Impulsive noise removal from color video with morphological filtering

NASA Astrophysics Data System (ADS)

Ruchay, Alexey; Kober, Vitaly

2017-09-01

This paper deals with impulse noise removal from color video. The proposed noise removal algorithm employs a switching filtering for denoising of color video; that is, detection of corrupted pixels by means of a novel morphological filtering followed by removal of the detected pixels on the base of estimation of uncorrupted pixels in the previous scenes. With the help of computer simulation we show that the proposed algorithm is able to well remove impulse noise in color video. The performance of the proposed algorithm is compared in terms of image restoration metrics with that of common successful algorithms.

A memetic optimization algorithm for multi-constrained multicast routing in ad hoc networks

PubMed Central

Hammad, Karim; El Bakly, Ahmed M.

2018-01-01

A mobile ad hoc network is a conventional self-configuring network where the routing optimization problem—subject to various Quality-of-Service (QoS) constraints—represents a major challenge. Unlike previously proposed solutions, in this paper, we propose a memetic algorithm (MA) employing an adaptive mutation parameter, to solve the multicast routing problem with higher search ability and computational efficiency. The proposed algorithm utilizes an updated scheme, based on statistical analysis, to estimate the best values for all MA parameters and enhance MA performance. The numerical results show that the proposed MA improved the delay and jitter of the network, while reducing computational complexity as compared to existing algorithms. PMID:29509760

A memetic optimization algorithm for multi-constrained multicast routing in ad hoc networks.

PubMed

Ramadan, Rahab M; Gasser, Safa M; El-Mahallawy, Mohamed S; Hammad, Karim; El Bakly, Ahmed M

2018-01-01

A mobile ad hoc network is a conventional self-configuring network where the routing optimization problem-subject to various Quality-of-Service (QoS) constraints-represents a major challenge. Unlike previously proposed solutions, in this paper, we propose a memetic algorithm (MA) employing an adaptive mutation parameter, to solve the multicast routing problem with higher search ability and computational efficiency. The proposed algorithm utilizes an updated scheme, based on statistical analysis, to estimate the best values for all MA parameters and enhance MA performance. The numerical results show that the proposed MA improved the delay and jitter of the network, while reducing computational complexity as compared to existing algorithms.

Affine Projection Algorithm with Improved Data-Selective Method Using the Condition Number

NASA Astrophysics Data System (ADS)

Ban, Sung Jun; Lee, Chang Woo; Kim, Sang Woo

Recently, a data-selective method has been proposed to achieve low misalignment in affine projection algorithm (APA) by keeping the condition number of an input data matrix small. We present an improved method, and a complexity reduction algorithm for the APA with the data-selective method. Experimental results show that the proposed algorithm has lower misalignment and a lower condition number for an input data matrix than both the conventional APA and the APA with the previous data-selective method.

Adaptive power allocation schemes based on IAFS algorithm for OFDM-based cognitive radio systems

NASA Astrophysics Data System (ADS)

Zhang, Shuying; Zhao, Xiaohui; Liang, Cong; Ding, Xu

2017-01-01

In cognitive radio (CR) systems, reasonable power allocation can increase transmission rate of CR users or secondary users (SUs) as much as possible and at the same time insure normal communication among primary users (PUs). This study proposes an optimal power allocation scheme for the OFDM-based CR system with one SU influenced by multiple PU interference constraints. This scheme is based on an improved artificial fish swarm (IAFS) algorithm in combination with the advantage of conventional artificial fish swarm (ASF) algorithm and particle swarm optimisation (PSO) algorithm. In performance comparison of IAFS algorithm with other intelligent algorithms by simulations, the superiority of the IAFS algorithm is illustrated; this superiority results in better performance of our proposed scheme than that of the power allocation algorithms proposed by the previous studies in the same scenario. Furthermore, our proposed scheme can obtain higher transmission data rate under the multiple PU interference constraints and the total power constraint of SU than that of the other mentioned works.

Novel and efficient tag SNPs selection algorithms.

PubMed

Chen, Wen-Pei; Hung, Che-Lun; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2014-01-01

SNPs are the most abundant forms of genetic variations amongst species; the association studies between complex diseases and SNPs or haplotypes have received great attention. However, these studies are restricted by the cost of genotyping all SNPs; thus, it is necessary to find smaller subsets, or tag SNPs, representing the rest of the SNPs. In fact, the existing tag SNP selection algorithms are notoriously time-consuming. An efficient algorithm for tag SNP selection was presented, which was applied to analyze the HapMap YRI data. The experimental results show that the proposed algorithm can achieve better performance than the existing tag SNP selection algorithms; in most cases, this proposed algorithm is at least ten times faster than the existing methods. In many cases, when the redundant ratio of the block is high, the proposed algorithm can even be thousands times faster than the previously known methods. Tools and web services for haplotype block analysis integrated by hadoop MapReduce framework are also developed using the proposed algorithm as computation kernels.

Efficient conjugate gradient algorithms for computation of the manipulator forward dynamics

NASA Technical Reports Server (NTRS)

Fijany, Amir; Scheid, Robert E.

1989-01-01

The applicability of conjugate gradient algorithms for computation of the manipulator forward dynamics is investigated. The redundancies in the previously proposed conjugate gradient algorithm are analyzed. A new version is developed which, by avoiding these redundancies, achieves a significantly greater efficiency. A preconditioned conjugate gradient algorithm is also presented. A diagonal matrix whose elements are the diagonal elements of the inertia matrix is proposed as the preconditioner. In order to increase the computational efficiency, an algorithm is developed which exploits the synergism between the computation of the diagonal elements of the inertia matrix and that required by the conjugate gradient algorithm.

Scenario Decomposition for 0-1 Stochastic Programs: Improvements and Asynchronous Implementation

DOE PAGES

Ryan, Kevin; Rajan, Deepak; Ahmed, Shabbir

2016-05-01

We recently proposed scenario decomposition algorithm for stochastic 0-1 programs finds an optimal solution by evaluating and removing individual solutions that are discovered by solving scenario subproblems. In our work, we develop an asynchronous, distributed implementation of the algorithm which has computational advantages over existing synchronous implementations of the algorithm. Improvements to both the synchronous and asynchronous algorithm are proposed. We also test the results on well known stochastic 0-1 programs from the SIPLIB test library and is able to solve one previously unsolved instance from the test set.

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.

A dynamic data source selection system for smartwatch platform.

PubMed

Nemati, Ebrahim; Sideris, Konstantinos; Kalantarian, Haik; Sarrafzadeh, Majid

2016-08-01

A novel data source selection algorithm is proposed for ambulatory activity tracking of elderly people. The algorithm introduces the concept of dynamic switching between the data collection modules (a smartwatch and a smartphone) to improve accuracy and battery life using contextual information. We show that by making offloading decisions as a function of activity, the proposed algorithm improves power consumption and accuracy of the previous work by 7 hours and 5% respectively compared to the baseline.

GraDit: graph-based data repair algorithm for multiple data edits rule violations

NASA Astrophysics Data System (ADS)

Ode Zuhayeni Madjida, Wa; Gusti Bagus Baskara Nugraha, I.

2018-03-01

Constraint-based data cleaning captures data violation to a set of rule called data quality rules. The rules consist of integrity constraint and data edits. Structurally, they are similar, where the rule contain left hand side and right hand side. Previous research proposed a data repair algorithm for integrity constraint violation. The algorithm uses undirected hypergraph as rule violation representation. Nevertheless, this algorithm can not be applied for data edits because of different rule characteristics. This study proposed GraDit, a repair algorithm for data edits rule. First, we use bipartite-directed hypergraph as model representation of overall defined rules. These representation is used for getting interaction between violation rules and clean rules. On the other hand, we proposed undirected graph as violation representation. Our experimental study showed that algorithm with undirected graph as violation representation model gave better data quality than algorithm with undirected hypergraph as representation model.

The efficiency of average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling in identifying homogeneous precipitation catchments

NASA Astrophysics Data System (ADS)

Chuan, Zun Liang; Ismail, Noriszura; Shinyie, Wendy Ling; Lit Ken, Tan; Fam, Soo-Fen; Senawi, Azlyna; Yusoff, Wan Nur Syahidah Wan

2018-04-01

Due to the limited of historical precipitation records, agglomerative hierarchical clustering algorithms widely used to extrapolate information from gauged to ungauged precipitation catchments in yielding a more reliable projection of extreme hydro-meteorological events such as extreme precipitation events. However, identifying the optimum number of homogeneous precipitation catchments accurately based on the dendrogram resulted using agglomerative hierarchical algorithms are very subjective. The main objective of this study is to propose an efficient regionalized algorithm to identify the homogeneous precipitation catchments for non-stationary precipitation time series. The homogeneous precipitation catchments are identified using average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling, while uncentered correlation coefficient as the similarity measure. The regionalized homogeneous precipitation is consolidated using K-sample Anderson Darling non-parametric test. The analysis result shows the proposed regionalized algorithm performed more better compared to the proposed agglomerative hierarchical clustering algorithm in previous studies.

Model-based Bayesian signal extraction algorithm for peripheral nerves

NASA Astrophysics Data System (ADS)

Eggers, Thomas E.; Dweiri, Yazan M.; McCallum, Grant A.; Durand, Dominique M.

2017-10-01

Objective. Multi-channel cuff electrodes have recently been investigated for extracting fascicular-level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques. These extracted signals, however, had low signal-to-noise ratios and thus limited their utility to binary classification. In this work a new algorithm is proposed which combines previous source localization approaches to create a model based method which operates in real time. Approach. To validate this algorithm, a saline benchtop setup was created to allow the precise placement of artificial sources within a cuff and interference sources outside the cuff. The artificial source was taken from five seconds of chronic neural activity to replicate realistic recordings. The proposed algorithm, hybrid Bayesian signal extraction (HBSE), is then compared to previous algorithms, beamforming and a Bayesian spatial filtering method, on this test data. An example chronic neural recording is also analyzed with all three algorithms. Main results. The proposed algorithm improved the signal to noise and signal to interference ratio of extracted test signals two to three fold, as well as increased the correlation coefficient between the original and recovered signals by 10-20%. These improvements translated to the chronic recording example and increased the calculated bit rate between the recovered signals and the recorded motor activity. Significance. HBSE significantly outperforms previous algorithms in extracting realistic neural signals, even in the presence of external noise sources. These results demonstrate the feasibility of extracting dynamic motor signals from a multi-fascicled intact nerve trunk, which in turn could extract motor command signals from an amputee for the end goal of controlling a prosthetic limb.

Approximation algorithms for the min-power symmetric connectivity problem

NASA Astrophysics Data System (ADS)

Plotnikov, Roman; Erzin, Adil; Mladenovic, Nenad

2016-10-01

We consider the NP-hard problem of synthesis of optimal spanning communication subgraph in a given arbitrary simple edge-weighted graph. This problem occurs in the wireless networks while minimizing the total transmission power consumptions. We propose several new heuristics based on the variable neighborhood search metaheuristic for the approximation solution of the problem. We have performed a numerical experiment where all proposed algorithms have been executed on the randomly generated test samples. For these instances, on average, our algorithms outperform the previously known heuristics.

Mining subspace clusters from DNA microarray data using large itemset techniques.

