A segmentation algorithm based on image projection for complex text layout

NASA Astrophysics Data System (ADS)

Zhu, Wangsheng; Chen, Qin; Wei, Chuanyi; Li, Ziyang

2017-10-01

Segmentation algorithm is an important part of layout analysis, considering the efficiency advantage of the top-down approach and the particularity of the object, a breakdown of projection layout segmentation algorithm. Firstly, the algorithm will algorithm first partitions the text image, and divided into several columns, then for each column scanning projection, the text image is divided into several sub regions through multiple projection. The experimental results show that, this method inherits the projection itself and rapid calculation speed, but also can avoid the effect of arc image information page segmentation, and also can accurate segmentation of the text image layout is complex.

Geometric modeling of subcellular structures, organelles, and multiprotein complexes

PubMed Central

Feng, Xin; Xia, Kelin; Tong, Yiying; Wei, Guo-Wei

2013-01-01

SUMMARY Recently, the structure, function, stability, and dynamics of subcellular structures, organelles, and multi-protein complexes have emerged as a leading interest in structural biology. Geometric modeling not only provides visualizations of shapes for large biomolecular complexes but also fills the gap between structural information and theoretical modeling, and enables the understanding of function, stability, and dynamics. This paper introduces a suite of computational tools for volumetric data processing, information extraction, surface mesh rendering, geometric measurement, and curvature estimation of biomolecular complexes. Particular emphasis is given to the modeling of cryo-electron microscopy data. Lagrangian-triangle meshes are employed for the surface presentation. On the basis of this representation, algorithms are developed for surface area and surface-enclosed volume calculation, and curvature estimation. Methods for volumetric meshing have also been presented. Because the technological development in computer science and mathematics has led to multiple choices at each stage of the geometric modeling, we discuss the rationales in the design and selection of various algorithms. Analytical models are designed to test the computational accuracy and convergence of proposed algorithms. Finally, we select a set of six cryo-electron microscopy data representing typical subcellular complexes to demonstrate the efficacy of the proposed algorithms in handling biomolecular surfaces and explore their capability of geometric characterization of binding targets. This paper offers a comprehensive protocol for the geometric modeling of subcellular structures, organelles, and multiprotein complexes. PMID:23212797

Vision-Aided Inertial Navigation

NASA Technical Reports Server (NTRS)

Roumeliotis, Stergios I. (Inventor); Mourikis, Anastasios I. (Inventor)

2017-01-01

This document discloses, among other things, a system and method for implementing an algorithm to determine pose, velocity, acceleration or other navigation information using feature tracking data. The algorithm has computational complexity that is linear with the number of features tracked.

The Goddard Profiling Algorithm (GPROF): Description and Current Applications

NASA Technical Reports Server (NTRS)

Olson, William S.; Yang, Song; Stout, John E.; Grecu, Mircea

2004-01-01

Atmospheric scientists use different methods for interpreting satellite data. In the early days of satellite meteorology, the analysis of cloud pictures from satellites was primarily subjective. As computer technology improved, satellite pictures could be processed digitally, and mathematical algorithms were developed and applied to the digital images in different wavelength bands to extract information about the atmosphere in an objective way. The kind of mathematical algorithm one applies to satellite data may depend on the complexity of the physical processes that lead to the observed image, and how much information is contained in the satellite images both spatially and at different wavelengths. Imagery from satellite-borne passive microwave radiometers has limited horizontal resolution, and the observed microwave radiances are the result of complex physical processes that are not easily modeled. For this reason, a type of algorithm called a Bayesian estimation method is utilized to interpret passive microwave imagery in an objective, yet computationally efficient manner.

ECG Sensor Card with Evolving RBP Algorithms for Human Verification.

PubMed

Tseng, Kuo-Kun; Huang, Huang-Nan; Zeng, Fufu; Tu, Shu-Yi

2015-08-21

It is known that cardiac and respiratory rhythms in electrocardiograms (ECGs) are highly nonlinear and non-stationary. As a result, most traditional time-domain algorithms are inadequate for characterizing the complex dynamics of the ECG. This paper proposes a new ECG sensor card and a statistical-based ECG algorithm, with the aid of a reduced binary pattern (RBP), with the aim of achieving faster ECG human identity recognition with high accuracy. The proposed algorithm has one advantage that previous ECG algorithms lack-the waveform complex information and de-noising preprocessing can be bypassed; therefore, it is more suitable for non-stationary ECG signals. Experimental results tested on two public ECG databases (MIT-BIH) from MIT University confirm that the proposed scheme is feasible with excellent accuracy, low complexity, and speedy processing. To be more specific, the advanced RBP algorithm achieves high accuracy in human identity recognition and is executed at least nine times faster than previous algorithms. Moreover, based on the test results from a long-term ECG database, the evolving RBP algorithm also demonstrates superior capability in handling long-term and non-stationary ECG signals.

Advanced Algorithms for Local Routing Strategy on Complex Networks

PubMed Central

Lin, Benchuan; Chen, Bokui; Gao, Yachun; Tse, Chi K.; Dong, Chuanfei; Miao, Lixin; Wang, Binghong

2016-01-01

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets Rc increases by over ten-fold and the average transmission time 〈T〉 decreases by 70–90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks. PMID:27434502

Advanced Algorithms for Local Routing Strategy on Complex Networks.

PubMed

Lin, Benchuan; Chen, Bokui; Gao, Yachun; Tse, Chi K; Dong, Chuanfei; Miao, Lixin; Wang, Binghong

2016-01-01

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets Rc increases by over ten-fold and the average transmission time 〈T〉 decreases by 70-90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks.

Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers

PubMed Central

2010-01-01

Background The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved. Results This article proposes an expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution of SNP effects. The posterior probability of being in LD with at least one QTL is calculated for each SNP along with estimates of the hyperparameters for the mixture prior. A simulated example of genomic selection from an international workshop is used to demonstrate the features of the EM algorithm. The accuracy of prediction is comparable to a full Bayesian analysis but the EM algorithm is considerably faster. The EM algorithm was accurate in locating QTL which explained more than 1% of the total genetic variation. A computational algorithm for very large SNP panels is described. Conclusions emBayesB is a fast and accurate EM algorithm for implementing genomic selection and predicting complex traits by mapping QTL in genome-wide dense SNP marker data. Its accuracy is similar to Bayesian methods but it takes only a fraction of the time. PMID:20969788

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.

Community structure from spectral properties in complex networks

NASA Astrophysics Data System (ADS)

Servedio, V. D. P.; Colaiori, F.; Capocci, A.; Caldarelli, G.

2005-06-01

We analyze the spectral properties of complex networks focusing on their relation to the community structure, and develop an algorithm based on correlations among components of different eigenvectors. The algorithm applies to general weighted networks, and, in a suitably modified version, to the case of directed networks. Our method allows to correctly detect communities in sharply partitioned graphs, however it is useful to the analysis of more complex networks, without a well defined cluster structure, as social and information networks. As an example, we test the algorithm on a large scale data-set from a psychological experiment of free word association, where it proves to be successful both in clustering words, and in uncovering mental association patterns.

Sampling from complex networks using distributed learning automata

NASA Astrophysics Data System (ADS)

Rezvanian, Alireza; Rahmati, Mohammad; Meybodi, Mohammad Reza

2014-02-01

A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due to a growing trend in analysis of online social networks as dynamic complex large-scale graphs. Due to the large scale and limited access of real networks, the network model is characterized using an appropriate part of a network by sampling approaches. In this paper, a new sampling algorithm based on distributed learning automata has been proposed for sampling from complex networks. In the proposed algorithm, a set of distributed learning automata cooperate with each other in order to take appropriate samples from the given network. To investigate the performance of the proposed algorithm, several simulation experiments are conducted on well-known complex networks. Experimental results are compared with several sampling methods in terms of different measures. The experimental results demonstrate the superiority of the proposed algorithm over the others.

Swarm Intelligence in Text Document Clustering

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

Cui, Xiaohui; Potok, Thomas E

2008-01-01

Social animals or insects in nature often exhibit a form of emergent collective behavior. The research field that attempts to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies is called Swarm Intelligence. Compared to the traditional algorithms, the swarm algorithms are usually flexible, robust, decentralized and self-organized. These characters make the swarm algorithms suitable for solving complex problems, such as document collection clustering. The major challenge of today's information society is being overwhelmed with information on any topic they are searching for. Fast and high-quality document clustering algorithms play an important role inmore » helping users to effectively navigate, summarize, and organize the overwhelmed information. In this chapter, we introduce three nature inspired swarm intelligence clustering approaches for document clustering analysis. These clustering algorithms use stochastic and heuristic principles discovered from observing bird flocks, fish schools and ant food forage.« less

Artificial Intelligence Methods: Choice of algorithms, their complexity, and appropriateness within the context of hydrology and water resources. (Invited)

NASA Astrophysics Data System (ADS)

Bastidas, L. A.; Pande, S.

2009-12-01

Pattern analysis deals with the automatic detection of patterns in the data and there are a variety of algorithms available for the purpose. These algorithms are commonly called Artificial Intelligence (AI) or data driven algorithms, and have been applied lately to a variety of problems in hydrology and are becoming extremely popular. When confronting such a range of algorithms, the question of which one is the “best” arises. Some algorithms may be preferred because of the lower computational complexity; others take into account prior knowledge of the form and the amount of the data; others are chosen based on a version of the Occam’s razor principle that a simple classifier performs better. Popper has argued, however, that Occam’s razor is without operational value because there is no clear measure or criterion for simplicity. An example of measures that can be used for this purpose are: the so called algorithmic complexity - also known as Kolmogorov complexity or Kolmogorov (algorithmic) entropy; the Bayesian information criterion; or the Vapnik-Chervonenkis dimension. On the other hand, the No Free Lunch Theorem states that there is no best general algorithm, and that specific algorithms are superior only for specific problems. It should be noted also that the appropriate algorithm and the appropriate complexity are constrained by the finiteness of the available data and the uncertainties associated with it. Thus, there is compromise between the complexity of the algorithm, the data properties, and the robustness of the predictions. We discuss the above topics; briefly review the historical development of applications with particular emphasis on statistical learning theory (SLT), also known as machine learning (ML) of which support vector machines and relevant vector machines are the most commonly known algorithms. We present some applications of such algorithms for distributed hydrologic modeling; and introduce an example of how the complexity measure can be applied for appropriate model choice within the context of applications in hydrologic modeling intended for use in studies about water resources and water resources management and their direct relation to extreme conditions or natural hazards.

High-performance technology for indexing of high volumes of Earth remote sensing data

NASA Astrophysics Data System (ADS)

Strotov, Valery V.; Taganov, Alexander I.; Kolesenkov, Aleksandr N.; Kostrov, Boris V.

2017-10-01

The present paper has suggested a technology for search, indexing, cataloging and distribution of aerospace images on the basis of geo-information approach, cluster and spectral analysis. It has considered information and algorithmic support of the system. Functional circuit of the system and structure of the geographical data base have been developed on the basis of the geographical online portal technology. Taking into account heterogeneity of information obtained from various sources it is reasonable to apply a geoinformation platform that allows analyzing space location of objects and territories and executing complex processing of information. Geoinformation platform is based on cartographic fundamentals with the uniform coordinate system, the geographical data base, a set of algorithms and program modules for execution of various tasks. The technology for adding by particular users and companies of images taken by means of professional and amateur devices and also processed by various software tools to the array system has been suggested. Complex usage of visual and instrumental approaches allows significantly expanding an application area of Earth remote sensing data. Development and implementation of new algorithms based on the complex usage of new methods for processing of structured and unstructured data of high volumes will increase periodicity and rate of data updating. The paper has shown that application of original algorithms for search, indexing and cataloging of aerospace images will provide an easy access to information spread by hundreds of suppliers and allow increasing an access rate to aerospace images up to 5 times in comparison with current analogues.

A novel vehicle tracking algorithm based on mean shift and active contour model in complex environment

NASA Astrophysics Data System (ADS)

Cai, Lei; Wang, Lin; Li, Bo; Zhang, Libao; Lv, Wen

2017-06-01

Vehicle tracking technology is currently one of the most active research topics in machine vision. It is an important part of intelligent transportation system. However, in theory and technology, it still faces many challenges including real-time and robustness. In video surveillance, the targets need to be detected in real-time and to be calculated accurate position for judging the motives. The contents of video sequence images and the target motion are complex, so the objects can't be expressed by a unified mathematical model. Object-tracking is defined as locating the interest moving target in each frame of a piece of video. The current tracking technology can achieve reliable results in simple environment over the target with easy identified characteristics. However, in more complex environment, it is easy to lose the target because of the mismatch between the target appearance and its dynamic model. Moreover, the target usually has a complex shape, but the tradition target tracking algorithm usually represents the tracking results by simple geometric such as rectangle or circle, so it cannot provide accurate information for the subsequent upper application. This paper combines a traditional object-tracking technology, Mean-Shift algorithm, with a kind of image segmentation algorithm, Active-Contour model, to get the outlines of objects while the tracking process and automatically handle topology changes. Meanwhile, the outline information is used to aid tracking algorithm to improve it.

A tunable algorithm for collective decision-making.

PubMed

Pratt, Stephen C; Sumpter, David J T

2006-10-24

Complex biological systems are increasingly understood in terms of the algorithms that guide the behavior of system components and the information pathways that link them. Much attention has been given to robust algorithms, or those that allow a system to maintain its functions in the face of internal or external perturbations. At the same time, environmental variation imposes a complementary need for algorithm versatility, or the ability to alter system function adaptively as external circumstances change. An important goal of systems biology is thus the identification of biological algorithms that can meet multiple challenges rather than being narrowly specified to particular problems. Here we show that emigrating colonies of the ant Temnothorax curvispinosus tune the parameters of a single decision algorithm to respond adaptively to two distinct problems: rapid abandonment of their old nest in a crisis and deliberative selection of the best available new home when their old nest is still intact. The algorithm uses a stepwise commitment scheme and a quorum rule to integrate information gathered by numerous individual ants visiting several candidate homes. By varying the rates at which they search for and accept these candidates, the ants yield a colony-level response that adaptively emphasizes either speed or accuracy. We propose such general but tunable algorithms as a design feature of complex systems, each algorithm providing elegant solutions to a wide range of problems.

Despeckling Polsar Images Based on Relative Total Variation Model

NASA Astrophysics Data System (ADS)

Jiang, C.; He, X. F.; Yang, L. J.; Jiang, J.; Wang, D. Y.; Yuan, Y.

2018-04-01

Relatively total variation (RTV) algorithm, which can effectively decompose structure information and texture in image, is employed in extracting main structures of the image. However, applying the RTV directly to polarimetric SAR (PolSAR) image filtering will not preserve polarimetric information. A new RTV approach based on the complex Wishart distribution is proposed considering the polarimetric properties of PolSAR. The proposed polarization RTV (PolRTV) algorithm can be used for PolSAR image filtering. The L-band Airborne SAR (AIRSAR) San Francisco data is used to demonstrate the effectiveness of the proposed algorithm in speckle suppression, structural information preservation, and polarimetric property preservation.

Adjoint-Based Algorithms for Adaptation and Design Optimizations on Unstructured Grids

NASA Technical Reports Server (NTRS)

Nielsen, Eric J.

2006-01-01

Schemes based on discrete adjoint algorithms present several exciting opportunities for significantly advancing the current state of the art in computational fluid dynamics. Such methods provide an extremely efficient means for obtaining discretely consistent sensitivity information for hundreds of design variables, opening the door to rigorous, automated design optimization of complex aerospace configuration using the Navier-Stokes equation. Moreover, the discrete adjoint formulation provides a mathematically rigorous foundation for mesh adaptation and systematic reduction of spatial discretization error. Error estimates are also an inherent by-product of an adjoint-based approach, valuable information that is virtually non-existent in today's large-scale CFD simulations. An overview of the adjoint-based algorithm work at NASA Langley Research Center is presented, with examples demonstrating the potential impact on complex computational problems related to design optimization as well as mesh adaptation.

Exact and heuristic algorithms for Space Information Flow.

PubMed

Uwitonze, Alfred; Huang, Jiaqing; Ye, Yuanqing; Cheng, Wenqing; Li, Zongpeng

2018-01-01

Space Information Flow (SIF) is a new promising research area that studies network coding in geometric space, such as Euclidean space. The design of algorithms that compute the optimal SIF solutions remains one of the key open problems in SIF. This work proposes the first exact SIF algorithm and a heuristic SIF algorithm that compute min-cost multicast network coding for N (N ≥ 3) given terminal nodes in 2-D Euclidean space. Furthermore, we find that the Butterfly network in Euclidean space is the second example besides the Pentagram network where SIF is strictly better than Euclidean Steiner minimal tree. The exact algorithm design is based on two key techniques: Delaunay triangulation and linear programming. Delaunay triangulation technique helps to find practically good candidate relay nodes, after which a min-cost multicast linear programming model is solved over the terminal nodes and the candidate relay nodes, to compute the optimal multicast network topology, including the optimal relay nodes selected by linear programming from all the candidate relay nodes and the flow rates on the connection links. The heuristic algorithm design is also based on Delaunay triangulation and linear programming techniques. The exact algorithm can achieve the optimal SIF solution with an exponential computational complexity, while the heuristic algorithm can achieve the sub-optimal SIF solution with a polynomial computational complexity. We prove the correctness of the exact SIF algorithm. The simulation results show the effectiveness of the heuristic SIF algorithm.

Computational complexity of ecological and evolutionary spatial dynamics

PubMed Central

Ibsen-Jensen, Rasmus; Chatterjee, Krishnendu; Nowak, Martin A.

2015-01-01

There are deep, yet largely unexplored, connections between computer science and biology. Both disciplines examine how information proliferates in time and space. Central results in computer science describe the complexity of algorithms that solve certain classes of problems. An algorithm is deemed efficient if it can solve a problem in polynomial time, which means the running time of the algorithm is a polynomial function of the length of the input. There are classes of harder problems for which the fastest possible algorithm requires exponential time. Another criterion is the space requirement of the algorithm. There is a crucial distinction between algorithms that can find a solution, verify a solution, or list several distinct solutions in given time and space. The complexity hierarchy that is generated in this way is the foundation of theoretical computer science. Precise complexity results can be notoriously difficult. The famous question whether polynomial time equals nondeterministic polynomial time (i.e., P = NP) is one of the hardest open problems in computer science and all of mathematics. Here, we consider simple processes of ecological and evolutionary spatial dynamics. The basic question is: What is the probability that a new invader (or a new mutant) will take over a resident population? We derive precise complexity results for a variety of scenarios. We therefore show that some fundamental questions in this area cannot be answered by simple equations (assuming that P is not equal to NP). PMID:26644569

Parana Basin Structure from Multi-Objective Inversion of Surface Wave and Receiver Function by Competent Genetic Algorithm

NASA Astrophysics Data System (ADS)

An, M.; Assumpcao, M.

2003-12-01

The joint inversion of receiver function and surface wave is an effective way to diminish the influences of the strong tradeoff among parameters and the different sensitivity to the model parameters in their respective inversions, but the inversion problem becomes more complex. Multi-objective problems can be much more complicated than single-objective inversion in the model selection and optimization. If objectives are involved and conflicting, models can be ordered only partially. In this case, Pareto-optimal preference should be used to select solutions. On the other hand, the inversion to get only a few optimal solutions can not deal properly with the strong tradeoff between parameters, the uncertainties in the observation, the geophysical complexities and even the incompetency of the inversion technique. The effective way is to retrieve the geophysical information statistically from many acceptable solutions, which requires more competent global algorithms. Competent genetic algorithms recently proposed are far superior to the conventional genetic algorithm and can solve hard problems quickly, reliably and accurately. In this work we used one of competent genetic algorithms, Bayesian Optimization Algorithm as the main inverse procedure. This algorithm uses Bayesian networks to draw out inherited information and can use Pareto-optimal preference in the inversion. With this algorithm, the lithospheric structure of Paran"› basin is inverted to fit both the observations of inter-station surface wave dispersion and receiver function.

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks.

PubMed

Zheng, Wei; Yan, Xiaoyong; Zhao, Wei; Qian, Chengshan

2017-12-20

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.

The application of dynamic programming in production planning

NASA Astrophysics Data System (ADS)

Wu, Run

2017-05-01

Nowadays, with the popularity of the computers, various industries and fields are widely applying computer information technology, which brings about huge demand for a variety of application software. In order to develop software meeting various needs with most economical cost and best quality, programmers must design efficient algorithms. A superior algorithm can not only soul up one thing, but also maximize the benefits and generate the smallest overhead. As one of the common algorithms, dynamic programming algorithms are used to solving problems with some sort of optimal properties. When solving problems with a large amount of sub-problems that needs repetitive calculations, the ordinary sub-recursive method requires to consume exponential time, and dynamic programming algorithm can reduce the time complexity of the algorithm to the polynomial level, according to which we can conclude that dynamic programming algorithm is a very efficient compared to other algorithms reducing the computational complexity and enriching the computational results. In this paper, we expound the concept, basic elements, properties, core, solving steps and difficulties of the dynamic programming algorithm besides, establish the dynamic programming model of the production planning problem.

A comparative study of Message Digest 5(MD5) and SHA256 algorithm

NASA Astrophysics Data System (ADS)

Rachmawati, D.; Tarigan, J. T.; Ginting, A. B. C.

2018-03-01

The document is a collection of written or printed data containing information. The more rapid advancement of technology, the integrity of a document should be kept. Because of the nature of an open document means the document contents can be read and modified by many parties so that the integrity of the information as a content of the document is not preserved. To maintain the integrity of the data, it needs to create a mechanism which is called a digital signature. A digital signature is a specific code which is generated from the function of producing a digital signature. One of the algorithms that used to create the digital signature is a hash function. There are many hash functions. Two of them are message digest 5 (MD5) and SHA256. Those both algorithms certainly have its advantages and disadvantages of each. The purpose of this research is to determine the algorithm which is better. The parameters which used to compare that two algorithms are the running time and complexity. The research results obtained from the complexity of the Algorithms MD5 and SHA256 is the same, i.e., ⊖ (N), but regarding the speed is obtained that MD5 is better compared to SHA256.

A selective-update affine projection algorithm with selective input vectors

NASA Astrophysics Data System (ADS)

Kong, NamWoong; Shin, JaeWook; Park, PooGyeon

2011-10-01

This paper proposes an affine projection algorithm (APA) with selective input vectors, which based on the concept of selective-update in order to reduce estimation errors and computations. The algorithm consists of two procedures: input- vector-selection and state-decision. The input-vector-selection procedure determines the number of input vectors by checking with mean square error (MSE) whether the input vectors have enough information for update. The state-decision procedure determines the current state of the adaptive filter by using the state-decision criterion. As the adaptive filter is in transient state, the algorithm updates the filter coefficients with the selected input vectors. On the other hand, as soon as the adaptive filter reaches the steady state, the update procedure is not performed. Through these two procedures, the proposed algorithm achieves small steady-state estimation errors, low computational complexity and low update complexity for colored input signals.

Practical image registration concerns overcome by the weighted and filtered mutual information metric

NASA Astrophysics Data System (ADS)

Keane, Tommy P.; Saber, Eli; Rhody, Harvey; Savakis, Andreas; Raj, Jeffrey

2012-04-01

Contemporary research in automated panorama creation utilizes camera calibration or extensive knowledge of camera locations and relations to each other to achieve successful results. Research in image registration attempts to restrict these same camera parameters or apply complex point-matching schemes to overcome the complications found in real-world scenarios. This paper presents a novel automated panorama creation algorithm by developing an affine transformation search based on maximized mutual information (MMI) for region-based registration. Standard MMI techniques have been limited to applications with airborne/satellite imagery or medical images. We show that a novel MMI algorithm can approximate an accurate registration between views of realistic scenes of varying depth distortion. The proposed algorithm has been developed using stationary, color, surveillance video data for a scenario with no a priori camera-to-camera parameters. This algorithm is robust for strict- and nearly-affine-related scenes, while providing a useful approximation for the overlap regions in scenes related by a projective homography or a more complex transformation, allowing for a set of efficient and accurate initial conditions for pixel-based registration.

AdaBoost-based algorithm for network intrusion detection.

PubMed

Hu, Weiming; Hu, Wei; Maybank, Steve

2008-04-01

Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.

An IPv6 routing lookup algorithm using weight-balanced tree based on prefix value for virtual router

NASA Astrophysics Data System (ADS)

Chen, Lingjiang; Zhou, Shuguang; Zhang, Qiaoduo; Li, Fenghua

2016-10-01

Virtual router enables the coexistence of different networks on the same physical facility and has lately attracted a great deal of attention from researchers. As the number of IPv6 addresses is rapidly increasing in virtual routers, designing an efficient IPv6 routing lookup algorithm is of great importance. In this paper, we present an IPv6 lookup algorithm called weight-balanced tree (WBT). WBT merges Forwarding Information Bases (FIBs) of virtual routers into one spanning tree, and compresses the space cost. WBT's average time complexity and the worst case time complexity of lookup and update process are both O(logN) and space complexity is O(cN) where N is the size of routing table and c is a constant. Experiments show that WBT helps reduce more than 80% Static Random Access Memory (SRAM) cost in comparison to those separation schemes. WBT also achieves the least average search depth comparing with other homogeneous algorithms.

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method

PubMed Central

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-01-01

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli, and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs. PMID:29113310

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

PubMed

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-10-06

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Identifying Optimal Measurement Subspace for the Ensemble Kalman Filter

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

Zhou, Ning; Huang, Zhenyu; Welch, Greg

2012-05-24

To reduce the computational load of the ensemble Kalman filter while maintaining its efficacy, an optimization algorithm based on the generalized eigenvalue decomposition method is proposed for identifying the most informative measurement subspace. When the number of measurements is large, the proposed algorithm can be used to make an effective tradeoff between computational complexity and estimation accuracy. This algorithm also can be extended to other Kalman filters for measurement subspace selection.

PDF file encryption on mobile phone using super-encryption of Variably Modified Permutation Composition (VMPC) and two square cipher algorithm

NASA Astrophysics Data System (ADS)

Rachmawati, D.; Budiman, M. A.; Atika, F.

2018-03-01

Data security is becoming one of the most significant challenges in the digital world. Retrieval of data by unauthorized parties will result in harm to the owner of the data. PDF data are also susceptible to data security disorder. These things affect the security of the information. To solve the security problem, it needs a method to maintain the protection of the data, such as cryptography. In cryptography, several algorithms can encode data, one of them is Two Square Cipher algorithm which is a symmetric algorithm. At this research, Two Square Cipher algorithm has already developed into a 16 x 16 key aims to enter the various plaintexts. However, for more enhancement security it will be combined with the VMPC algorithm which is a symmetric algorithm. The combination of the two algorithms is called with the super-encryption. At this point, the data already can be stored on a mobile phone allowing users to secure data flexibly and can be accessed anywhere. The application of PDF document security on this research built by Android-platform. At this study will also calculate the complexity of algorithms and process time. Based on the test results the complexity of the algorithm is θ (n) for Two Square Cipher and θ (n) for VMPC algorithm, so the complexity of the super-encryption is also θ (n). VMPC algorithm processing time results quicker than on Two Square Cipher. And the processing time is directly proportional to the length of the plaintext and passwords.

An automated method for finding molecular complexes in large protein interaction networks

PubMed Central

Bader, Gary D; Hogue, Christopher WV

2003-01-01

Background Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. Results This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Conclusion Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from . PMID:12525261

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

PubMed Central

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details. PMID:26158662

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

PubMed

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

A Type-2 fuzzy data fusion approach for building reliable weighted protein interaction networks with application in protein complex detection.

PubMed

Mehranfar, Adele; Ghadiri, Nasser; Kouhsar, Morteza; Golshani, Ashkan

2017-09-01

Detecting the protein complexes is an important task in analyzing the protein interaction networks. Although many algorithms predict protein complexes in different ways, surveys on the interaction networks indicate that about 50% of detected interactions are false positives. Consequently, the accuracy of existing methods needs to be improved. In this paper we propose a novel algorithm to detect the protein complexes in 'noisy' protein interaction data. First, we integrate several biological data sources to determine the reliability of each interaction and determine more accurate weights for the interactions. A data fusion component is used for this step, based on the interval type-2 fuzzy voter that provides an efficient combination of the information sources. This fusion component detects the errors and diminishes their effect on the detection protein complexes. So in the first step, the reliability scores have been assigned for every interaction in the network. In the second step, we have proposed a general protein complex detection algorithm by exploiting and adopting the strong points of other algorithms and existing hypotheses regarding real complexes. Finally, the proposed method has been applied for the yeast interaction datasets for predicting the interactions. The results show that our framework has a better performance regarding precision and F-measure than the existing approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Ranking in evolving complex networks

NASA Astrophysics Data System (ADS)

Liao, Hao; Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng; Zhou, Ming-Yang

2017-05-01

Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Many popular ranking algorithms (such as Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes.

Discovering protein complexes in protein interaction networks via exploring the weak ties effect

PubMed Central

2012-01-01

Background Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges. Results To provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes. Conclusions We proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting that the roles of edges are critical in discovering protein complexes. PMID:23046740

Informationally Efficient Multi-User Communication

DTIC Science & Technology

2010-01-01

DSM algorithms, the Op- timal Spectrum Balancing ( OSB ) algorithm and the Iterative Spectrum Balanc- ing (ISB) algorithm, were proposed to solve the...problem of maximization of a weighted rate-sum across all users [CYM06, YL06]. OSB has an exponential complexity in the number of users. ISB only has a...the duality gap min λ1,λ2 D (λ1, λ2) − max P1,P2 f (P1,P2) is not zero. Fig. 3.3 summarizes the three key steps of a dual method, the OSB algorithm

The effect of orthology and coregulation on detecting regulatory motifs.

PubMed

Storms, Valerie; Claeys, Marleen; Sanchez, Aminael; De Moor, Bart; Verstuyf, Annemieke; Marchal, Kathleen

2010-02-03

Computational de novo discovery of transcription factor binding sites is still a challenging problem. The growing number of sequenced genomes allows integrating orthology evidence with coregulation information when searching for motifs. Moreover, the more advanced motif detection algorithms explicitly model the phylogenetic relatedness between the orthologous input sequences and thus should be well adapted towards using orthologous information. In this study, we evaluated the conditions under which complementing coregulation with orthologous information improves motif detection for the class of probabilistic motif detection algorithms with an explicit evolutionary model. We designed datasets (real and synthetic) covering different degrees of coregulation and orthologous information to test how well Phylogibbs and Phylogenetic sampler, as representatives of the motif detection algorithms with evolutionary model performed as compared to MEME, a more classical motif detection algorithm that treats orthologs independently. Under certain conditions detecting motifs in the combined coregulation-orthology space is indeed more efficient than using each space separately, but this is not always the case. Moreover, the difference in success rate between the advanced algorithms and MEME is still marginal. The success rate of motif detection depends on the complex interplay between the added information and the specificities of the applied algorithms. Insights in this relation provide information useful to both developers and users. All benchmark datasets are available at http://homes.esat.kuleuven.be/~kmarchal/Supplementary_Storms_Valerie_PlosONE.

The Effect of Orthology and Coregulation on Detecting Regulatory Motifs

PubMed Central

Storms, Valerie; Claeys, Marleen; Sanchez, Aminael; De Moor, Bart; Verstuyf, Annemieke; Marchal, Kathleen

2010-01-01

Background Computational de novo discovery of transcription factor binding sites is still a challenging problem. The growing number of sequenced genomes allows integrating orthology evidence with coregulation information when searching for motifs. Moreover, the more advanced motif detection algorithms explicitly model the phylogenetic relatedness between the orthologous input sequences and thus should be well adapted towards using orthologous information. In this study, we evaluated the conditions under which complementing coregulation with orthologous information improves motif detection for the class of probabilistic motif detection algorithms with an explicit evolutionary model. Methodology We designed datasets (real and synthetic) covering different degrees of coregulation and orthologous information to test how well Phylogibbs and Phylogenetic sampler, as representatives of the motif detection algorithms with evolutionary model performed as compared to MEME, a more classical motif detection algorithm that treats orthologs independently. Results and Conclusions Under certain conditions detecting motifs in the combined coregulation-orthology space is indeed more efficient than using each space separately, but this is not always the case. Moreover, the difference in success rate between the advanced algorithms and MEME is still marginal. The success rate of motif detection depends on the complex interplay between the added information and the specificities of the applied algorithms. Insights in this relation provide information useful to both developers and users. All benchmark datasets are available at http://homes.esat.kuleuven.be/~kmarchal/Supplementary_Storms_Valerie_PlosONE. PMID:20140085

A hand tracking algorithm with particle filter and improved GVF snake model

NASA Astrophysics Data System (ADS)

Sun, Yi-qi; Wu, Ai-guo; Dong, Na; Shao, Yi-zhe

2017-07-01

To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow (GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion.

The Applications of Genetic Algorithms in Medicine.

PubMed

Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

2015-11-01

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.].

The Applications of Genetic Algorithms in Medicine

PubMed Central

Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

2015-01-01

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.] PMID:26676060

Fast algorithm for automatically computing Strahler stream order

USGS Publications Warehouse

Lanfear, Kenneth J.

1990-01-01

An efficient algorithm was developed to determine Strahler stream order for segments of stream networks represented in a Geographic Information System (GIS). The algorithm correctly assigns Strahler stream order in topologically complex situations such as braided streams and multiple drainage outlets. Execution time varies nearly linearly with the number of stream segments in the network. This technique is expected to be particularly useful for studying the topology of dense stream networks derived from digital elevation model data.

Locating the source of diffusion in complex networks by time-reversal backward spreading.

PubMed

Shen, Zhesi; Cao, Shinan; Wang, Wen-Xu; Di, Zengru; Stanley, H Eugene

2016-03-01

Locating the source that triggers a dynamical process is a fundamental but challenging problem in complex networks, ranging from epidemic spreading in society and on the Internet to cancer metastasis in the human body. An accurate localization of the source is inherently limited by our ability to simultaneously access the information of all nodes in a large-scale complex network. This thus raises two critical questions: how do we locate the source from incomplete information and can we achieve full localization of sources at any possible location from a given set of observable nodes. Here we develop a time-reversal backward spreading algorithm to locate the source of a diffusion-like process efficiently and propose a general locatability condition. We test the algorithm by employing epidemic spreading and consensus dynamics as typical dynamical processes and apply it to the H1N1 pandemic in China. We find that the sources can be precisely located in arbitrary networks insofar as the locatability condition is assured. Our tools greatly improve our ability to locate the source of diffusion in complex networks based on limited accessibility of nodal information. Moreover, they have implications for controlling a variety of dynamical processes taking place on complex networks, such as inhibiting epidemics, slowing the spread of rumors, pollution control, and environmental protection.

Locating the source of diffusion in complex networks by time-reversal backward spreading

NASA Astrophysics Data System (ADS)

Shen, Zhesi; Cao, Shinan; Wang, Wen-Xu; Di, Zengru; Stanley, H. Eugene

2016-03-01

Locating the source that triggers a dynamical process is a fundamental but challenging problem in complex networks, ranging from epidemic spreading in society and on the Internet to cancer metastasis in the human body. An accurate localization of the source is inherently limited by our ability to simultaneously access the information of all nodes in a large-scale complex network. This thus raises two critical questions: how do we locate the source from incomplete information and can we achieve full localization of sources at any possible location from a given set of observable nodes. Here we develop a time-reversal backward spreading algorithm to locate the source of a diffusion-like process efficiently and propose a general locatability condition. We test the algorithm by employing epidemic spreading and consensus dynamics as typical dynamical processes and apply it to the H1N1 pandemic in China. We find that the sources can be precisely located in arbitrary networks insofar as the locatability condition is assured. Our tools greatly improve our ability to locate the source of diffusion in complex networks based on limited accessibility of nodal information. Moreover, they have implications for controlling a variety of dynamical processes taking place on complex networks, such as inhibiting epidemics, slowing the spread of rumors, pollution control, and environmental protection.

Extended shortest path selection for package routing of complex networks

NASA Astrophysics Data System (ADS)

Ye, Fan; Zhang, Lei; Wang, Bing-Hong; Liu, Lu; Zhang, Xing-Yi

The routing strategy plays a very important role in complex networks such as Internet system and Peer-to-Peer networks. However, most of the previous work concentrates only on the path selection, e.g. Flooding and Random Walk, or finding the shortest path (SP) and rarely considering the local load information such as SP and Distance Vector Routing. Flow-based Routing mainly considers load balance and still cannot achieve best optimization. Thus, in this paper, we propose a novel dynamic routing strategy on complex network by incorporating the local load information into SP algorithm to enhance the traffic flow routing optimization. It was found that the flow in a network is greatly affected by the waiting time of the network, so we should not consider only choosing optimized path for package transformation but also consider node congestion. As a result, the packages should be transmitted with a global optimized path with smaller congestion and relatively short distance. Analysis work and simulation experiments show that the proposed algorithm can largely enhance the network flow with the maximum throughput within an acceptable calculating time. The detailed analysis of the algorithm will also be provided for explaining the efficiency.

A Novel Strategy Using Factor Graphs and the Sum-Product Algorithm for Satellite Broadcast Scheduling Problems

NASA Astrophysics Data System (ADS)

Chen, Jung-Chieh

This paper presents a low complexity algorithmic framework for finding a broadcasting schedule in a low-altitude satellite system, i. e., the satellite broadcast scheduling (SBS) problem, based on the recent modeling and computational methodology of factor graphs. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, in this paper, we transform the SBS problem into an LDPC-like problem through a factor graph instead of using the conventional neural network approaches to solve the SBS problem. Based on a factor graph framework, the soft-information, describing the probability that each satellite will broadcast information to a terminal at a specific time slot, is exchanged among the local processing in the proposed framework via the sum-product algorithm to iteratively optimize the satellite broadcasting schedule. Numerical results show that the proposed approach not only can obtain optimal solution but also enjoys the low complexity suitable for integral-circuit implementation.

Complex network approach to fractional time series

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

Manshour, Pouya

In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacencymore » matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.« less

Fractal Complexity-Based Feature Extraction Algorithm of Communication Signals

NASA Astrophysics Data System (ADS)

Wang, Hui; Li, Jingchao; Guo, Lili; Dou, Zheng; Lin, Yun; Zhou, Ruolin

How to analyze and identify the characteristics of radiation sources and estimate the threat level by means of detecting, intercepting and locating has been the central issue of electronic support in the electronic warfare, and communication signal recognition is one of the key points to solve this issue. Aiming at accurately extracting the individual characteristics of the radiation source for the increasingly complex communication electromagnetic environment, a novel feature extraction algorithm for individual characteristics of the communication radiation source based on the fractal complexity of the signal is proposed. According to the complexity of the received signal and the situation of environmental noise, use the fractal dimension characteristics of different complexity to depict the subtle characteristics of the signal to establish the characteristic database, and then identify different broadcasting station by gray relation theory system. The simulation results demonstrate that the algorithm can achieve recognition rate of 94% even in the environment with SNR of -10dB, and this provides an important theoretical basis for the accurate identification of the subtle features of the signal at low SNR in the field of information confrontation.

The Capabilities of Chaos and Complexity

PubMed Central

Abel, David L.

2009-01-01

To what degree could chaos and complexity have organized a Peptide or RNA World of crude yet necessarily integrated protometabolism? How far could such protolife evolve in the absence of a heritable linear digital symbol system that could mutate, instruct, regulate, optimize and maintain metabolic homeostasis? To address these questions, chaos, complexity, self-ordered states, and organization must all be carefully defined and distinguished. In addition their cause-and-effect relationships and mechanisms of action must be delineated. Are there any formal (non physical, abstract, conceptual, algorithmic) components to chaos, complexity, self-ordering and organization, or are they entirely physicodynamic (physical, mass/energy interaction alone)? Chaos and complexity can produce some fascinating self-ordered phenomena. But can spontaneous chaos and complexity steer events and processes toward pragmatic benefit, select function over non function, optimize algorithms, integrate circuits, produce computational halting, organize processes into formal systems, control and regulate existing systems toward greater efficiency? The question is pursued of whether there might be some yet-to-be discovered new law of biology that will elucidate the derivation of prescriptive information and control. “System” will be rigorously defined. Can a low-informational rapid succession of Prigogine’s dissipative structures self-order into bona fide organization? PMID:19333445

Detecting complexes from edge-weighted PPI networks via genes expression analysis.

PubMed

Zhang, Zehua; Song, Jian; Tang, Jijun; Xu, Xinying; Guo, Fei

2018-04-24

Identifying complexes from PPI networks has become a key problem to elucidate protein functions and identify signal and biological processes in a cell. Proteins binding as complexes are important roles of life activity. Accurate determination of complexes in PPI networks is crucial for understanding principles of cellular organization. We propose a novel method to identify complexes on PPI networks, based on different co-expression information. First, we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks. Then, we propose some significant features, such as graph information and gene expression analysis, to filter and modify complexes predicted by Markov Cluster Algorithm. To evaluate our method, we test on two experimental yeast PPI networks. On DIP network, our method has Precision and F-Measure values of 0.6004 and 0.5528. On MIPS network, our method has F-Measure and S n values of 0.3774 and 0.3453. Comparing to existing methods, our method improves Precision value by at least 0.1752, F-Measure value by at least 0.0448, S n value by at least 0.0771. Experiments show that our method achieves better results than some state-of-the-art methods for identifying complexes on PPI networks, with the prediction quality improved in terms of evaluation criteria.

FPGA implementation of sparse matrix algorithm for information retrieval

NASA Astrophysics Data System (ADS)

Bojanic, Slobodan; Jevtic, Ruzica; Nieto-Taladriz, Octavio

2005-06-01

Information text data retrieval requires a tremendous amount of processing time because of the size of the data and the complexity of information retrieval algorithms. In this paper the solution to this problem is proposed via hardware supported information retrieval algorithms. Reconfigurable computing may adopt frequent hardware modifications through its tailorable hardware and exploits parallelism for a given application through reconfigurable and flexible hardware units. The degree of the parallelism can be tuned for data. In this work we implemented standard BLAS (basic linear algebra subprogram) sparse matrix algorithm named Compressed Sparse Row (CSR) that is showed to be more efficient in terms of storage space requirement and query-processing timing over the other sparse matrix algorithms for information retrieval application. Although inverted index algorithm is treated as the de facto standard for information retrieval for years, an alternative approach to store the index of text collection in a sparse matrix structure gains more attention. This approach performs query processing using sparse matrix-vector multiplication and due to parallelization achieves a substantial efficiency over the sequential inverted index. The parallel implementations of information retrieval kernel are presented in this work targeting the Virtex II Field Programmable Gate Arrays (FPGAs) board from Xilinx. A recent development in scientific applications is the use of FPGA to achieve high performance results. Computational results are compared to implementations on other platforms. The design achieves a high level of parallelism for the overall function while retaining highly optimised hardware within processing unit.

Efficient, Decentralized Detection of Qualitative Spatial Events in a Dynamic Scalar Field

PubMed Central

Jeong, Myeong-Hun; Duckham, Matt

2015-01-01

This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes’ coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks. PMID:26343672

Efficient, Decentralized Detection of Qualitative Spatial Events in a Dynamic Scalar Field.

PubMed

Jeong, Myeong-Hun; Duckham, Matt

2015-08-28

This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes' coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks.

A real time QRS detection using delay-coordinate mapping for the microcontroller implementation.

PubMed

Lee, Jeong-Whan; Kim, Kyeong-Seop; Lee, Bongsoo; Lee, Byungchae; Lee, Myoung-Ho

2002-01-01

In this article, we propose a new algorithm using the characteristics of reconstructed phase portraits by delay-coordinate mapping utilizing lag rotundity for a real-time detection of QRS complexes in ECG signals. In reconstructing phase portrait the mapping parameters, time delay, and mapping dimension play important roles in shaping of portraits drawn in a new dimensional space. Experimentally, the optimal mapping time delay for detection of QRS complexes turned out to be 20 ms. To explore the meaning of this time delay and the proper mapping dimension, we applied a fill factor, mutual information, and autocorrelation function algorithm that were generally used to analyze the chaotic characteristics of sampled signals. From these results, we could find the fact that the performance of our proposed algorithms relied mainly on the geometrical property such as an area of the reconstructed phase portrait. For the real application, we applied our algorithm for designing a small cardiac event recorder. This system was to record patients' ECG and R-R intervals for 1 h to investigate HRV characteristics of the patients who had vasovagal syncope symptom and for the evaluation, we implemented our algorithm in C language and applied to MIT/BIH arrhythmia database of 48 subjects. Our proposed algorithm achieved a 99.58% detection rate of QRS complexes.

How Analysts Cognitively “Connect the Dots”

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

Bradel, Lauren; Self, Jessica S.; Endert, Alexander

2013-06-04

As analysts attempt to make sense of a collection of documents, such as intelligence analysis reports, they may wish to “connect the dots” between pieces of information that may initially seem unrelated. This process of synthesizing information between information requires users to make connections between pairs of documents, creating a conceptual story. We conducted a user study to analyze the process by which users connect pairs of documents and how they spatially arrange information. Users created conceptual stories that connected the dots using organizational strategies that ranged in complexity. We propose taxonomies for cognitive connections and physical structures used whenmore » trying to “connect the dots” between two documents. We compared the user-created stories with a data-mining algorithm that constructs chains of documents using co-occurrence metrics. Using the insight gained into the storytelling process, we offer design considerations for the existing data mining algorithm and corresponding tools to combine the power of data mining and the complex cognitive processing of analysts.« less

Testing a Variety of Encryption Technologies

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

Henson, T J

2001-04-09

Review and test speeds of various encryption technologies using Entrust Software. Multiple encryption algorithms are included in the product. Algorithms tested were IDEA, CAST, DES, and RC2. Test consisted of taking a 7.7 MB Word document file which included complex graphics and timing encryption, decryption and signing. Encryption is discussed in the GIAC Kickstart section: Information Security: The Big Picture--Part VI.

Identifying online user reputation of user-object bipartite networks

NASA Astrophysics Data System (ADS)

Liu, Xiao-Lu; Liu, Jian-Guo; Yang, Kai; Guo, Qiang; Han, Jing-Ti

2017-02-01

Identifying online user reputation based on the rating information of the user-object bipartite networks is important for understanding online user collective behaviors. Based on the Bayesian analysis, we present a parameter-free algorithm for ranking online user reputation, where the user reputation is calculated based on the probability that their ratings are consistent with the main part of all user opinions. The experimental results show that the AUC values of the presented algorithm could reach 0.8929 and 0.8483 for the MovieLens and Netflix data sets, respectively, which is better than the results generated by the CR and IARR methods. Furthermore, the experimental results for different user groups indicate that the presented algorithm outperforms the iterative ranking methods in both ranking accuracy and computation complexity. Moreover, the results for the synthetic networks show that the computation complexity of the presented algorithm is a linear function of the network size, which suggests that the presented algorithm is very effective and efficient for the large scale dynamic online systems.

Unified treatment algorithm for the management of crotaline snakebite in the United States: results of an evidence-informed consensus workshop

PubMed Central

2011-01-01

Background Envenomation by crotaline snakes (rattlesnake, cottonmouth, copperhead) is a complex, potentially lethal condition affecting thousands of people in the United States each year. Treatment of crotaline envenomation is not standardized, and significant variation in practice exists. Methods A geographically diverse panel of experts was convened for the purpose of deriving an evidence-informed unified treatment algorithm. Research staff analyzed the extant medical literature and performed targeted analyses of existing databases to inform specific clinical decisions. A trained external facilitator used modified Delphi and structured consensus methodology to achieve consensus on the final treatment algorithm. Results A unified treatment algorithm was produced and endorsed by all nine expert panel members. This algorithm provides guidance about clinical and laboratory observations, indications for and dosing of antivenom, adjunctive therapies, post-stabilization care, and management of complications from envenomation and therapy. Conclusions Clinical manifestations and ideal treatment of crotaline snakebite differ greatly, and can result in severe complications. Using a modified Delphi method, we provide evidence-informed treatment guidelines in an attempt to reduce variation in care and possibly improve clinical outcomes. PMID:21291549

Tactical Conflict Detection in Terminal Airspace

NASA Technical Reports Server (NTRS)

Tang, Huabin; Robinson, John E.; Denery, Dallas G.

2010-01-01

Air traffic systems have long relied on automated short-term conflict prediction algorithms to warn controllers of impending conflicts (losses of separation). The complexity of terminal airspace has proven difficult for such systems as it often leads to excessive false alerts. Thus, the legacy system, called Conflict Alert, which provides short-term alerts in both en-route and terminal airspace currently, is often inhibited or degraded in areas where frequent false alerts occur, even though the alerts are provided only when an aircraft is in dangerous proximity of other aircraft. This research investigates how a minimal level of flight intent information may be used to improve short-term conflict detection in terminal airspace such that it can be used by the controller to maintain legal aircraft separation. The flight intent information includes a site-specific nominal arrival route and inferred altitude clearances in addition to the flight plan that includes the RNAV (Area Navigation) departure route. A new tactical conflict detection algorithm is proposed, which uses a single analytic trajectory, determined by the flight intent and the current state information of the aircraft, and includes a complex set of current, dynamic separation standards for terminal airspace to define losses of separation. The new algorithm is compared with an algorithm that imitates a known en-route algorithm and another that imitates Conflict Alert by analysis of false-alert rate and alert lead time with recent real-world data of arrival and departure operations and a large set of operational error cases from Dallas/Fort Worth TRACON (Terminal Radar Approach Control). The new algorithm yielded a false-alert rate of two per hour and an average alert lead time of 38 seconds.

Localization of Pathology on Complex Architecture Building Surfaces

NASA Astrophysics Data System (ADS)

Sidiropoulos, A. A.; Lakakis, K. N.; Mouza, V. K.

2017-02-01

The technology of 3D laser scanning is considered as one of the most common methods for heritage documentation. The point clouds that are being produced provide information of high detail, both geometric and thematic. There are various studies that examine techniques of the best exploitation of this information. In this study, an algorithm of pathology localization, such as cracks and fissures, on complex building surfaces is being tested. The algorithm makes use of the points' position in the point cloud and tries to distinguish them in two groups-patterns; pathology and non-pathology. The extraction of the geometric information that is being used for recognizing the pattern of the points is being accomplished via Principal Component Analysis (PCA) in user-specified neighborhoods in the whole point cloud. The implementation of PCA leads to the definition of the normal vector at each point of the cloud. Two tests that operate separately examine both local and global geometric criteria among the points and conclude which of them should be categorized as pathology. The proposed algorithm was tested on parts of the Gazi Evrenos Baths masonry, which are located at the city of Giannitsa at Northern Greece.

The new approach for infrared target tracking based on the particle filter algorithm

NASA Astrophysics Data System (ADS)

Sun, Hang; Han, Hong-xia

2011-08-01

Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring, precision, and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection, the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure, but in order to capture the change of the state space, it need a certain amount of particles to ensure samples is enough, and this number will increase in accompany with dimension and increase exponentially, this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining", we expand the classic Mean Shift tracking framework .Based on the previous perspective, we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis, Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism, used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation, and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value .Last because of the gray and fusion target motion information, this approach also inhibit interference from the background, ultimately improve the stability and the real-time of the target track.

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

PubMed Central

Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

2015-01-01

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced. PMID:26690164

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms.

PubMed

Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

2015-12-04

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

Natural image statistics and low-complexity feature selection.

PubMed

Vasconcelos, Manuela; Vasconcelos, Nuno

2009-02-01

Low-complexity feature selection is analyzed in the context of visual recognition. It is hypothesized that high-order dependences of bandpass features contain little information for discrimination of natural images. This hypothesis is characterized formally by the introduction of the concepts of conjunctive interference and decomposability order of a feature set. Necessary and sufficient conditions for the feasibility of low-complexity feature selection are then derived in terms of these concepts. It is shown that the intrinsic complexity of feature selection is determined by the decomposability order of the feature set and not its dimension. Feature selection algorithms are then derived for all levels of complexity and are shown to be approximated by existing information-theoretic methods, which they consistently outperform. The new algorithms are also used to objectively test the hypothesis of low decomposability order through comparison of classification performance. It is shown that, for image classification, the gain of modeling feature dependencies has strongly diminishing returns: best results are obtained under the assumption of decomposability order 1. This suggests a generic law for bandpass features extracted from natural images: that the effect, on the dependence of any two features, of observing any other feature is constant across image classes.

Common Ground: An Interactive Visual Exploration and Discovery for Complex Health Data

DTIC Science & Technology

2015-04-01

working with Intermountain Healthcare on a new rich dataset extracted directly from medical notes using natural language processing ( NLP ) algorithms...probabilities based on a state- of-the-art NLP classifiers. At that stage the data did not include geographic information or temporal information but we

Evaluation of Dynamic Channel and Power Assignment for Cognitive Networks

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

Syed A. Ahmad; Umesh Shukla; Ryan E. Irwin

2011-03-01

In this paper, we develop a unifying optimization formulation to describe the Dynamic Channel and Power Assignment (DCPA) problem and evaluation method for comparing DCPA algorithms. DCPA refers to the allocation of transmit power and frequency channels to links in a cognitive network so as to maximize the total number of feasible links while minimizing the aggregate transmit power. We apply our evaluation method to five algorithms representative of DCPA used in literature. This comparison illustrates the tradeoffs between control modes (centralized versus distributed) and channel/power assignment techniques. We estimate the complexity of each algorithm. Through simulations, we evaluate themore » effectiveness of the algorithms in achieving feasible link allocations in the network, as well as their power efficiency. Our results indicate that, when few channels are available, the effectiveness of all algorithms is comparable and thus the one with smallest complexity should be selected. The Least Interfering Channel and Iterative Power Assignment (LICIPA) algorithm does not require cross-link gain information, has the overall lowest run time, and highest feasibility ratio of all the distributed algorithms; however, this comes at a cost of higher average power per link.« less

Improved Collaborative Filtering Algorithm via Information Transformation

NASA Astrophysics Data System (ADS)

Liu, Jian-Guo; Wang, Bing-Hong; Guo, Qiang

In this paper, we propose a spreading activation approach for collaborative filtering (SA-CF). By using the opinion spreading process, the similarity between any users can be obtained. The algorithm has remarkably higher accuracy than the standard collaborative filtering using the Pearson correlation. Furthermore, we introduce a free parameter β to regulate the contributions of objects to user-user correlations. The numerical results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy and personality. We argue that a better algorithm should simultaneously require less computation and generate higher accuracy. Accordingly, we further propose an algorithm involving only the top-N similar neighbors for each target user, which has both less computational complexity and higher algorithmic accuracy.

