appropriate registration and calibration. Using inexpensive consumer hardware such as smartphone-integrated cameras, calibration of geometry, color, and contrast is challenging. Some methods involve color calibration using a reference pattern such as a standard color card, which is located manually in the photographs. In this paper, we adopt the lattice detection algorithm by Park et al. from real world to medicine. At first, the algorithm extracts and clusters feature points according to their local intensity patterns. Groups of similar points are fed into a selection process, which tests for suitability as a lattice grid. The group which describes the largest probability of the meshes of a lattice is selected and from it a template for an initial lattice cell is extracted. Then, a Markov random field is modeled. Using the mean-shift belief propagation, the detection of the 2D lattice is solved iteratively as a spatial tracking problem. Least-squares geometric calibration of projective distortions and non-linear color calibration in RGB space is supported by 35 corner points of 24 color patches, respectively. The method is tested on 37 photographs taken from the German Calciphylaxis registry, where non-standardized photographic documentation is collected nationwide from all contributing trial sites. In all images, the reference card location is correctly identified. At least, 28 out of 35 lattice points were detected, outperforming the SIFT-based approach previously applied. Based on these coordinates, robust geometry and color registration is performed making the photographs comparable for quantitative analysis.

Attitude Determination Algorithm based on Relative Quaternion Geometry of Velocity Incremental Vectors for Cost Efficient AHRS Design

NASA Astrophysics Data System (ADS)

Lee, Byungjin; Lee, Young Jae; Sung, Sangkyung

2018-05-01

A novel attitude determination method is investigated that is computationally efficient and implementable in low cost sensor and embedded platform. Recent result on attitude reference system design is adapted to further develop a three-dimensional attitude determination algorithm through the relative velocity incremental measurements. For this, velocity incremental vectors, computed respectively from INS and GPS with different update rate, are compared to generate filter measurement for attitude estimation. In the quaternion-based Kalman filter configuration, an Euler-like attitude perturbation angle is uniquely introduced for reducing filter states and simplifying propagation processes. Furthermore, assuming a small angle approximation between attitude update periods, it is shown that the reduced order filter greatly simplifies the propagation processes. For performance verification, both simulation and experimental studies are completed. A low cost MEMS IMU and GPS receiver are employed for system integration, and comparison with the true trajectory or a high-grade navigation system demonstrates the performance of the proposed algorithm.

Alignment of leading-edge and peak-picking time of arrival methods to obtain accurate source locations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roussel-Dupre, R.; Symbalisty, E.; Fox, C.

2009-08-01

The location of a radiating source can be determined by time-tagging the arrival of the radiated signal at a network of spatially distributed sensors. The accuracy of this approach depends strongly on the particular time-tagging algorithm employed at each of the sensors. If different techniques are used across the network, then the time tags must be referenced to a common fiducial for maximum location accuracy. In this report we derive the time corrections needed to temporally align leading-edge, time-tagging techniques with peak-picking algorithms. We focus on broadband radio frequency (RF) sources, an ionospheric propagation channel, and narrowband receivers, but themore » final results can be generalized to apply to any source, propagation environment, and sensor. Our analytic results are checked against numerical simulations for a number of representative cases and agree with the specific leading-edge algorithm studied independently by Kim and Eng (1995) and Pongratz (2005 and 2007).« less

An algorithm for continuum modeling of rocks with multiple embedded nonlinearly-compliant joints [Continuum modeling of elasto-plastic media with multiple embedded nonlinearly-compliant joints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hurley, R. C.; Vorobiev, O. Y.; Ezzedine, S. M.

Here, we present a numerical method for modeling the mechanical effects of nonlinearly-compliant joints in elasto-plastic media. The method uses a series of strain-rate and stress update algorithms to determine joint closure, slip, and solid stress within computational cells containing multiple “embedded” joints. This work facilitates efficient modeling of nonlinear wave propagation in large spatial domains containing a large number of joints that affect bulk mechanical properties. We implement the method within the massively parallel Lagrangian code GEODYN-L and provide verification and examples. We highlight the ability of our algorithms to capture joint interactions and multiple weakness planes within individualmore » computational cells, as well as its computational efficiency. We also discuss the motivation for developing the proposed technique: to simulate large-scale wave propagation during the Source Physics Experiments (SPE), a series of underground explosions conducted at the Nevada National Security Site (NNSS).« less

An algorithm for continuum modeling of rocks with multiple embedded nonlinearly-compliant joints [Continuum modeling of elasto-plastic media with multiple embedded nonlinearly-compliant joints

DOE PAGES

Hurley, R. C.; Vorobiev, O. Y.; Ezzedine, S. M.

2017-04-06

Here, we present a numerical method for modeling the mechanical effects of nonlinearly-compliant joints in elasto-plastic media. The method uses a series of strain-rate and stress update algorithms to determine joint closure, slip, and solid stress within computational cells containing multiple “embedded” joints. This work facilitates efficient modeling of nonlinear wave propagation in large spatial domains containing a large number of joints that affect bulk mechanical properties. We implement the method within the massively parallel Lagrangian code GEODYN-L and provide verification and examples. We highlight the ability of our algorithms to capture joint interactions and multiple weakness planes within individualmore » computational cells, as well as its computational efficiency. We also discuss the motivation for developing the proposed technique: to simulate large-scale wave propagation during the Source Physics Experiments (SPE), a series of underground explosions conducted at the Nevada National Security Site (NNSS).« less

Numerical study of signal propagation in corrugated coaxial cables

DOE PAGES

Li, Jichun; Machorro, Eric A.; Shields, Sidney

2017-01-01

Our article focuses on high-fidelity modeling of signal propagation in corrugated coaxial cables. Taking advantage of the axisymmetry, the authors reduce the 3-D problem to a 2-D problem by solving time-dependent Maxwell's equations in cylindrical coordinates.They then develop a nodal discontinuous Galerkin method for solving their model equations. We prove stability and error analysis for the semi-discrete scheme. We we present our numerical results, we demonstrate that our algorithm not only converges as our theoretical analysis predicts, but it is also very effective in solving a variety of signal propagation problems in practical corrugated coaxial cables.

Multi-particle phase space integration with arbitrary set of singularities in CompHEP

NASA Astrophysics Data System (ADS)

Kovalenko, D. N.; Pukhov, A. E.

1997-02-01

We describe an algorithm of multi-particle phase space integration for collision and decay processes realized in CompHEP package version 3.2. In the framework of this algorithm it is possible to regularize an arbitrary set of singularities caused by virtual particle propagators. The algorithm is based on the method of the recursive representation of kinematics and on the multichannel Monte Carlo approach. CompHEP package is available by WWW: http://theory.npi.msu.su/pukhov/comphep.html

Wind velocity profile reconstruction from intensity fluctuations of a plane wave propagating in a turbulent atmosphere.

PubMed

Banakh, V A; Marakasov, D A

2007-08-01

Reconstruction of a wind profile based on the statistics of plane-wave intensity fluctuations in a turbulent atmosphere is considered. The algorithm for wind profile retrieval from the spatiotemporal spectrum of plane-wave weak intensity fluctuations is described, and the results of end-to-end computer experiments on wind profiling based on the developed algorithm are presented. It is shown that the reconstructing algorithm allows retrieval of a wind profile from turbulent plane-wave intensity fluctuations with acceptable accuracy.

Genetic algorithm for neural networks optimization

NASA Astrophysics Data System (ADS)

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

2004-11-01

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

Account of an optical beam spreading caused by turbulence for the problem of partially coherent wavefield propagation through inhomogeneous absorbing media

NASA Astrophysics Data System (ADS)

Dudorov, Vadim V.; Kolosov, Valerii V.

2003-04-01

The propagation problem for partially coherent wave fields in inhomogeneous media is considered in this work. The influence of refraction, inhomogeneity of gain medium properties and refraction parameter fluctuations on target characteristics of radiation are taken into consideration. Such problems arise in the study of laser propagation on atmosphere paths, under investigation of directional radiation pattern forming for lasers which gain media is characterized by strong fluctuation of dielectric constant and for lasers which resonator have an atmosphere area. The ray-tracing technique allows us to make effective algorithms for modeling of a partially coherent wave field propagation through inhomogeneous random media is presented for case when the influecne of an optical wave refraction, the influence of the inhomogeiety of radiaitn amplification or absorption, and also the influence of fluctuations of a refraction parameter on target radiation parameters are basic. Novelty of the technique consists in the account of the additional refraction caused by inhomogeneity of gain, and also in the method of an account of turbulent distortions of a beam with any initial coherence allowing to execute construction of effective numerical algorithms. The technique based on the solution of the equation for coherence function of the second order.

Light-Cone and Diffusive Propagation of Correlations in a Many-Body Dissipative System.

PubMed

Bernier, Jean-Sébastien; Tan, Ryan; Bonnes, Lars; Guo, Chu; Poletti, Dario; Kollath, Corinna

2018-01-12

We analyze the propagation of correlations after a sudden interaction change in a strongly interacting quantum system in contact with an environment. In particular, we consider an interaction quench in the Bose-Hubbard model, deep within the Mott-insulating phase, under the effect of dephasing. We observe that dissipation effectively speeds up the propagation of single-particle correlations while reducing their coherence. In contrast, for two-point density correlations, the initial ballistic propagation regime gives way to diffusion at intermediate times. Numerical simulations, based on a time-dependent matrix product state algorithm, are supplemented by a quantitatively accurate fermionic quasiparticle approach providing an intuitive description of the initial dynamics in terms of holon and doublon excitations.

Making the Impossible Possible: Strategies for Fast POMDP Monitoring

NASA Technical Reports Server (NTRS)

Washington, Richard; Lau, Sonie (Technical Monitor)

1998-01-01

Systems modeled as partially observable Markov decision processes (POMDPs) can be tracked quickly with three restrictions: all actions are grouped together, the out-degree of each system state is bounded by a constant, and the number of non-zero elements in the belief state is bounded by a (different) constant. With these restrictions, the tracking algorithm operates in constant time and linear space. The first restriction assumes that the action itself is unobservable. The second restriction defines a subclass of POMDPs that covers however a wide range of problems. The third restriction is an approximation technique that can lead to a potentially vexing problem: an observation may be received that has probability according to the restricted belief state. This problem of impossibility will cause the belief state to collapse. In this paper we discuss the tradeoffs between the constant bound on the belief state and the quality of the solution. We concentrate on strategies for overcoming the impossibility problem and demonstrate initial experimental results that indicate promising directions.

Adaptive multi-time-domain subcycling for crystal plasticity FE modeling of discrete twin evolution

NASA Astrophysics Data System (ADS)

Ghosh, Somnath; Cheng, Jiahao

2018-02-01

Crystal plasticity finite element (CPFE) models that accounts for discrete micro-twin nucleation-propagation have been recently developed for studying complex deformation behavior of hexagonal close-packed (HCP) materials (Cheng and Ghosh in Int J Plast 67:148-170, 2015, J Mech Phys Solids 99:512-538, 2016). A major difficulty with conducting high fidelity, image-based CPFE simulations of polycrystalline microstructures with explicit twin formation is the prohibitively high demands on computing time. High strain localization within fast propagating twin bands requires very fine simulation time steps and leads to enormous computational cost. To mitigate this shortcoming and improve the simulation efficiency, this paper proposes a multi-time-domain subcycling algorithm. It is based on adaptive partitioning of the evolving computational domain into twinned and untwinned domains. Based on the local deformation-rate, the algorithm accelerates simulations by adopting different time steps for each sub-domain. The sub-domains are coupled back after coarse time increments using a predictor-corrector algorithm at the interface. The subcycling-augmented CPFEM is validated with a comprehensive set of numerical tests. Significant speed-up is observed with this novel algorithm without any loss of accuracy that is advantageous for predicting twinning in polycrystalline microstructures.

NASA Perspective and Modeling of Thermal Runaway Propagation Mitigation in Aerospace Batteries

NASA Technical Reports Server (NTRS)

Shack, P.; Iannello, C.; Rickman, S.; Button, R.

2014-01-01

NASA has traditionally sought to reduce the likelihood of a single cell thermal runaway (TR) in their aerospace batteries to an absolute minimum by employing rigorous screening program of the cells. There was generally a belief that TR propagation resulting in catastrophic failure of the battery was a forgone conclusion for densely packed aerospace lithium-ion batteries. As it turns out, this may not be the case. An increasing number of purportedly TR propagation-resistant batteries are appearing among NASA partners in the commercial sector and the Department of Defense. In the recent update of the battery safety standard (JSC 20793) to address this paradigm shift, the NASA community included requirements for assessing TR severity and identifying simple, low-cost severity reduction measures. Unfortunately, there are no best-practice guidelines for this work in the Agency, so the first project team attempting to meet these requirements would have an undue burden placed upon them. A NASA engineering Safety Center (NESC) team set out to perform pathfinding activities for meeting those requirements. This presentation will provide contextual background to this effort, as well as initial results in attempting to model and simulate TR heat transfer and propagation within battery designs.

Parametric inference for biological sequence analysis.

PubMed

Pachter, Lior; Sturmfels, Bernd

2004-11-16

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

Randomness Testing of the Advanced Encryption Standard Finalist Candidates

DTIC Science & Technology

2000-03-28

Excursions Variant 18 168-185 Rank 1 7 Serial 2 186-187 Spectral DFT 1 8 Lempel - Ziv Compression 1 188 Aperiodic Templates 148 9-156 Linear Complexity...256 bits) for each of the algorithms , for a total of 80 different data sets10. These data sets were selected based on the belief that they would be...useful in evaluating the randomness of cryptographic algorithms . Table 2 lists the eight data types. For a description of the data types, see Appendix

Modeling and predicting abstract concept or idea introduction and propagation through geopolitical groups

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Hicklen, Michael L.

2007-04-01

This paper describes a novel capability for modeling known idea propagation transformations and predicting responses to new ideas from geopolitical groups. Ideas are captured using semantic words that are text based and bear cognitive definitions. We demonstrate a unique algorithm for converting these into analytical predictive equations. Using the illustrative idea of "proposing a gasoline price increase of 1 per gallon from 2" and its changing perceived impact throughout 5 demographic groups, we identify 13 cost of living Diplomatic, Information, Military, and Economic (DIME) features common across all 5 demographic groups. This enables the modeling and monitoring of Political, Military, Economic, Social, Information, and Infrastructure (PMESII) effects of each group to this idea and how their "perception" of this proposal changes. Our algorithm and results are summarized in this paper.

Algorithmic Extensions of Low-Dispersion Scheme and Modeling Effects for Acoustic Wave Simulation. Revised

NASA Technical Reports Server (NTRS)

Kaushik, Dinesh K.; Baysal, Oktay

1997-01-01

Accurate computation of acoustic wave propagation may be more efficiently performed when their dispersion relations are considered. Consequently, computational algorithms which attempt to preserve these relations have been gaining popularity in recent years. In the present paper, the extensions to one such scheme are discussed. By solving the linearized, 2-D Euler and Navier-Stokes equations with such a method for the acoustic wave propagation, several issues were investigated. Among them were higher-order accuracy, choice of boundary conditions and differencing stencils, effects of viscosity, low-storage time integration, generalized curvilinear coordinates, periodic series, their reflections and interference patterns from a flat wall and scattering from a circular cylinder. The results were found to be promising en route to the aeroacoustic simulations of realistic engineering problems.

Mathematics of Sensing, Exploitation, and Execution (MSEE) Hierarchical Representations for the Evaluation of Sensed Data

DTIC Science & Technology

2016-06-01

theories of the mammalian visual system, and exploiting descriptive text that may accompany a still image for improved inference. The focus of the Brown...test, computer vision, semantic description , street scenes, belief propagation, generative models, nonlinear filtering, sufficient statistics 16...visual system, and exploiting descriptive text that may accompany a still image for improved inference. The focus of the Brown team was on single images

Data Mining Meets HCI: Making Sense of Large Graphs

DTIC Science & Technology

2012-07-01

graph algo- rithms, won the Open Source Software World Challenge, Silver Award. We have released Pegasus as free , open-source software, downloaded by...METIS [77], spectral clustering [108], and the parameter- free “Cross-associations” (CA) [26]. Belief Propagation can also be used for clus- tering, as...number of tools have been developed to support “ landscape ” views of information. These include WebBook and Web- Forager [23], which use a book metaphor

An investigation of the information propagation and entropy transport aspects of Stirling machine numerical simulation

NASA Technical Reports Server (NTRS)

Goldberg, Louis F.

1992-01-01

Aspects of the information propagation modeling behavior of integral machine computer simulation programs are investigated in terms of a transmission line. In particular, the effects of pressure-linking and temporal integration algorithms on the amplitude ratio and phase angle predictions are compared against experimental and closed-form analytic data. It is concluded that the discretized, first order conservation balances may not be adequate for modeling information propagation effects at characteristic numbers less than about 24. An entropy transport equation suitable for generalized use in Stirling machine simulation is developed. The equation is evaluated by including it in a simulation of an incompressible oscillating flow apparatus designed to demonstrate the effect of flow oscillations on the enhancement of thermal diffusion. Numerical false diffusion is found to be a major factor inhibiting validation of the simulation predictions with experimental and closed-form analytic data. A generalized false diffusion correction algorithm is developed which allows the numerical results to match their analytic counterparts. Under these conditions, the simulation yields entropy predictions which satisfy Clausius' inequality.

Cognitive Network Modeling as a Basis for Characterizing Human Communication Dynamics and Belief Contagion in Technology Adoption

NASA Technical Reports Server (NTRS)

Hutto, Clayton; Briscoe, Erica; Trewhitt, Ethan

2012-01-01

Societal level macro models of social behavior do not sufficiently capture nuances needed to adequately represent the dynamics of person-to-person interactions. Likewise, individual agent level micro models have limited scalability - even minute parameter changes can drastically affect a model's response characteristics. This work presents an approach that uses agent-based modeling to represent detailed intra- and inter-personal interactions, as well as a system dynamics model to integrate societal-level influences via reciprocating functions. A Cognitive Network Model (CNM) is proposed as a method of quantitatively characterizing cognitive mechanisms at the intra-individual level. To capture the rich dynamics of interpersonal communication for the propagation of beliefs and attitudes, a Socio-Cognitive Network Model (SCNM) is presented. The SCNM uses socio-cognitive tie strength to regulate how agents influence--and are influenced by--one another's beliefs during social interactions. We then present experimental results which support the use of this network analytical approach, and we discuss its applicability towards characterizing and understanding human information processing.

Hybrid massively parallel fast sweeping method for static Hamilton-Jacobi equations

NASA Astrophysics Data System (ADS)

Detrixhe, Miles; Gibou, Frédéric

2016-10-01

The fast sweeping method is a popular algorithm for solving a variety of static Hamilton-Jacobi equations. Fast sweeping algorithms for parallel computing have been developed, but are severely limited. In this work, we present a multilevel, hybrid parallel algorithm that combines the desirable traits of two distinct parallel methods. The fine and coarse grained components of the algorithm take advantage of heterogeneous computer architecture common in high performance computing facilities. We present the algorithm and demonstrate its effectiveness on a set of example problems including optimal control, dynamic games, and seismic wave propagation. We give results for convergence, parallel scaling, and show state-of-the-art speedup values for the fast sweeping method.

GPU Accelerated Ultrasonic Tomography Using Propagation and Back Propagation Method

DTIC Science & Technology

2015-09-28

the medical imaging field using GPUs has been done for many years. In [1], Copeland et al. used 2D images , obtained by X - ray projections, to...Index Terms— Medical Imaging , Ultrasonic Tomography, GPU, CUDA, Parallel Computing I. INTRODUCTION GRAPHIC Processing Units (GPUs) are computation... Imaging Algorithm The process of reconstructing images from ultrasonic infor- mation starts with the following acoustical wave equation: ∂2 ∂t2 u ( x

Digital Oblique Remote Ionospheric Sensing (DORIS) Program Development

DTIC Science & Technology

1992-04-01

waveforms. A new with the ARTIST software (Reinisch and Iluang. autoscaling technique for oblique ionograms 1983, Gamache et al., 1985) which is...development and performance of a complete oblique ionogram autoscaling and inversion algorithm is presented. The inver.i-,n algorithm uses a three...OTIH radar. 14. SUBJECT TERMS 15. NUMBER OF PAGES Oblique Propagation; Oblique lonogram Autoscaling ; i Electron Density Profile Inversion; Simulated 16

Constraint Optimization Literature Review

DTIC Science & Technology

2015-11-01

COPs. 15. SUBJECT TERMS high-performance computing, mobile ad hoc network, optimization, constraint, satisfaction 16. SECURITY CLASSIFICATION OF: 17...Optimization Problems 1 2.1 Constraint Satisfaction Problems 1 2.2 Constraint Optimization Problems 3 3. Constraint Optimization Algorithms 9 3.1...Constraint Satisfaction Algorithms 9 3.1.1 Brute-Force search 9 3.1.2 Constraint Propagation 10 3.1.3 Depth-First Search 13 3.1.4 Local Search 18

Guided wave propagation and spectral element method for debonding damage assessment in RC structures

NASA Astrophysics Data System (ADS)

Wang, Ying; Zhu, Xinqun; Hao, Hong; Ou, Jinping

2009-07-01

A concrete-steel interface spectral element is developed to study the guided wave propagation along the steel rebar in the concrete. Scalar damage parameters characterizing changes in the interface (debonding damage) are incorporated into the formulation of the spectral finite element that is used for damage detection of reinforced concrete structures. Experimental tests are carried out on a reinforced concrete beam with embedded piezoelectric elements to verify the performance of the proposed model and algorithm. Parametric studies are performed to evaluate the effect of different damage scenarios on wave propagation in the reinforced concrete structures. Numerical simulations and experimental results show that the method is effective to model wave propagation along the steel rebar in concrete and promising to detect damage in the concrete-steel interface.

SU-F-19A-09: Propagation of Organ at Risk Contours for High Dose Rate Brachytherapy Planning for Cervical Cancer: A Deformable Image Registration Comparison

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bellon, M; Kumarasiri, A; Kim, J

Purpose: To compare the performance of two deformable image registration (DIR) algorithms for contour propagation and to evaluate the accuracy of DIR for use with high dose rate (HDR) brachytherapy planning for cervical cancer. Methods: Five patients undergoing HDR ring and tandem brachytherapy were included in this retrospective study. All patients underwent CT simulation and replanning prior to each fraction (3–5 fractions total). CT-to-CT DIR was performed using two commercially available software platforms: SmartAdapt, Varian Medical Systems (Demons) and Velocity AI, Velocity Medical Solutions (B-spline). Fraction 1 contours were deformed and propagated to each subsequent image set and compared tomore » contours manually drawn by an expert clinician. Dice similarity coefficients (DSC), defined as, DSC(A,B)=2(AandB)/(A+B) were calculated to quantify spatial overlap between manual (A) and deformed (B) contours. Additionally, clinician-assigned visual scores were used to describe and compare the performance of each DIR method and ultimately evaluate which was more clinically acceptable. Scoring was based on a 1–5 scale—with 1 meaning, “clinically acceptable with no contour changes” and 5 meaning, “clinically unacceptable”. Results: Statistically significant differences were not observed between the two DIR algorithms. The average DSC for the bladder, rectum and rectosigmoid were 0.82±0.08, 0.67±0.13 and 0.48±0.18, respectively. The poorest contour agreement was observed for the rectosigmoid due to limited soft tissue contrast and drastic anatomical changes, i.e., organ shape/filling. Two clinicians gave nearly equivalent average scores of 2.75±0.91 for SmartAdapt and 2.75±0.94 for Velocity AI—indicating that for a majority of the cases, more than one of the three contours evaluated required major modifications. Conclusion: Limitations of both DIR algorithms resulted in inaccuracies in contour propagation in the pelvic region, thus hampering the clinical utility of this technology. Further work is required to optimize these algorithms and take advantage of the potential of DIR for HDR brachytherapy planning.« less

The accuracy of dynamic attitude propagation

NASA Technical Reports Server (NTRS)

Harvie, E.; Chu, D.; Woodard, M.

1990-01-01

Propagating attitude by integrating Euler's equation for rigid body motion has long been suggested for the Earth Radiation Budget Satellite (ERBS) but until now has not been implemented. Because of limited Sun visibility, propagation is necessary for yaw determination. With the deterioration of the gyros, dynamic propagation has become more attractive. Angular rates are derived from integrating Euler's equation with a stepsize of 1 second, using torques computed from telemetered control system data. The environmental torque model was quite basic. It included gravity gradient and unshadowed aerodynamic torques. Knowledge of control torques is critical to the accuracy of dynamic modeling. Due to their coarseness and sparsity, control actuator telemetry were smoothed before integration. The dynamic model was incorporated into existing ERBS attitude determination software. Modeled rates were then used for attitude propagation in the standard ERBS fine-attitude algorithm. In spite of the simplicity of the approach, the dynamically propagated attitude matched the attitude propagated with good gyros well for roll and yaw but diverged up to 3 degrees for pitch because of the very low resolution in pitch momentum wheel telemetry. When control anomalies significantly perturb the nominal attitude, the effect of telemetry granularity is reduced and the dynamically propagated attitudes are accurate on all three axes.

The sixth generation robot in space

NASA Technical Reports Server (NTRS)

Butcher, A.; Das, A.; Reddy, Y. V.; Singh, H.

1990-01-01

The knowledge based simulator developed in the artificial intelligence laboratory has become a working test bed for experimenting with intelligent reasoning architectures. With this simulator, recently, small experiments have been done with an aim to simulate robot behavior to avoid colliding paths. An automatic extension of such experiments to intelligently planning robots in space demands advanced reasoning architectures. One such architecture for general purpose problem solving is explored. The robot, seen as a knowledge base machine, goes via predesigned abstraction mechanism for problem understanding and response generation. The three phases in one such abstraction scheme are: abstraction for representation, abstraction for evaluation, and abstraction for resolution. Such abstractions require multimodality. This multimodality requires the use of intensional variables to deal with beliefs in the system. Abstraction mechanisms help in synthesizing possible propagating lattices for such beliefs. The machine controller enters into a sixth generation paradigm.

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network

PubMed Central

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish–Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection. PMID:26447696

Supervised learning of probability distributions by neural networks

NASA Technical Reports Server (NTRS)

Baum, Eric B.; Wilczek, Frank

1988-01-01

Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

PubMed

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm.

PubMed

Wu, Haizhou; Zhou, Yongquan; Luo, Qifang; Basset, Mohamed Abdel

2016-01-01

Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.

Intelligent wear mode identification system for marine diesel engines based on multi-level belief rule base methodology

NASA Astrophysics Data System (ADS)

Yan, Xinping; Xu, Xiaojian; Sheng, Chenxing; Yuan, Chengqing; Li, Zhixiong

2018-01-01

Wear faults are among the chief causes of main-engine damage, significantly influencing the secure and economical operation of ships. It is difficult for engineers to utilize multi-source information to identify wear modes, so an intelligent wear mode identification model needs to be developed to assist engineers in diagnosing wear faults in diesel engines. For this purpose, a multi-level belief rule base (BBRB) system is proposed in this paper. The BBRB system consists of two-level belief rule bases, and the 2D and 3D characteristics of wear particles are used as antecedent attributes on each level. Quantitative and qualitative wear information with uncertainties can be processed simultaneously by the BBRB system. In order to enhance the efficiency of the BBRB, the silhouette value is adopted to determine referential points and the fuzzy c-means clustering algorithm is used to transform input wear information into belief degrees. In addition, the initial parameters of the BBRB system are constructed on the basis of expert-domain knowledge and then optimized by the genetic algorithm to ensure the robustness of the system. To verify the validity of the BBRB system, experimental data acquired from real-world diesel engines are analyzed. Five-fold cross-validation is conducted on the experimental data and the BBRB is compared with the other four models in the cross-validation. In addition, a verification dataset containing different wear particles is used to highlight the effectiveness of the BBRB system in wear mode identification. The verification results demonstrate that the proposed BBRB is effective and efficient for wear mode identification with better performance and stability than competing systems.

Accurate orbit propagation in the presence of planetary close encounters

NASA Astrophysics Data System (ADS)

Amato, Davide; Baù, Giulio; Bombardelli, Claudio

2017-09-01

We present an efficient strategy for the numerical propagation of small Solar system objects undergoing close encounters with massive bodies. The trajectory is split into several phases, each of them being the solution of a perturbed two-body problem. Formulations regularized with respect to different primaries are employed in two subsequent phases. In particular, we consider the Kustaanheimo-Stiefel regularization and a novel set of non-singular orbital elements pertaining to the Dromo family. In order to test the proposed strategy, we perform ensemble propagations in the Earth-Sun Circular Restricted 3-Body Problem (CR3BP) using a variable step size and order multistep integrator and an improved version of Everhart's radau solver of 15th order. By combining the trajectory splitting with regularized equations of motion in short-term propagations (1 year), we gain up to six orders of magnitude in accuracy with respect to the classical Cowell's method for the same computational cost. Moreover, in the propagation of asteroid (99942) Apophis through its 2029 Earth encounter, the position error stays within 100 metres after 100 years. In general, as to improve the performance of regularized formulations, the trajectory must be split between 1.2 and 3 Hill radii from the Earth. We also devise a robust iterative algorithm to stop the integration of regularized equations of motion at a prescribed physical time. The results rigorously hold in the CR3BP, and similar considerations may apply when considering more complex models. The methods and algorithms are implemented in the naples fortran 2003 code, which is available online as a GitHub repository.