PubMed

Chang, Ye-In; Chen, Jiun-Rung; Tsai, Yueh-Chi

2009-05-01

Mining subspace clusters from the DNA microarrays could help researchers identify those genes which commonly contribute to a disease, where a subspace cluster indicates a subset of genes whose expression levels are similar under a subset of conditions. Since in a DNA microarray, the number of genes is far larger than the number of conditions, those previous proposed algorithms which compute the maximum dimension sets (MDSs) for any two genes will take a long time to mine subspace clusters. In this article, we propose the Large Itemset-Based Clustering (LISC) algorithm for mining subspace clusters. Instead of constructing MDSs for any two genes, we construct only MDSs for any two conditions. Then, we transform the task of finding the maximal possible gene sets into the problem of mining large itemsets from the condition-pair MDSs. Since we are only interested in those subspace clusters with gene sets as large as possible, it is desirable to pay attention to those gene sets which have reasonable large support values in the condition-pair MDSs. From our simulation results, we show that the proposed algorithm needs shorter processing time than those previous proposed algorithms which need to construct gene-pair MDSs.

Image restoration for three-dimensional fluorescence microscopy using an orthonormal basis for efficient representation of depth-variant point-spread functions

PubMed Central

Patwary, Nurmohammed; Preza, Chrysanthe

2015-01-01

A depth-variant (DV) image restoration algorithm for wide field fluorescence microscopy, using an orthonormal basis decomposition of DV point-spread functions (PSFs), is investigated in this study. The efficient PSF representation is based on a previously developed principal component analysis (PCA), which is computationally intensive. We present an approach developed to reduce the number of DV PSFs required for the PCA computation, thereby making the PCA-based approach computationally tractable for thick samples. Restoration results from both synthetic and experimental images show consistency and that the proposed algorithm addresses efficiently depth-induced aberration using a small number of principal components. Comparison of the PCA-based algorithm with a previously-developed strata-based DV restoration algorithm demonstrates that the proposed method improves performance by 50% in terms of accuracy and simultaneously reduces the processing time by 64% using comparable computational resources. PMID:26504634

An adaptive scale factor based MPPT algorithm for changing solar irradiation levels in outer space

NASA Astrophysics Data System (ADS)

Kwan, Trevor Hocksun; Wu, Xiaofeng

2017-03-01

Maximum power point tracking (MPPT) techniques are popularly used for maximizing the output of solar panels by continuously tracking the maximum power point (MPP) of their P-V curves, which depend both on the panel temperature and the input insolation. Various MPPT algorithms have been studied in literature, including perturb and observe (P&O), hill climbing, incremental conductance, fuzzy logic control and neural networks. This paper presents an algorithm which improves the MPP tracking performance by adaptively scaling the DC-DC converter duty cycle. The principle of the proposed algorithm is to detect the oscillation by checking the sign (ie. direction) of the duty cycle perturbation between the current and previous time steps. If there is a difference in the signs then it is clear an oscillation is present and the DC-DC converter duty cycle perturbation is subsequently scaled down by a constant factor. By repeating this process, the steady state oscillations become negligibly small which subsequently allows for a smooth steady state MPP response. To verify the proposed MPPT algorithm, a simulation involving irradiances levels that are typically encountered in outer space is conducted. Simulation and experimental results prove that the proposed algorithm is fast and stable in comparison to not only the conventional fixed step counterparts, but also to previous variable step size algorithms.

Opposition-Based Memetic Algorithm and Hybrid Approach for Sorting Permutations by Reversals.

PubMed

Soncco-Álvarez, José Luis; Muñoz, Daniel M; Ayala-Rincón, Mauricio

2018-02-21

Sorting unsigned permutations by reversals is a difficult problem; indeed, it was proved to be NP-hard by Caprara (1997). Because of its high complexity, many approximation algorithms to compute the minimal reversal distance were proposed until reaching the nowadays best-known theoretical ratio of 1.375. In this article, two memetic algorithms to compute the reversal distance are proposed. The first one uses the technique of opposition-based learning leading to an opposition-based memetic algorithm; the second one improves the previous algorithm by applying the heuristic of two breakpoint elimination leading to a hybrid approach. Several experiments were performed with one-hundred randomly generated permutations, single benchmark permutations, and biological permutations. Results of the experiments showed that the proposed OBMA and Hybrid-OBMA algorithms achieve the best results for practical cases, that is, for permutations of length up to 120. Also, Hybrid-OBMA showed to improve the results of OBMA for permutations greater than or equal to 60. The applicability of our proposed algorithms was checked processing permutations based on biological data, in which case OBMA gave the best average results for all instances.

Improved argument-FFT frequency offset estimation for QPSK coherent optical Systems

NASA Astrophysics Data System (ADS)

Han, Jilong; Li, Wei; Yuan, Zhilin; Li, Haitao; Huang, Liyan; Hu, Qianggao

2016-02-01

A frequency offset estimation (FOE) algorithm based on fast Fourier transform (FFT) of the signal's argument is investigated, which does not require removing the modulated data phase. In this paper, we analyze the flaw of the argument-FFT algorithm and propose a combined FOE algorithm, in which the absolute of frequency offset (FO) is accurately calculated by argument-FFT algorithm with a relatively large number of samples and the sign of FO is determined by FFT-based interpolation discrete Fourier transformation (DFT) algorithm with a relatively small number of samples. Compared with the previous algorithms based on argument-FFT, the proposed one has low complexity and can still effectively work with a relatively less number of samples.

Automatic Boosted Flood Mapping from Satellite Data

NASA Technical Reports Server (NTRS)

Coltin, Brian; McMichael, Scott; Smith, Trey; Fong, Terrence

2016-01-01

Numerous algorithms have been proposed to map floods from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. However, most require human input to succeed, either to specify a threshold value or to manually annotate training data. We introduce a new algorithm based on Adaboost which effectively maps floods without any human input, allowing for a truly rapid and automatic response. The Adaboost algorithm combines multiple thresholds to achieve results comparable to state-of-the-art algorithms which do require human input. We evaluate Adaboost, as well as numerous previously proposed flood mapping algorithms, on multiple MODIS flood images, as well as on hundreds of non-flood MODIS lake images, demonstrating its effectiveness across a wide variety of conditions.

An efficient algorithm for function optimization: modified stem cells algorithm

NASA Astrophysics Data System (ADS)

Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad Hadi

2013-03-01

In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

Unmitigated numerical solution to the diffraction term in the parabolic nonlinear ultrasound wave equation.

PubMed

Hasani, Mojtaba H; Gharibzadeh, Shahriar; Farjami, Yaghoub; Tavakkoli, Jahan

2013-09-01

Various numerical algorithms have been developed to solve the Khokhlov-Kuznetsov-Zabolotskaya (KZK) parabolic nonlinear wave equation. In this work, a generalized time-domain numerical algorithm is proposed to solve the diffraction term of the KZK equation. This algorithm solves the transverse Laplacian operator of the KZK equation in three-dimensional (3D) Cartesian coordinates using a finite-difference method based on the five-point implicit backward finite difference and the five-point Crank-Nicolson finite difference discretization techniques. This leads to a more uniform discretization of the Laplacian operator which in turn results in fewer calculation gridding nodes without compromising accuracy in the diffraction term. In addition, a new empirical algorithm based on the LU decomposition technique is proposed to solve the system of linear equations obtained from this discretization. The proposed empirical algorithm improves the calculation speed and memory usage, while the order of computational complexity remains linear in calculation of the diffraction term in the KZK equation. For evaluating the accuracy of the proposed algorithm, two previously published algorithms are used as comparison references: the conventional 2D Texas code and its generalization for 3D geometries. The results show that the accuracy/efficiency performance of the proposed algorithm is comparable with the established time-domain methods.

Theory and algorithms for image reconstruction on chords and within regions of interest

NASA Astrophysics Data System (ADS)

Zou, Yu; Pan, Xiaochuan; Sidky, Emilâ Y.

2005-11-01

We introduce a formula for image reconstruction on a chord of a general source trajectory. We subsequently develop three algorithms for exact image reconstruction on a chord from data acquired with the general trajectory. Interestingly, two of the developed algorithms can accommodate data containing transverse truncations. The widely used helical trajectory and other trajectories discussed in literature can be interpreted as special cases of the general trajectory, and the developed theory and algorithms are thus directly applicable to reconstructing images exactly from data acquired with these trajectories. For instance, chords on a helical trajectory are equivalent to the n-PI-line segments. In this situation, the proposed algorithms become the algorithms that we proposed previously for image reconstruction on PI-line segments. We have performed preliminary numerical studies, which include the study on image reconstruction on chords of two-circle trajectory, which is nonsmooth, and on n-PI lines of a helical trajectory, which is smooth. Quantitative results of these studies verify and demonstrate the proposed theory and algorithms.

Figure-ground segmentation based on class-independent shape priors

NASA Astrophysics Data System (ADS)

Li, Yang; Liu, Yang; Liu, Guojun; Guo, Maozu

2018-01-01

We propose a method to generate figure-ground segmentation by incorporating shape priors into the graph-cuts algorithm. Given an image, we first obtain a linear representation of an image and then apply directional chamfer matching to generate class-independent, nonparametric shape priors, which provide shape clues for the graph-cuts algorithm. We then enforce shape priors in a graph-cuts energy function to produce object segmentation. In contrast to previous segmentation methods, the proposed method shares shape knowledge for different semantic classes and does not require class-specific model training. Therefore, the approach obtains high-quality segmentation for objects. We experimentally validate that the proposed method outperforms previous approaches using the challenging PASCAL VOC 2010/2012 and Berkeley (BSD300) segmentation datasets.

Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements.

PubMed

Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K; Cai, Chang; Nagarajan, Srikantan S

2018-06-01

Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements

NASA Astrophysics Data System (ADS)

Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K.; Cai, Chang; Nagarajan, Srikantan S.

2018-06-01

Objective. Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. Approach. The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. Main results. Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. Significance. The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Hybrid glowworm swarm optimization for task scheduling in the cloud environment

NASA Astrophysics Data System (ADS)

Zhou, Jing; Dong, Shoubin

2018-06-01

In recent years many heuristic algorithms have been proposed to solve task scheduling problems in the cloud environment owing to their optimization capability. This article proposes a hybrid glowworm swarm optimization (HGSO) based on glowworm swarm optimization (GSO), which uses a technique of evolutionary computation, a strategy of quantum behaviour based on the principle of neighbourhood, offspring production and random walk, to achieve more efficient scheduling with reasonable scheduling costs. The proposed HGSO reduces the redundant computation and the dependence on the initialization of GSO, accelerates the convergence and more easily escapes from local optima. The conducted experiments and statistical analysis showed that in most cases the proposed HGSO algorithm outperformed previous heuristic algorithms to deal with independent tasks.