Design of Restoration Method Based on Compressed Sensing and TwIST Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Fei; Piao, Yan

2018-04-01

In order to improve the subjective and objective quality of degraded images at low sampling rates effectively,save storage space and reduce computational complexity at the same time, this paper proposes a joint restoration algorithm of compressed sensing and two step iterative threshold shrinkage (TwIST). The algorithm applies the TwIST algorithm which used in image restoration to the compressed sensing theory. Then, a small amount of sparse high-frequency information is obtained in frequency domain. The TwIST algorithm based on compressed sensing theory is used to accurately reconstruct the high frequency image. The experimental results show that the proposed algorithm achieves better subjective visual effects and objective quality of degraded images while accurately restoring degraded images.

Adaptive identifier for uncertain complex nonlinear systems based on continuous neural networks.

PubMed

Alfaro-Ponce, Mariel; Cruz, Amadeo Argüelles; Chairez, Isaac

2014-03-01

This paper presents the design of a complex-valued differential neural network identifier for uncertain nonlinear systems defined in the complex domain. This design includes the construction of an adaptive algorithm to adjust the parameters included in the identifier. The algorithm is obtained based on a special class of controlled Lyapunov functions. The quality of the identification process is characterized using the practical stability framework. Indeed, the region where the identification error converges is derived by the same Lyapunov method. This zone is defined by the power of uncertainties and perturbations affecting the complex-valued uncertain dynamics. Moreover, this convergence zone is reduced to its lowest possible value using ideas related to the so-called ellipsoid methodology. Two simple but informative numerical examples are developed to show how the identifier proposed in this paper can be used to approximate uncertain nonlinear systems valued in the complex domain.

Real-time minimal-bit-error probability decoding of convolutional codes

NASA Technical Reports Server (NTRS)

Lee, L.-N.

1974-01-01

A recursive procedure is derived for decoding of rate R = 1/n binary convolutional codes which minimizes the probability of the individual decoding decisions for each information bit, subject to the constraint that the decoding delay be limited to Delta branches. This new decoding algorithm is similar to, but somewhat more complex than, the Viterbi decoding algorithm. A real-time, i.e., fixed decoding delay, version of the Viterbi algorithm is also developed and used for comparison to the new algorithm on simulated channels. It is shown that the new algorithm offers advantages over Viterbi decoding in soft-decision applications, such as in the inner coding system for concatenated coding.

Real-time minimal bit error probability decoding of convolutional codes

NASA Technical Reports Server (NTRS)

Lee, L. N.

1973-01-01

A recursive procedure is derived for decoding of rate R=1/n binary convolutional codes which minimizes the probability of the individual decoding decisions for each information bit subject to the constraint that the decoding delay be limited to Delta branches. This new decoding algorithm is similar to, but somewhat more complex than, the Viterbi decoding algorithm. A real-time, i.e. fixed decoding delay, version of the Viterbi algorithm is also developed and used for comparison to the new algorithm on simulated channels. It is shown that the new algorithm offers advantages over Viterbi decoding in soft-decision applications such as in the inner coding system for concatenated coding.

A Bayesian Uncertainty Framework for Conceptual Snowmelt and Hydrologic Models Applied to the Tenderfoot Creek Experimental Forest

NASA Astrophysics Data System (ADS)

Smith, T.; Marshall, L.

2007-12-01

In many mountainous regions, the single most important parameter in forecasting the controls on regional water resources is snowpack (Williams et al., 1999). In an effort to bridge the gap between theoretical understanding and functional modeling of snow-driven watersheds, a flexible hydrologic modeling framework is being developed. The aim is to create a suite of models that move from parsimonious structures, concentrated on aggregated watershed response, to those focused on representing finer scale processes and distributed response. This framework will operate as a tool to investigate the link between hydrologic model predictive performance, uncertainty, model complexity, and observable hydrologic processes. Bayesian methods, and particularly Markov chain Monte Carlo (MCMC) techniques, are extremely useful in uncertainty assessment and parameter estimation of hydrologic models. However, these methods have some difficulties in implementation. In a traditional Bayesian setting, it can be difficult to reconcile multiple data types, particularly those offering different spatial and temporal coverage, depending on the model type. These difficulties are also exacerbated by sensitivity of MCMC algorithms to model initialization and complex parameter interdependencies. As a way of circumnavigating some of the computational complications, adaptive MCMC algorithms have been developed to take advantage of the information gained from each successive iteration. Two adaptive algorithms are compared is this study, the Adaptive Metropolis (AM) algorithm, developed by Haario et al (2001), and the Delayed Rejection Adaptive Metropolis (DRAM) algorithm, developed by Haario et al (2006). While neither algorithm is truly Markovian, it has been proven that each satisfies the desired ergodicity and stationarity properties of Markov chains. Both algorithms were implemented as the uncertainty and parameter estimation framework for a conceptual rainfall-runoff model based on the Probability Distributed Model (PDM), developed by Moore (1985). We implement the modeling framework in Stringer Creek watershed in the Tenderfoot Creek Experimental Forest (TCEF), Montana. The snowmelt-driven watershed offers that additional challenge of modeling snow accumulation and melt and current efforts are aimed at developing a temperature- and radiation-index snowmelt model. Auxiliary data available from within TCEF's watersheds are used to support in the understanding of information value as it relates to predictive performance. Because the model is based on lumped parameters, auxiliary data are hard to incorporate directly. However, these additional data offer benefits through the ability to inform prior distributions of the lumped, model parameters. By incorporating data offering different information into the uncertainty assessment process, a cross-validation technique is engaged to better ensure that modeled results reflect real process complexity.

SMV⊥: Simplex of maximal volume based upon the Gram-Schmidt process

NASA Astrophysics Data System (ADS)

Salazar-Vazquez, Jairo; Mendez-Vazquez, Andres

2015-10-01

In recent years, different algorithms for Hyperspectral Image (HI) analysis have been introduced. The high spectral resolution of these images allows to develop different algorithms for target detection, material mapping, and material identification for applications in Agriculture, Security and Defense, Industry, etc. Therefore, from the computer science's point of view, there is fertile field of research for improving and developing algorithms in HI analysis. In some applications, the spectral pixels of a HI can be classified using laboratory spectral signatures. Nevertheless, for many others, there is no enough available prior information or spectral signatures, making any analysis a difficult task. One of the most popular algorithms for the HI analysis is the N-FINDR because it is easy to understand and provides a way to unmix the original HI in the respective material compositions. The N-FINDR is computationally expensive and its performance depends on a random initialization process. This paper proposes a novel idea to reduce the complexity of the N-FINDR by implementing a bottom-up approach based in an observation from linear algebra and the use of the Gram-Schmidt process. Therefore, the Simplex of Maximal Volume Perpendicular (SMV⊥) algorithm is proposed for fast endmember extraction in hyperspectral imagery. This novel algorithm has complexity O(n) with respect to the number of pixels. In addition, the evidence shows that SMV⊥ calculates a bigger volume, and has lower computational time complexity than other poular algorithms on synthetic and real scenarios.

A new complexity measure for time series analysis and classification

NASA Astrophysics Data System (ADS)

Nagaraj, Nithin; Balasubramanian, Karthi; Dey, Sutirth

2013-07-01

Complexity measures are used in a number of applications including extraction of information from data such as ecological time series, detection of non-random structure in biomedical signals, testing of random number generators, language recognition and authorship attribution etc. Different complexity measures proposed in the literature like Shannon entropy, Relative entropy, Lempel-Ziv, Kolmogrov and Algorithmic complexity are mostly ineffective in analyzing short sequences that are further corrupted with noise. To address this problem, we propose a new complexity measure ETC and define it as the "Effort To Compress" the input sequence by a lossless compression algorithm. Here, we employ the lossless compression algorithm known as Non-Sequential Recursive Pair Substitution (NSRPS) and define ETC as the number of iterations needed for NSRPS to transform the input sequence to a constant sequence. We demonstrate the utility of ETC in two applications. ETC is shown to have better correlation with Lyapunov exponent than Shannon entropy even with relatively short and noisy time series. The measure also has a greater rate of success in automatic identification and classification of short noisy sequences, compared to entropy and a popular measure based on Lempel-Ziv compression (implemented by Gzip).

Towards human-computer synergetic analysis of large-scale biological data.

PubMed

Singh, Rahul; Yang, Hui; Dalziel, Ben; Asarnow, Daniel; Murad, William; Foote, David; Gormley, Matthew; Stillman, Jonathan; Fisher, Susan

2013-01-01

Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself. In context of the aforementioned background, the results presented in this paper describe advancements along two methodological directions. First, given the context of biological data, we utilize and extend a design approach called experiential computing from multimedia information system design. This paradigm combines information visualization and human-computer interaction with algorithms for exploratory analysis of large-scale and complex data. In the proposed approach, emphasis is laid on: (1) allowing users to directly visualize, interact, experience, and explore the data through interoperable visualization-based and algorithmic components, (2) supporting unified query and presentation spaces to facilitate experimentation and exploration, (3) providing external contextual information by assimilating relevant supplementary data, and (4) encouraging user-directed information visualization, data exploration, and hypotheses formulation. Second, to illustrate the proposed design paradigm and measure its efficacy, we describe two prototype web applications. The first, called XMAS (Experiential Microarray Analysis System) is designed for analysis of time-series transcriptional data. The second system, called PSPACE (Protein Space Explorer) is designed for holistic analysis of structural and structure-function relationships using interactive low-dimensional maps of the protein structure space. Both these systems promote and facilitate human-computer synergy, where cognitive elements such as domain knowledge, contextual reasoning, and purpose-driven exploration, are integrated with a host of powerful algorithmic operations that support large-scale data analysis, multifaceted data visualization, and multi-source information integration. The proposed design philosophy, combines visualization, algorithmic components and cognitive expertise into a seamless processing-analysis-exploration framework that facilitates sense-making, exploration, and discovery. Using XMAS, we present case studies that analyze transcriptional data from two highly complex domains: gene expression in the placenta during human pregnancy and reaction of marine organisms to heat stress. With PSPACE, we demonstrate how complex structure-function relationships can be explored. These results demonstrate the novelty, advantages, and distinctions of the proposed paradigm. Furthermore, the results also highlight how domain insights can be combined with algorithms to discover meaningful knowledge and formulate evidence-based hypotheses during the data analysis process. Finally, user studies against comparable systems indicate that both XMAS and PSPACE deliver results with better interpretability while placing lower cognitive loads on the users. XMAS is available at: http://tintin.sfsu.edu:8080/xmas. PSPACE is available at: http://pspace.info/.

Towards human-computer synergetic analysis of large-scale biological data

PubMed Central

2013-01-01

Background Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself. Results In context of the aforementioned background, the results presented in this paper describe advancements along two methodological directions. First, given the context of biological data, we utilize and extend a design approach called experiential computing from multimedia information system design. This paradigm combines information visualization and human-computer interaction with algorithms for exploratory analysis of large-scale and complex data. In the proposed approach, emphasis is laid on: (1) allowing users to directly visualize, interact, experience, and explore the data through interoperable visualization-based and algorithmic components, (2) supporting unified query and presentation spaces to facilitate experimentation and exploration, (3) providing external contextual information by assimilating relevant supplementary data, and (4) encouraging user-directed information visualization, data exploration, and hypotheses formulation. Second, to illustrate the proposed design paradigm and measure its efficacy, we describe two prototype web applications. The first, called XMAS (Experiential Microarray Analysis System) is designed for analysis of time-series transcriptional data. The second system, called PSPACE (Protein Space Explorer) is designed for holistic analysis of structural and structure-function relationships using interactive low-dimensional maps of the protein structure space. Both these systems promote and facilitate human-computer synergy, where cognitive elements such as domain knowledge, contextual reasoning, and purpose-driven exploration, are integrated with a host of powerful algorithmic operations that support large-scale data analysis, multifaceted data visualization, and multi-source information integration. Conclusions The proposed design philosophy, combines visualization, algorithmic components and cognitive expertise into a seamless processing-analysis-exploration framework that facilitates sense-making, exploration, and discovery. Using XMAS, we present case studies that analyze transcriptional data from two highly complex domains: gene expression in the placenta during human pregnancy and reaction of marine organisms to heat stress. With PSPACE, we demonstrate how complex structure-function relationships can be explored. These results demonstrate the novelty, advantages, and distinctions of the proposed paradigm. Furthermore, the results also highlight how domain insights can be combined with algorithms to discover meaningful knowledge and formulate evidence-based hypotheses during the data analysis process. Finally, user studies against comparable systems indicate that both XMAS and PSPACE deliver results with better interpretability while placing lower cognitive loads on the users. XMAS is available at: http://tintin.sfsu.edu:8080/xmas. PSPACE is available at: http://pspace.info/. PMID:24267485

Algorithms, complexity, and the sciences

PubMed Central

Papadimitriou, Christos

2014-01-01

Algorithms, perhaps together with Moore’s law, compose the engine of the information technology revolution, whereas complexity—the antithesis of algorithms—is one of the deepest realms of mathematical investigation. After introducing the basic concepts of algorithms and complexity, and the fundamental complexity classes P (polynomial time) and NP (nondeterministic polynomial time, or search problems), we discuss briefly the P vs. NP problem. We then focus on certain classes between P and NP which capture important phenomena in the social and life sciences, namely the Nash equlibrium and other equilibria in economics and game theory, and certain processes in population genetics and evolution. Finally, an algorithm known as multiplicative weights update (MWU) provides an algorithmic interpretation of the evolution of allele frequencies in a population under sex and weak selection. All three of these equivalences are rife with domain-specific implications: The concept of Nash equilibrium may be less universal—and therefore less compelling—than has been presumed; selection on gene interactions may entail the maintenance of genetic variation for longer periods than selection on single alleles predicts; whereas MWU can be shown to maximize, for each gene, a convex combination of the gene’s cumulative fitness in the population and the entropy of the allele distribution, an insight that may be pertinent to the maintenance of variation in evolution. PMID:25349382

An efficient grid layout algorithm for biological networks utilizing various biological attributes

PubMed Central

Kojima, Kaname; Nagasaki, Masao; Jeong, Euna; Kato, Mitsuru; Miyano, Satoru

2007-01-01

Background Clearly visualized biopathways provide a great help in understanding biological systems. However, manual drawing of large-scale biopathways is time consuming. We proposed a grid layout algorithm that can handle gene-regulatory networks and signal transduction pathways by considering edge-edge crossing, node-edge crossing, distance measure between nodes, and subcellular localization information from Gene Ontology. Consequently, the layout algorithm succeeded in drastically reducing these crossings in the apoptosis model. However, for larger-scale networks, we encountered three problems: (i) the initial layout is often very far from any local optimum because nodes are initially placed at random, (ii) from a biological viewpoint, human layouts still exceed automatic layouts in understanding because except subcellular localization, it does not fully utilize biological information of pathways, and (iii) it employs a local search strategy in which the neighborhood is obtained by moving one node at each step, and automatic layouts suggest that simultaneous movements of multiple nodes are necessary for better layouts, while such extension may face worsening the time complexity. Results We propose a new grid layout algorithm. To address problem (i), we devised a new force-directed algorithm whose output is suitable as the initial layout. For (ii), we considered that an appropriate alignment of nodes having the same biological attribute is one of the most important factors of the comprehension, and we defined a new score function that gives an advantage to such configurations. For solving problem (iii), we developed a search strategy that considers swapping nodes as well as moving a node, while keeping the order of the time complexity. Though a naïve implementation increases by one order, the time complexity, we solved this difficulty by devising a method that caches differences between scores of a layout and its possible updates. Conclusion Layouts of the new grid layout algorithm are compared with that of the previous algorithm and human layout in an endothelial cell model, three times as large as the apoptosis model. The total cost of the result from the new grid layout algorithm is similar to that of the human layout. In addition, its convergence time is drastically reduced (40% reduction). PMID:17338825

Detecting communities in large networks

NASA Astrophysics Data System (ADS)

Capocci, A.; Servedio, V. D. P.; Caldarelli, G.; Colaiori, F.

2005-07-01

We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns.

Could a neuroscientist understand a microprocessor?

DOE PAGES

Jonas, Eric; Kording, Konrad Paul; Diedrichsen, Jorn

2017-01-12

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods frommore » neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Furthermore, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.« less

Could a Neuroscientist Understand a Microprocessor?

PubMed Central

Kording, Konrad Paul

2017-01-01

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods. PMID:28081141

Could a Neuroscientist Understand a Microprocessor?

PubMed

Jonas, Eric; Kording, Konrad Paul

2017-01-01

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.

Could a neuroscientist understand a microprocessor?

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

Jonas, Eric; Kording, Konrad Paul; Diedrichsen, Jorn

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods frommore » neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Furthermore, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.« less

Design and implementation of intelligent electronic warfare decision making algorithm

NASA Astrophysics Data System (ADS)

Peng, Hsin-Hsien; Chen, Chang-Kuo; Hsueh, Chi-Shun

2017-05-01

Electromagnetic signals and the requirements of timely response have been a rapid growth in modern electronic warfare. Although jammers are limited resources, it is possible to achieve the best electronic warfare efficiency by tactical decisions. This paper proposes the intelligent electronic warfare decision support system. In this work, we develop a novel hybrid algorithm, Digital Pheromone Particle Swarm Optimization, based on Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Shuffled Frog Leaping Algorithm (SFLA). We use PSO to solve the problem and combine the concept of pheromones in ACO to accumulate more useful information in spatial solving process and speed up finding the optimal solution. The proposed algorithm finds the optimal solution in reasonable computation time by using the method of matrix conversion in SFLA. The results indicated that jammer allocation was more effective. The system based on the hybrid algorithm provides electronic warfare commanders with critical information to assist commanders in effectively managing the complex electromagnetic battlefield.

Adaptive selection and validation of models of complex systems in the presence of uncertainty

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

Farrell-Maupin, Kathryn; Oden, J. T.

This study describes versions of OPAL, the Occam-Plausibility Algorithm in which the use of Bayesian model plausibilities is replaced with information theoretic methods, such as the Akaike Information Criterion and the Bayes Information Criterion. Applications to complex systems of coarse-grained molecular models approximating atomistic models of polyethylene materials are described. All of these model selection methods take into account uncertainties in the model, the observational data, the model parameters, and the predicted quantities of interest. A comparison of the models chosen by Bayesian model selection criteria and those chosen by the information-theoretic criteria is given.

Adaptive selection and validation of models of complex systems in the presence of uncertainty

DOE PAGES

Farrell-Maupin, Kathryn; Oden, J. T.

2017-08-01

This study describes versions of OPAL, the Occam-Plausibility Algorithm in which the use of Bayesian model plausibilities is replaced with information theoretic methods, such as the Akaike Information Criterion and the Bayes Information Criterion. Applications to complex systems of coarse-grained molecular models approximating atomistic models of polyethylene materials are described. All of these model selection methods take into account uncertainties in the model, the observational data, the model parameters, and the predicted quantities of interest. A comparison of the models chosen by Bayesian model selection criteria and those chosen by the information-theoretic criteria is given.

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach.

PubMed

Abduallah, Yasser; Turki, Turki; Byron, Kevin; Du, Zongxuan; Cervantes-Cervantes, Miguel; Wang, Jason T L

2017-01-01

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

Information mining in weighted complex networks with nonlinear rating projection

NASA Astrophysics Data System (ADS)

Liao, Hao; Zeng, An; Zhou, Mingyang; Mao, Rui; Wang, Bing-Hong

2017-10-01

Weighted rating networks are commonly used by e-commerce providers nowadays. In order to generate an objective ranking of online items' quality according to users' ratings, many sophisticated algorithms have been proposed in the complex networks domain. In this paper, instead of proposing new algorithms we focus on a more fundamental problem: the nonlinear rating projection. The basic idea is that even though the rating values given by users are linearly separated, the real preference of users to items between the different given values is nonlinear. We thus design an approach to project the original ratings of users to more representative values. This approach can be regarded as a data pretreatment method. Simulation in both artificial and real networks shows that the performance of the ranking algorithms can be improved when the projected ratings are used.

An Effective Hybrid Evolutionary Algorithm for Solving the Numerical Optimization Problems

NASA Astrophysics Data System (ADS)

Qian, Xiaohong; Wang, Xumei; Su, Yonghong; He, Liu

2018-04-01

There are many different algorithms for solving complex optimization problems. Each algorithm has been applied successfully in solving some optimization problems, but not efficiently in other problems. In this paper the Cauchy mutation and the multi-parent hybrid operator are combined to propose a hybrid evolutionary algorithm based on the communication (Mixed Evolutionary Algorithm based on Communication), hereinafter referred to as CMEA. The basic idea of the CMEA algorithm is that the initial population is divided into two subpopulations. Cauchy mutation operators and multiple paternal crossover operators are used to perform two subpopulations parallelly to evolve recursively until the downtime conditions are met. While subpopulation is reorganized, the individual is exchanged together with information. The algorithm flow is given and the performance of the algorithm is compared using a number of standard test functions. Simulation results have shown that this algorithm converges significantly faster than FEP (Fast Evolutionary Programming) algorithm, has good performance in global convergence and stability and is superior to other compared algorithms.

Generalizing Atoms in Constraint Logic

NASA Technical Reports Server (NTRS)

Page, C. David, Jr.; Frisch, Alan M.

1991-01-01

This paper studies the generalization of atomic formulas, or atoms, that are augmented with constraints on or among their terms. The atoms may also be viewed as definite clauses whose antecedents express the constraints. Atoms are generalized relative to a body of background information about the constraints. This paper first examines generalization of atoms with only monadic constraints. The paper develops an algorithm for the generalization task and discusses algorithm complexity. It then extends the algorithm to apply to atoms with constraints of arbitrary arity. The paper also presents semantic properties of the generalizations computed by the algorithms, making the algorithms applicable to such problems as abduction, induction, and knowledge base verification. The paper emphasizes the application to induction and presents a pac-learning result for constrained atoms.

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.

Electrodermal complexity during the Stroop colour word test.

PubMed

Svetlak, Miroslav; Bob, Petr; Cernik, Michal; Kukleta, Miloslav

2010-01-15

Several recent studies suggest that quantitative description of signal complexity using algorithms of nonlinear analysis could uncover new information about the autonomic system that is not reflected using common methods applied to measures of autonomic activity. With this aim we have performed complexity analysis of electrodermal activity (EDA) assessed in 106 healthy university students during rest conditions and non-conflicting and conflicting Stroop task. Complexity analysis applied to EDA was performed using Skinner's algorithm for pointwise correlation dimension (PD2). Results have shown that EDA responses during the Stroop Colour Word test are related to significantly increased or decreased complexity. Particularly significant result is that PD2 has a unique ability to predict to an extent the change in EDA response to stress i.e. that subjects with low initial PD2 tended to respond to experimental stress by its increase and subjects with high initial PD2 values tended to respond by its decrease. This response was not found in EDA measures where increase of the EDA presented predominant response to experimental stress in majority of the subjects. These findings suggest that PD2 is more sensitive to subtle aspects of functionally and spatially distributed modulatory influences of various parts of the brain that are involved in the EDA modulation and provides novel information in comparison to traditional methods.

Parametric boundary reconstruction algorithm for industrial CT metrology application.

PubMed

Yin, Zhye; Khare, Kedar; De Man, Bruno

2009-01-01

High-energy X-ray computed tomography (CT) systems have been recently used to produce high-resolution images in various nondestructive testing and evaluation (NDT/NDE) applications. The accuracy of the dimensional information extracted from CT images is rapidly approaching the accuracy achieved with a coordinate measuring machine (CMM), the conventional approach to acquire the metrology information directly. On the other hand, CT systems generate the sinogram which is transformed mathematically to the pixel-based images. The dimensional information of the scanned object is extracted later by performing edge detection on reconstructed CT images. The dimensional accuracy of this approach is limited by the grid size of the pixel-based representation of CT images since the edge detection is performed on the pixel grid. Moreover, reconstructed CT images usually display various artifacts due to the underlying physical process and resulting object boundaries from the edge detection fail to represent the true boundaries of the scanned object. In this paper, a novel algorithm to reconstruct the boundaries of an object with uniform material composition and uniform density is presented. There are three major benefits in the proposed approach. First, since the boundary parameters are reconstructed instead of image pixels, the complexity of the reconstruction algorithm is significantly reduced. The iterative approach, which can be computationally intensive, will be practical with the parametric boundary reconstruction. Second, the object of interest in metrology can be represented more directly and accurately by the boundary parameters instead of the image pixels. By eliminating the extra edge detection step, the overall dimensional accuracy and process time can be improved. Third, since the parametric reconstruction approach shares the boundary representation with other conventional metrology modalities such as CMM, boundary information from other modalities can be directly incorporated as prior knowledge to improve the convergence of an iterative approach. In this paper, the feasibility of parametric boundary reconstruction algorithm is demonstrated with both simple and complex simulated objects. Finally, the proposed algorithm is applied to the experimental industrial CT system data.

Imperishable Networks: Complexity Theory and Communication Networking-Bridging the Gap Between Algorithmic Information Theory and Communication Networking

DTIC Science & Technology

2003-04-01

gener- ally considered to be passive data . Instead the genetic material should be capable of being algorith - mic information, that is, program code or...information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and...maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other

An advanced algorithm for fetal heart rate estimation from non-invasive low electrode density recordings.

PubMed

Dessì, Alessia; Pani, Danilo; Raffo, Luigi

2014-08-01

Non-invasive fetal electrocardiography is still an open research issue. The recent publication of an annotated dataset on Physionet providing four-channel non-invasive abdominal ECG traces promoted an international challenge on the topic. Starting from that dataset, an algorithm for the identification of the fetal QRS complexes from a reduced number of electrodes and without any a priori information about the electrode positioning has been developed, entering into the top ten best-performing open-source algorithms presented at the challenge.In this paper, an improved version of that algorithm is presented and evaluated exploiting the same challenge metrics. It is mainly based on the subtraction of the maternal QRS complexes in every lead, obtained by synchronized averaging of morphologically similar complexes, the filtering of the maternal P and T waves and the enhancement of the fetal QRS through independent component analysis (ICA) applied on the processed signals before a final fetal QRS detection stage. The RR time series of both the mother and the fetus are analyzed to enhance pseudoperiodicity with the aim of correcting wrong annotations. The algorithm has been designed and extensively evaluated on the open dataset A (N = 75), and finally evaluated on datasets B (N = 100) and C (N = 272) to have the mean scores over data not used during the algorithm development. Compared to the results achieved by the previous version of the algorithm, the current version would mark the 5th and 4th position in the final ranking related to the events 1 and 2, reserved to the open-source challenge entries, taking into account both official and unofficial entrants. On dataset A, the algorithm achieves 0.982 median sensitivity and 0.976 median positive predictivity.

Iterative Neighbour-Information Gathering for Ranking Nodes in Complex Networks

NASA Astrophysics Data System (ADS)

Xu, Shuang; Wang, Pei; Lü, Jinhu

2017-01-01

Designing node influence ranking algorithms can provide insights into network dynamics, functions and structures. Increasingly evidences reveal that node’s spreading ability largely depends on its neighbours. We introduce an iterative neighbourinformation gathering (Ing) process with three parameters, including a transformation matrix, a priori information and an iteration time. The Ing process iteratively combines priori information from neighbours via the transformation matrix, and iteratively assigns an Ing score to each node to evaluate its influence. The algorithm appropriates for any types of networks, and includes some traditional centralities as special cases, such as degree, semi-local, LeaderRank. The Ing process converges in strongly connected networks with speed relying on the first two largest eigenvalues of the transformation matrix. Interestingly, the eigenvector centrality corresponds to a limit case of the algorithm. By comparing with eight renowned centralities, simulations of susceptible-infected-removed (SIR) model on real-world networks reveal that the Ing can offer more exact rankings, even without a priori information. We also observe that an optimal iteration time is always in existence to realize best characterizing of node influence. The proposed algorithms bridge the gaps among some existing measures, and may have potential applications in infectious disease control, designing of optimal information spreading strategies.

Information spread of emergency events: path searching on social networks.

PubMed

Dai, Weihui; Hu, Hongzhi; Wu, Tunan; Dai, Yonghui

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.

Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks

NASA Technical Reports Server (NTRS)

Fathpour, Nanaz; Hadaegh, Fred Y.; Mesbahi, Mehran; Rahmani, Amirreza

2011-01-01

Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation-flying estimation algorithms can only be supported via highly centralized, all-to-all, static relative sensing. New algorithms are proposed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms rely on a local information exchange network, relaxing the assumptions on existing algorithms. Distributed space systems rely on a signal transmission network among multiple spacecraft for their operation. Control and coordination among multiple spacecraft in a formation is facilitated via a network of relative sensing and interspacecraft communications. Guidance, navigation, and control rely on the sensing network. This network becomes more complex the more spacecraft are added, or as mission requirements become more complex. The observability of a formation state was observed by a set of local observations from a particular node in the formation. Formation observability can be parameterized in terms of the matrices appearing in the formation dynamics and observation matrices. An agreement protocol was used as a mechanism for observing formation states from local measurements. An agreement protocol is essentially an unforced dynamic system whose trajectory is governed by the interconnection geometry and initial condition of each node, with a goal of reaching a common value of interest. The observability of the interconnected system depends on the geometry of the network, as well as the position of the observer relative to the topology. For the first time, critical GN&C (guidance, navigation, and control estimation) subsystems are synthesized by bringing the contribution of the spacecraft information-exchange network to the forefront of algorithmic analysis and design. The result is a formation estimation algorithm that is modular and robust to variations in the topology and link properties of the underlying formation network.

Optimization of multicast optical networks with genetic algorithm

NASA Astrophysics Data System (ADS)

Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng

2007-11-01

In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.

Design and demonstration of automated data analysis algorithms for ultrasonic inspection of complex composite panels with bonds

NASA Astrophysics Data System (ADS)

Aldrin, John C.; Forsyth, David S.; Welter, John T.

2016-02-01

To address the data review burden and improve the reliability of the ultrasonic inspection of large composite structures, automated data analysis (ADA) algorithms have been developed to make calls on indications that satisfy the detection criteria and minimize false calls. The original design followed standard procedures for analyzing signals for time-of-flight indications and backwall amplitude dropout. However, certain complex panels with varying shape, ply drops and the presence of bonds can complicate this interpretation process. In this paper, enhancements to the automated data analysis algorithms are introduced to address these challenges. To estimate the thickness of the part and presence of bonds without prior information, an algorithm tracks potential backwall or bond-line signals, and evaluates a combination of spatial, amplitude, and time-of-flight metrics to identify bonded sections. Once part boundaries, thickness transitions and bonded regions are identified, feature extraction algorithms are applied to multiple sets of through-thickness and backwall C-scan images, for evaluation of both first layer through thickness and layers under bonds. ADA processing results are presented for a variety of complex test specimens with inserted materials and other test discontinuities. Lastly, enhancements to the ADA software interface are presented, which improve the software usability for final data review by the inspectors and support the certification process.

Rapid, topology-based particle tracking for high-resolution measurements of large complex 3D motion fields.

PubMed

Patel, Mohak; Leggett, Susan E; Landauer, Alexander K; Wong, Ian Y; Franck, Christian

2018-04-03

Spatiotemporal tracking of tracer particles or objects of interest can reveal localized behaviors in biological and physical systems. However, existing tracking algorithms are most effective for relatively low numbers of particles that undergo displacements smaller than their typical interparticle separation distance. Here, we demonstrate a single particle tracking algorithm to reconstruct large complex motion fields with large particle numbers, orders of magnitude larger than previously tractably resolvable, thus opening the door for attaining very high Nyquist spatial frequency motion recovery in the images. Our key innovations are feature vectors that encode nearest neighbor positions, a rigorous outlier removal scheme, and an iterative deformation warping scheme. We test this technique for its accuracy and computational efficacy using synthetically and experimentally generated 3D particle images, including non-affine deformation fields in soft materials, complex fluid flows, and cell-generated deformations. We augment this algorithm with additional particle information (e.g., color, size, or shape) to further enhance tracking accuracy for high gradient and large displacement fields. These applications demonstrate that this versatile technique can rapidly track unprecedented numbers of particles to resolve large and complex motion fields in 2D and 3D images, particularly when spatial correlations exist.

On the definition of the concepts thinking, consciousness, and conscience.

PubMed Central

Monin, A S

1992-01-01

A complex system (CS) is defined as a set of elements, with connections between them, singled out of the environment, capable of getting information from the environment, capable of making decisions (i.e., of choosing between alternatives), and having purposefulness (i.e., an urge towards preferable states or other goals). Thinking is a process that takes place (or which can take place) in some of the CS and consists of (i) receiving information from the environment (and from itself), (ii) memorizing the information, (iii) the subconscious, and (iv) consciousness. Life is a process that takes place in some CS and consists of functions i and ii, as well as (v) reproduction with passing of hereditary information to progeny, and (vi) oriented energy and matter exchange with the environment sufficient for the maintenance of all life processes. Memory is a complex of processes of placing information in memory banks, keeping it there, and producing it according to prescriptions available in the system or to inquiries arising in it. Consciousness is a process of realization by the thinking CS of some set of algorithms consisting of the comparison of its knowledge, intentions, decisions, and actions with reality--i.e., with accumulated and continuously received internal and external information. Conscience is a realization of an algorithm of good and evil pattern recognition. PMID:1631060

Multi scales based sparse matrix spectral clustering image segmentation

NASA Astrophysics Data System (ADS)

Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

2018-04-01

In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.

On The Computational Capabilities of Physical Systems. Part 2; Relationship With Conventional Computer Science

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Koga, Dennis (Technical Monitor)

2000-01-01

In the first of this pair of papers, it was proven that there cannot be a physical computer to which one can properly pose any and all computational tasks concerning the physical universe. It was then further proven that no physical computer C can correctly carry out all computational tasks that can be posed to C. As a particular example, this result means that no physical computer that can, for any physical system external to that computer, take the specification of that external system's state as input and then correctly predict its future state before that future state actually occurs; one cannot build a physical computer that can be assured of correctly "processing information faster than the universe does". These results do not rely on systems that are infinite, and/or non-classical, and/or obey chaotic dynamics. They also hold even if one uses an infinitely fast, infinitely dense computer, with computational powers greater than that of a Turing Machine. This generality is a direct consequence of the fact that a novel definition of computation - "physical computation" - is needed to address the issues considered in these papers, which concern real physical computers. While this novel definition does not fit into the traditional Chomsky hierarchy, the mathematical structure and impossibility results associated with it have parallels in the mathematics of the Chomsky hierarchy. This second paper of the pair presents a preliminary exploration of some of this mathematical structure. Analogues of Chomskian results concerning universal Turing Machines and the Halting theorem are derived, as are results concerning the (im)possibility of certain kinds of error-correcting codes. In addition, an analogue of algorithmic information complexity, "prediction complexity", is elaborated. A task-independent bound is derived on how much the prediction complexity of a computational task can differ for two different reference universal physical computers used to solve that task, a bound similar to the "encoding" bound governing how much the algorithm information complexity of a Turing machine calculation can differ for two reference universal Turing machines. Finally, it is proven that either the Hamiltonian of our universe proscribes a certain type of computation, or prediction complexity is unique (unlike algorithmic information complexity), in that there is one and only version of it that can be applicable throughout our universe.

The Creating an Optimal Warfarin Nomogram (CROWN) Study

PubMed Central

Perlstein, Todd S.; Goldhaber, Samuel Z.; Nelson, Kerrie; Joshi, Victoria; Morgan, T. Vance; Lesko, Lawrence J.; Lee, Joo-Yeon; Gobburu, Jogarao; Schoenfeld, David; Kucherlapati, Raju; Freeman, Mason W.; Creager, Mark A.

2014-01-01

A significant proportion of warfarin dose variability is explained by variation in the genotypes of the cytochrome P450 CYP2C9 and the vitamin K epoxide reductase complex, VKORC1, enzymes that influence warfarin metabolism and sensitivity, respectively. We sought to develop an optimal pharmacogenetic warfarin dosing algorithm that incorporated clinical and genetic information. We enroled patients initiating warfarin therapy. Genotyping was performed of the VKORC1, –1639G>A, the CYP2C9*2, 430C>T, and the CYP2C9*3, 1075C>A genotypes. The initial warfarin dosing algorithm (Algorithm A) was based upon established clinical practice and published warfarin pharmacogenetic information. Subsequent dosing algorithms (Algorithms B and Algorithm C) were derived from pharmacokinetic / pharmacodynamic (PK/PD) modelling of warfarin dose, international normalised ratio (INR), clinical and genetic factors from patients treated by the preceding algorithm(s). The primary outcome was the time in the therapeutic range, considered an INR of 1.8 to 3.2. A total of 344 subjects are included in the study analyses. The mean percentage time within the therapeutic range for each subject increased progressively from Algorithm A to Algorithm C from 58.9 (22.0), to 59.7 (23.0), to 65.8 (16.9) percent (p = 0.04). Improvement also occurred in most secondary endpoints, which included the per-patient percentage of INRs outside of the therapeutic range (p = 0.004), the time to the first therapeutic INR (p = 0.07), and the time to achieve stable therapeutic anticoagulation (p < 0.001). In conclusion, warfarin pharmacogenetic dosing can be optimised in real time utilising observed PK/PD information in an adaptive fashion. Clinical Trial Registration ClinicalTrials.gov (NCT00401414) PMID:22116191

Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling.

PubMed

Perdikaris, P; Raissi, M; Damianou, A; Lawrence, N D; Karniadakis, G E

2017-02-01

Multi-fidelity modelling enables accurate inference of quantities of interest by synergistically combining realizations of low-cost/low-fidelity models with a small set of high-fidelity observations. This is particularly effective when the low- and high-fidelity models exhibit strong correlations, and can lead to significant computational gains over approaches that solely rely on high-fidelity models. However, in many cases of practical interest, low-fidelity models can only be well correlated to their high-fidelity counterparts for a specific range of input parameters, and potentially return wrong trends and erroneous predictions if probed outside of their validity regime. Here we put forth a probabilistic framework based on Gaussian process regression and nonlinear autoregressive schemes that is capable of learning complex nonlinear and space-dependent cross-correlations between models of variable fidelity, and can effectively safeguard against low-fidelity models that provide wrong trends. This introduces a new class of multi-fidelity information fusion algorithms that provide a fundamental extension to the existing linear autoregressive methodologies, while still maintaining the same algorithmic complexity and overall computational cost. The performance of the proposed methods is tested in several benchmark problems involving both synthetic and real multi-fidelity datasets from computational fluid dynamics simulations.

Spectral gap optimization of order parameters for sampling complex molecular systems

PubMed Central

Tiwary, Pratyush; Berne, B. J.

2016-01-01

In modern-day simulations of many-body systems, much of the computational complexity is shifted to the identification of slowly changing molecular order parameters called collective variables (CVs) or reaction coordinates. A vast array of enhanced-sampling methods are based on the identification and biasing of these low-dimensional order parameters, whose fluctuations are important in driving rare events of interest. Here, we describe a new algorithm for finding optimal low-dimensional CVs for use in enhanced-sampling biasing methods like umbrella sampling, metadynamics, and related methods, when limited prior static and dynamic information is known about the system, and a much larger set of candidate CVs is specified. The algorithm involves estimating the best combination of these candidate CVs, as quantified by a maximum path entropy estimate of the spectral gap for dynamics viewed as a function of that CV. The algorithm is called spectral gap optimization of order parameters (SGOOP). Through multiple practical examples, we show how this postprocessing procedure can lead to optimization of CV and several orders of magnitude improvement in the convergence of the free energy calculated through metadynamics, essentially giving the ability to extract useful information even from unsuccessful metadynamics runs. PMID:26929365

A New Algorithm to Optimize Maximal Information Coefficient

PubMed Central

Luo, Feng; Yuan, Zheming

2016-01-01

The maximal information coefficient (MIC) captures dependences between paired variables, including both functional and non-functional relationships. In this paper, we develop a new method, ChiMIC, to calculate the MIC values. The ChiMIC algorithm uses the chi-square test to terminate grid optimization and then removes the restriction of maximal grid size limitation of original ApproxMaxMI algorithm. Computational experiments show that ChiMIC algorithm can maintain same MIC values for noiseless functional relationships, but gives much smaller MIC values for independent variables. For noise functional relationship, the ChiMIC algorithm can reach the optimal partition much faster. Furthermore, the MCN values based on MIC calculated by ChiMIC can capture the complexity of functional relationships in a better way, and the statistical powers of MIC calculated by ChiMIC are higher than those calculated by ApproxMaxMI. Moreover, the computational costs of ChiMIC are much less than those of ApproxMaxMI. We apply the MIC values tofeature selection and obtain better classification accuracy using features selected by the MIC values from ChiMIC. PMID:27333001

A stereo remote sensing feature selection method based on artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

2014-05-01

To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

Target-type probability combining algorithms for multisensor tracking

NASA Astrophysics Data System (ADS)

Wigren, Torbjorn

2001-08-01

Algorithms for the handing of target type information in an operational multi-sensor tracking system are presented. The paper discusses recursive target type estimation, computation of crosses from passive data (strobe track triangulation), as well as the computation of the quality of the crosses for deghosting purposes. The focus is on Bayesian algorithms that operate in the discrete target type probability space, and on the approximations introduced for computational complexity reduction. The centralized algorithms are able to fuse discrete data from a variety of sensors and information sources, including IFF equipment, ESM's, IRST's as well as flight envelopes estimated from track data. All algorithms are asynchronous and can be tuned to handle clutter, erroneous associations as well as missed and erroneous detections. A key to obtain this ability is the inclusion of data forgetting by a procedure for propagation of target type probability states between measurement time instances. Other important properties of the algorithms are their abilities to handle ambiguous data and scenarios. The above aspects are illustrated in a simulations study. The simulation setup includes 46 air targets of 6 different types that are tracked by 5 airborne sensor platforms using ESM's and IRST's as data sources.

Speckle-reduction algorithm for ultrasound images in complex wavelet domain using genetic algorithm-based mixture model.

PubMed

Uddin, Muhammad Shahin; Tahtali, Murat; Lambert, Andrew J; Pickering, Mark R; Marchese, Margaret; Stuart, Iain

2016-05-20

Compared with other medical-imaging modalities, ultrasound (US) imaging is a valuable way to examine the body's internal organs, and two-dimensional (2D) imaging is currently the most common technique used in clinical diagnoses. Conventional 2D US imaging systems are highly flexible cost-effective imaging tools that permit operators to observe and record images of a large variety of thin anatomical sections in real time. Recently, 3D US imaging has also been gaining popularity due to its considerable advantages over 2D US imaging. It reduces dependency on the operator and provides better qualitative and quantitative information for an effective diagnosis. Furthermore, it provides a 3D view, which allows the observation of volume information. The major shortcoming of any type of US imaging is the presence of speckle noise. Hence, speckle reduction is vital in providing a better clinical diagnosis. The key objective of any speckle-reduction algorithm is to attain a speckle-free image while preserving the important anatomical features. In this paper we introduce a nonlinear multi-scale complex wavelet-diffusion based algorithm for speckle reduction and sharp-edge preservation of 2D and 3D US images. In the proposed method we use a Rayleigh and Maxwell-mixture model for 2D and 3D US images, respectively, where a genetic algorithm is used in combination with an expectation maximization method to estimate mixture parameters. Experimental results using both 2D and 3D synthetic, physical phantom, and clinical data demonstrate that our proposed algorithm significantly reduces speckle noise while preserving sharp edges without discernible distortions. The proposed approach performs better than the state-of-the-art approaches in both qualitative and quantitative measures.

Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm.

PubMed

Wang, Xingmei; Liu, Shu; Liu, Zhipeng

2017-01-01

This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the method utilizing a novel filtering degree parameter. Then, a quantum-inspired shuffled frog leaping algorithm based on new search mechanism (QSFLA-NSM) is proposed to precisely and quickly detect sonar images. Each frog individual is directly encoded by real numbers, which can greatly simplify the evolution process of the quantum-inspired shuffled frog leaping algorithm (QSFLA). Meanwhile, a fitness function combining intra-class difference with inter-class difference is adopted to evaluate frog positions more accurately. On this basis, recurring to an analysis of the quantum-behaved particle swarm optimization (QPSO) and the shuffled frog leaping algorithm (SFLA), a new search mechanism is developed to improve the searching ability and detection accuracy. At the same time, the time complexity is further reduced. Finally, the results of comparative experiments using the original sonar images, the UCI data sets and the benchmark functions demonstrate the effectiveness and adaptability of the proposed method.

Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm

PubMed Central

Liu, Zhipeng

2017-01-01

This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the method utilizing a novel filtering degree parameter. Then, a quantum-inspired shuffled frog leaping algorithm based on new search mechanism (QSFLA-NSM) is proposed to precisely and quickly detect sonar images. Each frog individual is directly encoded by real numbers, which can greatly simplify the evolution process of the quantum-inspired shuffled frog leaping algorithm (QSFLA). Meanwhile, a fitness function combining intra-class difference with inter-class difference is adopted to evaluate frog positions more accurately. On this basis, recurring to an analysis of the quantum-behaved particle swarm optimization (QPSO) and the shuffled frog leaping algorithm (SFLA), a new search mechanism is developed to improve the searching ability and detection accuracy. At the same time, the time complexity is further reduced. Finally, the results of comparative experiments using the original sonar images, the UCI data sets and the benchmark functions demonstrate the effectiveness and adaptability of the proposed method. PMID:28542266

How artificial intelligence tools can be used to assess individual patient risk in cardiovascular disease: problems with the current methods.

PubMed

Grossi, Enzo

2006-05-03

In recent years a number of algorithms for cardiovascular risk assessment has been proposed to the medical community. These algorithms consider a number of variables and express their results as the percentage risk of developing a major fatal or non-fatal cardiovascular event in the following 10 to 20 years The author has identified three major pitfalls of these algorithms, linked to the limitation of the classical statistical approach in dealing with this kind of non linear and complex information. The pitfalls are the inability to capture the disease complexity, the inability to capture process dynamics, and the wide confidence interval of individual risk assessment. Artificial Intelligence tools can provide potential advantage in trying to overcome these limitations. The theoretical background and some application examples related to artificial neural networks and fuzzy logic have been reviewed and discussed. The use of predictive algorithms to assess individual absolute risk of cardiovascular future events is currently hampered by methodological and mathematical flaws. The use of newer approaches, such as fuzzy logic and artificial neural networks, linked to artificial intelligence, seems to better address both the challenge of increasing complexity resulting from a correlation between predisposing factors, data on the occurrence of cardiovascular events, and the prediction of future events on an individual level.

Envelopment filter and K-means for the detection of QRS waveforms in electrocardiogram.

PubMed

Merino, Manuel; Gómez, Isabel María; Molina, Alberto J

2015-06-01

The electrocardiogram (ECG) is a well-established technique for determining the electrical activity of the heart and studying its diseases. One of the most common pieces of information that can be read from the ECG is the heart rate (HR) through the detection of its most prominent feature: the QRS complex. This paper describes an offline version and a real-time implementation of a new algorithm to determine QRS localization in the ECG signal based on its envelopment and K-means clustering algorithm. The envelopment is used to obtain a signal with only QRS complexes, deleting P, T, and U waves and baseline wander. Two moving average filters are applied to smooth data. The K-means algorithm classifies data into QRS and non-QRS. The technique is validated using 22 h of ECG data from five Physionet databases. These databases were arbitrarily selected to analyze different morphologies of QRS complexes: three stored data with cardiac pathologies, and two had data with normal heartbeats. The algorithm has a low computational load, with no decision thresholds. Furthermore, it does not require any additional parameter. Sensitivity, positive prediction and accuracy from results are over 99.7%. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Properties of the numerical algorithms for problems of quantum information technologies: Benefits of deep analysis

NASA Astrophysics Data System (ADS)

Chernyavskiy, Andrey; Khamitov, Kamil; Teplov, Alexey; Voevodin, Vadim; Voevodin, Vladimir

2016-10-01

In recent years, quantum information technologies (QIT) showed great development, although, the way of the implementation of QIT faces the serious difficulties, some of which are challenging computational tasks. This work is devoted to the deep and broad analysis of the parallel algorithmic properties of such tasks. As an example we take one- and two-qubit transformations of a many-qubit quantum state, which are the most critical kernels of many important QIT applications. The analysis of the algorithms uses the methodology of the AlgoWiki project (algowiki-project.org) and consists of two parts: theoretical and experimental. Theoretical part includes features like sequential and parallel complexity, macro structure, and visual information graph. Experimental part was made by using the petascale Lomonosov supercomputer (Moscow State University, Russia) and includes the analysis of locality and memory access, scalability and the set of more specific dynamic characteristics of realization. This approach allowed us to obtain bottlenecks and generate ideas of efficiency improvement.

Information Retrieval and Graph Analysis Approaches for Book Recommendation.

PubMed

Benkoussas, Chahinez; Bellot, Patrice

2015-01-01

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments.

Information Retrieval and Graph Analysis Approaches for Book Recommendation

PubMed Central

Benkoussas, Chahinez; Bellot, Patrice

2015-01-01

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments. PMID:26504899

A Firefly Algorithm-based Approach for Pseudo-Relevance Feedback: Application to Medical Database.

PubMed

Khennak, Ilyes; Drias, Habiba

2016-11-01

The difficulty of disambiguating the sense of the incomplete and imprecise keywords that are extensively used in the search queries has caused the failure of search systems to retrieve the desired information. One of the most powerful and promising method to overcome this shortcoming and improve the performance of search engines is Query Expansion, whereby the user's original query is augmented by new keywords that best characterize the user's information needs and produce more useful query. In this paper, a new Firefly Algorithm-based approach is proposed to enhance the retrieval effectiveness of query expansion while maintaining low computational complexity. In contrast to the existing literature, the proposed approach uses a Firefly Algorithm to find the best expanded query among a set of expanded query candidates. Moreover, this new approach allows the determination of the length of the expanded query empirically. Experimental results on MEDLINE, the on-line medical information database, show that our proposed approach is more effective and efficient compared to the state-of-the-art.

Information Spread of Emergency Events: Path Searching on Social Networks

PubMed Central

Hu, Hongzhi; Wu, Tunan

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning. PMID:24600323

A Strapdown Interial Navigation System/Beidou/Doppler Velocity Log Integrated Navigation Algorithm Based on a Cubature Kalman Filter

PubMed Central

Gao, Wei; Zhang, Ya; Wang, Jianguo

2014-01-01

The integrated navigation system with strapdown inertial navigation system (SINS), Beidou (BD) receiver and Doppler velocity log (DVL) can be used in marine applications owing to the fact that the redundant and complementary information from different sensors can markedly improve the system accuracy. However, the existence of multisensor asynchrony will introduce errors into the system. In order to deal with the problem, conventionally the sampling interval is subdivided, which increases the computational complexity. In this paper, an innovative integrated navigation algorithm based on a Cubature Kalman filter (CKF) is proposed correspondingly. A nonlinear system model and observation model for the SINS/BD/DVL integrated system are established to more accurately describe the system. By taking multi-sensor asynchronization into account, a new sampling principle is proposed to make the best use of each sensor's information. Further, CKF is introduced in this new algorithm to enable the improvement of the filtering accuracy. The performance of this new algorithm has been examined through numerical simulations. The results have shown that the positional error can be effectively reduced with the new integrated navigation algorithm. Compared with the traditional algorithm based on EKF, the accuracy of the SINS/BD/DVL integrated navigation system is improved, making the proposed nonlinear integrated navigation algorithm feasible and efficient. PMID:24434842

Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection.

PubMed

Meng, Jiahui; Zhao, Danfeng; Tian, Hai; Zhang, Liang

2018-01-15

In order to improve the performance of non-binary low-density parity check codes (LDPC) hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA) and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes' (VN) magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF) algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER) of 10 -5 over an additive white Gaussian noise (AWGN) channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced.

Denni Algorithm An Enhanced Of SMS (Scan, Move and Sort) Algorithm

NASA Astrophysics Data System (ADS)

Aprilsyah Lubis, Denni; Salim Sitompul, Opim; Marwan; Tulus; Andri Budiman, M.

2017-12-01

Sorting has been a profound area for the algorithmic researchers, and many resources are invested to suggest a more working sorting algorithm. For this purpose many existing sorting algorithms were observed in terms of the efficiency of the algorithmic complexity. Efficient sorting is important to optimize the use of other algorithms that require sorted lists to work correctly. Sorting has been considered as a fundamental problem in the study of algorithms that due to many reasons namely, the necessary to sort information is inherent in many applications, algorithms often use sorting as a key subroutine, in algorithm design there are many essential techniques represented in the body of sorting algorithms, and many engineering issues come to the fore when implementing sorting algorithms., Many algorithms are very well known for sorting the unordered lists, and one of the well-known algorithms that make the process of sorting to be more economical and efficient is SMS (Scan, Move and Sort) algorithm, an enhancement of Quicksort invented Rami Mansi in 2010. This paper presents a new sorting algorithm called Denni-algorithm. The Denni algorithm is considered as an enhancement on the SMS algorithm in average, and worst cases. The Denni algorithm is compared with the SMS algorithm and the results were promising.

Tag SNP selection via a genetic algorithm.

PubMed

Mahdevar, Ghasem; Zahiri, Javad; Sadeghi, Mehdi; Nowzari-Dalini, Abbas; Ahrabian, Hayedeh

2010-10-01

Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved to be an NP-hard problem, so heuristic methods may be useful. In this paper we present a heuristic method based on genetic algorithm to find reasonable solution within acceptable time. The algorithm was tested on a variety of simulated and experimental data. In comparison with the exact algorithm, based on brute force approach, results show that our method can obtain optimal solutions in almost all cases and runs much faster than exact algorithm when the number of SNP sites is large. Our software is available upon request to the corresponding author.