TSaT-MUSIC: a novel algorithm for rapid and accurate ultrasonic 3D localization

NASA Astrophysics Data System (ADS)

Mizutani, Kyohei; Ito, Toshio; Sugimoto, Masanori; Hashizume, Hiromichi

2011-12-01

We describe a fast and accurate indoor localization technique using the multiple signal classification (MUSIC) algorithm. The MUSIC algorithm is known as a high-resolution method for estimating directions of arrival (DOAs) or propagation delays. A critical problem in using the MUSIC algorithm for localization is its computational complexity. Therefore, we devised a novel algorithm called Time Space additional Temporal-MUSIC, which can rapidly and simultaneously identify DOAs and delays of mul-ticarrier ultrasonic waves from transmitters. Computer simulations have proved that the computation time of the proposed algorithm is almost constant in spite of increasing numbers of incoming waves and is faster than that of existing methods based on the MUSIC algorithm. The robustness of the proposed algorithm is discussed through simulations. Experiments in real environments showed that the standard deviation of position estimations in 3D space is less than 10 mm, which is satisfactory for indoor localization.

The development of efficient numerical time-domain modeling methods for geophysical wave propagation

NASA Astrophysics Data System (ADS)

Zhu, Lieyuan

This Ph.D. dissertation focuses on the numerical simulation of geophysical wave propagation in the time domain including elastic waves in solid media, the acoustic waves in fluid media, and the electromagnetic waves in dielectric media. This thesis shows that a linear system model can describe accurately the physical processes of those geophysical waves' propagation and can be used as a sound basis for modeling geophysical wave propagation phenomena. The generalized stability condition for numerical modeling of wave propagation is therefore discussed in the context of linear system theory. The efficiency of a series of different numerical algorithms in the time-domain for modeling geophysical wave propagation are discussed and compared. These algorithms include the finite-difference time-domain method, pseudospectral time domain method, alternating directional implicit (ADI) finite-difference time domain method. The advantages and disadvantages of these numerical methods are discussed and the specific stability condition for each modeling scheme is carefully derived in the context of the linear system theory. Based on the review and discussion of these existing approaches, the split step, ADI pseudospectral time domain (SS-ADI-PSTD) method is developed and tested for several cases. Moreover, the state-of-the-art stretched-coordinate perfect matched layer (SCPML) has also been implemented in SS-ADI-PSTD algorithm as the absorbing boundary condition for truncating the computational domain and absorbing the artificial reflection from the domain boundaries. After algorithmic development, a few case studies serve as the real-world examples to verify the capacities of the numerical algorithms and understand the capabilities and limitations of geophysical methods for detection of subsurface contamination. The first case is a study using ground penetrating radar (GPR) amplitude variation with offset (AVO) for subsurface non-aqueous-liquid (NAPL) contamination. The numerical AVO study reveals that the normalized residual polarization (NRP) variation with offset does not respond to subsurface NAPL existence when the offset is close to or larger than its critical value (which corresponds to critical incident angle) because the air and head waves dominate the recorded wave field and severely interfere with reflected waves in the TEz wave field. Thus it can be concluded that the NRP AVO/GPR method is invalid when source-receiver angle offset is close to or greater than its critical value due to incomplete and severely distorted reflection information. In other words, AVO is not a promising technique for detection of the subsurface NAPL, as claimed by some researchers. In addition, the robustness of the newly developed numerical algorithms is also verified by the AVO study for randomly-arranged layered media. Meanwhile, this case study also demonstrates again that the full-wave numerical modeling algorithms are superior to ray tracing method. The second case study focuses on the effect of the existence of a near-surface fault on the vertically incident P- and S- plane waves. The modeling results show that both P-wave vertical incidence and S-wave vertical incidence cases are qualified fault indicators. For the plane S-wave vertical incidence case, the horizontal location of the upper tip of the fault (the footwall side) can be identified without much effort, because all the recorded parameters on the surface including the maximum velocities and the maximum accelerations, and even their ratios H/V, have shown dramatic changes when crossing the upper tip of the fault. The centers of the transition zone of the all the curves of parameters are almost directly above the fault tip (roughly the horizontal center of the model). Compared with the case of the vertically incident P-wave source, it has been found that the S-wave vertical source is a better indicator for fault location, because the horizontal location of the tip of that fault cannot be clearly identified with the ratio of the horizontal to vertical velocity for the P-wave incident case.

An introduction of component fusion extend Kalman filtering method

NASA Astrophysics Data System (ADS)

Geng, Yue; Lei, Xusheng

2018-05-01

In this paper, the Component Fusion Extend Kalman Filtering (CFEKF) algorithm is proposed. Assuming each component of error propagation are independent with Gaussian distribution. The CFEKF can be obtained through the maximum likelihood of propagation error, which can adjust the state transition matrix and the measured matrix adaptively. With minimize linearization error, CFEKF can an effectively improve the estimation accuracy of nonlinear system state. The computation of CFEKF is similar to EKF which is easy for application.

Quantum mechanical generalized phase-shift approach to atom-surface scattering: a Feshbach projection approach to dealing with closed channel effects.

PubMed

Maji, Kaushik; Kouri, Donald J

2011-03-28

We have developed a new method for solving quantum dynamical scattering problems, using the time-independent Schrödinger equation (TISE), based on a novel method to generalize a "one-way" quantum mechanical wave equation, impose correct boundary conditions, and eliminate exponentially growing closed channel solutions. The approach is readily parallelized to achieve approximate N(2) scaling, where N is the number of coupled equations. The full two-way nature of the TISE is included while propagating the wave function in the scattering variable and the full S-matrix is obtained. The new algorithm is based on a "Modified Cayley" operator splitting approach, generalizing earlier work where the method was applied to the time-dependent Schrödinger equation. All scattering variable propagation approaches to solving the TISE involve solving a Helmholtz-type equation, and for more than one degree of freedom, these are notoriously ill-behaved, due to the unavoidable presence of exponentially growing contributions to the numerical solution. Traditionally, the method used to eliminate exponential growth has posed a major obstacle to the full parallelization of such propagation algorithms. We stabilize by using the Feshbach projection operator technique to remove all the nonphysical exponentially growing closed channels, while retaining all of the propagating open channel components, as well as exponentially decaying closed channel components.

Interoperable Communications for Hierarchical Heterogeneous Wireless Networks

DTIC Science & Technology

2016-04-01

The PhD student , Nan Zou supervised by the PI, won the Best Poster Award in the STEAM Research Symposium on March 21, 2014. Received Book Chapter TOTAL...Belief Propagation for spectrum awareness within one network for the multiple channel case in a previous study [86] 39 Figure 2.2 An illustration of the...wireless networks enabled by cognitive radio technology. The PIs have been working closely with students to carry out all the proposed research tasks

Comparacion de Modelos de Educacion Sexual en El Conocimiento y Cambio de Actitudes en Practicas Sexuales por Alumnos de Nivel Superior en La Region De Caguas, Puerto Rico

ERIC Educational Resources Information Center

Juan, Vallejo Ramos L.

2012-01-01

In opposition to the Sexual Education Traditional Model (SETM) that is used in the state schools of Puerto Rico, the Health Beliefs Model (HBM) appears. It facilitates a curricular design that improves the ability of the students to respond to the group pressure by means of attitudes that stimulate sexual conducts of smaller risk of propagation of…

The method of belief scales as a means for dealing with uncertainty in tough regulatory decisions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pilch, Martin M.

Modeling and simulation is playing an increasing role in supporting tough regulatory decisions, which are typically characterized by variabilities and uncertainties in the scenarios, input conditions, failure criteria, model parameters, and even model form. Variability exists when there is a statistically significant database that is fully relevant to the application. Uncertainty, on the other hand, is characterized by some degree of ignorance. A simple algebraic problem was used to illustrate how various risk methodologies address variability and uncertainty in a regulatory context. These traditional risk methodologies include probabilistic methods (including frequensic and Bayesian perspectives) and second-order methods where variabilities andmore » uncertainties are treated separately. Representing uncertainties with (subjective) probability distributions and using probabilistic methods to propagate subjective distributions can lead to results that are not logically consistent with available knowledge and that may not be conservative. The Method of Belief Scales (MBS) is developed as a means to logically aggregate uncertain input information and to propagate that information through the model to a set of results that are scrutable, easily interpretable by the nonexpert, and logically consistent with the available input information. The MBS, particularly in conjunction with sensitivity analyses, has the potential to be more computationally efficient than other risk methodologies. The regulatory language must be tailored to the specific risk methodology if ambiguity and conflict are to be avoided.« less

An Ensemble-Based Smoother with Retrospectively Updated Weights for Highly Nonlinear Systems

NASA Technical Reports Server (NTRS)

Chin, T. M.; Turmon, M. J.; Jewell, J. B.; Ghil, M.

2006-01-01

Monte Carlo computational methods have been introduced into data assimilation for nonlinear systems in order to alleviate the computational burden of updating and propagating the full probability distribution. By propagating an ensemble of representative states, algorithms like the ensemble Kalman filter (EnKF) and the resampled particle filter (RPF) rely on the existing modeling infrastructure to approximate the distribution based on the evolution of this ensemble. This work presents an ensemble-based smoother that is applicable to the Monte Carlo filtering schemes like EnKF and RPF. At the minor cost of retrospectively updating a set of weights for ensemble members, this smoother has demonstrated superior capabilities in state tracking for two highly nonlinear problems: the double-well potential and trivariate Lorenz systems. The algorithm does not require retrospective adaptation of the ensemble members themselves, and it is thus suited to a streaming operational mode. The accuracy of the proposed backward-update scheme in estimating non-Gaussian distributions is evaluated by comparison to the more accurate estimates provided by a Markov chain Monte Carlo algorithm.

Mining Top K Spread Sources for a Specific Topic and a Given Node.

PubMed

Liu, Weiwei; Deng, Zhi-Hong; Cao, Longbing; Xu, Xiaoran; Liu, He; Gong, Xiuwen

2015-11-01

In social networks, nodes (or users) interested in specific topics are often influenced by others. The influence is usually associated with a set of nodes rather than a single one. An interesting but challenging task for any given topic and node is to find the set of nodes that represents the source or trigger for the topic and thus identify those nodes that have the greatest influence on the given node as the topic spreads. We find that it is an NP-hard problem. This paper proposes an effective framework to deal with this problem. First, the topic propagation is represented as the Bayesian network. We then construct the propagation model by a variant of the voter model. The probability transition matrix (PTM) algorithm is presented to conduct the probability inference with the complexity O(θ(3)log2θ), while θ is the number nodes in the given graph. To evaluate the PTM algorithm, we conduct extensive experiments on real datasets. The experimental results show that the PTM algorithm is both effective and efficient.

Classification of multispectral image data by the Binary Diamond neural network and by nonparametric, pixel-by-pixel methods

NASA Technical Reports Server (NTRS)

Salu, Yehuda; Tilton, James

1993-01-01

The classification of multispectral image data obtained from satellites has become an important tool for generating ground cover maps. This study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A new neural network, the Binary Diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The Binary Diamond is a multilayer, feed-forward neural network, which learns from examples in unsupervised, 'one-shot' mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done by using a realistic data base, consisting of approximately 90,000 Landsat 4 Thematic Mapper pixels. The Binary Diamond and the nearest neighbor performances were close, with some advantages to the Binary Diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories, and analyzing nonboundary pixels, are addressed and evaluated.

Optimization of training backpropagation algorithm using nguyen widrow for angina ludwig diagnosis

NASA Astrophysics Data System (ADS)

Aisyah, Siti; Harahap, Mawaddah; Mahmud Husein Siregar, Amir; Turnip, Mardi

2018-04-01

Tooth and mouth disease is a common disease, with a prevalence of more than 40% (children aged less than 7 years) in milk teeth and about 85% (adults aged 17 years and over) on permanent teeth. Angina Ludwig is one of mouth disease type that occurs due to infection of the tooth root and trauma of the mouth. ‘In this study back propagation algorithm applied to diagnose AnginaLudwig disease (using Nguyen Widrow method in optimization of training time). From the experimental results, it is known that the average BPNN by using Nguyen Widrow is much faster which is about 0.0624 seconds and 0.1019 seconds (without NguyenWidrow). In contrast, for pattern recognition needs, found that back propagation without Nguyen Widrow is much better that is with 90% accuracy (only 70% with NguyenWidrow).

The impact of robustness of deformable image registration on contour propagation and dose accumulation for head and neck adaptive radiotherapy.

PubMed

Zhang, Lian; Wang, Zhi; Shi, Chengyu; Long, Tengfei; Xu, X George

2018-05-30

Deformable image registration (DIR) is the key process for contour propagation and dose accumulation in adaptive radiation therapy (ART). However, currently, ART suffers from a lack of understanding of "robustness" of the process involving the image contour based on DIR and subsequent dose variations caused by algorithm itself and the presetting parameters. The purpose of this research is to evaluate the DIR caused variations for contour propagation and dose accumulation during ART using the RayStation treatment planning system. Ten head and neck cancer patients were selected for retrospective studies. Contours were performed by a single radiation oncologist and new treatment plans were generated on the weekly CT scans for all patients. For each DIR process, four deformation vector fields (DVFs) were generated to propagate contours and accumulate weekly dose by the following algorithms: (a) ANACONDA with simple presetting parameters, (b) ANACONDA with detailed presetting parameters, (c) MORFEUS with simple presetting parameters, and (d) MORFEUS with detailed presetting parameters. The geometric evaluation considered DICE coefficient and Hausdorff distance. The dosimetric evaluation included D 95 , D max , D mean , D min , and Homogeneity Index. For geometric evaluation, the DICE coefficient variations of the GTV were found to be 0.78 ± 0.11, 0.96 ± 0.02, 0.64 ± 0.15, and 0.91 ± 0.03 for simple ANACONDA, detailed ANACONDA, simple MORFEUS, and detailed MORFEUS, respectively. For dosimetric evaluation, the corresponding Homogeneity Index variations were found to be 0.137 ± 0.115, 0.006 ± 0.032, 0.197 ± 0.096, and 0.006 ± 0.033, respectively. The coherent geometric and dosimetric variations also consisted in large organs and small organs. Overall, the results demonstrated that the contour propagation and dose accumulation in clinical ART were influenced by the DIR algorithm, and to a greater extent by the presetting parameters. A quality assurance procedure should be established for the proper use of a commercial DIR for adaptive radiation therapy. © 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

SU-F-J-58: Evaluation of RayStation Hybrid Deformable Image Registration for Accurate Contour Propagation in Adaptive Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rong, Y; Rao, S; Daly, M

Purpose: Adaptive radiotherapy requires complete new sets of regions of interests (ROIs) delineation on the mid-treatment CT images. This work aims at evaluating the accuracy of the RayStation hybrid deformable image registration (DIR) algorithm for its overall integrity and accuracy in contour propagation for adaptive planning. Methods: The hybrid DIR is based on the combination of intensity-based algorithm and anatomical information provided by contours. Patients who received mid-treatment CT scans were identified for the study, including six lung patients (two mid-treatment CTs) and six head-and-neck (HN) patients (one mid-treatment CT). DIRpropagated ROIs were compared with physician-drawn ROIs for 8 ITVsmore » and 7 critical organs (lungs, heart, esophagus, and etc.) for the lung patients, as well as 14 GTVs and 20 critical organs (mandible, eyes, parotids, and etc.) for the HN patients. Volume difference, center of mass (COM) difference, and Dice index were used for evaluation. Clinical-relevance of each propagated ROI was scored by two physicians, and correlated with the Dice index. Results: For critical organs, good agreement (Dice>0.9) were seen on all 7 for lung patients and 13 out of 20 for HN patients, with the rest requiring minimal edits. For targets, COM differences were within 5 mm on average for all patients. For Lung, 5 out of 8 ITVs required minimal edits (Dice 0.8–0.9), with the rest 2 needed re-drawn due to their small volumes (<10 cc). However, the propagated HN GTVs resulted in relatively low Dice values (0.5–0.8) due to their small volumes (3–40 cc) and high variability, among which 2 required re-drawn due to new nodal target identified on the mid-treatment CT scans. Conclusion: The hybrid DIR algorithm was found to be clinically useful and efficient for lung and HN patients, especially for propagated critical organ ROIs. It has potential to significantly improve the workflow in adaptive planning.« less

Hybrid massively parallel fast sweeping method for static Hamilton–Jacobi equations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Detrixhe, Miles, E-mail: mdetrixhe@engineering.ucsb.edu; University of California Santa Barbara, Santa Barbara, CA, 93106; Gibou, Frédéric, E-mail: fgibou@engineering.ucsb.edu

The fast sweeping method is a popular algorithm for solving a variety of static Hamilton–Jacobi equations. Fast sweeping algorithms for parallel computing have been developed, but are severely limited. In this work, we present a multilevel, hybrid parallel algorithm that combines the desirable traits of two distinct parallel methods. The fine and coarse grained components of the algorithm take advantage of heterogeneous computer architecture common in high performance computing facilities. We present the algorithm and demonstrate its effectiveness on a set of example problems including optimal control, dynamic games, and seismic wave propagation. We give results for convergence, parallel scaling,more » and show state-of-the-art speedup values for the fast sweeping method.« less

TrustRank: a Cold-Start tolerant recommender system

NASA Astrophysics Data System (ADS)

Zou, Haitao; Gong, Zhiguo; Zhang, Nan; Zhao, Wei; Guo, Jingzhi

2015-02-01

The explosive growth of the World Wide Web leads to the fast advancing development of e-commerce techniques. Recommender systems, which use personalised information filtering techniques to generate a set of items suitable to a given user, have received considerable attention. User- and item-based algorithms are two popular techniques for the design of recommender systems. These two algorithms are known to have Cold-Start problems, i.e., they are unable to effectively handle Cold-Start users who have an extremely limited number of purchase records. In this paper, we develop TrustRank, a novel recommender system which handles the Cold-Start problem by leveraging the user-trust networks which are commonly available for e-commerce applications. A user-trust network is formed by friendships or trust relationships that users specify among them. While it is straightforward to conjecture that a user-trust network is helpful for improving the accuracy of recommendations, a key challenge for using user-trust network to facilitate Cold-Start users is that these users also tend to have a very limited number of trust relationships. To address this challenge, we propose a pre-processing propagation of the Cold-Start users' trust network. In particular, by applying the personalised PageRank algorithm, we expand the friends of a given user to include others with similar purchase records to his/her original friends. To make this propagation algorithm scalable to a large amount of users, as required by real-world recommender systems, we devise an iterative computation algorithm of the original personalised TrustRank which can incrementally compute trust vectors for Cold-Start users. We conduct extensive experiments to demonstrate the consistently improvement provided by our proposed algorithm over the existing recommender algorithms on the accuracy of recommendations for Cold-Start users.

Ray-tracing method for creeping waves on arbitrarily shaped nonuniform rational B-splines surfaces.

PubMed

Chen, Xi; He, Si-Yuan; Yu, Ding-Feng; Yin, Hong-Cheng; Hu, Wei-Dong; Zhu, Guo-Qiang

2013-04-01

An accurate creeping ray-tracing algorithm is presented in this paper to determine the tracks of creeping waves (or creeping rays) on arbitrarily shaped free-form parametric surfaces [nonuniform rational B-splines (NURBS) surfaces]. The main challenge in calculating the surface diffracted fields on NURBS surfaces is due to the difficulty in determining the geodesic paths along which the creeping rays propagate. On one single parametric surface patch, the geodesic paths need to be computed by solving the geodesic equations numerically. Furthermore, realistic objects are generally modeled as the union of several connected NURBS patches. Due to the discontinuity of the parameter between the patches, it is more complicated to compute geodesic paths on several connected patches than on one single patch. Thus, a creeping ray-tracing algorithm is presented in this paper to compute the geodesic paths of creeping rays on the complex objects that are modeled as the combination of several NURBS surface patches. In the algorithm, the creeping ray tracing on each surface patch is performed by solving the geodesic equations with a Runge-Kutta method. When the creeping ray propagates from one patch to another, a transition method is developed to handle the transition of the creeping ray tracing across the border between the patches. This creeping ray-tracing algorithm can meet practical requirements because it can be applied to the objects with complex shapes. The algorithm can also extend the applicability of NURBS for electromagnetic and optical applications. The validity and usefulness of the algorithm can be verified from the numerical results.

NEOPROP: A NEO Propagator for Space Situational Awareness

NASA Astrophysics Data System (ADS)

Zuccarelli, Valentino; Bancelin, David; Weikert, Sven; Thuillot, William; Hestroffer, Daniel; Yabar Valle, Celia; Koschny, Detlef

2013-09-01

The overall aim of the Space Situational Awareness (SSA) Preparatory Programme is to support the European independent utilisation of and access to space for research or services, through providing timely and quality data, information, services and knowledge regarding the environment, the threats and the sustainable exploitation of the outer space surrounding our planet Earth. The SSA system will comprise three main segments:• Space Weather (SWE) monitoring and forecast• Near-Earth Objects (NEO) survey and follow-up• Space Surveillance and Tracking (SST) of man-made space objectsCurrently, there are over 600.000 asteroids known in our Solar System, where more than 9.500 of these are NEOs. These could potentially hit our planet and depending on their size could produce considerable damage. For this reason NEOs deserve active detection and tracking efforts.The role of the SSA programme is to provide warning services against potential asteroid impact hazards, including discovery, identification, orbit prediction and civil alert capabilities. ESA is now working to develop a NEO Coordination Centre which will later evolve into a SSA-NEO Small Bodies Data Centre (SBDC), located at ESA/ESRIN, Italy. The Software prototype developed in the frame of this activity may be later implemented as a part of the SSA-NEO programme simulators aimed at assessing the trajectory of asteroids. There already exist different algorithms to predict orbits for NEOs. The objective of this activity is to come up with a different trajectory prediction algorithm, which allows an independent validation of the current algorithms within the SSA-NEO segment (e.g. NEODyS, JPL Sentry System).The key objective of this activity was to design, develop, test, verify, and validate trajectory prediction algorithm of NEOs in order to be able to computeanalytically and numerically the minimum orbital intersection distances (MOIDs).The NEOPROP software consists of two separate modules/tools:1. The Analytical Module makes use of analytical algorithms in order to rapidly assess the impact risk of a NEO. It is responsible for the preliminary analysis. Orbit Determination algorithms, as the Gauss and the Linear Least Squares (LLS) methods, will determine the initial state (from MPC observations), along with its uncertainty, and the MOID of the NEO (analytically).2. The Numerical Module makes use of numerical algorithms in order to refine and to better assess the impact probabilities. The initial state provided by the orbit determination process will be used to numerically propagate the trajectory. The numerical propagation can be run in two modes: one faster ("fast analysis"), in order to get a fast evaluation of the trajectory and one more precise ("complete analysis") taking into consideration more detailed perturbation models. Moreover, a configurable number of Virtual Asteroids (VAs) will be numerically propagated in order to determine the Earth closest approach. This new "MOID" computation differs from the analytical one since it takes into consideration the full dynamics of the problem.

Combat Identification of Synthetic Aperture Radar Images Using Contextual Features and Bayesian Belief Networks

DTIC Science & Technology

2012-03-01

algorithms. Alternatively, features invariant to aspect and de - pression angles must be found. This research uses simple segmentation algorithms and...FN5 Out of Library SA-8 TZM SA-8 Reload vehicle N 6 N OOL1 BMP-1 Tank w/small turret Y 0 Y OOL2 BTR-70 8-wheeled transport N 8 N OOL3 SA-13 Turret SAMs...the NDEC threshold is established by selecting a user de - fined quantile of class’ entropy distriubtion. During testing, any test exemplar with an

Model-Based IN SITU Parameter Estimation of Ultrasonic Guided Waves in AN Isotropic Plate

NASA Astrophysics Data System (ADS)

Hall, James S.; Michaels, Jennifer E.

2010-02-01

Most ultrasonic systems employing guided waves for flaw detection require information such as dispersion curves, transducer locations, and expected propagation loss. Degraded system performance may result if assumed parameter values do not accurately reflect the actual environment. By characterizing the propagating environment in situ at the time of test, potentially erroneous a priori estimates are avoided and performance of ultrasonic guided wave systems can be improved. A four-part model-based algorithm is described in the context of previous work that estimates model parameters whereby an assumed propagation model is used to describe the received signals. This approach builds upon previous work by demonstrating the ability to estimate parameters for the case of single mode propagation. Performance is demonstrated on signals obtained from theoretical dispersion curves, finite element modeling, and experimental data.

Fast Noncircular 2D-DOA Estimation for Rectangular Planar Array

PubMed Central

Xu, Lingyun; Wen, Fangqing

2017-01-01

A novel scheme is proposed for direction finding with uniform rectangular planar array. First, the characteristics of noncircular signals and Euler’s formula are exploited to construct a new real-valued rectangular array data. Then, the rotational invariance relations for real-valued signal space are depicted in a new way. Finally the real-valued propagator method is utilized to estimate the pairing two-dimensional direction of arrival (2D-DOA). The proposed algorithm provides better angle estimation performance and can discern more sources than the 2D propagator method. At the same time, it has very close angle estimation performance to the noncircular propagator method (NC-PM) with reduced computational complexity. PMID:28417926

Discovering weighted patterns in intron sequences using self-adaptive harmony search and back-propagation algorithms.

PubMed

Huang, Yin-Fu; Wang, Chia-Ming; Liou, Sing-Wu

2013-01-01

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete.

Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms

PubMed Central

Wang, Chia-Ming; Liou, Sing-Wu

2013-01-01

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete. PMID:23737711

A higher-order split-step Fourier parabolic-equation sound propagation solution scheme.

PubMed

Lin, Ying-Tsong; Duda, Timothy F

2012-08-01

A three-dimensional Cartesian parabolic-equation model with a higher-order approximation to the square-root Helmholtz operator is presented for simulating underwater sound propagation in ocean waveguides. The higher-order approximation includes cross terms with the free-space square-root Helmholtz operator and the medium phase speed anomaly. It can be implemented with a split-step Fourier algorithm to solve for sound pressure in the model. Two idealized ocean waveguide examples are presented to demonstrate the performance of this numerical technique.

A Statistical Analysis of IrisCode and Its Security Implications.

PubMed

Kong, Adams Wai-Kin

2015-03-01

IrisCode has been used to gather iris data for 430 million people. Because of the huge impact of IrisCode, it is vital that it is completely understood. This paper first studies the relationship between bit probabilities and a mean of iris images (The mean of iris images is defined as the average of independent iris images.) and then uses the Chi-square statistic, the correlation coefficient and a resampling algorithm to detect statistical dependence between bits. The results show that the statistical dependence forms a graph with a sparse and structural adjacency matrix. A comparison of this graph with a graph whose edges are defined by the inner product of the Gabor filters that produce IrisCodes shows that partial statistical dependence is induced by the filters and propagates through the graph. Using this statistical information, the security risk associated with two patented template protection schemes that have been deployed in commercial systems for producing application-specific IrisCodes is analyzed. To retain high identification speed, they use the same key to lock all IrisCodes in a database. The belief has been that if the key is not compromised, the IrisCodes are secure. This study shows that even without the key, application-specific IrisCodes can be unlocked and that the key can be obtained through the statistical dependence detected.

Fully coupled simulation of multiple hydraulic fractures to propagate simultaneously from a perforated horizontal wellbore

NASA Astrophysics Data System (ADS)

Zeng, Qinglei; Liu, Zhanli; Wang, Tao; Gao, Yue; Zhuang, Zhuo

2018-02-01

In hydraulic fracturing process in shale rock, multiple fractures perpendicular to a horizontal wellbore are usually driven to propagate simultaneously by the pumping operation. In this paper, a numerical method is developed for the propagation of multiple hydraulic fractures (HFs) by fully coupling the deformation and fracturing of solid formation, fluid flow in fractures, fluid partitioning through a horizontal wellbore and perforation entry loss effect. The extended finite element method (XFEM) is adopted to model arbitrary growth of the fractures. Newton's iteration is proposed to solve these fully coupled nonlinear equations, which is more efficient comparing to the widely adopted fixed-point iteration in the literatures and avoids the need to impose fluid pressure boundary condition when solving flow equations. A secant iterative method based on the stress intensity factor (SIF) is proposed to capture different propagation velocities of multiple fractures. The numerical results are compared with theoretical solutions in literatures to verify the accuracy of the method. The simultaneous propagation of multiple HFs is simulated by the newly proposed algorithm. The coupled influences of propagation regime, stress interaction, wellbore pressure loss and perforation entry loss on simultaneous propagation of multiple HFs are investigated.