Real-time estimation of ionospheric delay using GPS measurements

NASA Astrophysics Data System (ADS)

Lin, Lao-Sheng

1997-12-01

When radio waves such as the GPS signals propagate through the ionosphere, they experience an extra time delay. The ionospheric delay can be eliminated (to the first order) through a linear combination of L1 and L2 observations from dual-frequency GPS receivers. Taking advantage of this dispersive principle, one or more dual- frequency GPS receivers can be used to determine a model of the ionospheric delay across a region of interest and, if implemented in real-time, can support single-frequency GPS positioning and navigation applications. The research objectives of this thesis were: (1) to develop algorithms to obtain accurate absolute Total Electron Content (TEC) estimates from dual-frequency GPS observables, and (2) to develop an algorithm to improve the accuracy of real-time ionosphere modelling. In order to fulfil these objectives, four algorithms have been proposed in this thesis. A 'multi-day multipath template technique' is proposed to mitigate the pseudo-range multipath effects at static GPS reference stations. This technique is based on the assumption that the multipath disturbance at a static station will be constant if the physical environment remains unchanged from day to day. The multipath template, either single-day or multi-day, can be generated from the previous days' GPS data. A 'real-time failure detection and repair algorithm' is proposed to detect and repair the GPS carrier phase 'failures', such as the occurrence of cycle slips. The proposed algorithm uses two procedures: (1) application of a statistical test on the state difference estimated from robust and conventional Kalman filters in order to detect and identify the carrier phase failure, and (2) application of a Kalman filter algorithm to repair the 'identified carrier phase failure'. A 'L1/L2 differential delay estimation algorithm' is proposed to estimate GPS satellite transmitter and receiver L1/L2 differential delays. This algorithm, based on the single-site modelling technique, is able to estimate the sum of the satellite and receiver L1/L2 differential delay for each tracked GPS satellite. A 'UNSW grid-based algorithm' is proposed to improve the accuracy of real-time ionosphere modelling. The proposed algorithm is similar to the conventional grid-based algorithm. However, two modifications were made to the algorithm: (1) an 'exponential function' is adopted as the weighting function, and (2) the 'grid-based ionosphere model' estimated from the previous day is used to predict the ionospheric delay ratios between the grid point and reference points. (Abstract shortened by UMI.)

A physarum-inspired prize-collecting steiner tree approach to identify subnetworks for drug repositioning.

PubMed

Sun, Yahui; Hameed, Pathima Nusrath; Verspoor, Karin; Halgamuge, Saman

2016-12-05

Drug repositioning can reduce the time, costs and risks of drug development by identifying new therapeutic effects for known drugs. It is challenging to reposition drugs as pharmacological data is large and complex. Subnetwork identification has already been used to simplify the visualization and interpretation of biological data, but it has not been applied to drug repositioning so far. In this paper, we fill this gap by proposing a new Physarum-inspired Prize-Collecting Steiner Tree algorithm to identify subnetworks for drug repositioning. Drug Similarity Networks (DSN) are generated using the chemical, therapeutic, protein, and phenotype features of drugs. In DSNs, vertex prizes and edge costs represent the similarities and dissimilarities between drugs respectively, and terminals represent drugs in the cardiovascular class, as defined in the Anatomical Therapeutic Chemical classification system. A new Physarum-inspired Prize-Collecting Steiner Tree algorithm is proposed in this paper to identify subnetworks. We apply both the proposed algorithm and the widely-used GW algorithm to identify subnetworks in our 18 generated DSNs. In these DSNs, our proposed algorithm identifies subnetworks with an average Rand Index of 81.1%, while the GW algorithm can only identify subnetworks with an average Rand Index of 64.1%. We select 9 subnetworks with high Rand Index to find drug repositioning opportunities. 10 frequently occurring drugs in these subnetworks are identified as candidates to be repositioned for cardiovascular diseases. We find evidence to support previous discoveries that nitroglycerin, theophylline and acarbose may be able to be repositioned for cardiovascular diseases. Moreover, we identify seven previously unknown drug candidates that also may interact with the biological cardiovascular system. These discoveries show our proposed Prize-Collecting Steiner Tree approach as a promising strategy for drug repositioning.

Extending Correlation Filter-Based Visual Tracking by Tree-Structured Ensemble and Spatial Windowing.

PubMed

Gundogdu, Erhan; Ozkan, Huseyin; Alatan, A Aydin

2017-11-01

Correlation filters have been successfully used in visual tracking due to their modeling power and computational efficiency. However, the state-of-the-art correlation filter-based (CFB) tracking algorithms tend to quickly discard the previous poses of the target, since they consider only a single filter in their models. On the contrary, our approach is to register multiple CFB trackers for previous poses and exploit the registered knowledge when an appearance change occurs. To this end, we propose a novel tracking algorithm [of complexity O(D) ] based on a large ensemble of CFB trackers. The ensemble [of size O(2 D ) ] is organized over a binary tree (depth D ), and learns the target appearance subspaces such that each constituent tracker becomes an expert of a certain appearance. During tracking, the proposed algorithm combines only the appearance-aware relevant experts to produce boosted tracking decisions. Additionally, we propose a versatile spatial windowing technique to enhance the individual expert trackers. For this purpose, spatial windows are learned for target objects as well as the correlation filters and then the windowed regions are processed for more robust correlations. In our extensive experiments on benchmark datasets, we achieve a substantial performance increase by using the proposed tracking algorithm together with the spatial windowing.

Smooth Approximation l 0-Norm Constrained Affine Projection Algorithm and Its Applications in Sparse Channel Estimation

PubMed Central

2014-01-01

We propose a smooth approximation l 0-norm constrained affine projection algorithm (SL0-APA) to improve the convergence speed and the steady-state error of affine projection algorithm (APA) for sparse channel estimation. The proposed algorithm ensures improved performance in terms of the convergence speed and the steady-state error via the combination of a smooth approximation l 0-norm (SL0) penalty on the coefficients into the standard APA cost function, which gives rise to a zero attractor that promotes the sparsity of the channel taps in the channel estimation and hence accelerates the convergence speed and reduces the steady-state error when the channel is sparse. The simulation results demonstrate that our proposed SL0-APA is superior to the standard APA and its sparsity-aware algorithms in terms of both the convergence speed and the steady-state behavior in a designated sparse channel. Furthermore, SL0-APA is shown to have smaller steady-state error than the previously proposed sparsity-aware algorithms when the number of nonzero taps in the sparse channel increases. PMID:24790588

A dynamic scheduling algorithm for singe-arm two-cluster tools with flexible processing times

NASA Astrophysics Data System (ADS)

Li, Xin; Fung, Richard Y. K.

2018-02-01

This article presents a dynamic algorithm for job scheduling in two-cluster tools producing multi-type wafers with flexible processing times. Flexible processing times mean that the actual times for processing wafers should be within given time intervals. The objective of the work is to minimize the completion time of the newly inserted wafer. To deal with this issue, a two-cluster tool is decomposed into three reduced single-cluster tools (RCTs) in a series based on a decomposition approach proposed in this article. For each single-cluster tool, a dynamic scheduling algorithm based on temporal constraints is developed to schedule the newly inserted wafer. Three experiments have been carried out to test the dynamic scheduling algorithm proposed, comparing with the results the 'earliest starting time' heuristic (EST) adopted in previous literature. The results show that the dynamic algorithm proposed in this article is effective and practical.

Multiple Leader Candidate and Competitive Position Allocation for Robust Formation against Member Robot Faults

PubMed Central

Kwon, Ji-Wook; Kim, Jin Hyo; Seo, Jiwon

2015-01-01

This paper proposes a Multiple Leader Candidate (MLC) structure and a Competitive Position Allocation (CPA) algorithm which can be applicable for various applications including environmental sensing. Unlike previous formation structures such as virtual-leader and actual-leader structures with position allocation including a rigid allocation and an optimization based allocation, the formation employing the proposed MLC structure and CPA algorithm is robust against the fault (or disappearance) of the member robots and reduces the entire cost. In the MLC structure, a leader of the entire system is chosen among leader candidate robots. The CPA algorithm is the decentralized position allocation algorithm that assigns the robots to the vertex of the formation via the competition of the adjacent robots. The numerical simulations and experimental results are included to show the feasibility and the performance of the multiple robot system employing the proposed MLC structure and the CPA algorithm. PMID:25954956

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

Qin, SB; Cady, ST; Dominguez-Garcia, AD

This paper presents the theory and implementation of a distributed algorithm for controlling differential power processing converters in photovoltaic (PV) applications. This distributed algorithm achieves true maximum power point tracking of series-connected PV submodules by relying only on local voltage measurements and neighbor-to-neighbor communication between the differential power converters. Compared to previous solutions, the proposed algorithm achieves reduced number of perturbations at each step and potentially faster tracking without adding extra hardware; all these features make this algorithm well-suited for long submodule strings. The formulation of the algorithm, discussion of its properties, as well as three case studies are presented.more » The performance of the distributed tracking algorithm has been verified via experiments, which yielded quantifiable improvements over other techniques that have been implemented in practice. Both simulations and hardware experiments have confirmed the effectiveness of the proposed distributed algorithm.« less

Intra Frame Coding In Advanced Video Coding Standard (H.264) to Obtain Consistent PSNR and Reduce Bit Rate for Diagonal Down Left Mode Using Gaussian Pulse

NASA Astrophysics Data System (ADS)

Manjanaik, N.; Parameshachari, B. D.; Hanumanthappa, S. N.; Banu, Reshma

2017-08-01

Intra prediction process of H.264 video coding standard used to code first frame i.e. Intra frame of video to obtain good coding efficiency compare to previous video coding standard series. More benefit of intra frame coding is to reduce spatial pixel redundancy with in current frame, reduces computational complexity and provides better rate distortion performance. To code Intra frame it use existing process Rate Distortion Optimization (RDO) method. This method increases computational complexity, increases in bit rate and reduces picture quality so it is difficult to implement in real time applications, so the many researcher has been developed fast mode decision algorithm for coding of intra frame. The previous work carried on Intra frame coding in H.264 standard using fast decision mode intra prediction algorithm based on different techniques was achieved increased in bit rate, degradation of picture quality(PSNR) for different quantization parameters. Many previous approaches of fast mode decision algorithms on intra frame coding achieved only reduction of computational complexity or it save encoding time and limitation was increase in bit rate with loss of quality of picture. In order to avoid increase in bit rate and loss of picture quality a better approach was developed. In this paper developed a better approach i.e. Gaussian pulse for Intra frame coding using diagonal down left intra prediction mode to achieve higher coding efficiency in terms of PSNR and bitrate. In proposed method Gaussian pulse is multiplied with each 4x4 frequency domain coefficients of 4x4 sub macro block of macro block of current frame before quantization process. Multiplication of Gaussian pulse for each 4x4 integer transformed coefficients at macro block levels scales the information of the coefficients in a reversible manner. The resulting signal would turn abstract. Frequency samples are abstract in a known and controllable manner without intermixing of coefficients, it avoids picture getting bad hit for higher values of quantization parameters. The proposed work was implemented using MATLAB and JM 18.6 reference software. The proposed work measure the performance parameters PSNR, bit rate and compression of intra frame of yuv video sequences in QCIF resolution under different values of quantization parameter with Gaussian value for diagonal down left intra prediction mode. The simulation results of proposed algorithm are tabulated and compared with previous algorithm i.e. Tian et al method. The proposed algorithm achieved reduced in bit rate averagely 30.98% and maintain consistent picture quality for QCIF sequences compared to previous algorithm i.e. Tian et al method.