Hardware-software complex of informing passengers of forecasted route transport arrival at stop

NASA Astrophysics Data System (ADS)

Pogrebnoy, V. Yu; Pushkarev, M. I.; Fadeev, A. S.

2017-02-01

The paper presents the hardware-software complex of informing the passengers of the forecasted route transport arrival. A client-server architecture of the forecasting information system is represented and an electronic information board prototype is described. The scheme of information transfer and processing, starting with receiving navigating telemetric data from a transport vehicle and up to the time of passenger public transport arrival at the stop, as well as representation of the information on the electronic board is illustrated and described. Methods and algorithms of determination of the transport vehicle current location in the city route network are considered in detail. The description of the proposed forecasting model of transport vehicle arrival time at the stop is given. The obtained result is applied in Tomsk for forecasting and displaying the arrival time information at the stops.

Evaluating nodes importance in complex network based on PageRank algorithm

NASA Astrophysics Data System (ADS)

Li, Kai; He, Yongfeng

2018-04-01

To evaluate the important nodes in the complex network, and aim at the problems existing in the traditional PageRank algorithm, we propose a modified PageRank algorithm. The algorithm has convergence for the evaluation of the importance of the suspended nodes and the nodes with a directed loop network. The simulation example shows the effectiveness of the modified algorithm for the evaluation of the complexity of the complex network nodes.

Estimating Angle-of-Arrival and Time-of-Flight for Multipath Components Using WiFi Channel State Information.

PubMed

Ahmed, Afaz Uddin; Arablouei, Reza; Hoog, Frank de; Kusy, Branislav; Jurdak, Raja; Bergmann, Neil

2018-05-29

Channel state information (CSI) collected during WiFi packet transmissions can be used for localization of commodity WiFi devices in indoor environments with multipath propagation. To this end, the angle of arrival (AoA) and time of flight (ToF) for all dominant multipath components need to be estimated. A two-dimensional (2D) version of the multiple signal classification (MUSIC) algorithm has been shown to solve this problem using 2D grid search, which is computationally expensive and is therefore not suited for real-time localisation. In this paper, we propose using a modified matrix pencil (MMP) algorithm instead. Specifically, we show that the AoA and ToF estimates can be found independently of each other using the one-dimensional (1D) MMP algorithm and the results can be accurately paired to obtain the AoA⁻ToF pairs for all multipath components. Thus, the 2D estimation problem reduces to running 1D estimation multiple times, substantially reducing the computational complexity. We identify and resolve the problem of degenerate performance when two or more multipath components have the same AoA. In addition, we propose a packet aggregation model that uses the CSI data from multiple packets to improve the performance under noisy conditions. Simulation results show that our algorithm achieves two orders of magnitude reduction in the computational time over the 2D MUSIC algorithm while achieving similar accuracy. High accuracy and low computation complexity of our approach make it suitable for applications that require location estimation to run on resource-constrained embedded devices in real time.

Associations among multiple markers and complex disease: models, algorithms, and applications.

PubMed

Assimes, Themistocles L; Olshen, Adam B; Narasimhan, Balasubramanian; Olshen, Richard A

2008-01-01

This chapter is a report on collaborations among its authors and others over many years. It devolves from our goal of understanding genes, their main and epistatic effects combined with interactions involving demographic and environmental features also, as together they predict genetically complex diseases. Thus, our goal is "association." Particular phenotypes of interest to us are hypertension, insulin resistance, angina, and myocardial infarction. Prediction of complex disease is notoriously difficult, though it would be made easier were we given strand-specific information on genotype. Unfortunately, with current technology, genotypic information comes to us "unphased." While obviously we have strand-specific information when genotype is homozygous, we do not have such information when genotype is heterozygous. To summarize, the ultimate goals of approaches we provide is to predict phenotype, typically untoward or not, within a specific window of time. Our approach is neither through linkage nor from finding haplotype frequencies per se.

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks

PubMed Central

Pirbhulal, Sandeep; Zhang, Heye; Mukhopadhyay, Subhas Chandra; Li, Chunyue; Wang, Yumei; Li, Guanglin; Wu, Wanqing; Zhang, Yuan-Ting

2015-01-01

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption. PMID:26131666

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks.

PubMed

Pirbhulal, Sandeep; Zhang, Heye; Mukhopadhyay, Subhas Chandra; Li, Chunyue; Wang, Yumei; Li, Guanglin; Wu, Wanqing; Zhang, Yuan-Ting

2015-06-26

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

Lossless Video Sequence Compression Using Adaptive Prediction

NASA Technical Reports Server (NTRS)

Li, Ying; Sayood, Khalid

2007-01-01

We present an adaptive lossless video compression algorithm based on predictive coding. The proposed algorithm exploits temporal, spatial, and spectral redundancies in a backward adaptive fashion with extremely low side information. The computational complexity is further reduced by using a caching strategy. We also study the relationship between the operational domain for the coder (wavelet or spatial) and the amount of temporal and spatial redundancy in the sequence being encoded. Experimental results show that the proposed scheme provides significant improvements in compression efficiencies.

An efficient approach to the travelling salesman problem using self-organizing maps.

PubMed

Vieira, Frederico Carvalho; Dória Neto, Adrião Duarte; Costa, José Alfredo Ferreira

2003-04-01

This paper presents an approach to the well-known Travelling Salesman Problem (TSP) using Self-Organizing Maps (SOM). The SOM algorithm has interesting topological information about its neurons configuration on cartesian space, which can be used to solve optimization problems. Aspects of initialization, parameters adaptation, and complexity analysis of the proposed SOM based algorithm are discussed. The results show an average deviation of 3.7% from the optimal tour length for a set of 12 TSP instances.

A pruning algorithm for Meta-blocking based on cumulative weight

NASA Astrophysics Data System (ADS)

Zhang, Fulin; Gao, Zhipeng; Niu, Kun

2017-08-01

Entity Resolution is an important process in data cleaning and data integration. It usually employs a blocking method to avoid the quadratic complexity work when scales to large data sets. Meta-blocking can perform better in the context of highly heterogeneous information spaces. Yet, its precision and efficiency still have room to improve. In this paper, we present a new pruning algorithm for Meta-Blocking. It can achieve a higher precision than the existing WEP algorithm at a small cost of recall. In addition, can reduce the runtime of the blocking process. We evaluate our proposed method over five real-world data sets.

Wavelet phase extracting demodulation algorithm based on scale factor for optical fiber Fabry-Perot sensing.

PubMed

Zhang, Baolin; Tong, Xinglin; Hu, Pan; Guo, Qian; Zheng, Zhiyuan; Zhou, Chaoran

2016-12-26

Optical fiber Fabry-Perot (F-P) sensors have been used in various on-line monitoring of physical parameters such as acoustics, temperature and pressure. In this paper, a wavelet phase extracting demodulation algorithm for optical fiber F-P sensing is first proposed. In application of this demodulation algorithm, search range of scale factor is determined by estimated cavity length which is obtained by fast Fourier transform (FFT) algorithm. Phase information of each point on the optical interference spectrum can be directly extracted through the continuous complex wavelet transform without de-noising. And the cavity length of the optical fiber F-P sensor is calculated by the slope of fitting curve of the phase. Theorical analysis and experiment results show that this algorithm can greatly reduce the amount of computation and improve demodulation speed and accuracy.

Formation of the predicted training parameters in the form of a discrete information stream

NASA Astrophysics Data System (ADS)

Smolentseva, T. E.; Sumin, V. I.; Zolnikov, V. K.; Lavlinsky, V. V.

2018-03-01

In work process of training in the form of a discrete information stream is considered. On each of stages of the considered process portions of the training information and quality of their assimilation are analysed. Individual characteristics and reaction trained for every portion of information on appropriate sections are defined. The control algorithm of training with the predicted number of control checks of the trainee who allows to define what operating influence is considered it is necessary to create for the trainee. On the basis of this algorithm the vector of probabilities of ignorance of elements of the training information is received. As a result of the conducted researches the algorithm on formation of the predicted training parameters is developed. In work the task of comparison of duration of training received experimentally with predicted on the basis of it is solved the conclusion is drawn on efficiency of formation of the predicted training parameters. The program complex on the basis of the values of individual parameters received as a result of experiments on each trainee who allows to calculate individual characteristics is developed, to form rating and to monitor process of change of parameters of training.

Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

2017-12-01

In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

Automated information and control complex of hydro-gas endogenous mine processes

NASA Astrophysics Data System (ADS)

Davkaev, K. S.; Lyakhovets, M. V.; Gulevich, T. M.; Zolin, K. A.

2017-09-01

The automated information and control complex designed to prevent accidents, related to aerological situation in the underground workings, accounting of the received and handed over individual devices, transmission and display of measurement data, and the formation of preemptive solutions is considered. Examples for the automated workplace of an airgas control operator by individual means are given. The statistical characteristics of field data characterizing the aerological situation in the mine are obtained. The conducted studies of statistical characteristics confirm the feasibility of creating a subsystem of controlled gas distribution with an adaptive arrangement of points for gas control. The adaptive (multivariant) algorithm for processing measuring information of continuous multidimensional quantities and influencing factors has been developed.

An interactive ontology-driven information system for simulating background radiation and generating scenarios for testing special nuclear materials detection algorithms

DOE PAGES

Sorokine, Alexandre; Schlicher, Bob G.; Ward, Richard C.; ...

2015-05-22

This paper describes an original approach to generating scenarios for the purpose of testing the algorithms used to detect special nuclear materials (SNM) that incorporates the use of ontologies. Separating the signal of SNM from the background requires sophisticated algorithms. To assist in developing such algorithms, there is a need for scenarios that capture a very wide range of variables affecting the detection process, depending on the type of detector being used. To provide such a cpability, we developed an ontology-driven information system (ODIS) for generating scenarios that can be used in creating scenarios for testing of algorithms for SNMmore » detection. The ontology-driven scenario generator (ODSG) is an ODIS based on information supplied by subject matter experts and other documentation. The details of the creation of the ontology, the development of the ontology-driven information system, and the design of the web user interface (UI) are presented along with specific examples of scenarios generated using the ODSG. We demonstrate that the paradigm behind the ODSG is capable of addressing the problem of semantic complexity at both the user and developer levels. Compared to traditional approaches, an ODIS provides benefits such as faithful representation of the users' domain conceptualization, simplified management of very large and semantically diverse datasets, and the ability to handle frequent changes to the application and the UI. Furthermore, the approach makes possible the generation of a much larger number of specific scenarios based on limited user-supplied information« less

Kernel Temporal Differences for Neural Decoding

PubMed Central

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

Influence maximization based on partial network structure information: A comparative analysis on seed selection heuristics

NASA Astrophysics Data System (ADS)

Erkol, Şirag; Yücel, Gönenç

In this study, the problem of seed selection is investigated. This problem is mainly treated as an optimization problem, which is proved to be NP-hard. There are several heuristic approaches in the literature which mostly use algorithmic heuristics. These approaches mainly focus on the trade-off between computational complexity and accuracy. Although the accuracy of algorithmic heuristics are high, they also have high computational complexity. Furthermore, in the literature, it is generally assumed that complete information on the structure and features of a network is available, which is not the case in most of the times. For the study, a simulation model is constructed, which is capable of creating networks, performing seed selection heuristics, and simulating diffusion models. Novel metric-based seed selection heuristics that rely only on partial information are proposed and tested using the simulation model. These heuristics use local information available from nodes in the synthetically created networks. The performances of heuristics are comparatively analyzed on three different network types. The results clearly show that the performance of a heuristic depends on the structure of a network. A heuristic to be used should be selected after investigating the properties of the network at hand. More importantly, the approach of partial information provided promising results. In certain cases, selection heuristics that rely only on partial network information perform very close to similar heuristics that require complete network data.

General Quantum Meet-in-the-Middle Search Algorithm Based on Target Solution of Fixed Weight

NASA Astrophysics Data System (ADS)

Fu, Xiang-Qun; Bao, Wan-Su; Wang, Xiang; Shi, Jian-Hong

2016-10-01

Similar to the classical meet-in-the-middle algorithm, the storage and computation complexity are the key factors that decide the efficiency of the quantum meet-in-the-middle algorithm. Aiming at the target vector of fixed weight, based on the quantum meet-in-the-middle algorithm, the algorithm for searching all n-product vectors with the same weight is presented, whose complexity is better than the exhaustive search algorithm. And the algorithm can reduce the storage complexity of the quantum meet-in-the-middle search algorithm. Then based on the algorithm and the knapsack vector of the Chor-Rivest public-key crypto of fixed weight d, we present a general quantum meet-in-the-middle search algorithm based on the target solution of fixed weight, whose computational complexity is \\sumj = 0d {(O(\\sqrt {Cn - k + 1d - j }) + O(C_kj log C_k^j))} with Σd i =0 Ck i memory cost. And the optimal value of k is given. Compared to the quantum meet-in-the-middle search algorithm for knapsack problem and the quantum algorithm for searching a target solution of fixed weight, the computational complexity of the algorithm is lower. And its storage complexity is smaller than the quantum meet-in-the-middle-algorithm. Supported by the National Basic Research Program of China under Grant No. 2013CB338002 and the National Natural Science Foundation of China under Grant No. 61502526

Sorting on STAR. [CDC computer algorithm timing comparison

NASA Technical Reports Server (NTRS)

Stone, H. S.

1978-01-01

Timing comparisons are given for three sorting algorithms written for the CDC STAR computer. One algorithm is Hoare's (1962) Quicksort, which is the fastest or nearly the fastest sorting algorithm for most computers. A second algorithm is a vector version of Quicksort that takes advantage of the STAR's vector operations. The third algorithm is an adaptation of Batcher's (1968) sorting algorithm, which makes especially good use of vector operations but has a complexity of N(log N)-squared as compared with a complexity of N log N for the Quicksort algorithms. In spite of its worse complexity, Batcher's sorting algorithm is competitive with the serial version of Quicksort for vectors up to the largest that can be treated by STAR. Vector Quicksort outperforms the other two algorithms and is generally preferred. These results indicate that unusual instruction sets can introduce biases in program execution time that counter results predicted by worst-case asymptotic complexity analysis.

Insights on correlation dimension from dynamics mapping of three experimental nonlinear laser systems.

PubMed

McMahon, Christopher J; Toomey, Joshua P; Kane, Deb M

2017-01-01

We have analysed large data sets consisting of tens of thousands of time series from three Type B laser systems: a semiconductor laser in a photonic integrated chip, a semiconductor laser subject to optical feedback from a long free-space-external-cavity, and a solid-state laser subject to optical injection from a master laser. The lasers can deliver either constant, periodic, pulsed, or chaotic outputs when parameters such as the injection current and the level of external perturbation are varied. The systems represent examples of experimental nonlinear systems more generally and cover a broad range of complexity including systematically varying complexity in some regions. In this work we have introduced a new procedure for semi-automatically interrogating experimental laser system output power time series to calculate the correlation dimension (CD) using the commonly adopted Grassberger-Proccacia algorithm. The new CD procedure is called the 'minimum gradient detection algorithm'. A value of minimum gradient is returned for all time series in a data set. In some cases this can be identified as a CD, with uncertainty. Applying the new 'minimum gradient detection algorithm' CD procedure, we obtained robust measurements of the correlation dimension for many of the time series measured from each laser system. By mapping the results across an extended parameter space for operation of each laser system, we were able to confidently identify regions of low CD (CD < 3) and assign these robust values for the correlation dimension. However, in all three laser systems, we were not able to measure the correlation dimension at all parts of the parameter space. Nevertheless, by mapping the staged progress of the algorithm, we were able to broadly classify the dynamical output of the lasers at all parts of their respective parameter spaces. For two of the laser systems this included displaying regions of high-complexity chaos and dynamic noise. These high-complexity regions are differentiated from regions where the time series are dominated by technical noise. This is the first time such differentiation has been achieved using a CD analysis approach. More can be known of the CD for a system when it is interrogated in a mapping context, than from calculations using isolated time series. This has been shown for three laser systems and the approach is expected to be useful in other areas of nonlinear science where large data sets are available and need to be semi-automatically analysed to provide real dimensional information about the complex dynamics. The CD/minimum gradient algorithm measure provides additional information that complements other measures of complexity and relative complexity, such as the permutation entropy; and conventional physical measurements.

Pathway-based personalized analysis of cancer

PubMed Central

Drier, Yotam; Sheffer, Michal; Domany, Eytan

2013-01-01

We introduce Pathifier, an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. We demonstrate the algorithm’s performance on three colorectal cancer datasets and two glioblastoma multiforme datasets and show that our multipathway-based representation is reproducible, preserves much of the original information, and allows inference of complex biologically significant information. We discovered several pathways that were significantly associated with survival of glioblastoma patients and two whose scores are predictive of survival in colorectal cancer: CXCR3-mediated signaling and oxidative phosphorylation. We also identified a subclass of proneural and neural glioblastoma with significantly better survival, and an EGF receptor-deregulated subclass of colon cancers. PMID:23547110

Information Gain Based Dimensionality Selection for Classifying Text Documents

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

Dumidu Wijayasekara; Milos Manic; Miles McQueen

2013-06-01

Selecting the optimal dimensions for various knowledge extraction applications is an essential component of data mining. Dimensionality selection techniques are utilized in classification applications to increase the classification accuracy and reduce the computational complexity. In text classification, where the dimensionality of the dataset is extremely high, dimensionality selection is even more important. This paper presents a novel, genetic algorithm based methodology, for dimensionality selection in text mining applications that utilizes information gain. The presented methodology uses information gain of each dimension to change the mutation probability of chromosomes dynamically. Since the information gain is calculated a priori, the computational complexitymore » is not affected. The presented method was tested on a specific text classification problem and compared with conventional genetic algorithm based dimensionality selection. The results show an improvement of 3% in the true positives and 1.6% in the true negatives over conventional dimensionality selection methods.« less

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns.

PubMed

Ortuño, Francisco M; Valenzuela, Olga; Rojas, Fernando; Pomares, Hector; Florido, Javier P; Urquiza, Jose M; Rojas, Ignacio

2013-09-01

Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

Parallel consistent labeling algorithms

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

Samal, A.; Henderson, T.

Mackworth and Freuder have analyzed the time complexity of several constraint satisfaction algorithms. Mohr and Henderson have given new algorithms, AC-4 and PC-3, for arc and path consistency, respectively, and have shown that the arc consistency algorithm is optimal in time complexity and of the same order space complexity as the earlier algorithms. In this paper, they give parallel algorithms for solving node and arc consistency. They show that any parallel algorithm for enforcing arc consistency in the worst case must have O(na) sequential steps, where n is number of nodes, and a is the number of labels per node.more » They give several parallel algorithms to do arc consistency. It is also shown that they all have optimal time complexity. The results of running the parallel algorithms on a BBN Butterfly multiprocessor are also presented.« less

An algorithm for automated layout of process description maps drawn in SBGN

PubMed Central

Genc, Begum; Dogrusoz, Ugur

2016-01-01

Motivation: Evolving technology has increased the focus on genomics. The combination of today’s advanced techniques with decades of molecular biology research has yielded huge amounts of pathway data. A standard, named the Systems Biology Graphical Notation (SBGN), was recently introduced to allow scientists to represent biological pathways in an unambiguous, easy-to-understand and efficient manner. Although there are a number of automated layout algorithms for various types of biological networks, currently none specialize on process description (PD) maps as defined by SBGN. Results: We propose a new automated layout algorithm for PD maps drawn in SBGN. Our algorithm is based on a force-directed automated layout algorithm called Compound Spring Embedder (CoSE). On top of the existing force scheme, additional heuristics employing new types of forces and movement rules are defined to address SBGN-specific rules. Our algorithm is the only automatic layout algorithm that properly addresses all SBGN rules for drawing PD maps, including placement of substrates and products of process nodes on opposite sides, compact tiling of members of molecular complexes and extensively making use of nested structures (compound nodes) to properly draw cellular locations and molecular complex structures. As demonstrated experimentally, the algorithm results in significant improvements over use of a generic layout algorithm such as CoSE in addressing SBGN rules on top of commonly accepted graph drawing criteria. Availability and implementation: An implementation of our algorithm in Java is available within ChiLay library (https://github.com/iVis-at-Bilkent/chilay). Contact: ugur@cs.bilkent.edu.tr or dogrusoz@cbio.mskcc.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26363029

Novel density-based and hierarchical density-based clustering algorithms for uncertain data.

PubMed

Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

2017-09-01

Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing algorithms in accuracy and efficiency. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dense Matching Comparison Between Census and a Convolutional Neural Network Algorithm for Plant Reconstruction

NASA Astrophysics Data System (ADS)

Xia, Y.; Tian, J.; d'Angelo, P.; Reinartz, P.

2018-05-01

3D reconstruction of plants is hard to implement, as the complex leaf distribution highly increases the difficulty level in dense matching. Semi-Global Matching has been successfully applied to recover the depth information of a scene, but may perform variably when different matching cost algorithms are used. In this paper two matching cost computation algorithms, Census transform and an algorithm using a convolutional neural network, are tested for plant reconstruction based on Semi-Global Matching. High resolution close-range photogrammetric images from a handheld camera are used for the experiment. The disparity maps generated based on the two selected matching cost methods are comparable with acceptable quality, which shows the good performance of Census and the potential of neural networks to improve the dense matching.

Learning Time-Varying Coverage Functions

PubMed Central

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2015-01-01

Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data. PMID:25960624

Learning Time-Varying Coverage Functions.

PubMed

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2014-12-08

Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data.

Study of genetic direct search algorithms for function optimization

NASA Technical Reports Server (NTRS)

Zeigler, B. P.

1974-01-01

The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.

An Adaptive Immune Genetic Algorithm for Edge Detection

NASA Astrophysics Data System (ADS)

Li, Ying; Bai, Bendu; Zhang, Yanning

An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

Automatic extraction of building boundaries using aerial LiDAR data

NASA Astrophysics Data System (ADS)

Wang, Ruisheng; Hu, Yong; Wu, Huayi; Wang, Jian

2016-01-01

Building extraction is one of the main research topics of the photogrammetry community. This paper presents automatic algorithms for building boundary extractions from aerial LiDAR data. First, segmenting height information generated from LiDAR data, the outer boundaries of aboveground objects are expressed as closed chains of oriented edge pixels. Then, building boundaries are distinguished from nonbuilding ones by evaluating their shapes. The candidate building boundaries are reconstructed as rectangles or regular polygons by applying new algorithms, following the hypothesis verification paradigm. These algorithms include constrained searching in Hough space, enhanced Hough transformation, and the sequential linking technique. The experimental results show that the proposed algorithms successfully extract building boundaries at rates of 97%, 85%, and 92% for three LiDAR datasets with varying scene complexities.

Prediction of TF target sites based on atomistic models of protein-DNA complexes

PubMed Central

Angarica, Vladimir Espinosa; Pérez, Abel González; Vasconcelos, Ana T; Collado-Vides, Julio; Contreras-Moreira, Bruno

2008-01-01

Background The specific recognition of genomic cis-regulatory elements by transcription factors (TFs) plays an essential role in the regulation of coordinated gene expression. Studying the mechanisms determining binding specificity in protein-DNA interactions is thus an important goal. Most current approaches for modeling TF specific recognition rely on the knowledge of large sets of cognate target sites and consider only the information contained in their primary sequence. Results Here we describe a structure-based methodology for predicting sequence motifs starting from the coordinates of a TF-DNA complex. Our algorithm combines information regarding the direct and indirect readout of DNA into an atomistic statistical model, which is used to estimate the interaction potential. We first measure the ability of our method to correctly estimate the binding specificities of eight prokaryotic and eukaryotic TFs that belong to different structural superfamilies. Secondly, the method is applied to two homology models, finding that sampling of interface side-chain rotamers remarkably improves the results. Thirdly, the algorithm is compared with a reference structural method based on contact counts, obtaining comparable predictions for the experimental complexes and more accurate sequence motifs for the homology models. Conclusion Our results demonstrate that atomic-detail structural information can be feasibly used to predict TF binding sites. The computational method presented here is universal and might be applied to other systems involving protein-DNA recognition. PMID:18922190

Research on the electro-optical assistant landing system based on the dual camera photogrammetry algorithm

NASA Astrophysics Data System (ADS)

Mi, Yuhe; Huang, Yifan; Li, Lin

2015-08-01

Based on the location technique of beacon photogrammetry, Dual Camera Photogrammetry (DCP) algorithm was used to assist helicopters landing on the ship. In this paper, ZEMAX was used to simulate the two Charge Coupled Device (CCD) cameras imaging four beacons on both sides of the helicopter and output the image to MATLAB. Target coordinate systems, image pixel coordinate systems, world coordinate systems and camera coordinate systems were established respectively. According to the ideal pin-hole imaging model, the rotation matrix and translation vector of the target coordinate systems and the camera coordinate systems could be obtained by using MATLAB to process the image information and calculate the linear equations. On the basis mentioned above, ambient temperature and the positions of the beacons and cameras were changed in ZEMAX to test the accuracy of the DCP algorithm in complex sea status. The numerical simulation shows that in complex sea status, the position measurement accuracy can meet the requirements of the project.

How artificial intelligence tools can be used to assess individual patient risk in cardiovascular disease: problems with the current methods

PubMed Central

Grossi, Enzo

2006-01-01

Background In recent years a number of algorithms for cardiovascular risk assessment has been proposed to the medical community. These algorithms consider a number of variables and express their results as the percentage risk of developing a major fatal or non-fatal cardiovascular event in the following 10 to 20 years Discussion The author has identified three major pitfalls of these algorithms, linked to the limitation of the classical statistical approach in dealing with this kind of non linear and complex information. The pitfalls are the inability to capture the disease complexity, the inability to capture process dynamics, and the wide confidence interval of individual risk assessment. Artificial Intelligence tools can provide potential advantage in trying to overcome these limitations. The theoretical background and some application examples related to artificial neural networks and fuzzy logic have been reviewed and discussed. Summary The use of predictive algorithms to assess individual absolute risk of cardiovascular future events is currently hampered by methodological and mathematical flaws. The use of newer approaches, such as fuzzy logic and artificial neural networks, linked to artificial intelligence, seems to better address both the challenge of increasing complexity resulting from a correlation between predisposing factors, data on the occurrence of cardiovascular events, and the prediction of future events on an individual level. PMID:16672045

A method of minimum volume simplex analysis constrained unmixing for hyperspectral image

NASA Astrophysics Data System (ADS)

Zou, Jinlin; Lan, Jinhui; Zeng, Yiliang; Wu, Hongtao

2017-07-01

The signal recorded by a low resolution hyperspectral remote sensor from a given pixel, letting alone the effects of the complex terrain, is a mixture of substances. To improve the accuracy of classification and sub-pixel object detection, hyperspectral unmixing(HU) is a frontier-line in remote sensing area. Unmixing algorithm based on geometric has become popular since the hyperspectral image possesses abundant spectral information and the mixed model is easy to understand. However, most of the algorithms are based on pure pixel assumption, and since the non-linear mixed model is complex, it is hard to obtain the optimal endmembers especially under a highly mixed spectral data. To provide a simple but accurate method, we propose a minimum volume simplex analysis constrained (MVSAC) unmixing algorithm. The proposed approach combines the algebraic constraints that are inherent to the convex minimum volume with abundance soft constraint. While considering abundance fraction, we can obtain the pure endmember set and abundance fraction correspondingly, and the final unmixing result is closer to reality and has better accuracy. We illustrate the performance of the proposed algorithm in unmixing simulated data and real hyperspectral data, and the result indicates that the proposed method can obtain the distinct signatures correctly without redundant endmember and yields much better performance than the pure pixel based algorithm.

Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection

PubMed Central

Meng, Jiahui; Zhao, Danfeng; Tian, Hai; Zhang, Liang

2018-01-01

In order to improve the performance of non-binary low-density parity check codes (LDPC) hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA) and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes’ (VN) magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF) algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER) of 10−5 over an additive white Gaussian noise (AWGN) channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced. PMID:29342963

A novel clinical decision support algorithm for constructing complete medication histories.

PubMed

Long, Ju; Yuan, Michael Juntao

2017-07-01

A patient's complete medication history is a crucial element for physicians to develop a full understanding of the patient's medical conditions and treatment options. However, due to the fragmented nature of medical data, this process can be very time-consuming and often impossible for physicians to construct a complete medication history for complex patients. In this paper, we describe an accurate, computationally efficient and scalable algorithm to construct a medication history timeline. The algorithm is developed and validated based on 1 million random prescription records from a large national prescription data aggregator. Our evaluation shows that the algorithm can be scaled horizontally on-demand, making it suitable for future delivery in a cloud-computing environment. We also propose that this cloud-based medication history computation algorithm could be integrated into Electronic Medical Records, enabling informed clinical decision-making at the point of care. Copyright © 2017 Elsevier B.V. All rights reserved.

Scalable software-defined optical networking with high-performance routing and wavelength assignment algorithms.

PubMed

Lee, Chankyun; Cao, Xiaoyuan; Yoshikane, Noboru; Tsuritani, Takehiro; Rhee, June-Koo Kevin

2015-10-19

The feasibility of software-defined optical networking (SDON) for a practical application critically depends on scalability of centralized control performance. The paper, highly scalable routing and wavelength assignment (RWA) algorithms are investigated on an OpenFlow-based SDON testbed for proof-of-concept demonstration. Efficient RWA algorithms are proposed to achieve high performance in achieving network capacity with reduced computation cost, which is a significant attribute in a scalable centralized-control SDON. The proposed heuristic RWA algorithms differ in the orders of request processes and in the procedures of routing table updates. Combined in a shortest-path-based routing algorithm, a hottest-request-first processing policy that considers demand intensity and end-to-end distance information offers both the highest throughput of networks and acceptable computation scalability. We further investigate trade-off relationship between network throughput and computation complexity in routing table update procedure by a simulation study.

Learning to Predict Social Influence in Complex Networks

DTIC Science & Technology

2012-03-29

03/2010 – 17/03/2012 Abstract: First, we addressed the problem of analyzing information diffusion process in a social network using two kinds...algorithm which avoids the inner loop optimization during the search. We tested the performance using the structures of four real world networks, and...result of information diffusion that starts from the node. 2 We use “infected” and “activated” interchangeably. Efficient Discovery of Influential

Activity recognition using a single accelerometer placed at the wrist or ankle.

PubMed

Mannini, Andrea; Intille, Stephen S; Rosenberger, Mary; Sabatini, Angelo M; Haskell, William

2013-11-01

Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purposes of this work was to obtain an algorithm to process wrist and ankle raw data and to classify behavior into four broad activity classes: ambulation, cycling, sedentary, and other activities. Participants (N = 33) wearing accelerometers on the wrist and ankle performed 26 daily activities. The accelerometer data were collected, cleaned, and preprocessed to extract features that characterize 2-, 4-, and 12.8-s data windows. Feature vectors encoding information about frequency and intensity of motion extracted from analysis of the raw signal were used with a support vector machine classifier to identify a subject's activity. Results were compared with categories classified by a human observer. Algorithms were validated using a leave-one-subject-out strategy. The computational complexity of each processing step was also evaluated. With 12.8-s windows, the proposed strategy showed high classification accuracies for ankle data (95.0%) that decreased to 84.7% for wrist data. Shorter (4 s) windows only minimally decreased performances of the algorithm on the wrist to 84.2%. A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set. The algorithm is computationally efficient and could be implemented in real time on mobile devices with only 4-s latency.

Modeling of biological intelligence for SCM system optimization.

PubMed

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Modeling of Biological Intelligence for SCM System Optimization

PubMed Central

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

PubMed Central

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.

PubMed

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.

A fast algorithm for vertex-frequency representations of signals on graphs

PubMed Central

Jestrović, Iva; Coyle, James L.; Sejdić, Ervin

2016-01-01

The windowed Fourier transform (short time Fourier transform) and the S-transform are widely used signal processing tools for extracting frequency information from non-stationary signals. Previously, the windowed Fourier transform had been adopted for signals on graphs and has been shown to be very useful for extracting vertex-frequency information from graphs. However, high computational complexity makes these algorithms impractical. We sought to develop a fast windowed graph Fourier transform and a fast graph S-transform requiring significantly shorter computation time. The proposed schemes have been tested with synthetic test graph signals and real graph signals derived from electroencephalography recordings made during swallowing. The results showed that the proposed schemes provide significantly lower computation time in comparison with the standard windowed graph Fourier transform and the fast graph S-transform. Also, the results showed that noise has no effect on the results of the algorithm for the fast windowed graph Fourier transform or on the graph S-transform. Finally, we showed that graphs can be reconstructed from the vertex-frequency representations obtained with the proposed algorithms. PMID:28479645

Developing a Shuffled Complex-Self Adaptive Hybrid Evolution (SC-SAHEL) Framework for Water Resources Management and Water-Energy System Optimization

NASA Astrophysics Data System (ADS)

Rahnamay Naeini, M.; Sadegh, M.; AghaKouchak, A.; Hsu, K. L.; Sorooshian, S.; Yang, T.

2017-12-01

Meta-Heuristic optimization algorithms have gained a great deal of attention in a wide variety of fields. Simplicity and flexibility of these algorithms, along with their robustness, make them attractive tools for solving optimization problems. Different optimization methods, however, hold algorithm-specific strengths and limitations. Performance of each individual algorithm obeys the "No-Free-Lunch" theorem, which means a single algorithm cannot consistently outperform all possible optimization problems over a variety of problems. From users' perspective, it is a tedious process to compare, validate, and select the best-performing algorithm for a specific problem or a set of test cases. In this study, we introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme, and allows users to select the most suitable algorithm tailored to the problem at hand. The concept of SC-SAHEL is to execute different EAs as separate parallel search cores, and let all participating EAs to compete during the course of the search. The newly developed SC-SAHEL algorithm is designed to automatically select, the best performing algorithm for the given optimization problem. This algorithm is rigorously effective in finding the global optimum for several strenuous benchmark test functions, and computationally efficient as compared to individual EAs. We benchmark the proposed SC-SAHEL algorithm over 29 conceptual test functions, and two real-world case studies - one hydropower reservoir model and one hydrological model (SAC-SMA). Results show that the proposed framework outperforms individual EAs in an absolute majority of the test problems, and can provide competitive results to the fittest EA algorithm with more comprehensive information during the search. The proposed framework is also flexible for merging additional EAs, boundary-handling techniques, and sampling schemes, and has good potential to be used in Water-Energy system optimal operation and management.

Quantum Search in Hilbert Space

NASA Technical Reports Server (NTRS)

Zak, Michail

2003-01-01

A proposed quantum-computing algorithm would perform a search for an item of information in a database stored in a Hilbert-space memory structure. The algorithm is intended to make it possible to search relatively quickly through a large database under conditions in which available computing resources would otherwise be considered inadequate to perform such a task. The algorithm would apply, more specifically, to a relational database in which information would be stored in a set of N complex orthonormal vectors, each of N dimensions (where N can be exponentially large). Each vector would constitute one row of a unitary matrix, from which one would derive the Hamiltonian operator (and hence the evolutionary operator) of a quantum system. In other words, all the stored information would be mapped onto a unitary operator acting on a quantum state that would represent the item of information to be retrieved. Then one could exploit quantum parallelism: one could pose all search queries simultaneously by performing a quantum measurement on the system. In so doing, one would effectively solve the search problem in one computational step. One could exploit the direct- and inner-product decomposability of the unitary matrix to make the dimensionality of the memory space exponentially large by use of only linear resources. However, inasmuch as the necessary preprocessing (the mapping of the stored information into a Hilbert space) could be exponentially expensive, the proposed algorithm would likely be most beneficial in applications in which the resources available for preprocessing were much greater than those available for searching.

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.

The multiple complex exponential model and its application to EEG analysis

NASA Astrophysics Data System (ADS)

Chen, Dao-Mu; Petzold, J.

The paper presents a novel approach to the analysis of the EEG signal, which is based on a multiple complex exponential (MCE) model. Parameters of the model are estimated using a nonharmonic Fourier expansion algorithm. The central idea of the algorithm is outlined, and the results, estimated on the basis of simulated data, are presented and compared with those obtained by the conventional methods of signal analysis. Preliminary work on various application possibilities of the MCE model in EEG data analysis is described. It is shown that the parameters of the MCE model reflect the essential information contained in an EEG segment. These parameters characterize the EEG signal in a more objective way because they are closer to the recent supposition of the nonlinear character of the brain's dynamic behavior.

Neural imaging to track mental states while using an intelligent tutoring system.

PubMed

Anderson, John R; Betts, Shawn; Ferris, Jennifer L; Fincham, Jon M

2010-04-13

Hemodynamic measures of brain activity can be used to interpret a student's mental state when they are interacting with an intelligent tutoring system. Functional magnetic resonance imaging (fMRI) data were collected while students worked with a tutoring system that taught an algebra isomorph. A cognitive model predicted the distribution of solution times from measures of problem complexity. Separately, a linear discriminant analysis used fMRI data to predict whether or not students were engaged in problem solving. A hidden Markov algorithm merged these two sources of information to predict the mental states of students during problem-solving episodes. The algorithm was trained on data from 1 day of interaction and tested with data from a later day. In terms of predicting what state a student was in during a 2-s period, the algorithm achieved 87% accuracy on the training data and 83% accuracy on the test data. The results illustrate the importance of integrating the bottom-up information from imaging data with the top-down information from a cognitive model.

Information theoretic methods for image processing algorithm optimization

NASA Astrophysics Data System (ADS)

Prokushkin, Sergey F.; Galil, Erez

2015-01-01

Modern image processing pipelines (e.g., those used in digital cameras) are full of advanced, highly adaptive filters that often have a large number of tunable parameters (sometimes > 100). This makes the calibration procedure for these filters very complex, and the optimal results barely achievable in the manual calibration; thus an automated approach is a must. We will discuss an information theory based metric for evaluation of algorithm adaptive characteristics ("adaptivity criterion") using noise reduction algorithms as an example. The method allows finding an "orthogonal decomposition" of the filter parameter space into the "filter adaptivity" and "filter strength" directions. This metric can be used as a cost function in automatic filter optimization. Since it is a measure of a physical "information restoration" rather than perceived image quality, it helps to reduce the set of the filter parameters to a smaller subset that is easier for a human operator to tune and achieve a better subjective image quality. With appropriate adjustments, the criterion can be used for assessment of the whole imaging system (sensor plus post-processing).

The H0 function, a new index for detecting structural/topological complexity information in undirected graphs

NASA Astrophysics Data System (ADS)

Buscema, Massimo; Asadi-Zeydabadi, Masoud; Lodwick, Weldon; Breda, Marco

2016-04-01

Significant applications such as the analysis of Alzheimer's disease differentiated from dementia, or in data mining of social media, or in extracting information of drug cartel structural composition, are often modeled as graphs. The structural or topological complexity or lack of it in a graph is quite often useful in understanding and more importantly, resolving the problem. We are proposing a new index we call the H0function to measure the structural/topological complexity of a graph. To do this, we introduce the concept of graph pruning and its associated algorithm that is used in the development of our measure. We illustrate the behavior of our measure, the H0 function, through different examples found in the appendix. These examples indicate that the H0 function contains information that is useful and important characteristics of a graph. Here, we restrict ourselves to undirected.

Universal data compression

NASA Astrophysics Data System (ADS)

Lindsay, R. A.; Cox, B. V.

Universal and adaptive data compression techniques have the capability to globally compress all types of data without loss of information but have the disadvantage of complexity and computation speed. Advances in hardware speed and the reduction of computational costs have made universal data compression feasible. Implementations of the Adaptive Huffman and Lempel-Ziv compression algorithms are evaluated for performance. Compression ratios versus run times for different size data files are graphically presented and discussed in the paper. Required adjustments needed for optimum performance of the algorithms relative to theoretical achievable limits will be outlined.

Application of machine learning methods in bioinformatics

NASA Astrophysics Data System (ADS)

Yang, Haoyu; An, Zheng; Zhou, Haotian; Hou, Yawen

2018-05-01

Faced with the development of bioinformatics, high-throughput genomic technology have enabled biology to enter the era of big data. [1] Bioinformatics is an interdisciplinary, including the acquisition, management, analysis, interpretation and application of biological information, etc. It derives from the Human Genome Project. The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets.[2]. This paper analyzes and compares various algorithms of machine learning and their applications in bioinformatics.

Approach to recognition of flexible form for credit card expiration date recognition as example

NASA Astrophysics Data System (ADS)

Sheshkus, Alexander; Nikolaev, Dmitry P.; Ingacheva, Anastasia; Skoryukina, Natalya

2015-12-01

In this paper we consider a task of finding information fields within document with flexible form for credit card expiration date field as example. We discuss main difficulties and suggest possible solutions. In our case this task is to be solved on mobile devices therefore computational complexity has to be as low as possible. In this paper we provide results of the analysis of suggested algorithm. Error distribution of the recognition system shows that suggested algorithm solves the task with required accuracy.

Information processing for aerospace structural health monitoring

NASA Astrophysics Data System (ADS)

Lichtenwalner, Peter F.; White, Edward V.; Baumann, Erwin W.

1998-06-01

Structural health monitoring (SHM) technology provides a means to significantly reduce life cycle of aerospace vehicles by eliminating unnecessary inspections, minimizing inspection complexity, and providing accurate diagnostics and prognostics to support vehicle life extension. In order to accomplish this, a comprehensive SHM system will need to acquire data from a wide variety of diverse sensors including strain gages, accelerometers, acoustic emission sensors, crack growth gages, corrosion sensors, and piezoelectric transducers. Significant amounts of computer processing will then be required to convert this raw sensor data into meaningful information which indicates both the diagnostics of the current structural integrity as well as the prognostics necessary for planning and managing the future health of the structure in a cost effective manner. This paper provides a description of the key types of information processing technologies required in an effective SHM system. These include artificial intelligence techniques such as neural networks, expert systems, and fuzzy logic for nonlinear modeling, pattern recognition, and complex decision making; signal processing techniques such as Fourier and wavelet transforms for spectral analysis and feature extraction; statistical algorithms for optimal detection, estimation, prediction, and fusion; and a wide variety of other algorithms for data analysis and visualization. The intent of this paper is to provide an overview of the role of information processing for SHM, discuss various technologies which can contribute to accomplishing this role, and present some example applications of information processing for SHM implemented at the Boeing Company.

Information-optimal genome assembly via sparse read-overlap graphs.

PubMed

Shomorony, Ilan; Kim, Samuel H; Courtade, Thomas A; Tse, David N C

2016-09-01

In the context of third-generation long-read sequencing technologies, read-overlap-based approaches are expected to play a central role in the assembly step. A fundamental challenge in assembling from a read-overlap graph is that the true sequence corresponds to a Hamiltonian path on the graph, and, under most formulations, the assembly problem becomes NP-hard, restricting practical approaches to heuristics. In this work, we avoid this seemingly fundamental barrier by first setting the computational complexity issue aside, and seeking an algorithm that targets information limits In particular, we consider a basic feasibility question: when does the set of reads contain enough information to allow unambiguous reconstruction of the true sequence? Based on insights from this information feasibility question, we present an algorithm-the Not-So-Greedy algorithm-to construct a sparse read-overlap graph. Unlike most other assembly algorithms, Not-So-Greedy comes with a performance guarantee: whenever information feasibility conditions are satisfied, the algorithm reduces the assembly problem to an Eulerian path problem on the resulting graph, and can thus be solved in linear time. In practice, this theoretical guarantee translates into assemblies of higher quality. Evaluations on both simulated reads from real genomes and a PacBio Escherichia coli K12 dataset demonstrate that Not-So-Greedy compares favorably with standard string graph approaches in terms of accuracy of the resulting read-overlap graph and contig N50. Available at github.com/samhykim/nsg courtade@eecs.berkeley.edu or dntse@stanford.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

STREAMFINDER - I. A new algorithm for detecting stellar streams

NASA Astrophysics Data System (ADS)

Malhan, Khyati; Ibata, Rodrigo A.

2018-07-01

We have designed a powerful new algorithm to detect stellar streams in an automated and systematic way. The algorithm, which we call the STREAMFINDER, is well suited for finding dynamically cold and thin stream structures that may lie along any simple or complex orbits in Galactic stellar surveys containing any combination of positional and kinematic information. In the present contribution, we introduce the algorithm, lay out the ideas behind it, explain the methodology adopted to detect streams, and detail its workings by running it on a suite of simulations of mock Galactic survey data of similar quality to that expected from the European Space Agency/Gaia mission. We show that our algorithm is able to detect even ultra-faint stream features lying well below previous detection limits. Tests show that our algorithm will be able to detect distant halo stream structures >10° long containing as few as ˜15 members (ΣG ˜ 33.6 mag arcsec-2) in the Gaia data set.

LiveWire interactive boundary extraction algorithm based on Haar wavelet transform and control point set direction search

NASA Astrophysics Data System (ADS)

Cheng, Jun; Zhang, Jun; Tian, Jinwen

2015-12-01

Based on deep analysis of the LiveWire interactive boundary extraction algorithm, a new algorithm focusing on improving the speed of LiveWire algorithm is proposed in this paper. Firstly, the Haar wavelet transform is carried on the input image, and the boundary is extracted on the low resolution image obtained by the wavelet transform of the input image. Secondly, calculating LiveWire shortest path is based on the control point set direction search by utilizing the spatial relationship between the two control points users provide in real time. Thirdly, the search order of the adjacent points of the starting node is set in advance. An ordinary queue instead of a priority queue is taken as the storage pool of the points when optimizing their shortest path value, thus reducing the complexity of the algorithm from O[n2] to O[n]. Finally, A region iterative backward projection method based on neighborhood pixel polling has been used to convert dual-pixel boundary of the reconstructed image to single-pixel boundary after Haar wavelet inverse transform. The algorithm proposed in this paper combines the advantage of the Haar wavelet transform and the advantage of the optimal path searching method based on control point set direction search. The former has fast speed of image decomposition and reconstruction and is more consistent with the texture features of the image and the latter can reduce the time complexity of the original algorithm. So that the algorithm can improve the speed in interactive boundary extraction as well as reflect the boundary information of the image more comprehensively. All methods mentioned above have a big role in improving the execution efficiency and the robustness of the algorithm.

A problem of optimal control and observation for distributed homogeneous multi-agent system

NASA Astrophysics Data System (ADS)

Kruglikov, Sergey V.

2017-12-01

The paper considers the implementation of a algorithm for controlling a distributed complex of several mobile multi-robots. The concept of a unified information space of the controlling system is applied. The presented information and mathematical models of participants and obstacles, as real agents, and goals and scenarios, as virtual agents, create the base forming the algorithmic and software background for computer decision support system. The controlling scheme assumes the indirect management of the robotic team on the basis of optimal control and observation problem predicting intellectual behavior in a dynamic, hostile environment. A basic content problem is a compound cargo transportation by a group of participants in the case of a distributed control scheme in the terrain with multiple obstacles.

Hidden Markov model analysis of force/torque information in telemanipulation

NASA Technical Reports Server (NTRS)

Hannaford, Blake; Lee, Paul

1991-01-01

A model for the prediction and analysis of sensor information recorded during robotic performance of telemanipulation tasks is presented. The model uses the hidden Markov model to describe the task structure, the operator's or intelligent controller's goal structure, and the sensor signals. A methodology for constructing the model parameters based on engineering knowledge of the task is described. It is concluded that the model and its optimal state estimation algorithm, the Viterbi algorithm, are very succesful at the task of segmenting the data record into phases corresponding to subgoals of the task. The model provides a rich modeling structure within a statistical framework, which enables it to represent complex systems and be robust to real-world sensory signals.

Development of a meta-algorithm for guiding primary care encounters for patients with multimorbidity using evidence-based and case-based guideline development methodology.

PubMed

Muche-Borowski, Cathleen; Lühmann, Dagmar; Schäfer, Ingmar; Mundt, Rebekka; Wagner, Hans-Otto; Scherer, Martin

2017-06-22

The study aimed to develop a comprehensive algorithm (meta-algorithm) for primary care encounters of patients with multimorbidity. We used a novel, case-based and evidence-based procedure to overcome methodological difficulties in guideline development for patients with complex care needs. Systematic guideline development methodology including systematic evidence retrieval (guideline synopses), expert opinions and informal and formal consensus procedures. Primary care. The meta-algorithm was developed in six steps:1. Designing 10 case vignettes of patients with multimorbidity (common, epidemiologically confirmed disease patterns and/or particularly challenging health care needs) in a multidisciplinary workshop.2. Based on the main diagnoses, a systematic guideline synopsis of evidence-based and consensus-based clinical practice guidelines was prepared. The recommendations were prioritised according to the clinical and psychosocial characteristics of the case vignettes.3. Case vignettes along with the respective guideline recommendations were validated and specifically commented on by an external panel of practicing general practitioners (GPs).4. Guideline recommendations and experts' opinions were summarised as case specific management recommendations (N-of-one guidelines).5. Healthcare preferences of patients with multimorbidity were elicited from a systematic literature review and supplemented with information from qualitative interviews.6. All N-of-one guidelines were analysed using pattern recognition to identify common decision nodes and care elements. These elements were put together to form a generic meta-algorithm. The resulting meta-algorithm reflects the logic of a GP's encounter of a patient with multimorbidity regarding decision-making situations, communication needs and priorities. It can be filled with the complex problems of individual patients and hereby offer guidance to the practitioner. Contrary to simple, symptom-oriented algorithms, the meta-algorithm illustrates a superordinate process that permanently keeps the entire patient in view. The meta-algorithm represents the back bone of the multimorbidity guideline of the German College of General Practitioners and Family Physicians. This article presents solely the development phase; the meta-algorithm needs to be piloted before it can be implemented. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery.

PubMed

Orzechowski, Patryk; Sipper, Moshe; Huang, Xiuzhen; Moore, Jason H

2018-05-22

Biclustering algorithms are commonly used for gene expression data analysis. However, accurate identification of meaningful structures is very challenging and state-of-the-art methods are incapable of discovering with high accuracy different patterns of high biological relevance. In this paper a novel biclustering algorithm based on evolutionary computation, a subfield of artificial intelligence (AI), is introduced. The method called EBIC aims to detect order-preserving patterns in complex data. EBIC is capable of discovering multiple complex patterns with unprecedented accuracy in real gene expression datasets. It is also one of the very few biclustering methods designed for parallel environments with multiple graphics processing units (GPUs). We demonstrate that EBIC greatly outperforms state-of-the-art biclustering methods, in terms of recovery and relevance, on both synthetic and genetic datasets. EBIC also yields results over 12 times faster than the most accurate reference algorithms. EBIC source code is available on GitHub at https://github.com/EpistasisLab/ebic. Correspondence and requests for materials should be addressed to P.O. (email: patryk.orzechowski@gmail.com) and J.H.M. (email: jhmoore@upenn.edu). Supplementary Data with results of analyses and additional information on the method is available at Bioinformatics online.

Indoor positioning algorithm combined with angular vibration compensation and the trust region technique based on received signal strength-visible light communication

NASA Astrophysics Data System (ADS)

Wang, Jin; Li, Haoxu; Zhang, Xiaofeng; Wu, Rangzhong

2017-05-01

Indoor positioning using visible light communication has become a topic of intensive research in recent years. Because the normal of the receiver always deviates from that of the transmitter in application, the positioning systems which require that the normal of the receiver be aligned with that of the transmitter have large positioning errors. Some algorithms take the angular vibrations into account; nevertheless, these positioning algorithms cannot meet the requirement of high accuracy or low complexity. A visible light positioning algorithm combined with angular vibration compensation is proposed. The angle information from the accelerometer or other angle acquisition devices is used to calculate the angle of incidence even when the receiver is not horizontal. Meanwhile, a received signal strength technique with high accuracy is employed to determine the location. Moreover, an eight-light-emitting-diode (LED) system model is provided to improve the accuracy. The simulation results show that the proposed system can achieve a low positioning error with low complexity, and the eight-LED system exhibits improved performance. Furthermore, trust region-based positioning is proposed to determine three-dimensional locations and achieves high accuracy in both the horizontal and the vertical components.

Multi-viewpoint Image Array Virtual Viewpoint Rapid Generation Algorithm Based on Image Layering

NASA Astrophysics Data System (ADS)

Jiang, Lu; Piao, Yan

2018-04-01

The use of multi-view image array combined with virtual viewpoint generation technology to record 3D scene information in large scenes has become one of the key technologies for the development of integrated imaging. This paper presents a virtual viewpoint rendering method based on image layering algorithm. Firstly, the depth information of reference viewpoint image is quickly obtained. During this process, SAD is chosen as the similarity measure function. Then layer the reference image and calculate the parallax based on the depth information. Through the relative distance between the virtual viewpoint and the reference viewpoint, the image layers are weighted and panned. Finally the virtual viewpoint image is rendered layer by layer according to the distance between the image layers and the viewer. This method avoids the disadvantages of the algorithm DIBR, such as high-precision requirements of depth map and complex mapping operations. Experiments show that, this algorithm can achieve the synthesis of virtual viewpoints in any position within 2×2 viewpoints range, and the rendering speed is also very impressive. The average result proved that this method can get satisfactory image quality. The average SSIM value of the results relative to real viewpoint images can reaches 0.9525, the PSNR value can reaches 38.353 and the image histogram similarity can reaches 93.77%.

AxTract: Toward microstructure informed tractography.