High-order nonuniformly correlated beams

NASA Astrophysics Data System (ADS)

Wu, Dan; Wang, Fei; Cai, Yangjian

2018-02-01

We have introduced a class of partially coherent beams with spatially varying correlations named high-order nonuniformly correlated (HNUC) beams, as an extension of conventional nonuniformly correlated (NUC) beams. Such beams bring a new parameter (mode order) which is used to tailor the spatial coherence properties. The behavior of the spectral density of the HNUC beams on propagation has been investigated through numerical examples with the help of discrete model decomposition and fast Fourier transform (FFT) algorithm. Our results reveal that by selecting the mode order appropriately, the more sharpened intensity maxima can be achieved at a certain propagation distance compared to that of the NUC beams, and the lateral shift of the intensity maxima on propagation is closed related to the mode order. Furthermore, analytical expressions for the r.m.s width and the propagation factor of the HNUC beams on free-space propagation are derived by means of Wigner distribution function. The influence of initial beam parameters on the evolution of the r.m.s width and the propagation factor, and the relation between the r.m.s width and the occurring of the sharpened intensity maxima on propagation have been studied and discussed in detail.

Topological leakage detection and freeze-and-grow propagation for improved CT-based airway segmentation

NASA Astrophysics Data System (ADS)

Nadeem, Syed Ahmed; Hoffman, Eric A.; Sieren, Jered P.; Saha, Punam K.

2018-03-01

Numerous large multi-center studies are incorporating the use of computed tomography (CT)-based characterization of the lung parenchyma and bronchial tree to understand chronic obstructive pulmonary disease status and progression. To the best of our knowledge, there are no fully automated airway tree segmentation methods, free of the need for user review. A failure in even a fraction of segmentation results necessitates manual revision of all segmentation masks which is laborious considering the thousands of image data sets evaluated in large studies. In this paper, we present a novel CT-based airway tree segmentation algorithm using topological leakage detection and freeze-and-grow propagation. The method is fully automated requiring no manual inputs or post-segmentation editing. It uses simple intensity-based connectivity and a freeze-and-grow propagation algorithm to iteratively grow the airway tree starting from an initial seed inside the trachea. It begins with a conservative parameter and then, gradually shifts toward more generous parameter values. The method was applied on chest CT scans of fifteen subjects at total lung capacity. Airway segmentation results were qualitatively assessed and performed comparably to established airway segmentation method with no major visual leakages.

Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks*

PubMed Central

Bandeira, Nuno

2016-01-01

Peptide and protein identification remains challenging in organisms with poorly annotated or rapidly evolving genomes, as are commonly encountered in environmental or biofuels research. Such limitations render tandem mass spectrometry (MS/MS) database search algorithms ineffective as they lack corresponding sequences required for peptide-spectrum matching. We address this challenge with the spectral networks approach to (1) match spectra of orthologous peptides across multiple related species and then (2) propagate peptide annotations from identified to unidentified spectra. We here present algorithms to assess the statistical significance of spectral alignments (Align-GF), reduce the impurity in spectral networks, and accurately estimate the error rate in propagated identifications. Analyzing three related Cyanothece species, a model organism for biohydrogen production, spectral networks identified peptides from highly divergent sequences from networks with dozens of variant peptides, including thousands of peptides in species lacking a sequenced genome. Our analysis further detected the presence of many novel putative peptides even in genomically characterized species, thus suggesting the possibility of gaps in our understanding of their proteomic and genomic expression. A web-based pipeline for spectral networks analysis is available at http://proteomics.ucsd.edu/software. PMID:27609420

Localization of marine mammals near Hawaii using an acoustic propagation model

NASA Astrophysics Data System (ADS)

Tiemann, Christopher O.; Porter, Michael B.; Frazer, L. Neil

2004-06-01

Humpback whale songs were recorded on six widely spaced receivers of the Pacific Missile Range Facility (PMRF) hydrophone network near Hawaii during March of 2001. These recordings were used to test a new approach to localizing the whales that exploits the time-difference of arrival (time lag) of their calls as measured between receiver pairs in the PMRF network. The usual technique for estimating source position uses the intersection of hyperbolic curves of constant time lag, but a drawback of this approach is its assumption of a constant wave speed and straight-line propagation to associate acoustic travel time with range. In contrast to hyperbolic fixing, the algorithm described here uses an acoustic propagation model to account for waveguide and multipath effects when estimating travel time from hypothesized source positions. A comparison between predicted and measured time lags forms an ambiguity surface, or visual representation of the most probable whale position in a horizontal plane around the array. This is an important benefit because it allows for automated peak extraction to provide a location estimate. Examples of whale localizations using real and simulated data in algorithms of increasing complexity are provided.

Best-Fit Conic Approximation of Spacecraft Trajectory

NASA Technical Reports Server (NTRS)

Singh, Gurkipal

2005-01-01

A computer program calculates a best conic fit of a given spacecraft trajectory. Spacecraft trajectories are often propagated as conics onboard. The conic-section parameters as a result of the best-conic-fit are uplinked to computers aboard the spacecraft for use in updating predictions of the spacecraft trajectory for operational purposes. In the initial application for which this program was written, there is a requirement to fit a single conic section (necessitated by onboard memory constraints) accurate within 200 microradians to a sequence of positions measured over a 4.7-hour interval. The present program supplants a prior one that could not cover the interval with fewer than four successive conic sections. The present program is based on formulating the best-fit conic problem as a parameter-optimization problem and solving the problem numerically, on the ground, by use of a modified steepest-descent algorithm. For the purpose of this algorithm, optimization is defined as minimization of the maximum directional propagation error across the fit interval. In the specific initial application, the program generates a single 4.7-hour conic, the directional propagation of which is accurate to within 34 microradians easily exceeding the mission constraints by a wide margin.

Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm

PubMed Central

Wu, Haizhou; Luo, Qifang

2016-01-01

Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared. PMID:28105044

Accumulate Repeat Accumulate Coded Modulation

NASA Technical Reports Server (NTRS)

Abbasfar, Aliazam; Divsalar, Dariush; Yao, Kung

2004-01-01

In this paper we propose an innovative coded modulation scheme called 'Accumulate Repeat Accumulate Coded Modulation' (ARA coded modulation). This class of codes can be viewed as serial turbo-like codes, or as a subclass of Low Density Parity Check (LDPC) codes that are combined with high level modulation. Thus at the decoder belief propagation can be used for iterative decoding of ARA coded modulation on a graph, provided a demapper transforms the received in-phase and quadrature samples to reliability of the bits.

Propagation of Bayesian Belief for Near-Real Time Statistical Assessment of Geosynchronous Satellite Status Based on Non-Resolved Photometry Data

DTIC Science & Technology

2014-09-01

of the BRDF for the Body and Panel. In order to provide a continuously updated baseline, the Photometry Model application is performed using a...brightness to its predicted brightness. The brightness predictions can be obtained using any analytical model chosen by the user. The inference for a...the analytical model as possible; and to mitigate the effect of bias that could be introduced by the choice of analytical model . It considers that a

Third-dimension information retrieval from a single convergent-beam transmission electron diffraction pattern using an artificial neural network

NASA Astrophysics Data System (ADS)

Pennington, Robert S.; Van den Broek, Wouter; Koch, Christoph T.

2014-05-01

We have reconstructed third-dimension specimen information from convergent-beam electron diffraction (CBED) patterns simulated using the stacked-Bloch-wave method. By reformulating the stacked-Bloch-wave formalism as an artificial neural network and optimizing with resilient back propagation, we demonstrate specimen orientation reconstructions with depth resolutions down to 5 nm. To show our algorithm's ability to analyze realistic data, we also discuss and demonstrate our algorithm reconstructing from noisy data and using a limited number of CBED disks. Applicability of this reconstruction algorithm to other specimen parameters is discussed.

Born approximation, multiple scattering, and butterfly algorithm

NASA Astrophysics Data System (ADS)

Martinez, Alex; Qiao, Zhijun

2014-06-01

Many imaging algorithms have been designed assuming the absence of multiple scattering. In the 2013 SPIE proceeding, we discussed an algorithm for removing high order scattering components from collected data. In this paper, our goal is to continue this work. First, we survey the current state of multiple scattering in SAR. Then, we revise our method and test it. Given an estimate of our target reflectivity, we compute the multi scattering effects in our target region for various frequencies. Furthermore, we propagate this energy through free space towards our antenna, and remove it from the collected data.

Evaluation of Swift Start TCP in Long-Delay Environment

NASA Technical Reports Server (NTRS)

Lawas-Grodek, Frances J.; Tran, Diepchi T.

2004-01-01

This report presents the test results of the Swift Start algorithm in single-flow and multiple-flow testbeds under the effects of high propagation delays, various slow bottlenecks, and small queue sizes. Although this algorithm estimates capacity and implements packet pacing, the findings were that in a heavily congested link, the Swift Start algorithm will not be applicable. The reason is that the bottleneck estimation is falsely influenced by timeouts induced by retransmissions and the expiration of delayed acknowledgment (ACK) timers, thus causing the modified Swift Start code to fall back to regular transmission control protocol (TCP).

Elastic Reverse Time Migration (RTM) From Surface Topography

NASA Astrophysics Data System (ADS)

Akram, Naveed; Chen, Xiaofei

2017-04-01

Seismic Migration is a promising data processing technique to construct subsurface images by projecting the recorded seismic data at surface back to their origins. There are numerous Migration methods. Among them, Reverse Time Migration (RTM) is considered a robust and standard imaging technology in present day exploration industry as well as in academic research field because of its superior performance compared to traditional migration methods. Although RTM is extensive computing and time consuming but it can efficiently handle the complex geology, highly dipping reflectors and strong lateral velocity variation all together. RTM takes data recorded at the surface as a boundary condition and propagates the data backwards in time until the imaging condition is met. It can use the same modeling algorithm that we use for forward modeling. The classical seismic exploration theory assumes flat surface which is almost impossible in practice for land data. So irregular surface topography has to be considered in simulation of seismic wave propagation, which is not always a straightforward undertaking. In this study, Curved grid finite difference method (CG-FDM) is adapted to model elastic seismic wave propagation to investigate the effect of surface topography on RTM results and explore its advantages and limitations with synthetic data experiments by using Foothill model with topography as the true model. We focus on elastic wave propagation rather than acoustic wave because earth actually behaves as an elastic body. Our results strongly emphasize on the fact that irregular surface topography must be considered for modeling of seismic wave propagation to get better subsurface images specially in mountainous scenario and suggest practitioners to properly handled the geometry of data acquired on irregular topographic surface in their imaging algorithms.

Elastic Reverse Time Migration (RTM) From Surface Topography

NASA Astrophysics Data System (ADS)

Naveed, A.; Chen, X.

2016-12-01

Seismic Migration is a promising data processing technique to construct subsurface images by projecting the recorded seismic data at surface back to their origins. There are numerous Migration methods. Among them, Reverse Time Migration (RTM) is considered a robust and standard imaging technology in present day exploration industry as well as in academic research field because of its superior performance compared to traditional migration methods. Although RTM is extensive computing and time consuming but it can efficiently handle the complex geology, highly dipping reflectors and strong lateral velocity variation all together. RTM takes data recorded at the surface as a boundary condition and propagates the data backwards in time until the imaging condition is met. It can use the same modeling algorithm that we use for forward modeling. The classical seismic exploration theory assumes flat surface which is almost impossible in practice for land data. So irregular surface topography has to be considered in simulation of seismic wave propagation, which is not always a straightforward undertaking. In this study, Curved grid finite difference method (CG-FDM) is adapted to model elastic seismic wave propagation to investigate the effect of surface topography on RTM results and explore its advantages and limitations with synthetic data experiments by using Foothill model with topography as the true model. We focus on elastic wave propagation rather than acoustic wave because earth actually behaves as an elastic body. Our results strongly emphasize on the fact that irregular surface topography must be considered for modeling of seismic wave propagation to get better subsurface images specially in mountainous scenario and suggest practitioners to properly handled the geometry of data acquired on irregular topographic surface in their imaging algorithms.

A local crack-tracking strategy to model three-dimensional crack propagation with embedded methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Annavarapu, Chandrasekhar; Settgast, Randolph R.; Vitali, Efrem

We develop a local, implicit crack tracking approach to propagate embedded failure surfaces in three-dimensions. We build on the global crack-tracking strategy of Oliver et al. (Int J. Numer. Anal. Meth. Geomech., 2004; 28:609–632) that tracks all potential failure surfaces in a problem at once by solving a Laplace equation with anisotropic conductivity. We discuss important modifications to this algorithm with a particular emphasis on the effect of the Dirichlet boundary conditions for the Laplace equation on the resultant crack path. Algorithmic and implementational details of the proposed method are provided. Finally, several three-dimensional benchmark problems are studied and resultsmore » are compared with available literature. Lastly, the results indicate that the proposed method addresses pathological cases, exhibits better behavior in the presence of closely interacting fractures, and provides a viable strategy to robustly evolve embedded failure surfaces in 3D.« less

Combination of the discontinuous Galerkin method with finite differences for simulation of seismic wave propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lisitsa, Vadim, E-mail: lisitsavv@ipgg.sbras.ru; Novosibirsk State University, Novosibirsk; Tcheverda, Vladimir

We present an algorithm for the numerical simulation of seismic wave propagation in models with a complex near surface part and free surface topography. The approach is based on the combination of finite differences with the discontinuous Galerkin method. The discontinuous Galerkin method can be used on polyhedral meshes; thus, it is easy to handle the complex surfaces in the models. However, this approach is computationally intense in comparison with finite differences. Finite differences are computationally efficient, but in general, they require rectangular grids, leading to the stair-step approximation of the interfaces, which causes strong diffraction of the wavefield. Inmore » this research we present a hybrid algorithm where the discontinuous Galerkin method is used in a relatively small upper part of the model and finite differences are applied to the main part of the model.« less

A local crack-tracking strategy to model three-dimensional crack propagation with embedded methods

DOE PAGES

Annavarapu, Chandrasekhar; Settgast, Randolph R.; Vitali, Efrem; ...

2016-09-29

We develop a local, implicit crack tracking approach to propagate embedded failure surfaces in three-dimensions. We build on the global crack-tracking strategy of Oliver et al. (Int J. Numer. Anal. Meth. Geomech., 2004; 28:609–632) that tracks all potential failure surfaces in a problem at once by solving a Laplace equation with anisotropic conductivity. We discuss important modifications to this algorithm with a particular emphasis on the effect of the Dirichlet boundary conditions for the Laplace equation on the resultant crack path. Algorithmic and implementational details of the proposed method are provided. Finally, several three-dimensional benchmark problems are studied and resultsmore » are compared with available literature. Lastly, the results indicate that the proposed method addresses pathological cases, exhibits better behavior in the presence of closely interacting fractures, and provides a viable strategy to robustly evolve embedded failure surfaces in 3D.« less

Dictionary Learning on the Manifold of Square Root Densities and Application to Reconstruction of Diffusion Propagator Fields*

PubMed Central

Sun, Jiaqi; Xie, Yuchen; Ye, Wenxing; Ho, Jeffrey; Entezari, Alireza; Blackband, Stephen J.

2013-01-01

In this paper, we present a novel dictionary learning framework for data lying on the manifold of square root densities and apply it to the reconstruction of diffusion propagator (DP) fields given a multi-shell diffusion MRI data set. Unlike most of the existing dictionary learning algorithms which rely on the assumption that the data points are vectors in some Euclidean space, our dictionary learning algorithm is designed to incorporate the intrinsic geometric structure of manifolds and performs better than traditional dictionary learning approaches when applied to data lying on the manifold of square root densities. Non-negativity as well as smoothness across the whole field of the reconstructed DPs is guaranteed in our approach. We demonstrate the advantage of our approach by comparing it with an existing dictionary based reconstruction method on synthetic and real multi-shell MRI data. PMID:24684004

Distributed Synchronization Technique for OFDMA-Based Wireless Mesh Networks Using a Bio-Inspired Algorithm

PubMed Central

Kim, Mi Jeong; Maeng, Sung Joon; Cho, Yong Soo

2015-01-01

In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA)-based wireless mesh network (WMN) with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity because it is operated in a distributed manner, not requiring any feedback channel for the compensation of the propagation delays. In addition, a self-organization scheme that can be effectively used to construct 1-hop neighbor nodes is proposed for an OFDMA-based WMN with a large number of nodes. The performance of the proposed technique is evaluated with regard to the convergence property and synchronization success probability using a computer simulation. PMID:26225974

Distributed Synchronization Technique for OFDMA-Based Wireless Mesh Networks Using a Bio-Inspired Algorithm.

PubMed

Kim, Mi Jeong; Maeng, Sung Joon; Cho, Yong Soo

2015-07-28

In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA)-based wireless mesh network (WMN) with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity because it is operated in a distributed manner, not requiring any feedback channel for the compensation of the propagation delays. In addition, a self-organization scheme that can be effectively used to construct 1-hop neighbor nodes is proposed for an OFDMA-based WMN with a large number of nodes. The performance of the proposed technique is evaluated with regard to the convergence property and synchronization success probability using a computer simulation.

An economical semi-analytical orbit theory for micro-computer applications

NASA Technical Reports Server (NTRS)

Gordon, R. A.

1988-01-01

An economical algorithm is presented for predicting the position of a satellite perturbed by drag and zonal harmonics J sub 2 through J sub 4. Simplicity being of the essence, drag is modeled as a secular decay rate in the semi-axis (retarded motion); with the zonal perturbations modeled from a modified version of the Brouwers formulas. The algorithm is developed as: an alternative on-board orbit predictor; a back up propagator requiring low energy consumption; or a ground based propagator for microcomputer applications (e.g., at the foot of an antenna). An O(J sub 2) secular retarded state partial matrix (matrizant) is also given to employ with state estimation. The theory was implemented in BASIC on an inexpensive microcomputer, the program occupying under 8K bytes of memory. Simulated trajectory data and real tracking data are employed to illustrate the theory's ability to accurately accommodate oblateness and drag effects.

Hacia la predicción del Número R de Wolf de manchas solares utilizando Redes Neuronales con retardos temporales

NASA Astrophysics Data System (ADS)

Francile, C.; Luoni, M. L.

We present a prediction of the time series of the Wolf number R of sunspots using "time lagged feed forward neural networks". We use two types of networks: the focused and distributed ones which were trained with the back propagation of errors algorithm and the temporal back propagation algorithm respectively. As inputs to neural networks we use the time series of the number R averaged annually and monthly with the method IR5. As data sets for training and test we choose certain intervals of the time series similar to other works, in order to compare the results. Finally we discuss the topology of the networks used, the number of delays used, the number of neurons per layer, the number of hidden layers and the results in the prediction of the series between one and six steps ahead. FULL TEXT IN SPANISH

An economical semi-analytical orbit theory for micro-computer applications

NASA Technical Reports Server (NTRS)

Gordon, R. A.

1986-01-01

An economical algorithm is presented for predicting the position of a satellite perturbed by drag and zonal harmonics J2 through J4. Simplicity being of the essence, drag is modeled as a secular decay rate in the semimajor axis (retarded motion) with the zonal perturbations modeled from a modified version of Brouwers formulas. The algorithm is developed as an alternative on-board orbit predictor; a back up propagator requiring low energy consumption; or a ground based propagator for microcomputer applications (e.g., at the foot of an antenna). An O(J2) secular retarded state partial matrix (matrizant) is also given to employ with state estimation. The theory has been implemented in BASIC on an inexpensive microcomputer, the program occupying under 8K bytes of memory. Simulated trajectory data and real tracking data are employed to illustrate the theory's ability to accurately accommodate oblateness and drag effects.

Shot boundary detection and label propagation for spatio-temporal video segmentation

NASA Astrophysics Data System (ADS)

Piramanayagam, Sankaranaryanan; Saber, Eli; Cahill, Nathan D.; Messinger, David

2015-02-01

This paper proposes a two stage algorithm for streaming video segmentation. In the first stage, shot boundaries are detected within a window of frames by comparing dissimilarity between 2-D segmentations of each frame. In the second stage, the 2-D segments are propagated across the window of frames in both spatial and temporal direction. The window is moved across the video to find all shot transitions and obtain spatio-temporal segments simultaneously. As opposed to techniques that operate on entire video, the proposed approach consumes significantly less memory and enables segmentation of lengthy videos. We tested our segmentation based shot detection method on the TRECVID 2007 video dataset and compared it with block-based technique. Cut detection results on the TRECVID 2007 dataset indicate that our algorithm has comparable results to the best of the block-based methods. The streaming video segmentation routine also achieves promising results on a challenging video segmentation benchmark database.

The Temporal Morphology of Infrasound Propagation

NASA Astrophysics Data System (ADS)

Drob, Douglas P.; Garcés, Milton; Hedlin, Michael; Brachet, Nicolas

2010-05-01

Expert knowledge suggests that the performance of automated infrasound event association and source location algorithms could be greatly improved by the ability to continually update station travel-time curves to properly account for the hourly, daily, and seasonal changes of the atmospheric state. With the goal of reducing false alarm rates and improving network detection capability we endeavor to develop, validate, and integrate this capability into infrasound processing operations at the International Data Centre of the Comprehensive Nuclear Test-Ban Treaty Organization. Numerous studies have demonstrated that incorporation of hybrid ground-to-space (G2S) enviromental specifications in numerical calculations of infrasound signal travel time and azimuth deviation yields significantly improved results over that of climatological atmospheric specifications, specifically for tropospheric and stratospheric modes. A robust infrastructure currently exists to generate hybrid G2S vector spherical harmonic coefficients, based on existing operational and emperical models on a real-time basis (every 3- to 6-hours) (D rob et al., 2003). Thus the next requirement in this endeavor is to refine numerical procedures to calculate infrasound propagation characteristics for robust automatic infrasound arrival identification and network detection, location, and characterization algorithms. We present results from a new code that integrates the local (range-independent) τp ray equations to provide travel time, range, turning point, and azimuth deviation for any location on the globe given a G2S vector spherical harmonic coefficient set. The code employs an accurate numerical technique capable of handling square-root singularities. We investigate the seasonal variability of propagation characteristics over a five-year time series for two different stations within the International Monitoring System with the aim of understanding the capabilities of current working knowledge of the atmosphere and infrasound propagation models. The statistical behaviors or occurrence frequency of various propagation configurations are discussed. Representative examples of some of these propagation configuration states are also shown.

SU-E-J-226: Propagation of Pancreas Target Contours On Respiratory Correlated CT Images Using Deformable Image Registration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, F; Yorke, E; Mageras, G

2014-06-01

Purpose: Respiratory Correlated CT (RCCT) scans to assess intra-fraction motion among pancreatic cancer patients undergoing radiotherapy allow for dose sparing of normal tissues, in particular for the duodenum. Contour propagation of the gross tumor volume (GTV) from one reference respiratory phase to 9 other phases is time consuming. Deformable image registration (DIR) has been successfully used for high contrast disease sites but lower contrast for pancreatic tumors may compromise accuracy. This study evaluates the accuracy of Fast Free Form (FFF) registration-based contour propagation of the GTV on RCCT scans of pancreas cancer patients. Methods: Twenty-four pancreatic cancer patients were retrospectivelymore » studied; 20 had tumors in the pancreatic head/neck, 4 in the body/tail. Patients were simulated with RCCT and images were sorted into 10 respiratory phases. A radiation oncologist manually delineated the GTV for 5 phases (0%, 30%, 50%, 70% and 90%). The FFF algorithm was used to map deformations between the EE (50%) phase and each of the other 4 phases. The resultant deformation fields served to propagate GTV contours from EE to the other phases. The Dice Similarity Coefficient (DSC), which measures agreement between the DIR-propagated and manually-delineated GTVs, was used to quantitatively examine DIR accuracy. Results: Average DSC over all scans and patients is 0.82 and standard deviation is 0.09 (DSC range 0.97–0.57). For GTV volumes above and below the median volume of 20.2 cc, a Wilcoxon rank-sum test shows significantly different DSC (p=0.0000002). For the GTVs above the median volume, average +/− SD is 0.85 +/− 0.07; and for the GTVs below, the average +/− SD is 0.75 +/−0.08. Conclusion: For pancreatic tumors, the FFF DIR algorithm accurately propagated the GTV between the images in different phases of RCCT, with improved performance for larger tumors.« less

Integrating uncertainty propagation in GNSS radio occultation retrieval: from excess phase to atmospheric bending angle profiles

NASA Astrophysics Data System (ADS)

Schwarz, Jakob; Kirchengast, Gottfried; Schwaerz, Marc

2018-05-01

Global Navigation Satellite System (GNSS) radio occultation (RO) observations are highly accurate, long-term stable data sets and are globally available as a continuous record from 2001. Essential climate variables for the thermodynamic state of the free atmosphere - such as pressure, temperature, and tropospheric water vapor profiles (involving background information) - can be derived from these records, which therefore have the potential to serve as climate benchmark data. However, to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties quantified and traced throughout the retrieval chain from raw observations to essential climate variables. The new Reference Occultation Processing System (rOPS) at the Wegener Center aims to deliver such an accurate RO retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented in the rOPS for uncertainty propagation from excess phase to atmospheric bending angle profiles, for estimated systematic and random uncertainties, including vertical error correlations and resolution estimates. We estimated systematic uncertainty profiles with the same operators as used for the basic state profiles retrieval. The random uncertainty is traced through covariance propagation and validated using Monte Carlo ensemble methods. The algorithm performance is demonstrated using test day ensembles of simulated data as well as real RO event data from the satellite missions CHAllenging Minisatellite Payload (CHAMP); Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC); and Meteorological Operational Satellite A (MetOp). The results of the Monte Carlo validation show that our covariance propagation delivers correct uncertainty quantification from excess phase to bending angle profiles. The results from the real RO event ensembles demonstrate that the new uncertainty estimation chain performs robustly. Together with the other parts of the rOPS processing chain this part is thus ready to provide integrated uncertainty propagation through the whole RO retrieval chain for the benefit of climate monitoring and other applications.

Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange.

PubMed

Hula, Andreas; Montague, P Read; Dayan, Peter

2015-06-01

Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent's preference for equity with their partner, beliefs about the partner's appetite for equity, beliefs about the partner's model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference.

Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange

PubMed Central

Hula, Andreas; Montague, P. Read; Dayan, Peter

2015-01-01

Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent’s preference for equity with their partner, beliefs about the partner’s appetite for equity, beliefs about the partner’s model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference. PMID:26053429

A coarse to fine minutiae-based latent palmprint matching.

PubMed

Liu, Eryun; Jain, Anil K; Tian, Jie

2013-10-01

With the availability of live-scan palmprint technology, high resolution palmprint recognition has started to receive significant attention in forensics and law enforcement. In forensic applications, latent palmprints provide critical evidence as it is estimated that about 30 percent of the latents recovered at crime scenes are those of palms. Most of the available high-resolution palmprint matching algorithms essentially follow the minutiae-based fingerprint matching strategy. Considering the large number of minutiae (about 1,000 minutiae in a full palmprint compared to about 100 minutiae in a rolled fingerprint) and large area of foreground region in full palmprints, novel strategies need to be developed for efficient and robust latent palmprint matching. In this paper, a coarse to fine matching strategy based on minutiae clustering and minutiae match propagation is designed specifically for palmprint matching. To deal with the large number of minutiae, a local feature-based minutiae clustering algorithm is designed to cluster minutiae into several groups such that minutiae belonging to the same group have similar local characteristics. The coarse matching is then performed within each cluster to establish initial minutiae correspondences between two palmprints. Starting with each initial correspondence, a minutiae match propagation algorithm searches for mated minutiae in the full palmprint. The proposed palmprint matching algorithm has been evaluated on a latent-to-full palmprint database consisting of 446 latents and 12,489 background full prints. The matching results show a rank-1 identification accuracy of 79.4 percent, which is significantly higher than the 60.8 percent identification accuracy of a state-of-the-art latent palmprint matching algorithm on the same latent database. The average computation time of our algorithm for a single latent-to-full match is about 141 ms for genuine match and 50 ms for impostor match, on a Windows XP desktop system with 2.2-GHz CPU and 1.00-GB RAM. The computation time of our algorithm is an order of magnitude faster than a previously published state-of-the-art-algorithm.

Corneal Stability following Hyperopic LASIK with Advanced Laser Ablation Profiles Analyzed by a Light Propagation Study

PubMed Central

Gharaibeh, Almutez M.; Villanueva, Asier; Mas, David; Espinosa, Julian

2018-01-01

Purpose To assess anterior corneal surface stability 12 months following hyperopic LASIK correction with a light propagation algorithm. Setting Vissum Instituto Oftalmológico de Alicante, Universidad Miguel Hernández, Alicante, Spain. Methods This retrospective consecutive observational study includes 37 eyes of 37 patients treated with 6th-generation excimer laser platform (Schwind Amaris). Hyperopic LASIK was performed in all of them by the same surgeon (JLA) and completed 12-month follow-up. Corneal topography was analyzed with a light propagation algorithm, to assess the stability of the corneal outcomes along one year of follow-up. Results Between three and twelve months postoperatively, an objective corneal power (OCP) regression of 0.39 D and 0.41 D was found for 6 mm and 9 mm central corneal zone, respectively. Subjective outcomes at the end of the follow-up period were as follows: 65% of eyes had spherical equivalent within ±0.50 D. 70% of eyes had an uncorrected distance visual acuity 20/20 or better. 86% of eyes had the same or better corrected distance visual acuity. In terms of stability, 0.14 D of regression was found. No statistically significant differences were found for all the study parameters evaluated at different postoperative moments over the 12-month period. Conclusions Light propagation analysis confirms corneal surface stability following modern hyperopic LASIK with a 6th-generation excimer laser technology over a 12-month period. PMID:29785300

Low-momentum ghost dressing function and the gluon mass

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boucaud, Ph.; Leroy, J. P.; Le Yaouanc, A.