A Robust Sound Source Localization Approach for Microphone Array with Model Errors

NASA Astrophysics Data System (ADS)

Xiao, Hua; Shao, Huai-Zong; Peng, Qi-Cong

In this paper, a robust sound source localization approach is proposed. The approach retains good performance even when model errors exist. Compared with previous work in this field, the contributions of this paper are as follows. First, an improved broad-band and near-field array model is proposed. It takes array gain, phase perturbations into account and is based on the actual positions of the elements. It can be used in arbitrary planar geometry arrays. Second, a subspace model errors estimation algorithm and a Weighted 2-Dimension Multiple Signal Classification (W2D-MUSIC) algorithm are proposed. The subspace model errors estimation algorithm estimates unknown parameters of the array model, i. e., gain, phase perturbations, and positions of the elements, with high accuracy. The performance of this algorithm is improved with the increasing of SNR or number of snapshots. The W2D-MUSIC algorithm based on the improved array model is implemented to locate sound sources. These two algorithms compose the robust sound source approach. The more accurate steering vectors can be provided for further processing such as adaptive beamforming algorithm. Numerical examples confirm effectiveness of this proposed approach.

Scalable Nearest Neighbor Algorithms for High Dimensional Data.

PubMed

Muja, Marius; Lowe, David G

2014-11-01

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.

SemiBoost: boosting for semi-supervised learning.

PubMed

Mallapragada, Pavan Kumar; Jin, Rong; Jain, Anil K; Liu, Yi

2009-11-01

Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled data. Our goal is to improve the classification accuracy of any given supervised learning algorithm by using the available unlabeled examples. We call this as the Semi-supervised improvement problem, to distinguish the proposed approach from the existing approaches. We design a metasemi-supervised learning algorithm that wraps around the underlying supervised algorithm and improves its performance using unlabeled data. This problem is particularly important when we need to train a supervised learning algorithm with a limited number of labeled examples and a multitude of unlabeled examples. We present a boosting framework for semi-supervised learning, termed as SemiBoost. The key advantages of the proposed semi-supervised learning approach are: 1) performance improvement of any supervised learning algorithm with a multitude of unlabeled data, 2) efficient computation by the iterative boosting algorithm, and 3) exploiting both manifold and cluster assumption in training classification models. An empirical study on 16 different data sets and text categorization demonstrates that the proposed framework improves the performance of several commonly used supervised learning algorithms, given a large number of unlabeled examples. We also show that the performance of the proposed algorithm, SemiBoost, is comparable to the state-of-the-art semi-supervised learning algorithms.

From Determinism and Probability to Chaos: Chaotic Evolution towards Philosophy and Methodology of Chaotic Optimization

PubMed Central

2015-01-01

We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed. PMID:25879067

From determinism and probability to chaos: chaotic evolution towards philosophy and methodology of chaotic optimization.

PubMed

Pei, Yan

2015-01-01

We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed.

Multi-scale graph-cut algorithm for efficient water-fat separation.

PubMed

Berglund, Johan; Skorpil, Mikael

2017-09-01

To improve the accuracy and robustness to noise in water-fat separation by unifying the multiscale and graph cut based approaches to B 0 -correction. A previously proposed water-fat separation algorithm that corrects for B 0 field inhomogeneity in 3D by a single quadratic pseudo-Boolean optimization (QPBO) graph cut was incorporated into a multi-scale framework, where field map solutions are propagated from coarse to fine scales for voxels that are not resolved by the graph cut. The accuracy of the single-scale and multi-scale QPBO algorithms was evaluated against benchmark reference datasets. The robustness to noise was evaluated by adding noise to the input data prior to water-fat separation. Both algorithms achieved the highest accuracy when compared with seven previously published methods, while computation times were acceptable for implementation in clinical routine. The multi-scale algorithm was more robust to noise than the single-scale algorithm, while causing only a small increase (+10%) of the reconstruction time. The proposed 3D multi-scale QPBO algorithm offers accurate water-fat separation, robustness to noise, and fast reconstruction. The software implementation is freely available to the research community. Magn Reson Med 78:941-949, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Wavelet tree structure based speckle noise removal for optical coherence tomography

NASA Astrophysics Data System (ADS)

Yuan, Xin; Liu, Xuan; Liu, Yang

2018-02-01

We report a new speckle noise removal algorithm in optical coherence tomography (OCT). Though wavelet domain thresholding algorithms have demonstrated superior advantages in suppressing noise magnitude and preserving image sharpness in OCT, the wavelet tree structure has not been investigated in previous applications. In this work, we propose an adaptive wavelet thresholding algorithm via exploiting the tree structure in wavelet coefficients to remove the speckle noise in OCT images. The threshold for each wavelet band is adaptively selected following a special rule to retain the structure of the image across different wavelet layers. Our results demonstrate that the proposed algorithm outperforms conventional wavelet thresholding, with significant advantages in preserving image features.

Searching for efficient Markov chain Monte Carlo proposal kernels

PubMed Central

Yang, Ziheng; Rodríguez, Carlos E.

2013-01-01

Markov chain Monte Carlo (MCMC) or the Metropolis–Hastings algorithm is a simulation algorithm that has made modern Bayesian statistical inference possible. Nevertheless, the efficiency of different Metropolis–Hastings proposal kernels has rarely been studied except for the Gaussian proposal. Here we propose a unique class of Bactrian kernels, which avoid proposing values that are very close to the current value, and compare their efficiency with a number of proposals for simulating different target distributions, with efficiency measured by the asymptotic variance of a parameter estimate. The uniform kernel is found to be more efficient than the Gaussian kernel, whereas the Bactrian kernel is even better. When optimal scales are used for both, the Bactrian kernel is at least 50% more efficient than the Gaussian. Implementation in a Bayesian program for molecular clock dating confirms the general applicability of our results to generic MCMC algorithms. Our results refute a previous claim that all proposals had nearly identical performance and will prompt further research into efficient MCMC proposals. PMID:24218600

Zero-block mode decision algorithm for H.264/AVC.

PubMed

Lee, Yu-Ming; Lin, Yinyi

2009-03-01

In the previous paper , we proposed a zero-block intermode decision algorithm for H.264 video coding based upon the number of zero-blocks of 4 x 4 DCT coefficients between the current macroblock and the co-located macroblock. The proposed algorithm can achieve significant improvement in computation, but the computation performance is limited for high bit-rate coding. To improve computation efficiency, in this paper, we suggest an enhanced zero-block decision algorithm, which uses an early zero-block detection method to compute the number of zero-blocks instead of direct DCT and quantization (DCT/Q) calculation and incorporates two adequate decision methods into semi-stationary and nonstationary regions of a video sequence. In addition, the zero-block decision algorithm is also applied to the intramode prediction in the P frame. The enhanced zero-block decision algorithm brings out a reduction of average 27% of total encoding time compared to the zero-block decision algorithm.

Skull removal in MR images using a modified artificial bee colony optimization algorithm.

PubMed

Taherdangkoo, Mohammad

2014-01-01

Removal of the skull from brain Magnetic Resonance (MR) images is an important preprocessing step required for other image analysis techniques such as brain tissue segmentation. In this paper, we propose a new algorithm based on the Artificial Bee Colony (ABC) optimization algorithm to remove the skull region from brain MR images. We modify the ABC algorithm using a different strategy for initializing the coordinates of scout bees and their direction of search. Moreover, we impose an additional constraint to the ABC algorithm to avoid the creation of discontinuous regions. We found that our algorithm successfully removed all bony skull from a sample of de-identified MR brain images acquired from different model scanners. The obtained results of the proposed algorithm compared with those of previously introduced well known optimization algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) demonstrate the superior results and computational performance of our algorithm, suggesting its potential for clinical applications.

Semi-blind sparse image reconstruction with application to MRFM.

PubMed

Park, Se Un; Dobigeon, Nicolas; Hero, Alfred O

2012-09-01

We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high-dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data.

An Adaptive Tradeoff Algorithm for Multi-issue SLA Negotiation

NASA Astrophysics Data System (ADS)

Son, Seokho; Sim, Kwang Mong

Since participants in a Cloud may be independent bodies, mechanisms are necessary for resolving different preferences in leasing Cloud services. Whereas there are currently mechanisms that support service-level agreement negotiation, there is little or no negotiation support for concurrent price and timeslot for Cloud service reservations. For the concurrent price and timeslot negotiation, a tradeoff algorithm to generate and evaluate a proposal which consists of price and timeslot proposal is necessary. The contribution of this work is thus to design an adaptive tradeoff algorithm for multi-issue negotiation mechanism. The tradeoff algorithm referred to as "adaptive burst mode" is especially designed to increase negotiation speed and total utility and to reduce computational load by adaptively generating concurrent set of proposals. The empirical results obtained from simulations carried out using a testbed suggest that due to the concurrent price and timeslot negotiation mechanism with adaptive tradeoff algorithm: 1) both agents achieve the best performance in terms of negotiation speed and utility; 2) the number of evaluations of each proposal is comparatively lower than previous scheme (burst-N).

An efficient variable projection formulation for separable nonlinear least squares problems.

PubMed

Gan, Min; Li, Han-Xiong

2014-05-01

We consider in this paper a class of nonlinear least squares problems in which the model can be represented as a linear combination of nonlinear functions. The variable projection algorithm projects the linear parameters out of the problem, leaving the nonlinear least squares problems involving only the nonlinear parameters. To implement the variable projection algorithm more efficiently, we propose a new variable projection functional based on matrix decomposition. The advantage of the proposed formulation is that the size of the decomposed matrix may be much smaller than those of previous ones. The Levenberg-Marquardt algorithm using finite difference method is then applied to minimize the new criterion. Numerical results show that the proposed approach achieves significant reduction in computing time.

Mining User Dwell Time for Personalized Web Search Re-Ranking

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

Xu, Songhua; Jiang, Hao; Lau, Francis

We propose a personalized re-ranking algorithm through mining user dwell times derived from a user's previously online reading or browsing activities. We acquire document level user dwell times via a customized web browser, from which we then infer conceptword level user dwell times in order to understand a user's personal interest. According to the estimated concept word level user dwell times, our algorithm can estimate a user's potential dwell time over a new document, based on which personalized webpage re-ranking can be carried out. We compare the rankings produced by our algorithm with rankings generated by popular commercial search enginesmore » and a recently proposed personalized ranking algorithm. The results clearly show the superiority of our method. In this paper, we propose a new personalized webpage ranking algorithmthrough mining dwell times of a user. We introduce a quantitative model to derive concept word level user dwell times from the observed document level user dwell times. Once we have inferred a user's interest over the set of concept words the user has encountered in previous readings, we can then predict the user's potential dwell time over a new document. Such predicted user dwell time allows us to carry out personalized webpage re-ranking. To explore the effectiveness of our algorithm, we measured the performance of our algorithm under two conditions - one with a relatively limited amount of user dwell time data and the other with a doubled amount. Both evaluation cases put our algorithm for generating personalized webpage rankings to satisfy a user's personal preference ahead of those by Google, Yahoo!, and Bing, as well as a recent personalized webpage ranking algorithm.« less

GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.