PubMed

Girard, Gabriel; Daducci, Alessandro; Petit, Laurent; Thiran, Jean-Philippe; Whittingstall, Kevin; Deriche, Rachid; Wassermann, Demian; Descoteaux, Maxime

2017-11-01

Diffusion-weighted (DW) magnetic resonance imaging (MRI) tractography has become the tool of choice to probe the human brain's white matter in vivo. However, tractography algorithms produce a large number of erroneous streamlines (false positives), largely due to complex ambiguous tissue configurations. Moreover, the relationship between the resulting streamlines and the underlying white matter microstructure characteristics remains poorly understood. In this work, we introduce a new approach to simultaneously reconstruct white matter fascicles and characterize the apparent distribution of axon diameters within fascicles. To achieve this, our method, AxTract, takes full advantage of the recent development DW-MRI microstructure acquisition, modeling, and reconstruction techniques. This enables AxTract to separate parallel fascicles with different microstructure characteristics, hence reducing ambiguities in areas of complex tissue configuration. We report a decrease in the incidence of erroneous streamlines compared to the conventional deterministic tractography algorithms on simulated data. We also report an average increase in streamline density over 15 known fascicles of the 34 healthy subjects. Our results suggest that microstructure information improves tractography in crossing areas of the white matter. Moreover, AxTract provides additional microstructure information along the fascicle that can be studied alongside other streamline-based indices. Overall, AxTract provides the means to distinguish and follow white matter fascicles using their microstructure characteristics, bringing new insights into the white matter organization. This is a step forward in microstructure informed tractography, paving the way to a new generation of algorithms able to deal with intricate configurations of white matter fibers and providing quantitative brain connectivity analysis. Hum Brain Mapp 38:5485-5500, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Complexity, information loss, and model building: from neuro- to cognitive dynamics

NASA Astrophysics Data System (ADS)

Arecchi, F. Tito

2007-06-01

A scientific problem described within a given code is mapped by a corresponding computational problem, We call complexity (algorithmic) the bit length of the shortest instruction which solves the problem. Deterministic chaos in general affects a dynamical systems making the corresponding problem experimentally and computationally heavy, since one must reset the initial conditions at a rate higher than that of information loss (Kolmogorov entropy). One can control chaos by adding to the system new degrees of freedom (information swapping: information lost by chaos is replaced by that arising from the new degrees of freedom). This implies a change of code, or a new augmented model. Within a single code, changing hypotheses is equivalent to fixing different sets of control parameters, each with a different a-priori probability, to be then confirmed and transformed to an a-posteriori probability via Bayes theorem. Sequential application of Bayes rule is nothing else than the Darwinian strategy in evolutionary biology. The sequence is a steepest ascent algorithm, which stops once maximum probability has been reached. At this point the hypothesis exploration stops. By changing code (and hence the set of relevant variables) one can start again to formulate new classes of hypotheses . We call semantic complexity the number of accessible scientific codes, or models, that describe a situation. It is however a fuzzy concept, in so far as this number changes due to interaction of the operator with the system under investigation. These considerations are illustrated with reference to a cognitive task, starting from synchronization of neuron arrays in a perceptual area and tracing the putative path toward a model building.

Hybrid sparse blind deconvolution: an implementation of SOOT algorithm to real data

NASA Astrophysics Data System (ADS)

Pakmanesh, Parvaneh; Goudarzi, Alireza; Kourki, Meisam

2018-06-01

Getting information of seismic data depends on deconvolution as an important processing step; it provides the reflectivity series by signal compression. This compression can be obtained by removing the wavelet effects on the traces. The recently blind deconvolution has provided reliable performance for sparse signal recovery. In this study, two deconvolution methods have been implemented to the seismic data; the convolution of these methods provides a robust spiking deconvolution approach. This hybrid deconvolution is applied using the sparse deconvolution (MM algorithm) and the Smoothed-One-Over-Two algorithm (SOOT) in a chain. The MM algorithm is based on the minimization of the cost function defined by standards l1 and l2. After applying the two algorithms to the seismic data, the SOOT algorithm provided well-compressed data with a higher resolution than the MM algorithm. The SOOT algorithm requires initial values to be applied for real data, such as the wavelet coefficients and reflectivity series that can be achieved through the MM algorithm. The computational cost of the hybrid method is high, and it is necessary to be implemented on post-stack or pre-stack seismic data of complex structure regions.

Hierarchical heuristic search using a Gaussian mixture model for UAV coverage planning.

PubMed

Lin, Lanny; Goodrich, Michael A

2014-12-01

During unmanned aerial vehicle (UAV) search missions, efficient use of UAV flight time requires flight paths that maximize the probability of finding the desired subject. The probability of detecting the desired subject based on UAV sensor information can vary in different search areas due to environment elements like varying vegetation density or lighting conditions, making it likely that the UAV can only partially detect the subject. This adds another dimension of complexity to the already difficult (NP-Hard) problem of finding an optimal search path. We present a new class of algorithms that account for partial detection in the form of a task difficulty map and produce paths that approximate the payoff of optimal solutions. The algorithms use the mode goodness ratio heuristic that uses a Gaussian mixture model to prioritize search subregions. The algorithms search for effective paths through the parameter space at different levels of resolution. We compare the performance of the new algorithms against two published algorithms (Bourgault's algorithm and LHC-GW-CONV algorithm) in simulated searches with three real search and rescue scenarios, and show that the new algorithms outperform existing algorithms significantly and can yield efficient paths that yield payoffs near the optimal.

CCOMP: An efficient algorithm for complex roots computation of determinantal equations

NASA Astrophysics Data System (ADS)

Zouros, Grigorios P.

2018-01-01

In this paper a free Python algorithm, entitled CCOMP (Complex roots COMPutation), is developed for the efficient computation of complex roots of determinantal equations inside a prescribed complex domain. The key to the method presented is the efficient determination of the candidate points inside the domain which, in their close neighborhood, a complex root may lie. Once these points are detected, the algorithm proceeds to a two-dimensional minimization problem with respect to the minimum modulus eigenvalue of the system matrix. In the core of CCOMP exist three sub-algorithms whose tasks are the efficient estimation of the minimum modulus eigenvalues of the system matrix inside the prescribed domain, the efficient computation of candidate points which guarantee the existence of minima, and finally, the computation of minima via bound constrained minimization algorithms. Theoretical results and heuristics support the development and the performance of the algorithm, which is discussed in detail. CCOMP supports general complex matrices, and its efficiency, applicability and validity is demonstrated to a variety of microwave applications.

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.

Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks

NASA Astrophysics Data System (ADS)

Cui, Yaozu; Wang, Xingyuan; Eustace, Justine

2014-12-01

Community structure is a common phenomenon in complex networks, and it has been shown that some communities in complex networks often overlap each other. So in this paper we propose a new algorithm to detect overlapping community structure in complex networks. To identify the overlapping community structure, our algorithm firstly extracts fully connected sub-graphs which are maximal sub-graphs from original networks. Then two maximal sub-graphs having the key pair-vertices can be merged into a new larger sub-graph using some belonging degree functions. Furthermore we extend the modularity function to evaluate the proposed algorithm. In addition, overlapping nodes between communities are founded successfully. Finally we report the comparison between the modularity and the computational complexity of the proposed algorithm with some other existing algorithms. The experimental results show that the proposed algorithm gives satisfactory results.

Quantitative assessment of 12-lead ECG synthesis using CAVIAR.

PubMed

Scherer, J A; Rubel, P; Fayn, J; Willems, J L

1992-01-01

The objective of this study is to assess the performance of patient-specific segment-specific (PSSS) synthesis in QRST complexes using CAVIAR, a new method of the serial comparison for electrocardiograms and vectorcardiograms. A collection of 250 multi-lead recordings from the Common Standards for Quantitative Electrocardiography (CSE) diagnostic pilot study is employed. QRS and ST-T segments are independently synthesized using the PSSS algorithm so that the mean-squared error between the original and estimated waveforms is minimized. CAVIAR compares the recorded and synthesized QRS and ST-T segments and calculates the mean-quadratic deviation as a measure of error. The results of this study indicate that estimated QRS complexes are good representatives of their recorded counterparts, and the integrity of the spatial information is maintained by the PSSS synthesis process. Analysis of the ST-T segments suggests that the deviations between recorded and synthesized waveforms are considerably greater than those associated with the QRS complexes. The poorer performance of the ST-T segments is attributed to magnitude normalization of the spatial loops, low-voltage passages, and noise interference. Using the mean-quadratic deviation and CAVIAR as methods of performance assessment, this study indicates that the PSSS-synthesis algorithm accurately maintains the signal information within the 12-lead electrocardiogram.

Automation of a high risk medication regime algorithm in a home health care population.

PubMed

Olson, Catherine H; Dierich, Mary; Westra, Bonnie L

2014-10-01

Create an automated algorithm for predicting elderly patients' medication-related risks for readmission and validate it by comparing results with a manual analysis of the same patient population. Outcome and Assessment Information Set (OASIS) and medication data were reused from a previous, manual study of 911 patients from 15 Medicare-certified home health care agencies. The medication data was converted into standardized drug codes using APIs managed by the National Library of Medicine (NLM), and then integrated in an automated algorithm that calculates patients' high risk medication regime scores (HRMRs). A comparison of the results between algorithm and manual process was conducted to determine how frequently the HRMR scores were derived which are predictive of readmission. HRMR scores are composed of polypharmacy (number of drugs), Potentially Inappropriate Medications (PIM) (drugs risky to the elderly), and Medication Regimen Complexity Index (MRCI) (complex dose forms, instructions or administration). The algorithm produced polypharmacy, PIM, and MRCI scores that matched with 99%, 87% and 99% of the scores, respectively, from the manual analysis. Imperfect match rates resulted from discrepancies in how drugs were classified and coded by the manual analysis vs. the automated algorithm. HRMR rules lack clarity, resulting in clinical judgments for manual coding that were difficult to replicate in the automated analysis. The high comparison rates for the three measures suggest that an automated clinical tool could use patients' medication records to predict their risks of avoidable readmissions. Copyright © 2014 Elsevier Inc. All rights reserved.

Community detection in complex networks using deep auto-encoded extreme learning machine

NASA Astrophysics Data System (ADS)

Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

2018-06-01

Community detection has long been a fascinating topic in complex networks since the community structure usually unveils valuable information of interest. The prevalence and evolution of deep learning and neural networks have been pushing forward the advancement in various research fields and also provide us numerous useful and off the shelf techniques. In this paper, we put the cascaded stacked autoencoders and the unsupervised extreme learning machine (ELM) together in a two-level embedding process and propose a novel community detection algorithm. Extensive comparison experiments in circumstances of both synthetic and real-world networks manifest the advantages of the proposed algorithm. On one hand, it outperforms the k-means clustering in terms of the accuracy and stability thus benefiting from the determinate dimensions of the ELM block and the integration of sparsity restrictions. On the other hand, it endures smaller complexity than the spectral clustering method on account of the shrinkage in time spent on the eigenvalue decomposition procedure.

Analysis and Recognition of Curve Type as The Basis of Object Recognition in Image

NASA Astrophysics Data System (ADS)

Nugraha, Nurma; Madenda, Sarifuddin; Indarti, Dina; Dewi Agushinta, R.; Ernastuti

2016-06-01

An object in an image when analyzed further will show the characteristics that distinguish one object with another object in an image. Characteristics that are used in object recognition in an image can be a color, shape, pattern, texture and spatial information that can be used to represent objects in the digital image. The method has recently been developed for image feature extraction on objects that share characteristics curve analysis (simple curve) and use the search feature of chain code object. This study will develop an algorithm analysis and the recognition of the type of curve as the basis for object recognition in images, with proposing addition of complex curve characteristics with maximum four branches that will be used for the process of object recognition in images. Definition of complex curve is the curve that has a point of intersection. By using some of the image of the edge detection, the algorithm was able to do the analysis and recognition of complex curve shape well.

Bat-Inspired Algorithm Based Query Expansion for Medical Web Information Retrieval.

PubMed

Khennak, Ilyes; Drias, Habiba

2017-02-01

With the increasing amount of medical data available on the Web, looking for health information has become one of the most widely searched topics on the Internet. Patients and people of several backgrounds are now using Web search engines to acquire medical information, including information about a specific disease, medical treatment or professional advice. Nonetheless, due to a lack of medical knowledge, many laypeople have difficulties in forming appropriate queries to articulate their inquiries, which deem their search queries to be imprecise due the use of unclear keywords. The use of these ambiguous and vague queries to describe the patients' needs has resulted in a failure of Web search engines to retrieve accurate and relevant information. One of the most natural and promising method to overcome this drawback is Query Expansion. In this paper, an original approach based on Bat Algorithm is proposed to improve the retrieval effectiveness of query expansion in medical field. In contrast to the existing literature, the proposed approach uses Bat Algorithm to find the best expanded query among a set of expanded query candidates, while maintaining low computational complexity. Moreover, this new approach allows the determination of the length of the expanded query empirically. Numerical results on MEDLINE, the on-line medical information database, show that the proposed approach is more effective and efficient compared to the baseline.

Comparison of Different Machine Learning Algorithms for Lithological Mapping Using Remote Sensing Data and Morphological Features: A Case Study in Kurdistan Region, NE Iraq

NASA Astrophysics Data System (ADS)

Othman, Arsalan; Gloaguen, Richard

2015-04-01

Topographic effects and complex vegetation cover hinder lithology classification in mountain regions based not only in field, but also in reflectance remote sensing data. The area of interest "Bardi-Zard" is located in the NE of Iraq. It is part of the Zagros orogenic belt, where seven lithological units outcrop and is known for its chromite deposit. The aim of this study is to compare three machine learning algorithms (MLAs): Maximum Likelihood (ML), Support Vector Machines (SVM), and Random Forest (RF) in the context of a supervised lithology classification task using Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite, its derived, spatial information (spatial coordinates) and geomorphic data. We emphasize the enhancement in remote sensing lithological mapping accuracy that arises from the integration of geomorphic features and spatial information (spatial coordinates) in classifications. This study identifies that RF is better than ML and SVM algorithms in almost the sixteen combination datasets, which were tested. The overall accuracy of the best dataset combination with the RF map for the all seven classes reach ~80% and the producer and user's accuracies are ~73.91% and 76.09% respectively while the kappa coefficient is ~0.76. TPI is more effective with SVM algorithm than an RF algorithm. This paper demonstrates that adding geomorphic indices such as TPI and spatial information in the dataset increases the lithological classification accuracy.

Effects of visualization on algorithm comprehension

NASA Astrophysics Data System (ADS)

Mulvey, Matthew

Computer science students are expected to learn and apply a variety of core algorithms which are an essential part of the field. Any one of these algorithms by itself is not necessarily extremely complex, but remembering the large variety of algorithms and the differences between them is challenging. To address this challenge, we present a novel algorithm visualization tool designed to enhance students understanding of Dijkstra's algorithm by allowing them to discover the rules of the algorithm for themselves. It is hoped that a deeper understanding of the algorithm will help students correctly select, adapt and apply the appropriate algorithm when presented with a problem to solve, and that what is learned here will be applicable to the design of other visualization tools designed to teach different algorithms. Our visualization tool is currently in the prototype stage, and this thesis will discuss the pedagogical approach that informs its design, as well as the results of some initial usability testing. Finally, to clarify the direction for further development of the tool, four different variations of the prototype were implemented, and the instructional effectiveness of each was assessed by having a small sample participants use the different versions of the prototype and then take a quiz to assess their comprehension of the algorithm.

An efficient non-dominated sorting method for evolutionary algorithms.

PubMed

Fang, Hongbing; Wang, Qian; Tu, Yi-Cheng; Horstemeyer, Mark F

2008-01-01

We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-dominated fronts takes the majority of total computational time (excluding the cost of fitness evaluations) of NSGA-II, making this algorithm faster will significantly improve the overall efficiency of NSGA-II and other genetic algorithms using non-dominated sorting. The new non-dominated sorting algorithm proposed in this study reduces the number of redundant comparisons existing in the algorithm of NSGA-II by recording the dominance information among solutions from their first comparisons. By utilizing a new data structure called the dominance tree and the divide-and-conquer mechanism, the new algorithm is faster than NSGA-II for different numbers of objective functions. Although the number of solution comparisons by the proposed algorithm is close to that of NSGA-II when the number of objectives becomes large, the total computational time shows that the proposed algorithm still has better efficiency because of the adoption of the dominance tree structure and the divide-and-conquer mechanism.

The infection algorithm: an artificial epidemic approach for dense stereo correspondence.

PubMed

Olague, Gustavo; Fernández, Francisco; Pérez, Cynthia B; Lutton, Evelyne

2006-01-01

We present a new bio-inspired approach applied to a problem of stereo image matching. This approach is based on an artificial epidemic process, which we call the infection algorithm. The problem at hand is a basic one in computer vision for 3D scene reconstruction. It has many complex aspects and is known as an extremely difficult one. The aim is to match the contents of two images in order to obtain 3D information that allows the generation of simulated projections from a viewpoint that is different from the ones of the initial photographs. This process is known as view synthesis. The algorithm we propose exploits the image contents in order to produce only the necessary 3D depth information, while saving computational time. It is based on a set of distributed rules, which propagate like an artificial epidemic over the images. Experiments on a pair of real images are presented, and realistic reprojected images have been generated.

A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations: CESM/CAM EVALUATION BY DECISION TREES

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

Soner Yorgun, M.; Rood, Richard B.

An object-based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smoothmore » topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small-scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales.« less

A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations: CESM/CAM EVALUATION BY DECISION TREES

DOE PAGES

Soner Yorgun, M.; Rood, Richard B.

2016-11-11

An object-based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smoothmore » topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small-scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales.« less

Twelve automated thresholding methods for segmentation of PET images: a phantom study.

PubMed

Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M

2012-06-21

Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical (18)F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.

Twelve automated thresholding methods for segmentation of PET images: a phantom study

NASA Astrophysics Data System (ADS)

Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M.

2012-06-01

Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical 18F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.

Data-directed RNA secondary structure prediction using probabilistic modeling

PubMed Central

Deng, Fei; Ledda, Mirko; Vaziri, Sana; Aviran, Sharon

2016-01-01

Structure dictates the function of many RNAs, but secondary RNA structure analysis is either labor intensive and costly or relies on computational predictions that are often inaccurate. These limitations are alleviated by integration of structure probing data into prediction algorithms. However, existing algorithms are optimized for a specific type of probing data. Recently, new chemistries combined with advances in sequencing have facilitated structure probing at unprecedented scale and sensitivity. These novel technologies and anticipated wealth of data highlight a need for algorithms that readily accommodate more complex and diverse input sources. We implemented and investigated a recently outlined probabilistic framework for RNA secondary structure prediction and extended it to accommodate further refinement of structural information. This framework utilizes direct likelihood-based calculations of pseudo-energy terms per considered structural context and can readily accommodate diverse data types and complex data dependencies. We use real data in conjunction with simulations to evaluate performances of several implementations and to show that proper integration of structural contexts can lead to improvements. Our tests also reveal discrepancies between real data and simulations, which we show can be alleviated by refined modeling. We then propose statistical preprocessing approaches to standardize data interpretation and integration into such a generic framework. We further systematically quantify the information content of data subsets, demonstrating that high reactivities are major drivers of SHAPE-directed predictions and that better understanding of less informative reactivities is key to further improvements. Finally, we provide evidence for the adaptive capability of our framework using mock probe simulations. PMID:27251549

Blind separation of overlapping partials in harmonic musical notes using amplitude and phase reconstruction

NASA Astrophysics Data System (ADS)

de León, Jesús Ponce; Beltrán, José Ramón

2012-12-01

In this study, a new method of blind audio source separation (BASS) of monaural musical harmonic notes is presented. The input (mixed notes) signal is processed using a flexible analysis and synthesis algorithm (complex wavelet additive synthesis, CWAS), which is based on the complex continuous wavelet transform. When the harmonics from two or more sources overlap in a certain frequency band (or group of bands), a new technique based on amplitude similarity criteria is used to obtain an approximation to the original partial information. The aim is to show that the CWAS algorithm can be a powerful tool in BASS. Compared with other existing techniques, the main advantages of the proposed algorithm are its accuracy in the instantaneous phase estimation, its synthesis capability and that the only input information needed is the mixed signal itself. A set of synthetically mixed monaural isolated notes have been analyzed using this method, in eight different experiments: the same instrument playing two notes within the same octave and two harmonically related notes (5th and 12th intervals), two different musical instruments playing 5th and 12th intervals, two different instruments playing non-harmonic notes, major and minor chords played by the same musical instrument, three different instruments playing non-harmonically related notes and finally the mixture of a inharmonic instrument (piano) and one harmonic instrument. The results obtained show the strength of the technique.

Use of information entropy measures of sitting postural sway to quantify developmental delay in infants

PubMed Central

Deffeyes, Joan E; Harbourne, Regina T; DeJong, Stacey L; Kyvelidou, Anastasia; Stuberg, Wayne A; Stergiou, Nicholas

2009-01-01

Background By quantifying the information entropy of postural sway data, the complexity of the postural movement of different populations can be assessed, giving insight into pathologic motor control functioning. Methods In this study, developmental delay of motor control function in infants was assessed by analysis of sitting postural sway data acquired from force plate center of pressure measurements. Two types of entropy measures were used: symbolic entropy, including a new asymmetric symbolic entropy measure, and approximate entropy, a more widely used entropy measure. For each method of analysis, parameters were adjusted to optimize the separation of the results from the infants with delayed development from infants with typical development. Results The method that gave the widest separation between the populations was the asymmetric symbolic entropy method, which we developed by modification of the symbolic entropy algorithm. The approximate entropy algorithm also performed well, using parameters optimized for the infant sitting data. The infants with delayed development were found to have less complex patterns of postural sway in the medial-lateral direction, and were found to have different left-right symmetry in their postural sway, as compared to typically developing infants. Conclusion The results of this study indicate that optimization of the entropy algorithm for infant sitting postural sway data can greatly improve the ability to separate the infants with developmental delay from typically developing infants. PMID:19671183

Rate distortion optimal bit allocation methods for volumetric data using JPEG 2000.

PubMed

Kosheleva, Olga M; Usevitch, Bryan E; Cabrera, Sergio D; Vidal, Edward

2006-08-01

Computer modeling programs that generate three-dimensional (3-D) data on fine grids are capable of generating very large amounts of information. These data sets, as well as 3-D sensor/measured data sets, are prime candidates for the application of data compression algorithms. A very flexible and powerful compression algorithm for imagery data is the newly released JPEG 2000 standard. JPEG 2000 also has the capability to compress volumetric data, as described in Part 2 of the standard, by treating the 3-D data as separate slices. As a decoder standard, JPEG 2000 does not describe any specific method to allocate bits among the separate slices. This paper proposes two new bit allocation algorithms for accomplishing this task. The first procedure is rate distortion optimal (for mean squared error), and is conceptually similar to postcompression rate distortion optimization used for coding codeblocks within JPEG 2000. The disadvantage of this approach is its high computational complexity. The second bit allocation algorithm, here called the mixed model (MM) approach, mathematically models each slice's rate distortion curve using two distinct regions to get more accurate modeling at low bit rates. These two bit allocation algorithms are applied to a 3-D Meteorological data set. Test results show that the MM approach gives distortion results that are nearly identical to the optimal approach, while significantly reducing computational complexity.

Coherent multiscale image processing using dual-tree quaternion wavelets.

PubMed

Chan, Wai Lam; Choi, Hyeokho; Baraniuk, Richard G

2008-07-01

The dual-tree quaternion wavelet transform (QWT) is a new multiscale analysis tool for geometric image features. The QWT is a near shift-invariant tight frame representation whose coefficients sport a magnitude and three phases: two phases encode local image shifts while the third contains image texture information. The QWT is based on an alternative theory for the 2-D Hilbert transform and can be computed using a dual-tree filter bank with linear computational complexity. To demonstrate the properties of the QWT's coherent magnitude/phase representation, we develop an efficient and accurate procedure for estimating the local geometrical structure of an image. We also develop a new multiscale algorithm for estimating the disparity between a pair of images that is promising for image registration and flow estimation applications. The algorithm features multiscale phase unwrapping, linear complexity, and sub-pixel estimation accuracy.

Fire detection system using random forest classification for image sequences of complex background

NASA Astrophysics Data System (ADS)

Kim, Onecue; Kang, Dong-Joong

2013-06-01

We present a fire alarm system based on image processing that detects fire accidents in various environments. To reduce false alarms that frequently appeared in earlier systems, we combined image features including color, motion, and blinking information. We specifically define the color conditions of fires in hue, saturation and value, and RGB color space. Fire features are represented as intensity variation, color mean and variance, motion, and image differences. Moreover, blinking fire features are modeled by using crossing patches. We propose an algorithm that classifies patches into fire or nonfire areas by using random forest supervised learning. We design an embedded surveillance device made with acrylonitrile butadiene styrene housing for stable fire detection in outdoor environments. The experimental results show that our algorithm works robustly in complex environments and is able to detect fires in real time.

Binding Direction-Based Two-Dimensional Flattened Contact Area Computing Algorithm for Protein-Protein Interactions.

PubMed

Kang, Beom Sik; Pugalendhi, GaneshKumar; Kim, Ku-Jin

2017-10-13

Interactions between protein molecules are essential for the assembly, function, and regulation of proteins. The contact region between two protein molecules in a protein complex is usually complementary in shape for both molecules and the area of the contact region can be used to estimate the binding strength between two molecules. Although the area is a value calculated from the three-dimensional surface, it cannot represent the three-dimensional shape of the surface. Therefore, we propose an original concept of two-dimensional contact area which provides further information such as the ruggedness of the contact region. We present a novel algorithm for calculating the binding direction between two molecules in a protein complex, and then suggest a method to compute the two-dimensional flattened area of the contact region between two molecules based on the binding direction.

Quantum Associative Neural Network with Nonlinear Search Algorithm

NASA Astrophysics Data System (ADS)

Zhou, Rigui; Wang, Huian; Wu, Qian; Shi, Yang

2012-03-01

Based on analysis on properties of quantum linear superposition, to overcome the complexity of existing quantum associative memory which was proposed by Ventura, a new storage method for multiply patterns is proposed in this paper by constructing the quantum array with the binary decision diagrams. Also, the adoption of the nonlinear search algorithm increases the pattern recalling speed of this model which has multiply patterns to O( {log2}^{2^{n -t}} ) = O( n - t ) time complexity, where n is the number of quantum bit and t is the quantum information of the t quantum bit. Results of case analysis show that the associative neural network model proposed in this paper based on quantum learning is much better and optimized than other researchers' counterparts both in terms of avoiding the additional qubits or extraordinary initial operators, storing pattern and improving the recalling speed.

Cognitive foundations for model-based sensor fusion

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.; Weijers, Bertus; Mutz, Chris W.

2003-08-01

Target detection, tracking, and sensor fusion are complicated problems, which usually are performed sequentially. First detecting targets, then tracking, then fusing multiple sensors reduces computations. This procedure however is inapplicable to difficult targets which cannot be reliably detected using individual sensors, on individual scans or frames. In such more complicated cases one has to perform functions of fusing, tracking, and detecting concurrently. This often has led to prohibitive combinatorial complexity and, as a consequence, to sub-optimal performance as compared to the information-theoretic content of all the available data. It is well appreciated that in this task the human mind is by far superior qualitatively to existing mathematical methods of sensor fusion, however, the human mind is limited in the amount of information and speed of computation it can cope with. Therefore, research efforts have been devoted toward incorporating "biological lessons" into smart algorithms, yet success has been limited. Why is this so, and how to overcome existing limitations? The fundamental reasons for current limitations are analyzed and a potentially breakthrough research and development effort is outlined. We utilize the way our mind combines emotions and concepts in the thinking process and present the mathematical approach to accomplishing this in the current technology computers. The presentation will summarize the difficulties encountered by intelligent systems over the last 50 years related to combinatorial complexity, analyze the fundamental limitations of existing algorithms and neural networks, and relate it to the type of logic underlying the computational structure: formal, multivalued, and fuzzy logic. A new concept of dynamic logic will be introduced along with algorithms capable of pulling together all the available information from multiple sources. This new mathematical technique, like our brain, combines conceptual understanding with emotional evaluation and overcomes the combinatorial complexity of concurrent fusion, tracking, and detection. The presentation will discuss examples of performance, where computational speedups of many orders of magnitude were attained leading to performance improvements of up to 10 dB (and better).

A novel complex networks clustering algorithm based on the core influence of nodes.

PubMed

Tong, Chao; Niu, Jianwei; Dai, Bin; Xie, Zhongyu

2014-01-01

In complex networks, cluster structure, identified by the heterogeneity of nodes, has become a common and important topological property. Network clustering methods are thus significant for the study of complex networks. Currently, many typical clustering algorithms have some weakness like inaccuracy and slow convergence. In this paper, we propose a clustering algorithm by calculating the core influence of nodes. The clustering process is a simulation of the process of cluster formation in sociology. The algorithm detects the nodes with core influence through their betweenness centrality, and builds the cluster's core structure by discriminant functions. Next, the algorithm gets the final cluster structure after clustering the rest of the nodes in the network by optimizing method. Experiments on different datasets show that the clustering accuracy of this algorithm is superior to the classical clustering algorithm (Fast-Newman algorithm). It clusters faster and plays a positive role in revealing the real cluster structure of complex networks precisely.

An image overall complexity evaluation method based on LSD line detection

NASA Astrophysics Data System (ADS)

Li, Jianan; Duan, Jin; Yang, Xu; Xiao, Bo

2017-04-01

In the artificial world, whether it is the city's traffic roads or engineering buildings contain a lot of linear features. Therefore, the research on the image complexity of linear information has become an important research direction in digital image processing field. This paper, by detecting the straight line information in the image and using the straight line as the parameter index, establishing the quantitative and accurate mathematics relationship. In this paper, we use LSD line detection algorithm which has good straight-line detection effect to detect the straight line, and divide the detected line by the expert consultation strategy. Then we use the neural network to carry on the weight training and get the weight coefficient of the index. The image complexity is calculated by the complexity calculation model. The experimental results show that the proposed method is effective. The number of straight lines in the image, the degree of dispersion, uniformity and so on will affect the complexity of the image.

Anatomisation with slicing: a new privacy preservation approach for multiple sensitive attributes.

PubMed

Susan, V Shyamala; Christopher, T

2016-01-01

An enormous quantity of personal health information is available in recent decades and tampering of any part of this information imposes a great risk to the health care field. Existing anonymization methods are only apt for single sensitive and low dimensional data to keep up with privacy specifically like generalization and bucketization. In this paper, an anonymization technique is proposed that is a combination of the benefits of anatomization, and enhanced slicing approach adhering to the principle of k-anonymity and l-diversity for the purpose of dealing with high dimensional data along with multiple sensitive data. The anatomization approach dissociates the correlation observed between the quasi identifier attributes and sensitive attributes (SA) and yields two separate tables with non-overlapping attributes. In the enhanced slicing algorithm, vertical partitioning does the grouping of the correlated SA in ST together and thereby minimizes the dimensionality by employing the advanced clustering algorithm. In order to get the optimal size of buckets, tuple partitioning is conducted by MFA. The experimental outcomes indicate that the proposed method can preserve privacy of data with numerous SA. The anatomization approach minimizes the loss of information and slicing algorithm helps in the preservation of correlation and utility which in turn results in reducing the data dimensionality and information loss. The advanced clustering algorithms prove its efficiency by minimizing the time and complexity. Furthermore, this work sticks to the principle of k-anonymity, l-diversity and thus avoids privacy threats like membership, identity and attributes disclosure.

Low complexity 1D IDCT for 16-bit parallel architectures

NASA Astrophysics Data System (ADS)

Bivolarski, Lazar

2007-09-01

This paper shows that using the Loeffler, Ligtenberg, and Moschytz factorization of 8-point IDCT [2] one-dimensional (1-D) algorithm as a fast approximation of the Discrete Cosine Transform (DCT) and using only 16 bit numbers, it is possible to create in an IEEE 1180-1990 compliant and multiplierless algorithm with low computational complexity. This algorithm as characterized by its structure is efficiently implemented on parallel high performance architectures as well as due to its low complexity is sufficient for wide range of other architectures. Additional constraint on this work was the requirement of compliance with the existing MPEG standards. The hardware implementation complexity and low resources where also part of the design criteria for this algorithm. This implementation is also compliant with the precision requirements described in MPEG IDCT precision specification ISO/IEC 23002-1. Complexity analysis is performed as an extension to the simple measure of shifts and adds for the multiplierless algorithm as additional operations are included in the complexity measure to better describe the actual transform implementation complexity.

Analyzing milestoning networks for molecular kinetics: definitions, algorithms, and examples.

PubMed

Viswanath, Shruthi; Kreuzer, Steven M; Cardenas, Alfredo E; Elber, Ron

2013-11-07

Network representations are becoming increasingly popular for analyzing kinetic data from techniques like Milestoning, Markov State Models, and Transition Path Theory. Mapping continuous phase space trajectories into a relatively small number of discrete states helps in visualization of the data and in dissecting complex dynamics to concrete mechanisms. However, not only are molecular networks derived from molecular dynamics simulations growing in number, they are also getting increasingly complex, owing partly to the growth in computer power that allows us to generate longer and better converged trajectories. The increased complexity of the networks makes simple interpretation and qualitative insight of the molecular systems more difficult to achieve. In this paper, we focus on various network representations of kinetic data and algorithms to identify important edges and pathways in these networks. The kinetic data can be local and partial (such as the value of rate coefficients between states) or an exact solution to kinetic equations for the entire system (such as the stationary flux between vertices). In particular, we focus on the Milestoning method that provides fluxes as the main output. We proposed Global Maximum Weight Pathways as a useful tool for analyzing molecular mechanism in Milestoning networks. A closely related definition was made in the context of Transition Path Theory. We consider three algorithms to find Global Maximum Weight Pathways: Recursive Dijkstra's, Edge-Elimination, and Edge-List Bisection. The asymptotic efficiency of the algorithms is analyzed and numerical tests on finite networks show that Edge-List Bisection and Recursive Dijkstra's algorithms are most efficient for sparse and dense networks, respectively. Pathways are illustrated for two examples: helix unfolding and membrane permeation. Finally, we illustrate that networks based on local kinetic information can lead to incorrect interpretation of molecular mechanisms.

On-Demand Associative Cross-Language Information Retrieval

NASA Astrophysics Data System (ADS)

Geraldo, André Pinto; Moreira, Viviane P.; Gonçalves, Marcos A.

This paper proposes the use of algorithms for mining association rules as an approach for Cross-Language Information Retrieval. These algorithms have been widely used to analyse market basket data. The idea is to map the problem of finding associations between sales items to the problem of finding term translations over a parallel corpus. The proposal was validated by means of experiments using queries in two distinct languages: Portuguese and Finnish to retrieve documents in English. The results show that the performance of our proposed approach is comparable to the performance of the monolingual baseline and to query translation via machine translation, even though these systems employ more complex Natural Language Processing techniques. The combination between machine translation and our approach yielded the best results, even outperforming the monolingual baseline.

Problems of Automation and Management Principles Information Flow in Manufacturing

NASA Astrophysics Data System (ADS)

Grigoryuk, E. N.; Bulkin, V. V.

2017-07-01

Automated control systems of technological processes are complex systems that are characterized by the presence of elements of the overall focus, the systemic nature of the implemented algorithms for the exchange and processing of information, as well as a large number of functional subsystems. The article gives examples of automatic control systems and automated control systems of technological processes held parallel between them by identifying strengths and weaknesses. Other proposed non-standard control system of technological process.

A joint swarm intelligence algorithm for multi-user detection in MIMO-OFDM system

NASA Astrophysics Data System (ADS)

Hu, Fengye; Du, Dakun; Zhang, Peng; Wang, Zhijun

2014-11-01

In the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, traditional multi-user detection (MUD) algorithms that usually used to suppress multiple access interference are difficult to balance system detection performance and the complexity of the algorithm. To solve this problem, this paper proposes a joint swarm intelligence algorithm called Ant Colony and Particle Swarm Optimisation (AC-PSO) by integrating particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms. According to simulation results, it has been shown that, with low computational complexity, the MUD for the MIMO-OFDM system based on AC-PSO algorithm gains comparable MUD performance with maximum likelihood algorithm. Thus, the proposed AC-PSO algorithm provides a satisfactory trade-off between computational complexity and detection performance.

An efficient link prediction index for complex military organization

NASA Astrophysics Data System (ADS)

Fan, Changjun; Liu, Zhong; Lu, Xin; Xiu, Baoxin; Chen, Qing

2017-03-01

Quality of information is crucial for decision-makers to judge the battlefield situations and design the best operation plans, however, real intelligence data are often incomplete and noisy, where missing links prediction methods and spurious links identification algorithms can be applied, if modeling the complex military organization as the complex network where nodes represent functional units and edges denote communication links. Traditional link prediction methods usually work well on homogeneous networks, but few for the heterogeneous ones. And the military network is a typical heterogeneous network, where there are different types of nodes and edges. In this paper, we proposed a combined link prediction index considering both the nodes' types effects and nodes' structural similarities, and demonstrated that it is remarkably superior to all the 25 existing similarity-based methods both in predicting missing links and identifying spurious links in a real military network data; we also investigated the algorithms' robustness under noisy environment, and found the mistaken information is more misleading than incomplete information in military areas, which is different from that in recommendation systems, and our method maintained the best performance under the condition of small noise. Since the real military network intelligence must be carefully checked at first due to its significance, and link prediction methods are just adopted to purify the network with the left latent noise, the method proposed here is applicable in real situations. In the end, as the FINC-E model, here used to describe the complex military organizations, is also suitable to many other social organizations, such as criminal networks, business organizations, etc., thus our method has its prospects in these areas for many tasks, like detecting the underground relationships between terrorists, predicting the potential business markets for decision-makers, and so on.

Automatic Detection and Recognition of Craters Based on the Spectral Features of Lunar Rocks and Minerals

NASA Astrophysics Data System (ADS)

Ye, L.; Xu, X.; Luan, D.; Jiang, W.; Kang, Z.

2017-07-01

Crater-detection approaches can be divided into four categories: manual recognition, shape-profile fitting algorithms, machine-learning methods and geological information-based analysis using terrain and spectral data. The mainstream method is Shape-profile fitting algorithms. Many scholars throughout the world use the illumination gradient information to fit standard circles by least square method. Although this method has achieved good results, it is difficult to identify the craters with poor "visibility", complex structure and composition. Moreover, the accuracy of recognition is difficult to be improved due to the multiple solutions and noise interference. Aiming at the problem, we propose a method for the automatic extraction of impact craters based on spectral characteristics of the moon rocks and minerals: 1) Under the condition of sunlight, the impact craters are extracted from MI by condition matching and the positions as well as diameters of the craters are obtained. 2) Regolith is spilled while lunar is impacted and one of the elements of lunar regolith is iron. Therefore, incorrectly extracted impact craters can be removed by judging whether the crater contains "non iron" element. 3) Craters which are extracted correctly, are divided into two types: simple type and complex type according to their diameters. 4) Get the information of titanium and match the titanium distribution of the complex craters with normal distribution curve, then calculate the goodness of fit and set the threshold. The complex craters can be divided into two types: normal distribution curve type of titanium and non normal distribution curve type of titanium. We validated our proposed method with MI acquired by SELENE. Experimental results demonstrate that the proposed method has good performance in the test area.

Stereo vision tracking of multiple objects in complex indoor environments.

PubMed

Marrón-Romera, Marta; García, Juan C; Sotelo, Miguel A; Pizarro, Daniel; Mazo, Manuel; Cañas, José M; Losada, Cristina; Marcos, Alvaro

2010-01-01

This paper presents a novel system capable of solving the problem of tracking multiple targets in a crowded, complex and dynamic indoor environment, like those typical of mobile robot applications. The proposed solution is based on a stereo vision set in the acquisition step and a probabilistic algorithm in the obstacles position estimation process. The system obtains 3D position and speed information related to each object in the robot's environment; then it achieves a classification between building elements (ceiling, walls, columns and so on) and the rest of items in robot surroundings. All objects in robot surroundings, both dynamic and static, are considered to be obstacles but the structure of the environment itself. A combination of a Bayesian algorithm and a deterministic clustering process is used in order to obtain a multimodal representation of speed and position of detected obstacles. Performance of the final system has been tested against state of the art proposals; test results validate the authors' proposal. The designed algorithms and procedures provide a solution to those applications where similar multimodal data structures are found.

New approaches to optimization in aerospace conceptual design

NASA Technical Reports Server (NTRS)

Gage, Peter J.

1995-01-01

Aerospace design can be viewed as an optimization process, but conceptual studies are rarely performed using formal search algorithms. Three issues that restrict the success of automatic search are identified in this work. New approaches are introduced to address the integration of analyses and optimizers, to avoid the need for accurate gradient information and a smooth search space (required for calculus-based optimization), and to remove the restrictions imposed by fixed complexity problem formulations. (1) Optimization should be performed in a flexible environment. A quasi-procedural architecture is used to conveniently link analysis modules and automatically coordinate their execution. It efficiently controls a large-scale design tasks. (2) Genetic algorithms provide a search method for discontinuous or noisy domains. The utility of genetic optimization is demonstrated here, but parameter encodings and constraint-handling schemes must be carefully chosen to avoid premature convergence to suboptimal designs. The relationship between genetic and calculus-based methods is explored. (3) A variable-complexity genetic algorithm is created to permit flexible parameterization, so that the level of description can change during optimization. This new optimizer automatically discovers novel designs in structural and aerodynamic tasks.

Classifier dependent feature preprocessing methods

NASA Astrophysics Data System (ADS)

Rodriguez, Benjamin M., II; Peterson, Gilbert L.

2008-04-01

In mobile applications, computational complexity is an issue that limits sophisticated algorithms from being implemented on these devices. This paper provides an initial solution to applying pattern recognition systems on mobile devices by combining existing preprocessing algorithms for recognition. In pattern recognition systems, it is essential to properly apply feature preprocessing tools prior to training classification models in an attempt to reduce computational complexity and improve the overall classification accuracy. The feature preprocessing tools extended for the mobile environment are feature ranking, feature extraction, data preparation and outlier removal. Most desktop systems today are capable of processing a majority of the available classification algorithms without concern of processing while the same is not true on mobile platforms. As an application of pattern recognition for mobile devices, the recognition system targets the problem of steganalysis, determining if an image contains hidden information. The measure of performance shows that feature preprocessing increases the overall steganalysis classification accuracy by an average of 22%. The methods in this paper are tested on a workstation and a Nokia 6620 (Symbian operating system) camera phone with similar results.

Low-complexity R-peak detection in ECG signals: a preliminary step towards ambulatory fetal monitoring.

PubMed

Rooijakkers, Michiel; Rabotti, Chiara; Bennebroek, Martijn; van Meerbergen, Jef; Mischi, Massimo

2011-01-01

Non-invasive fetal health monitoring during pregnancy has become increasingly important. Recent advances in signal processing technology have enabled fetal monitoring during pregnancy, using abdominal ECG recordings. Ubiquitous ambulatory monitoring for continuous fetal health measurement is however still unfeasible due to the computational complexity of noise robust solutions. In this paper an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm. The proposed algorithm is optimized to reduce computational complexity, while increasing the R-peak detection quality compared to existing R-peak detection schemes. Validation of the algorithm is performed on two manually annotated datasets, the MIT/BIH Arrhythmia database and an in-house abdominal database. Both R-peak detection quality and computational complexity are compared to state-of-the-art algorithms as described in the literature. With a detection error rate of 0.22% and 0.12% on the MIT/BIH Arrhythmia and in-house databases, respectively, the quality of the proposed algorithm is comparable to the best state-of-the-art algorithms, at a reduced computational complexity.

An efficient and scalable graph modeling approach for capturing information at different levels in next generation sequencing reads

PubMed Central

2013-01-01

Background Next generation sequencing technologies have greatly advanced many research areas of the biomedical sciences through their capability to generate massive amounts of genetic information at unprecedented rates. The advent of next generation sequencing has led to the development of numerous computational tools to analyze and assemble the millions to billions of short sequencing reads produced by these technologies. While these tools filled an important gap, current approaches for storing, processing, and analyzing short read datasets generally have remained simple and lack the complexity needed to efficiently model the produced reads and assemble them correctly. Results Previously, we presented an overlap graph coarsening scheme for modeling read overlap relationships on multiple levels. Most current read assembly and analysis approaches use a single graph or set of clusters to represent the relationships among a read dataset. Instead, we use a series of graphs to represent the reads and their overlap relationships across a spectrum of information granularity. At each information level our algorithm is capable of generating clusters of reads from the reduced graph, forming an integrated graph modeling and clustering approach for read analysis and assembly. Previously we applied our algorithm to simulated and real 454 datasets to assess its ability to efficiently model and cluster next generation sequencing data. In this paper we extend our algorithm to large simulated and real Illumina datasets to demonstrate that our algorithm is practical for both sequencing technologies. Conclusions Our overlap graph theoretic algorithm is able to model next generation sequencing reads at various levels of granularity through the process of graph coarsening. Additionally, our model allows for efficient representation of the read overlap relationships, is scalable for large datasets, and is practical for both Illumina and 454 sequencing technologies. PMID:24564333

A novel image encryption algorithm using chaos and reversible cellular automata

NASA Astrophysics Data System (ADS)

Wang, Xingyuan; Luan, Dapeng

2013-11-01

In this paper, a novel image encryption scheme is proposed based on reversible cellular automata (RCA) combining chaos. In this algorithm, an intertwining logistic map with complex behavior and periodic boundary reversible cellular automata are used. We split each pixel of image into units of 4 bits, then adopt pseudorandom key stream generated by the intertwining logistic map to permute these units in confusion stage. And in diffusion stage, two-dimensional reversible cellular automata which are discrete dynamical systems are applied to iterate many rounds to achieve diffusion on bit-level, in which we only consider the higher 4 bits in a pixel because the higher 4 bits carry almost the information of an image. Theoretical analysis and experimental results demonstrate the proposed algorithm achieves a high security level and processes good performance against common attacks like differential attack and statistical attack. This algorithm belongs to the class of symmetric systems.

Label-based routing for a family of small-world Farey graphs.

PubMed

Zhai, Yinhu; Wang, Yinhe

2016-05-11

We introduce an informative labelling method for vertices in a family of Farey graphs, and deduce a routing algorithm on all the shortest paths between any two vertices in Farey graphs. The label of a vertex is composed of the precise locating position in graphs and the exact time linking to graphs. All the shortest paths routing between any pair of vertices, which number is exactly the product of two Fibonacci numbers, are determined only by their labels, and the time complexity of the algorithm is O(n). It is the first algorithm to figure out all the shortest paths between any pair of vertices in a kind of deterministic graphs. For Farey networks, the existence of an efficient routing protocol is of interest to design practical communication algorithms in relation to dynamical processes (including synchronization and structural controllability) and also to understand the underlying mechanisms that have shaped their particular structure.

Label-based routing for a family of small-world Farey graphs

NASA Astrophysics Data System (ADS)

Zhai, Yinhu; Wang, Yinhe

2016-05-01

We introduce an informative labelling method for vertices in a family of Farey graphs, and deduce a routing algorithm on all the shortest paths between any two vertices in Farey graphs. The label of a vertex is composed of the precise locating position in graphs and the exact time linking to graphs. All the shortest paths routing between any pair of vertices, which number is exactly the product of two Fibonacci numbers, are determined only by their labels, and the time complexity of the algorithm is O(n). It is the first algorithm to figure out all the shortest paths between any pair of vertices in a kind of deterministic graphs. For Farey networks, the existence of an efficient routing protocol is of interest to design practical communication algorithms in relation to dynamical processes (including synchronization and structural controllability) and also to understand the underlying mechanisms that have shaped their particular structure.

Neural network explanation using inversion.

PubMed

Saad, Emad W; Wunsch, Donald C

2007-01-01

An important drawback of many artificial neural networks (ANN) is their lack of explanation capability [Andrews, R., Diederich, J., & Tickle, A. B. (1996). A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems, 8, 373-389]. This paper starts with a survey of algorithms which attempt to explain the ANN output. We then present HYPINV, a new explanation algorithm which relies on network inversion; i.e. calculating the ANN input which produces a desired output. HYPINV is a pedagogical algorithm, that extracts rules, in the form of hyperplanes. It is able to generate rules with arbitrarily desired fidelity, maintaining a fidelity-complexity tradeoff. To our knowledge, HYPINV is the only pedagogical rule extraction method, which extracts hyperplane rules from continuous or binary attribute neural networks. Different network inversion techniques, involving gradient descent as well as an evolutionary algorithm, are presented. An information theoretic treatment of rule extraction is presented. HYPINV is applied to example synthetic problems, to a real aerospace problem, and compared with similar algorithms using benchmark problems.

Enhancement of A5/1 encryption algorithm

NASA Astrophysics Data System (ADS)

Thomas, Ria Elin; Chandhiny, G.; Sharma, Katyayani; Santhi, H.; Gayathri, P.

2017-11-01

Mobiles have become an integral part of today’s world. Various standards have been proposed for the mobile communication, one of them being GSM. With the rising increase of mobile-based crimes, it is necessary to improve the security of the information passed in the form of voice or data. GSM uses A5/1 for its encryption. It is known that various attacks have been implemented, exploiting the vulnerabilities present within the A5/1 algorithm. Thus, in this paper, we proceed to look at what these vulnerabilities are, and propose the enhanced A5/1 (E-A5/1) where, we try to improve the security provided by the A5/1 algorithm by XORing the key stream generated with a pseudo random number, without increasing the time complexity. We need to study what the vulnerabilities of the base algorithm (A5/1) is, and try to improve upon its security. This will help in the future releases of the A5 family of algorithms.

Definition of an Enhanced Map-Matching Algorithm for Urban Environments with Poor GNSS Signal Quality.

PubMed

Jiménez, Felipe; Monzón, Sergio; Naranjo, Jose Eugenio

2016-02-04

Vehicle positioning is a key factor for numerous information and assistance applications that are included in vehicles and for which satellite positioning is mainly used. However, this positioning process can result in errors and lead to measurement uncertainties. These errors come mainly from two sources: errors and simplifications of digital maps and errors in locating the vehicle. From that inaccurate data, the task of assigning the vehicle's location to a link on the digital map at every instant is carried out by map-matching algorithms. These algorithms have been developed to fulfil that need and attempt to amend these errors to offer the user a suitable positioning. In this research; an algorithm is developed that attempts to solve the errors in positioning when the Global Navigation Satellite System (GNSS) signal reception is frequently lost. The algorithm has been tested with satisfactory results in a complex urban environment of narrow streets and tall buildings where errors and signal reception losses of the GPS receiver are frequent.

Navigable points estimation for mobile robots using binary image skeletonization

NASA Astrophysics Data System (ADS)

Martinez S., Fernando; Jacinto G., Edwar; Montiel A., Holman

2017-02-01

This paper describes the use of image skeletonization for the estimation of all the navigable points, inside a scene of mobile robots navigation. Those points are used for computing a valid navigation path, using standard methods. The main idea is to find the middle and the extreme points of the obstacles in the scene, taking into account the robot size, and create a map of navigable points, in order to reduce the amount of information for the planning algorithm. Those points are located by means of the skeletonization of a binary image of the obstacles and the scene background, along with some other digital image processing algorithms. The proposed algorithm automatically gives a variable number of navigable points per obstacle, depending on the complexity of its shape. As well as, the way how the algorithm can change some of their parameters in order to change the final number of the resultant key points is shown. The results shown here were obtained applying different kinds of digital image processing algorithms on static scenes.

Definition of an Enhanced Map-Matching Algorithm for Urban Environments with Poor GNSS Signal Quality

PubMed Central

Jiménez, Felipe; Monzón, Sergio; Naranjo, Jose Eugenio

2016-01-01

Vehicle positioning is a key factor for numerous information and assistance applications that are included in vehicles and for which satellite positioning is mainly used. However, this positioning process can result in errors and lead to measurement uncertainties. These errors come mainly from two sources: errors and simplifications of digital maps and errors in locating the vehicle. From that inaccurate data, the task of assigning the vehicle’s location to a link on the digital map at every instant is carried out by map-matching algorithms. These algorithms have been developed to fulfil that need and attempt to amend these errors to offer the user a suitable positioning. In this research; an algorithm is developed that attempts to solve the errors in positioning when the Global Navigation Satellite System (GNSS) signal reception is frequently lost. The algorithm has been tested with satisfactory results in a complex urban environment of narrow streets and tall buildings where errors and signal reception losses of the GPS receiver are frequent. PMID:26861320

Quantum computation and analysis of Wigner and Husimi functions: toward a quantum image treatment.

PubMed

Terraneo, M; Georgeot, B; Shepelyansky, D L

2005-06-01

We study the efficiency of quantum algorithms which aim at obtaining phase-space distribution functions of quantum systems. Wigner and Husimi functions are considered. Different quantum algorithms are envisioned to build these functions, and compared with the classical computation. Different procedures to extract more efficiently information from the final wave function of these algorithms are studied, including coarse-grained measurements, amplitude amplification, and measure of wavelet-transformed wave function. The algorithms are analyzed and numerically tested on a complex quantum system showing different behavior depending on parameters: namely, the kicked rotator. The results for the Wigner function show in particular that the use of the quantum wavelet transform gives a polynomial gain over classical computation. For the Husimi distribution, the gain is much larger than for the Wigner function and is larger with the help of amplitude amplification and wavelet transforms. We discuss the generalization of these results to the simulation of other quantum systems. We also apply the same set of techniques to the analysis of real images. The results show that the use of the quantum wavelet transform allows one to lower dramatically the number of measurements needed, but at the cost of a large loss of information.

The DISC Quotient

NASA Astrophysics Data System (ADS)

Elliott, John R.; Baxter, Stephen

2012-09-01

D.I.S.C: Decipherment Impact of a Signal's Content. The authors present a numerical method to characterise the significance of the receipt of a complex and potentially decipherable signal from extraterrestrial intelligence (ETI). The purpose of the scale is to facilitate the public communication of work on any such claimed signal, as such work proceeds, and to assist in its discussion and interpretation. Building on a "position" paper rationale, this paper looks at the DISC quotient proposed and develops the algorithmic steps and comprising measures that form this post detection strategy for information dissemination, based on prior work on message detection, decipherment. As argued, we require a robust and incremental strategy, to disseminate timely, accurate and meaningful information, to the scientific community and the general public, in the event we receive an "alien" signal that displays decipherable information. This post-detection strategy is to serve as a stepwise algorithm for a logical approach to information extraction and a vehicle for sequential information dissemination, to manage societal impact. The "DISC Quotient", which is based on signal analysis processing stages, includes factors based on the signal's data quantity, structure, affinity to known human languages, and likely decipherment times. Comparisons with human and other phenomena are included as a guide to assessing likely societal impact. It is submitted that the development, refinement and implementation of DISC as an integral strategy, during the complex processes involved in post detection and decipherment, is essential if we wish to minimize disruption and optimize dissemination.