2010-09-01

We study the low-momentum ghost propagator Dyson-Schwinger equation in the Landau gauge, assuming for the truncation a constant ghost-gluon vertex, as it is extensively done, and a simple model for a massive gluon propagator. Then, regular Dyson-Schwinger equation solutions (the zero-momentum ghost dressing function not diverging) appear to emerge, and we show the ghost propagator to be described by an asymptotic expression reliable up to the order O(q{sup 2}). That expression, depending on the gluon mass and the zero-momentum Taylor-scheme effective charge, is proven to fit pretty well some low-momentum ghost propagator data [I. L. Bogolubsky, E. M. Ilgenfritz, M.more » Muller-Preussker, and A. Sternbeck, Phys. Lett. B 676, 69 (2009); Proc. Sci., LAT2007 (2007) 290] from big-volume lattice simulations where the so-called ''simulated annealing algorithm'' is applied to fix the Landau gauge.« less

Clustering by soft-constraint affinity propagation: applications to gene-expression data.

PubMed

Leone, Michele; Sumedha; Weigt, Martin

2007-10-15

Similarity-measure-based clustering is a crucial problem appearing throughout scientific data analysis. Recently, a powerful new algorithm called Affinity Propagation (AP) based on message-passing techniques was proposed by Frey and Dueck (2007a). In AP, each cluster is identified by a common exemplar all other data points of the same cluster refer to, and exemplars have to refer to themselves. Albeit its proved power, AP in its present form suffers from a number of drawbacks. The hard constraint of having exactly one exemplar per cluster restricts AP to classes of regularly shaped clusters, and leads to suboptimal performance, e.g. in analyzing gene expression data. This limitation can be overcome by relaxing the AP hard constraints. A new parameter controls the importance of the constraints compared to the aim of maximizing the overall similarity, and allows to interpolate between the simple case where each data point selects its closest neighbor as an exemplar and the original AP. The resulting soft-constraint affinity propagation (SCAP) becomes more informative, accurate and leads to more stable clustering. Even though a new a priori free parameter is introduced, the overall dependence of the algorithm on external tuning is reduced, as robustness is increased and an optimal strategy for parameter selection emerges more naturally. SCAP is tested on biological benchmark data, including in particular microarray data related to various cancer types. We show that the algorithm efficiently unveils the hierarchical cluster structure present in the data sets. Further on, it allows to extract sparse gene expression signatures for each cluster.

Blind Channel Equalization Using Constrained Generalized Pattern Search Optimization and Reinitialization Strategy

NASA Astrophysics Data System (ADS)

Zaouche, Abdelouahib; Dayoub, Iyad; Rouvaen, Jean Michel; Tatkeu, Charles

2008-12-01

We propose a global convergence baud-spaced blind equalization method in this paper. This method is based on the application of both generalized pattern optimization and channel surfing reinitialization. The potentially used unimodal cost function relies on higher- order statistics, and its optimization is achieved using a pattern search algorithm. Since the convergence to the global minimum is not unconditionally warranted, we make use of channel surfing reinitialization (CSR) strategy to find the right global minimum. The proposed algorithm is analyzed, and simulation results using a severe frequency selective propagation channel are given. Detailed comparisons with constant modulus algorithm (CMA) are highlighted. The proposed algorithm performances are evaluated in terms of intersymbol interference, normalized received signal constellations, and root mean square error vector magnitude. In case of nonconstant modulus input signals, our algorithm outperforms significantly CMA algorithm with full channel surfing reinitialization strategy. However, comparable performances are obtained for constant modulus signals.

Two algorithms for neural-network design and training with application to channel equalization.

PubMed

Sweatman, C Z; Mulgrew, B; Gibson, G J

1998-01-01

We describe two algorithms for designing and training neural-network classifiers. The first, the linear programming slab algorithm (LPSA), is motivated by the problem of reconstructing digital signals corrupted by passage through a dispersive channel and by additive noise. It constructs a multilayer perceptron (MLP) to separate two disjoint sets by using linear programming methods to identify network parameters. The second, the perceptron learning slab algorithm (PLSA), avoids the computational costs of linear programming by using an error-correction approach to identify parameters. Both algorithms operate in highly constrained parameter spaces and are able to exploit symmetry in the classification problem. Using these algorithms, we develop a number of procedures for the adaptive equalization of a complex linear 4-quadrature amplitude modulation (QAM) channel, and compare their performance in a simulation study. Results are given for both stationary and time-varying channels, the latter based on the COST 207 GSM propagation model.

Efficient matrix approach to optical wave propagation and Linear Canonical Transforms.

PubMed

Shakir, Sami A; Fried, David L; Pease, Edwin A; Brennan, Terry J; Dolash, Thomas M

2015-10-05

The Fresnel diffraction integral form of optical wave propagation and the more general Linear Canonical Transforms (LCT) are cast into a matrix transformation form. Taking advantage of recent efficient matrix multiply algorithms, this approach promises an efficient computational and analytical tool that is competitive with FFT based methods but offers better behavior in terms of aliasing, transparent boundary condition, and flexibility in number of sampling points and computational window sizes of the input and output planes being independent. This flexibility makes the method significantly faster than FFT based propagators when only a single point, as in Strehl metrics, or a limited number of points, as in power-in-the-bucket metrics, are needed in the output observation plane.

Predicting How Close Near-Earth Asteroids Will Come to Earth in the Next Five Years Using Only Kepler's Algorithm

NASA Astrophysics Data System (ADS)

Wright, Melissa J.

1998-04-01

There are estimated to be over 150,000 near-earth asteroids in our solar system that are large enough to pose a significant threat to Earth. In order to determine which of them may be a hazard in the future, their orbits must be propagated through time. The goal of this investigation was to see if using only Kepler's algorithm, which ignores the gravitational pull of other planets, our moon, and Jupiter, was sufficient to predict close encounters with Earth. The results were very rough, and about half of the closest approaches were near the dates of those predicted by more refined models. The distances were in general off by a magnitude often, showing that asteroid orbits must be very perturbed by other planets, particularly Jupiter, over time and these must be taken into account for a precise distance estimate. A noted correlation was that the difference in the angular distance from the I vector was very small when the asteroid and Earth were supposed to be closest. In conclusion, using Kepler's algorithm alone can narrow down intervals of time of nearest approaches, which can then be looked at using more accurate propagators.

Graph-based surface reconstruction from stereo pairs using image segmentation

NASA Astrophysics Data System (ADS)

Bleyer, Michael; Gelautz, Margrit

2005-01-01

This paper describes a novel stereo matching algorithm for epipolar rectified images. The method applies colour segmentation on the reference image. The use of segmentation makes the algorithm capable of handling large untextured regions, estimating precise depth boundaries and propagating disparity information to occluded regions, which are challenging tasks for conventional stereo methods. We model disparity inside a segment by a planar equation. Initial disparity segments are clustered to form a set of disparity layers, which are planar surfaces that are likely to occur in the scene. Assignments of segments to disparity layers are then derived by minimization of a global cost function via a robust optimization technique that employs graph cuts. The cost function is defined on the pixel level, as well as on the segment level. While the pixel level measures the data similarity based on the current disparity map and detects occlusions symmetrically in both views, the segment level propagates the segmentation information and incorporates a smoothness term. New planar models are then generated based on the disparity layers' spatial extents. Results obtained for benchmark and self-recorded image pairs indicate that the proposed method is able to compete with the best-performing state-of-the-art algorithms.

Boundary identification and error analysis of shocked material images

NASA Astrophysics Data System (ADS)

Hock, Margaret; Howard, Marylesa; Cooper, Leora; Meehan, Bernard; Nelson, Keith

2017-06-01

To compute quantities such as pressure and velocity from laser-induced shock waves propagating through materials, high-speed images are captured and analyzed. Shock images typically display high noise and spatially-varying intensities, causing conventional analysis techniques to have difficulty identifying boundaries in the images without making significant assumptions about the data. We present a novel machine learning algorithm that efficiently segments, or partitions, images with high noise and spatially-varying intensities, and provides error maps that describe a level of uncertainty in the partitioning. The user trains the algorithm by providing locations of known materials within the image but no assumptions are made on the geometries in the image. The error maps are used to provide lower and upper bounds on quantities of interest, such as velocity and pressure, once boundaries have been identified and propagated through equations of state. This algorithm will be demonstrated on images of shock waves with noise and aberrations to quantify properties of the wave as it progresses. DOE/NV/25946-3126 This work was done by National Security Technologies, LLC, under Contract No. DE- AC52-06NA25946 with the U.S. Department of Energy and supported by the SDRD Program.

Phase accumulation tracking algorithm for effective index retrieval of fishnet metamaterials and other resonant guided wave networks

NASA Astrophysics Data System (ADS)

Feigenbaum, Eyal; Hiszpanski, Anna M.

2017-07-01

A phase accumulation tracking (PAT) algorithm is proposed and demonstrated for the retrieval of the effective index of fishnet metamaterials (FMMs) in order to avoid the multi-branch uncertainty problem. This algorithm tracks the phase and amplitude of the dominant propagation mode across the FMM slab. The suggested PAT algorithm applies to resonant guided wave networks having only one mode that carries the light between the two slab ends, where the FMM is one example of this metamaterials sub-class. The effective index is a net effect of positive and negative accumulated phase in the alternating FMM metal and dielectric layers, with a negative effective index occurring when negative phase accumulation dominates.

High-resolution seismic data regularization and wavefield separation

NASA Astrophysics Data System (ADS)

Cao, Aimin; Stump, Brian; DeShon, Heather

2018-04-01

We present a new algorithm, non-equispaced fast antileakage Fourier transform (NFALFT), for irregularly sampled seismic data regularization. Synthetic tests from 1-D to 5-D show that the algorithm may efficiently remove leaked energy in the frequency wavenumber domain, and its corresponding regularization process is accurate and fast. Taking advantage of the NFALFT algorithm, we suggest a new method (wavefield separation) for the detection of the Earth's inner core shear wave with irregularly distributed seismic arrays or networks. All interfering seismic phases that propagate along the minor arc are removed from the time window around the PKJKP arrival. The NFALFT algorithm is developed for seismic data, but may also be used for other irregularly sampled temporal or spatial data processing.

Probabilistic location estimation of acoustic emission sources in isotropic plates with one sensor

NASA Astrophysics Data System (ADS)

Ebrahimkhanlou, Arvin; Salamone, Salvatore

2017-04-01

This paper presents a probabilistic acoustic emission (AE) source localization algorithm for isotropic plate structures. The proposed algorithm requires only one sensor and uniformly monitors the entire area of such plates without any blind zones. In addition, it takes a probabilistic approach and quantifies localization uncertainties. The algorithm combines a modal acoustic emission (MAE) and a reflection-based technique to obtain information pertaining to the location of AE sources. To estimate confidence contours for the location of sources, uncertainties are quantified and propagated through the two techniques. The approach was validated using standard pencil lead break (PLB) tests on an Aluminum plate. The results demonstrate that the proposed source localization algorithm successfully estimates confidence contours for the location of AE sources.

Reducing On-Board Computer Propagation Errors Due to Omitted Geopotential Terms by Judicious Selection of Uploaded State Vector

NASA Technical Reports Server (NTRS)

Greatorex, Scott (Editor); Beckman, Mark

1996-01-01

Several future, and some current missions, use an on-board computer (OBC) force model that is very limited. The OBC geopotential force model typically includes only the J(2), J(3), J(4), C(2,2) and S(2,2) terms to model non-spherical Earth gravitational effects. The Tropical Rainfall Measuring Mission (TRMM), Wide-field Infrared Explorer (WIRE), Transition Region and Coronal Explorer (TRACE), Submillimeter Wave Astronomy Satellite (SWAS), and X-ray Timing Explorer (XTE) all plan to use this geopotential force model on-board. The Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX) is already flying this geopotential force model. Past analysis has shown that one of the leading sources of error in the OBC propagated ephemeris is the omission of the higher order geopotential terms. However, these same analyses have shown a wide range of accuracies for the OBC ephemerides. Analysis was performed using EUVE state vectors that showed the EUVE four day OBC propagated ephemerides varied in accuracy from 200 m. to 45 km. depending on the initial vector used to start the propagation. The vectors used in the study were from a single EUVE orbit at one minute intervals in the ephemeris. Since each vector propagated practically the same path as the others, the differences seen had to be due to differences in the inital state vector only. An algorithm was developed that will optimize the epoch of the uploaded state vector. Proper selection can reduce the previous errors of anywhere from 200 m. to 45 km. to generally less than one km. over four days of propagation. This would enable flight projects to minimize state vector uploads to the spacecraft. Additionally, this method is superior to other methods in that no additional orbit estimates need be done. The definitive ephemeris generated on the ground can be used as long as the proper epoch is chosen. This algorithm can be easily coded in software that would pick the epoch within a specified time range that would minimize the OBC propagation error. This techniques should greatly improve the accuracy of the OBC propagation on-board future spacecraft such as TRMM, WIRE, SWAS, and XTE without increasing complexity in the ground processing.

The Behavioral and Neural Mechanisms Underlying the Tracking of Expertise

PubMed Central

Boorman, Erie D.; O’Doherty, John P.; Adolphs, Ralph; Rangel, Antonio

2013-01-01

Summary Evaluating the abilities of others is fundamental for successful economic and social behavior. We investigated the computational and neurobiological basis of ability tracking by designing an fMRI task that required participants to use and update estimates of both people and algorithms’ expertise through observation of their predictions. Behaviorally, we find a model-based algorithm characterized subject predictions better than several alternative models. Notably, when the agent’s prediction was concordant rather than discordant with the subject’s own likely prediction, participants credited people more than algorithms for correct predictions and penalized them less for incorrect predictions. Neurally, many components of the mentalizing network—medial prefrontal cortex, anterior cingulate gyrus, temporoparietal junction, and precuneus—represented or updated expertise beliefs about both people and algorithms. Moreover, activity in lateral orbitofrontal and medial prefrontal cortex reflected behavioral differences in learning about people and algorithms. These findings provide basic insights into the neural basis of social learning. PMID:24360551

Uncovering the community structure in signed social networks based on greedy optimization

NASA Astrophysics Data System (ADS)

Chen, Yan; Yan, Jiaqi; Yang, Yu; Chen, Junhua

2017-05-01

The formality of signed relationships has been recently adopted in a lot of complicated systems. The relations among these entities are complicated and multifarious. We cannot indicate these relationships only by positive links, and signed networks have been becoming more and more universal in the study of social networks when community is being significant. In this paper, to identify communities in signed networks, we exploit a new greedy algorithm, taking signs and the density of these links into account. The main idea of the algorithm is the initial procedure of signed modularity and the corresponding update rules. Specially, we employ the “Asymmetric and Constrained Belief Evolution” procedure to evaluate the optimal number of communities. According to the experimental results, the algorithm is proved to be able to run well. More specifically, the proposed algorithm is very efficient for these networks with medium size, both dense and sparse.

Win-Stay, Lose-Sample: a simple sequential algorithm for approximating Bayesian inference.

PubMed

Bonawitz, Elizabeth; Denison, Stephanie; Gopnik, Alison; Griffiths, Thomas L

2014-11-01

People can behave in a way that is consistent with Bayesian models of cognition, despite the fact that performing exact Bayesian inference is computationally challenging. What algorithms could people be using to make this possible? We show that a simple sequential algorithm "Win-Stay, Lose-Sample", inspired by the Win-Stay, Lose-Shift (WSLS) principle, can be used to approximate Bayesian inference. We investigate the behavior of adults and preschoolers on two causal learning tasks to test whether people might use a similar algorithm. These studies use a "mini-microgenetic method", investigating how people sequentially update their beliefs as they encounter new evidence. Experiment 1 investigates a deterministic causal learning scenario and Experiments 2 and 3 examine how people make inferences in a stochastic scenario. The behavior of adults and preschoolers in these experiments is consistent with our Bayesian version of the WSLS principle. This algorithm provides both a practical method for performing Bayesian inference and a new way to understand people's judgments. Copyright © 2014 Elsevier Inc. All rights reserved.

Unsupervised chunking based on graph propagation from bilingual corpus.

PubMed

Zhu, Ling; Wong, Derek F; Chao, Lidia S

2014-01-01

This paper presents a novel approach for unsupervised shallow parsing model trained on the unannotated Chinese text of parallel Chinese-English corpus. In this approach, no information of the Chinese side is applied. The exploitation of graph-based label propagation for bilingual knowledge transfer, along with an application of using the projected labels as features in unsupervised model, contributes to a better performance. The experimental comparisons with the state-of-the-art algorithms show that the proposed approach is able to achieve impressive higher accuracy in terms of F-score.

co2amp: A software program for modeling the dynamics of ultrashort pulses in optical systems with CO 2 amplifiers

DOE PAGES

Polyanskiy, Mikhail N.

2015-01-01

We describe a computer code for simulating the amplification of ultrashort mid-infrared laser pulses in CO 2 amplifiers and their propagation through arbitrary optical systems. This code is based on a comprehensive model that includes an accurate consideration of the CO 2 active medium and a physical optics propagation algorithm, and takes into account the interaction of the laser pulse with the material of the optical elements. Finally, the application of the code for optimizing an isotopic regenerative amplifier is described.

Computing Satellite Maneuvers For A Repeating Ground Track

NASA Technical Reports Server (NTRS)

Shapiro, Bruce

1994-01-01

TOPEX/POSEIDON Ground Track Maintenance Maneuver Targeting Program (GTARG) assists in designing maneuvers to maintain orbit of TOPEX/POSEIDON satellite. Targeting strategies used either maximize time between maneuvers or force control band exit to occur at specified intervals. Runout mode allows for ground-track propagation without targeting. GTARG incorporates analytic mean-element propagation algorithm accounting for all perturbations known to cause significant variations in ground track. Perturbations include oblateness of Earth, luni-solar gravitation, drag, thrusts associated with impulsive maneuvers, and unspecified fixed forces acting on satellite in direction along trajectory. Written in VAX-FORTRAN.

Geographic and demographic correlates of autism-related anti-vaccine beliefs on Twitter, 2009-15.

PubMed

Tomeny, Theodore S; Vargo, Christopher J; El-Toukhy, Sherine

2017-10-01

This study examines temporal trends, geographic distribution, and demographic correlates of anti-vaccine beliefs on Twitter, 2009-2015. A total of 549,972 tweets were downloaded and coded for the presence of anti-vaccine beliefs through a machine learning algorithm. Tweets with self-disclosed geographic information were resolved and United States Census data were collected for corresponding areas at the micropolitan/metropolitan level. Trends in number of anti-vaccine tweets were examined at the national and state levels over time. A least absolute shrinkage and selection operator regression model was used to determine census variables that were correlated with anti-vaccination tweet volume. Fifty percent of our sample of 549,972 tweets collected between 2009 and 2015 contained anti-vaccine beliefs. Anti-vaccine tweet volume increased after vaccine-related news coverage. California, Connecticut, Massachusetts, New York, and Pennsylvania had anti-vaccination tweet volume that deviated from the national average. Demographic characteristics explained 67% of variance in geographic clustering of anti-vaccine tweets, which were associated with a larger population and higher concentrations of women who recently gave birth, households with high income levels, men aged 40 to 44, and men with minimal college education. Monitoring anti-vaccination beliefs on Twitter can uncover vaccine-related concerns and misconceptions, serve as an indicator of shifts in public opinion, and equip pediatricians to refute anti-vaccine arguments. Real-time interventions are needed to counter anti-vaccination beliefs online. Identifying clusters of anti-vaccination beliefs can help public health professionals disseminate targeted/tailored interventions to geographic locations and demographic sectors of the population. Copyright © 2017 Elsevier Ltd. All rights reserved.

Suboptimal foraging behavior: A new perspective on gambling

PubMed Central

Addicott, Merideth A.; Pearson, John M.; Kaiser, Nicole; Platt, Michael L.; McClernon, F. Joseph

2015-01-01

Why do people gamble? Conventional views hold that gambling may be motivated by irrational beliefs, risk-seeking, impulsive temperament, or dysfunction within the same reward circuitry affected by drugs of abuse. An alternate, unexplored perspective is that gambling is an extension of natural foraging behavior to a financial environment. However, when these foraging algorithms are applied to stochastic gambling outcomes, undesirable results may occur. To test this hypothesis, we recruited participants based on their frequency of gambling – yearly (or less), monthly, and weekly – and investigated how gambling frequency related to irrational beliefs, risk-taking/impulsivity, and foraging behavior. We found that increased gambling frequency corresponded to greater gambling-related beliefs, more exploratory choices on an explore/exploit foraging task, and fewer points earned on a patchy foraging task. Gambling-related beliefs negatively related to performance on the patchy foraging task, indicating that individuals with more gambling-related cognitions tended to leave a patch too quickly. This indicates that frequent gamblers have reduced foraging ability to maximize rewards; however, gambling frequency- and by extension, poor foraging ability- was not related to risk-taking or impulsive behavior. These results suggest that gambling reflects the application of a dysfunctional foraging process to financial outcomes. PMID:26191945

Suboptimal foraging behavior: a new perspective on gambling.

PubMed

Addicott, Merideth A; Pearson, John M; Kaiser, Nicole; Platt, Michael L; McClernon, F Joseph

2015-10-01

Why do people gamble? Conventional views hold that gambling may be motivated by irrational beliefs, risk-seeking, impulsive temperament, or dysfunction within the same reward circuitry affected by drugs of abuse. An alternate, unexplored perspective is that gambling is an extension of natural foraging behavior to a financial environment. However, when these foraging algorithms are applied to stochastic gambling outcomes, undesirable results may occur. To test this hypothesis, we recruited participants based on their frequency of gambling-yearly (or less), monthly, and weekly-and investigated how gambling frequency related to irrational beliefs, risk-taking/impulsivity, and foraging behavior. We found that increased gambling frequency corresponded to greater gambling-related beliefs, more exploratory choices on an explore/exploit foraging task, and fewer points earned on a Patchy Foraging Task. Gambling-related beliefs negatively related to performance on the Patchy Foraging Task, indicating that individuals with more gambling-related cognitions tended to leave a patch too quickly. This indicates that frequent gamblers have reduced foraging ability to maximize rewards; however, gambling frequency -and by extension, poor foraging ability- was not related to risk-taking or impulsive behavior. These results suggest that gambling reflects the application of a dysfunctional foraging process to financial outcomes. (c) 2015 APA, all rights reserved).

Optimizing a spectral element for modeling PZT-induced Lamb wave propagation in thin plates

NASA Astrophysics Data System (ADS)

Ha, Sungwon; Chang, Fu-Kuo

2010-01-01

Use of surface-mounted piezoelectric actuators to generate acoustic ultrasound has been demonstrated to be a key component of built-in nondestructive detection evaluation (NDE) techniques, which can automatically inspect and interrogate damage in hard-to-access areas in real time without disassembly of the structural parts. However, piezoelectric actuators create complex waves, which propagate through the structure. Having the capability to model piezoelectric actuator-induced wave propagation and understanding its physics are essential to developing advanced algorithms for the built-in NDE techniques. Therefore, the objective of this investigation was to develop an efficient hybrid spectral element for modeling piezoelectric actuator-induced high-frequency wave propagation in thin plates. With the hybrid element we take advantage of both a high-order spectral element in the in-plane direction and a linear finite element in the thickness direction in order to efficiently analyze Lamb wave propagation in thin plates. The hybrid spectral element out-performs other elements in terms of leading to significantly faster computation and smaller memory requirements. Use of the hybrid spectral element is proven to be an efficient technique for modeling PZT-induced (PZT: lead zirconate titanate) wave propagation in thin plates. The element enables fundamental understanding of PZT-induced wave propagation.

Development and Validation of a Lifecycle-based Prognostics Architecture with Test Bed Validation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hines, J. Wesley; Upadhyaya, Belle; Sharp, Michael

On-line monitoring and tracking of nuclear plant system and component degradation is being investigated as a method for improving the safety, reliability, and maintainability of aging nuclear power plants. Accurate prediction of the current degradation state of system components and structures is important for accurate estimates of their remaining useful life (RUL). The correct quantification and propagation of both the measurement uncertainty and model uncertainty is necessary for quantifying the uncertainty of the RUL prediction. This research project developed and validated methods to perform RUL estimation throughout the lifecycle of plant components. Prognostic methods should seamlessly operate from beginning ofmore » component life (BOL) to end of component life (EOL). We term this "Lifecycle Prognostics." When a component is put into use, the only information available may be past failure times of similar components used in similar conditions, and the predicted failure distribution can be estimated with reliability methods such as Weibull Analysis (Type I Prognostics). As the component operates, it begins to degrade and consume its available life. This life consumption may be a function of system stresses, and the failure distribution should be updated to account for the system operational stress levels (Type II Prognostics). When degradation becomes apparent, this information can be used to again improve the RUL estimate (Type III Prognostics). This research focused on developing prognostics algorithms for the three types of prognostics, developing uncertainty quantification methods for each of the algorithms, and, most importantly, developing a framework using Bayesian methods to transition between prognostic model types and update failure distribution estimates as new information becomes available. The developed methods were then validated on a range of accelerated degradation test beds. The ultimate goal of prognostics is to provide an accurate assessment for RUL predictions, with as little uncertainty as possible. From a reliability and maintenance standpoint, there would be improved safety by avoiding all failures. Calculated risk would decrease, saving money by avoiding unnecessary maintenance. One major bottleneck for data-driven prognostics is the availability of run-to-failure degradation data. Without enough degradation data leading to failure, prognostic models can yield RUL distributions with large uncertainty or mathematically unsound predictions. To address these issues a "Lifecycle Prognostics" method was developed to create RUL distributions from Beginning of Life (BOL) to End of Life (EOL). This employs established Type I, II, and III prognostic methods, and Bayesian transitioning between each Type. Bayesian methods, as opposed to classical frequency statistics, show how an expected value, a priori, changes with new data to form a posterior distribution. For example, when you purchase a component you have a prior belief, or estimation, of how long it will operate before failing. As you operate it, you may collect information related to its condition that will allow you to update your estimated failure time. Bayesian methods are best used when limited data are available. The use of a prior also means that information is conserved when new data are available. The weightings of the prior belief and information contained in the sampled data are dependent on the variance (uncertainty) of the prior, the variance (uncertainty) of the data, and the amount of measured data (number of samples). If the variance of the prior is small compared to the uncertainty of the data, the prior will be weighed more heavily. However, as more data are collected, the data will be weighted more heavily and will eventually swamp out the prior in calculating the posterior distribution of model parameters. Fundamentally Bayesian analysis updates a prior belief with new data to get a posterior belief. The general approach to applying the Bayesian method to lifecycle prognostics consisted of identifying the prior, which is the RUL estimate and uncertainty from the previous prognostics type, and combining it with observational data related to the newer prognostics type. The resulting lifecycle prognostics algorithm uses all available information throughout the component lifecycle.« less

Oceanographic applications of the Kalman filter

NASA Technical Reports Server (NTRS)

Barbieri, R. W.; Schopf, P. S.

1982-01-01

The Kalman filter is a data-processing algorithm with a distinguished history in systems theory. Its application to oceanographic problems is in the embryo stage. The behavior of the filter is demonstrated in the context of an internal equatorial Rossby wave propagation problem.

Classification of neocortical interneurons using affinity propagation.

PubMed

Santana, Roberto; McGarry, Laura M; Bielza, Concha; Larrañaga, Pedro; Yuste, Rafael

2013-01-01

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets. However, better and more robust classification methods are needed for increasingly complex and larger datasets. Here, we explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. Affinity propagation outperformed Ward's method, a current standard clustering approach, in classifying the neurons into 4 subtypes. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

Source and listener directivity for interactive wave-based sound propagation.

PubMed

Mehra, Ravish; Antani, Lakulish; Kim, Sujeong; Manocha, Dinesh

2014-04-01

We present an approach to model dynamic, data-driven source and listener directivity for interactive wave-based sound propagation in virtual environments and computer games. Our directional source representation is expressed as a linear combination of elementary spherical harmonic (SH) sources. In the preprocessing stage, we precompute and encode the propagated sound fields due to each SH source. At runtime, we perform the SH decomposition of the varying source directivity interactively and compute the total sound field at the listener position as a weighted sum of precomputed SH sound fields. We propose a novel plane-wave decomposition approach based on higher-order derivatives of the sound field that enables dynamic HRTF-based listener directivity at runtime. We provide a generic framework to incorporate our source and listener directivity in any offline or online frequency-domain wave-based sound propagation algorithm. We have integrated our sound propagation system in Valve's Source game engine and use it to demonstrate realistic acoustic effects such as sound amplification, diffraction low-passing, scattering, localization, externalization, and spatial sound, generated by wave-based propagation of directional sources and listener in complex scenarios. We also present results from our preliminary user study.