PubMed

Tae Joon Jun; Hyun Ji Park; Hyuk Yoo; Young-Hak Kim; Daeyoung Kim

2016-08-01

In this paper, we propose an GPU based Cloud system for high-performance arrhythmia detection. Pan-Tompkins algorithm is used for QRS detection and we optimized beat classification algorithm with K-Nearest Neighbor (K-NN). To support high performance beat classification on the system, we parallelized beat classification algorithm with CUDA to execute the algorithm on virtualized GPU devices on the Cloud system. MIT-BIH Arrhythmia database is used for validation of the algorithm. The system achieved about 93.5% of detection rate which is comparable to previous researches while our algorithm shows 2.5 times faster execution time compared to CPU only detection algorithm.

Efficient shortest-path-tree computation in network routing based on pulse-coupled neural networks.

PubMed

Qu, Hong; Yi, Zhang; Yang, Simon X

2013-06-01

Shortest path tree (SPT) computation is a critical issue for routers using link-state routing protocols, such as the most commonly used open shortest path first and intermediate system to intermediate system. Each router needs to recompute a new SPT rooted from itself whenever a change happens in the link state. Most commercial routers do this computation by deleting the current SPT and building a new one using static algorithms such as the Dijkstra algorithm at the beginning. Such recomputation of an entire SPT is inefficient, which may consume a considerable amount of CPU time and result in a time delay in the network. Some dynamic updating methods using the information in the updated SPT have been proposed in recent years. However, there are still many limitations in those dynamic algorithms. In this paper, a new modified model of pulse-coupled neural networks (M-PCNNs) is proposed for the SPT computation. It is rigorously proved that the proposed model is capable of solving some optimization problems, such as the SPT. A static algorithm is proposed based on the M-PCNNs to compute the SPT efficiently for large-scale problems. In addition, a dynamic algorithm that makes use of the structure of the previously computed SPT is proposed, which significantly improves the efficiency of the algorithm. Simulation results demonstrate the effective and efficient performance of the proposed approach.

Fast and efficient search for MPEG-4 video using adjacent pixel intensity difference quantization histogram feature

NASA Astrophysics Data System (ADS)

Lee, Feifei; Kotani, Koji; Chen, Qiu; Ohmi, Tadahiro

2010-02-01

In this paper, a fast search algorithm for MPEG-4 video clips from video database is proposed. An adjacent pixel intensity difference quantization (APIDQ) histogram is utilized as the feature vector of VOP (video object plane), which had been reliably applied to human face recognition previously. Instead of fully decompressed video sequence, partially decoded data, namely DC sequence of the video object are extracted from the video sequence. Combined with active search, a temporal pruning algorithm, fast and robust video search can be realized. The proposed search algorithm has been evaluated by total 15 hours of video contained of TV programs such as drama, talk, news, etc. to search for given 200 MPEG-4 video clips which each length is 15 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 80ms, and Equal Error Rate (ERR) of 2 % in drama and news categories are achieved, which are more accurately and robust than conventional fast video search algorithm.

Symbolic Computation of Strongly Connected Components Using Saturation

NASA Technical Reports Server (NTRS)

Zhao, Yang; Ciardo, Gianfranco

2010-01-01

Finding strongly connected components (SCCs) in the state-space of discrete-state models is a critical task in formal verification of LTL and fair CTL properties, but the potentially huge number of reachable states and SCCs constitutes a formidable challenge. This paper is concerned with computing the sets of states in SCCs or terminal SCCs of asynchronous systems. Because of its advantages in many applications, we employ saturation on two previously proposed approaches: the Xie-Beerel algorithm and transitive closure. First, saturation speeds up state-space exploration when computing each SCC in the Xie-Beerel algorithm. Then, our main contribution is a novel algorithm to compute the transitive closure using saturation. Experimental results indicate that our improved algorithms achieve a clear speedup over previous algorithms in some cases. With the help of the new transitive closure computation algorithm, up to 10(exp 150) SCCs can be explored within a few seconds.

Optimization of memory use of fragment extension-based protein-ligand docking with an original fast minimum cost flow algorithm.

PubMed

Yanagisawa, Keisuke; Komine, Shunta; Kubota, Rikuto; Ohue, Masahito; Akiyama, Yutaka

2018-06-01

The need to accelerate large-scale protein-ligand docking in virtual screening against a huge compound database led researchers to propose a strategy that entails memorizing the evaluation result of the partial structure of a compound and reusing it to evaluate other compounds. However, the previous method required frequent disk accesses, resulting in insufficient acceleration. Thus, more efficient memory usage can be expected to lead to further acceleration, and optimal memory usage could be achieved by solving the minimum cost flow problem. In this research, we propose a fast algorithm for the minimum cost flow problem utilizing the characteristics of the graph generated for this problem as constraints. The proposed algorithm, which optimized memory usage, was approximately seven times faster compared to existing minimum cost flow algorithms. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Online Coregularization for Multiview Semisupervised Learning

PubMed Central

Li, Guohui; Huang, Kuihua

2013-01-01

We propose a novel online coregularization framework for multiview semisupervised learning based on the notion of duality in constrained optimization. Using the weak duality theorem, we reduce the online coregularization to the task of increasing the dual function. We demonstrate that the existing online coregularization algorithms in previous work can be viewed as an approximation of our dual ascending process using gradient ascent. New algorithms are derived based on the idea of ascending the dual function more aggressively. For practical purpose, we also propose two sparse approximation approaches for kernel representation to reduce the computational complexity. Experiments show that our derived online coregularization algorithms achieve risk and accuracy comparable to offline algorithms while consuming less time and memory. Specially, our online coregularization algorithms are able to deal with concept drift and maintain a much smaller error rate. This paper paves a way to the design and analysis of online coregularization algorithms. PMID:24194680

Scene-based nonuniformity correction with reduced ghosting using a gated LMS algorithm.

PubMed

Hardie, Russell C; Baxley, Frank; Brys, Brandon; Hytla, Patrick

2009-08-17

In this paper, we present a scene-based nouniformity correction (NUC) method using a modified adaptive least mean square (LMS) algorithm with a novel gating operation on the updates. The gating is designed to significantly reduce ghosting artifacts produced by many scene-based NUC algorithms by halting updates when temporal variation is lacking. We define the algorithm and present a number of experimental results to demonstrate the efficacy of the proposed method in comparison to several previously published methods including other LMS and constant statistics based methods. The experimental results include simulated imagery and a real infrared image sequence. We show that the proposed method significantly reduces ghosting artifacts, but has a slightly longer convergence time. (c) 2009 Optical Society of America

Methodology for the Evaluation of the Algorithms for Text Line Segmentation Based on Extended Binary Classification

NASA Astrophysics Data System (ADS)

Brodic, D.

2011-01-01

Text line segmentation represents the key element in the optical character recognition process. Hence, testing of text line segmentation algorithms has substantial relevance. All previously proposed testing methods deal mainly with text database as a template. They are used for testing as well as for the evaluation of the text segmentation algorithm. In this manuscript, methodology for the evaluation of the algorithm for text segmentation based on extended binary classification is proposed. It is established on the various multiline text samples linked with text segmentation. Their results are distributed according to binary classification. Final result is obtained by comparative analysis of cross linked data. At the end, its suitability for different types of scripts represents its main advantage.

Smart Phase Tuning in Microwave Photonic Integrated Circuits Toward Automated Frequency Multiplication by Design

NASA Astrophysics Data System (ADS)

Nabavi, N.

2018-07-01

The author investigates the monitoring methods for fine adjustment of the previously proposed on-chip architecture for frequency multiplication and translation of harmonics by design. Digital signal processing (DSP) algorithms are utilized to create an optimized microwave photonic integrated circuit functionality toward automated frequency multiplication. The implemented DSP algorithms are formed on discrete Fourier transform and optimization-based algorithms (Greedy and gradient-based algorithms), which are analytically derived and numerically compared based on the accuracy and speed of convergence criteria.

An algorithm of discovering signatures from DNA databases on a computer cluster.

PubMed

Lee, Hsiao Ping; Sheu, Tzu-Fang

2014-10-05

Signatures are short sequences that are unique and not similar to any other sequence in a database that can be used as the basis to identify different species. Even though several signature discovery algorithms have been proposed in the past, these algorithms require the entirety of databases to be loaded in the memory, thus restricting the amount of data that they can process. It makes those algorithms unable to process databases with large amounts of data. Also, those algorithms use sequential models and have slower discovery speeds, meaning that the efficiency can be improved. In this research, we are debuting the utilization of a divide-and-conquer strategy in signature discovery and have proposed a parallel signature discovery algorithm on a computer cluster. The algorithm applies the divide-and-conquer strategy to solve the problem posed to the existing algorithms where they are unable to process large databases and uses a parallel computing mechanism to effectively improve the efficiency of signature discovery. Even when run with just the memory of regular personal computers, the algorithm can still process large databases such as the human whole-genome EST database which were previously unable to be processed by the existing algorithms. The algorithm proposed in this research is not limited by the amount of usable memory and can rapidly find signatures in large databases, making it useful in applications such as Next Generation Sequencing and other large database analysis and processing. The implementation of the proposed algorithm is available at http://www.cs.pu.edu.tw/~fang/DDCSDPrograms/DDCSD.htm.

Low Cost Design of an Advanced Encryption Standard (AES) Processor Using a New Common-Subexpression-Elimination Algorithm

NASA Astrophysics Data System (ADS)

Chen, Ming-Chih; Hsiao, Shen-Fu

In this paper, we propose an area-efficient design of Advanced Encryption Standard (AES) processor by applying a new common-expression-elimination (CSE) method to the sub-functions of various transformations required in AES. The proposed method reduces the area cost of realizing the sub-functions by extracting the common factors in the bit-level XOR/AND-based sum-of-product expressions of these sub-functions using a new CSE algorithm. Cell-based implementation results show that the AES processor with our proposed CSE method has significant area improvement compared with previous designs.

A hybrid algorithm for speckle noise reduction of ultrasound images.

PubMed

Singh, Karamjeet; Ranade, Sukhjeet Kaur; Singh, Chandan

2017-09-01

Medical images are contaminated by multiplicative speckle noise which significantly reduce the contrast of ultrasound images and creates a negative effect on various image interpretation tasks. In this paper, we proposed a hybrid denoising approach which collaborate the both local and nonlocal information in an efficient manner. The proposed hybrid algorithm consist of three stages in which at first stage the use of local statistics in the form of guided filter is used to reduce the effect of speckle noise initially. Then, an improved speckle reducing bilateral filter (SRBF) is developed to further reduce the speckle noise from the medical images. Finally, to reconstruct the diffused edges we have used the efficient post-processing technique which jointly considered the advantages of both bilateral and nonlocal mean (NLM) filter for the attenuation of speckle noise efficiently. The performance of proposed hybrid algorithm is evaluated on synthetic, simulated and real ultrasound images. The experiments conducted on various test images demonstrate that our proposed hybrid approach outperforms the various traditional speckle reduction approaches included recently proposed NLM and optimized Bayesian-based NLM. The results of various quantitative, qualitative measures and by visual inspection of denoise synthetic and real ultrasound images demonstrate that the proposed hybrid algorithm have strong denoising capability and able to preserve the fine image details such as edge of a lesion better than previously developed methods for speckle noise reduction. The denoising and edge preserving capability of hybrid algorithm is far better than existing traditional and recently proposed speckle reduction (SR) filters. The success of proposed algorithm would help in building the lay foundation for inventing the hybrid algorithms for denoising of ultrasound images. Copyright © 2017 Elsevier B.V. All rights reserved.