GPU accelerated edge-region based level set evolution constrained by 2D gray-scale histogram.

PubMed

Balla-Arabé, Souleymane; Gao, Xinbo; Wang, Bin

2013-07-01

Due to its intrinsic nature which allows to easily handle complex shapes and topological changes, the level set method (LSM) has been widely used in image segmentation. Nevertheless, LSM is computationally expensive, which limits its applications in real-time systems. For this purpose, we propose a new level set algorithm, which uses simultaneously edge, region, and 2D histogram information in order to efficiently segment objects of interest in a given scene. The computational complexity of the proposed LSM is greatly reduced by using the highly parallelizable lattice Boltzmann method (LBM) with a body force to solve the level set equation (LSE). The body force is the link with image data and is defined from the proposed LSE. The proposed LSM is then implemented using an NVIDIA graphics processing units to fully take advantage of the LBM local nature. The new algorithm is effective, robust against noise, independent to the initial contour, fast, and highly parallelizable. The edge and region information enable to detect objects with and without edges, and the 2D histogram information enable the effectiveness of the method in a noisy environment. Experimental results on synthetic and real images demonstrate subjectively and objectively the performance of the proposed method.

Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

NASA Astrophysics Data System (ADS)

Pullanagari, Reddy; Kereszturi, Gábor; Yule, Ian J.; Ghamisi, Pedram

2017-04-01

Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification accuracy. We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines (SVM) and an artificial neural network. The spatial features considered are produced by a gray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.

Scale invariant SURF detector and automatic clustering segmentation for infrared small targets detection

NASA Astrophysics Data System (ADS)

Zhang, Haiying; Bai, Jiaojiao; Li, Zhengjie; Liu, Yan; Liu, Kunhong

2017-06-01

The detection and discrimination of infrared small dim targets is a challenge in automatic target recognition (ATR), because there is no salient information of size, shape and texture. Many researchers focus on mining more discriminative information of targets in temporal-spatial. However, such information may not be available with the change of imaging environments, and the targets size and intensity keep changing in different imaging distance. So in this paper, we propose a novel research scheme using density-based clustering and backtracking strategy. In this scheme, the speeded up robust feature (SURF) detector is applied to capture candidate targets in single frame at first. And then, these points are mapped into one frame, so that target traces form a local aggregation pattern. In order to isolate the targets from noises, a newly proposed density-based clustering algorithm, fast search and find of density peak (FSFDP for short), is employed to cluster targets by the spatial intensive distribution. Two important factors of the algorithm, percent and γ , are exploited fully to determine the clustering scale automatically, so as to extract the trace with highest clutter suppression ratio. And at the final step, a backtracking algorithm is designed to detect and discriminate target trace as well as to eliminate clutter. The consistence and continuity of the short-time target trajectory in temporal-spatial is incorporated into the bounding function to speed up the pruning. Compared with several state-of-arts methods, our algorithm is more effective for the dim targets with lower signal-to clutter ratio (SCR). Furthermore, it avoids constructing the candidate target trajectory searching space, so its time complexity is limited to a polynomial level. The extensive experimental results show that it has superior performance in probability of detection (Pd) and false alarm suppressing rate aiming at variety of complex backgrounds.

Information Search and Decision Making: The Effects of Age and Complexity on Strategy Use

PubMed Central

Queen, Tara L.; Hess, Thomas M.; Ennis, Gilda E.; Dowd, Keith; Grühn, Daniel

2012-01-01

The impact of task complexity on information search strategy and decision quality was examined in a sample of 135 young, middle-aged, and older adults. We were particularly interested in the competing roles of fluid cognitive ability and domain knowledge and experience, with the former being a negative influence and the latter being a positive influence on older adults’ performance. Participants utilized two decision matrices, which varied in complexity, regarding a consumer purchase. Using process tracing software and an algorithm developed to assess decision strategy, we recorded search behavior, strategy selection, and final decision. Contrary to expectations, older adults were not more likely than the younger age groups to engage in information-minimizing search behaviors in response to increases in task complexity. Similarly, adults of all ages used comparable decision strategies and adapted their strategies to the demands of the task. We also examined decision outcomes in relation to participants’ preferences. Overall, it seems that older adults utilize simpler sets of information primarily reflecting the most valued attributes in making their choice. The results of this study suggest that older adults are adaptive in their approach to decision making and this ability may benefit from accrued knowledge and experience. PMID:22663157

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.

Genetic Algorithms for Multiple-Choice Problems

NASA Astrophysics Data System (ADS)

Aickelin, Uwe

2010-04-01

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.

The bilinear complexity and practical algorithms for matrix multiplication

NASA Astrophysics Data System (ADS)

Smirnov, A. V.

2013-12-01

A method for deriving bilinear algorithms for matrix multiplication is proposed. New estimates for the bilinear complexity of a number of problems of the exact and approximate multiplication of rectangular matrices are obtained. In particular, the estimate for the boundary rank of multiplying 3 × 3 matrices is improved and a practical algorithm for the exact multiplication of square n × n matrices is proposed. The asymptotic arithmetic complexity of this algorithm is O( n 2.7743).

Smart algorithms and adaptive methods in computational fluid dynamics

NASA Astrophysics Data System (ADS)

Tinsley Oden, J.

1989-05-01

A review is presented of the use of smart algorithms which employ adaptive methods in processing large amounts of data in computational fluid dynamics (CFD). Smart algorithms use a rationally based set of criteria for automatic decision making in an attempt to produce optimal simulations of complex fluid dynamics problems. The information needed to make these decisions is not known beforehand and evolves in structure and form during the numerical solution of flow problems. Once the code makes a decision based on the available data, the structure of the data may change, and criteria may be reapplied in order to direct the analysis toward an acceptable end. Intelligent decisions are made by processing vast amounts of data that evolve unpredictably during the calculation. The basic components of adaptive methods and their application to complex problems of fluid dynamics are reviewed. The basic components of adaptive methods are: (1) data structures, that is what approaches are available for modifying data structures of an approximation so as to reduce errors; (2) error estimation, that is what techniques exist for estimating error evolution in a CFD calculation; and (3) solvers, what algorithms are available which can function in changing meshes. Numerical examples which demonstrate the viability of these approaches are presented.

Secret information reconciliation based on punctured low-density parity-check codes for continuous-variable quantum key distribution

NASA Astrophysics Data System (ADS)

Jiang, Xue-Qin; Huang, Peng; Huang, Duan; Lin, Dakai; Zeng, Guihua

2017-02-01

Achieving information theoretic security with practical complexity is of great interest to continuous-variable quantum key distribution in the postprocessing procedure. In this paper, we propose a reconciliation scheme based on the punctured low-density parity-check (LDPC) codes. Compared to the well-known multidimensional reconciliation scheme, the present scheme has lower time complexity. Especially when the chosen punctured LDPC code achieves the Shannon capacity, the proposed reconciliation scheme can remove the information that has been leaked to an eavesdropper in the quantum transmission phase. Therefore, there is no information leaked to the eavesdropper after the reconciliation stage. This indicates that the privacy amplification algorithm of the postprocessing procedure is no more needed after the reconciliation process. These features lead to a higher secret key rate, optimal performance, and availability for the involved quantum key distribution scheme.

Comparison of subpixel image registration algorithms

NASA Astrophysics Data System (ADS)

Boye, R. R.; Nelson, C. L.

2009-02-01

Research into the use of multiframe superresolution has led to the development of algorithms for providing images with enhanced resolution using several lower resolution copies. An integral component of these algorithms is the determination of the registration of each of the low resolution images to a reference image. Without this information, no resolution enhancement can be attained. We have endeavored to find a suitable method for registering severely undersampled images by comparing several approaches. To test the algorithms, an ideal image is input to a simulated image formation program, creating several undersampled images with known geometric transformations. The registration algorithms are then applied to the set of low resolution images and the estimated registration parameters compared to the actual values. This investigation is limited to monochromatic images (extension to color images is not difficult) and only considers global geometric transformations. Each registration approach will be reviewed and evaluated with respect to the accuracy of the estimated registration parameters as well as the computational complexity required. In addition, the effects of image content, specifically spatial frequency content, as well as the immunity of the registration algorithms to noise will be discussed.

Combining De Novo Peptide Sequencing Algorithms, A Synergistic Approach to Boost Both Identifications and Confidence in Bottom-up Proteomics.

PubMed

Blank-Landeshammer, Bernhard; Kollipara, Laxmikanth; Biß, Karsten; Pfenninger, Markus; Malchow, Sebastian; Shuvaev, Konstantin; Zahedi, René P; Sickmann, Albert

2017-09-01

Complex mass spectrometry based proteomics data sets are mostly analyzed by protein database searches. While this approach performs considerably well for sequenced organisms, direct inference of peptide sequences from tandem mass spectra, i.e., de novo peptide sequencing, oftentimes is the only way to obtain information when protein databases are absent. However, available algorithms suffer from drawbacks such as lack of validation and often high rates of false positive hits (FP). Here we present a simple method of combining results from commonly available de novo peptide sequencing algorithms, which in conjunction with minor tweaks in data acquisition ensues lower empirical FDR compared to the analysis using single algorithms. Results were validated using state-of-the art database search algorithms as well specifically synthesized reference peptides. Thus, we could increase the number of PSMs meeting a stringent FDR of 5% more than 3-fold compared to the single best de novo sequencing algorithm alone, accounting for an average of 11 120 PSMs (combined) instead of 3476 PSMs (alone) in triplicate 2 h LC-MS runs of tryptic HeLa digestion.

Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks.

PubMed

He, Jieyue; Li, Chaojun; Ye, Baoliu; Zhong, Wei

2012-06-25

Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures. In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules. The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms. Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.

Universal Decoder for PPM of any Order

NASA Technical Reports Server (NTRS)

Moision, Bruce E.

2010-01-01

A recently developed algorithm for demodulation and decoding of a pulse-position- modulation (PPM) signal is suitable as a basis for designing a single hardware decoding apparatus to be capable of handling any PPM order. Hence, this algorithm offers advantages of greater flexibility and lower cost, in comparison with prior such algorithms, which necessitate the use of a distinct hardware implementation for each PPM order. In addition, in comparison with the prior algorithms, the present algorithm entails less complexity in decoding at large orders. An unavoidably lengthy presentation of background information, including definitions of terms, is prerequisite to a meaningful summary of this development. As an aid to understanding, the figure illustrates the relevant processes of coding, modulation, propagation, demodulation, and decoding. An M-ary PPM signal has M time slots per symbol period. A pulse (signifying 1) is transmitted during one of the time slots; no pulse (signifying 0) is transmitted during the other time slots. The information intended to be conveyed from the transmitting end to the receiving end of a radio or optical communication channel is a K-bit vector u. This vector is encoded by an (N,K) binary error-correcting code, producing an N-bit vector a. In turn, the vector a is subdivided into blocks of m = log2(M) bits and each such block is mapped to an M-ary PPM symbol. The resultant coding/modulation scheme can be regarded as equivalent to a nonlinear binary code. The binary vector of PPM symbols, x is transmitted over a Poisson channel, such that there is obtained, at the receiver, a Poisson-distributed photon count characterized by a mean background count nb during no-pulse time slots and a mean signal-plus-background count of ns+nb during a pulse time slot. In the receiver, demodulation of the signal is effected in an iterative soft decoding process that involves consideration of relationships among photon counts and conditional likelihoods of m-bit vectors of coded bits. Inasmuch as the likelihoods of all the m-bit vectors of coded bits mapping to the same PPM symbol are correlated, the best performance is obtained when the joint mbit conditional likelihoods are utilized. Unfortunately, the complexity of decoding, measured in the number of operations per bit, grows exponentially with m, and can thus become prohibitively expensive for large PPM orders. For a system required to handle multiple PPM orders, the cost is even higher because it is necessary to have separate decoding hardware for each order. This concludes the prerequisite background information. In the present algorithm, the decoding process as described above is modified by, among other things, introduction of an lbit marginalizer sub-algorithm. The term "l-bit marginalizer" signifies that instead of m-bit conditional likelihoods, the decoder computes l-bit conditional likelihoods, where l is fixed. Fixing l, regardless of the value of m, makes it possible to use a single hardware implementation for any PPM order. One could minimize the decoding complexity and obtain an especially simple design by fixing l at 1, but this would entail some loss of performance. An intermediate solution is to fix l at some value, greater than 1, that may be less than or greater than m. This solution makes it possible to obtain the desired flexibility to handle any PPM order while compromising between complexity and loss of performance.

A fast complex integer convolution using a hybrid transform

NASA Technical Reports Server (NTRS)

Reed, I. S.; K Truong, T.

1978-01-01

It is shown that the Winograd transform can be combined with a complex integer transform over the Galois field GF(q-squared) to yield a new algorithm for computing the discrete cyclic convolution of complex number points. By this means a fast method for accurately computing the cyclic convolution of a sequence of complex numbers for long convolution lengths can be obtained. This new hybrid algorithm requires fewer multiplications than previous algorithms.

An information theory criteria based blind method for enumerating active users in DS-CDMA system

NASA Astrophysics Data System (ADS)

Samsami Khodadad, Farid; Abed Hodtani, Ghosheh

2014-11-01

In this paper, a new and blind algorithm for active user enumeration in asynchronous direct sequence code division multiple access (DS-CDMA) in multipath channel scenario is proposed. The proposed method is based on information theory criteria. There are two main categories of information criteria which are widely used in active user enumeration, Akaike Information Criterion (AIC) and Minimum Description Length (MDL) information theory criteria. The main difference between these two criteria is their penalty functions. Due to this difference, MDL is a consistent enumerator which has better performance in higher signal-to-noise ratios (SNR) but AIC is preferred in lower SNRs. In sequel, we propose a SNR compliance method based on subspace and training genetic algorithm to have the performance of both of them. Moreover, our method uses only a single antenna, in difference to the previous methods which decrease hardware complexity. Simulation results show that the proposed method is capable of estimating the number of active users without any prior knowledge and the efficiency of the method.

Visual saliency-based fast intracoding algorithm for high efficiency video coding

NASA Astrophysics Data System (ADS)

Zhou, Xin; Shi, Guangming; Zhou, Wei; Duan, Zhemin

2017-01-01

Intraprediction has been significantly improved in high efficiency video coding over H.264/AVC with quad-tree-based coding unit (CU) structure from size 64×64 to 8×8 and more prediction modes. However, these techniques cause a dramatic increase in computational complexity. An intracoding algorithm is proposed that consists of perceptual fast CU size decision algorithm and fast intraprediction mode decision algorithm. First, based on the visual saliency detection, an adaptive and fast CU size decision method is proposed to alleviate intraencoding complexity. Furthermore, a fast intraprediction mode decision algorithm with step halving rough mode decision method and early modes pruning algorithm is presented to selectively check the potential modes and effectively reduce the complexity of computation. Experimental results show that our proposed fast method reduces the computational complexity of the current HM to about 57% in encoding time with only 0.37% increases in BD rate. Meanwhile, the proposed fast algorithm has reasonable peak signal-to-noise ratio losses and nearly the same subjective perceptual quality.

Fast and stable algorithms for computing the principal square root of a complex matrix

NASA Technical Reports Server (NTRS)

Shieh, Leang S.; Lian, Sui R.; Mcinnis, Bayliss C.

1987-01-01

This note presents recursive algorithms that are rapidly convergent and more stable for finding the principal square root of a complex matrix. Also, the developed algorithms are utilized to derive the fast and stable matrix sign algorithms which are useful in developing applications to control system problems.

Algorithms for computing solvents of unilateral second-order matrix polynomials over prime finite fields using lambda-matrices

NASA Astrophysics Data System (ADS)

Burtyka, Filipp

2018-01-01

The paper considers algorithms for finding diagonalizable and non-diagonalizable roots (so called solvents) of monic arbitrary unilateral second-order matrix polynomial over prime finite field. These algorithms are based on polynomial matrices (lambda-matrices). This is an extension of existing general methods for computing solvents of matrix polynomials over field of complex numbers. We analyze how techniques for complex numbers can be adapted for finite field and estimate asymptotic complexity of the obtained algorithms.

Algorithm for detection the QRS complexes based on support vector machine

NASA Astrophysics Data System (ADS)

Van, G. V.; Podmasteryev, K. V.

2017-11-01

The efficiency of computer ECG analysis depends on the accurate detection of QRS-complexes. This paper presents an algorithm for QRS complex detection based of support vector machine (SVM). The proposed algorithm is evaluated on annotated standard databases such as MIT-BIH Arrhythmia database. The QRS detector obtained a sensitivity Se = 98.32% and specificity Sp = 95.46% for MIT-BIH Arrhythmia database. This algorithm can be used as the basis for the software to diagnose electrical activity of the heart.

Computationally efficient algorithm for high sampling-frequency operation of active noise control

NASA Astrophysics Data System (ADS)

Rout, Nirmal Kumar; Das, Debi Prasad; Panda, Ganapati

2015-05-01

In high sampling-frequency operation of active noise control (ANC) system the length of the secondary path estimate and the ANC filter are very long. This increases the computational complexity of the conventional filtered-x least mean square (FXLMS) algorithm. To reduce the computational complexity of long order ANC system using FXLMS algorithm, frequency domain block ANC algorithms have been proposed in past. These full block frequency domain ANC algorithms are associated with some disadvantages such as large block delay, quantization error due to computation of large size transforms and implementation difficulties in existing low-end DSP hardware. To overcome these shortcomings, the partitioned block ANC algorithm is newly proposed where the long length filters in ANC are divided into a number of equal partitions and suitably assembled to perform the FXLMS algorithm in the frequency domain. The complexity of this proposed frequency domain partitioned block FXLMS (FPBFXLMS) algorithm is quite reduced compared to the conventional FXLMS algorithm. It is further reduced by merging one fast Fourier transform (FFT)-inverse fast Fourier transform (IFFT) combination to derive the reduced structure FPBFXLMS (RFPBFXLMS) algorithm. Computational complexity analysis for different orders of filter and partition size are presented. Systematic computer simulations are carried out for both the proposed partitioned block ANC algorithms to show its accuracy compared to the time domain FXLMS algorithm.

Efficient 3D geometric and Zernike moments computation from unstructured surface meshes.

PubMed

Pozo, José María; Villa-Uriol, Maria-Cruz; Frangi, Alejandro F

2011-03-01

This paper introduces and evaluates a fast exact algorithm and a series of faster approximate algorithms for the computation of 3D geometric moments from an unstructured surface mesh of triangles. Being based on the object surface reduces the computational complexity of these algorithms with respect to volumetric grid-based algorithms. In contrast, it can only be applied for the computation of geometric moments of homogeneous objects. This advantage and restriction is shared with other proposed algorithms based on the object boundary. The proposed exact algorithm reduces the computational complexity for computing geometric moments up to order N with respect to previously proposed exact algorithms, from N(9) to N(6). The approximate series algorithm appears as a power series on the rate between triangle size and object size, which can be truncated at any desired degree. The higher the number and quality of the triangles, the better the approximation. This approximate algorithm reduces the computational complexity to N(3). In addition, the paper introduces a fast algorithm for the computation of 3D Zernike moments from the computed geometric moments, with a computational complexity N(4), while the previously proposed algorithm is of order N(6). The error introduced by the proposed approximate algorithms is evaluated in different shapes and the cost-benefit ratio in terms of error, and computational time is analyzed for different moment orders.

The physics of complex systems in information and biology

NASA Astrophysics Data System (ADS)

Walker, Dylan

Citation networks have re-emerged as a topic intense interest in the complex networks community with the recent availability of large-scale data sets. The ranking of citation networks is a necessary practice as a means to improve information navigability and search. Unlike many information networks, the aging characteristics of citation networks require the development of new ranking methods. To account for strong aging characteristics of citation networks, we modify the PageRank algorithm by initially distributing random surfers exponentially with age, in favor of more recent publications. The output of this algorithm, which we call CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information. We optimize parameters of our algorithm to achieve the best performance. The results are compared for two rather different citation networks: all American Physical Society publications between 1893-2003 and the set of high-energy physics theory (hep-th) preprints. Despite major differences between these two networks, we find that their optimal parameters for the CiteRank algorithm are remarkably similar. The advantages and performance of CiteRank over more conventional methods of ranking publications are discussed. Collaborative voting systems have emerged as an abundant form of real-world, complex information systems that exist in a variety of online applications. These systems are comprised of large populations of users that collectively submit and vote on objects. While the specific properties of these systems vary widely, many of them share a core set of features and dynamical behaviors that govern their evolution. We study a subset of these systems that involve material of a time-critical nature as in the popular example of news items. We consider a general model system in which articles are introduced, voted on by a population of users, and subsequently expire after a proscribed period of time. To study the interaction between popularity and quality, we introduce simple stochastic models of user behavior that approximate differing user quality and susceptibility to the common notion of popularity. We define a metric to quantify user reputation in a manner that is self-consistent, adaptable and content-blind and shows good correlation with the probability that a user behaves in an optimal fashion. We further construct a mechanism for ranking documents that take into account user reputation and provides substantial improvement in the time-critical performance of the system. The structure of complex systems have been well studied in the context of both information and biological systems. More recently, dynamics in complex systems that occur over the background of the underlying network has received a great deal of attention. In particular, the study of fluctuations in complex systems has emerged as an issue central to understanding dynamical behavior. We approach the problem of collective effects of the underlying network on dynamical fluctuations by considering the protein-protein interaction networks for the system of the living cell. We consider two types of fluctuations in the mass-action equilibrium in protein binding networks. The first type is driven by relatively slow changes in total concentrations (copy numbers) of interacting proteins. The second type, to which we refer to as spontaneous, is caused by quickly decaying thermodynamic deviations away from the mass-action equilibrium of the system. As such they are amenable to methods of equilibrium statistical mechanics used in our study. We investigate the effects of network connectivity on these fluctuations by comparing them to different scenarios in which the interacting pair is isolated form the rest of the network. Such comparison allows us to analytically derive upper and lower bounds on network fluctuations. The collective effects are shown to sometimes lead to relatively large amplification of spontaneous fluctuations as compared to the expectation for isolated dimers. As a consequence of this, the strength of both types of fluctuations is positively correlated with the overall network connectivity of proteins forming the complex. On the other hand, the relative amplitude of fluctuations is negatively correlated with the equilibrium concentration of the complex. Our general findings are illustrated using a curated network of protein-protein interactions and multi-protein complexes in bakers yeast with experimentally determined protein concentrations.

Angle-of-Arrival Assisted GNSS Collaborative Positioning.

PubMed

Huang, Bin; Yao, Zheng; Cui, Xiaowei; Lu, Mingquan

2016-06-20

For outdoor and global navigation satellite system (GNSS) challenged scenarios, collaborative positioning algorithms are proposed to fuse information from GNSS satellites and terrestrial wireless systems. This paper derives the Cramer-Rao lower bound (CRLB) and algorithms for the angle-of-arrival (AOA)-assisted GNSS collaborative positioning. Based on the CRLB model and collaborative positioning algorithms, theoretical analysis are performed to specify the effects of various factors on the accuracy of collaborative positioning, including the number of users, their distribution and AOA measurements accuracy. Besides, the influences of the relative location of the collaborative users are also discussed in order to choose appropriate neighboring users, which is in favor of reducing computational complexity. Simulations and actual experiment are carried out with several GNSS receivers in different scenarios, and the results are consistent with theoretical analysis.

Data compression and genomes: a two-dimensional life domain map.

PubMed

Menconi, Giulia; Benci, Vieri; Buiatti, Marcello

2008-07-21

We define the complexity of DNA sequences as the information content per nucleotide, calculated by means of some Lempel-Ziv data compression algorithm. It is possible to use the statistics of the complexity values of the functional regions of different complete genomes to distinguish among genomes of different domains of life (Archaea, Bacteria and Eukarya). We shall focus on the distribution function of the complexity of non-coding regions. We show that the three domains may be plotted in separate regions within the two-dimensional space where the axes are the skewness coefficient and the curtosis coefficient of the aforementioned distribution. Preliminary results on 15 genomes are introduced.

RAVEN: a GUI and an Artificial Intelligence Engine in a Dynamic PRA Framework

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

C. Rabiti; D. Mandelli; A. Alfonsi

Increases in computational power and pressure for more accurate simulations and estimations of accident scenario consequences are driving the need for Dynamic Probabilistic Risk Assessment (PRA) [1] of very complex models. While more sophisticated algorithms and computational power address the back end of this challenge, the front end is still handled by engineers that need to extract meaningful information from the large amount of data and build these complex models. Compounding this problem is the difficulty in knowledge transfer and retention, and the increasing speed of software development. The above-described issues would have negatively impacted deployment of the new highmore » fidelity plant simulator RELAP-7 (Reactor Excursion and Leak Analysis Program) at Idaho National Laboratory. Therefore, RAVEN that was initially focused to be the plant controller for RELAP-7 will help mitigate future RELAP-7 software engineering risks. In order to accomplish this task, Reactor Analysis and Virtual Control Environment (RAVEN) has been designed to provide an easy to use Graphical User Interface (GUI) for building plant models and to leverage artificial intelligence algorithms in order to reduce computational time, improve results, and help the user to identify the behavioral pattern of the Nuclear Power Plants (NPPs). In this paper we will present the GUI implementation and its current capability status. We will also introduce the support vector machine algorithms and show our evaluation of their potentiality in increasing the accuracy and reducing the computational costs of PRA analysis. In this evaluation we will refer to preliminary studies performed under the Risk Informed Safety Margins Characterization (RISMC) project of the Light Water Reactors Sustainability (LWRS) campaign [3]. RISMC simulation needs and algorithm testing are currently used as a guidance to prioritize RAVEN developments relevant to PRA.« less

Recursive least-squares learning algorithms for neural networks

NASA Astrophysics Data System (ADS)

Lewis, Paul S.; Hwang, Jenq N.

1990-11-01

This paper presents the development of a pair of recursive least squares (ItLS) algorithms for online training of multilayer perceptrons which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is 0(N2) where N is the number of network parameters. This is due to the estimation of the N x N inverse Hessian matrix. Less computationally intensive approximations of the ilLS algorithms can be easily derived by using only block diagonal elements of this matrix thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6 1 BACKGROUND Artificial neural networks (ANNs) offer an interesting and potentially useful paradigm for signal processing and pattern recognition. The majority of ANN applications employ the feed-forward multilayer perceptron (MLP) network architecture in which network parameters are " trained" by a supervised learning algorithm employing the generalized delta rule (GDIt) [1 2]. The GDR algorithm approximates a fixed step steepest descent algorithm using derivatives computed by error backpropagatiori. The GDII algorithm is sometimes referred to as the backpropagation algorithm. However in this paper we will use the term backpropagation to refer only to the process of computing error derivatives. While multilayer perceptrons provide a very powerful nonlinear modeling capability GDR training can be very slow and inefficient. In linear adaptive filtering the analog of the GDR algorithm is the leastmean- squares (LMS) algorithm. Steepest descent-based algorithms such as GDR or LMS are first order because they use only first derivative or gradient information about the training error to be minimized. To speed up the training process second order algorithms may be employed that take advantage of second derivative or Hessian matrix information. Second order information can be incorporated into MLP training in different ways. In many applications especially in the area of pattern recognition the training set is finite. In these cases block learning can be applied using standard nonlinear optimization techniques [3 4 5].

Protecting Information

NASA Astrophysics Data System (ADS)

Loepp, Susan; Wootters, William K.

2006-09-01

For many everyday transmissions, it is essential to protect digital information from noise or eavesdropping. This undergraduate introduction to error correction and cryptography is unique in devoting several chapters to quantum cryptography and quantum computing, thus providing a context in which ideas from mathematics and physics meet. By covering such topics as Shor's quantum factoring algorithm, this text informs the reader about current thinking in quantum information theory and encourages an appreciation of the connections between mathematics and science.Of particular interest are the potential impacts of quantum physics:(i) a quantum computer, if built, could crack our currently used public-key cryptosystems; and (ii) quantum cryptography promises to provide an alternative to these cryptosystems, basing its security on the laws of nature rather than on computational complexity. No prior knowledge of quantum mechanics is assumed, but students should have a basic knowledge of complex numbers, vectors, and matrices. Accessible to readers familiar with matrix algebra, vector spaces and complex numbers First undergraduate text to cover cryptography, error-correction, and quantum computation together Features exercises designed to enhance understanding, including a number of computational problems, available from www.cambridge.org/9780521534765

Queue and stack sorting algorithm optimization and performance analysis

NASA Astrophysics Data System (ADS)

Qian, Mingzhu; Wang, Xiaobao

2018-04-01

Sorting algorithm is one of the basic operation of a variety of software development, in data structures course specializes in all kinds of sort algorithm. The performance of the sorting algorithm is directly related to the efficiency of the software. A lot of excellent scientific research queue is constantly optimizing algorithm, algorithm efficiency better as far as possible, the author here further research queue combined with stacks of sorting algorithms, the algorithm is mainly used for alternating operation queue and stack storage properties, Thus avoiding the need for a large number of exchange or mobile operations in the traditional sort. Before the existing basis to continue research, improvement and optimization, the focus on the optimization of the time complexity of the proposed optimization and improvement, The experimental results show that the improved effectively, at the same time and the time complexity and space complexity of the algorithm, the stability study corresponding research. The improvement and optimization algorithm, improves the practicability.

Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering.

PubMed

Theofilatos, Konstantinos; Pavlopoulou, Niki; Papasavvas, Christoforos; Likothanassis, Spiros; Dimitrakopoulos, Christos; Georgopoulos, Efstratios; Moschopoulos, Charalampos; Mavroudi, Seferina

2015-03-01

Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein-protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs. The proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms. Using public available datasets, EE-MC outperformed existing methodologies (in some datasets the separation metric was increased by 10-20%). Moreover, when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity. In specific, 5737 protein complexes were predicted and 72.58% of them are enriched for at least one gene ontology (GO) function term. EE-MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks, their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning. This fact was experimentally validated and moreover, new potentially true human protein complexes were suggested as candidates for further validation using experimental techniques. Copyright © 2015 Elsevier B.V. All rights reserved.

Neural network fusion capabilities for efficient implementation of tracking algorithms

NASA Astrophysics Data System (ADS)

Sundareshan, Malur K.; Amoozegar, Farid

1996-05-01

The ability to efficiently fuse information of different forms for facilitating intelligent decision-making is one of the major capabilities of trained multilayer neural networks that is being recognized int eh recent times. While development of innovative adaptive control algorithms for nonlinear dynamical plants which attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. In this paper we describe the capabilities and functionality of neural network algorithms for data fusion and implementation of nonlinear tracking filters. For a discussion of details and for serving as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes form the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. Such an approach results in an overall nonlinear tracking filter which has several advantages over the popular efforts at designing nonlinear estimation algorithms for tracking applications, the principle one being the reduction of mathematical and computational complexities. A system architecture that efficiently integrates the processing capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described in this paper.

An Out-of-Core GPU based dimensionality reduction algorithm for Big Mass Spectrometry Data and its application in bottom-up Proteomics.

PubMed

Awan, Muaaz Gul; Saeed, Fahad

2017-08-01

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and computational operations inside GPU. These novel data structures include Binary Spectra and Quantized Indexed Spectra (QIS) . The former helps in communicating essential information between CPU and GPU using minimum amount of data while latter enables us to store and process complex 3-D data structure into a 1-D array structure while maintaining the integrity of MS data. Our proposed algorithm also takes into account the limited memory of GPUs and switches between in-core and out-of-core modes based upon the size of input data. G-MSR achieves a peak speed-up of 386x over its sequential counterpart and is shown to process over a million spectra in just 32 seconds. The code for this algorithm is available as a GPL open-source at GitHub at the following link: https://github.com/pcdslab/G-MSR.

Detection of protein complex from protein-protein interaction network using Markov clustering

NASA Astrophysics Data System (ADS)

Ochieng, P. J.; Kusuma, W. A.; Haryanto, T.

2017-05-01

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.

Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using feedback control algorithm.

PubMed

Venugopal, G; Deepak, P; Ghosh, Diptasree M; Ramakrishnan, S

2017-11-01

Surface electromyography is a non-invasive technique used for recording the electrical activity of neuromuscular systems. These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. This model can be used to study the influence of various signal parameters under fatigue and non-fatigue conditions.

Combining a Deconvolution and a Universal Library Search Algorithm for the Nontarget Analysis of Data-Independent Acquisition Mode Liquid Chromatography-High-Resolution Mass Spectrometry Results.

PubMed

Samanipour, Saer; Reid, Malcolm J; Bæk, Kine; Thomas, Kevin V

2018-04-17

Nontarget analysis is considered one of the most comprehensive tools for the identification of unknown compounds in a complex sample analyzed via liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Due to the complexity of the data generated via LC-HRMS, the data-dependent acquisition mode, which produces the MS 2 spectra of a limited number of the precursor ions, has been one of the most common approaches used during nontarget screening. However, data-independent acquisition mode produces highly complex spectra that require proper deconvolution and library search algorithms. We have developed a deconvolution algorithm and a universal library search algorithm (ULSA) for the analysis of complex spectra generated via data-independent acquisition. These algorithms were validated and tested using both semisynthetic and real environmental data. A total of 6000 randomly selected spectra from MassBank were introduced across the total ion chromatograms of 15 sludge extracts at three levels of background complexity for the validation of the algorithms via semisynthetic data. The deconvolution algorithm successfully extracted more than 60% of the added ions in the analytical signal for 95% of processed spectra (i.e., 3 complexity levels multiplied by 6000 spectra). The ULSA ranked the correct spectra among the top three for more than 95% of cases. We further tested the algorithms with 5 wastewater effluent extracts for 59 artificial unknown analytes (i.e., their presence or absence was confirmed via target analysis). These algorithms did not produce any cases of false identifications while correctly identifying ∼70% of the total inquiries. The implications, capabilities, and the limitations of both algorithms are further discussed.

A novel encoding Lempel-Ziv complexity algorithm for quantifying the irregularity of physiological time series.

PubMed

Zhang, Yatao; Wei, Shoushui; Liu, Hai; Zhao, Lina; Liu, Chengyu

2016-09-01

The Lempel-Ziv (LZ) complexity and its variants have been extensively used to analyze the irregularity of physiological time series. To date, these measures cannot explicitly discern between the irregularity and the chaotic characteristics of physiological time series. Our study compared the performance of an encoding LZ (ELZ) complexity algorithm, a novel variant of the LZ complexity algorithm, with those of the classic LZ (CLZ) and multistate LZ (MLZ) complexity algorithms. Simulation experiments on Gaussian noise, logistic chaotic, and periodic time series showed that only the ELZ algorithm monotonically declined with the reduction in irregularity in time series, whereas the CLZ and MLZ approaches yielded overlapped values for chaotic time series and time series mixed with Gaussian noise, demonstrating the accuracy of the proposed ELZ algorithm in capturing the irregularity, rather than the complexity, of physiological time series. In addition, the effect of sequence length on the ELZ algorithm was more stable compared with those on CLZ and MLZ, especially when the sequence length was longer than 300. A sensitivity analysis for all three LZ algorithms revealed that both the MLZ and the ELZ algorithms could respond to the change in time sequences, whereas the CLZ approach could not. Cardiac interbeat (RR) interval time series from the MIT-BIH database were also evaluated, and the results showed that the ELZ algorithm could accurately measure the inherent irregularity of the RR interval time series, as indicated by lower LZ values yielded from a congestive heart failure group versus those yielded from a normal sinus rhythm group (p < 0.01). Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Algorithms and applications of aberration correction and American standard-based digital evaluation in surface defects evaluating system

NASA Astrophysics Data System (ADS)

Wu, Fan; Cao, Pin; Yang, Yongying; Li, Chen; Chai, Huiting; Zhang, Yihui; Xiong, Haoliang; Xu, Wenlin; Yan, Kai; Zhou, Lin; Liu, Dong; Bai, Jian; Shen, Yibing

2016-11-01

The inspection of surface defects is one of significant sections of optical surface quality evaluation. Based on microscopic scattering dark-field imaging, sub-aperture scanning and stitching, the Surface Defects Evaluating System (SDES) can acquire full-aperture image of defects on optical elements surface and then extract geometric size and position information of defects with image processing such as feature recognization. However, optical distortion existing in the SDES badly affects the inspection precision of surface defects. In this paper, a distortion correction algorithm based on standard lattice pattern is proposed. Feature extraction, polynomial fitting and bilinear interpolation techniques in combination with adjacent sub-aperture stitching are employed to correct the optical distortion of the SDES automatically in high accuracy. Subsequently, in order to digitally evaluate surface defects with American standard by using American military standards MIL-PRF-13830B to judge the surface defects information obtained from the SDES, an American standard-based digital evaluation algorithm is proposed, which mainly includes a judgment method of surface defects concentration. The judgment method establishes weight region for each defect and adopts the method of overlap of weight region to calculate defects concentration. This algorithm takes full advantage of convenience of matrix operations and has merits of low complexity and fast in running, which makes itself suitable very well for highefficiency inspection of surface defects. Finally, various experiments are conducted and the correctness of these algorithms are verified. At present, these algorithms have been used in SDES.

Analysis of decision fusion algorithms in handling uncertainties for integrated health monitoring systems

NASA Astrophysics Data System (ADS)

Zein-Sabatto, Saleh; Mikhail, Maged; Bodruzzaman, Mohammad; DeSimio, Martin; Derriso, Mark; Behbahani, Alireza

2012-06-01

It has been widely accepted that data fusion and information fusion methods can improve the accuracy and robustness of decision-making in structural health monitoring systems. It is arguably true nonetheless, that decision-level is equally beneficial when applied to integrated health monitoring systems. Several decisions at low-levels of abstraction may be produced by different decision-makers; however, decision-level fusion is required at the final stage of the process to provide accurate assessment about the health of the monitored system as a whole. An example of such integrated systems with complex decision-making scenarios is the integrated health monitoring of aircraft. Thorough understanding of the characteristics of the decision-fusion methodologies is a crucial step for successful implementation of such decision-fusion systems. In this paper, we have presented the major information fusion methodologies reported in the literature, i.e., probabilistic, evidential, and artificial intelligent based methods. The theoretical basis and characteristics of these methodologies are explained and their performances are analyzed. Second, candidate methods from the above fusion methodologies, i.e., Bayesian, Dempster-Shafer, and fuzzy logic algorithms are selected and their applications are extended to decisions fusion. Finally, fusion algorithms are developed based on the selected fusion methods and their performance are tested on decisions generated from synthetic data and from experimental data. Also in this paper, a modeling methodology, i.e. cloud model, for generating synthetic decisions is presented and used. Using the cloud model, both types of uncertainties; randomness and fuzziness, involved in real decision-making are modeled. Synthetic decisions are generated with an unbiased process and varying interaction complexities among decisions to provide for fair performance comparison of the selected decision-fusion algorithms. For verification purposes, implementation results of the developed fusion algorithms on structural health monitoring data collected from experimental tests are reported in this paper.

Kalman/Map filtering-aided fast normalized cross correlation-based Wi-Fi fingerprinting location sensing.

PubMed

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-11-13

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

PubMed Central

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-01-01

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results. PMID:24233027

Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes.

PubMed

Brock, Guy N; Shaffer, John R; Blakesley, Richard E; Lotz, Meredith J; Tseng, George C

2008-01-10

Gene expression data frequently contain missing values, however, most down-stream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures x time series data. We then introduce two complementary selection schemes for determining the most appropriate imputation method for any given data set. We found that the optimal imputation algorithms (LSA, LLS, and BPCA) are all highly competitive with each other, and that no method is uniformly superior in all the data sets we examined. The success of each method can also depend on the underlying "complexity" of the expression data, where we take complexity to indicate the difficulty in mapping the gene expression matrix to a lower-dimensional subspace. We developed an entropy measure to quantify the complexity of expression matrixes and found that, by incorporating this information, the entropy-based selection (EBS) scheme is useful for selecting an appropriate imputation algorithm. We further propose a simulation-based self-training selection (STS) scheme. This technique has been used previously for microarray data imputation, but for different purposes. The scheme selects the optimal or near-optimal method with high accuracy but at an increased computational cost. Our findings provide insight into the problem of which imputation method is optimal for a given data set. Three top-performing methods (LSA, LLS and BPCA) are competitive with each other. Global-based imputation methods (PLS, SVD, BPCA) performed better on mcroarray data with lower complexity, while neighbour-based methods (KNN, OLS, LSA, LLS) performed better in data with higher complexity. We also found that the EBS and STS schemes serve as complementary and effective tools for selecting the optimal imputation algorithm.

Fast and accurate detection of spread source in large complex networks.

PubMed

Paluch, Robert; Lu, Xiaoyan; Suchecki, Krzysztof; Szymański, Bolesław K; Hołyst, Janusz A

2018-02-06

Spread over complex networks is a ubiquitous process with increasingly wide applications. Locating spread sources is often important, e.g. finding the patient one in epidemics, or source of rumor spreading in social network. Pinto, Thiran and Vetterli introduced an algorithm (PTVA) to solve the important case of this problem in which a limited set of nodes act as observers and report times at which the spread reached them. PTVA uses all observers to find a solution. Here we propose a new approach in which observers with low quality information (i.e. with large spread encounter times) are ignored and potential sources are selected based on the likelihood gradient from high quality observers. The original complexity of PTVA is O(N α ), where α ∈ (3,4) depends on the network topology and number of observers (N denotes the number of nodes in the network). Our Gradient Maximum Likelihood Algorithm (GMLA) reduces this complexity to O (N 2 log (N)). Extensive numerical tests performed on synthetic networks and real Gnutella network with limitation that id's of spreaders are unknown to observers demonstrate that for scale-free networks with such limitation GMLA yields higher quality localization results than PTVA does.

Solving complex band structure problems with the FEAST eigenvalue algorithm

NASA Astrophysics Data System (ADS)

Laux, S. E.

2012-08-01

With straightforward extension, the FEAST eigenvalue algorithm [Polizzi, Phys. Rev. B 79, 115112 (2009)] is capable of solving the generalized eigenvalue problems representing traveling-wave problems—as exemplified by the complex band-structure problem—even though the matrices involved are complex, non-Hermitian, and singular, and hence outside the originally stated range of applicability of the algorithm. The obtained eigenvalues/eigenvectors, however, contain spurious solutions which must be detected and removed. The efficiency and parallel structure of the original algorithm are unaltered. The complex band structures of Si layers of varying thicknesses and InAs nanowires of varying radii are computed as test problems.

Subband Image Coding with Jointly Optimized Quantizers

NASA Technical Reports Server (NTRS)

Kossentini, Faouzi; Chung, Wilson C.; Smith Mark J. T.

1995-01-01

An iterative design algorithm for the joint design of complexity- and entropy-constrained subband quantizers and associated entropy coders is proposed. Unlike conventional subband design algorithms, the proposed algorithm does not require the use of various bit allocation algorithms. Multistage residual quantizers are employed here because they provide greater control of the complexity-performance tradeoffs, and also because they allow efficient and effective high-order statistical modeling. The resulting subband coder exploits statistical dependencies within subbands, across subbands, and across stages, mainly through complexity-constrained high-order entropy coding. Experimental results demonstrate that the complexity-rate-distortion performance of the new subband coder is exceptional.

Associating optical measurements and estimating orbits of geocentric objects with a Genetic Algorithm: performance limitations.

NASA Astrophysics Data System (ADS)

Zittersteijn, Michiel; Schildknecht, Thomas; Vananti, Alessandro; Dolado Perez, Juan Carlos; Martinot, Vincent

2016-07-01

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the correlation and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention. This problem is also known as the Multiple Target Tracking (MTT) problem. The complexity of the MTT problem is defined by its dimension S. Current research tends to focus on the S = 2 MTT problem. The reason for this is that for S = 2 the problem has a P-complexity. However, with S = 2 the decision to associate a set of observations is based on the minimum amount of information, in ambiguous situations (e.g. satellite clusters) this will lead to incorrect associations. The S > 2 MTT problem is an NP-hard combinatorial optimization problem. In previous work an Elitist Genetic Algorithm (EGA) was proposed as a method to approximately solve this problem. It was shown that the EGA is able to find a good approximate solution with a polynomial time complexity. The EGA relies on solving the Lambert problem in order to perform the necessary orbit determinations. This means that the algorithm is restricted to orbits that are described by Keplerian motion. The work presented in this paper focuses on the impact that this restriction has on the algorithm performance.

A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks.

PubMed

Le, Duc-Hau

2015-01-01

Protein complexes formed by non-covalent interaction among proteins play important roles in cellular functions. Computational and purification methods have been used to identify many protein complexes and their cellular functions. However, their roles in terms of causing disease have not been well discovered yet. There exist only a few studies for the identification of disease-associated protein complexes. However, they mostly utilize complicated heterogeneous networks which are constructed based on an out-of-date database of phenotype similarity network collected from literature. In addition, they only apply for diseases for which tissue-specific data exist. In this study, we propose a method to identify novel disease-protein complex associations. First, we introduce a framework to construct functional similarity protein complex networks where two protein complexes are functionally connected by either shared protein elements, shared annotating GO terms or based on protein interactions between elements in each protein complex. Second, we propose a simple but effective neighborhood-based algorithm, which yields a local similarity measure, to rank disease candidate protein complexes. Comparing the predictive performance of our proposed algorithm with that of two state-of-the-art network propagation algorithms including one we used in our previous study, we found that it performed statistically significantly better than that of these two algorithms for all the constructed functional similarity protein complex networks. In addition, it ran about 32 times faster than these two algorithms. Moreover, our proposed method always achieved high performance in terms of AUC values irrespective of the ways to construct the functional similarity protein complex networks and the used algorithms. The performance of our method was also higher than that reported in some existing methods which were based on complicated heterogeneous networks. Finally, we also tested our method with prostate cancer and selected the top 100 highly ranked candidate protein complexes. Interestingly, 69 of them were evidenced since at least one of their protein elements are known to be associated with prostate cancer. Our proposed method, including the framework to construct functional similarity protein complex networks and the neighborhood-based algorithm on these networks, could be used for identification of novel disease-protein complex associations.

Approximate Joint Diagonalization and Geometric Mean of Symmetric Positive Definite Matrices

PubMed Central

Congedo, Marco; Afsari, Bijan; Barachant, Alexandre; Moakher, Maher

2015-01-01

We explore the connection between two problems that have arisen independently in the signal processing and related fields: the estimation of the geometric mean of a set of symmetric positive definite (SPD) matrices and their approximate joint diagonalization (AJD). Today there is a considerable interest in estimating the geometric mean of a SPD matrix set in the manifold of SPD matrices endowed with the Fisher information metric. The resulting mean has several important invariance properties and has proven very useful in diverse engineering applications such as biomedical and image data processing. While for two SPD matrices the mean has an algebraic closed form solution, for a set of more than two SPD matrices it can only be estimated by iterative algorithms. However, none of the existing iterative algorithms feature at the same time fast convergence, low computational complexity per iteration and guarantee of convergence. For this reason, recently other definitions of geometric mean based on symmetric divergence measures, such as the Bhattacharyya divergence, have been considered. The resulting means, although possibly useful in practice, do not satisfy all desirable invariance properties. In this paper we consider geometric means of covariance matrices estimated on high-dimensional time-series, assuming that the data is generated according to an instantaneous mixing model, which is very common in signal processing. We show that in these circumstances we can approximate the Fisher information geometric mean by employing an efficient AJD algorithm. Our approximation is in general much closer to the Fisher information geometric mean as compared to its competitors and verifies many invariance properties. Furthermore, convergence is guaranteed, the computational complexity is low and the convergence rate is quadratic. The accuracy of this new geometric mean approximation is demonstrated by means of simulations. PMID:25919667

Riding the Hype Wave: Evaluating new AI Techniques for their Applicability in Earth Science

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Zhang, J.; Maskey, M.; Lee, T. J.

2016-12-01

Every few years a new technology rides the hype wave generated by the computer science community. Converts to this new technology who surface from both the science community and the informatics community promulgate that it can radically improve or even change the existing scientific process. Recent examples of new technology following in the footsteps of "big data" now include deep learning algorithms and knowledge graphs. Deep learning algorithms mimic the human brain and process information through multiple stages of transformation and representation. These algorithms are able to learn complex functions that map pixels directly to outputs without relying on human-crafted features and solve some of the complex classification problems that exist in science. Similarly, knowledge graphs aggregate information around defined topics that enable users to resolve their query without having to navigate and assemble information manually. Knowledge graphs could potentially be used in scientific research to assist in hypothesis formulation, testing, and review. The challenge for the Earth science research community is to evaluate these new technologies by asking the right questions and considering what-if scenarios. What is this new technology enabling/providing that is innovative and different? Can one justify the adoption costs with respect to the research returns? Since nothing comes for free, utilizing a new technology entails adoption costs that may outweigh the benefits. Furthermore, these technologies may require significant computing infrastructure in order to be utilized effectively. Results from two different projects will be presented along with lessons learned from testing these technologies. The first project primarily evaluates deep learning techniques for different applications of image retrieval within Earth science while the second project builds a prototype knowledge graph constructed for Hurricane science.

The threshold algorithm: Description of the methodology and new developments

NASA Astrophysics Data System (ADS)

Neelamraju, Sridhar; Oligschleger, Christina; Schön, J. Christian

2017-10-01

Understanding the dynamics of complex systems requires the investigation of their energy landscape. In particular, the flow of probability on such landscapes is a central feature in visualizing the time evolution of complex systems. To obtain such flows, and the concomitant stable states of the systems and the generalized barriers among them, the threshold algorithm has been developed. Here, we describe the methodology of this approach starting from the fundamental concepts in complex energy landscapes and present recent new developments, the threshold-minimization algorithm and the molecular dynamics threshold algorithm. For applications of these new algorithms, we draw on landscape studies of three disaccharide molecules: lactose, maltose, and sucrose.

Neural network fusion capabilities for efficient implementation of tracking algorithms

NASA Astrophysics Data System (ADS)

Sundareshan, Malur K.; Amoozegar, Farid

1997-03-01

The ability to efficiently fuse information of different forms to facilitate intelligent decision making is one of the major capabilities of trained multilayer neural networks that is now being recognized. While development of innovative adaptive control algorithms for nonlinear dynamical plants that attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. We describe the capabilities and functionality of neural network algorithms for data fusion and implementation of tracking filters. To discuss details and to serve as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target- tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The innovation lies in the way the fusion of multisensor data is accomplished to facilitate improved estimation without increasing the computational complexity of the dynamical state estimator itself.

The architecture of the management system of complex steganographic information

NASA Astrophysics Data System (ADS)

Evsutin, O. O.; Meshcheryakov, R. V.; Kozlova, A. S.; Solovyev, T. M.

2017-01-01

The aim of the study is to create a wide area information system that allows one to control processes of generation, embedding, extraction, and detection of steganographic information. In this paper, the following problems are considered: the definition of the system scope and the development of its architecture. For creation of algorithmic maintenance of the system, classic methods of steganography are used to embed information. Methods of mathematical statistics and computational intelligence are used to identify the embedded information. The main result of the paper is the development of the architecture of the management system of complex steganographic information. The suggested architecture utilizes cloud technology in order to provide service using the web-service via the Internet. It is meant to provide streams of multimedia data processing that are streams with many sources of different types. The information system, built in accordance with the proposed architecture, will be used in the following areas: hidden transfer of documents protected by medical secrecy in telemedicine systems; copyright protection of online content in public networks; prevention of information leakage caused by insiders.

Comparative study of classification algorithms for immunosignaturing data

PubMed Central

2012-01-01

Background High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data. Results We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found ‘Naïve Bayes’ far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy. Conclusions ‘Naïve Bayes’ algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties. PMID:22720696

Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes. Part 3

NASA Technical Reports Server (NTRS)

Lin, Shu

1998-01-01

Decoding algorithms based on the trellis representation of a code (block or convolutional) drastically reduce decoding complexity. The best known and most commonly used trellis-based decoding algorithm is the Viterbi algorithm. It is a maximum likelihood decoding algorithm. Convolutional codes with the Viterbi decoding have been widely used for error control in digital communications over the last two decades. This chapter is concerned with the application of the Viterbi decoding algorithm to linear block codes. First, the Viterbi algorithm is presented. Then, optimum sectionalization of a trellis to minimize the computational complexity of a Viterbi decoder is discussed and an algorithm is presented. Some design issues for IC (integrated circuit) implementation of a Viterbi decoder are considered and discussed. Finally, a new decoding algorithm based on the principle of compare-select-add is presented. This new algorithm can be applied to both block and convolutional codes and is more efficient than the conventional Viterbi algorithm based on the add-compare-select principle. This algorithm is particularly efficient for rate 1/n antipodal convolutional codes and their high-rate punctured codes. It reduces computational complexity by one-third compared with the Viterbi algorithm.

Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis.

PubMed

Aboy, Mateo; Hornero, Roberto; Abásolo, Daniel; Alvarez, Daniel

2006-11-01

Lempel-Ziv complexity (LZ) and derived LZ algorithms have been extensively used to solve information theoretic problems such as coding and lossless data compression. In recent years, LZ has been widely used in biomedical applications to estimate the complexity of discrete-time signals. Despite its popularity as a complexity measure for biosignal analysis, the question of LZ interpretability and its relationship to other signal parameters and to other metrics has not been previously addressed. We have carried out an investigation aimed at gaining a better understanding of the LZ complexity itself, especially regarding its interpretability as a biomedical signal analysis technique. Our results indicate that LZ is particularly useful as a scalar metric to estimate the bandwidth of random processes and the harmonic variability in quasi-periodic signals.