Channel estimation based on quantized MMP for FDD massive MIMO downlink

NASA Astrophysics Data System (ADS)

Guo, Yao-ting; Wang, Bing-he; Qu, Yi; Cai, Hua-jie

2016-10-01

In this paper, we consider channel estimation for Massive MIMO systems operating in frequency division duplexing mode. By exploiting the sparsity of propagation paths in Massive MIMO channel, we develop a compressed sensing(CS) based channel estimator which can reduce the pilot overhead. As compared with the conventional least squares (LS) and linear minimum mean square error(LMMSE) estimation, the proposed algorithm is based on the quantized multipath matching pursuit - MMP - reduced the pilot overhead and performs better than other CS algorithms. The simulation results demonstrate the advantage of the proposed algorithm over various existing methods including the LS, LMMSE, CoSaMP and conventional MMP estimators.

Global Linking of Cell Tracks Using the Viterbi Algorithm

PubMed Central

Jaldén, Joakim; Gilbert, Penney M.; Blau, Helen M.

2016-01-01

Automated tracking of living cells in microscopy image sequences is an important and challenging problem. With this application in mind, we propose a global track linking algorithm, which links cell outlines generated by a segmentation algorithm into tracks. The algorithm adds tracks to the image sequence one at a time, in a way which uses information from the complete image sequence in every linking decision. This is achieved by finding the tracks which give the largest possible increases to a probabilistically motivated scoring function, using the Viterbi algorithm. We also present a novel way to alter previously created tracks when new tracks are created, thus mitigating the effects of error propagation. The algorithm can handle mitosis, apoptosis, and migration in and out of the imaged area, and can also deal with false positives, missed detections, and clusters of jointly segmented cells. The algorithm performance is demonstrated on two challenging datasets acquired using bright-field microscopy, but in principle, the algorithm can be used with any cell type and any imaging technique, presuming there is a suitable segmentation algorithm. PMID:25415983

On-line prognosis of fatigue crack propagation based on Gaussian weight-mixture proposal particle filter.

PubMed

Chen, Jian; Yuan, Shenfang; Qiu, Lei; Wang, Hui; Yang, Weibo

2018-01-01

Accurate on-line prognosis of fatigue crack propagation is of great meaning for prognostics and health management (PHM) technologies to ensure structural integrity, which is a challenging task because of uncertainties which arise from sources such as intrinsic material properties, loading, and environmental factors. The particle filter algorithm has been proved to be a powerful tool to deal with prognostic problems those are affected by uncertainties. However, most studies adopted the basic particle filter algorithm, which uses the transition probability density function as the importance density and may suffer from serious particle degeneracy problem. This paper proposes an on-line fatigue crack propagation prognosis method based on a novel Gaussian weight-mixture proposal particle filter and the active guided wave based on-line crack monitoring. Based on the on-line crack measurement, the mixture of the measurement probability density function and the transition probability density function is proposed to be the importance density. In addition, an on-line dynamic update procedure is proposed to adjust the parameter of the state equation. The proposed method is verified on the fatigue test of attachment lugs which are a kind of important joint components in aircraft structures. Copyright © 2017 Elsevier B.V. All rights reserved.

Implementation of neural network for color properties of polycarbonates

NASA Astrophysics Data System (ADS)

Saeed, U.; Ahmad, S.; Alsadi, J.; Ross, D.; Rizvi, G.

2014-05-01

In present paper, the applicability of artificial neural networks (ANN) is investigated for color properties of plastics. The neural networks toolbox of Matlab 6.5 is used to develop and test the ANN model on a personal computer. An optimal design is completed for 10, 12, 14,16,18 & 20 hidden neurons on single hidden layer with five different algorithms: batch gradient descent (GD), batch variable learning rate (GDX), resilient back-propagation (RP), scaled conjugate gradient (SCG), levenberg-marquardt (LM) in the feed forward back-propagation neural network model. The training data for ANN is obtained from experimental measurements. There were twenty two inputs including resins, additives & pigments while three tristimulus color values L*, a* and b* were used as output layer. Statistical analysis in terms of Root-Mean-Squared (RMS), absolute fraction of variance (R squared), as well as mean square error is used to investigate the performance of ANN. LM algorithm with fourteen neurons on hidden layer in Feed Forward Back-Propagation of ANN model has shown best result in the present study. The degree of accuracy of the ANN model in reduction of errors is proven acceptable in all statistical analysis and shown in results. However, it was concluded that ANN provides a feasible method in error reduction in specific color tristimulus values.

Experimental studies of applications of time-reversal acoustics to noncoherent underwater communications.

PubMed

Heinemann, M; Larraza, A; Smith, K B

2003-06-01

The most difficult problem in shallow underwater acoustic communications is considered to be the time-varying multipath propagation because it impacts negatively on data rates. At high data rates the intersymbol interference requires adaptive algorithms on the receiver side that lead to computationally intensive and complex signal processing. A novel technique called time-reversal acoustics (TRA) can environmentally adapt the acoustic propagation effects of a complex medium in order to focus energy at a particular target range and depth. Using TRA, the multipath structure is reduced because all the propagation paths add coherently at the intended target location. This property of time-reversal acoustics suggests a potential application in the field of noncoherent acoustic communications. This work presents results of a tank scale experiment using an algorithm for rapid transmission of binary data in a complex underwater environment with the TRA approach. A simple 15-symbol code provides an example of the simplicity and feasibility of the approach. Covert coding due to the inherent scrambling induced by the environment at points other than the intended receiver is also investigated. The experiments described suggest a high potential in data rate for the time-reversal approach in underwater acoustic communications while keeping the computational complexity low.

Application guide for universal source encoding for space

NASA Technical Reports Server (NTRS)

Yeh, Pen-Shu; Miller, Warner H.

1993-01-01

Lossless data compression was studied for many NASA missions. The Rice algorithm was demonstrated to provide better performance than other available techniques on most scientific data. A top-level description of the Rice algorithm is first given, along with some new capabilities implemented in both software and hardware forms. Systems issues important for onboard implementation, including sensor calibration, error propagation, and data packetization, are addressed. The latter part of the guide provides twelve case study examples drawn from a broad spectrum of science instruments.

Numerical modeling of transverse mode competition in strongly pumped multimode fiber lasers and amplifiers.

PubMed

Gong, Mali; Yuan, Yanyang; Li, Chen; Yan, Ping; Zhang, Haitao; Liao, Suying

2007-03-19

A model based on propagation-rate equations with consideration of transverse gain distribution is built up to describe the transverse mode competition in strongly pumped multimode fiber lasers and amplifiers. An approximate practical numerical algorithm by multilayer method is presented. Based on the model and the numerical algorithm, the behaviors of multitransverse mode competition are demonstrated and individual transverse modes power distributions of output are simulated numerically for both fiber lasers and amplifiers under various conditions.

Three-Dimensional Stable Nonorthogonal FDTD Algorithm with Adaptive Mesh Refinement for Solving Maxwell’s Equations

DTIC Science & Technology

2013-03-01

Räisänen. An efficient FDTD algorithm for the analysis of microstrip patch antennas printed on a general anisotropic dielectric substrate. IEEE...applications [3, 21, 22], including antenna , microwave circuits, geophysics, optics, etc. The Ground Penetrating Radar (GPR) is a popular and...IEEE Trans. Antennas Propag., 41:994–999, 1993. 16 [6] S. G. Garcia, T. M. Hung-Bao, R. G. Martin, and B. G. Olmedo. On the application of finite

A space-time neural network

NASA Technical Reports Server (NTRS)

Villarreal, James A.; Shelton, Robert O.

1991-01-01

Introduced here is a novel technique which adds the dimension of time to the well known back propagation neural network algorithm. Cited here are several reasons why the inclusion of automated spatial and temporal associations are crucial to effective systems modeling. An overview of other works which also model spatiotemporal dynamics is furnished. A detailed description is given of the processes necessary to implement the space-time network algorithm. Several demonstrations that illustrate the capabilities and performance of this new architecture are given.

Neural-Network-Development Program

NASA Technical Reports Server (NTRS)

Phillips, Todd A.

1993-01-01

NETS, software tool for development and evaluation of neural networks, provides simulation of neural-network algorithms plus computing environment for development of such algorithms. Uses back-propagation learning method for all of networks it creates. Enables user to customize patterns of connections between layers of network. Also provides features for saving, during learning process, values of weights, providing more-precise control over learning process. Written in ANSI standard C language. Machine-independent version (MSC-21588) includes only code for command-line-interface version of NETS 3.0.

Spherical Harmonic Decomposition of Gravitational Waves Across Mesh Refinement Boundaries

NASA Technical Reports Server (NTRS)

Fiske, David R.; Baker, John; vanMeter, James R.; Centrella, Joan M.

2005-01-01

We evolve a linearized (Teukolsky) solution of the Einstein equations with a non-linear Einstein solver. Using this testbed, we are able to show that such gravitational waves, defined by the Weyl scalars in the Newman-Penrose formalism, propagate faithfully across mesh refinement boundaries, and use, for the first time to our knowledge, a novel algorithm due to Misner to compute spherical harmonic components of our waveforms. We show that the algorithm performs extremely well, even when the extraction sphere intersects refinement boundaries.

Time-Reversal Based Range Extension Technique for Ultra-Wideband (UWB) Sensors and Applications in Tactical Communications and Networking

DTIC Science & Technology

2007-07-16

issue to find a proper acquisition strategy and to optimize the algorithm. So far a two-stage acquisition algorithm based on the optical orthogonal...vol.5, May 11-15, 2003, pp. 3530-3534. [23] M. Weisenhorn and W. Hirt, "Robust noncoherent receiver exploiting UWB channel properties," in Proc. IEEE...PRF) and data rate, are programmable. I Depending on the propagation environments, either the Barker code or the optical orthogonal codes (OOC) [53

Arbitrary-level hanging nodes for adaptive hphp-FEM approximations in 3D

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pavel Kus; Pavel Solin; David Andrs

2014-11-01

In this paper we discuss constrained approximation with arbitrary-level hanging nodes in adaptive higher-order finite element methods (hphp-FEM) for three-dimensional problems. This technique enables using highly irregular meshes, and it greatly simplifies the design of adaptive algorithms as it prevents refinements from propagating recursively through the finite element mesh. The technique makes it possible to design efficient adaptive algorithms for purely hexahedral meshes. We present a detailed mathematical description of the method and illustrate it with numerical examples.

Nothing Happens by Accident, or Does It? A Low Prior for Randomness Does Not Explain Belief in Conspiracy Theories.

PubMed

Dieguez, Sebastian; Wagner-Egger, Pascal; Gauvrit, Nicolas

2015-11-01

Belief in conspiracy theories has often been associated with a biased perception of randomness, akin to a nothing-happens-by-accident heuristic. Indeed, a low prior for randomness (i.e., believing that randomness is a priori unlikely) could plausibly explain the tendency to believe that a planned deception lies behind many events, as well as the tendency to perceive meaningful information in scattered and irrelevant details; both of these tendencies are traits diagnostic of conspiracist ideation. In three studies, we investigated this hypothesis and failed to find the predicted association between low prior for randomness and conspiracist ideation, even when randomness was explicitly opposed to malevolent human intervention. Conspiracy believers' and nonbelievers' perceptions of randomness were not only indistinguishable from each other but also accurate compared with the normative view arising from the algorithmic information framework. Thus, the motto "nothing happens by accident," taken at face value, does not explain belief in conspiracy theories. © The Author(s) 2015.

Accurate numerical solution of the Helmholtz equation by iterative Lanczos reduction.

PubMed

Ratowsky, R P; Fleck, J A

1991-06-01

The Lanczos recursion algorithm is used to determine forward-propagating solutions for both the paraxial and Helmholtz wave equations for longitudinally invariant refractive indices. By eigenvalue analysis it is demonstrated that the method gives extremely accurate solutions to both equations.

Investigation on financial crises with the negative-information-propagation-induced model

NASA Astrophysics Data System (ADS)

Fan, Feng-Hua; Deng, Yanbin; Huang, Yong-Chang

2017-03-01

We first argue about the similarity between the propagation phenomenon of negative information about potential deterioration of economic situation in group of investors and the propagation phenomenon of infectious disease in crowd Applying the negative-information-propagation-induced model built based on above argument, we investigate the relationship between the generation of financial crises and propagation effects of negative information We introduce the discrimination parameter to distinguish whether or not negative information will be propagated extensively in group of investors. We also introduce the target critical value of financial crises. By comparing the theoretically predicted ratio of the long term projected number of total investors to the total number of investors at some time as initial time with target critical value of financial crises, the model can provide real-time monitoring of whether the curve of total number of investors is progressing toward the direction of generating financial crises or running on track of financial markets safety. If at some time this ratio is computed to be less than the target critical value of financial crises, governments can take relevant measures to prevent the generation of financial crises in advance Governments' interference helps to recover the confidence of investors so that they never will again believe in negative information to continue their investment. Results from theoretical and numerical analysis show that the number of investors who hold the belief of potential deterioration of economic situation, and the number of investors who withdraw capital and depart from financial markets for avoiding business loss when governments make appropriate interference are lowered compared to that without appropriate governments' interference. The results show the effectiveness of governments in preventing financial crises from the viewpoint of the negative information-propagation-induced model, namely governments' prevention against financial crises can reduce the possibility of the generation of financial crises.

Method for validating cloud mask obtained from satellite measurements using ground-based sky camera.

PubMed

Letu, Husi; Nagao, Takashi M; Nakajima, Takashi Y; Matsumae, Yoshiaki

2014-11-01

Error propagation in Earth's atmospheric, oceanic, and land surface parameters of the satellite products caused by misclassification of the cloud mask is a critical issue for improving the accuracy of satellite products. Thus, characterizing the accuracy of the cloud mask is important for investigating the influence of the cloud mask on satellite products. In this study, we proposed a method for validating multiwavelength satellite data derived cloud masks using ground-based sky camera (GSC) data. First, a cloud cover algorithm for GSC data has been developed using sky index and bright index. Then, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived cloud masks by two cloud-screening algorithms (i.e., MOD35 and CLAUDIA) were validated using the GSC cloud mask. The results indicate that MOD35 is likely to classify ambiguous pixels as "cloudy," whereas CLAUDIA is likely to classify them as "clear." Furthermore, the influence of error propagations caused by misclassification of the MOD35 and CLAUDIA cloud masks on MODIS derived reflectance, brightness temperature, and normalized difference vegetation index (NDVI) in clear and cloudy pixels was investigated using sky camera data. It shows that the influence of the error propagation by the MOD35 cloud mask on the MODIS derived monthly mean reflectance, brightness temperature, and NDVI for clear pixels is significantly smaller than for the CLAUDIA cloud mask; the influence of the error propagation by the CLAUDIA cloud mask on MODIS derived monthly mean cloud products for cloudy pixels is significantly smaller than that by the MOD35 cloud mask.

VLF remote sensing of the ambient and modified lower ionosphere

NASA Astrophysics Data System (ADS)

Demirkol, Mehmet Kursad

2000-08-01

Electron density and temperature changes in the D region are sensitively manifested as changes in the amplitude and phase of subionospheric Very Low Frequency (VLF) signals propagating beneath the perturbed region. Both localized and large scale disturbances (either in electron density or temperature) in the D region cause significant scattering of VLF waves propagating in the earth- ionosphere waveguide, leading to measurable changes in the amplitude and phase of the VLF waves. Large scale auroral disturbances, associated with intensification of the auroral electrojet, as well as ionospheric disturbances produced during relativistic electron enhancements, cause characteristic changes over relatively long time scales that allow the assessment of the `ambient' ionosphere. Localized ionospheric disturbances are also produced by powerful VLF transmitting facilities such as the High Power Auroral Stimulation (HIPAS) facility, the High frequency Active Auroral Research Program (HAARP), and also by lightning discharges. Amplitude and phase changes of VLF waveguide signals scattered from such artificially heated ionospheric patches are known to be detectable. In this study, we describe a new inversion algorithm to determine altitude profiles of electron density and collision frequency within such a localized disturbance by using the measured amplitude and phase of three different VLF signals at three separate receiving sites. For this purpose a new optimization algorithm is developed which is primarily based on the recursive usage of the three dimensional version of the Long Wave Propagation, Capability (LWPC) code used to model the subionospheric propagation and scattering of VLF signals in the earth- ionosphere waveguide in the presence of ionospheric disturbances.

Enhancement of high-order harmonic generation by a two-color field: Influence of propagation effects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schiessl, K.; Persson, E.; Burgdoerfer, J.

2006-11-15

Recent calculations of the response of a single atom subjected to a two-color laser pulse with the higher frequency being resonant with an excitation of the target atom revealed a significant enhancement of photoionization as well as high-order harmonic generation [K. Ishikawa, Phy. Rev. Lett. 91, 043002 (2003)]. We investigate the problem in the framework a fully quantum-mechanical pulse propagation algorithm and perform calculations for rare gases in the single-active-electron approximation. The enhancement of harmonic output compared to the corresponding one-color pulse remains intact for short propagation lengths, promising the feasibility of experimental realization. We also study weak second colorsmore » resonant via a two-photon transition where significant enhancements in harmonic yields can be observed as well.« less

Millimeter wave propagation modeling of inhomogeneous rain media for satellite communications systems

NASA Technical Reports Server (NTRS)

Persinger, R. R.; Stutzman, W. L.

1978-01-01

A theoretical propagation model that represents the scattering properties of an inhomogeneous rain often found on a satellite communications link is presented. The model includes the scattering effects of an arbitrary distribution of particle type (rain or ice), particle shape, particle size, and particle orientation within a given rain cell. An associated rain propagation prediction program predicts attenuation, isolation and phase shift as a function of ground rain rate. A frequency independent synthetic storm algorithm is presented that models nonuniform rain rates present on a satellite link. Antenna effects are included along with a discussion of rain reciprocity. The model is verified using the latest available multiple frequency data from the CTS and COMSTAR satellites. The data covers a wide range of frequencies, elevation angles, and ground site locations.

A three-dimensional wide-angle BPM for optical waveguide structures.

PubMed

Ma, Changbao; Van Keuren, Edward

2007-01-22

Algorithms for effective modeling of optical propagation in three- dimensional waveguide structures are critical for the design of photonic devices. We present a three-dimensional (3-D) wide-angle beam propagation method (WA-BPM) using Hoekstra's scheme. A sparse matrix algebraic equation is formed and solved using iterative methods. The applicability, accuracy and effectiveness of our method are demonstrated by applying it to simulations of wide-angle beam propagation, along with a technique for shifting the simulation window to reduce the dimension of the numerical equation and a threshold technique to further ensure its convergence. These techniques can ensure the implementation of iterative methods for waveguide structures by relaxing the convergence problem, which will further enable us to develop higher-order 3-D WA-BPMs based on Padé approximant operators.

A three-dimensional wide-angle BPM for optical waveguide structures

NASA Astrophysics Data System (ADS)

Ma, Changbao; van Keuren, Edward

2007-01-01

Algorithms for effective modeling of optical propagation in three- dimensional waveguide structures are critical for the design of photonic devices. We present a three-dimensional (3-D) wide-angle beam propagation method (WA-BPM) using Hoekstra’s scheme. A sparse matrix algebraic equation is formed and solved using iterative methods. The applicability, accuracy and effectiveness of our method are demonstrated by applying it to simulations of wide-angle beam propagation, along with a technique for shifting the simulation window to reduce the dimension of the numerical equation and a threshold technique to further ensure its convergence. These techniques can ensure the implementation of iterative methods for waveguide structures by relaxing the convergence problem, which will further enable us to develop higher-order 3-D WA-BPMs based on Padé approximant operators.

System Design for Nano-Network Communications

NASA Astrophysics Data System (ADS)

ShahMohammadian, Hoda

The potential applications of nanotechnology in a wide range of areas necessities nano-networking research. Nano-networking is a new type of networking which has emerged by applying nanotechnology to communication theory. Therefore, this dissertation presents a framework for physical layer communications in a nano-network and addresses some of the pressing unsolved challenges in designing a molecular communication system. The contribution of this dissertation is proposing well-justified models for signal propagation, noise sources, optimum receiver design and synchronization in molecular communication channels. The design of any communication system is primarily based on the signal propagation channel and noise models. Using the Brownian motion and advection molecular statistics, separate signal propagation and noise models are presented for diffusion-based and flow-based molecular communication channels. It is shown that the corrupting noise of molecular channels is uncorrelated and non-stationary with a signal dependent magnitude. The next key component of any communication system is the reception and detection process. This dissertation provides a detailed analysis of the effect of the ligand-receptor binding mechanism on the received signal, and develops the first optimal receiver design for molecular communications. The bit error rate performance of the proposed receiver is evaluated and the impact of medium motion on the receiver performance is investigated. Another important feature of any communication system is synchronization. In this dissertation, the first blind synchronization algorithm is presented for the molecular communication channels. The proposed algorithm uses a non-decision directed maximum likelihood criterion for estimating the channel delay. The Cramer-Rao lower bound is also derived and the performance of the proposed synchronization algorithm is evaluated by investigating its mean square error.

The Numerical Technique for the Landslide Tsunami Simulations Based on Navier-Stokes Equations

NASA Astrophysics Data System (ADS)

Kozelkov, A. S.

2017-12-01

The paper presents an integral technique simulating all phases of a landslide-driven tsunami. The technique is based on the numerical solution of the system of Navier-Stokes equations for multiphase flows. The numerical algorithm uses a fully implicit approximation method, in which the equations of continuity and momentum conservation are coupled through implicit summands of pressure gradient and mass flow. The method we propose removes severe restrictions on the time step and allows simulation of tsunami propagation to arbitrarily large distances. The landslide origin is simulated as an individual phase being a Newtonian fluid with its own density and viscosity and separated from the water and air phases by an interface. The basic formulas of equation discretization and expressions for coefficients are presented, and the main steps of the computation procedure are described in the paper. To enable simulations of tsunami propagation across wide water areas, we propose a parallel algorithm of the technique implementation, which employs an algebraic multigrid method. The implementation of the multigrid method is based on the global level and cascade collection algorithms that impose no limitations on the paralleling scale and make this technique applicable to petascale systems. We demonstrate the possibility of simulating all phases of a landslide-driven tsunami, including its generation, propagation and uprush. The technique has been verified against the problems supported by experimental data. The paper describes the mechanism of incorporating bathymetric data to simulate tsunamis in real water areas of the world ocean. Results of comparison with the nonlinear dispersion theory, which has demonstrated good agreement, are presented for the case of a historical tsunami of volcanic origin on the Montserrat Island in the Caribbean Sea.

RF tomography of metallic objects in free space: preliminary results

NASA Astrophysics Data System (ADS)

Li, Jia; Ewing, Robert L.; Berdanier, Charles; Baker, Christopher

2015-05-01

RF tomography has great potential in defense and homeland security applications. A distributed sensing research facility is under development at Air Force Research Lab. To develop a RF tomographic imaging system for the facility, preliminary experiments have been performed in an indoor range with 12 radar sensors distributed on a circle of 3m radius. Ultra-wideband pulses are used to illuminate single and multiple metallic targets. The echoes received by distributed sensors were processed and combined for tomography reconstruction. Traditional matched filter algorithm and truncated singular value decomposition (SVD) algorithm are compared in terms of their complexity, accuracy, and suitability for distributed processing. A new algorithm is proposed for shape reconstruction, which jointly estimates the object boundary and scatter points on the waveform's propagation path. The results show that the new algorithm allows accurate reconstruction of object shape, which is not available through the matched filter and truncated SVD algorithms.

160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA)

PubMed Central

Li, Isaac TS; Shum, Warren; Truong, Kevin

2007-01-01

Background To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching sequence databases that may contain hundreds of millions of sequences, this algorithm becomes computationally expensive. Results In this paper, we focused on accelerating the Smith-Waterman algorithm by using FPGA-based hardware that implemented a module for computing the score of a single cell of the SW matrix. Then using a grid of this module, the entire SW matrix was computed at the speed of field propagation through the FPGA circuit. These modifications dramatically accelerated the algorithm's computation time by up to 160 folds compared to a pure software implementation running on the same FPGA with an Altera Nios II softprocessor. Conclusion This design of FPGA accelerated hardware offers a new promising direction to seeking computation improvement of genomic database searching. PMID:17555593

160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA).

PubMed

Li, Isaac T S; Shum, Warren; Truong, Kevin

2007-06-07

To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching sequence databases that may contain hundreds of millions of sequences, this algorithm becomes computationally expensive. In this paper, we focused on accelerating the Smith-Waterman algorithm by using FPGA-based hardware that implemented a module for computing the score of a single cell of the SW matrix. Then using a grid of this module, the entire SW matrix was computed at the speed of field propagation through the FPGA circuit. These modifications dramatically accelerated the algorithm's computation time by up to 160 folds compared to a pure software implementation running on the same FPGA with an Altera Nios II softprocessor. This design of FPGA accelerated hardware offers a new promising direction to seeking computation improvement of genomic database searching.

Error Estimation for the Linearized Auto-Localization Algorithm

PubMed Central

Guevara, Jorge; Jiménez, Antonio R.; Prieto, Jose Carlos; Seco, Fernando

2012-01-01

The Linearized Auto-Localization (LAL) algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs), using only the distance measurements to a mobile node whose position is also unknown. The LAL algorithm calculates the inter-beacon distances, used for the estimation of the beacons’ positions, from the linearized trilateration equations. In this paper we propose a method to estimate the propagation of the errors of the inter-beacon distances obtained with the LAL algorithm, based on a first order Taylor approximation of the equations. Since the method depends on such approximation, a confidence parameter τ is defined to measure the reliability of the estimated error. Field evaluations showed that by applying this information to an improved weighted-based auto-localization algorithm (WLAL), the standard deviation of the inter-beacon distances can be improved by more than 30% on average with respect to the original LAL method. PMID:22736965

Ultrasound Algorithm Derivation for Soil Moisture Content Estimation

NASA Technical Reports Server (NTRS)

Belisle, W.R.; Metzl, R.; Choi, J.; Aggarwal, M. D.; Coleman, T.

1997-01-01

Soil moisture content can be estimated by evaluating the velocity at which sound waves travel through a known volume of solid material. This research involved the development of three soil algorithms relating the moisture content to the velocity at which sound waves moved through dry and moist media. Pressure and shear wave propagation equations were used in conjunction with soil property descriptions to derive algorithms appropriate for describing the effects of moisture content variation on the velocity of sound waves in soils with and without complete soil pore water volumes, An elementary algorithm was used to estimate soil moisture contents ranging from 0.08 g/g to 0.5 g/g from sound wave velocities ranging from 526 m/s to 664 m/s. Secondary algorithms were also used to estimate soil moisture content from sound wave velocities through soils with pores that were filled predominantly with air or water.

Active contour based segmentation of resected livers in CT images

NASA Astrophysics Data System (ADS)

Oelmann, Simon; Oyarzun Laura, Cristina; Drechsler, Klaus; Wesarg, Stefan

2015-03-01

The majority of state of the art segmentation algorithms are able to give proper results in healthy organs but not in pathological ones. However, many clinical applications require an accurate segmentation of pathological organs. The determination of the target boundaries for radiotherapy or liver volumetry calculations are examples of this. Volumetry measurements are of special interest after tumor resection for follow up of liver regrow. The segmentation of resected livers presents additional challenges that were not addressed by state of the art algorithms. This paper presents a snakes based algorithm specially developed for the segmentation of resected livers. The algorithm is enhanced with a novel dynamic smoothing technique that allows the active contour to propagate with different speeds depending on the intensities visible in its neighborhood. The algorithm is evaluated in 6 clinical CT images as well as 18 artificial datasets generated from additional clinical CT images.

On-line Flagging of Anomalies and Adaptive Sequential Hypothesis Testing for Fine-feature Characterization of Geosynchronous Satellites

NASA Astrophysics Data System (ADS)

Chaudhary, A.; Payne, T.; Kinateder, K.; Dao, P.; Beecher, E.; Boone, D.; Elliott, B.