Score-Level Fusion of Phase-Based and Feature-Based Fingerprint Matching Algorithms

NASA Astrophysics Data System (ADS)

Ito, Koichi; Morita, Ayumi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. In our previous work, we have already proposed an efficient fingerprint recognition algorithm using Phase-Only Correlation (POC), and developed commercial fingerprint verification units for access control applications. The use of Fourier phase information of fingerprint images makes it possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. This paper presents an idea of improving the performance of POC-based fingerprint matching by combining it with feature-based matching, where feature-based matching is introduced in order to improve recognition efficiency for images with nonlinear distortion. Experimental evaluation using two different types of fingerprint image databases demonstrates efficient recognition performance of the combination of the POC-based algorithm and the feature-based algorithm.

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

NASA Astrophysics Data System (ADS)

Lee, Sanggyun; Kim, Hyun-cheol; Im, Jungho

2018-05-01

We propose a waveform mixture algorithm to detect leads from CryoSat-2 data, which is novel and different from the existing threshold-based lead detection methods. The waveform mixture algorithm adopts the concept of spectral mixture analysis, which is widely used in the field of hyperspectral image analysis. This lead detection method was evaluated with high-resolution (250 m) MODIS images and showed comparable and promising performance in detecting leads when compared to the previous methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters (i.e., stack standard deviation, stack skewness, stack kurtosis, pulse peakiness, and backscatter σ0), as it directly uses L1B waveform data, unlike the existing threshold-based methods. Monthly lead fraction maps were produced by the waveform mixture algorithm, which shows interannual variability of recent sea ice cover during 2011-2016, excluding the summer season (i.e., June to September). We also compared the lead fraction maps to other lead fraction maps generated from previously published data sets, resulting in similar spatiotemporal patterns.

DNABIT Compress - Genome compression algorithm.

PubMed

Rajarajeswari, Pothuraju; Apparao, Allam

2011-01-22

Data compression is concerned with how information is organized in data. Efficient storage means removal of redundancy from the data being stored in the DNA molecule. Data compression algorithms remove redundancy and are used to understand biologically important molecules. We present a compression algorithm, "DNABIT Compress" for DNA sequences based on a novel algorithm of assigning binary bits for smaller segments of DNA bases to compress both repetitive and non repetitive DNA sequence. Our proposed algorithm achieves the best compression ratio for DNA sequences for larger genome. Significantly better compression results show that "DNABIT Compress" algorithm is the best among the remaining compression algorithms. While achieving the best compression ratios for DNA sequences (Genomes),our new DNABIT Compress algorithm significantly improves the running time of all previous DNA compression programs. Assigning binary bits (Unique BIT CODE) for (Exact Repeats, Reverse Repeats) fragments of DNA sequence is also a unique concept introduced in this algorithm for the first time in DNA compression. This proposed new algorithm could achieve the best compression ratio as much as 1.58 bits/bases where the existing best methods could not achieve a ratio less than 1.72 bits/bases.

Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification.

PubMed

Liu, Yanqiu; Lu, Huijuan; Yan, Ke; Xia, Haixia; An, Chunlin

2016-01-01

Embedding cost-sensitive factors into the classifiers increases the classification stability and reduces the classification costs for classifying high-scale, redundant, and imbalanced datasets, such as the gene expression data. In this study, we extend our previous work, that is, Dissimilar ELM (D-ELM), by introducing misclassification costs into the classifier. We name the proposed algorithm as the cost-sensitive D-ELM (CS-D-ELM). Furthermore, we embed rejection cost into the CS-D-ELM to increase the classification stability of the proposed algorithm. Experimental results show that the rejection cost embedded CS-D-ELM algorithm effectively reduces the average and overall cost of the classification process, while the classification accuracy still remains competitive. The proposed method can be extended to classification problems of other redundant and imbalanced data.

Probabilistic choice between symmetric disparities in motion stereo matching for a lateral navigation system

NASA Astrophysics Data System (ADS)

Ershov, Egor; Karnaukhov, Victor; Mozerov, Mikhail

2016-02-01

Two consecutive frames of a lateral navigation camera video sequence can be considered as an appropriate approximation to epipolar stereo. To overcome edge-aware inaccuracy caused by occlusion, we propose a model that matches the current frame to the next and to the previous ones. The positive disparity of matching to the previous frame has its symmetric negative disparity to the next frame. The proposed algorithm performs probabilistic choice for each matched pixel between the positive disparity and its symmetric disparity cost. A disparity map obtained by optimization over the cost volume composed of the proposed probabilistic choice is more accurate than the traditional left-to-right and right-to-left disparity maps cross-check. Also, our algorithm needs two times less computational operations per pixel than the cross-check technique. The effectiveness of our approach is demonstrated on synthetic data and real video sequences, with ground-truth value.

The Search for Effective Algorithms for Recovery from Loss of Separation

NASA Technical Reports Server (NTRS)

Butler, Ricky W.; Hagen, George E.; Maddalon, Jeffrey M.; Munoz, Cesar A.; Narawicz, Anthony J.

2012-01-01

Our previous work presented an approach for developing high confidence algorithms for recovering aircraft from loss of separation situations. The correctness theorems for the algorithms relied on several key assumptions, namely that state data for all local aircraft is perfectly known, that resolution maneuvers can be achieved instantaneously, and that all aircraft compute resolutions using exactly the same data. Experiments showed that these assumptions were adequate in cases where the aircraft are far away from losing separation, but are insufficient when the aircraft have already lost separation. This paper describes the results of this experimentation and proposes a new criteria specification for loss of separation recovery that preserves the formal safety properties of the previous criteria while overcoming some key limitations. Candidate algorithms that satisfy the new criteria are presented.

Identification of robust adaptation gene regulatory network parameters using an improved particle swarm optimization algorithm.

PubMed

Huang, X N; Ren, H P

2016-05-13

Robust adaptation is a critical ability of gene regulatory network (GRN) to survive in a fluctuating environment, which represents the system responding to an input stimulus rapidly and then returning to its pre-stimulus steady state timely. In this paper, the GRN is modeled using the Michaelis-Menten rate equations, which are highly nonlinear differential equations containing 12 undetermined parameters. The robust adaption is quantitatively described by two conflicting indices. To identify the parameter sets in order to confer the GRNs with robust adaptation is a multi-variable, multi-objective, and multi-peak optimization problem, which is difficult to acquire satisfactory solutions especially high-quality solutions. A new best-neighbor particle swarm optimization algorithm is proposed to implement this task. The proposed algorithm employs a Latin hypercube sampling method to generate the initial population. The particle crossover operation and elitist preservation strategy are also used in the proposed algorithm. The simulation results revealed that the proposed algorithm could identify multiple solutions in one time running. Moreover, it demonstrated a superior performance as compared to the previous methods in the sense of detecting more high-quality solutions within an acceptable time. The proposed methodology, owing to its universality and simplicity, is useful for providing the guidance to design GRN with superior robust adaptation.

Separation of irradiance and reflectance from observed color images by logarithmical nonlinear diffusion process

NASA Astrophysics Data System (ADS)

Saito, Takahiro; Takahashi, Hiromi; Komatsu, Takashi

2006-02-01

The Retinex theory was first proposed by Land, and deals with separation of irradiance from reflectance in an observed image. The separation problem is an ill-posed problem. Land and others proposed various Retinex separation algorithms. Recently, Kimmel and others proposed a variational framework that unifies the previous Retinex algorithms such as the Poisson-equation-type Retinex algorithms developed by Horn and others, and presented a Retinex separation algorithm with the time-evolution of a linear diffusion process. However, the Kimmel's separation algorithm cannot achieve physically rational separation, if true irradiance varies among color channels. To cope with this problem, we introduce a nonlinear diffusion process into the time-evolution. Moreover, as to its extension to color images, we present two approaches to treat color channels: the independent approach to treat each color channel separately and the collective approach to treat all color channels collectively. The latter approach outperforms the former. Furthermore, we apply our separation algorithm to a high quality chroma key in which before combining a foreground frame and a background frame into an output image a color of each pixel in the foreground frame are spatially adaptively corrected through transformation of the separated irradiance. Experiments demonstrate superiority of our separation algorithm over the Kimmel's separation algorithm.

Multi-period project portfolio selection under risk considerations and stochastic income

NASA Astrophysics Data System (ADS)

Tofighian, Ali Asghar; Moezzi, Hamid; Khakzar Barfuei, Morteza; Shafiee, Mahmood

2018-02-01

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each time period. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedure is presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solve this type of problems. The GA is enhanced by a new solution representation and well selected operators. It also is hybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposed algorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization (PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation results show the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, the proposed algorithm is wisely combined with PSO to improve the computing time considerably.

Data depth based clustering analysis

DOE PAGES

Jeong, Myeong -Hun; Cai, Yaping; Sullivan, Clair J.; ...

2016-01-01

Here, this paper proposes a new algorithm for identifying patterns within data, based on data depth. Such a clustering analysis has an enormous potential to discover previously unknown insights from existing data sets. Many clustering algorithms already exist for this purpose. However, most algorithms are not affine invariant. Therefore, they must operate with different parameters after the data sets are rotated, scaled, or translated. Further, most clustering algorithms, based on Euclidean distance, can be sensitive to noises because they have no global perspective. Parameter selection also significantly affects the clustering results of each algorithm. Unlike many existing clustering algorithms, themore » proposed algorithm, called data depth based clustering analysis (DBCA), is able to detect coherent clusters after the data sets are affine transformed without changing a parameter. It is also robust to noises because using data depth can measure centrality and outlyingness of the underlying data. Further, it can generate relatively stable clusters by varying the parameter. The experimental comparison with the leading state-of-the-art alternatives demonstrates that the proposed algorithm outperforms DBSCAN and HDBSCAN in terms of affine invariance, and exceeds or matches the ro-bustness to noises of DBSCAN or HDBSCAN. The robust-ness to parameter selection is also demonstrated through the case study of clustering twitter data.« less

A real-time and closed-loop control algorithm for cascaded multilevel inverter based on artificial neural network.

PubMed

Wang, Libing; Mao, Chengxiong; Wang, Dan; Lu, Jiming; Zhang, Junfeng; Chen, Xun

2014-01-01

In order to control the cascaded H-bridges (CHB) converter with staircase modulation strategy in a real-time manner, a real-time and closed-loop control algorithm based on artificial neural network (ANN) for three-phase CHB converter is proposed in this paper. It costs little computation time and memory. It has two steps. In the first step, hierarchical particle swarm optimizer with time-varying acceleration coefficient (HPSO-TVAC) algorithm is employed to minimize the total harmonic distortion (THD) and generate the optimal switching angles offline. In the second step, part of optimal switching angles are used to train an ANN and the well-designed ANN can generate optimal switching angles in a real-time manner. Compared with previous real-time algorithm, the proposed algorithm is suitable for a wider range of modulation index and results in a smaller THD and a lower calculation time. Furthermore, the well-designed ANN is embedded into a closed-loop control algorithm for CHB converter with variable direct voltage (DC) sources. Simulation results demonstrate that the proposed closed-loop control algorithm is able to quickly stabilize load voltage and minimize the line current's THD (<5%) when subjecting the DC sources disturbance or load disturbance. In real design stage, a switching angle pulse generation scheme is proposed and experiment results verify its correctness.