Spatial inventory integrating raster databases and point sample data. [Geographic Information System for timber inventory

NASA Technical Reports Server (NTRS)

Strahler, A. H.; Woodcock, C. E.; Logan, T. L.

1983-01-01

A timber inventory of the Eldorado National Forest, located in east-central California, provides an example of the use of a Geographic Information System (GIS) to stratify large areas of land for sampling and the collection of statistical data. The raster-based GIS format of the VICAR/IBIS software system allows simple and rapid tabulation of areas, and facilitates the selection of random locations for ground sampling. Algorithms that simplify the complex spatial pattern of raster-based information, and convert raster format data to strings of coordinate vectors, provide a link to conventional vector-based geographic information systems.

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.

Study of robot landmark recognition with complex background

NASA Astrophysics Data System (ADS)

Huang, Yuqing; Yang, Jia

2007-12-01

It's of great importance for assisting robot in path planning, position navigating and task performing by perceiving and recognising environment characteristic. To solve the problem of monocular-vision-oriented landmark recognition for mobile intelligent robot marching with complex background, a kind of nested region growing algorithm which fused with transcendental color information and based on current maximum convergence center is proposed, allowing invariance localization to changes in position, scale, rotation, jitters and weather conditions. Firstly, a novel experiment threshold based on RGB vision model is used for the first image segmentation, which allowing some objects and partial scenes with similar color to landmarks also are detected with landmarks together. Secondly, with current maximum convergence center on segmented image as each growing seed point, the above region growing algorithm accordingly starts to establish several Regions of Interest (ROI) orderly. According to shape characteristics, a quick and effectual contour analysis based on primitive element is applied in deciding whether current ROI could be reserved or deleted after each region growing, then each ROI is judged initially and positioned. When the position information as feedback is conveyed to the gray image, the whole landmarks are extracted accurately with the second segmentation on the local image that exclusive to landmark area. Finally, landmarks are recognised by Hopfield neural network. Results issued from experiments on a great number of images with both campus and urban district as background show the effectiveness of the proposed algorithm.

Efficient Pricing Technique for Resource Allocation Problem in Downlink OFDM Cognitive Radio Networks

NASA Astrophysics Data System (ADS)

Abdulghafoor, O. B.; Shaat, M. M. R.; Ismail, M.; Nordin, R.; Yuwono, T.; Alwahedy, O. N. A.

2017-05-01

In this paper, the problem of resource allocation in OFDM-based downlink cognitive radio (CR) networks has been proposed. The purpose of this research is to decrease the computational complexity of the resource allocation algorithm for downlink CR network while concerning the interference constraint of primary network. The objective has been secured by adopting pricing scheme to develop power allocation algorithm with the following concerns: (i) reducing the complexity of the proposed algorithm and (ii) providing firm power control to the interference introduced to primary users (PUs). The performance of the proposed algorithm is tested for OFDM- CRNs. The simulation results show that the performance of the proposed algorithm approached the performance of the optimal algorithm at a lower computational complexity, i.e., O(NlogN), which makes the proposed algorithm suitable for more practical applications.

Variable forgetting factor mechanisms for diffusion recursive least squares algorithm in sensor networks

NASA Astrophysics Data System (ADS)

Zhang, Ling; Cai, Yunlong; Li, Chunguang; de Lamare, Rodrigo C.

2017-12-01

In this work, we present low-complexity variable forgetting factor (VFF) techniques for diffusion recursive least squares (DRLS) algorithms. Particularly, we propose low-complexity VFF-DRLS algorithms for distributed parameter and spectrum estimation in sensor networks. For the proposed algorithms, they can adjust the forgetting factor automatically according to the posteriori error signal. We develop detailed analyses in terms of mean and mean square performance for the proposed algorithms and derive mathematical expressions for the mean square deviation (MSD) and the excess mean square error (EMSE). The simulation results show that the proposed low-complexity VFF-DRLS algorithms achieve superior performance to the existing DRLS algorithm with fixed forgetting factor when applied to scenarios of distributed parameter and spectrum estimation. Besides, the simulation results also demonstrate a good match for our proposed analytical expressions.

Plasma Parameters From Reentry Signal Attenuation

DOE PAGES

Statom, T. K.

2018-02-27

This study presents the application of a theoretically developed method that provides plasma parameter solution space information from measured RF attenuation that occurs during reentry. The purpose is to provide reentry plasma parameter information from the communication signal attenuation. The theoretical development centers around the attenuation and the complex index of refraction. The methodology uses an imaginary index of the refraction matching algorithm with a tolerance to find suitable solutions that satisfy the theory. The imaginary matching terms are then used to determine the real index of refraction resulting in the complex index of refraction. Then a filter is usedmore » to reject nonphysical solutions. Signal attenuation-based plasma parameter properties investigated include the complex index of refraction, plasma frequency, electron density, collision frequency, propagation constant, attenuation constant, phase constant, complex plasma conductivity, and electron mobility. RF plasma thickness attenuation is investigated and compared to the literature. Finally, similar plasma thickness for a specific signal attenuation can have different plasma properties.« less

Plasma Parameters From Reentry Signal Attenuation

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

Statom, T. K.

This study presents the application of a theoretically developed method that provides plasma parameter solution space information from measured RF attenuation that occurs during reentry. The purpose is to provide reentry plasma parameter information from the communication signal attenuation. The theoretical development centers around the attenuation and the complex index of refraction. The methodology uses an imaginary index of the refraction matching algorithm with a tolerance to find suitable solutions that satisfy the theory. The imaginary matching terms are then used to determine the real index of refraction resulting in the complex index of refraction. Then a filter is usedmore » to reject nonphysical solutions. Signal attenuation-based plasma parameter properties investigated include the complex index of refraction, plasma frequency, electron density, collision frequency, propagation constant, attenuation constant, phase constant, complex plasma conductivity, and electron mobility. RF plasma thickness attenuation is investigated and compared to the literature. Finally, similar plasma thickness for a specific signal attenuation can have different plasma properties.« less

An Integrated Processing Strategy for Mountain Glacier Motion Monitoring Based on SAR Images

NASA Astrophysics Data System (ADS)

Ruan, Z.; Yan, S.; Liu, G.; LV, M.

2017-12-01

Mountain glacier dynamic variables are important parameters in studies of environment and climate change in High Mountain Asia. Due to the increasing events of abnormal glacier-related hazards, research of monitoring glacier movements has attracted more interest during these years. Glacier velocities are sensitive and changing fast under complex conditions of high mountain regions, which implies that analysis of glacier dynamic changes requires comprehensive and frequent observations with relatively high accuracy. Synthetic aperture radar (SAR) has been successfully exploited to detect glacier motion in a number of previous studies, usually with pixel-tracking and interferometry methods. However, the traditional algorithms applied to mountain glacier regions are constrained by the complex terrain and diverse glacial motion types. Interferometry techniques are prone to fail in mountain glaciers because of their narrow size and the steep terrain, while pixel-tracking algorithm, which is more robust in high mountain areas, is subject to accuracy loss. In order to derive glacier velocities continually and efficiently, we propose a modified strategy to exploit SAR data information for mountain glaciers. In our approach, we integrate a set of algorithms for compensating non-glacial-motion-related signals which exist in the offset values retrieved by sub-pixel cross-correlation of SAR image pairs. We exploit modified elastic deformation model to remove the offsets associated with orbit and sensor attitude, and for the topographic residual offset we utilize a set of operations including DEM-assisted compensation algorithm and wavelet-based algorithm. At the last step of the flow, an integrated algorithm combining phase and intensity information of SAR images will be used to improve regional motion results failed in cross-correlation related processing. The proposed strategy is applied to the West Kunlun Mountain and Muztagh Ata region in western China using ALOS/PALSAR data. The results show that the strategy can effectively improve the accuracy of velocity estimation by reducing the mean and standard deviation values from 0.32 m and 0.4 m to 0.16 m. It is proved to be highly appropriate for monitoring glacier motion over a widely varying range of ice velocities with a relatively high accuracy.

Multiscale computing.

PubMed

Kobayashi, M; Irino, T; Sweldens, W

2001-10-23

Multiscale computing (MSC) involves the computation, manipulation, and analysis of information at different resolution levels. Widespread use of MSC algorithms and the discovery of important relationships between different approaches to implementation were catalyzed, in part, by the recent interest in wavelets. We present two examples that demonstrate how MSC can help scientists understand complex data. The first is from acoustical signal processing and the second is from computer graphics.

Scalability, Complexity and Reliability in Quantum Information Processing

DTIC Science & Technology

2007-03-01

hidden subgroup framework to abelian groups which are not finitely generated. An extension of the basic algorithm breaks the Buchmann-Williams...finding short lattice vectors . In [2], we showed that the generalization of the standard method --- random coset state preparation followed by fourier...sampling --- required exponential time for sufficiently non-abelian groups including the symmetric group , at least when the fourier transforms are

Institute for Brain and Neural Systems

DTIC Science & Technology

2009-10-06

to deal with computational complexity when analyzing large amounts of information in visual scenes. It seems natural that in addition to exploring...algorithms using methods from statistical pattern recognition and machine learning. Over the last fifteen years, significant advances had been made in...recognition, robustness to noise and ability to cope with significant variations in lighting conditions. Identifying an occluded target adds another layer of

Information Dynamics as Foundation for Network Management

DTIC Science & Technology

2014-12-04

developed to adapt to channel dynamics in a mobile network environment. We devise a low- complexity online scheduling algorithm integrated with the...has been accepted for the Journal on Network and Systems Management in 2014. - RINC programmable platform for Infrastructure -as-a-Service public... backend servers. Rather than implementing load balancing in dedicated appliances, commodity SDN switches can perform this function. We design

An Algorithm for Integrated Subsystem Embodiment and System Synthesis

NASA Technical Reports Server (NTRS)

Lewis, Kemper

1997-01-01

Consider the statement,'A system has two coupled subsystems, one of which dominates the design process. Each subsystem consists of discrete and continuous variables, and is solved using sequential analysis and solution.' To address this type of statement in the design of complex systems, three steps are required, namely, the embodiment of the statement in terms of entities on a computer, the mathematical formulation of subsystem models, and the resulting solution and system synthesis. In complex system decomposition, the subsystems are not isolated, self-supporting entities. Information such as constraints, goals, and design variables may be shared between entities. But many times in engineering problems, full communication and cooperation does not exist, information is incomplete, or one subsystem may dominate the design. Additionally, these engineering problems give rise to mathematical models involving nonlinear functions of both discrete and continuous design variables. In this dissertation an algorithm is developed to handle these types of scenarios for the domain-independent integration of subsystem embodiment, coordination, and system synthesis using constructs from Decision-Based Design, Game Theory, and Multidisciplinary Design Optimization. Implementation of the concept in this dissertation involves testing of the hypotheses using example problems and a motivating case study involving the design of a subsonic passenger aircraft.

Sequential Test Strategies for Multiple Fault Isolation

NASA Technical Reports Server (NTRS)

Shakeri, M.; Pattipati, Krishna R.; Raghavan, V.; Patterson-Hine, Ann; Kell, T.

1997-01-01

In this paper, we consider the problem of constructing near optimal test sequencing algorithms for diagnosing multiple faults in redundant (fault-tolerant) systems. The computational complexity of solving the optimal multiple-fault isolation problem is super-exponential, that is, it is much more difficult than the single-fault isolation problem, which, by itself, is NP-hard. By employing concepts from information theory and Lagrangian relaxation, we present several static and dynamic (on-line or interactive) test sequencing algorithms for the multiple fault isolation problem that provide a trade-off between the degree of suboptimality and computational complexity. Furthermore, we present novel diagnostic strategies that generate a static diagnostic directed graph (digraph), instead of a static diagnostic tree, for multiple fault diagnosis. Using this approach, the storage complexity of the overall diagnostic strategy reduces substantially. Computational results based on real-world systems indicate that the size of a static multiple fault strategy is strictly related to the structure of the system, and that the use of an on-line multiple fault strategy can diagnose faults in systems with as many as 10,000 failure sources.

Hidden state prediction: a modification of classic ancestral state reconstruction algorithms helps unravel complex symbioses.

PubMed

Zaneveld, Jesse R R; Thurber, Rebecca L V

2014-01-01

Complex symbioses between animal or plant hosts and their associated microbiotas can involve thousands of species and millions of genes. Because of the number of interacting partners, it is often impractical to study all organisms or genes in these host-microbe symbioses individually. Yet new phylogenetic predictive methods can use the wealth of accumulated data on diverse model organisms to make inferences into the properties of less well-studied species and gene families. Predictive functional profiling methods use evolutionary models based on the properties of studied relatives to put bounds on the likely characteristics of an organism or gene that has not yet been studied in detail. These techniques have been applied to predict diverse features of host-associated microbial communities ranging from the enzymatic function of uncharacterized genes to the gene content of uncultured microorganisms. We consider these phylogenetically informed predictive techniques from disparate fields as examples of a general class of algorithms for Hidden State Prediction (HSP), and argue that HSP methods have broad value in predicting organismal traits in a variety of contexts, including the study of complex host-microbe symbioses.

More About Vector Adaptive/Predictive Coding Of Speech

NASA Technical Reports Server (NTRS)

Jedrey, Thomas C.; Gersho, Allen

1992-01-01

Report presents additional information about digital speech-encoding and -decoding system described in "Vector Adaptive/Predictive Encoding of Speech" (NPO-17230). Summarizes development of vector adaptive/predictive coding (VAPC) system and describes basic functions of algorithm. Describes refinements introduced enabling receiver to cope with errors. VAPC algorithm implemented in integrated-circuit coding/decoding processors (codecs). VAPC and other codecs tested under variety of operating conditions. Tests designed to reveal effects of various background quiet and noisy environments and of poor telephone equipment. VAPC found competitive with and, in some respects, superior to other 4.8-kb/s codecs and other codecs of similar complexity.

Robust algorithm for aligning two-dimensional chromatograms.

PubMed

Gros, Jonas; Nabi, Deedar; Dimitriou-Christidis, Petros; Rutler, Rebecca; Arey, J Samuel

2012-11-06

Comprehensive two-dimensional gas chromatography (GC × GC) chromatograms typically exhibit run-to-run retention time variability. Chromatogram alignment is often a desirable step prior to further analysis of the data, for example, in studies of environmental forensics or weathering of complex mixtures. We present a new algorithm for aligning whole GC × GC chromatograms. This technique is based on alignment points that have locations indicated by the user both in a target chromatogram and in a reference chromatogram. We applied the algorithm to two sets of samples. First, we aligned the chromatograms of twelve compositionally distinct oil spill samples, all analyzed using the same instrument parameters. Second, we applied the algorithm to two compositionally distinct wastewater extracts analyzed using two different instrument temperature programs, thus involving larger retention time shifts than the first sample set. For both sample sets, the new algorithm performed favorably compared to two other available alignment algorithms: that of Pierce, K. M.; Wood, Lianna F.; Wright, B. W.; Synovec, R. E. Anal. Chem.2005, 77, 7735-7743 and 2-D COW from Zhang, D.; Huang, X.; Regnier, F. E.; Zhang, M. Anal. Chem.2008, 80, 2664-2671. The new algorithm achieves the best matches of retention times for test analytes, avoids some artifacts which result from the other alignment algorithms, and incurs the least modification of quantitative signal information.

iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D

PubMed Central

Liluashvili, Vaja; Kalayci, Selim; Fluder, Eugene; Wilson, Manda; Gabow, Aaron

2017-01-01

Abstract Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field. PMID:28814063

A generic implementation of replica exchange with solute tempering (REST2) algorithm in NAMD for complex biophysical simulations

NASA Astrophysics Data System (ADS)

Jo, Sunhwan; Jiang, Wei

2015-12-01

Replica Exchange with Solute Tempering (REST2) is a powerful sampling enhancement algorithm of molecular dynamics (MD) in that it needs significantly smaller number of replicas but achieves higher sampling efficiency relative to standard temperature exchange algorithm. In this paper, we extend the applicability of REST2 for quantitative biophysical simulations through a robust and generic implementation in greatly scalable MD software NAMD. The rescaling procedure of force field parameters controlling REST2 "hot region" is implemented into NAMD at the source code level. A user can conveniently select hot region through VMD and write the selection information into a PDB file. The rescaling keyword/parameter is written in NAMD Tcl script interface that enables an on-the-fly simulation parameter change. Our implementation of REST2 is within communication-enabled Tcl script built on top of Charm++, thus communication overhead of an exchange attempt is vanishingly small. Such a generic implementation facilitates seamless cooperation between REST2 and other modules of NAMD to provide enhanced sampling for complex biomolecular simulations. Three challenging applications including native REST2 simulation for peptide folding-unfolding transition, free energy perturbation/REST2 for absolute binding affinity of protein-ligand complex and umbrella sampling/REST2 Hamiltonian exchange for free energy landscape calculation were carried out on IBM Blue Gene/Q supercomputer to demonstrate efficacy of REST2 based on the present implementation.

iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D.

PubMed

Liluashvili, Vaja; Kalayci, Selim; Fluder, Eugene; Wilson, Manda; Gabow, Aaron; Gümüs, Zeynep H

2017-08-01

Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field. © The Authors 2017. Published by Oxford University Press.

Multiple Two-Way Time Message Exchange (TTME) Time Synchronization for Bridge Monitoring Wireless Sensor Networks

PubMed Central

Shi, Fanrong; Tuo, Xianguo; Yang, Simon X.; Li, Huailiang; Shi, Rui

2017-01-01

Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring. PMID:28471418

Multiple Two-Way Time Message Exchange (TTME) Time Synchronization for Bridge Monitoring Wireless Sensor Networks.

PubMed

Shi, Fanrong; Tuo, Xianguo; Yang, Simon X; Li, Huailiang; Shi, Rui

2017-05-04

Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring.

A Statistical Physics Characterization of the Complex Systems Dynamics: Quantifying Complexity from Spatio-Temporal Interactions

PubMed Central

Koorehdavoudi, Hana; Bogdan, Paul

2016-01-01

Biological systems are frequently categorized as complex systems due to their capabilities of generating spatio-temporal structures from apparent random decisions. In spite of research on analyzing biological systems, we lack a quantifiable framework for measuring their complexity. To fill this gap, in this paper, we develop a new paradigm to study a collective group of N agents moving and interacting in a three-dimensional space. Our paradigm helps to identify the spatio-temporal states of the motion of the group and their associated transition probabilities. This framework enables the estimation of the free energy landscape corresponding to the identified states. Based on the energy landscape, we quantify missing information, emergence, self-organization and complexity for a collective motion. We show that the collective motion of the group of agents evolves to reach the most probable state with relatively lowest energy level and lowest missing information compared to other possible states. Our analysis demonstrates that the natural group of animals exhibit a higher degree of emergence, self-organization and complexity over time. Consequently, this algorithm can be integrated into new frameworks to engineer collective motions to achieve certain degrees of emergence, self-organization and complexity. PMID:27297496

A Statistical Physics Characterization of the Complex Systems Dynamics: Quantifying Complexity from Spatio-Temporal Interactions

NASA Astrophysics Data System (ADS)

Koorehdavoudi, Hana; Bogdan, Paul

2016-06-01

Biological systems are frequently categorized as complex systems due to their capabilities of generating spatio-temporal structures from apparent random decisions. In spite of research on analyzing biological systems, we lack a quantifiable framework for measuring their complexity. To fill this gap, in this paper, we develop a new paradigm to study a collective group of N agents moving and interacting in a three-dimensional space. Our paradigm helps to identify the spatio-temporal states of the motion of the group and their associated transition probabilities. This framework enables the estimation of the free energy landscape corresponding to the identified states. Based on the energy landscape, we quantify missing information, emergence, self-organization and complexity for a collective motion. We show that the collective motion of the group of agents evolves to reach the most probable state with relatively lowest energy level and lowest missing information compared to other possible states. Our analysis demonstrates that the natural group of animals exhibit a higher degree of emergence, self-organization and complexity over time. Consequently, this algorithm can be integrated into new frameworks to engineer collective motions to achieve certain degrees of emergence, self-organization and complexity.

Lithologic analysis from multispectral thermal infrared data of the alkalic rock complex at Iron Hill, Colorado

USGS Publications Warehouse

Watson, K.; Rowan, L.C.; Bowers, T.L.; Anton-Pacheco, C.; Gumiel, P.; Miller, S.H.

1996-01-01

Airborne thermal-infrared multispectral scanner (TIMS) data of the Iron Hill carbonatite-alkalic igneous rock complex in south-central Colorado are analyzed using a new spectral emissivity ratio algorithm and confirmed by field examination using existing 1:24 000-scale geologic maps and petrographic studies. Color composite images show that the alkalic rocks could be clearly identified and that differences existed among alkalic rocks in several parts of the complex. An unsupervised classification algorithm defines four alkalic rock classes within the complex: biotitic pyroxenite, uncompahgrite, augitic pyroxenite, and fenite + nepheline syenite. Felsic rock classes defined in the surrounding country rock are an extensive class consisting of tuff, granite, and felsite, a less extensive class of granite and felsite, and quartzite. The general composition of the classes can be determined from comparisons of the TIMS spectra with laboratory spectra. Carbonatite rocks are not classified, and we attribute that to the fact that dolomite, the predominant carbonate mineral in the complex, has a spectral feature that falls between TIMS channels 5 and 6. Mineralogical variability in the fenitized granite contributed to the nonuniform pattern of the fenite-nepheline syenite class. The biotitic pyroxenite, which resulted from alteration of the pyroxenite, is spatially associated and appears to be related to narrow carbonatite dikes and sills. Results from a linear unmixing algorithm suggest that the detected spatial extent of the two mixed felsic rock classes was sensitive to the amount of vegetation cover. These results illustrate that spectral thermal infrared data can be processed to yield compositional information that can be a cost-effective tool to target mineral exploration, particularly in igneous terranes.

A parallel algorithm for the eigenvalues and eigenvectors for a general complex matrix

NASA Technical Reports Server (NTRS)

Shroff, Gautam

1989-01-01

A new parallel Jacobi-like algorithm is developed for computing the eigenvalues of a general complex matrix. Most parallel methods for this parallel typically display only linear convergence. Sequential norm-reducing algorithms also exit and they display quadratic convergence in most cases. The new algorithm is a parallel form of the norm-reducing algorithm due to Eberlein. It is proven that the asymptotic convergence rate of this algorithm is quadratic. Numerical experiments are presented which demonstrate the quadratic convergence of the algorithm and certain situations where the convergence is slow are also identified. The algorithm promises to be very competitive on a variety of parallel architectures.

Computational complexity of algorithms for sequence comparison, short-read assembly and genome alignment.

PubMed

Baichoo, Shakuntala; Ouzounis, Christos A

A multitude of algorithms for sequence comparison, short-read assembly and whole-genome alignment have been developed in the general context of molecular biology, to support technology development for high-throughput sequencing, numerous applications in genome biology and fundamental research on comparative genomics. The computational complexity of these algorithms has been previously reported in original research papers, yet this often neglected property has not been reviewed previously in a systematic manner and for a wider audience. We provide a review of space and time complexity of key sequence analysis algorithms and highlight their properties in a comprehensive manner, in order to identify potential opportunities for further research in algorithm or data structure optimization. The complexity aspect is poised to become pivotal as we will be facing challenges related to the continuous increase of genomic data on unprecedented scales and complexity in the foreseeable future, when robust biological simulation at the cell level and above becomes a reality. Copyright © 2017 Elsevier B.V. All rights reserved.

δ-dependency for privacy-preserving XML data publishing.

PubMed

Landberg, Anders H; Nguyen, Kinh; Pardede, Eric; Rahayu, J Wenny

2014-08-01

An ever increasing amount of medical data such as electronic health records, is being collected, stored, shared and managed in large online health information systems and electronic medical record systems (EMR) (Williams et al., 2001; Virtanen, 2009; Huang and Liou, 2007) [1-3]. From such rich collections, data is often published in the form of census and statistical data sets for the purpose of knowledge sharing and enabling medical research. This brings with it an increasing need for protecting individual people privacy, and it becomes an issue of great importance especially when information about patients is exposed to the public. While the concept of data privacy has been comprehensively studied for relational data, models and algorithms addressing the distinct differences and complex structure of XML data are yet to be explored. Currently, the common compromise method is to convert private XML data into relational data for publication. This ad hoc approach results in significant loss of useful semantic information previously carried in the private XML data. Health data often has very complex structure, which is best expressed in XML. In fact, XML is the standard format for exchanging (e.g. HL7 version 3(1)) and publishing health information. Lack of means to deal directly with data in XML format is inevitably a serious drawback. In this paper we propose a novel privacy protection model for XML, and an algorithm for implementing this model. We provide general rules, both for transforming a private XML schema into a published XML schema, and for mapping private XML data to the new privacy-protected published XML data. In addition, we propose a new privacy property, δ-dependency, which can be applied to both relational and XML data, and that takes into consideration the hierarchical nature of sensitive data (as opposed to "quasi-identifiers"). Lastly, we provide an implementation of our model, algorithm and privacy property, and perform an experimental analysis, to demonstrate the proposed privacy scheme in practical application. Copyright © 2014. Published by Elsevier Inc.

Low-complexity R-peak detection for ambulatory fetal monitoring.

PubMed

Rooijakkers, Michael J; Rabotti, Chiara; Oei, S Guid; Mischi, Massimo

2012-07-01

Non-invasive fetal health monitoring during pregnancy is becoming increasingly important because of the increasing number of high-risk pregnancies. Despite recent advances in signal-processing technology, which have enabled fetal monitoring during pregnancy using abdominal electrocardiogram (ECG) recordings, ubiquitous fetal health monitoring is still unfeasible due to the computational complexity of noise-robust solutions. In this paper, an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm. The proposed algorithm is optimized to reduce computational complexity, without reducing the R-peak detection performance compared to the existing R-peak detection schemes. Validation of the algorithm is performed on three manually annotated datasets. With a detection error rate of 0.23%, 1.32% and 9.42% on the MIT/BIH Arrhythmia and in-house maternal and fetal databases, respectively, the detection rate of the proposed algorithm is comparable to the best state-of-the-art algorithms, at a reduced computational complexity.

Vision-Based Haptic Feedback for Remote Micromanipulation in-SEM Environment

NASA Astrophysics Data System (ADS)

Bolopion, Aude; Dahmen, Christian; Stolle, Christian; Haliyo, Sinan; Régnier, Stéphane; Fatikow, Sergej

2012-07-01

This article presents an intuitive environment for remote micromanipulation composed of both haptic feedback and virtual reconstruction of the scene. To enable nonexpert users to perform complex teleoperated micromanipulation tasks, it is of utmost importance to provide them with information about the 3-D relative positions of the objects and the tools. Haptic feedback is an intuitive way to transmit such information. Since position sensors are not available at this scale, visual feedback is used to derive information about the scene. In this work, three different techniques are implemented, evaluated, and compared to derive the object positions from scanning electron microscope images. The modified correlation matching with generated template algorithm is accurate and provides reliable detection of objects. To track the tool, a marker-based approach is chosen since fast detection is required for stable haptic feedback. Information derived from these algorithms is used to propose an intuitive remote manipulation system that enables users situated in geographically distant sites to benefit from specific equipments, such as SEMs. Stability of the haptic feedback is ensured by the minimization of the delays, the computational efficiency of vision algorithms, and the proper tuning of the haptic coupling. Virtual guides are proposed to avoid any involuntary collisions between the tool and the objects. This approach is validated by a teleoperation involving melamine microspheres with a diameter of less than 2 μ m between Paris, France and Oldenburg, Germany.

Life-histories from Landsat: Algorithmic approaches to distilling Earth's recent ecological dynamics

NASA Astrophysics Data System (ADS)

Kennedy, R. E.; Yang, Z.; Braaten, J.; Cohen, W. B.; Ohmann, J.; Gregory, M.; Roberts, H.; Meigs, G. W.; Nelson, P.; Pfaff, E.

2012-12-01

As the longest running continuous satellite Earth-observation record, data from the Landsat family of sensors have the potential to uniquely reveal temporal dynamics critical to many terrestrial disciplines. The convergence of a free-data access policy in the late 2000s with a rapid rise in computing and storage capacity has highlighted an increasinagly common challenge: effective distillation of information from large digital datasets. Here, we describe how an algorithmic workflow informed by basic understanding of ecological processes is being used to convert multi-terabyte image time-series datasets into concise renditions of landscape dynamics. Using examples from our own work, we show how these are in turn applied to monitor vegetative disturbance and growth dynamics in national parks, to evaluate effectiveness of natural resource policy in national forests, to constrain and inform biogeochemical models, to measure carbon impacts of natural and anthropogenic stressors, to assess impacts of land use change on threatened species, to educate and inform students, and to better characterize complex links between changing climate, insect pathogens, and wildfire in forests.

Solution of the problem of superposing image and digital map for detection of new objects

NASA Astrophysics Data System (ADS)

Rizaev, I. S.; Miftakhutdinov, D. I.; Takhavova, E. G.

2018-01-01

The problem of superposing the map of the terrain with the image of the terrain is considered. The image of the terrain may be represented in different frequency bands. Further analysis of the results of collation the digital map with the image of the appropriate terrain is described. Also the approach to detection of differences between information represented on the digital map and information of the image of the appropriate area is offered. The algorithm for calculating the values of brightness of the converted image area on the original picture is offered. The calculation is based on using information about the navigation parameters and information according to arranged bench marks. For solving the posed problem the experiments were performed. The results of the experiments are shown in this paper. The presented algorithms are applicable to the ground complex of remote sensing data to assess differences between resulting images and accurate geopositional data. They are also suitable for detecting new objects in the image, based on the analysis of the matching the digital map and the image of corresponding locality.

Information dynamics algorithm for detecting communities in networks

NASA Astrophysics Data System (ADS)

Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro

2012-11-01

The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.

PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm.

PubMed

Xu, Qian; Xiong, Yi; Dai, Hao; Kumari, Kotni Meena; Xu, Qin; Ou, Hong-Yu; Wei, Dong-Qing

2017-03-21

Combinatorial therapy is a promising strategy for combating complex diseases by improving the efficacy and reducing the side effects. To facilitate the identification of drug combinations in pharmacology, we proposed a new computational model, termed PDC-SGB, to predict effective drug combinations by integrating biological, chemical and pharmacological information based on a stochastic gradient boosting algorithm. To begin with, a set of 352 golden positive samples were collected from the public drug combination database. Then, a set of 732 dimensional feature vector involving biological, chemical and pharmaceutical information was constructed for each drug combination to describe its properties. To avoid overfitting, the maximum relevance & minimum redundancy (mRMR) method was performed to extract useful ones by removing redundant subsets. Based on the selected features, the three different type of classification algorithms were employed to build the drug combination prediction models. Our results demonstrated that the model based on the stochastic gradient boosting algorithm yield out the best performance. Furthermore, it is indicated that the feature patterns of therapy had powerful ability to discriminate effective drug combinations from non-effective ones. By analyzing various features, it is shown that the enriched features occurred frequently in golden positive samples can help predict novel drug combinations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Decision-making without a brain: how an amoeboid organism solves the two-armed bandit.

PubMed

Reid, Chris R; MacDonald, Hannelore; Mann, Richard P; Marshall, James A R; Latty, Tanya; Garnier, Simon

2016-06-01

Several recent studies hint at shared patterns in decision-making between taxonomically distant organisms, yet few studies demonstrate and dissect mechanisms of decision-making in simpler organisms. We examine decision-making in the unicellular slime mould Physarum polycephalum using a classical decision problem adapted from human and animal decision-making studies: the two-armed bandit problem. This problem has previously only been used to study organisms with brains, yet here we demonstrate that a brainless unicellular organism compares the relative qualities of multiple options, integrates over repeated samplings to perform well in random environments, and combines information on reward frequency and magnitude in order to make correct and adaptive decisions. We extend our inquiry by using Bayesian model selection to determine the most likely algorithm used by the cell when making decisions. We deduce that this algorithm centres around a tendency to exploit environments in proportion to their reward experienced through past sampling. The algorithm is intermediate in computational complexity between simple, reactionary heuristics and calculation-intensive optimal performance algorithms, yet it has very good relative performance. Our study provides insight into ancestral mechanisms of decision-making and suggests that fundamental principles of decision-making, information processing and even cognition are shared among diverse biological systems. © 2016 The Authors.

A community detection algorithm using network topologies and rule-based hierarchical arc-merging strategies

PubMed Central

2017-01-01

The authors use four criteria to examine a novel community detection algorithm: (a) effectiveness in terms of producing high values of normalized mutual information (NMI) and modularity, using well-known social networks for testing; (b) examination, meaning the ability to examine mitigating resolution limit problems using NMI values and synthetic networks; (c) correctness, meaning the ability to identify useful community structure results in terms of NMI values and Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks; and (d) scalability, or the ability to produce comparable modularity values with fast execution times when working with large-scale real-world networks. In addition to describing a simple hierarchical arc-merging (HAM) algorithm that uses network topology information, we introduce rule-based arc-merging strategies for identifying community structures. Five well-studied social network datasets and eight sets of LFR benchmark networks were employed to validate the correctness of a ground-truth community, eight large-scale real-world complex networks were used to measure its efficiency, and two synthetic networks were used to determine its susceptibility to two resolution limit problems. Our experimental results indicate that the proposed HAM algorithm exhibited satisfactory performance efficiency, and that HAM-identified and ground-truth communities were comparable in terms of social and LFR benchmark networks, while mitigating resolution limit problems. PMID:29121100

Efficient terrestrial laser scan segmentation exploiting data structure

NASA Astrophysics Data System (ADS)

Mahmoudabadi, Hamid; Olsen, Michael J.; Todorovic, Sinisa

2016-09-01

New technologies such as lidar enable the rapid collection of massive datasets to model a 3D scene as a point cloud. However, while hardware technology continues to advance, processing 3D point clouds into informative models remains complex and time consuming. A common approach to increase processing efficiently is to segment the point cloud into smaller sections. This paper proposes a novel approach for point cloud segmentation using computer vision algorithms to analyze panoramic representations of individual laser scans. These panoramas can be quickly created using an inherent neighborhood structure that is established during the scanning process, which scans at fixed angular increments in a cylindrical or spherical coordinate system. In the proposed approach, a selected image segmentation algorithm is applied on several input layers exploiting this angular structure including laser intensity, range, normal vectors, and color information. These segments are then mapped back to the 3D point cloud so that modeling can be completed more efficiently. This approach does not depend on pre-defined mathematical models and consequently setting parameters for them. Unlike common geometrical point cloud segmentation methods, the proposed method employs the colorimetric and intensity data as another source of information. The proposed algorithm is demonstrated on several datasets encompassing variety of scenes and objects. Results show a very high perceptual (visual) level of segmentation and thereby the feasibility of the proposed algorithm. The proposed method is also more efficient compared to Random Sample Consensus (RANSAC), which is a common approach for point cloud segmentation.

Information search and decision making: effects of age and complexity on strategy use.

PubMed

Queen, Tara L; Hess, Thomas M; Ennis, Gilda E; Dowd, Keith; Grühn, Daniel

2012-12-01

The impact of task complexity on information search strategy and decision quality was examined in a sample of 135 young, middle-aged, and older adults. We were particularly interested in the competing roles of fluid cognitive ability and domain knowledge and experience, with the former being a negative influence and the latter being a positive influence on older adults' performance. Participants utilized 2 decision matrices, which varied in complexity, regarding a consumer purchase. Using process tracing software and an algorithm developed to assess decision strategy, we recorded search behavior, strategy selection, and final decision. Contrary to expectations, older adults were not more likely than the younger age groups to engage in information-minimizing search behaviors in response to increases in task complexity. Similarly, adults of all ages used comparable decision strategies and adapted their strategies to the demands of the task. We also examined decision outcomes in relation to participants' preferences. Overall, it seems that older adults utilize simpler sets of information primarily reflecting the most valued attributes in making their choice. The results of this study suggest that older adults are adaptive in their approach to decision making and that this ability may benefit from accrued knowledge and experience. 2013 APA, all rights reserved

Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks.

PubMed

Zhang, Jing; Zhou, Qingjie; Ng, Derrick Wing Kwan; Jo, Minho

2017-09-15

In wireless powered communication networks (WPCNs), it is essential to research energy efficiency fairness in order to evaluate the balance of nodes for receiving information and harvesting energy. In this paper, we propose an efficient iterative algorithm for optimal energy efficiency proportional fairness in WPCN. The main idea is to use stochastic geometry to derive the mean proportionally fairness utility function with respect to user association probability and receive threshold. Subsequently, we prove that the relaxed proportionally fairness utility function is a concave function for user association probability and receive threshold, respectively. At the same time, a sub-optimal algorithm by exploiting alternating optimization approach is proposed. Through numerical simulations, we demonstrate that our sub-optimal algorithm can obtain a result close to optimal energy efficiency proportional fairness with significant reduction of computational complexity.

Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks

PubMed Central

Zhou, Qingjie; Ng, Derrick Wing Kwan; Jo, Minho

2017-01-01

In wireless powered communication networks (WPCNs), it is essential to research energy efficiency fairness in order to evaluate the balance of nodes for receiving information and harvesting energy. In this paper, we propose an efficient iterative algorithm for optimal energy efficiency proportional fairness in WPCN. The main idea is to use stochastic geometry to derive the mean proportionally fairness utility function with respect to user association probability and receive threshold. Subsequently, we prove that the relaxed proportionally fairness utility function is a concave function for user association probability and receive threshold, respectively. At the same time, a sub-optimal algorithm by exploiting alternating optimization approach is proposed. Through numerical simulations, we demonstrate that our sub-optimal algorithm can obtain a result close to optimal energy efficiency proportional fairness with significant reduction of computational complexity. PMID:28914818

Quantum red-green-blue image steganography

NASA Astrophysics Data System (ADS)

Heidari, Shahrokh; Pourarian, Mohammad Rasoul; Gheibi, Reza; Naseri, Mosayeb; Houshmand, Monireh

One of the most considering matters in the field of quantum information processing is quantum data hiding including quantum steganography and quantum watermarking. This field is an efficient tool for protecting any kind of digital data. In this paper, three quantum color images steganography algorithms are investigated based on Least Significant Bit (LSB). The first algorithm employs only one of the image’s channels to cover secret data. The second procedure is based on LSB XORing technique, and the last algorithm utilizes two channels to cover the color image for hiding secret quantum data. The performances of the proposed schemes are analyzed by using software simulations in MATLAB environment. The analysis of PSNR, BER and Histogram graphs indicate that the presented schemes exhibit acceptable performances and also theoretical analysis demonstrates that the networks complexity of the approaches scales squarely.

Fast parallel algorithm for slicing STL based on pipeline

NASA Astrophysics Data System (ADS)

Ma, Xulong; Lin, Feng; Yao, Bo

2016-05-01

In Additive Manufacturing field, the current researches of data processing mainly focus on a slicing process of large STL files or complicated CAD models. To improve the efficiency and reduce the slicing time, a parallel algorithm has great advantages. However, traditional algorithms can't make full use of multi-core CPU hardware resources. In the paper, a fast parallel algorithm is presented to speed up data processing. A pipeline mode is adopted to design the parallel algorithm. And the complexity of the pipeline algorithm is analyzed theoretically. To evaluate the performance of the new algorithm, effects of threads number and layers number are investigated by a serial of experiments. The experimental results show that the threads number and layers number are two remarkable factors to the speedup ratio. The tendency of speedup versus threads number reveals a positive relationship which greatly agrees with the Amdahl's law, and the tendency of speedup versus layers number also keeps a positive relationship agreeing with Gustafson's law. The new algorithm uses topological information to compute contours with a parallel method of speedup. Another parallel algorithm based on data parallel is used in experiments to show that pipeline parallel mode is more efficient. A case study at last shows a suspending performance of the new parallel algorithm. Compared with the serial slicing algorithm, the new pipeline parallel algorithm can make full use of the multi-core CPU hardware, accelerate the slicing process, and compared with the data parallel slicing algorithm, the new slicing algorithm in this paper adopts a pipeline parallel model, and a much higher speedup ratio and efficiency is achieved.

Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

NASA Astrophysics Data System (ADS)

Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

2016-03-01

The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

DOGMA: A Disk-Oriented Graph Matching Algorithm for RDF Databases

NASA Astrophysics Data System (ADS)

Bröcheler, Matthias; Pugliese, Andrea; Subrahmanian, V. S.

RDF is an increasingly important paradigm for the representation of information on the Web. As RDF databases increase in size to approach tens of millions of triples, and as sophisticated graph matching queries expressible in languages like SPARQL become increasingly important, scalability becomes an issue. To date, there is no graph-based indexing method for RDF data where the index was designed in a way that makes it disk-resident. There is therefore a growing need for indexes that can operate efficiently when the index itself resides on disk. In this paper, we first propose the DOGMA index for fast subgraph matching on disk and then develop a basic algorithm to answer queries over this index. This algorithm is then significantly sped up via an optimized algorithm that uses efficient (but correct) pruning strategies when combined with two different extensions of the index. We have implemented a preliminary system and tested it against four existing RDF database systems developed by others. Our experiments show that our algorithm performs very well compared to these systems, with orders of magnitude improvements for complex graph queries.

Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation

PubMed Central

Liu, Yang; Liu, Junfei

2016-01-01

This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid artificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm the new growth operators of branching, regrowing, and shrinkage are initially designed to optimize continuous space search by combining root-to-root communication and coevolution mechanism. With the auxin-regulated scheme, various root growth operators are guided systematically. With root-to-root communication, individuals exchange information in different efficient topologies, which essentially improve the exploration ability. With coevolution mechanism, the hierarchical spatial population driven by evolutionary pressure of multiple subpopulations is structured, which ensure that the diversity of root population is well maintained. The comparative results on a suit of benchmarks show the superiority of the proposed algorithm. Finally, the proposed HARFO algorithm is applied to handle the complex image segmentation problem based on multilevel threshold. Computational results of this approach on a set of tested images show the outperformance of the proposed algorithm in terms of optimization accuracy computation efficiency. PMID:27725826

Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation.

PubMed

Liu, Yang; Liu, Junfei; Tian, Liwei; Ma, Lianbo

2016-01-01

This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid artificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm the new growth operators of branching, regrowing, and shrinkage are initially designed to optimize continuous space search by combining root-to-root communication and coevolution mechanism. With the auxin-regulated scheme, various root growth operators are guided systematically. With root-to-root communication, individuals exchange information in different efficient topologies, which essentially improve the exploration ability. With coevolution mechanism, the hierarchical spatial population driven by evolutionary pressure of multiple subpopulations is structured, which ensure that the diversity of root population is well maintained. The comparative results on a suit of benchmarks show the superiority of the proposed algorithm. Finally, the proposed HARFO algorithm is applied to handle the complex image segmentation problem based on multilevel threshold. Computational results of this approach on a set of tested images show the outperformance of the proposed algorithm in terms of optimization accuracy computation efficiency.

Audiovisual focus of attention and its application to Ultra High Definition video compression

NASA Astrophysics Data System (ADS)

Rerabek, Martin; Nemoto, Hiromi; Lee, Jong-Seok; Ebrahimi, Touradj

2014-02-01

Using Focus of Attention (FoA) as a perceptual process in image and video compression belongs to well-known approaches to increase coding efficiency. It has been shown that foveated coding, when compression quality varies across the image according to region of interest, is more efficient than the alternative coding, when all region are compressed in a similar way. However, widespread use of such foveated compression has been prevented due to two main conflicting causes, namely, the complexity and the efficiency of algorithms for FoA detection. One way around these is to use as much information as possible from the scene. Since most video sequences have an associated audio, and moreover, in many cases there is a correlation between the audio and the visual content, audiovisual FoA can improve efficiency of the detection algorithm while remaining of low complexity. This paper discusses a simple yet efficient audiovisual FoA algorithm based on correlation of dynamics between audio and video signal components. Results of audiovisual FoA detection algorithm are subsequently taken into account for foveated coding and compression. This approach is implemented into H.265/HEVC encoder producing a bitstream which is fully compliant to any H.265/HEVC decoder. The influence of audiovisual FoA in the perceived quality of high and ultra-high definition audiovisual sequences is explored and the amount of gain in compression efficiency is analyzed.

A multistage selective weighting method for improved microwave breast tomography.

PubMed

Shahzad, Atif; O'Halloran, Martin; Jones, Edward; Glavin, Martin

2016-12-01

Microwave tomography has shown potential to successfully reconstruct the dielectric properties of the human breast, thereby providing an alternative to other imaging modalities used in breast imaging applications. Considering the costly forward solution and complex iterative algorithms, computational complexity becomes a major bottleneck in practical applications of microwave tomography. In addition, the natural tendency of microwave inversion algorithms to reward high contrast breast tissue boundaries, such as the skin-adipose interface, usually leads to a very slow reconstruction of the internal tissue structure of human breast. This paper presents a multistage selective weighting method to improve the reconstruction quality of breast dielectric properties and minimize the computational cost of microwave breast tomography. In the proposed two stage approach, the skin layer is approximated using scaled microwave measurements in the first pass of the inversion algorithm; a numerical skin model is then constructed based on the estimated skin layer and the assumed dielectric properties of the skin tissue. In the second stage of the algorithm, the skin model is used as a priori information to reconstruct the internal tissue structure of the breast using a set of temporal scaling functions. The proposed method is evaluated on anatomically accurate MRI-derived breast phantoms and a comparison with the standard single-stage technique is presented. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

An O({radical}nL) primal-dual affine scaling algorithm for linear programming

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

Huang, Siming

1994-12-31

We present a new primal-dual affine scaling algorithm for linear programming. The search direction of the algorithm is a combination of classical affine scaling direction of Dikin and a recent new affine scaling direction of Jansen, Roos and Terlaky. The algorithm has an iteration complexity of O({radical}nL), comparing to O(nL) complexity of Jansen, Roos and Terlaky.

A multistage motion vector processing method for motion-compensated frame interpolation.

PubMed

Huang, Ai- Mei; Nguyen, Truong Q

2008-05-01

In this paper, a novel, low-complexity motion vector processing algorithm at the decoder is proposed for motion-compensated frame interpolation or frame rate up-conversion. We address the problems of having broken edges and deformed structures in an interpolated frame by hierarchically refining motion vectors on different block sizes. Our method explicitly considers the reliability of each received motion vector and has the capability of preserving the structure information. This is achieved by analyzing the distribution of residual energies and effectively merging blocks that have unreliable motion vectors. The motion vector reliability information is also used as a prior knowledge in motion vector refinement using a constrained vector median filter to avoid choosing identical unreliable one. We also propose using chrominance information in our method. Experimental results show that the proposed scheme has better visual quality and is also robust, even in video sequences with complex scenes and fast motion.

Algorithms and architectures for robot vision

NASA Technical Reports Server (NTRS)

Schenker, Paul S.

1990-01-01

The scope of the current work is to develop practical sensing implementations for robots operating in complex, partially unstructured environments. A focus in this work is to develop object models and estimation techniques which are specific to requirements of robot locomotion, approach and avoidance, and grasp and manipulation. Such problems have to date received limited attention in either computer or human vision - in essence, asking not only how perception is in general modeled, but also what is the functional purpose of its underlying representations. As in the past, researchers are drawing on ideas from both the psychological and machine vision literature. Of particular interest is the development 3-D shape and motion estimates for complex objects when given only partial and uncertain information and when such information is incrementally accrued over time. Current studies consider the use of surface motion, contour, and texture information, with the longer range goal of developing a fused sensing strategy based on these sources and others.

Numerical characteristics of quantum computer simulation

NASA Astrophysics Data System (ADS)

Chernyavskiy, A.; Khamitov, K.; Teplov, A.; Voevodin, V.; Voevodin, Vl.

2016-12-01

The simulation of quantum circuits is significantly important for the implementation of quantum information technologies. The main difficulty of such modeling is the exponential growth of dimensionality, thus the usage of modern high-performance parallel computations is relevant. As it is well known, arbitrary quantum computation in circuit model can be done by only single- and two-qubit gates, and we analyze the computational structure and properties of the simulation of such gates. We investigate the fact that the unique properties of quantum nature lead to the computational properties of the considered algorithms: the quantum parallelism make the simulation of quantum gates highly parallel, and on the other hand, quantum entanglement leads to the problem of computational locality during simulation. We use the methodology of the AlgoWiki project (algowiki-project.org) to analyze the algorithm. This methodology consists of theoretical (sequential and parallel complexity, macro structure, and visual informational graph) and experimental (locality and memory access, scalability and more specific dynamic characteristics) parts. Experimental part was made by using the petascale Lomonosov supercomputer (Moscow State University, Russia). We show that the simulation of quantum gates is a good base for the research and testing of the development methods for data intense parallel software, and considered methodology of the analysis can be successfully used for the improvement of the algorithms in quantum information science.

Detecting the red tide based on remote sensing data in optically complex East China Sea

NASA Astrophysics Data System (ADS)

Xu, Xiaohui; Pan, Delu; Mao, Zhihua; Tao, Bangyi; Liu, Qiong

2012-09-01

Red tide not only destroys marine fishery production, deteriorates the marine environment, affects coastal tourist industry, but also causes human poison, even death by eating toxic seafood contaminated by red tide organisms. Remote sensing technology has the characteristics of large-scale, synchronized, rapid monitoring, so it is one of the most important and most effective means of red tide monitoring. This paper selects the high frequency red tides areas of the East China Sea as study area, MODIS/Aqua L2 data as the data source, analysis and compares the spectral differences in the red tide water bodies and non-red tide water bodies of many historical events. Based on the spectral differences, this paper develops the algorithm of Rrs555/Rrs488> 1.5 to extract the red tide information. Apply the algorithm on red tide event happened in the East China Sea on May 28, 2009 to extract the information of red tide, and found that the method can determine effectively the location of the occurrence of red tide; there is a good corresponding relationship between red tide extraction result and chlorophyll a concentration extracted by remote sensing, shows that these algorithm can determine effectively the location and extract the red tide information.

A General, Adaptive, Roadmap-Based Algorithm for Protein Motion Computation.

PubMed

Molloy, Kevin; Shehu, Amarda

2016-03-01

Precious information on protein function can be extracted from a detailed characterization of protein equilibrium dynamics. This remains elusive in wet and dry laboratories, as function-modulating transitions of a protein between functionally-relevant, thermodynamically-stable and meta-stable structural states often span disparate time scales. In this paper we propose a novel, robotics-inspired algorithm that circumvents time-scale challenges by drawing analogies between protein motion and robot motion. The algorithm adapts the popular roadmap-based framework in robot motion computation to handle the more complex protein conformation space and its underlying rugged energy surface. Given known structures representing stable and meta-stable states of a protein, the algorithm yields a time- and energy-prioritized list of transition paths between the structures, with each path represented as a series of conformations. The algorithm balances computational resources between a global search aimed at obtaining a global view of the network of protein conformations and their connectivity and a detailed local search focused on realizing such connections with physically-realistic models. Promising results are presented on a variety of proteins that demonstrate the general utility of the algorithm and its capability to improve the state of the art without employing system-specific insight.

Standardized shrinking LORETA-FOCUSS (SSLOFO): a new algorithm for spatio-temporal EEG source reconstruction.

PubMed

Liu, Hesheng; Schimpf, Paul H; Dong, Guoya; Gao, Xiaorong; Yang, Fusheng; Gao, Shangkai

2005-10-01

This paper presents a new algorithm called Standardized Shrinking LORETA-FOCUSS (SSLOFO) for solving the electroencephalogram (EEG) inverse problem. Multiple techniques are combined in a single procedure to robustly reconstruct the underlying source distribution with high spatial resolution. This algorithm uses a recursive process which takes the smooth estimate of sLORETA as initialization and then employs the re-weighted minimum norm introduced by FOCUSS. An important technique called standardization is involved in the recursive process to enhance the localization ability. The algorithm is further improved by automatically adjusting the source space according to the estimate of the previous step, and by the inclusion of temporal information. Simulation studies are carried out on both spherical and realistic head models. The algorithm achieves very good localization ability on noise-free data. It is capable of recovering complex source configurations with arbitrary shapes and can produce high quality images of extended source distributions. We also characterized the performance with noisy data in a realistic head model. An important feature of this algorithm is that the temporal waveforms are clearly reconstructed, even for closely spaced sources. This provides a convenient way to estimate neural dynamics directly from the cortical sources.

A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization

PubMed Central

Lin, Jingjing; Jing, Honglei

2016-01-01

Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive. PMID:27698662

An optimized routing algorithm for the automated assembly of standard multimode ribbon fibers in a full-mesh optical backplane

NASA Astrophysics Data System (ADS)

Basile, Vito; Guadagno, Gianluca; Ferrario, Maddalena; Fassi, Irene

2018-03-01

In this paper a parametric, modular and scalable algorithm allowing a fully automated assembly of a backplane fiber-optic interconnection circuit is presented. This approach guarantees the optimization of the optical fiber routing inside the backplane with respect to specific criteria (i.e. bending power losses), addressing both transmission performance and overall costs issues. Graph theory has been exploited to simplify the complexity of the NxN full-mesh backplane interconnection topology, firstly, into N independent sub-circuits and then, recursively, into a limited number of loops easier to be generated. Afterwards, the proposed algorithm selects a set of geometrical and architectural parameters whose optimization allows to identify the optimal fiber optic routing for each sub-circuit of the backplane. The topological and numerical information provided by the algorithm are then exploited to control a robot which performs the automated assembly of the backplane sub-circuits. The proposed routing algorithm can be extended to any array architecture and number of connections thanks to its modularity and scalability. Finally, the algorithm has been exploited for the automated assembly of an 8x8 optical backplane realized with standard multimode (MM) 12-fiber ribbons.