The objective of on-line flagging in this paper is to perform interactive assessment of geosynchronous satellites anomalies such as cross-tagging of a satellites in a cluster, solar panel offset change, etc. This assessment will utilize a Bayesian belief propagation procedure and will include automated update of baseline signature data for the satellite, while accounting for the seasonal changes. Its purpose is to enable an ongoing, automated assessment of satellite behavior through its life cycle using the photometry data collected during the synoptic search performed by a ground or space-based sensor as a part of its metrics mission. The change in the satellite features will be reported along with the probabilities of Type I and Type II errors. The objective of adaptive sequential hypothesis testing in this paper is to define future sensor tasking for the purpose of characterization of fine features of the satellite. The tasking will be designed in order to maximize new information with the least number of photometry data points to be collected during the synoptic search by a ground or space-based sensor. Its calculation is based on the utilization of information entropy techniques. The tasking is defined by considering a sequence of hypotheses in regard to the fine features of the satellite. The optimal observation conditions are then ordered in order to maximize new information about a chosen fine feature. The combined objective of on-line flagging and adaptive sequential hypothesis testing is to progressively discover new information about the features of a geosynchronous satellites by leveraging the regular but sparse cadence of data collection during the synoptic search performed by a ground or space-based sensor. Automated Algorithm to Detect Changes in Geostationary Satellite's Configuration and Cross-Tagging Phan Dao, Air Force Research Laboratory/RVB By characterizing geostationary satellites based on photometry and color photometry, analysts can evaluate satellite operational status and affirm its true identity. The process of ingesting photometry data and deriving satellite physical characteristics can be directed by analysts in a batch mode, meaning using a batch of recent data, or by automated algorithms in an on-line mode in which the assessment is updated with each new data point. Tools used for detecting change to satellite's status or identity, whether performed with a human in the loop or automated algorithms, are generally not built to detect with minimum latency and traceable confidence intervals. To alleviate those deficiencies, we investigate the use of Hidden Markov Models (HMM), in a Bayesian Network framework, to infer the hidden state (changed or unchanged) of a three-axis stabilized geostationary satellite using broadband and color photometry. Unlike frequentist statistics which exploit only the stationary statistics of the observables in the database, HMM also exploits the temporal pattern of the observables as well. The algorithm also operates in “learning” mode to gradually evolve the HMM and accommodate natural changes such as due to the seasonal dependence of GEO satellite's light curve. Our technique is designed to operate with missing color data. The version that ingests both panchromatic and color data can accommodate gaps in color photometry data. That attribute is important because while color indices, e.g. Johnson R and B, enhance the belief (probability) of a hidden state, in real world situations, flux data is collected sporadically in an untasked collect, and color data is limited and sometimes absent. Fluxes are measured with experimental error whose effect on the algorithm will be studied. Photometry data in the AFRL's Geo Color Photometry Catalog and Geo Observations with Latitudinal Diversity Simultaneously (GOLDS) data sets are used to simulate a wide variety of operational changes and identity cross tags. The algorithm is tested against simulated sequences of observed magnitudes, mimicking both the cadence of untasked SSN and other ground sensors, occasional operational changes and possible occurrence of cross tags of in-cluster satellites. We would like to show that the on-line algorithm can detect change; sometimes right after the first post-change data point is analyzed, for zero latency. We also want to show the unsupervised “learning” capability that allows the HMM to evolve with time without user's assistance. For example, the users are not required to “label” the true state of the data points.

Correction of Non-Linear Propagation Artifact in Contrast-Enhanced Ultrasound Imaging of Carotid Arteries: Methods and in Vitro Evaluation.

PubMed

Yildiz, Yesna O; Eckersley, Robert J; Senior, Roxy; Lim, Adrian K P; Cosgrove, David; Tang, Meng-Xing

2015-07-01

Non-linear propagation of ultrasound creates artifacts in contrast-enhanced ultrasound images that significantly affect both qualitative and quantitative assessments of tissue perfusion. This article describes the development and evaluation of a new algorithm to correct for this artifact. The correction is a post-processing method that estimates and removes non-linear artifact in the contrast-specific image using the simultaneously acquired B-mode image data. The method is evaluated on carotid artery flow phantoms with large and small vessels containing microbubbles of various concentrations at different acoustic pressures. The algorithm significantly reduces non-linear artifacts while maintaining the contrast signal from bubbles to increase the contrast-to-tissue ratio by up to 11 dB. Contrast signal from a small vessel 600 μm in diameter buried in tissue artifacts before correction was recovered after the correction. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Uncertainties of optical parameters and their propagations in an analytical ocean color inversion algorithm.

PubMed

Lee, ZhongPing; Arnone, Robert; Hu, Chuanmin; Werdell, P Jeremy; Lubac, Bertrand

2010-01-20

Following the theory of error propagation, we developed analytical functions to illustrate and evaluate the uncertainties of inherent optical properties (IOPs) derived by the quasi-analytical algorithm (QAA). In particular, we evaluated the effects of uncertainties of these optical parameters on the inverted IOPs: the absorption coefficient at the reference wavelength, the extrapolation of particle backscattering coefficient, and the spectral ratios of absorption coefficients of phytoplankton and detritus/gelbstoff, respectively. With a systematically simulated data set (46,200 points), we found that the relative uncertainty of QAA-derived total absorption coefficients in the blue-green wavelengths is generally within +/-10% for oceanic waters. The results of this study not only establish theoretical bases to evaluate and understand the effects of the various variables on IOPs derived from remote-sensing reflectance, but also lay the groundwork to analytically estimate uncertainties of these IOPs for each pixel. These are required and important steps for the generation of quality maps of IOP products derived from satellite ocean color remote sensing.

Modeling local extinction in turbulent combustion using an embedding method

NASA Astrophysics Data System (ADS)

Knaus, Robert; Pantano, Carlos

2012-11-01

Local regions of extinction in diffusion flames, called ``flame holes,'' can reduce the efficiency of combustion and increase the production of certain pollutants. At sufficiently high speeds, a flame may also be lifted from the rim of the burner to a downstream location that may be stable. These two phenomena share a common underlying mechanism of propagation related to edge-flame dynamics where chemistry and fluid mechanics are equally important. We present a formulation that describes the formation, propagation, and growth of flames holes on the stoichiometric surface using edge flame dynamics. The boundary separating the flame from the quenched region is modeled using a progress variable defined on the moving stoichiometric surface that is embedded in the three-dimensional space using an extension algorithm. This Cartesian problem is solved using a high-order finite-volume WENO method extended to this nonconservative problem. This algorithm can track the dynamics of flame holes in a turbulent reacting-shear layer and model flame liftoff without requiring full chemistry calculations.

Application of Fourier-wavelet regularized deconvolution for improving image quality of free space propagation x-ray phase contrast imaging.

PubMed

Zhou, Zhongxing; Gao, Feng; Zhao, Huijuan; Zhang, Lixin

2012-11-21

New x-ray phase contrast imaging techniques without using synchrotron radiation confront a common problem from the negative effects of finite source size and limited spatial resolution. These negative effects swamp the fine phase contrast fringes and make them almost undetectable. In order to alleviate this problem, deconvolution procedures should be applied to the blurred x-ray phase contrast images. In this study, three different deconvolution techniques, including Wiener filtering, Tikhonov regularization and Fourier-wavelet regularized deconvolution (ForWaRD), were applied to the simulated and experimental free space propagation x-ray phase contrast images of simple geometric phantoms. These algorithms were evaluated in terms of phase contrast improvement and signal-to-noise ratio. The results demonstrate that the ForWaRD algorithm is most appropriate for phase contrast image restoration among above-mentioned methods; it can effectively restore the lost information of phase contrast fringes while reduce the amplified noise during Fourier regularization.

Three dimensional iterative beam propagation method for optical waveguide devices

NASA Astrophysics Data System (ADS)

Ma, Changbao; Van Keuren, Edward

2006-10-01

The finite difference beam propagation method (FD-BPM) is an effective model for simulating a wide range of optical waveguide structures. The classical FD-BPMs are based on the Crank-Nicholson scheme, and in tridiagonal form can be solved using the Thomas method. We present a different type of algorithm for 3-D structures. In this algorithm, the wave equation is formulated into a large sparse matrix equation which can be solved using iterative methods. The simulation window shifting scheme and threshold technique introduced in our earlier work are utilized to overcome the convergence problem of iterative methods for large sparse matrix equation and wide-angle simulations. This method enables us to develop higher-order 3-D wide-angle (WA-) BPMs based on Pade approximant operators and the multistep method, which are commonly used in WA-BPMs for 2-D structures. Simulations using the new methods will be compared to the analytical results to assure its effectiveness and applicability.

Magnetometer-only attitude and rate determination for a gyro-less spacecraft

NASA Technical Reports Server (NTRS)

Natanson, G. A.; Challa, M. S.; Deutschmann, J.; Baker, D. F.

1994-01-01

Attitude determination algorithms that requires only the earth's magnetic field will be useful for contingency conditions. One way to determine attitude is to use the time derivative of the magnetic field as the second vector in the attitude determination process. When no gyros are available, however, attitude determination becomes difficult because the rates must be propagated via integration of Euler's equation, which in turn requires knowledge of the initial rates. The spacecraft state to be determined must then include not only the attitude but also rates. This paper describes a magnetometer-only attitude determination scheme with no a priori knowledge of the spacecraft state, which uses a deterministic algorithm to initialize an extended Kalman filter. The deterministic algorithm uses Euler's equation to relate the time derivatives of the magnetic field in the reference and body frames and solves the resultant transcendental equations for the coarse attitude and rates. An important feature of the filter is that its state vector also includes corrections to the propagated rates, thus enabling it to generate highly accurate solutions. The method was tested using in-flight data from the Solar, Anomalous, and Magnetospheric Particles Explorer (SAMPEX), a Small Explorer spacecraft. SAMPEX data using several eclipse periods were used to simulate conditions that may exist during the failure of the on-board digital sun sensor. The combined algorithm has been found effective, yielding accuracies of 1.5 deg in attitude (within even nominal mission requirements) and 0.01 degree per second (deg/sec) in the rates.

New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method

NASA Astrophysics Data System (ADS)

Mofavvaz, Shirin; Sohrabi, Mahmoud Reza; Nezamzadeh-Ejhieh, Alireza

2017-07-01

In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300 nm have been used for determination of antihistamine decongestant contents. In the first step, one type of network (feed-forward back-propagation) from the artificial neural network with two different training algorithms, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back-propagation (GDX) algorithm, were employed and their performance was evaluated. The performance of the LM algorithm was better than the GDX algorithm. In the second one, the radial basis network was utilized and results compared with the previous network. In the last one, the other intelligent method named least squares support vector machine was proposed to construct the antihistamine decongestant prediction model and the results were compared with two of the aforementioned networks. The values of the statistical parameters mean square error (MSE), Regression coefficient (R2), correlation coefficient (r) and also mean recovery (%), relative standard deviation (RSD) used for selecting the best model between these methods. Moreover, the proposed methods were compared to the high- performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them.

Two-terminal video coding.

PubMed

Yang, Yang; Stanković, Vladimir; Xiong, Zixiang; Zhao, Wei

2009-03-01

Following recent works on the rate region of the quadratic Gaussian two-terminal source coding problem and limit-approaching code designs, this paper examines multiterminal source coding of two correlated, i.e., stereo, video sequences to save the sum rate over independent coding of both sequences. Two multiterminal video coding schemes are proposed. In the first scheme, the left sequence of the stereo pair is coded by H.264/AVC and used at the joint decoder to facilitate Wyner-Ziv coding of the right video sequence. The first I-frame of the right sequence is successively coded by H.264/AVC Intracoding and Wyner-Ziv coding. An efficient stereo matching algorithm based on loopy belief propagation is then adopted at the decoder to produce pixel-level disparity maps between the corresponding frames of the two decoded video sequences on the fly. Based on the disparity maps, side information for both motion vectors and motion-compensated residual frames of the right sequence are generated at the decoder before Wyner-Ziv encoding. In the second scheme, source splitting is employed on top of classic and Wyner-Ziv coding for compression of both I-frames to allow flexible rate allocation between the two sequences. Experiments with both schemes on stereo video sequences using H.264/AVC, LDPC codes for Slepian-Wolf coding of the motion vectors, and scalar quantization in conjunction with LDPC codes for Wyner-Ziv coding of the residual coefficients give a slightly lower sum rate than separate H.264/AVC coding of both sequences at the same video quality.

Using level set based inversion of arrival times to recover shear wave speed in transient elastography and supersonic imaging

NASA Astrophysics Data System (ADS)

McLaughlin, Joyce; Renzi, Daniel

2006-04-01

Transient elastography and supersonic imaging are promising new techniques for characterizing the elasticity of soft tissues. Using this method, an 'ultrafast imaging' system (up to 10 000 frames s-1) follows in real time the propagation of a low-frequency shear wave. The displacement of the propagating shear wave is measured as a function of time and space. Here we develop a fast level set based algorithm for finding the shear wave speed from the interior positions of the propagating front. We compare the performance of level curve methods developed here and our previously developed (McLaughlin J and Renzi D 2006 Shear wave speed recovery in transient elastography and supersonic imaging using propagating fronts Inverse Problems 22 681-706) distance methods. We give reconstruction examples from synthetic data and from data obtained from a phantom experiment accomplished by Mathias Fink's group (the Laboratoire Ondes et Acoustique, ESPCI, Université Paris VII).

Influence of turbulent atmosphere on laser beams from confocal unstable resonators

NASA Astrophysics Data System (ADS)

Peng, Yu-feng; Wang, Juan; Bi, Xiao-qun; Zhang, Ming-gao; Cheng, Zu-hai

2009-07-01

Based on the laser fields from a positive confocal unstable resonator (ab initio), the propagation characteristics of the beam through turbulent atmosphere are investigated by means of fast Fourier transform algorithm (FFT). To conveniently investigate the propagation characteristics of laser beam through the atmosphere, as far as known, in the previous many works, a mathematical expression was generally artificially predefined to represent the given laser beam, such as Gaussian beam, Hermite-cosh-Gaussian beam, flat topped beam, dark-hollow (annular) beam, etc. In this paper, by basing on the initial built in oscillation of a laser resonator, such as a positive confocal unstable resonator (CUR), we studied the intensity distributions of the output laser field to obtain the propagation characteristics of laser beam through the turbulent atmosphere as functions of different propagation distances. The results show that the turbulence will result in the degradation of the peak value of the laser intensity in the far field, the spread of the far field diagram patterns, and the beam quality characteristics greatly degraded.

Deriving Continuous Fields of Tree Cover at 1-m over the Continental United States From the National Agriculture Imagery Program (NAIP) Imagery to Reduce Uncertainties in Forest Carbon Stock Estimation

NASA Astrophysics Data System (ADS)

Ganguly, S.; Basu, S.; Mukhopadhyay, S.; Michaelis, A.; Milesi, C.; Votava, P.; Nemani, R. R.

2013-12-01

An unresolved issue with coarse-to-medium resolution satellite-based forest carbon mapping over regional to continental scales is the high level of uncertainty in above ground biomass (AGB) estimates caused by the absence of forest cover information at a high enough spatial resolution (current spatial resolution is limited to 30-m). To put confidence in existing satellite-derived AGB density estimates, it is imperative to create continuous fields of tree cover at a sufficiently high resolution (e.g. 1-m) such that large uncertainties in forested area are reduced. The proposed work will provide means to reduce uncertainty in present satellite-derived AGB maps and Forest Inventory and Analysis (FIA) based regional estimates. Our primary objective will be to create Very High Resolution (VHR) estimates of tree cover at a spatial resolution of 1-m for the Continental United States using all available National Agriculture Imaging Program (NAIP) color-infrared imagery from 2010 till 2012. We will leverage the existing capabilities of the NASA Earth Exchange (NEX) high performance computing and storage facilities. The proposed 1-m tree cover map can be further aggregated to provide percent tree cover at any medium-to-coarse resolution spatial grid, which will aid in reducing uncertainties in AGB density estimation at the respective grid and overcome current limitations imposed by medium-to-coarse resolution land cover maps. We have implemented a scalable and computationally-efficient parallelized framework for tree-cover delineation - the core components of the algorithm [that] include a feature extraction process, a Statistical Region Merging image segmentation algorithm and a classification algorithm based on Deep Belief Network and a Feedforward Backpropagation Neural Network algorithm. An initial pilot exercise has been performed over the state of California (~11,000 scenes) to create a wall-to-wall 1-m tree cover map and the classification accuracy has been assessed. Results show an improvement in accuracy of tree-cover delineation as compared to existing forest cover maps from NLCD, especially over fragmented, heterogeneous and urban landscapes. Estimates of VHR tree cover will complement and enhance the accuracy of present remote-sensing based AGB modeling approaches and forest inventory based estimates at both national and local scales. A requisite step will be to characterize the inherent uncertainties in tree cover estimates and propagate them to estimate AGB.

[Monitoring of Crack Propagation in Repaired Structures Based on Characteristics of FBG Sensors Reflecting Spectra].

PubMed

Yuan, Shen-fang; Jin, Xin; Qiu, Lei; Huang, Hong-mei

2015-03-01

In order to improve the security of aircraft repaired structures, a method of crack propagation monitoring in repaired structures is put forward basing on characteristics of Fiber Bragg Grating (FBG) reflecting spectra in this article. With the cyclic loading effecting on repaired structure, cracks propagate, while non-uniform strain field appears nearby the tip of crack which leads to the FBG sensors' reflecting spectra deformations. The crack propagating can be monitored by extracting the characteristics of FBG sensors' reflecting spectral deformations. A finite element model (FEM) of the specimen is established. Meanwhile, the distributions of strains which are under the action of cracks of different angles and lengths are obtained. The characteristics, such as main peak wavelength shift, area of reflecting spectra, second and third peak value and so on, are extracted from the FBGs' reflecting spectral which are calculated by transfer matrix algorithm. An artificial neural network is built to act as the model between the characteristics of the reflecting spectral and the propagation of crack. As a result, the crack propagation of repaired structures is monitored accurately and the error of crack length is less than 0.5 mm, the error of crack angle is less than 5 degree. The accurately monitoring problem of crack propagation of repaired structures is solved by taking use of this method. It has important significance in aircrafts safety improvement and maintenance cost reducing.

Spatiotemporal Mapping of Interictal Spike Propagation: A Novel Methodology Applied to Pediatric Intracranial EEG Recordings

PubMed Central

Tomlinson, Samuel B.; Bermudez, Camilo; Conley, Chiara; Brown, Merritt W.; Porter, Brenda E.; Marsh, Eric D.

2016-01-01

Synchronized cortical activity is implicated in both normative cognitive functioning and many neurologic disorders. For epilepsy patients with intractable seizures, irregular synchronization within the epileptogenic zone (EZ) is believed to provide the network substrate through which seizures initiate and propagate. Mapping the EZ prior to epilepsy surgery is critical for detecting seizure networks in order to achieve postsurgical seizure control. However, automated techniques for characterizing epileptic networks have yet to gain traction in the clinical setting. Recent advances in signal processing and spike detection have made it possible to examine the spatiotemporal propagation of interictal spike discharges across the epileptic cortex. In this study, we present a novel methodology for detecting, extracting, and visualizing spike propagation and demonstrate its potential utility as a biomarker for the EZ. Eighteen presurgical intracranial EEG recordings were obtained from pediatric patients ultimately experiencing favorable (i.e., seizure-free, n = 9) or unfavorable (i.e., seizure-persistent, n = 9) surgical outcomes. Novel algorithms were applied to extract multichannel spike discharges and visualize their spatiotemporal propagation. Quantitative analysis of spike propagation was performed using trajectory clustering and spatial autocorrelation techniques. Comparison of interictal propagation patterns revealed an increase in trajectory organization (i.e., spatial autocorrelation) among Sz-Free patients compared with Sz-Persist patients. The pathophysiological basis and clinical implications of these findings are considered. PMID:28066315

Pruning Neural Networks with Distribution Estimation Algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cantu-Paz, E

2003-01-15

This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than themore » original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.« less

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blom, Philip Stephen; Marcillo, Omar Eduardo; Euler, Garrett Gene

InfraPy is a Python-based analysis toolkit being development at LANL. The algorithms are intended for ground-based nuclear detonation detection applications to detect, locate, and characterize explosive sources using infrasonic observations. The implementation is usable as a stand-alone Python library or as a command line driven tool operating directly on a database. With multiple scientists working on the project, we've begun using a LANL git repository for collaborative development and version control. Current and planned work on InfraPy focuses on the development of new algorithms and propagation models. Collaboration with Southern Methodist University (SMU) has helped identify bugs and limitations ofmore » the algorithms. The current focus of usage development is focused on library imports and CLI.« less

An adaptive DPCM algorithm for predicting contours in NTSC composite video signals

NASA Astrophysics Data System (ADS)

Cox, N. R.

An adaptive DPCM algorithm is proposed for encoding digitized National Television Systems Committee (NTSC) color video signals. This algorithm essentially predicts picture contours in the composite signal without resorting to component separation. The contour parameters (slope thresholds) are optimized using four 'typical' television frames that have been sampled at three times the color subcarrier frequency. Three variations of the basic predictor are simulated and compared quantitatively with three non-adaptive predictors of similar complexity. By incorporating a dual-word-length coder and buffer memory, high quality color pictures can be encoded at 4.0 bits/pel or 42.95 Mbit/s. The effect of channel error propagation is also investigated.

Wind profiling based on the optical beam intensity statistics in a turbulent atmosphere.

PubMed

Banakh, Victor A; Marakasov, Dimitrii A

2007-10-01

Reconstruction of the wind profile from the statistics of intensity fluctuations of an optical beam propagating in a turbulent atmosphere is considered. The equations for the spatiotemporal correlation function and the spectrum of weak intensity fluctuations of a Gaussian beam are obtained. The algorithms of wind profile retrieval from the spatiotemporal intensity spectrum are described and the results of end-to-end computer experiments on wind profiling based on the developed algorithms are presented. It is shown that the developed algorithms allow retrieval of the wind profile from the turbulent optical beam intensity fluctuations with acceptable accuracy in many practically feasible laser measurements set up in the atmosphere.

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale

PubMed Central

Kobourov, Stephen; Gallant, Mike; Börner, Katy

2016-01-01

Overview Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms—Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. Cluster Quality Metrics We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Network Clustering Algorithms Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters. PMID:27391786

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale.

PubMed

Emmons, Scott; Kobourov, Stephen; Gallant, Mike; Börner, Katy

2016-01-01

Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms-Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters.

Acoustic Inversion in Optoacoustic Tomography: A Review

PubMed Central

Rosenthal, Amir; Ntziachristos, Vasilis; Razansky, Daniel

2013-01-01

Optoacoustic tomography enables volumetric imaging with optical contrast in biological tissue at depths beyond the optical mean free path by the use of optical excitation and acoustic detection. The hybrid nature of optoacoustic tomography gives rise to two distinct inverse problems: The optical inverse problem, related to the propagation of the excitation light in tissue, and the acoustic inverse problem, which deals with the propagation and detection of the generated acoustic waves. Since the two inverse problems have different physical underpinnings and are governed by different types of equations, they are often treated independently as unrelated problems. From an imaging standpoint, the acoustic inverse problem relates to forming an image from the measured acoustic data, whereas the optical inverse problem relates to quantifying the formed image. This review focuses on the acoustic aspects of optoacoustic tomography, specifically acoustic reconstruction algorithms and imaging-system practicalities. As these two aspects are intimately linked, and no silver bullet exists in the path towards high-performance imaging, we adopt a holistic approach in our review and discuss the many links between the two aspects. Four classes of reconstruction algorithms are reviewed: time-domain (so called back-projection) formulae, frequency-domain formulae, time-reversal algorithms, and model-based algorithms. These algorithms are discussed in the context of the various acoustic detectors and detection surfaces which are commonly used in experimental studies. We further discuss the effects of non-ideal imaging scenarios on the quality of reconstruction and review methods that can mitigate these effects. Namely, we consider the cases of finite detector aperture, limited-view tomography, spatial under-sampling of the acoustic signals, and acoustic heterogeneities and losses. PMID:24772060

Operationalizing hippocampal volume as an enrichment biomarker for amnestic mild cognitive impairment trials: effect of algorithm, test-retest variability, and cut point on trial cost, duration, and sample size.

PubMed

Yu, Peng; Sun, Jia; Wolz, Robin; Stephenson, Diane; Brewer, James; Fox, Nick C; Cole, Patricia E; Jack, Clifford R; Hill, Derek L G; Schwarz, Adam J

2014-04-01

The objective of this study was to evaluate the effect of computational algorithm, measurement variability, and cut point on hippocampal volume (HCV)-based patient selection for clinical trials in mild cognitive impairment (MCI). We used normal control and amnestic MCI subjects from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) as normative reference and screening cohorts. We evaluated the enrichment performance of 4 widely used hippocampal segmentation algorithms (FreeSurfer, Hippocampus Multi-Atlas Propagation and Segmentation (HMAPS), Learning Embeddings Atlas Propagation (LEAP), and NeuroQuant) in terms of 2-year changes in Mini-Mental State Examination (MMSE), Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), and Clinical Dementia Rating Sum of Boxes (CDR-SB). We modeled the implications for sample size, screen fail rates, and trial cost and duration. HCV based patient selection yielded reduced sample sizes (by ∼40%-60%) and lower trial costs (by ∼30%-40%) across a wide range of cut points. These results provide a guide to the choice of HCV cut point for amnestic MCI clinical trials, allowing an informed tradeoff between statistical and practical considerations. Copyright © 2014 Elsevier Inc. All rights reserved.

SWRT: A package for semi-analytical solutions of surface wave propagation, including mode conversion, across transversely aligned vertical discontinuities

NASA Astrophysics Data System (ADS)

Datta, Arjun

2018-03-01

We present a suite of programs that implement decades-old algorithms for computation of seismic surface wave reflection and transmission coefficients at a welded contact between two laterally homogeneous quarter-spaces. For Love as well as Rayleigh waves, the algorithms are shown to be capable of modelling multiple mode conversions at a lateral discontinuity, which was not shown in the original publications or in the subsequent literature. Only normal incidence at a lateral boundary is considered so there is no Love-Rayleigh coupling, but incidence of any mode and coupling to any (other) mode can be handled. The code is written in Python and makes use of SciPy's Simpson's rule integrator and NumPy's linear algebra solver for its core functionality. Transmission-side results from this code are found to be in good agreement with those from finite-difference simulations. In today's research environment of extensive computing power, the coded algorithms are arguably redundant but SWRT can be used as a valuable testing tool for the ever evolving numerical solvers of seismic wave propagation. SWRT is available via GitHub (https://github.com/arjundatta23/SWRT.git).

Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems

NASA Technical Reports Server (NTRS)

Innocenti, M.; Napolitano, M.

2003-01-01

Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the use of learning algorithms more efficient than the classic Back-Propagation, and through the implementation of the neural schemes in parallel hardware. The results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model are presented. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural architectures were compared. The first architecture is based on a Multi Layer Perceptron (MLP) NN trained with the Extended Back Propagation algorithm (EBPA). The second architecture is based on a Radial Basis Function (RBF) NN trained with the Extended-MRAN (EMRAN) algorithms. In addition, alternative methods for communications links fault detection and accomodation are presented, relative to multiple unmanned aircraft applications.

Energy Efficient and Stable Weight Based Clustering for Mobile Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Bouk, Safdar H.; Sasase, Iwao

Recently several weighted clustering algorithms have been proposed, however, to the best of our knowledge; there is none that propagates weights to other nodes without weight message for leader election, normalizes node parameters and considers neighboring node parameters to calculate node weights. In this paper, we propose an Energy Efficient and Stable Weight Based Clustering (EE-SWBC) algorithm that elects cluster heads without sending any additional weight message. It propagates node parameters to its neighbors through neighbor discovery message (HELLO Message) and stores these parameters in neighborhood list. Each node normalizes parameters and efficiently calculates its own weight and the weights of neighboring nodes from that neighborhood table using Grey Decision Method (GDM). GDM finds the ideal solution (best node parameters in neighborhood list) and calculates node weights in comparison to the ideal solution. The node(s) with maximum weight (parameters closer to the ideal solution) are elected as cluster heads. In result, EE-SWBC fairly selects potential nodes with parameters closer to ideal solution with less overhead. Different performance metrics of EE-SWBC and Distributed Weighted Clustering Algorithm (DWCA) are compared through simulations. The simulation results show that EE-SWBC maintains fewer average numbers of stable clusters with minimum overhead, less energy consumption and fewer changes in cluster structure within network compared to DWCA.

Development of new tsunami detection algorithms for high frequency radars and application to tsunami warning in British Columbia, Canada

NASA Astrophysics Data System (ADS)

Grilli, S. T.; Guérin, C. A.; Shelby, M. R.; Grilli, A. R.; Insua, T. L.; Moran, P., Jr.

2016-12-01

A High-Frequency (HF) radar was installed by Ocean Networks Canada in Tofino, BC, to detect tsunamis from far- and near-field seismic sources; in particular, from the Cascadia Subduction Zone. This HF radar can measure ocean surface currents up to a 70-85 km range, depending on atmospheric conditions, based on the Doppler shift they cause in ocean waves at the Bragg frequency. In earlier work, we showed that tsunami currents must be at least 0.15 m/s to be directly detectable by a HF radar, when considering environmental noise and background currents (from tide/mesoscale circulation). This limits a direct tsunami detection to shallow water areas where currents are sufficiently strong due to wave shoaling and, hence, to the continental shelf. It follows that, in locations with a narrow shelf, warning times using a direct inversion method will be small. To detect tsunamis in deeper water, beyond the continental shelf, we proposed a new algorithm that does not require directly inverting currents, but instead is based on observing changes in patterns of spatial correlations of the raw radar signal between two radar cells located along the same wave ray, after time is shifted by the tsunami propagation time along the ray. A pattern change will indicate the presence of a tsunami. We validated this new algorithm for idealized tsunami wave trains propagating over a simple seafloor geometry in a direction normally incident to shore. Here, we further develop, extend, and validate the algorithm for realistic case studies of seismic tsunami sources impacting Vancouver Island, BC. Tsunami currents, computed with a state-of-the-art long wave model are spatially averaged over cells aligned along individual wave rays, located within the radar sweep area, obtained by solving the wave geometric optic equation; for long waves, such rays and tsunami propagation times along those are only function of the seafloor bathymetry, and hence can be precalculated for different incident tsunami directions. A model simulating the radar backscattered signal in space and time as a function of simulated tsunami currents is applied to the sweep area. Numerical experiments show that the new algorithm can detect a realistic tsunami further offshore than a direct detection method. Correlation thresholds for tsunami detection will be derived from the results.

Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines

PubMed Central

Teodoro, George; Pan, Tony; Kurc, Tahsin; Kong, Jun; Cooper, Lee; Saltz, Joel

2013-01-01

We address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50× and 85× with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively. PMID:23908562

Quantitative ultrasonic testing of acoustically anisotropic materials with verification on austenitic and dissimilar weld joints

NASA Astrophysics Data System (ADS)

Boller, C.; Pudovikov, S.; Bulavinov, A.

2012-05-01

Austenitic stainless steel materials are widely used in a variety of industry sectors. In particular, the material is qualified to meet the design criteria of high quality in safety related applications. For example, the primary loop of the most of the nuclear power plants in the world, due to high durability and corrosion resistance, is made of this material. Certain operating conditions may cause a range of changes in the integrity of the component, and therefore require nondestructive testing at reasonable intervals. These in-service inspections are often performed using ultrasonic techniques, in particular when cracking is of specific concern. However, the coarse, dendritic grain structure of the weld material, formed during the welding process, is extreme and unpredictably anisotropic. Such structure is no longer direction-independent to the ultrasonic wave propagation; therefore, the ultrasonic beam deflects and redirects and the wave front becomes distorted. Thus, the use of conventional ultrasonic testing techniques using fixed beam angles is very limited and the application of ultrasonic Phased Array techniques becomes desirable. The "Sampling Phased Array" technique, invented and developed by Fraunhofer IZFP, allows the acquisition of time signals (A-scans) for each individual transducer element of the array along with fast image reconstruction techniques based on synthetic focusing algorithms. The reconstruction considers the sound propagation from each image pixel to the individual sensor element. For anisotropic media, where the sound beam is deflected and the sound path is not known a-priori, a novel phase adjustment technique called "Reverse Phase Matching" is implemented. By taking into account the anisotropy and inhomogeneity of the weld structure, a ray tracing algorithm for modeling the acoustic wave propagation and calculating the sound propagation time is applied. This technique can be utilized for 2D and 3D real time image reconstruction. The "Gradient Constant Descent Method" (GECDM), an iterative algorithm, is implemented, which is essential for examination of inhomogeneous anisotropic media having unknown properties (elastic constants). The Sampling Phased Array technique with Reverse Phase Matching extended by GECDM-technique determines unknown elastic constants and provides reliable and efficient quantitative flaw detection in the austenitic welds. The validation of ray-tracing algorithm and GECDM-method is performed by number of experiments on test specimens with artificial as well as natural material flaws. A mechanized system for ultrasonic testing of stainless steel and dissimilar welds is developed. The system works on both conventional and Sampling Phased Array techniques. The new frontend ultrasonic unit with optical data link allows the 3D visualization of the inspection results in real time.

Open Energy Information System version 2.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

OpenEIS was created to provide standard methods for authoring, sharing, testing, using, and improving algorithms for operational building energy efficiency with building managers and building owners. OpenEIS is designed as a no-cost/low-cost solution that will propagate the fault detection and diagnostic (FDD) solutions into the marketplace by providing state- of- the-art analytical and diagnostic algorithms. As OpenEIS penetrates the market, demand by control system manufacturers and integrators serving small and medium commercial customers will help push these types of commercial software tool offerings into the broader marketplace.

Algorithm for Cosmic Noise Suppression in Free Space Optical Communications

NASA Astrophysics Data System (ADS)

Yuvaraj, George; Himani Sharma, Goyal, Dr.

2017-08-01

This article describes an algorithm to reduce cosmic noise in free space optical communication system. This method is intended to increase communication system’s performance and to increase the sustainability of the communication system by means of image processing technique. Apart from these, methods employed in testing the model are also presented for the communication system that uses either terrestrial or extraterrestrial medium to propagate message using optics or visible light without considering environmental impact that is turbulence, atmospheric absorption, beam dispersion and light intensity on its performance.

Frequency Estimator Performance for a Software-Based Beacon Receiver

NASA Technical Reports Server (NTRS)

Zemba, Michael J.; Morse, Jacquelynne Rose; Nessel, James A.; Miranda, Felix

2014-01-01

As propagation terminals have evolved, their design has trended more toward a software-based approach that facilitates convenient adjustment and customization of the receiver algorithms. One potential improvement is the implementation of a frequency estimation algorithm, through which the primary frequency component of the received signal can be estimated with a much greater resolution than with a simple peak search of the FFT spectrum. To select an estimator for usage in a QV-band beacon receiver, analysis of six frequency estimators was conducted to characterize their effectiveness as they relate to beacon receiver design.

Analysis of foliage effects on mobile propagation in dense urban environments

NASA Astrophysics Data System (ADS)

Bronshtein, Alexander; Mazar, Reuven; Lu, I.-Tai

2000-07-01

Attempts to reduce the interference level and to increase the spectral efficiency of cellular radio communication systems operating in dense urban and suburban areas lead to the microcellular approach with a consequent requirement to lower antenna heights. In large metropolitan areas having high buildings this requirement causes a situation where the transmitting and receiving antennas are both located below the rooftops, and the city street acts as a type of a waveguiding channel for the propagating signal. In this work, the city street is modeled as a random multislit waveguide with randomly distributed regions of foliage parallel to the building boundaries. The statistical propagation characteristics are expressed in terms of multiple ray-fields approaching the observer. Algorithms for predicting the path-loss along the waveguide and for computing the transverse field structure are presented.

Digital backpropagation accounting for polarization-mode dispersion.

PubMed

Czegledi, Cristian B; Liga, Gabriele; Lavery, Domaniç; Karlsson, Magnus; Agrell, Erik; Savory, Seb J; Bayvel, Polina

2017-02-06

Digital backpropagation (DBP) is a promising digital-domain technique to mitigate Kerr-induced nonlinear interference. While it successfully removes deterministic signal-signal interactions, the performance of ideal DBP is limited by stochastic effects, such as polarization-mode dispersion (PMD). In this paper, we consider an ideal full-field DBP implementation and modify it to additionally account for PMD; reversing the PMD effects in the backward propagation by passing the reverse propagated signal also through PMD sections, which concatenated equal the inverse of the PMD in the forward propagation. These PMD sections are calculated analytically at the receiver based on the total accumulated PMD of the link estimated from channel equalizers. Numerical simulations show that, accounting for nonlinear polarization-related interactions in the modified DBP algorithm, additional signal-to-noise ratio gains of 1.1 dB are obtained for transmission over 1000 km.

JDiffraction: A GPGPU-accelerated JAVA library for numerical propagation of scalar wave fields

NASA Astrophysics Data System (ADS)

Piedrahita-Quintero, Pablo; Trujillo, Carlos; Garcia-Sucerquia, Jorge

2017-05-01

JDiffraction, a GPGPU-accelerated JAVA library for numerical propagation of scalar wave fields, is presented. Angular spectrum, Fresnel transform, and Fresnel-Bluestein transform are the numerical algorithms implemented in the methods and functions of the library to compute the scalar propagation of the complex wavefield. The functionality of the library is tested with the modeling of easy to forecast numerical experiments and also with the numerical reconstruction of a digitally recorded hologram. The performance of JDiffraction is contrasted with a library written for C++, showing great competitiveness in the apparently less complex environment of JAVA language. JDiffraction also includes JAVA easy-to-use methods and functions that take advantage of the computation power of the graphic processing units to accelerate the processing times of 2048×2048 pixel images up to 74 frames per second.

A Smartphone Indoor Localization Algorithm Based on WLAN Location Fingerprinting with Feature Extraction and Clustering.

PubMed

Luo, Junhai; Fu, Liang

2017-06-09

With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS), which is collected from Access Points (APs). The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA) is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC) algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML) estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity.

The wavenumber algorithm for full-matrix imaging using an ultrasonic array.

PubMed

Hunter, Alan J; Drinkwater, Bruce W; Wilcox, Paul D

2008-11-01

Ultrasonic imaging using full-matrix capture, e.g., via the total focusing method (TFM), has been shown to increase angular inspection coverage and improve sensitivity to small defects in nondestructive evaluation. In this paper, we develop a Fourier-domain approach to full-matrix imaging based on the wavenumber algorithm used in synthetic aperture radar and sonar. The extension to the wavenumber algorithm for full-matrix data is described and the performance of the new algorithm compared with the TFM, which we use as a representative benchmark for the time-domain algorithms. The wavenumber algorithm provides a mathematically rigorous solution to the inverse problem for the assumed forward wave propagation model, whereas the TFM employs heuristic delay-and-sum beamforming. Consequently, the wavenumber algorithm has an improved point-spread function and provides better imagery. However, the major advantage of the wavenumber algorithm is its superior computational performance. For large arrays and images, the wavenumber algorithm is several orders of magnitude faster than the TFM. On the other hand, the key advantage of the TFM is its flexibility. The wavenumber algorithm requires a regularly sampled linear array, while the TFM can handle arbitrary imaging geometries. The TFM and the wavenumber algorithm are compared using simulated and experimental data.

Wave propagation, scattering and emission in complex media

NASA Astrophysics Data System (ADS)

Jin, Ya-Qiu

I. Polarimetric scattering and SAR imagery. EM wave propagation and scattering in polarimetric SAR interferometry / S. R. Cloude. Terrain topographic inversion from single-pass polarimetric SAR image data by using polarimetric stokes parameters and morphological algorithm / Y. Q. Jin, L. Luo. Road detection in forested area using polarimetric SAR / G. W. Dong ... [et al.]. Research on some problems about SAR radiometric resolution / G. Dong ... [et al.]. A fast image matching algorithm for remote sensing applications / Z. Q. Hou ... [et al.]. A new algorithm of noised remote sensing image fusion based on steerable filters / X. Kang ... [et al.]. Adaptive noise reduction of InSAR data based on anisotropic diffusion models and their applications to phase unwrapping / C. Wang, X. Gao, H. Zhang -- II. Scattering from randomly rough surfaces. Modeling tools for backscattering from rough surfaces / A. K. Fung, K. S. Chen. Pseudo-nondiffracting beams from rough surface scattering / E. R. Méndez, T. A. Leskova, A. A. Maradudin. Surface roughness clutter effects in GPR modeling and detection / C. Rappaport. Scattering from rough surfaces with small slopes / M. Saillard, G. Soriano. Polarization and spectral characteristics of radar signals reflected by sea-surface / V. A. Butko, V. A. Khlusov, L. I. Sharygina. Simulation of microwave scattering from wind-driven ocean surfaces / M. Y. Xia ... [et al.]. HF surface wave radar tests at the Eastern China Sea / X. B. Wu ... [et al.] -- III. Electromagnetics of complex materials. Wave propagation in plane-parallel metamaterial and constitutive relations / A. Ishimaru ... [et al.]. Two dimensional periodic approach for the study of left-handed metamaterials / T. M. Grzegorczyk ... [et al.]. Numerical analysis of the effective constitutive parameters of a random medium containing small chiral spheres / Y. Nanbu, T. Matsuoka, M. Tateiba. Wave propagation in inhomogeneous media: from the Helmholtz to the Ginzburg -Landau equation / M. Gitterman. Transformation of the spectrum of scattered radiation in randomly inhomogeneous absorptive plasma layer / G. V. Jandieri, G. D. Aburjunia, V. G. Jandieri. Numerical analysis of microwave heating on saponification reaction / K. Huang, K. Jia -- IV. Scattering from complex targets. Analysis of electromagnetic scattering from layered crossed-gratings of circular cylinders using lattice sums technique / K. Yasumoto, H. T. Jia. Scattering by a body in a random medium / M. Tateiba, Z. Q. Meng, H. El-Ocla. A rigorous analysis of electromagnetic scattering from multilayered crossed-arrays of metallic cylinders / H. T. Jia, K. Yasumoto. Vector models of non-stable and spatially-distributed radar objects / A. Surkov ... [et al.]. Simulation of algorithm of orthogonal signals forming and processing used to estimate back scattering matrix of non-stable radar objects / D. Nosov ... [et al.]. New features of scattering from a dielectric film on a reflecting metal substrate / Z. H. Gu, I. M. Fuks, M. Ciftan. A higher order FDTD method for EM wave propagation in collision plasmas / S. B. Liu, J. J. Mo, N. C. Yuan -- V. Radiative transfer and remote sensing. Simulating microwave emission from Antarctica ice sheet with a coherent model / M. Tedesco, P. Pampaloni. Scattering and emission from inhomogeneous vegetation canopy and alien target by using three-dimensional Vector Radiative Transfer (3D-VRT) equation / Y. Q. Jin, Z. C. Liang. Analysis of land types using high-resolution satellite images and fractal approach / H. G. Zhang ... [et al.]. Data fusion of RADARSAT SAR and DMSP SSM/I for monitoring sea ice of China's Bohai Sea / Y. Q. Jin. Retrieving atmospheric temperature profiles from simulated microwave radiometer data with artificial neural networks / Z. G. Yao, H. B. Chen -- VI. Wave propagation and wireless communication. Wireless propagation in urban environments: modeling and experimental verification / D. Erricolo ... [et al.]. An overview of physics-based wave propagation in forested environment / K. Sarabandi, I. Koh. Angle-of-arrival fluctuations due to meteorological conditions in the diffraction zone of C-band radio waves, propagated over the ground surface / T. A. Tyufilina, A. A. Meschelyakov, M. V. Krutikov. Simulating radio channel statistics using ray based prediction codes / H. L. Bertoni. Measurement and simulation of ultra wideband antenna elements / W. Sörgel, W. Wiesbeck. The experimental investigation of a ground-placed radio complex synchronization system / V. P. Denisov ... [et al.] -- VII. Computational electromagnetics. Analysis of 3-D electromagnetic wave scattering with the Krylov subspace FFT iterative methods / R. S. Chen ... [et al.]. Sparse approximate inverse preconditioned iterative algorithm with block toeplitz matrix for fast analysis of microstrip circuits / L. Mo, R. S. Chen, E. K. N. Yung. An Efficient modified interpolation technique for the translation operators in MLFMA / J. Hu, Z. P. Nie, G. X. Zou. Efficient solution of 3-D vector electromagnetic scattering by CG-MLFMA with partly approximate iteration / J. Hu, Z. P. Nie. The effective constitution at interface of different media / L. G. Zheng, W. X. Zhang. Novel basis functions for quadratic hexahedral edge element / P. Liu ... [et al.]. A higher order FDTD method for EM wave propagation in collision plasmas / S. B. Liu, J. J. Mo, N. C. Yuan. Attenuation of electric field eradiated by underground source / J. P. Dong, Y. G. Gao.

Hybrid radiosity-SP3 equation based bioluminescence tomography reconstruction for turbid medium with low- and non-scattering regions

NASA Astrophysics Data System (ADS)

Chen, Xueli; Zhang, Qitan; Yang, Defu; Liang, Jimin

2014-01-01

To provide an ideal solution for a specific problem of gastric cancer detection in which low-scattering regions simultaneously existed with both the non- and high-scattering regions, a novel hybrid radiosity-SP3 equation based reconstruction algorithm for bioluminescence tomography was proposed in this paper. In the algorithm, the third-order simplified spherical harmonics approximation (SP3) was combined with the radiosity equation to describe the bioluminescent light propagation in tissues, which provided acceptable accuracy for the turbid medium with both low- and non-scattering regions. The performance of the algorithm was evaluated with digital mouse based simulations and a gastric cancer-bearing mouse based in situ experiment. Primary results demonstrated the feasibility and superiority of the proposed algorithm for the turbid medium with low- and non-scattering regions.

A non-stochastic iterative computational method to model light propagation in turbid media

NASA Astrophysics Data System (ADS)

McIntyre, Thomas J.; Zemp, Roger J.

2015-03-01

Monte Carlo models are widely used to model light transport in turbid media, however their results implicitly contain stochastic variations. These fluctuations are not ideal, especially for inverse problems where Jacobian matrix errors can lead to large uncertainties upon matrix inversion. Yet Monte Carlo approaches are more computationally favorable than solving the full Radiative Transport Equation. Here, a non-stochastic computational method of estimating fluence distributions in turbid media is proposed, which is called the Non-Stochastic Propagation by Iterative Radiance Evaluation method (NSPIRE). Rather than using stochastic means to determine a random walk for each photon packet, the propagation of light from any element to all other elements in a grid is modelled simultaneously. For locally homogeneous anisotropic turbid media, the matrices used to represent scattering and projection are shown to be block Toeplitz, which leads to computational simplifications via convolution operators. To evaluate the accuracy of the algorithm, 2D simulations were done and compared against Monte Carlo models for the cases of an isotropic point source and a pencil beam incident on a semi-infinite turbid medium. The model was shown to have a mean percent error less than 2%. The algorithm represents a new paradigm in radiative transport modelling and may offer a non-stochastic alternative to modeling light transport in anisotropic scattering media for applications where the diffusion approximation is insufficient.

Application of the graphics processor unit to simulate a near field diffraction

NASA Astrophysics Data System (ADS)

Zinchik, Alexander A.; Topalov, Oleg K.; Muzychenko, Yana B.

2017-06-01

For many years, computer modeling program used for lecture demonstrations. Most of the existing commercial software, such as Virtual Lab, LightTrans GmbH company are quite expensive and have a surplus capabilities for educational tasks. The complexity of the diffraction demonstrations in the near zone, due to the large amount of calculations required to obtain the two-dimensional distribution of the amplitude and phase. At this day, there are no demonstrations, allowing to show the resulting distribution of amplitude and phase without much time delay. Even when using Fast Fourier Transform (FFT) algorithms diffraction calculation speed in the near zone for the input complex amplitude distributions with size more than 2000 × 2000 pixels is tens of seconds. Our program selects the appropriate propagation operator from a prescribed set of operators including Spectrum of Plane Waves propagation and Rayleigh-Sommerfeld propagation (using convolution). After implementation, we make a comparison between the calculation time for the near field diffraction: calculations made on GPU and CPU, showing that using GPU for calculations diffraction pattern in near zone does increase the overall speed of algorithm for an image of size 2048×2048 sampling points and more. The modules are implemented as separate dynamic-link libraries and can be used for lecture demonstrations, workshops, selfstudy and students in solving various problems such as the phase retrieval task.

Comparison of propagation-based phase-contrast tomography approaches for the evaluation of dentin microstructure

NASA Astrophysics Data System (ADS)

Deyhle, Hans; Weitkamp, Timm; Lang, Sabrina; Schulz, Georg; Rack, Alexander; Zanette, Irene; Müller, Bert

2012-10-01

The complex hierarchical structure of human tooth hard tissues, enamel and dentin, guarantees function for decades. On the micrometer level the dentin morphology is dominated by the tubules, micrometer-narrow channels extending from the dentin-enamel junction to the pulp chamber. Their structure has been extensively studied, mainly with two-dimensional approaches. Dentin tubules are formed during tooth growth and their orientation is linked to the morphology of the nanometer-sized components, which is of interest for example for the development of bio-inspired dental fillings. Therefore, a method has to be identified that can access the three-dimensional organization of the tubules, e.g. density and orientation. Tomographic setups with pixel sizes in the sub-micrometer range allow for the three-dimensional visualization of tooth dentin tubules both in phase and absorption contrast modes. We compare high-resolution tomographic scans reconstructed with propagation based phase retrieval algorithms as well as reconstructions without phase retrieval concerning spatial and density resolution as well as rendering of the dentin microstructure to determine the approach best suited for dentin tubule imaging. Reasonable results were obtained with a single-distance phase retrieval algorithm and a propagation distance of about 75% of the critical distance of d2/λ, where d is the size of the smallest objects identifiable in the specimen and λ is the X-ray wavelength.

Alignment-free detection of horizontal gene transfer between closely related bacterial genomes.

PubMed

Domazet-Lošo, Mirjana; Haubold, Bernhard

2011-09-01

Bacterial epidemics are often caused by strains that have acquired their increased virulence through horizontal gene transfer. Due to this association with disease, the detection of horizontal gene transfer continues to receive attention from microbiologists and bioinformaticians alike. Most software for detecting transfer events is based on alignments of sets of genes or of entire genomes. But despite great advances in the design of algorithms and computer programs, genome alignment remains computationally challenging. We have therefore developed an alignment-free algorithm for rapidly detecting horizontal gene transfer between closely related bacterial genomes. Our implementation of this algorithm is called alfy for "ALignment Free local homologY" and is freely available from http://guanine.evolbio.mpg.de/alfy/. In this comment we demonstrate the application of alfy to the genomes of Staphylococcus aureus. We also argue that-contrary to popular belief and in spite of increasing computer speed-algorithmic optimization is becoming more, not less, important if genome data continues to accumulate at the present rate.

Dreaming of Atmospheres

NASA Astrophysics Data System (ADS)

Waldmann, I. P.

2016-04-01

Here, we introduce the RobERt (Robotic Exoplanet Recognition) algorithm for the classification of exoplanetary emission spectra. Spectral retrieval of exoplanetary atmospheres frequently requires the preselection of molecular/atomic opacities to be defined by the user. In the era of open-source, automated, and self-sufficient retrieval algorithms, manual input should be avoided. User dependent input could, in worst-case scenarios, lead to incomplete models and biases in the retrieval. The RobERt algorithm is based on deep-belief neural (DBN) networks trained to accurately recognize molecular signatures for a wide range of planets, atmospheric thermal profiles, and compositions. Reconstructions of the learned features, also referred to as the “dreams” of the network, indicate good convergence and an accurate representation of molecular features in the DBN. Using these deep neural networks, we work toward retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data, and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.

High Energy Laser Beam Propagation in the Atmosphere: The Integral Invariants of the Nonlinear Parabolic Equation and the Method of Moments

NASA Technical Reports Server (NTRS)

Manning, Robert M.

2012-01-01

The method of moments is used to define and derive expressions for laser beam deflection and beam radius broadening for high-energy propagation through the Earth s atmosphere. These expressions are augmented with the integral invariants of the corresponding nonlinear parabolic equation that describes the electric field of high-energy laser beam to propagation to yield universal equations for the aforementioned quantities; the beam deflection is a linear function of the propagation distance whereas the beam broadening is a quadratic function of distance. The coefficients of these expressions are then derived from a thin screen approximation solution of the nonlinear parabolic equation to give corresponding analytical expressions for a target located outside the Earth s atmospheric layer. These equations, which are graphically presented for a host of propagation scenarios, as well as the thin screen model, are easily amenable to the phase expansions of the wave front for the specification and design of adaptive optics algorithms to correct for the inherent phase aberrations. This work finds application in, for example, the analysis of beamed energy propulsion for space-based vehicles.

Use of a porous material description of forests in infrasonic propagation algorithms.

PubMed

Swearingen, Michelle E; White, Michael J; Ketcham, Stephen A; McKenna, Mihan H

2013-10-01

Infrasound can propagate very long distances and remain at measurable levels. As a result infrasound sensing is used for remote monitoring in many applications. At local ranges, on the order of 10 km, the influence of the presence or absence of forests on the propagation of infrasonic signals is considered. Because the wavelengths of interest are much larger than the scale of individual components, the forest is modeled as a porous material. This approximation is developed starting with the relaxation model of porous materials. This representation is then incorporated into a Crank-Nicholson method parabolic equation solver to determine the relative impacts of the physical parameters of a forest (trunk size and basal area), the presence of gaps/trees in otherwise continuous forest/open terrain, and the effects of meteorology coupled with the porous layer. Finally, the simulations are compared to experimental data from a 10.9 kg blast propagated 14.5 km. Comparison to the experimental data shows that appropriate inclusion of a forest layer along the propagation path provides a closer fit to the data than solely changing the ground type across the frequency range from 1 to 30 Hz.

Modeling of an electrohydraulic lithotripter with the KZK equation.

PubMed

Averkiou, M A; Cleveland, R O

1999-07-01

The acoustic pressure field of an electrohydraulic extracorporeal shock wave lithotripter is modeled with a nonlinear parabolic wave equation (the KZK equation). The model accounts for diffraction, nonlinearity, and thermoviscous absorption. A numerical algorithm for solving the KZK equation in the time domain is used to model sound propagation from the mouth of the ellipsoidal reflector of the lithotripter. Propagation within the reflector is modeled with geometrical acoustics. It is shown that nonlinear distortion within the ellipsoidal reflector can play an important role for certain parameters. Calculated waveforms are compared with waveforms measured in a clinical lithotripter and good agreement is found. It is shown that the spatial location of the maximum negative pressure occurs pre-focally which suggests that the strongest cavitation activity will also be in front of the focus. Propagation of shock waves from a lithotripter with a pressure release reflector is considered and because of nonlinear propagation the focal waveform is not the inverse of the rigid reflector. Results from propagation through tissue are presented; waveforms are similar to those predicted in water except that the higher absorption in the tissue decreases the peak amplitude and lengthens the rise time of the shock.

Finite-difference time-domain synthesis of infrasound propagation through an absorbing atmosphere.

PubMed

de Groot-Hedlin, C

2008-09-01

Equations applicable to finite-difference time-domain (FDTD) computation of infrasound propagation through an absorbing atmosphere are derived and examined in this paper. It is shown that over altitudes up to 160 km, and at frequencies relevant to global infrasound propagation, i.e., 0.02-5 Hz, the acoustic absorption in dB/m varies approximately as the square of the propagation frequency plus a small constant term. A second-order differential equation is presented for an atmosphere modeled as a compressible Newtonian fluid with low shear viscosity, acted on by a small external damping force. It is shown that the solution to this equation represents pressure fluctuations with the attenuation indicated above. Increased dispersion is predicted at altitudes over 100 km at infrasound frequencies. The governing propagation equation is separated into two partial differential equations that are first order in time for FDTD implementation. A numerical analysis of errors inherent to this FDTD method shows that the attenuation term imposes additional stability constraints on the FDTD algorithm. Comparison of FDTD results for models with and without attenuation shows that the predicted transmission losses for the attenuating media agree with those computed from synthesized waveforms.

SU-C-17A-03: Evaluation of Deformable Image Registration Methods Between MRI and CT for Prostate Cancer Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wen, N; Glide-Hurst, C; Zhong, H

2014-06-15

Purpose: We evaluated the performance of two commercially available and one open source B-Spline deformable image registration (DIR) algorithms between T2-weighted MRI and treatment planning CT using the DICE indices. Methods: CT simulation (CT-SIM) and MR simulation (MR-SIM) for four prostate cancer patients were conducted on the same day using the same setup and immobilization devices. CT images (120 kVp, 500 mAs, voxel size = 1.1x1.1x3.0 mm3) were acquired using an open-bore CT scanner. T2-weighted Turbo Spine Echo (T2W-TSE) images (TE/TR/α = 80/4560 ms/90°, voxel size = 0.7×0.7×2.5 mm3) were scanned on a 1.0T high field open MR-SIM. Prostates, seminalmore » vesicles, rectum and bladders were delineated on both T2W-TSE and CT images by the attending physician. T2W-TSE images were registered to CT images using three DIR algorithms, SmartAdapt (Varian), Velocity AI (Velocity) and Elastix (Klein et al 2010) and contours were propagated. DIR results were evaluated quantitatively or qualitatively by image comparison and calculating organ DICE indices. Results: Significant differences in the contours of prostate and seminal vesicles were observed between MR and CT. On average, volume changes of the propagated contours were 5%, 2%, 160% and 8% for the prostate, seminal vesicles, bladder and rectum respectively. Corresponding mean DICE indices were 0.7, 0.5, 0.8, and 0.7. The intraclass correlation coefficient (ICC) was 0.9 among three algorithms for the Dice indices. Conclusion: Three DIR algorithms for CT/MR registration yielded similar results for organ propagation. Due to the different soft tissue contrasts between MRI and CT, organ delineation of prostate and SVs varied significantly, thus efforts to develop other DIR evaluation metrics are warranted. Conflict of interest: Submitting institution has research agreements with Varian Medical System and Philips Healthcare.« less

Modeling the blockage of Lg waves from 3-D variations in crustal structure

NASA Astrophysics Data System (ADS)

Sanborn, Christopher J.; Cormier, Vernon F.

2018-05-01

Comprised of S waves trapped in Earth's crust, the high frequency (2-10 Hz) Lg wave is important to discriminating earthquakes from explosions by comparing its amplitude and waveform to those of Pg and Pn waves. Lateral variations in crustal structure, including variations in crustal thickness, intrinsic attenuation, and scattering, affect the efficiency of Lg propagation and its consistency as a source discriminant at regional (200-1500 km) distances. To investigate the effects of laterally varying Earth structure on the efficiency of propagation of Lg and Pg, we apply a radiative transport algorithm to model complete, high-frequency (2-4 Hz), regional coda envelopes. The algorithm propagates packets of energy with ray theory through large-scale 3-D structure, and includes stochastic effects of multiple-scattering by small-scale heterogeneities within the large-scale structure. Source-radiation patterns are described by moment tensors. Seismograms of explosion and earthquake sources are synthesized in canonical models to predict effects on waveforms of paths crossing regions of crustal thinning (pull-apart basins and ocean/continent transitions) and thickening (collisional mountain belts), For paths crossing crustal thinning regions, Lg is amplified at receivers within the thinned region but strongly disrupted and attenuated at receivers beyond the thinned region. For paths crossing regions of crustal thickening, Lg amplitude is attenuated at receivers within the thickened region, but experiences little or no reduction in amplitude at receivers beyond the thickened region. The length of the Lg propagation within a thickened region and the complexity of over- and under-thrust crustal layers, can produce localized zones of Lg amplification or attenuation. Regions of intense scattering within laterally homogeneous models of the crust increase Lg attenuation but do not disrupt its coda shape.