A Dual Frequency Carrier Phase Error Difference Checking Algorithm for the GNSS Compass.

PubMed

Liu, Shuo; Zhang, Lei; Li, Jian

2016-11-24

The performance of the Global Navigation Satellite System (GNSS) compass is related to the quality of carrier phase measurement. How to process the carrier phase error properly is important to improve the GNSS compass accuracy. In this work, we propose a dual frequency carrier phase error difference checking algorithm for the GNSS compass. The algorithm aims at eliminating large carrier phase error in dual frequency double differenced carrier phase measurement according to the error difference between two frequencies. The advantage of the proposed algorithm is that it does not need additional environment information and has a good performance on multiple large errors compared with previous research. The core of the proposed algorithm is removing the geographical distance from the dual frequency carrier phase measurement, then the carrier phase error is separated and detectable. We generate the Double Differenced Geometry-Free (DDGF) measurement according to the characteristic that the different frequency carrier phase measurements contain the same geometrical distance. Then, we propose the DDGF detection to detect the large carrier phase error difference between two frequencies. The theoretical performance of the proposed DDGF detection is analyzed. An open sky test, a manmade multipath test and an urban vehicle test were carried out to evaluate the performance of the proposed algorithm. The result shows that the proposed DDGF detection is able to detect large error in dual frequency carrier phase measurement by checking the error difference between two frequencies. After the DDGF detection, the accuracy of the baseline vector is improved in the GNSS compass.

Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions.

PubMed

Chen, Ke; Wang, Shihai

2011-01-01

Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smoothness, cluster, and manifold assumptions, together into account during boosting learning. In this paper, we propose a novel cost functional consisting of the margin cost on labeled data and the regularization penalty on unlabeled data based on three fundamental semi-supervised assumptions. Thus, minimizing our proposed cost functional with a greedy yet stagewise functional optimization procedure leads to a generic boosting framework for semi-supervised learning. Extensive experiments demonstrate that our algorithm yields favorite results for benchmark and real-world classification tasks in comparison to state-of-the-art semi-supervised learning algorithms, including newly developed boosting algorithms. Finally, we discuss relevant issues and relate our algorithm to the previous work.

Multiparty Quantum Key Agreement Based on Quantum Search Algorithm

PubMed Central

Cao, Hao; Ma, Wenping

2017-01-01

Quantum key agreement is an important topic that the shared key must be negotiated equally by all participants, and any nontrivial subset of participants cannot fully determine the shared key. To date, the embed modes of subkey in all the previously proposed quantum key agreement protocols are based on either BB84 or entangled states. The research of the quantum key agreement protocol based on quantum search algorithms is still blank. In this paper, on the basis of investigating the properties of quantum search algorithms, we propose the first quantum key agreement protocol whose embed mode of subkey is based on a quantum search algorithm known as Grover’s algorithm. A novel example of protocols with 5 – party is presented. The efficiency analysis shows that our protocol is prior to existing MQKA protocols. Furthermore it is secure against both external attack and internal attacks. PMID:28332610

Quantum adiabatic computation and adiabatic conditions

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

Wei Zhaohui; Ying Mingsheng

2007-08-15

Recently, quantum adiabatic computation has attracted more and more attention in the literature. It is a novel quantum computation model based on adiabatic approximation, and the analysis of a quantum adiabatic algorithm depends highly on the adiabatic conditions. However, it has been pointed out that the traditional adiabatic conditions are problematic. Thus, results obtained previously should be checked and sufficient adiabatic conditions applicable to adiabatic computation should be proposed. Based on a result of Tong et al. [Phys. Rev. Lett. 98, 150402 (2007)], we propose a modified adiabatic criterion which is more applicable to the analysis of adiabatic algorithms. Asmore » an example, we prove the validity of the local adiabatic search algorithm by employing our criterion.« less

Generalization Performance of Regularized Ranking With Multiscale Kernels.

PubMed

Zhou, Yicong; Chen, Hong; Lan, Rushi; Pan, Zhibin

2016-05-01

The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.

Semantic Drift in Espresso-style Bootstrapping: Graph-theoretic Analysis and Evaluation in Word Sense Disambiguation

NASA Astrophysics Data System (ADS)

Komachi, Mamoru; Kudo, Taku; Shimbo, Masashi; Matsumoto, Yuji

Bootstrapping has a tendency, called semantic drift, to select instances unrelated to the seed instances as the iteration proceeds. We demonstrate the semantic drift of Espresso-style bootstrapping has the same root as the topic drift of Kleinberg's HITS, using a simplified graph-based reformulation of bootstrapping. We confirm that two graph-based algorithms, the von Neumann kernels and the regularized Laplacian, can reduce the effect of semantic drift in the task of word sense disambiguation (WSD) on Senseval-3 English Lexical Sample Task. Proposed algorithms achieve superior performance to Espresso and previous graph-based WSD methods, even though the proposed algorithms have less parameters and are easy to calibrate.

DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach

NASA Astrophysics Data System (ADS)

Tchagang, Alain B.; Tewfik, Ahmed H.

2006-12-01

Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneous row-column clustering. Biclustering problems arise in DNA microarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth. When dealing with DNA microarray experimental data for example, the goal of biclustering algorithms is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this study, we develop novel biclustering algorithms using basic linear algebra and arithmetic tools. The proposed biclustering algorithms can be used to search for all biclusters with constant values, biclusters with constant values on rows, biclusters with constant values on columns, and biclusters with coherent values from a set of data in a timely manner and without solving any optimization problem. We also show how one of the proposed biclustering algorithms can be adapted to identify biclusters with coherent evolution. The algorithms developed in this study discover all valid biclusters of each type, while almost all previous biclustering approaches will miss some.

Designing algorithm visualization on mobile platform: The proposed guidelines

NASA Astrophysics Data System (ADS)

Supli, A. A.; Shiratuddin, N.

2017-09-01

This paper entails an ongoing study about the design guidelines of algorithm visualization (AV) on mobile platform, helping students learning data structures and algorithm (DSA) subject effectively. Our previous review indicated that design guidelines of AV on mobile platform are still few. Mostly, previous guidelines of AV are developed for AV on desktop and website platform. In fact, mobile learning has been proved to enhance engagement in learning circumstances, and thus effect student's performance. In addition, the researchers highly recommend including UI design and Interactivity in designing effective AV system. However, the discussions of these two aspects in previous AV design guidelines are not comprehensive. The UI design in this paper describes the arrangement of AV features in mobile environment, whereas interactivity is about the active learning strategy features based on learning experiences (how to engage learners). Thus, this study main objective is to propose design guidelines of AV on mobile platform (AVOMP) that entails comprehensively UI design and interactivity aspects. These guidelines are developed through content analysis and comparative analysis from various related studies. These guidelines are useful for AV designers to help them constructing AVOMP for various topics on DSA.

Multicast backup reprovisioning problem for Hamiltonian cycle-based protection on WDM networks

NASA Astrophysics Data System (ADS)

Din, Der-Rong; Huang, Jen-Shen

2014-03-01

As networks grow in size and complexity, the chance and the impact of failures increase dramatically. The pre-allocated backup resources cannot provide 100% protection guarantee when continuous failures occur in a network. In this paper, the multicast backup re-provisioning problem (MBRP) for Hamiltonian cycle (HC)-based protection on WDM networks for the link-failure case is studied. We focus on how to recover the protecting capabilities of Hamiltonian cycle against the subsequent link-failures on WDM networks for multicast transmissions, after recovering the multicast trees affected by the previous link-failure. Since this problem is a hard problem, an algorithm, which consists of several heuristics and a genetic algorithm (GA), is proposed to solve it. The simulation results of the proposed method are also given. Experimental results indicate that the proposed algorithm can solve this problem efficiently.

Bandwidth reduction for video-on-demand broadcasting using secondary content insertion

NASA Astrophysics Data System (ADS)

Golynski, Alexander; Lopez-Ortiz, Alejandro; Poirier, Guillaume; Quimper, Claude-Guy

2005-01-01

An optimal broadcasting scheme under the presence of secondary content (i.e. advertisements) is proposed. The proposed scheme works both for movies encoded in a Constant Bit Rate (CBR) or a Variable Bit Rate (VBR) format. It is shown experimentally that secondary content in movies can make Video-on-Demand (VoD) broadcasting systems more efficient. An efficient algorithm is given to compute the optimal broadcasting schedule with secondary content, which in particular significantly improves over the best previously known algorithm for computing the optimal broadcasting schedule without secondary content.

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

NASA Technical Reports Server (NTRS)

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

1975-01-01

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

Automated Discovery of Elementary Chemical Reaction Steps Using Freezing String and Berny Optimization Methods.

PubMed

Suleimanov, Yury V; Green, William H

2015-09-08

We present a simple protocol which allows fully automated discovery of elementary chemical reaction steps using in cooperation double- and single-ended transition-state optimization algorithms--the freezing string and Berny optimization methods, respectively. To demonstrate the utility of the proposed approach, the reactivity of several single-molecule systems of combustion and atmospheric chemistry importance is investigated. The proposed algorithm allowed us to detect without any human intervention not only "known" reaction pathways, manually detected in the previous studies, but also new, previously "unknown", reaction pathways which involve significant atom rearrangements. We believe that applying such a systematic approach to elementary reaction path finding will greatly accelerate the discovery of new chemistry and will lead to more accurate computer simulations of various chemical processes.

Human arm joints reconstruction algorithm in rehabilitation therapies assisted by end-effector robotic devices.

PubMed

Bertomeu-Motos, Arturo; Blanco, Andrea; Badesa, Francisco J; Barios, Juan A; Zollo, Loredana; Garcia-Aracil, Nicolas

2018-02-20

End-effector robots are commonly used in robot-assisted neuro-rehabilitation therapies for upper limbs where the patient's hand can be easily attached to a splint. Nevertheless, they are not able to estimate and control the kinematic configuration of the upper limb during the therapy. However, the Range of Motion (ROM) together with the clinical assessment scales offers a comprehensive assessment to the therapist. Our aim is to present a robust and stable kinematic reconstruction algorithm to accurately measure the upper limb joints using only an accelerometer placed onto the upper arm. The proposed algorithm is based on the inverse of the augmented Jaciobian as the algorithm (Papaleo, et al., Med Biol Eng Comput 53(9):815-28, 2015). However, the estimation of the elbow joint location is performed through the computation of the rotation measured by the accelerometer during the arm movement, making the algorithm more robust against shoulder movements. Furthermore, we present a method to compute the initial configuration of the upper limb necessary to start the integration method, a protocol to manually measure the upper arm and forearm lengths, and a shoulder position estimation. An optoelectronic system was used to test the accuracy of the proposed algorithm whilst healthy subjects were performing upper limb movements holding the end effector of the seven Degrees of Freedom (DoF) robot. In addition, the previous and the proposed algorithms were studied during a neuro-rehabilitation therapy assisted by the 'PUPArm' planar robot with three post-stroke patients. The proposed algorithm reports a Root Mean Square Error (RMSE) of 2.13cm in the elbow joint location and 1.89cm in the wrist joint location with high correlation. These errors lead to a RMSE about 3.5 degrees (mean of the seven joints) with high correlation in all the joints with respect to the real upper limb acquired through the optoelectronic system. Then, the estimation of the upper limb joints through both algorithms reveal an instability on the previous when shoulder movement appear due to the inevitable trunk compensation in post-stroke patients. The proposed algorithm is able to accurately estimate the human upper limb joints during a neuro-rehabilitation therapy assisted by end-effector robots. In addition, the implemented protocol can be followed in a clinical environment without optoelectronic systems using only one accelerometer attached in the upper arm. Thus, the ROM can be perfectly determined and could become an objective assessment parameter for a comprehensive assessment.