An ant colony based algorithm for overlapping community detection in complex networks

NASA Astrophysics Data System (ADS)

Zhou, Xu; Liu, Yanheng; Zhang, Jindong; Liu, Tuming; Zhang, Di

2015-06-01

Community detection is of great importance to understand the structures and functions of networks. Overlap is a significant feature of networks and overlapping community detection has attracted an increasing attention. Many algorithms have been presented to detect overlapping communities. In this paper, we present an ant colony based overlapping community detection algorithm which mainly includes ants' location initialization, ants' movement and post processing phases. An ants' location initialization strategy is designed to identify initial location of ants and initialize label list stored in each node. During the ants' movement phase, the entire ants move according to the transition probability matrix, and a new heuristic information computation approach is redefined to measure similarity between two nodes. Every node keeps a label list through the cooperation made by ants until a termination criterion is reached. A post processing phase is executed on the label list to get final overlapping community structure naturally. We illustrate the capability of our algorithm by making experiments on both synthetic networks and real world networks. The results demonstrate that our algorithm will have better performance in finding overlapping communities and overlapping nodes in synthetic datasets and real world datasets comparing with state-of-the-art algorithms.

Biclustering Protein Complex Interactions with a Biclique FindingAlgorithm

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

Ding, Chris; Zhang, Anne Ya; Holbrook, Stephen

2006-12-01

Biclustering has many applications in text mining, web clickstream mining, and bioinformatics. When data entries are binary, the tightest biclusters become bicliques. We propose a flexible and highly efficient algorithm to compute bicliques. We first generalize the Motzkin-Straus formalism for computing the maximal clique from L{sub 1} constraint to L{sub p} constraint, which enables us to provide a generalized Motzkin-Straus formalism for computing maximal-edge bicliques. By adjusting parameters, the algorithm can favor biclusters with more rows less columns, or vice verse, thus increasing the flexibility of the targeted biclusters. We then propose an algorithm to solve the generalized Motzkin-Straus optimizationmore » problem. The algorithm is provably convergent and has a computational complexity of O(|E|) where |E| is the number of edges. It relies on a matrix vector multiplication and runs efficiently on most current computer architectures. Using this algorithm, we bicluster the yeast protein complex interaction network. We find that biclustering protein complexes at the protein level does not clearly reflect the functional linkage among protein complexes in many cases, while biclustering at protein domain level can reveal many underlying linkages. We show several new biologically significant results.« less

Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

PubMed

Guo, Xiaobo; Zhang, Ye; Hu, Wenhao; Tan, Haizhu; Wang, Xueqin

2014-01-01

Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

Inferring Nonlinear Gene Regulatory Networks from Gene Expression Data Based on Distance Correlation

PubMed Central

Guo, Xiaobo; Zhang, Ye; Hu, Wenhao; Tan, Haizhu; Wang, Xueqin

2014-01-01

Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference. PMID:24551058

On the use of Schwarz-Christoffel conformal mappings to the grid generation for global ocean models

NASA Astrophysics Data System (ADS)

Xu, S.; Wang, B.; Liu, J.

2015-02-01

In this article we propose two conformal mapping based grid generation algorithms for global ocean general circulation models (OGCMs). Contrary to conventional, analytical forms based dipolar or tripolar grids, the new algorithms are based on Schwarz-Christoffel (SC) conformal mapping with prescribed boundary information. While dealing with the basic grid design problem of pole relocation, these new algorithms also address more advanced issues such as smoothed scaling factor, or the new requirements on OGCM grids arisen from the recent trend of high-resolution and multi-scale modeling. The proposed grid generation algorithm could potentially achieve the alignment of grid lines to coastlines, enhanced spatial resolution in coastal regions, and easier computational load balance. Since the generated grids are still orthogonal curvilinear, they can be readily utilized in existing Bryan-Cox-Semtner type ocean models. The proposed methodology can also be applied to the grid generation task for regional ocean modeling where complex land-ocean distribution is present.

A method for feature selection of APT samples based on entropy

NASA Astrophysics Data System (ADS)

Du, Zhenyu; Li, Yihong; Hu, Jinsong

2018-05-01

By studying the known APT attack events deeply, this paper propose a feature selection method of APT sample and a logic expression generation algorithm IOCG (Indicator of Compromise Generate). The algorithm can automatically generate machine readable IOCs (Indicator of Compromise), to solve the existing IOCs logical relationship is fixed, the number of logical items unchanged, large scale and cannot generate a sample of the limitations of the expression. At the same time, it can reduce the redundancy and useless APT sample processing time consumption, and improve the sharing rate of information analysis, and actively respond to complex and volatile APT attack situation. The samples were divided into experimental set and training set, and then the algorithm was used to generate the logical expression of the training set with the IOC_ Aware plug-in. The contrast expression itself was different from the detection result. The experimental results show that the algorithm is effective and can improve the detection effect.

Applied algorithm in the liner inspection of solid rocket motors

NASA Astrophysics Data System (ADS)

Hoffmann, Luiz Felipe Simões; Bizarria, Francisco Carlos Parquet; Bizarria, José Walter Parquet

2018-03-01

In rocket motors, the bonding between the solid propellant and thermal insulation is accomplished by a thin adhesive layer, known as liner. The liner application method involves a complex sequence of tasks, which includes in its final stage, the surface integrity inspection. Nowadays in Brazil, an expert carries out a thorough visual inspection to detect defects on the liner surface that may compromise the propellant interface bonding. Therefore, this paper proposes an algorithm that uses the photometric stereo technique and the K-nearest neighbor (KNN) classifier to assist the expert in the surface inspection. Photometric stereo allows the surface information recovery of the test images, while the KNN method enables image pixels classification into two classes: non-defect and defect. Tests performed on a computer vision based prototype validate the algorithm. The positive results suggest that the algorithm is feasible and when implemented in a real scenario, will be able to help the expert in detecting defective areas on the liner surface.

Effects of model complexity and priors on estimation using sequential importance sampling/resampling for species conservation

USGS Publications Warehouse

Dunham, Kylee; Grand, James B.

2016-01-01

We examined the effects of complexity and priors on the accuracy of models used to estimate ecological and observational processes, and to make predictions regarding population size and structure. State-space models are useful for estimating complex, unobservable population processes and making predictions about future populations based on limited data. To better understand the utility of state space models in evaluating population dynamics, we used them in a Bayesian framework and compared the accuracy of models with differing complexity, with and without informative priors using sequential importance sampling/resampling (SISR). Count data were simulated for 25 years using known parameters and observation process for each model. We used kernel smoothing to reduce the effect of particle depletion, which is common when estimating both states and parameters with SISR. Models using informative priors estimated parameter values and population size with greater accuracy than their non-informative counterparts. While the estimates of population size and trend did not suffer greatly in models using non-informative priors, the algorithm was unable to accurately estimate demographic parameters. This model framework provides reasonable estimates of population size when little to no information is available; however, when information on some vital rates is available, SISR can be used to obtain more precise estimates of population size and process. Incorporating model complexity such as that required by structured populations with stage-specific vital rates affects precision and accuracy when estimating latent population variables and predicting population dynamics. These results are important to consider when designing monitoring programs and conservation efforts requiring management of specific population segments.

A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory

PubMed Central

Li, Jingchao; Cao, Yunpeng; Ying, Yulong; Li, Shuying

2016-01-01

Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing fault pattern recognition. The experimental results show that the proposed method can identify various bearing fault types as well as severities effectively and accurately. PMID:28036329

A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory.

PubMed

Li, Jingchao; Cao, Yunpeng; Ying, Yulong; Li, Shuying

2016-01-01

Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing fault pattern recognition. The experimental results show that the proposed method can identify various bearing fault types as well as severities effectively and accurately.

Functional Classification of Immune Regulatory Proteins

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

Rubinstein, Rotem; Ramagopal, Udupi A.; Nathenson, Stanley G.

2013-05-01

Members of the immunoglobulin superfamily (IgSF) control innate and adaptive immunity and are prime targets for the treatment of autoimmune diseases, infectious diseases, and malignancies. We describe a computational method, termed the Brotherhood algorithm, which utilizes intermediate sequence information to classify proteins into functionally related families. This approach identifies functional relationships within the IgSF and predicts additional receptor-ligand interactions. As a specific example, we examine the nectin/nectin-like family of cell adhesion and signaling proteins and propose receptor-ligand interactions within this family. We were guided by the Brotherhood approach and present the high-resolution structural characterization of a homophilic interaction involving themore » class-I MHC-restricted T-cell-associated molecule, which we now classify as a nectin-like family member. The Brotherhood algorithm is likely to have a significant impact on structural immunology by identifying those proteins and complexes for which structural characterization will be particularly informative.« less

A novel medical information management and decision model for uncertain demand optimization.

PubMed

Bi, Ya

2015-01-01

Accurately planning the procurement volume is an effective measure for controlling the medicine inventory cost. Due to uncertain demand it is difficult to make accurate decision on procurement volume. As to the biomedicine sensitive to time and season demand, the uncertain demand fitted by the fuzzy mathematics method is obviously better than general random distribution functions. To establish a novel medical information management and decision model for uncertain demand optimization. A novel optimal management and decision model under uncertain demand has been presented based on fuzzy mathematics and a new comprehensive improved particle swarm algorithm. The optimal management and decision model can effectively reduce the medicine inventory cost. The proposed improved particle swarm optimization is a simple and effective algorithm to improve the Fuzzy interference and hence effectively reduce the calculation complexity of the optimal management and decision model. Therefore the new model can be used for accurate decision on procurement volume under uncertain demand.

SA-SOM algorithm for detecting communities in complex networks

NASA Astrophysics Data System (ADS)

Chen, Luogeng; Wang, Yanran; Huang, Xiaoming; Hu, Mengyu; Hu, Fang

2017-10-01

Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does.

Data association approaches in bearings-only multi-target tracking

NASA Astrophysics Data System (ADS)

Xu, Benlian; Wang, Zhiquan

2008-03-01

According to requirements of time computation complexity and correctness of data association of the multi-target tracking, two algorithms are suggested in this paper. The proposed Algorithm 1 is developed from the modified version of dual Simplex method, and it has the advantage of direct and explicit form of the optimal solution. The Algorithm 2 is based on the idea of Algorithm 1 and rotational sort method, it combines not only advantages of Algorithm 1, but also reduces the computational burden, whose complexity is only 1/ N times that of Algorithm 1. Finally, numerical analyses are carried out to evaluate the performance of the two data association algorithms.

Quantum Algorithms for Fermionic Quantum Field Theories

DTIC Science & Technology

2014-04-28

preskill@theory.caltech.edu 1 Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is...NAME OF RESPONSIBLE PERSON a. REPORT unclassified b. ABSTRACT unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98...operators of momentum modes. (The choice between these forms of measurement depends on the application.) 2.3 Complexity In this section we bound the

Audio-based, unsupervised machine learning reveals cyclic changes in earthquake mechanisms in the Geysers geothermal field, California

NASA Astrophysics Data System (ADS)

Holtzman, B. K.; Paté, A.; Paisley, J.; Waldhauser, F.; Repetto, D.; Boschi, L.

2017-12-01

The earthquake process reflects complex interactions of stress, fracture and frictional properties. New machine learning methods reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Our methods are based closely on those developed for music information retrieval and voice recognition, using the spectrogram instead of the waveform directly. Unsupervised learning involves identification of patterns based on differences among signals without any additional information provided to the algorithm. Clustering of 46,000 earthquakes of $0.3

An optical Fourier transform coprocessor with direct phase determination.

PubMed

Macfaden, Alexander J; Gordon, George S D; Wilkinson, Timothy D

2017-10-20

The Fourier transform is a ubiquitous mathematical operation which arises naturally in optics. We propose and demonstrate a practical method to optically evaluate a complex-to-complex discrete Fourier transform. By implementing the Fourier transform optically we can overcome the limiting O(nlogn) complexity of fast Fourier transform algorithms. Efficiently extracting the phase from the well-known optical Fourier transform is challenging. By appropriately decomposing the input and exploiting symmetries of the Fourier transform we are able to determine the phase directly from straightforward intensity measurements, creating an optical Fourier transform with O(n) apparent complexity. Performing larger optical Fourier transforms requires higher resolution spatial light modulators, but the execution time remains unchanged. This method could unlock the potential of the optical Fourier transform to permit 2D complex-to-complex discrete Fourier transforms with a performance that is currently untenable, with applications across information processing and computational physics.

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

Vatsavai, Raju; Cheriyadat, Anil M; Bhaduri, Budhendra L

The high rate of urbanization, political conflicts and ensuing internal displacement of population, and increased poverty in the 20th century has resulted in rapid increase of informal settlements. These unplanned, unauthorized, and/or unstructured homes, known as informal settlements, shantytowns, barrios, or slums, pose several challenges to the nations, as these settlements are often located in most hazardous regions and lack basic services. Though several World Bank and United Nations sponsored studies stress the importance of poverty maps in designing better policies and interventions, mapping slums of the world is a daunting and challenging task. In this paper, we summarize ourmore » ongoing research on settlement mapping through the utilization of Very high resolution (VHR) remote sensing imagery. Most existing approaches used to classify VHR images are single instance (or pixel-based) learning algorithms, which are inadequate for analyzing VHR imagery, as single pixels do not contain sufficient contextual information (see Figure 1). However, much needed spatial contextual information can be captured via feature extraction and/or through newer machine learning algorithms in order to extract complex spatial patterns that distinguish informal settlements from formal ones. In recent years, we made significant progress in advancing the state of art in both directions. This paper summarizes these results.« less

Pulse retrieval algorithm for interferometric frequency-resolved optical gating based on differential evolution.

PubMed

Hyyti, Janne; Escoto, Esmerando; Steinmeyer, Günter

2017-10-01

A novel algorithm for the ultrashort laser pulse characterization method of interferometric frequency-resolved optical gating (iFROG) is presented. Based on a genetic method, namely, differential evolution, the algorithm can exploit all available information of an iFROG measurement to retrieve the complex electric field of a pulse. The retrieval is subjected to a series of numerical tests to prove the robustness of the algorithm against experimental artifacts and noise. These tests show that the integrated error-correction mechanisms of the iFROG method can be successfully used to remove the effect from timing errors and spectrally varying efficiency in the detection. Moreover, the accuracy and noise resilience of the new algorithm are shown to outperform retrieval based on the generalized projections algorithm, which is widely used as the standard method in FROG retrieval. The differential evolution algorithm is further validated with experimental data, measured with unamplified three-cycle pulses from a mode-locked Ti:sapphire laser. Additionally introducing group delay dispersion in the beam path, the retrieval results show excellent agreement with independent measurements with a commercial pulse measurement device based on spectral phase interferometry for direct electric-field retrieval. Further experimental tests with strongly attenuated pulses indicate resilience of differential-evolution-based retrieval against massive measurement noise.

Occam’s Quantum Strop: Synchronizing and Compressing Classical Cryptic Processes via a Quantum Channel

NASA Astrophysics Data System (ADS)

Mahoney, John R.; Aghamohammadi, Cina; Crutchfield, James P.

2016-02-01

A stochastic process’ statistical complexity stands out as a fundamental property: the minimum information required to synchronize one process generator to another. How much information is required, though, when synchronizing over a quantum channel? Recent work demonstrated that representing causal similarity as quantum state-indistinguishability provides a quantum advantage. We generalize this to synchronization and offer a sequence of constructions that exploit extended causal structures, finding substantial increase of the quantum advantage. We demonstrate that maximum compression is determined by the process’ cryptic order-a classical, topological property closely allied to Markov order, itself a measure of historical dependence. We introduce an efficient algorithm that computes the quantum advantage and close noting that the advantage comes at a cost-one trades off prediction for generation complexity.

Attributed community mining using joint general non-negative matrix factorization with graph Laplacian

NASA Astrophysics Data System (ADS)

Chen, Zigang; Li, Lixiang; Peng, Haipeng; Liu, Yuhong; Yang, Yixian

2018-04-01

Community mining for complex social networks with link and attribute information plays an important role according to different application needs. In this paper, based on our proposed general non-negative matrix factorization (GNMF) algorithm without dimension matching constraints in our previous work, we propose the joint GNMF with graph Laplacian (LJGNMF) to implement community mining of complex social networks with link and attribute information according to different application needs. Theoretical derivation result shows that the proposed LJGNMF is fully compatible with previous methods of integrating traditional NMF and symmetric NMF. In addition, experimental results show that the proposed LJGNMF can meet the needs of different community minings by adjusting its parameters, and the effect is better than traditional NMF in the community vertices attributes entropy.

Occam's Quantum Strop: Synchronizing and Compressing Classical Cryptic Processes via a Quantum Channel.

PubMed

Mahoney, John R; Aghamohammadi, Cina; Crutchfield, James P

2016-02-15

A stochastic process' statistical complexity stands out as a fundamental property: the minimum information required to synchronize one process generator to another. How much information is required, though, when synchronizing over a quantum channel? Recent work demonstrated that representing causal similarity as quantum state-indistinguishability provides a quantum advantage. We generalize this to synchronization and offer a sequence of constructions that exploit extended causal structures, finding substantial increase of the quantum advantage. We demonstrate that maximum compression is determined by the process' cryptic order--a classical, topological property closely allied to Markov order, itself a measure of historical dependence. We introduce an efficient algorithm that computes the quantum advantage and close noting that the advantage comes at a cost-one trades off prediction for generation complexity.

Theory of post-block 2 VLBI observable extraction

NASA Technical Reports Server (NTRS)

Lowe, Stephen T.

1992-01-01

The algorithms used in the post-Block II fringe-fitting software called 'Fit' are described. The steps needed to derive the very long baseline interferometry (VLBI) charged-particle corrected group delay, phase delay rate, and phase delay (the latter without resolving cycle ambiguities) are presented beginning with the set of complex fringe phasors as a function of observation frequency and time. The set of complex phasors is obtained from the JPL/CIT Block II correlator. The output of Fit is the set of charged-particle corrected observables (along with ancillary information) in a form amenable to the software program 'Modest.'

Computational path planner for product assembly in complex environments

NASA Astrophysics Data System (ADS)

Shang, Wei; Liu, Jianhua; Ning, Ruxin; Liu, Mi

2013-03-01

Assembly path planning is a crucial problem in assembly related design and manufacturing processes. Sampling based motion planning algorithms are used for computational assembly path planning. However, the performance of such algorithms may degrade much in environments with complex product structure, narrow passages or other challenging scenarios. A computational path planner for automatic assembly path planning in complex 3D environments is presented. The global planning process is divided into three phases based on the environment and specific algorithms are proposed and utilized in each phase to solve the challenging issues. A novel ray test based stochastic collision detection method is proposed to evaluate the intersection between two polyhedral objects. This method avoids fake collisions in conventional methods and degrades the geometric constraint when a part has to be removed with surface contact with other parts. A refined history based rapidly-exploring random tree (RRT) algorithm which bias the growth of the tree based on its planning history is proposed and employed in the planning phase where the path is simple but the space is highly constrained. A novel adaptive RRT algorithm is developed for the path planning problem with challenging scenarios and uncertain environment. With extending values assigned on each tree node and extending schemes applied, the tree can adapts its growth to explore complex environments more efficiently. Experiments on the key algorithms are carried out and comparisons are made between the conventional path planning algorithms and the presented ones. The comparing results show that based on the proposed algorithms, the path planner can compute assembly path in challenging complex environments more efficiently and with higher success. This research provides the references to the study of computational assembly path planning under complex environments.

Network Community Detection based on the Physarum-inspired Computational Framework.

PubMed

Gao, Chao; Liang, Mingxin; Li, Xianghua; Zhang, Zili; Wang, Zhen; Zhou, Zhili

2016-12-13

Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, which is a large amoeba-like cell consisting of a dendritic network of tube-like pseudopodia, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost. Moreover, a computational complexity analysis verifies the scalability of our framework.

Using quantum theory to simplify input-output processes

NASA Astrophysics Data System (ADS)

Thompson, Jayne; Garner, Andrew J. P.; Vedral, Vlatko; Gu, Mile

2017-02-01

All natural things process and transform information. They receive environmental information as input, and transform it into appropriate output responses. Much of science is dedicated to building models of such systems-algorithmic abstractions of their input-output behavior that allow us to simulate how such systems can behave in the future, conditioned on what has transpired in the past. Here, we show that classical models cannot avoid inefficiency-storing past information that is unnecessary for correct future simulation. We construct quantum models that mitigate this waste, whenever it is physically possible to do so. This suggests that the complexity of general input-output processes depends fundamentally on what sort of information theory we use to describe them.

Protein docking prediction using predicted protein-protein interface.

PubMed

Li, Bin; Kihara, Daisuke

2012-01-10

Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

Large-Scale Optimization for Bayesian Inference in Complex Systems

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

Willcox, Karen; Marzouk, Youssef

2013-11-12

The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimization) Project focused on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimization and inversion methods. The project was a collaborative effort among MIT, the University of Texas at Austin, Georgia Institute of Technology, and Sandia National Laboratories. The research was directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. The MIT--Sandia component of themore » SAGUARO Project addressed the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas--Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to-observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as ``reduce then sample'' and ``sample then reduce.'' In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.« less

Final Report: Large-Scale Optimization for Bayesian Inference in Complex Systems

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

Ghattas, Omar

2013-10-15

The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimiza- tion) Project focuses on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimiza- tion and inversion methods. Our research is directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. Our efforts are integrated in the context of a challenging testbed problem that considers subsurface reacting flow and transport. The MIT component of the SAGUAROmore » Project addresses the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas-Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to- observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as "reduce then sample" and "sample then reduce." In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.« less

Neurient: An Algorithm for Automatic Tracing of Confluent Neuronal Images to Determine Alignment

PubMed Central

Mitchel, J.A.; Martin, I.S.

2013-01-01

A goal of neural tissue engineering is the development and evaluation of materials that guide neuronal growth and alignment. However, the methods available to quantitatively evaluate the response of neurons to guidance materials are limited and/or expensive, and may require manual tracing to be performed by the researcher. We have developed an open source, automated Matlab-based algorithm, building on previously published methods, to trace and quantify alignment of fluorescent images of neurons in culture. The algorithm is divided into three phases, including computation of a lookup table which contains directional information for each image, location of a set of seed points which may lie along neurite centerlines, and tracing neurites starting with each seed point and indexing into the lookup table. This method was used to obtain quantitative alignment data for complex images of densely cultured neurons. Complete automation of tracing allows for unsupervised processing of large numbers of images. Following image processing with our algorithm, available metrics to quantify neurite alignment include angular histograms, percent of neurite segments in a given direction, and mean neurite angle. The alignment information obtained from traced images can be used to compare the response of neurons to a range of conditions. This tracing algorithm is freely available to the scientific community under the name Neurient, and its implementation in Matlab allows a wide range of researchers to use a standardized, open source method to quantitatively evaluate the alignment of dense neuronal cultures. PMID:23384629

Diagonalization of complex symmetric matrices: Generalized Householder reflections, iterative deflation and implicit shifts

NASA Astrophysics Data System (ADS)

Noble, J. H.; Lubasch, M.; Stevens, J.; Jentschura, U. D.

2017-12-01

We describe a matrix diagonalization algorithm for complex symmetric (not Hermitian) matrices, A ̲ =A̲T, which is based on a two-step algorithm involving generalized Householder reflections based on the indefinite inner product 〈 u ̲ , v ̲ 〉 ∗ =∑iuivi. This inner product is linear in both arguments and avoids complex conjugation. The complex symmetric input matrix is transformed to tridiagonal form using generalized Householder transformations (first step). An iterative, generalized QL decomposition of the tridiagonal matrix employing an implicit shift converges toward diagonal form (second step). The QL algorithm employs iterative deflation techniques when a machine-precision zero is encountered "prematurely" on the super-/sub-diagonal. The algorithm allows for a reliable and computationally efficient computation of resonance and antiresonance energies which emerge from complex-scaled Hamiltonians, and for the numerical determination of the real energy eigenvalues of pseudo-Hermitian and PT-symmetric Hamilton matrices. Numerical reference values are provided.

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

PubMed

Li, Min; Li, Dongyan; Tang, Yu; Wu, Fangxiang; Wang, Jianxin

2017-08-31

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks

PubMed Central

Li, Min; Li, Dongyan; Tang, Yu; Wang, Jianxin

2017-01-01

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster. PMID:28858211

Complexity control algorithm based on adaptive mode selection for interframe coding in high efficiency video coding

NASA Astrophysics Data System (ADS)

Chen, Gang; Yang, Bing; Zhang, Xiaoyun; Gao, Zhiyong

2017-07-01

The latest high efficiency video coding (HEVC) standard significantly increases the encoding complexity for improving its coding efficiency. Due to the limited computational capability of handheld devices, complexity constrained video coding has drawn great attention in recent years. A complexity control algorithm based on adaptive mode selection is proposed for interframe coding in HEVC. Considering the direct proportionality between encoding time and computational complexity, the computational complexity is measured in terms of encoding time. First, complexity is mapped to a target in terms of prediction modes. Then, an adaptive mode selection algorithm is proposed for the mode decision process. Specifically, the optimal mode combination scheme that is chosen through offline statistics is developed at low complexity. If the complexity budget has not been used up, an adaptive mode sorting method is employed to further improve coding efficiency. The experimental results show that the proposed algorithm achieves a very large complexity control range (as low as 10%) for the HEVC encoder while maintaining good rate-distortion performance. For the lowdelayP condition, compared with the direct resource allocation method and the state-of-the-art method, an average gain of 0.63 and 0.17 dB in BDPSNR is observed for 18 sequences when the target complexity is around 40%.

Aiding the Detection of QRS Complex in ECG Signals by Detecting S Peaks Independently.

PubMed

Sabherwal, Pooja; Singh, Latika; Agrawal, Monika

2018-03-30

In this paper, a novel algorithm for the accurate detection of QRS complex by combining the independent detection of R and S peaks, using fusion algorithm is proposed. R peak detection has been extensively studied and is being used to detect the QRS complex. Whereas, S peaks, which is also part of QRS complex can be independently detected to aid the detection of QRS complex. In this paper, we suggest a method to first estimate S peak from raw ECG signal and then use them to aid the detection of QRS complex. The amplitude of S peak in ECG signal is relatively weak than corresponding R peak, which is traditionally used for the detection of QRS complex, therefore, an appropriate digital filter is designed to enhance the S peaks. These enhanced S peaks are then detected by adaptive thresholding. The algorithm is validated on all the signals of MIT-BIH arrhythmia database and noise stress database taken from physionet.org. The algorithm performs reasonably well even for the signals highly corrupted by noise. The algorithm performance is confirmed by sensitivity and positive predictivity of 99.99% and the detection accuracy of 99.98% for QRS complex detection. The number of false positives and false negatives resulted while analysis has been drastically reduced to 80 and 42 against the 98 and 84 the best results reported so far.

Study on a low complexity adaptive modulation algorithm in OFDM-ROF system with sub-carrier grouping technology

NASA Astrophysics Data System (ADS)

Liu, Chong-xin; Liu, Bo; Zhang, Li-jia; Xin, Xiang-jun; Tian, Qing-hua; Tian, Feng; Wang, Yong-jun; Rao, Lan; Mao, Yaya; Li, Deng-ao

2018-01-01

During the last decade, the orthogonal frequency division multiplexing radio-over-fiber (OFDM-ROF) system with adaptive modulation technology is of great interest due to its capability of raising the spectral efficiency dramatically, reducing the effects of fiber link or wireless channel, and improving the communication quality. In this study, according to theoretical analysis of nonlinear distortion and frequency selective fading on the transmitted signal, a low-complexity adaptive modulation algorithm is proposed in combination with sub-carrier grouping technology. This algorithm achieves the optimal performance of the system by calculating the average combined signal-to-noise ratio of each group and dynamically adjusting the origination modulation format according to the preset threshold and user's requirements. At the same time, this algorithm takes the sub-carrier group as the smallest unit in the initial bit allocation and the subsequent bit adjustment. So, the algorithm complexity is only 1 /M (M is the number of sub-carriers in each group) of Fischer algorithm, which is much smaller than many classic adaptive modulation algorithms, such as Hughes-Hartogs algorithm, Chow algorithm, and is in line with the development direction of green and high speed communication. Simulation results show that the performance of OFDM-ROF system with the improved algorithm is much better than those without adaptive modulation, and the BER of the former achieves 10e1 to 10e2 times lower than the latter when SNR values gets larger. We can obtain that this low complexity adaptive modulation algorithm is extremely useful for the OFDM-ROF system.

Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm

PubMed Central

Li, Zhenping; Zhang, Xiang-Sun; Wang, Rui-Sheng; Liu, Hongwei; Zhang, Shihua

2013-01-01

Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks. PMID:24386268

3dRPC: a web server for 3D RNA-protein structure prediction.

PubMed

Huang, Yangyu; Li, Haotian; Xiao, Yi

2018-04-01

RNA-protein interactions occur in many biological processes. To understand the mechanism of these interactions one needs to know three-dimensional (3D) structures of RNA-protein complexes. 3dRPC is an algorithm for prediction of 3D RNA-protein complex structures and consists of a docking algorithm RPDOCK and a scoring function 3dRPC-Score. RPDOCK is used to sample possible complex conformations of an RNA and a protein by calculating the geometric and electrostatic complementarities and stacking interactions at the RNA-protein interface according to the features of atom packing of the interface. 3dRPC-Score is a knowledge-based potential that uses the conformations of nucleotide-amino-acid pairs as statistical variables and that is used to choose the near-native complex-conformations obtained from the docking method above. Recently, we built a web server for 3dRPC. The users can easily use 3dRPC without installing it locally. RNA and protein structures in PDB (Protein Data Bank) format are the only needed input files. It can also incorporate the information of interface residues or residue-pairs obtained from experiments or theoretical predictions to improve the prediction. The address of 3dRPC web server is http://biophy.hust.edu.cn/3dRPC. yxiao@hust.edu.cn.

The value of prior knowledge in machine learning of complex network systems.

PubMed

Ferranti, Dana; Krane, David; Craft, David

2017-11-15

Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available. The simulations are based on Boolean networks-directed graphs with 0/1 node states and logical node update rules-which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics and as such provide a useful framework for developing machine-learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine-learning algorithms to predict network steady-state node values ('phenotypes') and perturbation responses ('drug effects'). Links to codes and datasets here: https://gray.mgh.harvard.edu/people-directory/71-david-craft-phd. dcraft@broadinstitute.org. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A Comparison of Experimental EPMA Data and Monte Carlo Simulations

NASA Technical Reports Server (NTRS)

Carpenter, P. K.

2004-01-01

Monte Carlo (MC) modeling shows excellent prospects for simulating electron scattering and x-ray emission from complex geometries, and can be compared to experimental measurements using electron-probe microanalysis (EPMA) and phi(rho z) correction algorithms. Experimental EPMA measurements made on NIST SRM 481 (AgAu) and 482 (CuAu) alloys, at a range of accelerating potential and instrument take-off angles, represent a formal microanalysis data set that has been used to develop phi(rho z) correction algorithms. The accuracy of MC calculations obtained using the NIST, WinCasino, WinXray, and Penelope MC packages will be evaluated relative to these experimental data. There is additional information contained in the extended abstract.

Some Novel Design Principles for Collective Behaviors in Mobile Robots

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

OSBOURN, GORDON C.

2002-09-01

We present a set of novel design principles to aid in the development of complex collective behaviors in fleets of mobile robots. The key elements are: the use of a graph algorithm that we have created, with certain proven properties, that guarantee scalable local communications for fleets of arbitrary size; the use of artificial forces to simplify the design of motion control; the use of certain proximity values in the graph algorithm to simplify the sharing of robust navigation and sensor information among the robots. We describe these design elements and present a computer simulation that illustrates the behaviors readilymore » achievable with these design tools.« less

Faster than classical quantum algorithm for dense formulas of exact satisfiability and occupation problems

NASA Astrophysics Data System (ADS)

Mandrà, Salvatore; Giacomo Guerreschi, Gian; Aspuru-Guzik, Alán

2016-07-01

We present an exact quantum algorithm for solving the Exact Satisfiability problem, which belongs to the important NP-complete complexity class. The algorithm is based on an intuitive approach that can be divided into two parts: the first step consists in the identification and efficient characterization of a restricted subspace that contains all the valid assignments of the Exact Satisfiability; while the second part performs a quantum search in such restricted subspace. The quantum algorithm can be used either to find a valid assignment (or to certify that no solution exists) or to count the total number of valid assignments. The query complexities for the worst-case are respectively bounded by O(\\sqrt{{2}n-{M\\prime }}) and O({2}n-{M\\prime }), where n is the number of variables and {M}\\prime the number of linearly independent clauses. Remarkably, the proposed quantum algorithm results to be faster than any known exact classical algorithm to solve dense formulas of Exact Satisfiability. As a concrete application, we provide the worst-case complexity for the Hamiltonian cycle problem obtained after mapping it to a suitable Occupation problem. Specifically, we show that the time complexity for the proposed quantum algorithm is bounded by O({2}n/4) for 3-regular undirected graphs, where n is the number of nodes. The same worst-case complexity holds for (3,3)-regular bipartite graphs. As a reference, the current best classical algorithm has a (worst-case) running time bounded by O({2}31n/96). Finally, when compared to heuristic techniques for Exact Satisfiability problems, the proposed quantum algorithm is faster than the classical WalkSAT and Adiabatic Quantum Optimization for random instances with a density of constraints close to the satisfiability threshold, the regime in which instances are typically the hardest to solve. The proposed quantum algorithm can be straightforwardly extended to the generalized version of the Exact Satisfiability known as Occupation problem. The general version of the algorithm is presented and analyzed.

Fast parallel approach for 2-D DHT-based real-valued discrete Gabor transform.

PubMed

Tao, Liang; Kwan, Hon Keung

2009-12-01

Two-dimensional fast Gabor transform algorithms are useful for real-time applications due to the high computational complexity of the traditional 2-D complex-valued discrete Gabor transform (CDGT). This paper presents two block time-recursive algorithms for 2-D DHT-based real-valued discrete Gabor transform (RDGT) and its inverse transform and develops a fast parallel approach for the implementation of the two algorithms. The computational complexity of the proposed parallel approach is analyzed and compared with that of the existing 2-D CDGT algorithms. The results indicate that the proposed parallel approach is attractive for real time image processing.

Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network

PubMed Central

Hao, Chuangbo; Song, Ping; Yang, Cheng; Liu, Xiongjun

2017-01-01

Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links. PMID:28282899

Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network.

PubMed

Hao, Chuangbo; Song, Ping; Yang, Cheng; Liu, Xiongjun

2017-03-08

Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links.

Magnetic localization and orientation of the capsule endoscope based on a random complex algorithm.

PubMed

He, Xiaoqi; Zheng, Zizhao; Hu, Chao

2015-01-01

The development of the capsule endoscope has made possible the examination of the whole gastrointestinal tract without much pain. However, there are still some important problems to be solved, among which, one important problem is the localization of the capsule. Currently, magnetic positioning technology is a suitable method for capsule localization, and this depends on a reliable system and algorithm. In this paper, based on the magnetic dipole model as well as magnetic sensor array, we propose nonlinear optimization algorithms using a random complex algorithm, applied to the optimization calculation for the nonlinear function of the dipole, to determine the three-dimensional position parameters and two-dimensional direction parameters. The stability and the antinoise ability of the algorithm is compared with the Levenberg-Marquart algorithm. The simulation and experiment results show that in terms of the error level of the initial guess of magnet location, the random complex algorithm is more accurate, more stable, and has a higher "denoise" capacity, with a larger range for initial guess values.

Functional Entropy Variables: A New Methodology for Deriving Thermodynamically Consistent Algorithms for Complex Fluids, with Particular Reference to the Isothermal Navier-Stokes-Korteweg Equations

DTIC Science & Technology

2012-11-01

multicorrector algorithm . Predictor stage: Set Cρn+1,(0) = C ρ n, (157) Cun+1,(0) = C u n, (158) Cvn+1,(0) = C v n. (159) Multicorrector stage: Repeat the... corrector algorithm given by (157)-(178). Remark 20. We adopt the preconditioned GMRES algorithm [53] from PETSc [2] to solve the linear system given by (175...ICES REPORT 12-43 November 2012 Functional Entropy Variables: A New Methodology for Deriving Thermodynamically Consistent Algorithms for Complex

A synthetic genetic edge detection program.

PubMed

Tabor, Jeffrey J; Salis, Howard M; Simpson, Zachary Booth; Chevalier, Aaron A; Levskaya, Anselm; Marcotte, Edward M; Voigt, Christopher A; Ellington, Andrew D

2009-06-26

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

Automated fiber tracking and tissue characterization of the anterior cruciate ligament with optical coherence tomography

NASA Astrophysics Data System (ADS)

Balasubramanian, Priya S.; Guo, Jiaqi; Yao, Xinwen; Qu, Dovina; Lu, Helen H.; Hendon, Christine P.

2017-02-01

The directionality of collagen fibers across the anterior cruciate ligament (ACL) as well as the insertion of this key ligament into bone are important for understanding the mechanical integrity and functionality of this complex tissue. Quantitative analysis of three-dimensional fiber directionality is of particular interest due to the physiological, mechanical, and biological heterogeneity inherent across the ACL-to-bone junction, the behavior of the ligament under mechanical stress, and the usefulness of this information in designing tissue engineered grafts. We have developed an algorithm to characterize Optical Coherence Tomography (OCT) image volumes of the ACL. We present an automated algorithm for measuring ligamentous fiber angles, and extracting attenuation and backscattering coefficients of ligament, interface, and bone regions within mature and immature bovine ACL insertion samples. Future directions include translating this algorithm for real time processing to allow three-dimensional volumetric analysis within dynamically moving samples.

A duality theorem-based algorithm for inexact quadratic programming problems: Application to waste management under uncertainty

NASA Astrophysics Data System (ADS)

Kong, X. M.; Huang, G. H.; Fan, Y. R.; Li, Y. P.

2016-04-01

In this study, a duality theorem-based algorithm (DTA) for inexact quadratic programming (IQP) is developed for municipal solid waste (MSW) management under uncertainty. It improves upon the existing numerical solution method for IQP problems. The comparison between DTA and derivative algorithm (DAM) shows that the DTA method provides better solutions than DAM with lower computational complexity. It is not necessary to identify the uncertain relationship between the objective function and decision variables, which is required for the solution process of DAM. The developed method is applied to a case study of MSW management and planning. The results indicate that reasonable solutions have been generated for supporting long-term MSW management and planning. They could provide more information as well as enable managers to make better decisions to identify desired MSW management policies in association with minimized cost under uncertainty.

Schwarz-Christoffel Conformal Mapping based Grid Generation for Global Oceanic Circulation Models

NASA Astrophysics Data System (ADS)

Xu, Shiming

2015-04-01

We propose new grid generation algorithms for global ocean general circulation models (OGCMs). Contrary to conventional, analytical forms based dipolar or tripolar grids, the new algorithm are based on Schwarz-Christoffel (SC) conformal mapping with prescribed boundary information. While dealing with the conventional grid design problem of pole relocation, it also addresses more advanced issues of computational efficiency and the new requirements on OGCM grids arisen from the recent trend of high-resolution and multi-scale modeling. The proposed grid generation algorithm could potentially achieve the alignment of grid lines to coastlines, enhanced spatial resolution in coastal regions, and easier computational load balance. Since the generated grids are still orthogonal curvilinear, they can be readily 10 utilized in existing Bryan-Cox-Semtner type ocean models. The proposed methodology can also be applied to the grid generation task for regional ocean modeling when complex land-ocean distribution is present.

Analytical optimal pulse shapes obtained with the aid of genetic algorithms

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

Guerrero, Rubén D., E-mail: rdguerrerom@unal.edu.co; Arango, Carlos A.; Reyes, Andrés

2015-09-28

We propose a methodology to design optimal pulses for achieving quantum optimal control on molecular systems. Our approach constrains pulse shapes to linear combinations of a fixed number of experimentally relevant pulse functions. Quantum optimal control is obtained by maximizing a multi-target fitness function using genetic algorithms. As a first application of the methodology, we generated an optimal pulse that successfully maximized the yield on a selected dissociation channel of a diatomic molecule. Our pulse is obtained as a linear combination of linearly chirped pulse functions. Data recorded along the evolution of the genetic algorithm contained important information regarding themore » interplay between radiative and diabatic processes. We performed a principal component analysis on these data to retrieve the most relevant processes along the optimal path. Our proposed methodology could be useful for performing quantum optimal control on more complex systems by employing a wider variety of pulse shape functions.« less

Phase-and-amplitude recovery from a single phase-contrast image using partially spatially coherent x-ray radiation

NASA Astrophysics Data System (ADS)

Beltran, Mario A.; Paganin, David M.; Pelliccia, Daniele

2018-05-01

A simple method of phase-and-amplitude extraction is derived that corrects for image blurring induced by partially spatially coherent incident illumination using only a single intensity image as input. The method is based on Fresnel diffraction theory for the case of high Fresnel number, merged with the space-frequency description formalism used to quantify partially coherent fields and assumes the object under study is composed of a single-material. A priori knowledge of the object’s complex refractive index and information obtained by characterizing the spatial coherence of the source is required. The algorithm was applied to propagation-based phase-contrast data measured with a laboratory-based micro-focus x-ray source. The blurring due to the finite spatial extent of the source is embedded within the algorithm as a simple correction term to the so-called Paganin algorithm and is also numerically stable in the presence of noise.

A Synthetic Genetic Edge Detection Program

PubMed Central

Tabor, Jeffrey J.; Salis, Howard; Simpson, Zachary B.; Chevalier, Aaron A.; Levskaya, Anselm; Marcotte, Edward M.; Voigt, Christopher A.; Ellington, Andrew D.

2009-01-01

Summary Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks. PMID:19563759

Ubiquitousness of link-density and link-pattern communities in real-world networks

NASA Astrophysics Data System (ADS)

Šubelj, L.; Bajec, M.

2012-01-01

Community structure appears to be an intrinsic property of many complex real-world networks. However, recent work shows that real-world networks reveal even more sophisticated modules than classical cohesive (link-density) communities. In particular, networks can also be naturally partitioned according to similar patterns of connectedness among the nodes, revealing link-pattern communities. We here propose a propagation based algorithm that can extract both link-density and link-pattern communities, without any prior knowledge of the true structure. The algorithm was first validated on different classes of synthetic benchmark networks with community structure, and also on random networks. We have further applied the algorithm to different social, information, technological and biological networks, where it indeed reveals meaningful (composites of) link-density and link-pattern communities. The results thus seem to imply that, similarly as link-density counterparts, link-pattern communities appear ubiquitous in nature and design.

Simple and efficient self-healing strategy for damaged complex networks

NASA Astrophysics Data System (ADS)

Gallos, Lazaros K.; Fefferman, Nina H.

2015-11-01

The process of destroying a complex network through node removal has been the subject of extensive interest and research. Node loss typically leaves the network disintegrated into many small and isolated clusters. Here we show that these clusters typically remain close to each other and we suggest a simple algorithm that is able to reverse the inflicted damage by restoring the network's functionality. After damage, each node decides independently whether to create a new link depending on the fraction of neighbors it has lost. In addition to relying only on local information, where nodes do not need knowledge of the global network status, we impose the additional constraint that new links should be as short as possible (i.e., that the new edge completes a shortest possible new cycle). We demonstrate that this self-healing method operates very efficiently, both in model and real networks. For example, after removing the most connected airports in the USA, the self-healing algorithm rejoined almost 90% of the surviving airports.

Meshing complex macro-scale objects into self-assembling bricks

PubMed Central

Hacohen, Adar; Hanniel, Iddo; Nikulshin, Yasha; Wolfus, Shuki; Abu-Horowitz, Almogit; Bachelet, Ido

2015-01-01

Self-assembly provides an information-economical route to the fabrication of objects at virtually all scales. However, there is no known algorithm to program self-assembly in macro-scale, solid, complex 3D objects. Here such an algorithm is described, which is inspired by the molecular assembly of DNA, and based on bricks designed by tetrahedral meshing of arbitrary objects. Assembly rules are encoded by topographic cues imprinted on brick faces while attraction between bricks is provided by embedded magnets. The bricks can then be mixed in a container and agitated, leading to properly assembled objects at high yields and zero errors. The system and its assembly dynamics were characterized by video and audio analysis, enabling the precise time- and space-resolved characterization of its performance and accuracy. Improved designs inspired by our system could lead to successful implementation of self-assembly at the macro-scale, allowing rapid, on-demand fabrication of objects without the need for assembly lines. PMID:26226488

Predicting Human Preferences Using the Block Structure of Complex Social Networks

PubMed Central

Guimerà, Roger; Llorente, Alejandro; Moro, Esteban; Sales-Pardo, Marta

2012-01-01

With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a “new” computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups. PMID:22984533

Scalable service architecture for providing strong service guarantees

NASA Astrophysics Data System (ADS)

Christin, Nicolas; Liebeherr, Joerg

2002-07-01

For the past decade, a lot of Internet research has been devoted to providing different levels of service to applications. Initial proposals for service differentiation provided strong service guarantees, with strict bounds on delays, loss rates, and throughput, but required high overhead in terms of computational complexity and memory, both of which raise scalability concerns. Recently, the interest has shifted to service architectures with low overhead. However, these newer service architectures only provide weak service guarantees, which do not always address the needs of applications. In this paper, we describe a service architecture that supports strong service guarantees, can be implemented with low computational complexity, and only requires to maintain little state information. A key mechanism of the proposed service architecture is that it addresses scheduling and buffer management in a single algorithm. The presented architecture offers no solution for controlling the amount of traffic that enters the network. Instead, we plan on exploiting feedback mechanisms of TCP congestion control algorithms for the purpose of regulating the traffic entering the network.

Spatio-Temporal Process Variability in Watershed Scale Wetland Restoration Planning

NASA Astrophysics Data System (ADS)

Evenson, G. R.

2012-12-01

Watershed scale restoration decision making processes are increasingly informed by quantitative methodologies providing site-specific restoration recommendations - sometimes referred to as "systematic planning." The more advanced of these methodologies are characterized by a coupling of search algorithms and ecological models to discover restoration plans that optimize environmental outcomes. Yet while these methods have exhibited clear utility as decision support toolsets, they may be critiqued for flawed evaluations of spatio-temporally variable processes fundamental to watershed scale restoration. Hydrologic and non-hydrologic mediated process connectivity along with post-restoration habitat dynamics, for example, are commonly ignored yet known to appreciably affect restoration outcomes. This talk will present a methodology to evaluate such spatio-temporally complex processes in the production of watershed scale wetland restoration plans. Using the Tuscarawas Watershed in Eastern Ohio as a case study, a genetic algorithm will be coupled with the Soil and Water Assessment Tool (SWAT) to reveal optimal wetland restoration plans as measured by their capacity to maximize nutrient reductions. Then, a so-called "graphical" representation of the optimization problem will be implemented in-parallel to promote hydrologic and non-hydrologic mediated connectivity amongst existing wetlands and sites selected for restoration. Further, various search algorithm mechanisms will be discussed as a means of accounting for temporal complexities such as post-restoration habitat dynamics. Finally, generalized patterns of restoration plan optimality will be discussed as an alternative and possibly superior decision support toolset given the complexity and stochastic nature of spatio-temporal process variability.

Simulated parallel annealing within a neighborhood for optimization of biomechanical systems.

PubMed

Higginson, J S; Neptune, R R; Anderson, F C

2005-09-01

Optimization problems for biomechanical systems have become extremely complex. Simulated annealing (SA) algorithms have performed well in a variety of test problems and biomechanical applications; however, despite advances in computer speed, convergence to optimal solutions for systems of even moderate complexity has remained prohibitive. The objective of this study was to develop a portable parallel version of a SA algorithm for solving optimization problems in biomechanics. The algorithm for simulated parallel annealing within a neighborhood (SPAN) was designed to minimize interprocessor communication time and closely retain the heuristics of the serial SA algorithm. The computational speed of the SPAN algorithm scaled linearly with the number of processors on different computer platforms for a simple quadratic test problem and for a more complex forward dynamic simulation of human pedaling.

The Correlation Fractal Dimension of Complex Networks

NASA Astrophysics Data System (ADS)

Wang, Xingyuan; Liu, Zhenzhen; Wang, Mogei

2013-05-01

The fractality of complex networks is studied by estimating the correlation dimensions of the networks. Comparing with the previous algorithms of estimating the box dimension, our algorithm achieves a significant reduction in time complexity. For four benchmark cases tested, that is, the Escherichia coli (E. Coli) metabolic network, the Homo sapiens protein interaction network (H. Sapiens PIN), the Saccharomyces cerevisiae protein interaction network (S. Cerevisiae PIN) and the World Wide Web (WWW), experiments are provided to demonstrate the validity of our algorithm.

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.

An Innovative Thinking-Based Intelligent Information Fusion Algorithm

PubMed Central

Hu, Liang; Liu, Gang; Zhou, Jin

2013-01-01

This study proposes an intelligent algorithm that can realize information fusion in reference to the relative research achievements in brain cognitive theory and innovative computation. This algorithm treats knowledge as core and information fusion as a knowledge-based innovative thinking process. Furthermore, the five key parts of this algorithm including information sense and perception, memory storage, divergent thinking, convergent thinking, and evaluation system are simulated and modeled. This algorithm fully develops innovative thinking skills of knowledge in information fusion and is a try to converse the abstract conception of brain cognitive science to specific and operable research routes and strategies. Furthermore, the influences of each parameter of this algorithm on algorithm performance are analyzed and compared with those of classical intelligent algorithms trough test. Test results suggest that the algorithm proposed in this study can obtain the optimum problem solution by less target evaluation times, improve optimization effectiveness, and achieve the effective fusion of information. PMID:23956699

An innovative thinking-based intelligent information fusion algorithm.

PubMed

Lu, Huimin; Hu, Liang; Liu, Gang; Zhou, Jin

2013-01-01

This study proposes an intelligent algorithm that can realize information fusion in reference to the relative research achievements in brain cognitive theory and innovative computation. This algorithm treats knowledge as core and information fusion as a knowledge-based innovative thinking process. Furthermore, the five key parts of this algorithm including information sense and perception, memory storage, divergent thinking, convergent thinking, and evaluation system are simulated and modeled. This algorithm fully develops innovative thinking skills of knowledge in information fusion and is a try to converse the abstract conception of brain cognitive science to specific and operable research routes and strategies. Furthermore, the influences of each parameter of this algorithm on algorithm performance are analyzed and compared with those of classical intelligent algorithms trough test. Test results suggest that the algorithm proposed in this study can obtain the optimum problem solution by less target evaluation times, improve optimization effectiveness, and achieve the effective fusion of information.

A VLSI architecture for simplified arithmetic Fourier transform algorithm

NASA Technical Reports Server (NTRS)

Reed, Irving S.; Shih, Ming-Tang; Truong, T. K.; Hendon, E.; Tufts, D. W.

1992-01-01

The arithmetic Fourier transform (AFT) is a number-theoretic approach to Fourier analysis which has been shown to perform competitively with the classical FFT in terms of accuracy, complexity, and speed. Theorems developed in a previous paper for the AFT algorithm are used here to derive the original AFT algorithm which Bruns found in 1903. This is shown to yield an algorithm of less complexity and of improved performance over certain recent AFT algorithms. A VLSI architecture is suggested for this simplified AFT algorithm. This architecture uses a butterfly structure which reduces the number of additions by 25 percent of that used in the direct method.

A Program Complexity Metric Based on Variable Usage for Algorithmic Thinking Education of Novice Learners

ERIC Educational Resources Information Center

Fuwa, Minori; Kayama, Mizue; Kunimune, Hisayoshi; Hashimoto, Masami; Asano, David K.

2015-01-01

We have explored educational methods for algorithmic thinking for novices and implemented a block programming editor and a simple learning management system. In this paper, we propose a program/algorithm complexity metric specified for novice learners. This metric is based on the variable usage in arithmetic and relational formulas in learner's…

Characterizing Responses of Translation Invariant Neurons to Natural Stimuli: Maximally Informative Invariant Dimensions

PubMed Central

Eickenberg, Michael; Rowekamp, Ryan J.; Kouh, Minjoon; Sharpee, Tatyana O.

2012-01-01

Our visual system is capable of recognizing complex objects even when their appearances change drastically under various viewing conditions. Especially in the higher cortical areas, the sensory neurons reflect such functional capacity in their selectivity for complex visual features and invariance to certain object transformations, such as image translation. Due to the strong nonlinearities necessary to achieve both the selectivity and invariance, characterizing and predicting the response patterns of these neurons represents a formidable computational challenge. A related problem is that such neurons are poorly driven by randomized inputs, such as white noise, and respond strongly only to stimuli with complex high-order correlations, such as natural stimuli. Here we describe a novel two-step optimization technique that can characterize both the shape selectivity and the range and coarseness of position invariance from neural responses to natural stimuli. One step in the optimization involves finding the template as the maximally informative dimension given the estimated spatial location where the response could have been triggered within each image. The estimates of the locations that triggered the response are subsequently updated in the next step. Under the assumption of a monotonic relationship between the firing rate and stimulus projections on the template at a given position, the most likely location is the one that has the largest projection on the estimate of the template. The algorithm shows quick convergence during optimization, and the estimation results are reliable even in the regime of small signal-to-noise ratios. When we apply the algorithm to responses of complex cells in the primary visual cortex (V1) to natural movies, we find that responses of the majority of cells were significantly better described by translation invariant models based on one template compared with position-specific models with several relevant features. PMID:22734487

a Comparative Case Study of Reflection Seismic Imaging Method

NASA Astrophysics Data System (ADS)

Alamooti, M.; Aydin, A.

2017-12-01

Seismic imaging is the most common means of gathering information about subsurface structural features. The accuracy of seismic images may be highly variable depending on the complexity of the subsurface and on how seismic data is processed. One of the crucial steps in this process, especially in layered sequences with complicated structure, is the time and/or depth migration of seismic data.The primary purpose of the migration is to increase the spatial resolution of seismic images by repositioning the recorded seismic signal back to its original point of reflection in time/space, which enhances information about complex structure. In this study, our objective is to process a seismic data set (courtesy of the University of South Carolina) to generate an image on which the Magruder fault near Allendale SC can be clearly distinguished and its attitude can be accurately depicted. The data was gathered by common mid-point method with 60 geophones equally spaced along an about 550 m long traverse over a nearly flat ground. The results obtained from the application of different migration algorithms (including finite-difference and Kirchhoff) are compared in time and depth domains to investigate the efficiency of each algorithm in reducing the processing time and improving the accuracy of seismic images in reflecting the correct position of the Magruder fault.