Effects of the approximations of light propagation on quantitative photoacoustic tomography using two-dimensional photon diffusion equation and linearization

NASA Astrophysics Data System (ADS)

Okawa, Shinpei; Hirasawa, Takeshi; Kushibiki, Toshihiro; Ishihara, Miya

2017-12-01

Quantitative photoacoustic tomography (QPAT) employing a light propagation model will play an important role in medical diagnoses by quantifying the concentration of hemoglobin or a contrast agent. However, QPAT by the light propagation model with the three-dimensional (3D) radiative transfer equation (RTE) requires a huge computational load in the iterative forward calculations involved in the updating process to reconstruct the absorption coefficient. The approximations of the light propagation improve the efficiency of the image reconstruction for the QPAT. In this study, we compared the 3D/two-dimensional (2D) photon diffusion equation (PDE) approximating 3D RTE with the Monte Carlo simulation based on 3D RTE. Then, the errors in a 2D PDE-based linearized image reconstruction caused by the approximations were quantitatively demonstrated and discussed in the numerical simulations. It was clearly observed that the approximations affected the reconstructed absorption coefficient. The 2D PDE-based linearized algorithm succeeded in the image reconstruction of the region with a large absorption coefficient in the 3D phantom. The value reconstructed in the phantom experiment agreed with that in the numerical simulation, so that it was validated that the numerical simulation of the image reconstruction predicted the relationship between the true absorption coefficient of the target in the 3D medium and the reconstructed value with the 2D PDE-based linearized algorithm. Moreover, the the true absorption coefficient in 3D medium was estimated from the 2D reconstructed image on the basis of the prediction by the numerical simulation. The estimation was successful in the phantom experiment, although some limitations were revealed.

Ultrasound attenuation estimation using backscattered echoes from multiple sources.

PubMed

Bigelow, Timothy A

2008-08-01

The objective of this study was to devise an algorithm that can accurately estimate the attenuation along the propagation path (i.e., the total attenuation) from backscattered echoes. It was shown that the downshift in the center frequency of the backscattered ultrasound echoes compared to echoes obtained in a water bath was calculated to have the form Deltaf=mf(o)+b after normalizing with respect to the source bandwidth where m depends on the correlation length, b depends on the total attenuation, and f(o) is the center frequency of the source as measured from a reference echo. Therefore, the total attenuation can be determined independent of the scatterer correlation length by measuring the downshift in center frequency from multiple sources (i.e., different f(o)) and fitting a line to the measured shifts versus f(o). The intercept of the line gives the total attenuation along the propagation path. The calculations were verified using computer simulations of five spherically focused sources with 50% bandwidths and center frequencies of 6, 8, 10, 12, and 14 MHz. The simulated tissue had Gaussian scattering structures with effective radii of 25 mum placed at a density of 250 mm(3). The attenuation of the tissue was varied from 0.1 to 0.9 dB / cm-MHz. The error in the attenuation along the propagation path ranged from -3.5+/-14.7% for a tissue attenuation of 0.1 dB / cm-MHz to -7.0+/-3.1% for a tissue attenuation of 0.9 dB / cm-MHz demonstrating that the attenuation along the propagation path could be accurately determined using backscattered echoes from multiple sources using the derived algorithm.

Automated detection of secondary slip fronts in Cascadia

NASA Astrophysics Data System (ADS)

Bletery, Q.; Thomas, A.; Krogstad, R. D.; Hawthorne, J. C.; Skarbek, R. M.; Rempel, A. W.; Bostock, M. G.

2016-12-01

Slow slip events (SSEs) in subduction zones propagate along the plate interface at velocities on the order of 5 km/day and are largely confined to the region known as the transition zone, located down-dip of the seismogenically locked zone. As SSEs propagate, small on-fault asperities capable of generating seismic radiation fail in earthquake-like events known as low-frequency earthquakes. Recently, low-frequency earthquakes have been used to image smaller scale secondary slip fronts (SSFs) that occur within the actively slipping region of the fault after the main front associated with the SSE has passed. SSFs appear to occur over several different length and timescales and propagate both along dip and along strike. To date, most studies that have documented SSFs have relied on subjective methods, such as visual selection, to identify them. While such approaches have met with considerable success, it is likely that many small-scale fronts remain unidentifiable by visual inspection alone. We implement an algorithm to automatically detect SSFs from 2009 to 2015 along the Cascadia subduction zone. We also apply our algorithm to three large SSEs that were detected by campaign seismic instrumentation in the Vancouver Island area between 2003 and 2005. We find numerous SSFs at different time scales (from 30 min to 32 h duration). We provide a catalog of 1076 SSFs in Cascadia, including time, location, duration, area, propagation velocity, moment, stress drop, slip, slip velocity, and fracture energy for each of the detected SSFs. Analysis of their basic features indicate a wide spectra of stress drops, slip velocities, and fracture energy, as well as an intriguing relationship between SSF direction and duration that could potentially help discriminate between the different physical models proposed to explain slow slip phenomena.

Radio Propagation Prediction Software for Complex Mixed Path Physical Channels

DTIC Science & Technology

2006-08-14

63 4.4.6. Applied Linear Regression Analysis in the Frequency Range 1-50 MHz 69 4.4.7. Projected Scaling to...4.4.6. Applied Linear Regression Analysis in the Frequency Range 1-50 MHz In order to construct a comprehensive numerical algorithm capable of

Reflectivity retrieval in a networked radar environment

NASA Astrophysics Data System (ADS)

Lim, Sanghun

Monitoring of precipitation using a high-frequency radar system such as X-band is becoming increasingly popular due to its lower cost compared to its counterpart at S-band. Networks of meteorological radar systems at higher frequencies are being pursued for targeted applications such as coverage over a city or a small basin. However, at higher frequencies, the impact of attenuation due to precipitation needs to be resolved for successful implementation. In this research, new attenuation correction algorithms are introduced to compensate the attenuation impact due to rain medium. In order to design X-band radar systems as well as evaluate algorithm development, it is useful to have simultaneous X-band observation with and without the impact of path attenuation. One way to obtain that data set is through theoretical models. Methodologies for generating realistic range profiles of radar variables at attenuating frequencies such as X-band for rain medium are presented here. Fundamental microphysical properties of precipitation, namely size and shape distribution information, are used to generate realistic profiles of X-band starting with S-band observations. Conditioning the simulation from S-band radar measurements maintains the natural distribution of microphysical parameters associated with rainfall. In this research, data taken by the CSU-CHILL radar and the National Center for Atmospheric Research S-POL radar are used to simulate X-band radar variables. Three procedures to simulate the radar variables at X-band and sample applications are presented. A new attenuation correction algorithm based on profiles of reflectivity, differential reflectivity, and differential propagation phase shift is presented. A solution for specific attenuation retrieval in rain medium is proposed that solves the integral equations for reflectivity and differential reflectivity with cumulative differential propagation phase shift constraint. The conventional rain profiling algorithms that connect reflectivity and specific attenuation can retrieve specific attenuation values along the radar path assuming a constant intercept parameter of the normalized drop size distribution. However, in convective storms, the drop size distribution parameters can have significant variation along the path. In this research, a dual-polarization rain profiling algorithm for horizontal-looking radars incorporating reflectivity as well as differential reflectivity profiles is developed. The dual-polarization rain profiling algorithm has been evaluated with X-band radar observations simulated from drop size distribution derived from high-resolution S-band measurements collected by the CSU-CHILL radar. The analysis shows that the dual-polarization rain profiling algorithm provides significant improvement over the current algorithms. A methodology for reflectivity and attenuation retrieval for rain medium in a networked radar environment is described. Electromagnetic waves backscattered from a common volume in networked radar systems are attenuated differently along the different paths. A solution for the specific attenuation distribution is proposed by solving the integral equation for reflectivity. The set of governing integral equations describing the backscatter and propagation of common resolution volume are solved simultaneously with constraints on total path attenuation. The proposed algorithm is evaluated based on simulated X-band radar observations synthesized from S-band measurements collected by the CSU-CHILL radar. Retrieved reflectivity and specific attenuation using the proposed method show good agreement with simulated reflectivity and specific attenuation.

Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes

NASA Technical Reports Server (NTRS)

Lin, Shu

1998-01-01

A code trellis is a graphical representation of a code, block or convolutional, in which every path represents a codeword (or a code sequence for a convolutional code). This representation makes it possible to implement Maximum Likelihood Decoding (MLD) of a code with reduced decoding complexity. The most well known trellis-based MLD algorithm is the Viterbi algorithm. The trellis representation was first introduced and used for convolutional codes [23]. This representation, together with the Viterbi decoding algorithm, has resulted in a wide range of applications of convolutional codes for error control in digital communications over the last two decades. There are two major reasons for this inactive period of research in this area. First, most coding theorists at that time believed that block codes did not have simple trellis structure like convolutional codes and maximum likelihood decoding of linear block codes using the Viterbi algorithm was practically impossible, except for very short block codes. Second, since almost all of the linear block codes are constructed algebraically or based on finite geometries, it was the belief of many coding theorists that algebraic decoding was the only way to decode these codes. These two reasons seriously hindered the development of efficient soft-decision decoding methods for linear block codes and their applications to error control in digital communications. This led to a general belief that block codes are inferior to convolutional codes and hence, that they were not useful. Chapter 2 gives a brief review of linear block codes. The goal is to provide the essential background material for the development of trellis structure and trellis-based decoding algorithms for linear block codes in the later chapters. Chapters 3 through 6 present the fundamental concepts, finite-state machine model, state space formulation, basic structural properties, state labeling, construction procedures, complexity, minimality, and sectionalization of trellises. Chapter 7 discusses trellis decomposition and subtrellises for low-weight codewords. Chapter 8 first presents well known methods for constructing long powerful codes from short component codes or component codes of smaller dimensions, and then provides methods for constructing their trellises which include Shannon and Cartesian product techniques. Chapter 9 deals with convolutional codes, puncturing, zero-tail termination and tail-biting.Chapters 10 through 13 present various trellis-based decoding algorithms, old and new. Chapter 10 first discusses the application of the well known Viterbi decoding algorithm to linear block codes, optimum sectionalization of a code trellis to minimize computation complexity, and design issues for IC (integrated circuit) implementation of a Viterbi decoder. Then it presents a new decoding algorithm for convolutional codes, named Differential Trellis Decoding (DTD) algorithm. Chapter 12 presents a suboptimum reliability-based iterative decoding algorithm with a low-weight trellis search for the most likely codeword. This decoding algorithm provides a good trade-off between error performance and decoding complexity. All the decoding algorithms presented in Chapters 10 through 12 are devised to minimize word error probability. Chapter 13 presents decoding algorithms that minimize bit error probability and provide the corresponding soft (reliability) information at the output of the decoder. Decoding algorithms presented are the MAP (maximum a posteriori probability) decoding algorithm and the Soft-Output Viterbi Algorithm (SOVA) algorithm. Finally, the minimization of bit error probability in trellis-based MLD is discussed.

Incorporating individual health-protective decisions into disease transmission models: a mathematical framework.

PubMed

Durham, David P; Casman, Elizabeth A

2012-03-07

It is anticipated that the next generation of computational epidemic models will simulate both infectious disease transmission and dynamic human behaviour change. Individual agents within a simulation will not only infect one another, but will also have situational awareness and a decision algorithm that enables them to modify their behaviour. This paper develops such a model of behavioural response, presenting a mathematical interpretation of a well-known psychological model of individual decision making, the health belief model, suitable for incorporation within an agent-based disease-transmission model. We formalize the health belief model and demonstrate its application in modelling the prevalence of facemask use observed over the course of the 2003 Hong Kong SARS epidemic, a well-documented example of behaviour change in response to a disease outbreak.

Incorporating individual health-protective decisions into disease transmission models: a mathematical framework

PubMed Central

Durham, David P.; Casman, Elizabeth A.

2012-01-01

It is anticipated that the next generation of computational epidemic models will simulate both infectious disease transmission and dynamic human behaviour change. Individual agents within a simulation will not only infect one another, but will also have situational awareness and a decision algorithm that enables them to modify their behaviour. This paper develops such a model of behavioural response, presenting a mathematical interpretation of a well-known psychological model of individual decision making, the health belief model, suitable for incorporation within an agent-based disease-transmission model. We formalize the health belief model and demonstrate its application in modelling the prevalence of facemask use observed over the course of the 2003 Hong Kong SARS epidemic, a well-documented example of behaviour change in response to a disease outbreak. PMID:21775324

CHAMPION: Intelligent Hierarchical Reasoning Agents for Enhanced Decision Support

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hohimer, Ryan E.; Greitzer, Frank L.; Noonan, Christine F.

2011-11-15

We describe the design and development of an advanced reasoning framework employing semantic technologies, organized within a hierarchy of computational reasoning agents that interpret domain specific information. Designed based on an inspirational metaphor of the pattern recognition functions performed by the human neocortex, the CHAMPION reasoning framework represents a new computational modeling approach that derives invariant knowledge representations through memory-prediction belief propagation processes that are driven by formal ontological language specification and semantic technologies. The CHAMPION framework shows promise for enhancing complex decision making in diverse problem domains including cyber security, nonproliferation and energy consumption analysis.

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks.

PubMed

Chai, Rifai; Ling, Sai Ho; San, Phyo Phyo; Naik, Ganesh R; Nguyen, Tuan N; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-01-01

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks

PubMed Central

Chai, Rifai; Ling, Sai Ho; San, Phyo Phyo; Naik, Ganesh R.; Nguyen, Tuan N.; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T.

2017-01-01

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively. PMID:28326009

A Prize-Collecting Steiner Tree Approach for Transduction Network Inference

NASA Astrophysics Data System (ADS)

Bailly-Bechet, Marc; Braunstein, Alfredo; Zecchina, Riccardo

Into the cell, information from the environment is mainly propagated via signaling pathways which form a transduction network. Here we propose a new algorithm to infer transduction networks from heterogeneous data, using both the protein interaction network and expression datasets. We formulate the inference problem as an optimization task, and develop a message-passing, probabilistic and distributed formalism to solve it. We apply our algorithm to the pheromone response in the baker’s yeast S. cerevisiae. We are able to find the backbone of the known structure of the MAPK cascade of pheromone response, validating our algorithm. More importantly, we make biological predictions about some proteins whose role could be at the interface between pheromone response and other cellular functions.

Computational algorithms for simulations in atmospheric optics.

PubMed

Konyaev, P A; Lukin, V P

2016-04-20

A computer simulation technique for atmospheric and adaptive optics based on parallel programing is discussed. A parallel propagation algorithm is designed and a modified spectral-phase method for computer generation of 2D time-variant random fields is developed. Temporal power spectra of Laguerre-Gaussian beam fluctuations are considered as an example to illustrate the applications discussed. Implementation of the proposed algorithms using Intel MKL and IPP libraries and NVIDIA CUDA technology is shown to be very fast and accurate. The hardware system for the computer simulation is an off-the-shelf desktop with an Intel Core i7-4790K CPU operating at a turbo-speed frequency up to 5 GHz and an NVIDIA GeForce GTX-960 graphics accelerator with 1024 1.5 GHz processors.

Hybrid light transport model based bioluminescence tomography reconstruction for early gastric cancer detection

NASA Astrophysics Data System (ADS)

Chen, Xueli; Liang, Jimin; Hu, Hao; Qu, Xiaochao; Yang, Defu; Chen, Duofang; Zhu, Shouping; Tian, Jie

2012-03-01

Gastric cancer is the second cause of cancer-related death in the world, and it remains difficult to cure because it has been in late-stage once that is found. Early gastric cancer detection becomes an effective approach to decrease the gastric cancer mortality. Bioluminescence tomography (BLT) has been applied to detect early liver cancer and prostate cancer metastasis. However, the gastric cancer commonly originates from the gastric mucosa and grows outwards. The bioluminescent light will pass through a non-scattering region constructed by gastric pouch when it transports in tissues. Thus, the current BLT reconstruction algorithms based on the approximation model of radiative transfer equation are not optimal to handle this problem. To address the gastric cancer specific problem, this paper presents a novel reconstruction algorithm that uses a hybrid light transport model to describe the bioluminescent light propagation in tissues. The radiosity theory integrated with the diffusion equation to form the hybrid light transport model is utilized to describe light propagation in the non-scattering region. After the finite element discretization, the hybrid light transport model is converted into a minimization problem which fuses an l1 norm based regularization term to reveal the sparsity of bioluminescent source distribution. The performance of the reconstruction algorithm is first demonstrated with a digital mouse based simulation with the reconstruction error less than 1mm. An in situ gastric cancer-bearing nude mouse based experiment is then conducted. The primary result reveals the ability of the novel BLT reconstruction algorithm in early gastric cancer detection.

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network

PubMed Central

Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method. PMID:29420584

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

PubMed

Guan, Hongjun; Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.

An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation

PubMed Central

Zhang, Wei; Bao, Zhangmin; Jiang, Shan; He, Jingjing

2016-01-01

In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, etc., it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN) is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF)-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects. PMID:28773606

An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation.

PubMed

Zhang, Wei; Bao, Zhangmin; Jiang, Shan; He, Jingjing

2016-06-17

In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, etc. , it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN) is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF)-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects.

Cascade Back-Propagation Learning in Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2003-01-01

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

Extracting atmospheric turbulence and aerosol characteristics from passive imagery

NASA Astrophysics Data System (ADS)

Reinhardt, Colin N.; Wayne, D.; McBryde, K.; Cauble, G.

2013-09-01

Obtaining accurate, precise and timely information about the local atmospheric turbulence and extinction conditions and aerosol/particulate content remains a difficult problem with incomplete solutions. It has important applications in areas such as optical and IR free-space communications, imaging systems performance, and the propagation of directed energy. The capability to utilize passive imaging data to extract parameters characterizing atmospheric turbulence and aerosol/particulate conditions would represent a valuable addition to the current piecemeal toolset for atmospheric sensing. Our research investigates an application of fundamental results from optical turbulence theory and aerosol extinction theory combined with recent advances in image-quality-metrics (IQM) and image-quality-assessment (IQA) methods. We have developed an algorithm which extracts important parameters used for characterizing atmospheric turbulence and extinction along the propagation channel, such as the refractive-index structure parameter C2n , the Fried atmospheric coherence width r0 , and the atmospheric extinction coefficient βext , from passive image data. We will analyze the algorithm performance using simulations based on modeling with turbulence modulation transfer functions. An experimental field campaign was organized and data were collected from passive imaging through turbulence of Siemens star resolution targets over several short littoral paths in Point Loma, San Diego, under conditions various turbulence intensities. We present initial results of the algorithm's effectiveness using this field data and compare against measurements taken concurrently with other standard atmospheric characterization equipment. We also discuss some of the challenges encountered with the algorithm, tasks currently in progress, and approaches planned for improving the performance in the near future.

Wave Propagation, Scattering and Imaging Using Dual-domain One-way and One-return Propagators

NASA Astrophysics Data System (ADS)

Wu, R.-S.

- Dual-domain one-way propagators implement wave propagation in heterogeneous media in mixed domains (space-wavenumber domains). One-way propagators neglect wave reverberations between heterogeneities but correctly handle the forward multiple-scattering including focusing/defocusing, diffraction, refraction and interference of waves. The algorithm shuttles between space-domain and wavenumber-domain using FFT, and the operations in the two domains are self-adaptive to the complexity of the media. The method makes the best use of the operations in each domain, resulting in efficient and accurate propagators. Due to recent progress, new versions of dual-domain methods overcame some limitations of the classical dual-domain methods (phase-screen or split-step Fourier methods) and can propagate large-angle waves quite accurately in media with strong velocity contrasts. These methods can deliver superior image quality (high resolution/high fidelity) for complex subsurface structures. One-way and one-return (De Wolf approximation) propagators can be also applied to wave-field modeling and simulations for some geophysical problems. In the article, a historical review and theoretical analysis of the Born, Rytov, and De Wolf approximations are given. A review on classical phase-screen or split-step Fourier methods is also given, followed by a summary and analysis of the new dual-domain propagators. The applications of the new propagators to seismic imaging and modeling are reviewed with several examples. For seismic imaging, the advantages and limitations of the traditional Kirchhoff migration and time-space domain finite-difference migration, when applied to 3-D complicated structures, are first analyzed. Then the special features, and applications of the new dual-domain methods are presented. Three versions of GSP (generalized screen propagators), the hybrid pseudo-screen, the wide-angle Padé-screen, and the higher-order generalized screen propagators are discussed. Recent progress also makes it possible to use the dual-domain propagators for modeling elastic reflections for complex structures and long-range propagations of crustal guided waves. Examples of 2-D and 3-D imaging and modeling using GSP methods are given.

Towards predictive data-driven simulations of wildfire spread - Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation

NASA Astrophysics Data System (ADS)

Rochoux, M. C.; Ricci, S.; Lucor, D.; Cuenot, B.; Trouvé, A.

2014-05-01

This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: a level-set-based fire propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the non-linearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially-uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an ensemble-based data assimilation algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model based on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically-generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of data assimilation strongly relate to the spatial and temporal variability of the errors in the ROS model parameters.

Towards predictive data-driven simulations of wildfire spread - Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation

NASA Astrophysics Data System (ADS)

Rochoux, M. C.; Ricci, S.; Lucor, D.; Cuenot, B.; Trouvé, A.

2014-11-01

This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: an Eulerian front propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation (DA) algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the nonlinearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an ensemble-based DA algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model based on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach, as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of DA strongly relate to the spatial and temporal variability of the errors in the ROS model parameters.

Acoustic wave propagation in continuous functionally graded plates: an extension of the Legendre polynomial approach.

PubMed

Lefebvre, J E; Zhang, V; Gazalet, J; Gryba, T; Sadaune, V

2001-09-01

The propagation of guided waves in continuous functionally graded plates is studied by using Legendre polynomials. Dispersion curves, and power and field profiles are easily obtained. Our computer program is validated by comparing our results against other calculations from the literature. Numerical results are also given for a graded semiconductor plate. It is felt that the present method could be of quite practical interest in waveguiding engineering, non-destructive testing of functionally graded materials (FGMs) to identify the best inspection strategies, or by means of a numerical inversion algorithm to determine through-thickness gradients in material parameters.

Two papers on feed-forward networks

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Weigend, Andreas S.

1991-01-01

Connectionist feed-forward networks, trained with back-propagation, can be used both for nonlinear regression and for (discrete one-of-C) classification, depending on the form of training. This report contains two papers on feed-forward networks. The papers can be read independently. They are intended for the theoretically-aware practitioner or algorithm-designer; however, they also contain a review and comparison of several learning theories so they provide a perspective for the theoretician. The first paper works through Bayesian methods to complement back-propagation in the training of feed-forward networks. The second paper addresses a problem raised by the first: how to efficiently calculate second derivatives on feed-forward networks.

Enhanced backscatter of optical beams reflected in turbulent air.

PubMed

Nelson, W; Palastro, J P; Wu, C; Davis, C C

2015-07-01

Optical beams propagating through air acquire phase distortions from turbulent fluctuations in the refractive index. While these distortions are usually deleterious to propagation, beams reflected in a turbulent medium can undergo a local recovery of spatial coherence and intensity enhancement referred to as enhanced backscatter (EBS). Here we validate the commonly used phase screen simulation with experimental results obtained from lab-scale experiments. We also verify theoretical predictions of the dependence of the turbulence strength on EBS. Finally, we present a novel algorithm called the "tilt-shift method" which allows detection of EBS in frozen turbulence, reducing the time required to detect the EBS signal.

Using sparsity information for iterative phase retrieval in x-ray propagation imaging.

PubMed

Pein, A; Loock, S; Plonka, G; Salditt, T

2016-04-18

For iterative phase retrieval algorithms in near field x-ray propagation imaging experiments with a single distance measurement, it is indispensable to have a strong constraint based on a priori information about the specimen; for example, information about the specimen's support. Recently, Loock and Plonka proposed to use the a priori information that the exit wave is sparsely represented in a certain directional representation system, a so-called shearlet system. In this work, we extend this approach to complex-valued signals by applying the new shearlet constraint to amplitude and phase separately. Further, we demonstrate its applicability to experimental data.

Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china.

PubMed

Gan, Ruijing; Chen, Xiaojun; Yan, Yu; Huang, Daizheng

2015-01-01

Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.

Tsunami Detection by High-Frequency Radar Beyond the Continental Shelf

NASA Astrophysics Data System (ADS)

Grilli, Stéphan T.; Grosdidier, Samuel; Guérin, Charles-Antoine

2016-12-01

Where coastal tsunami hazard is governed by near-field sources, such as submarine mass failures or meteo-tsunamis, tsunami propagation times may be too small for a detection based on deep or shallow water buoys. To offer sufficient warning time, it has been proposed to implement early warning systems relying on high-frequency (HF) radar remote sensing, that can provide a dense spatial coverage as far offshore as 200-300 km (e.g., for Diginext Ltd.'s Stradivarius radar). Shore-based HF radars have been used to measure nearshore currents (e.g., CODAR SeaSonde® system; http://www.codar.com/), by inverting the Doppler spectral shifts, these cause on ocean waves at the Bragg frequency. Both modeling work and an analysis of radar data following the Tohoku 2011 tsunami, have shown that, given proper detection algorithms, such radars could be used to detect tsunami-induced currents and issue a warning. However, long wave physics is such that tsunami currents will only rise above noise and background currents (i.e., be at least 10-15 cm/s), and become detectable, in fairly shallow water which would limit the direct detection of tsunami currents by HF radar to nearshore areas, unless there is a very wide shallow shelf. Here, we use numerical simulations of both HF radar remote sensing and tsunami propagation to develop and validate a new type of tsunami detection algorithm that does not have these limitations. To simulate the radar backscattered signal, we develop a numerical model including second-order effects in both wind waves and radar signal, with the wave angular frequency being modulated by a time-varying surface current, combining tsunami and background currents. In each "radar cell", the model represents wind waves with random phases and amplitudes extracted from a specified (wind speed dependent) energy density frequency spectrum, and includes effects of random environmental noise and background current; phases, noise, and background current are extracted from independent Gaussian distributions. The principle of the new algorithm is to compute correlations of HF radar signals measured/simulated in many pairs of distant "cells" located along the same tsunami wave ray, shifted in time by the tsunami propagation time between these cell locations; both rays and travel time are easily obtained as a function of long wave phase speed and local bathymetry. It is expected that, in the presence of a tsunami current, correlations computed as a function of range and an additional time lag will show a narrow elevated peak near the zero time lag, whereas no pattern in correlation will be observed in the absence of a tsunami current; this is because surface waves and background current are uncorrelated between pair of cells, particularly when time-shifted by the long-wave propagation time. This change in correlation pattern can be used as a threshold for tsunami detection. To validate the algorithm, we first identify key features of tsunami propagation in the Western Mediterranean Basin, where Stradivarius is deployed, by way of direct numerical simulations with a long wave model. Then, for the purpose of validating the algorithm we only model HF radar detection for idealized tsunami wave trains and bathymetry, but verify that such idealized case studies capture well the salient tsunami wave physics. Results show that, in the presence of strong background currents, the proposed method still allows detecting a tsunami with currents as low as 0.05 m/s, whereas a standard direct inversion based on radar signal Doppler spectra fails to reproduce tsunami currents weaker than 0.15-0.2 m/s. Hence, the new algorithm allows detecting tsunami arrival in deeper water, beyond the shelf and further away from the coast, and providing an early warning. Because the standard detection of tsunami currents works well at short range, we envision that, in a field situation, the new algorithm could complement the standard approach of direct near-field detection by providing a warning that a tsunami is approaching, at larger range and in greater depth. This warning would then be confirmed at shorter range by a direct inversion of tsunami currents, from which the magnitude of the tsunami would also estimated. Hence, both algorithms would be complementary. In future work, the algorithm will be applied to actual tsunami case studies performed using a state-of-the-art long wave model, such as briefly presented here in the Mediterranean Basin.

GDPC: Gravitation-based Density Peaks Clustering algorithm

NASA Astrophysics Data System (ADS)

Jiang, Jianhua; Hao, Dehao; Chen, Yujun; Parmar, Milan; Li, Keqin

2018-07-01

The Density Peaks Clustering algorithm, which we refer to as DPC, is a novel and efficient density-based clustering approach, and it is published in Science in 2014. The DPC has advantages of discovering clusters with varying sizes and varying densities, but has some limitations of detecting the number of clusters and identifying anomalies. We develop an enhanced algorithm with an alternative decision graph based on gravitation theory and nearby distance to identify centroids and anomalies accurately. We apply our method to some UCI and synthetic data sets. We report comparative clustering performances using F-Measure and 2-dimensional vision. We also compare our method to other clustering algorithms, such as K-Means, Affinity Propagation (AP) and DPC. We present F-Measure scores and clustering accuracies of our GDPC algorithm compared to K-Means, AP and DPC on different data sets. We show that the GDPC has the superior performance in its capability of: (1) detecting the number of clusters obviously; (2) aggregating clusters with varying sizes, varying densities efficiently; (3) identifying anomalies accurately.