A novel artificial immune clonal selection classification and rule mining with swarm learning model

NASA Astrophysics Data System (ADS)

Al-Sheshtawi, Khaled A.; Abdul-Kader, Hatem M.; Elsisi, Ashraf B.

2013-06-01

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

Individual Rocks Segmentation in Terrestrial Laser Scanning Point Cloud Using Iterative Dbscan Algorithm

NASA Astrophysics Data System (ADS)

Walicka, A.; Jóźków, G.; Borkowski, A.

2018-05-01

The fluvial transport is an important aspect of hydrological and geomorphologic studies. The knowledge about the movement parameters of different-size fractions is essential in many applications, such as the exploration of the watercourse changes, the calculation of the river bed parameters or the investigation of the frequency and the nature of the weather events. Traditional techniques used for the fluvial transport investigations do not provide any information about the long-term horizontal movement of the rocks. This information can be gained by means of terrestrial laser scanning (TLS). However, this is a complex issue consisting of several stages of data processing. In this study the methodology for individual rocks segmentation from TLS point cloud has been proposed, which is the first step for the semi-automatic algorithm for movement detection of individual rocks. The proposed algorithm is executed in two steps. Firstly, the point cloud is classified as rocks or background using only geometrical information. Secondly, the DBSCAN algorithm is executed iteratively on points classified as rocks until only one stone is detected in each segment. The number of rocks in each segment is determined using principal component analysis (PCA) and simple derivative method for peak detection. As a result, several segments that correspond to individual rocks are formed. Numerical tests were executed on two test samples. The results of the semi-automatic segmentation were compared to results acquired by manual segmentation. The proposed methodology enabled to successfully segment 76 % and 72 % of rocks in the test sample 1 and test sample 2, respectively.

A new theory of development: the generation of complexity in ontogenesis.

PubMed

Barbieri, Marcello

2016-03-13

Today there is a very wide consensus on the idea that embryonic development is the result of a genetic programme and of epigenetic processes. Many models have been proposed in this theoretical framework to account for the various aspects of development, and virtually all of them have one thing in common: they do not acknowledge the presence of organic codes (codes between organic molecules) in ontogenesis. Here it is argued instead that embryonic development is a convergent increase in complexity that necessarily requires organic codes and organic memories, and a few examples of such codes are described. This is the code theory of development, a theory that was originally inspired by an algorithm that is capable of reconstructing structures from incomplete information, an algorithm that here is briefly summarized because it makes it intuitively appealing how a convergent increase in complexity can be achieved. The main thesis of the new theory is that the presence of organic codes in ontogenesis is not only a theoretical necessity but, first and foremost, an idea that can be tested and that has already been found to be in agreement with the evidence. © 2016 The Author(s).

Quantitative phase and amplitude imaging using Differential-Interference Contrast (DIC) microscopy

NASA Astrophysics Data System (ADS)

Preza, Chrysanthe; O'Sullivan, Joseph A.

2009-02-01

We present an extension of the development of an alternating minimization (AM) method for the computation of a specimen's complex transmittance function (magnitude and phase) from DIC images. The ability to extract both quantitative phase and amplitude information from two rotationally-diverse DIC images (i.e., acquired by rotating the sample) extends previous efforts in computational DIC microscopy that have focused on quantitative phase imaging only. Simulation results show that the inverse problem at hand is sensitive to noise as well as to the choice of the AM algorithm parameters. The AM framework allows constraints and penalties on the magnitude and phase estimates to be incorporated in a principled manner. Towards this end, Green and De Pierro's "log-cosh" regularization penalty is applied to the magnitude of differences of neighboring values of the complex-valued function of the specimen during the AM iterations. The penalty is shown to be convex in the complex space. A procedure to approximate the penalty within the iterations is presented. In addition, a methodology to pre-compute AM parameters that are optimal with respect to the convergence rate of the AM algorithm is also presented. Both extensions of the AM method are investigated with simulations.

Hidden state prediction: a modification of classic ancestral state reconstruction algorithms helps unravel complex symbioses

PubMed Central

Zaneveld, Jesse R. R.; Thurber, Rebecca L. V.

2014-01-01

Complex symbioses between animal or plant hosts and their associated microbiotas can involve thousands of species and millions of genes. Because of the number of interacting partners, it is often impractical to study all organisms or genes in these host-microbe symbioses individually. Yet new phylogenetic predictive methods can use the wealth of accumulated data on diverse model organisms to make inferences into the properties of less well-studied species and gene families. Predictive functional profiling methods use evolutionary models based on the properties of studied relatives to put bounds on the likely characteristics of an organism or gene that has not yet been studied in detail. These techniques have been applied to predict diverse features of host-associated microbial communities ranging from the enzymatic function of uncharacterized genes to the gene content of uncultured microorganisms. We consider these phylogenetically informed predictive techniques from disparate fields as examples of a general class of algorithms for Hidden State Prediction (HSP), and argue that HSP methods have broad value in predicting organismal traits in a variety of contexts, including the study of complex host-microbe symbioses. PMID:25202302

Retrieval of aerosol properties and water leaving radiance from multi-angle spectro-polarimetric measurement over coastal waters

NASA Astrophysics Data System (ADS)

Gao, M.; Zhai, P.; Franz, B. A.; Hu, Y.; Knobelspiesse, K. D.; Xu, F.; Ibrahim, A.

2017-12-01

Ocean color remote sensing in coastal waters remains a challenging task due to the complex optical properties of aerosols and ocean water properties. It is highly desirable to develop an advanced ocean color and aerosol retrieval algorithm for coastal waters, to advance our capabilities in monitoring water quality, improve our understanding of coastal carbon cycle dynamics, and allow for the development of more accurate circulation models. However, distinguishing the dissolved and suspended material from absorbing aerosols over coastal waters is challenging as they share similar absorption spectrum within the deep blue to UV range. In this paper we report a research algorithm on aerosol and ocean color retrieval with emphasis on coastal waters. The main features of our algorithm include: 1) combining co-located measurements from a hyperspectral ocean color instrument (OCI) and a multi-angle polarimeter (MAP); 2) using the radiative transfer model for coupled atmosphere and ocean system (CAOS), which is based on the highly accurate and efficient successive order of scattering method; and 3) incorporating a generalized bio-optical model with direct accounting of the total absorption of phytoplankton, CDOM and non-algal particles(NAP), and the total scattering of phytoplankton and NAP for improved description of ocean light scattering. The non-linear least square fitting algorithm is used to optimize the bio-optical model parameters and the aerosol optical and microphysical properties including refractive indices and size distributions for both fine and coarse modes. The retrieved aerosol information is used to calculate the atmospheric path radiance, which is then subtracted from the OCI observations to obtain the water leaving radiance contribution. Our work aims to maximize the use of available information from the co-located dataset and conduct the atmospheric correction with minimal assumptions. The algorithm will contribute to the success of current MAP instruments, such as the Research Scanning Polarimeter (RSP), and future ocean color missions, such as the Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission, by enabling retrieval of ocean biogeochemical properties under optically-complex atmospheric and oceanic conditions.

Transforming graph states using single-qubit operations.

PubMed

Dahlberg, Axel; Wehner, Stephanie

2018-07-13

Stabilizer states form an important class of states in quantum information, and are of central importance in quantum error correction. Here, we provide an algorithm for deciding whether one stabilizer (target) state can be obtained from another stabilizer (source) state by single-qubit Clifford operations (LC), single-qubit Pauli measurements (LPM) and classical communication (CC) between sites holding the individual qubits. What is more, we provide a recipe to obtain the sequence of LC+LPM+CC operations which prepare the desired target state from the source state, and show how these operations can be applied in parallel to reach the target state in constant time. Our algorithm has applications in quantum networks, quantum computing, and can also serve as a design tool-for example, to find transformations between quantum error correcting codes. We provide a software implementation of our algorithm that makes this tool easier to apply. A key insight leading to our algorithm is to show that the problem is equivalent to one in graph theory, which is to decide whether some graph G ' is a vertex-minor of another graph G The vertex-minor problem is, in general, [Formula: see text]-Complete, but can be solved efficiently on graphs which are not too complex. A measure of the complexity of a graph is the rank-width which equals the Schmidt-rank width of a subclass of stabilizer states called graph states, and thus intuitively is a measure of entanglement. Here, we show that the vertex-minor problem can be solved in time O (| G | 3 ), where | G | is the size of the graph G , whenever the rank-width of G and the size of G ' are bounded. Our algorithm is based on techniques by Courcelle for solving fixed parameter tractable problems, where here the relevant fixed parameter is the rank width. The second half of this paper serves as an accessible but far from exhausting introduction to these concepts, that could be useful for many other problems in quantum information.This article is part of a discussion meeting issue 'Foundations of quantum mechanics and their impact on contemporary society'. © 2018 The Author(s).

Aspects of Complexity in Sleep Analysis

NASA Astrophysics Data System (ADS)

Leitão, José M. N.; Da Rosa, Agostinho C.

The paper presents a selection of sleep analysis problems where some aspects and concepts of complexity come about. Emphasis is given to the electroencephalogram (EEG) as the most important sleep related variable. The conception of the EEG as a message to be deciphered stresses the importance of the communication and information theories in this field. An optimal detector of K complexes and vertex sharp waves based on a stochastic model of sleep EEG is considered. Besides detecting, the algorithm is also able to follow the evolution of the basic ongoing activity. It is shown that both the ostructure and microstructure of sleep can be described in terms of symbols and interpreted as sentences of a language. Syntactic models and Markov chain representations play in this context an important role.

Imbalanced learning for pattern recognition: an empirical study

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Man, Hong; Desai, Sachi; Quoraishee, Shafik

2010-10-01

The imbalanced learning problem (learning from imbalanced data) presents a significant new challenge to the pattern recognition and machine learning society because in most instances real-world data is imbalanced. When considering military applications, the imbalanced learning problem becomes much more critical because such skewed distributions normally carry the most interesting and critical information. This critical information is necessary to support the decision-making process in battlefield scenarios, such as anomaly or intrusion detection. The fundamental issue with imbalanced learning is the ability of imbalanced data to compromise the performance of standard learning algorithms, which assume balanced class distributions or equal misclassification penalty costs. Therefore, when presented with complex imbalanced data sets these algorithms may not be able to properly represent the distributive characteristics of the data. In this paper we present an empirical study of several popular imbalanced learning algorithms on an army relevant data set. Specifically we will conduct various experiments with SMOTE (Synthetic Minority Over-Sampling Technique), ADASYN (Adaptive Synthetic Sampling), SMOTEBoost (Synthetic Minority Over-Sampling in Boosting), and AdaCost (Misclassification Cost-Sensitive Boosting method) schemes. Detailed experimental settings and simulation results are presented in this work, and a brief discussion of future research opportunities/challenges is also presented.

Dilated contour extraction and component labeling algorithm for object vector representation

NASA Astrophysics Data System (ADS)

Skourikhine, Alexei N.

2005-08-01

Object boundary extraction from binary images is important for many applications, e.g., image vectorization, automatic interpretation of images containing segmentation results, printed and handwritten documents and drawings, maps, and AutoCAD drawings. Efficient and reliable contour extraction is also important for pattern recognition due to its impact on shape-based object characterization and recognition. The presented contour tracing and component labeling algorithm produces dilated (sub-pixel) contours associated with corresponding regions. The algorithm has the following features: (1) it always produces non-intersecting, non-degenerate contours, including the case of one-pixel wide objects; (2) it associates the outer and inner (i.e., around hole) contours with the corresponding regions during the process of contour tracing in a single pass over the image; (3) it maintains desired connectivity of object regions as specified by 8-neighbor or 4-neighbor connectivity of adjacent pixels; (4) it avoids degenerate regions in both background and foreground; (5) it allows an easy augmentation that will provide information about the containment relations among regions; (6) it has a time complexity that is dominantly linear in the number of contour points. This early component labeling (contour-region association) enables subsequent efficient object-based processing of the image information.

Informed-Proteomics: open-source software package for top-down proteomics

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

Park, Jungkap; Piehowski, Paul D.; Wilkins, Christopher

Top-down proteomics involves the analysis of intact proteins. This approach is very attractive as it allows for analyzing proteins in their endogenous form without proteolysis, preserving valuable information about post-translation modifications, isoforms, proteolytic processing or their combinations collectively called proteoforms. Moreover, the quality of the top-down LC-MS/MS datasets is rapidly increasing due to advances in the liquid chromatography and mass spectrometry instrumentation and sample processing protocols. However, the top-down mass spectra are substantially more complex compare to the more conventional bottom-up data. To take full advantage of the increasing quality of the top-down LC-MS/MS datasets there is an urgent needmore » to develop algorithms and software tools for confident proteoform identification and quantification. In this study we present a new open source software suite for top-down proteomics analysis consisting of an LC-MS feature finding algorithm, a database search algorithm, and an interactive results viewer. The presented tool along with several other popular tools were evaluated using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis.« less

Improved Passive Microwave Algorithms for North America and Eurasia

NASA Technical Reports Server (NTRS)

Foster, James; Chang, Alfred; Hall, Dorothy

1997-01-01

Microwave algorithms simplify complex physical processes in order to estimate geophysical parameters such as snow cover and snow depth. The microwave radiances received at the satellite sensor and expressed as brightness temperatures are a composite of contributions from the Earth's surface, the Earth's atmosphere and from space. Owing to the coarse resolution inherent to passive microwave sensors, each pixel value represents a mixture of contributions from different surface types including deep snow, shallow snow, forests and open areas. Algorithms are generated in order to resolve these mixtures. The accuracy of the retrieved information is affected by uncertainties in the assumptions used in the radiative transfer equation (Steffen et al., 1992). One such uncertainty in the Chang et al., (1987) snow algorithm is that the snow grain radius is 0.3 mm for all layers of the snowpack and for all physiographic regions. However, this is not usually the case. The influence of larger grain sizes appears to be of more importance for deeper snowpacks in the interior of Eurasia. Based on this consideration and the effects of forests, a revised SMMR snow algorithm produces more realistic snow mass values. The purpose of this study is to present results of the revised algorithm (referred to for the remainder of this paper as the GSFC 94 snow algorithm) which incorporates differences in both fractional forest cover and snow grain size. Results from the GSFC 94 algorithm will be compared to the original Chang et al. (1987) algorithm and to climatological snow depth data as well.

ROBNCA: robust network component analysis for recovering transcription factor activities.

PubMed

Noor, Amina; Ahmad, Aitzaz; Serpedin, Erchin; Nounou, Mohamed; Nounou, Hazem

2013-10-01

Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF)-gene regulations. Most of the contemporary algorithms either exhibit the drawback of inconsistency and poor reliability, or suffer from prohibitive computational complexity. In addition, the existing algorithms do not possess the ability to counteract the presence of outliers in the microarray data. Hence, robust and computationally efficient algorithms are needed to enable practical applications. We propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm that explicitly models the possible outliers in the microarray data. An attractive feature of the ROBNCA algorithm is the derivation of a closed form solution for estimating the connectivity matrix, which was not available in prior contributions. The ROBNCA algorithm is compared with FastNCA and the non-iterative NCA (NI-NCA). ROBNCA estimates the TF activity profiles as well as the TF-gene control strength matrix with a much higher degree of accuracy than FastNCA and NI-NCA, irrespective of varying noise, correlation and/or amount of outliers in case of synthetic data. The ROBNCA algorithm is also tested on Saccharomyces cerevisiae data and Escherichia coli data, and it is observed to outperform the existing algorithms. The run time of the ROBNCA algorithm is comparable with that of FastNCA, and is hundreds of times faster than NI-NCA. The ROBNCA software is available at http://people.tamu.edu/∼amina/ROBNCA

A generic EEG artifact removal algorithm based on the multi-channel Wiener filter

NASA Astrophysics Data System (ADS)

Somers, Ben; Francart, Tom; Bertrand, Alexander

2018-06-01

Objective. The electroencephalogram (EEG) is an essential neuro-monitoring tool for both clinical and research purposes, but is susceptible to a wide variety of undesired artifacts. Removal of these artifacts is often done using blind source separation techniques, relying on a purely data-driven transformation, which may sometimes fail to sufficiently isolate artifacts in only one or a few components. Furthermore, some algorithms perform well for specific artifacts, but not for others. In this paper, we aim to develop a generic EEG artifact removal algorithm, which allows the user to annotate a few artifact segments in the EEG recordings to inform the algorithm. Approach. We propose an algorithm based on the multi-channel Wiener filter (MWF), in which the artifact covariance matrix is replaced by a low-rank approximation based on the generalized eigenvalue decomposition. The algorithm is validated using both hybrid and real EEG data, and is compared to other algorithms frequently used for artifact removal. Main results. The MWF-based algorithm successfully removes a wide variety of artifacts with better performance than current state-of-the-art methods. Significance. Current EEG artifact removal techniques often have limited applicability due to their specificity to one kind of artifact, their complexity, or simply because they are too ‘blind’. This paper demonstrates a fast, robust and generic algorithm for removal of EEG artifacts of various types, i.e. those that were annotated as unwanted by the user.

Mathematical model and coordination algorithms for ensuring complex security of an organization

NASA Astrophysics Data System (ADS)

Novoseltsev, V. I.; Orlova, D. E.; Dubrovin, A. S.; Irkhin, V. P.

2018-03-01

The mathematical model of coordination when ensuring complex security of the organization is considered. On the basis of use of a method of casual search three types of algorithms of effective coordination adequate to mismatch level concerning security are developed: a coordination algorithm at domination of instructions of the coordinator; a coordination algorithm at domination of decisions of performers; a coordination algorithm at parity of interests of the coordinator and performers. Assessment of convergence of the algorithms considered above it was made by carrying out a computing experiment. The described algorithms of coordination have property of convergence in the sense stated above. And, the following regularity is revealed: than more simply in the structural relation the algorithm, for the smaller number of iterations is provided to those its convergence.

Algorithm for predicting the evolution of series of dynamics of complex systems in solving information problems

NASA Astrophysics Data System (ADS)

Kasatkina, T. I.; Dushkin, A. V.; Pavlov, V. A.; Shatovkin, R. R.

2018-03-01

In the development of information, systems and programming to predict the series of dynamics, neural network methods have recently been applied. They are more flexible, in comparison with existing analogues and are capable of taking into account the nonlinearities of the series. In this paper, we propose a modified algorithm for predicting the series of dynamics, which includes a method for training neural networks, an approach to describing and presenting input data, based on the prediction by the multilayer perceptron method. To construct a neural network, the values of a series of dynamics at the extremum points and time values corresponding to them, formed based on the sliding window method, are used as input data. The proposed algorithm can act as an independent approach to predicting the series of dynamics, and be one of the parts of the forecasting system. The efficiency of predicting the evolution of the dynamics series for a short-term one-step and long-term multi-step forecast by the classical multilayer perceptron method and a modified algorithm using synthetic and real data is compared. The result of this modification was the minimization of the magnitude of the iterative error that arises from the previously predicted inputs to the inputs to the neural network, as well as the increase in the accuracy of the iterative prediction of the neural network.

[Algorithms for treatment of complex hand injuries].

PubMed

Pillukat, T; Prommersberger, K-J

2011-07-01

The primary treatment strongly influences the course and prognosis of hand injuries. Complex injuries which compromise functional recovery are especially challenging. Despite an apparently unlimited number of injury patterns it is possible to develop strategies which facilitate a standardized approach to operative treatment. In this situation algorithms can be important guidelines for a rational approach. The following algorithms have been proven in the treatment of complex injuries of the hand by our own experience. They were modified according to the current literature and refer to prehospital care, emergency room management, basic strategy in general and reconstruction of bone and joints, vessels, nerves, tendons and soft tissue coverage in detail. Algorithms facilitate the treatment of severe hand injuries. Applying simple yes/no decisions complex injury patterns are split into distinct partial problems which can be managed step by step.

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

NASA Astrophysics Data System (ADS)

Perotti, Juan Ignacio; Tessone, Claudio Juan; Caldarelli, Guido

2015-12-01

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks.

Research on polarization imaging information parsing method

NASA Astrophysics Data System (ADS)

Yuan, Hongwu; Zhou, Pucheng; Wang, Xiaolong

2016-11-01

Polarization information parsing plays an important role in polarization imaging detection. This paper focus on the polarization information parsing method: Firstly, the general process of polarization information parsing is given, mainly including polarization image preprocessing, multiple polarization parameters calculation, polarization image fusion and polarization image tracking, etc.; And then the research achievements of the polarization information parsing method are presented, in terms of polarization image preprocessing, the polarization image registration method based on the maximum mutual information is designed. The experiment shows that this method can improve the precision of registration and be satisfied the need of polarization information parsing; In terms of multiple polarization parameters calculation, based on the omnidirectional polarization inversion model is built, a variety of polarization parameter images are obtained and the precision of inversion is to be improve obviously; In terms of polarization image fusion , using fuzzy integral and sparse representation, the multiple polarization parameters adaptive optimal fusion method is given, and the targets detection in complex scene is completed by using the clustering image segmentation algorithm based on fractal characters; In polarization image tracking, the average displacement polarization image characteristics of auxiliary particle filtering fusion tracking algorithm is put forward to achieve the smooth tracking of moving targets. Finally, the polarization information parsing method is applied to the polarization imaging detection of typical targets such as the camouflage target, the fog and latent fingerprints.

Prime Numbers Comparison using Sieve of Eratosthenes and Sieve of Sundaram Algorithm

NASA Astrophysics Data System (ADS)

Abdullah, D.; Rahim, R.; Apdilah, D.; Efendi, S.; Tulus, T.; Suwilo, S.

2018-03-01

Prime numbers are numbers that have their appeal to researchers due to the complexity of these numbers, many algorithms that can be used to generate prime numbers ranging from simple to complex computations, Sieve of Eratosthenes and Sieve of Sundaram are two algorithm that can be used to generate Prime numbers of randomly generated or sequential numbered random numbers, testing in this study to find out which algorithm is better used for large primes in terms of time complexity, the test also assisted with applications designed using Java language with code optimization and Maximum memory usage so that the testing process can be simultaneously and the results obtained can be objective

Predicting missing links in complex networks based on common neighbors and distance

PubMed Central

Yang, Jinxuan; Zhang, Xiao-Dong

2016-01-01

The algorithms based on common neighbors metric to predict missing links in complex networks are very popular, but most of these algorithms do not account for missing links between nodes with no common neighbors. It is not accurate enough to reconstruct networks by using these methods in some cases especially when between nodes have less common neighbors. We proposed in this paper a new algorithm based on common neighbors and distance to improve accuracy of link prediction. Our proposed algorithm makes remarkable effect in predicting the missing links between nodes with no common neighbors and performs better than most existing currently used methods for a variety of real-world networks without increasing complexity. PMID:27905526

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.

Low complexity Reed-Solomon-based low-density parity-check design for software defined optical transmission system based on adaptive puncturing decoding algorithm

NASA Astrophysics Data System (ADS)

Pan, Xiaolong; Liu, Bo; Zheng, Jianglong; Tian, Qinghua

2016-08-01

We propose and demonstrate a low complexity Reed-Solomon-based low-density parity-check (RS-LDPC) code with adaptive puncturing decoding algorithm for elastic optical transmission system. Partial received codes and the relevant column in parity-check matrix can be punctured to reduce the calculation complexity by adaptive parity-check matrix during decoding process. The results show that the complexity of the proposed decoding algorithm is reduced by 30% compared with the regular RS-LDPC system. The optimized code rate of the RS-LDPC code can be obtained after five times iteration.

Arrange and average algorithm for the retrieval of aerosol parameters from multiwavelength high-spectral-resolution lidar/Raman lidar data.

PubMed

Chemyakin, Eduard; Müller, Detlef; Burton, Sharon; Kolgotin, Alexei; Hostetler, Chris; Ferrare, Richard

2014-11-01

We present the results of a feasibility study in which a simple, automated, and unsupervised algorithm, which we call the arrange and average algorithm, is used to infer microphysical parameters (complex refractive index, effective radius, total number, surface area, and volume concentrations) of atmospheric aerosol particles. The algorithm uses backscatter coefficients at 355, 532, and 1064 nm and extinction coefficients at 355 and 532 nm as input information. Testing of the algorithm is based on synthetic optical data that are computed from prescribed monomodal particle size distributions and complex refractive indices that describe spherical, primarily fine mode pollution particles. We tested the performance of the algorithm for the "3 backscatter (β)+2 extinction (α)" configuration of a multiwavelength aerosol high-spectral-resolution lidar (HSRL) or Raman lidar. We investigated the degree to which the microphysical results retrieved by this algorithm depends on the number of input backscatter and extinction coefficients. For example, we tested "3β+1α," "2β+1α," and "3β" lidar configurations. This arrange and average algorithm can be used in two ways. First, it can be applied for quick data processing of experimental data acquired with lidar. Fast automated retrievals of microphysical particle properties are needed in view of the enormous amount of data that can be acquired by the NASA Langley Research Center's airborne "3β+2α" High-Spectral-Resolution Lidar (HSRL-2). It would prove useful for the growing number of ground-based multiwavelength lidar networks, and it would provide an option for analyzing the vast amount of optical data acquired with a future spaceborne multiwavelength lidar. The second potential application is to improve the microphysical particle characterization with our existing inversion algorithm that uses Tikhonov's inversion with regularization. This advanced algorithm has recently undergone development to allow automated and unsupervised processing; the arrange and average algorithm can be used as a preclassifier to further improve its speed and precision. First tests of the performance of arrange and average algorithm are encouraging. We used a set of 48 different monomodal particle size distributions, 4 real parts and 15 imaginary parts of the complex refractive index. All in all we tested 2880 different optical data sets for 0%, 10%, and 20% Gaussian measurement noise (one-standard deviation). In the case of the "3β+2α" configuration with 10% measurement noise, we retrieve the particle effective radius to within 27% for 1964 (68.2%) of the test optical data sets. The number concentration is obtained to 76%, the surface area concentration to 16%, and the volume concentration to 30% precision. The "3β" configuration performs significantly poorer. The performance of the "3β+1α" and "2β+1α" configurations is intermediate between the "3β+2α" and the "3β."

Implantable electronics: emerging design issues and an ultra light-weight security solution.

PubMed

Narasimhan, Seetharam; Wang, Xinmu; Bhunia, Swarup

2010-01-01

Implantable systems that monitor biological signals require increasingly complex digital signal processing (DSP) electronics for real-time in-situ analysis and compression of the recorded signals. While it is well-known that such signal processing hardware needs to be implemented under tight area and power constraints, new design requirements emerge with their increasing complexity. Use of nanoscale technology shows tremendous benefits in implementing these advanced circuits due to dramatic improvement in integration density and power dissipation per operation. However, it also brings in new challenges such as reliability and large idle power (due to higher leakage current). Besides, programmability of the device as well as security of the recorded information are rapidly becoming major design considerations of such systems. In this paper, we analyze the emerging issues associated with the design of the DSP unit in an implantable system. Next, we propose a novel ultra light-weight solution to address the information security issue. Unlike the conventional information security approaches like data encryption, which come at large area and power overhead and hence are not amenable for resource-constrained implantable systems, we propose a multilevel key-based scrambling algorithm, which exploits the nature of the biological signal to effectively obfuscate it. Analysis of the proposed algorithm in the context of neural signal processing and its hardware implementation shows that we can achieve high level of security with ∼ 13X lower power and ∼ 5X lower area overhead than conventional cryptographic solutions.

Querying graphs in protein-protein interactions networks using feedback vertex set.

PubMed

Blin, Guillaume; Sikora, Florian; Vialette, Stéphane

2010-01-01

Recent techniques increase rapidly the amount of our knowledge on interactions between proteins. The interpretation of these new information depends on our ability to retrieve known substructures in the data, the Protein-Protein Interactions (PPIs) networks. In an algorithmic point of view, it is an hard task since it often leads to NP-hard problems. To overcome this difficulty, many authors have provided tools for querying patterns with a restricted topology, i.e., paths or trees in PPI networks. Such restriction leads to the development of fixed parameter tractable (FPT) algorithms, which can be practicable for restricted sizes of queries. Unfortunately, Graph Homomorphism is a W[1]-hard problem, and hence, no FPT algorithm can be found when patterns are in the shape of general graphs. However, Dost et al. gave an algorithm (which is not implemented) to query graphs with a bounded treewidth in PPI networks (the treewidth of the query being involved in the time complexity). In this paper, we propose another algorithm for querying pattern in the shape of graphs, also based on dynamic programming and the color-coding technique. To transform graphs queries into trees without loss of informations, we use feedback vertex set coupled to a node duplication mechanism. Hence, our algorithm is FPT for querying graphs with a bounded size of their feedback vertex set. It gives an alternative to the treewidth parameter, which can be better or worst for a given query. We provide a python implementation which allows us to validate our implementation on real data. Especially, we retrieve some human queries in the shape of graphs into the fly PPI network.

Investigation of the optimum location of external markers for patient setup accuracy enhancement at external beam radiotherapy

PubMed Central

Torshabi, Ahmad Esmaili; Nankali, Saber

2016-01-01

In external beam radiotherapy, one of the most common and reliable methods for patient geometrical setup and/or predicting the tumor location is use of external markers. In this study, the main challenging issue is increasing the accuracy of patient setup by investigating external markers location. Since the location of each external marker may yield different patient setup accuracy, it is important to assess different locations of external markers using appropriate selective algorithms. To do this, two commercially available algorithms entitled a) canonical correlation analysis (CCA) and b) principal component analysis (PCA) were proposed as input selection algorithms. They work on the basis of maximum correlation coefficient and minimum variance between given datasets. The proposed input selection algorithms work in combination with an adaptive neuro‐fuzzy inference system (ANFIS) as a correlation model to give patient positioning information as output. Our proposed algorithms provide input file of ANFIS correlation model accurately. The required dataset for this study was prepared by means of a NURBS‐based 4D XCAT anthropomorphic phantom that can model the shape and structure of complex organs in human body along with motion information of dynamic organs. Moreover, a database of four real patients undergoing radiation therapy for lung cancers was utilized in this study for validation of proposed strategy. Final analyzed results demonstrate that input selection algorithms can reasonably select specific external markers from those areas of the thorax region where root mean square error (RMSE) of ANFIS model has minimum values at that given area. It is also found that the selected marker locations lie closely in those areas where surface point motion has a large amplitude and a high correlation. PACS number(s): 87.55.km, 87.55.N PMID:27929479

Challenges in the Verification of Reinforcement Learning Algorithms

NASA Technical Reports Server (NTRS)

Van Wesel, Perry; Goodloe, Alwyn E.

2017-01-01

Machine learning (ML) is increasingly being applied to a wide array of domains from search engines to autonomous vehicles. These algorithms, however, are notoriously complex and hard to verify. This work looks at the assumptions underlying machine learning algorithms as well as some of the challenges in trying to verify ML algorithms. Furthermore, we focus on the specific challenges of verifying reinforcement learning algorithms. These are highlighted using a specific example. Ultimately, we do not offer a solution to the complex problem of ML verification, but point out possible approaches for verification and interesting research opportunities.

Efficient FFT Algorithm for Psychoacoustic Model of the MPEG-4 AAC

NASA Astrophysics Data System (ADS)

Lee, Jae-Seong; Lee, Chang-Joon; Park, Young-Cheol; Youn, Dae-Hee

This paper proposes an efficient FFT algorithm for the Psycho-Acoustic Model (PAM) of MPEG-4 AAC. The proposed algorithm synthesizes FFT coefficients using MDCT and MDST coefficients through circular convolution. The complexity of the MDCT and MDST coefficients is approximately half of the original FFT. We also design a new PAM based on the proposed FFT algorithm, which has 15% lower computational complexity than the original PAM without degradation of sound quality. Subjective as well as objective test results are presented to confirm the efficiency of the proposed FFT computation algorithm and the PAM.

Volumetric three-dimensional intravascular ultrasound visualization using shape-based nonlinear interpolation

PubMed Central

2013-01-01

Background Intravascular ultrasound (IVUS) is a standard imaging modality for identification of plaque formation in the coronary and peripheral arteries. Volumetric three-dimensional (3D) IVUS visualization provides a powerful tool to overcome the limited comprehensive information of 2D IVUS in terms of complex spatial distribution of arterial morphology and acoustic backscatter information. Conventional 3D IVUS techniques provide sub-optimal visualization of arterial morphology or lack acoustic information concerning arterial structure due in part to low quality of image data and the use of pixel-based IVUS image reconstruction algorithms. In the present study, we describe a novel volumetric 3D IVUS reconstruction algorithm to utilize IVUS signal data and a shape-based nonlinear interpolation. Methods We developed an algorithm to convert a series of IVUS signal data into a fully volumetric 3D visualization. Intermediary slices between original 2D IVUS slices were generated utilizing the natural cubic spline interpolation to consider the nonlinearity of both vascular structure geometry and acoustic backscatter in the arterial wall. We evaluated differences in image quality between the conventional pixel-based interpolation and the shape-based nonlinear interpolation methods using both virtual vascular phantom data and in vivo IVUS data of a porcine femoral artery. Volumetric 3D IVUS images of the arterial segment reconstructed using the two interpolation methods were compared. Results In vitro validation and in vivo comparative studies with the conventional pixel-based interpolation method demonstrated more robustness of the shape-based nonlinear interpolation algorithm in determining intermediary 2D IVUS slices. Our shape-based nonlinear interpolation demonstrated improved volumetric 3D visualization of the in vivo arterial structure and more realistic acoustic backscatter distribution compared to the conventional pixel-based interpolation method. Conclusions This novel 3D IVUS visualization strategy has the potential to improve ultrasound imaging of vascular structure information, particularly atheroma determination. Improved volumetric 3D visualization with accurate acoustic backscatter information can help with ultrasound molecular imaging of atheroma component distribution. PMID:23651569

P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations.

PubMed

Young-Rae Cho; Yanan Xin; Speegle, Greg

2015-01-01

Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

Experimental realization of Shor's quantum factoring algorithm using nuclear magnetic resonance.

PubMed

Vandersypen, L M; Steffen, M; Breyta, G; Yannoni, C S; Sherwood, M H; Chuang, I L

The number of steps any classical computer requires in order to find the prime factors of an l-digit integer N increases exponentially with l, at least using algorithms known at present. Factoring large integers is therefore conjectured to be intractable classically, an observation underlying the security of widely used cryptographic codes. Quantum computers, however, could factor integers in only polynomial time, using Shor's quantum factoring algorithm. Although important for the study of quantum computers, experimental demonstration of this algorithm has proved elusive. Here we report an implementation of the simplest instance of Shor's algorithm: factorization of N = 15 (whose prime factors are 3 and 5). We use seven spin-1/2 nuclei in a molecule as quantum bits, which can be manipulated with room temperature liquid-state nuclear magnetic resonance techniques. This method of using nuclei to store quantum information is in principle scalable to systems containing many quantum bits, but such scalability is not implied by the present work. The significance of our work lies in the demonstration of experimental and theoretical techniques for precise control and modelling of complex quantum computers. In particular, we present a simple, parameter-free but predictive model of decoherence effects in our system.

A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization

PubMed Central

Zhu, Wenyong; Liu, Zijuan; Duan, Qingyan; Cao, Long

2016-01-01

This paper proposes a novel quantum-behaved bat algorithm with the direction of mean best position (QMBA). In QMBA, the position of each bat is mainly updated by the current optimal solution in the early stage of searching and in the late search it also depends on the mean best position which can enhance the convergence speed of the algorithm. During the process of searching, quantum behavior of bats is introduced which is beneficial to jump out of local optimal solution and make the quantum-behaved bats not easily fall into local optimal solution, and it has better ability to adapt complex environment. Meanwhile, QMBA makes good use of statistical information of best position which bats had experienced to generate better quality solutions. This approach not only inherits the characteristic of quick convergence, simplicity, and easy implementation of original bat algorithm, but also increases the diversity of population and improves the accuracy of solution. Twenty-four benchmark test functions are tested and compared with other variant bat algorithms for numerical optimization the simulation results show that this approach is simple and efficient and can achieve a more accurate solution. PMID:27293424

Rotation-invariant features for multi-oriented text detection in natural images.

PubMed

Yao, Cong; Zhang, Xin; Bai, Xiang; Liu, Wenyu; Ma, Yi; Tu, Zhuowen

2013-01-01

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes.

MoleculeNet: a benchmark for molecular machine learning† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a

PubMed Central

Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N.; Gomes, Joseph; Geniesse, Caleb; Pappu, Aneesh S.; Leswing, Karl

2017-01-01

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm. PMID:29629118

EDDA: An Efficient Distributed Data Replication Algorithm in VANETs.

PubMed

Zhu, Junyu; Huang, Chuanhe; Fan, Xiying; Guo, Sipei; Fu, Bin

2018-02-10

Efficient data dissemination in vehicular ad hoc networks (VANETs) is a challenging issue due to the dynamic nature of the network. To improve the performance of data dissemination, we study distributed data replication algorithms in VANETs for exchanging information and computing in an arbitrarily-connected network of vehicle nodes. To achieve low dissemination delay and improve the network performance, we control the number of message copies that can be disseminated in the network and then propose an efficient distributed data replication algorithm (EDDA). The key idea is to let the data carrier distribute the data dissemination tasks to multiple nodes to speed up the dissemination process. We calculate the number of communication stages for the network to enter into a balanced status and show that the proposed distributed algorithm can converge to a consensus in a small number of communication stages. Most of the theoretical results described in this paper are to study the complexity of network convergence. The lower bound and upper bound are also provided in the analysis of the algorithm. Simulation results show that the proposed EDDA can efficiently disseminate messages to vehicles in a specific area with low dissemination delay and system overhead.

EDDA: An Efficient Distributed Data Replication Algorithm in VANETs

PubMed Central

Zhu, Junyu; Huang, Chuanhe; Fan, Xiying; Guo, Sipei; Fu, Bin

2018-01-01

Efficient data dissemination in vehicular ad hoc networks (VANETs) is a challenging issue due to the dynamic nature of the network. To improve the performance of data dissemination, we study distributed data replication algorithms in VANETs for exchanging information and computing in an arbitrarily-connected network of vehicle nodes. To achieve low dissemination delay and improve the network performance, we control the number of message copies that can be disseminated in the network and then propose an efficient distributed data replication algorithm (EDDA). The key idea is to let the data carrier distribute the data dissemination tasks to multiple nodes to speed up the dissemination process. We calculate the number of communication stages for the network to enter into a balanced status and show that the proposed distributed algorithm can converge to a consensus in a small number of communication stages. Most of the theoretical results described in this paper are to study the complexity of network convergence. The lower bound and upper bound are also provided in the analysis of the algorithm. Simulation results show that the proposed EDDA can efficiently disseminate messages to vehicles in a specific area with low dissemination delay and system overhead. PMID:29439443

Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics.

PubMed

Stolper, Charles D; Perer, Adam; Gotz, David

2014-12-01

As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records.

Autonomous Navigation for Autonomous Underwater Vehicles Based on Information Filters and Active Sensing

PubMed Central

He, Bo; Zhang, Hongjin; Li, Chao; Zhang, Shujing; Liang, Yan; Yan, Tianhong

2011-01-01

This paper addresses an autonomous navigation method for the autonomous underwater vehicle (AUV) C-Ranger applying information-filter-based simultaneous localization and mapping (SLAM), and its sea trial experiments in Tuandao Bay (Shangdong Province, P.R. China). Weak links in the information matrix in an extended information filter (EIF) can be pruned to achieve an efficient approach-sparse EIF algorithm (SEIF-SLAM). All the basic update formulae can be implemented in constant time irrespective of the size of the map; hence the computational complexity is significantly reduced. The mechanical scanning imaging sonar is chosen as the active sensing device for the underwater vehicle, and a compensation method based on feedback of the AUV pose is presented to overcome distortion of the acoustic images due to the vehicle motion. In order to verify the feasibility of the navigation methods proposed for the C-Ranger, a sea trial was conducted in Tuandao Bay. Experimental results and analysis show that the proposed navigation approach based on SEIF-SLAM improves the accuracy of the navigation compared with conventional method; moreover the algorithm has a low computational cost when compared with EKF-SLAM. PMID:22346682

Autonomous navigation for autonomous underwater vehicles based on information filters and active sensing.

PubMed

He, Bo; Zhang, Hongjin; Li, Chao; Zhang, Shujing; Liang, Yan; Yan, Tianhong

2011-01-01

This paper addresses an autonomous navigation method for the autonomous underwater vehicle (AUV) C-Ranger applying information-filter-based simultaneous localization and mapping (SLAM), and its sea trial experiments in Tuandao Bay (Shangdong Province, P.R. China). Weak links in the information matrix in an extended information filter (EIF) can be pruned to achieve an efficient approach-sparse EIF algorithm (SEIF-SLAM). All the basic update formulae can be implemented in constant time irrespective of the size of the map; hence the computational complexity is significantly reduced. The mechanical scanning imaging sonar is chosen as the active sensing device for the underwater vehicle, and a compensation method based on feedback of the AUV pose is presented to overcome distortion of the acoustic images due to the vehicle motion. In order to verify the feasibility of the navigation methods proposed for the C-Ranger, a sea trial was conducted in Tuandao Bay. Experimental results and analysis show that the proposed navigation approach based on SEIF-SLAM improves the accuracy of the navigation compared with conventional method; moreover the algorithm has a low computational cost when compared with EKF-SLAM.

A new FOD recognition algorithm based on multi-source information fusion and experiment analysis

NASA Astrophysics Data System (ADS)

Li, Yu; Xiao, Gang

2011-08-01

Foreign Object Debris (FOD) is a kind of substance, debris or article alien to an aircraft or system, which would potentially cause huge damage when it appears on the airport runway. Due to the airport's complex circumstance, quick and precise detection of FOD target on the runway is one of the important protections for airplane's safety. A multi-sensor system including millimeter-wave radar and Infrared image sensors is introduced and a developed new FOD detection and recognition algorithm based on inherent feature of FOD is proposed in this paper. Firstly, the FOD's location and coordinate can be accurately obtained by millimeter-wave radar, and then according to the coordinate IR camera will take target images and background images. Secondly, in IR image the runway's edges which are straight lines can be extracted by using Hough transformation method. The potential target region, that is, runway region, can be segmented from the whole image. Thirdly, background subtraction is utilized to localize the FOD target in runway region. Finally, in the detailed small images of FOD target, a new characteristic is discussed and used in target classification. The experiment results show that this algorithm can effectively reduce the computational complexity, satisfy the real-time requirement and possess of high detection and recognition probability.

Point-in-convex polygon and point-in-convex polyhedron algorithms with O(1) complexity using space subdivision

NASA Astrophysics Data System (ADS)

Skala, Vaclav

2016-06-01

There are many space subdivision and space partitioning techniques used in many algorithms to speed up computations. They mostly rely on orthogonal space subdivision, resp. using hierarchical data structures, e.g. BSP trees, quadtrees, octrees, kd-trees, bounding volume hierarchies etc. However in some applications a non-orthogonal space subdivision can offer new ways for actual speed up. In the case of convex polygon in E2 a simple Point-in-Polygon test is of the O(N) complexity and the optimal algorithm is of O(log N) computational complexity. In the E3 case, the complexity is O(N) even for the convex polyhedron as no ordering is defined. New Point-in-Convex Polygon and Point-in-Convex Polyhedron algorithms are presented based on space subdivision in the preprocessing stage resulting to O(1) run-time complexity. The presented approach is simple to implement. Due to the principle of duality, dual problems, e.g. line-convex polygon, line clipping, can be solved in a similarly.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

PubMed

Srinivasulu, Yerukala Sathipati; Wang, Jyun-Rong; Hsu, Kai-Ti; Tsai, Ming-Ju; Charoenkwan, Phasit; Huang, Wen-Lin; Huang, Hui-Ling; Ho, Shinn-Ying

2015-01-01

Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes

PubMed Central

2015-01-01

Background Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. Results This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. Conclusions The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes. PMID:26681483

A Demons algorithm for image registration with locally adaptive regularization.

PubMed

Cahill, Nathan D; Noble, J Alison; Hawkes, David J

2009-01-01

Thirion's Demons is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. It approximately solves the diffusion registration problem by successively estimating force vectors that drive the deformation toward alignment and smoothing the force vectors by Gaussian convolution. In this article, we show how the Demons algorithm can be generalized to allow image-driven locally adaptive regularization in a manner that preserves both the linear complexity and ease of implementation of the original Demons algorithm. We show that the proposed algorithm exhibits lower target registration error and requires less computational effort than the original Demons algorithm on the registration of serial chest CT scans of patients with lung nodules.

Encryption and decryption algorithm using algebraic matrix approach

NASA Astrophysics Data System (ADS)

Thiagarajan, K.; Balasubramanian, P.; Nagaraj, J.; Padmashree, J.

2018-04-01

Cryptographic algorithms provide security of data against attacks during encryption and decryption. However, they are computationally intensive process which consume large amount of CPU time and space at time of encryption and decryption. The goal of this paper is to study the encryption and decryption algorithm and to find space complexity of the encrypted and decrypted data by using of algorithm. In this paper, we encrypt and decrypt the message using key with the help of cyclic square matrix provides the approach applicable for any number of words having more number of characters and longest word. Also we discussed about the time complexity of the algorithm. The proposed algorithm is simple but difficult to break the process.

Mobile robot dynamic path planning based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.

A constraint logic programming approach to associate 1D and 3D structural components for large protein complexes.

PubMed

Dal Palù, Alessandro; Pontelli, Enrico; He, Jing; Lu, Yonggang

2007-01-01

The paper describes a novel framework, constructed using Constraint Logic Programming (CLP) and parallelism, to determine the association between parts of the primary sequence of a protein and alpha-helices extracted from 3D low-resolution descriptions of large protein complexes. The association is determined by extracting constraints from the 3D information, regarding length, relative position and connectivity of helices, and solving these constraints with the guidance of a secondary structure prediction algorithm. Parallelism is employed to enhance performance on large proteins. The framework provides a fast, inexpensive alternative to determine the exact tertiary structure of unknown proteins.

An Improved Text Localization Method for Natural Scene Images

NASA Astrophysics Data System (ADS)

Jiang, Mengdi; Cheng, Jianghua; Chen, Minghui; Ku, Xishu

2018-01-01

In order to extract text information effectively from natural scene image with complex background, multi-orientation perspective and multilingual languages, we present a new method based on the improved Stroke Feature Transform (SWT). Firstly, The Maximally Stable Extremal Region (MSER) method is used to detect text candidate regions. Secondly, the SWT algorithm is used in the candidate regions, which can improve the edge detection compared with tradition SWT method. Finally, the Frequency-tuned (FT) visual saliency is introduced to remove non-text candidate regions. The experiment results show that, the method can achieve good robustness for complex background with multi-orientation perspective, various characters and font sizes.

Sparsity-aware multiple relay selection in large multi-hop decode-and-forward relay networks

NASA Astrophysics Data System (ADS)

Gouissem, A.; Hamila, R.; Al-Dhahir, N.; Foufou, S.

2016-12-01

In this paper, we propose and investigate two novel techniques to perform multiple relay selection in large multi-hop decode-and-forward relay networks. The two proposed techniques exploit sparse signal recovery theory to select multiple relays using the orthogonal matching pursuit algorithm and outperform state-of-the-art techniques in terms of outage probability and computation complexity. To reduce the amount of collected channel state information (CSI), we propose a limited-feedback scheme where only a limited number of relays feedback their CSI. Furthermore, a detailed performance-complexity tradeoff investigation is conducted for the different studied techniques and verified by Monte Carlo simulations.

Application of artificial neural networks to composite ply micromechanics

NASA Technical Reports Server (NTRS)

Brown, D. A.; Murthy, P. L. N.; Berke, L.

1991-01-01

Artificial neural networks can provide improved computational efficiency relative to existing methods when an algorithmic description of functional relationships is either totally unavailable or is complex in nature. For complex calculations, significant reductions in elapsed computation time are possible. The primary goal is to demonstrate the applicability of artificial neural networks to composite material characterization. As a test case, a neural network was trained to accurately predict composite hygral, thermal, and mechanical properties when provided with basic information concerning the environment, constituent materials, and component ratios used in the creation of the composite. A brief introduction on neural networks is provided along with a description of the project itself.

Simple Algorithms for Distributed Leader Election in Anonymous Synchronous Rings and Complete Networks Inspired by Neural Development in Fruit Flies.

PubMed

Xu, Lei; Jeavons, Peter

2015-11-01

Leader election in anonymous rings and complete networks is a very practical problem in distributed computing. Previous algorithms for this problem are generally designed for a classical message passing model where complex messages are exchanged. However, the need to send and receive complex messages makes such algorithms less practical for some real applications. We present some simple synchronous algorithms for distributed leader election in anonymous rings and complete networks that are inspired by the development of the neural system of the fruit fly. Our leader election algorithms all assume that only one-bit messages are broadcast by nodes in the network and processors are only able to distinguish between silence and the arrival of one or more messages. These restrictions allow implementations to use a simpler message-passing architecture. Even with these harsh restrictions our algorithms are shown to achieve good time and message complexity both analytically and experimentally.

Modal characterization of the ASCIE segmented optics testbed: New algorithms and experimental results

NASA Technical Reports Server (NTRS)

Carrier, Alain C.; Aubrun, Jean-Noel

1993-01-01

New frequency response measurement procedures, on-line modal tuning techniques, and off-line modal identification algorithms are developed and applied to the modal identification of the Advanced Structures/Controls Integrated Experiment (ASCIE), a generic segmented optics telescope test-bed representative of future complex space structures. The frequency response measurement procedure uses all the actuators simultaneously to excite the structure and all the sensors to measure the structural response so that all the transfer functions are measured simultaneously. Structural responses to sinusoidal excitations are measured and analyzed to calculate spectral responses. The spectral responses in turn are analyzed as the spectral data become available and, which is new, the results are used to maintain high quality measurements. Data acquisition, processing, and checking procedures are fully automated. As the acquisition of the frequency response progresses, an on-line algorithm keeps track of the actuator force distribution that maximizes the structural response to automatically tune to a structural mode when approaching a resonant frequency. This tuning is insensitive to delays, ill-conditioning, and nonproportional damping. Experimental results show that is useful for modal surveys even in high modal density regions. For thorough modeling, a constructive procedure is proposed to identify the dynamics of a complex system from its frequency response with the minimization of a least-squares cost function as a desirable objective. This procedure relies on off-line modal separation algorithms to extract modal information and on least-squares parameter subset optimization to combine the modal results and globally fit the modal parameters to the measured data. The modal separation algorithms resolved modal density of 5 modes/Hz in the ASCIE experiment. They promise to be useful in many challenging applications.

Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System

NASA Technical Reports Server (NTRS)

Aranki, Nazeeh I.; Keymeulen, Didier; Kimesh, Matthew A.

2012-01-01

Modern hyperspectral imaging systems are able to acquire far more data than can be downlinked from a spacecraft. Onboard data compression helps to alleviate this problem, but requires a system capable of power efficiency and high throughput. Software solutions have limited throughput performance and are power-hungry. Dedicated hardware solutions can provide both high throughput and power efficiency, while taking the load off of the main processor. Thus a hardware compression system was developed. The implementation uses a field-programmable gate array (FPGA). The implementation is based on the fast lossless (FL) compression algorithm reported in Fast Lossless Compression of Multispectral-Image Data (NPO-42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26, which achieves excellent compression performance and has low complexity. This algorithm performs predictive compression using an adaptive filtering method, and uses adaptive Golomb coding. The implementation also packetizes the coded data. The FL algorithm is well suited for implementation in hardware. In the FPGA implementation, one sample is compressed every clock cycle, which makes for a fast and practical realtime solution for space applications. Benefits of this implementation are: 1) The underlying algorithm achieves a combination of low complexity and compression effectiveness that exceeds that of techniques currently in use. 2) The algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. 3) Hardware acceleration provides a throughput improvement of 10 to 100 times vs. the software implementation. A prototype of the compressor is available in software, but it runs at a speed that does not meet spacecraft requirements. The hardware implementation targets the Xilinx Virtex IV FPGAs, and makes the use of this compressor practical for Earth satellites as well as beyond-Earth missions with hyperspectral instruments.

Surface-Height Determination of Crevassed Glaciers-Mathematical Principles of an Autoadaptive Density-Dimension Algorithm and Validation Using ICESat-2 Simulator (SIMPL) Data

NASA Technical Reports Server (NTRS)

Herzfeld, Ute C.; Trantow, Thomas M.; Harding, David; Dabney, Philip W.

2017-01-01

Glacial acceleration is a main source of uncertainty in sea-level-change assessment. Measurement of ice-surface heights with a spatial and temporal resolution that not only allows elevation-change calculation, but also captures ice-surface morphology and its changes is required to aid in investigations of the geophysical processes associated with glacial acceleration.The Advanced Topographic Laser Altimeter System aboard NASAs future ICESat-2 Mission (launch 2017) will implement multibeam micropulse photon-counting lidar altimetry aimed at measuring ice-surface heights at 0.7-m along-track spacing. The instrument is designed to resolve spatial and temporal variability of rapidly changing glaciers and ice sheets and the Arctic sea ice. The new technology requires the development of a new mathematical algorithm for the retrieval of height information.We introduce the density-dimension algorithm (DDA) that utilizes the radial basis function to calculate a weighted density as a form of data aggregation in the photon cloud and considers density an additional dimension as an aid in auto-adaptive threshold determination. The auto-adaptive capability of the algorithm is necessary to separate returns from noise and signal photons under changing environmental conditions. The algorithm is evaluated using data collected with an ICESat-2 simulator instrument, the Slope Imaging Multi-polarization Photon-counting Lidar, over the heavily crevassed Giesecke Braer in Northwestern Greenland in summer 2015. Results demonstrate that ICESat-2 may be expected to provide ice-surface height measurements over crevassed glaciers and other complex ice surfaces. The DDA is generally applicable for the analysis of airborne and spaceborne micropulse photon-counting lidar data over complex and simple surfaces.