Script-independent text line segmentation in freestyle handwritten documents.

PubMed

Li, Yi; Zheng, Yefeng; Doermann, David; Jaeger, Stefan; Li, Yi

2008-08-01

Text line segmentation in freestyle handwritten documents remains an open document analysis problem. Curvilinear text lines and small gaps between neighboring text lines present a challenge to algorithms developed for machine printed or hand-printed documents. In this paper, we propose a novel approach based on density estimation and a state-of-the-art image segmentation technique, the level set method. From an input document image, we estimate a probability map, where each element represents the probability that the underlying pixel belongs to a text line. The level set method is then exploited to determine the boundary of neighboring text lines by evolving an initial estimate. Unlike connected component based methods ( [1], [2] for example), the proposed algorithm does not use any script-specific knowledge. Extensive quantitative experiments on freestyle handwritten documents with diverse scripts, such as Arabic, Chinese, Korean, and Hindi, demonstrate that our algorithm consistently outperforms previous methods [1]-[3]. Further experiments show the proposed algorithm is robust to scale change, rotation, and noise.

DNABIT Compress – Genome compression algorithm

PubMed Central

Rajarajeswari, Pothuraju; Apparao, Allam

2011-01-01

Data compression is concerned with how information is organized in data. Efficient storage means removal of redundancy from the data being stored in the DNA molecule. Data compression algorithms remove redundancy and are used to understand biologically important molecules. We present a compression algorithm, “DNABIT Compress” for DNA sequences based on a novel algorithm of assigning binary bits for smaller segments of DNA bases to compress both repetitive and non repetitive DNA sequence. Our proposed algorithm achieves the best compression ratio for DNA sequences for larger genome. Significantly better compression results show that “DNABIT Compress” algorithm is the best among the remaining compression algorithms. While achieving the best compression ratios for DNA sequences (Genomes),our new DNABIT Compress algorithm significantly improves the running time of all previous DNA compression programs. Assigning binary bits (Unique BIT CODE) for (Exact Repeats, Reverse Repeats) fragments of DNA sequence is also a unique concept introduced in this algorithm for the first time in DNA compression. This proposed new algorithm could achieve the best compression ratio as much as 1.58 bits/bases where the existing best methods could not achieve a ratio less than 1.72 bits/bases. PMID:21383923

Network immunization under limited budget using graph spectra

NASA Astrophysics Data System (ADS)

Zahedi, R.; Khansari, M.

2016-03-01

In this paper, we propose a new algorithm that minimizes the worst expected growth of an epidemic by reducing the size of the largest connected component (LCC) of the underlying contact network. The proposed algorithm is applicable to any level of available resources and, despite the greedy approaches of most immunization strategies, selects nodes simultaneously. In each iteration, the proposed method partitions the LCC into two groups. These are the best candidates for communities in that component, and the available resources are sufficient to separate them. Using Laplacian spectral partitioning, the proposed method performs community detection inference with a time complexity that rivals that of the best previous methods. Experiments show that our method outperforms targeted immunization approaches in both real and synthetic networks.

An item-oriented recommendation algorithm on cold-start problem

NASA Astrophysics Data System (ADS)

Qiu, Tian; Chen, Guang; Zhang, Zi-Ke; Zhou, Tao

2011-09-01

Based on a hybrid algorithm incorporating the heat conduction and probability spreading processes (Proc. Natl. Acad. Sci. U.S.A., 107 (2010) 4511), in this letter, we propose an improved method by introducing an item-oriented function, focusing on solving the dilemma of the recommendation accuracy between the cold and popular items. Differently from previous works, the present algorithm does not require any additional information (e.g., tags). Further experimental results obtained in three real datasets, RYM, Netflix and MovieLens, show that, compared with the original hybrid method, the proposed algorithm significantly enhances the recommendation accuracy of the cold items, while it keeps the recommendation accuracy of the overall and the popular items. This work might shed some light on both understanding and designing effective methods for long-tailed online applications of recommender systems.

Rapid code acquisition algorithms employing PN matched filters

NASA Technical Reports Server (NTRS)

Su, Yu T.

1988-01-01

The performance of four algorithms using pseudonoise matched filters (PNMFs), for direct-sequence spread-spectrum systems, is analyzed. They are: parallel search with fix dwell detector (PL-FDD), parallel search with sequential detector (PL-SD), parallel-serial search with fix dwell detector (PS-FDD), and parallel-serial search with sequential detector (PS-SD). The operation characteristic for each detector and the mean acquisition time for each algorithm are derived. All the algorithms are studied in conjunction with the noncoherent integration technique, which enables the system to operate in the presence of data modulation. Several previous proposals using PNMF are seen as special cases of the present algorithms.

Investigating prior probabilities in a multiple hypothesis test for use in space domain awareness

NASA Astrophysics Data System (ADS)

Hardy, Tyler J.; Cain, Stephen C.

2016-05-01

The goal of this research effort is to improve Space Domain Awareness (SDA) capabilities of current telescope systems through improved detection algorithms. Ground-based optical SDA telescopes are often spatially under-sampled, or aliased. This fact negatively impacts the detection performance of traditionally proposed binary and correlation-based detection algorithms. A Multiple Hypothesis Test (MHT) algorithm has been previously developed to mitigate the effects of spatial aliasing. This is done by testing potential Resident Space Objects (RSOs) against several sub-pixel shifted Point Spread Functions (PSFs). A MHT has been shown to increase detection performance for the same false alarm rate. In this paper, the assumption of a priori probability used in a MHT algorithm is investigated. First, an analysis of the pixel decision space is completed to determine alternate hypothesis prior probabilities. These probabilities are then implemented into a MHT algorithm, and the algorithm is then tested against previous MHT algorithms using simulated RSO data. Results are reported with Receiver Operating Characteristic (ROC) curves and probability of detection, Pd, analysis.

A color gamut description algorithm for liquid crystal displays in CIELAB space.

PubMed

Sun, Bangyong; Liu, Han; Li, Wenli; Zhou, Shisheng

2014-01-01

Because the accuracy of gamut boundary description is significant for gamut mapping process, a gamut boundary calculating method for LCD monitors is proposed in this paper. Within most of the previous gamut boundary calculation algorithms, the gamut boundary is calculated in CIELAB space directly, and part of inside-gamut points are mistaken for the boundary points. While, in the new proposed algorithm, the points on the surface of RGB cube are selected as the boundary points, and then converted and described in CIELAB color space. Thus, in our algorithm, the true gamut boundary points are found and a more accurate gamut boundary is described. In experiment, a Toshiba LCD monitor's 3D CIELAB gamut for evaluation is firstly described which has regular-shaped outer surface, and then two 2D gamut boundaries ( CIE-a*b* boundary and CIE-C*L* boundary) are calculated which are often used in gamut mapping process. When our algorithm is compared with several famous gamut calculating algorithms, the gamut volumes are very close, which indicates that our algorithm's accuracy is precise and acceptable.

A Color Gamut Description Algorithm for Liquid Crystal Displays in CIELAB Space

PubMed Central

Sun, Bangyong; Liu, Han; Li, Wenli; Zhou, Shisheng

2014-01-01

Because the accuracy of gamut boundary description is significant for gamut mapping process, a gamut boundary calculating method for LCD monitors is proposed in this paper. Within most of the previous gamut boundary calculation algorithms, the gamut boundary is calculated in CIELAB space directly, and part of inside-gamut points are mistaken for the boundary points. While, in the new proposed algorithm, the points on the surface of RGB cube are selected as the boundary points, and then converted and described in CIELAB color space. Thus, in our algorithm, the true gamut boundary points are found and a more accurate gamut boundary is described. In experiment, a Toshiba LCD monitor's 3D CIELAB gamut for evaluation is firstly described which has regular-shaped outer surface, and then two 2D gamut boundaries ( CIE-a*b* boundary and CIE-C*L* boundary) are calculated which are often used in gamut mapping process. When our algorithm is compared with several famous gamut calculating algorithms, the gamut volumes are very close, which indicates that our algorithm's accuracy is precise and acceptable. PMID:24892068

Mining connected global and local dense subgraphs for bigdata

NASA Astrophysics Data System (ADS)

Wu, Bo; Shen, Haiying

2016-01-01

The problem of discovering connected dense subgraphs of natural graphs is important in data analysis. Discovering dense subgraphs that do not contain denser subgraphs or are not contained in denser subgraphs (called significant dense subgraphs) is also critical for wide-ranging applications. In spite of many works on discovering dense subgraphs, there are no algorithms that can guarantee the connectivity of the returned subgraphs or discover significant dense subgraphs. Hence, in this paper, we define two subgraph discovery problems to discover connected and significant dense subgraphs, propose polynomial-time algorithms and theoretically prove their validity. We also propose an algorithm to further improve the time and space efficiency of our basic algorithm for discovering significant dense subgraphs in big data by taking advantage of the unique features of large natural graphs. In the experiments, we use massive natural graphs to evaluate our algorithms in comparison with previous algorithms. The experimental results show the effectiveness of our algorithms for the two problems and their efficiency. This work is also the first that reveals the physical significance of significant dense subgraphs in natural graphs from different domains.

Terminal iterative learning control based station stop control of a train

NASA Astrophysics Data System (ADS)

Hou, Zhongsheng; Wang, Yi; Yin, Chenkun; Tang, Tao

2011-07-01

The terminal iterative learning control (TILC) method is introduced for the first time into the field of train station stop control and three TILC-based algorithms are proposed in this study. The TILC-based train station stop control approach utilises the terminal stop position error in previous braking process to update the current control profile. The initial braking position, or the braking force, or their combination is chosen as the control input, and corresponding learning law is developed. The terminal stop position error of each algorithm is guaranteed to converge to a small region related with the initial offset of braking position with rigorous analysis. The validity of the proposed algorithms is verified by illustrative numerical examples.

Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

PubMed

Liu, Bo; Tian, Meihong; Zhang, Chunhua; Li, Xiangtao

2015-04-01

Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Tracking control of concentration profiles in a fed-batch bioreactor using a linear algebra methodology.

PubMed

Rómoli, Santiago; Serrano, Mario Emanuel; Ortiz, Oscar Alberto; Vega, Jorge Rubén; Eduardo Scaglia, Gustavo Juan

2015-07-01

Based on a linear algebra approach, this paper aims at developing a novel control law able to track reference profiles that were previously-determined in the literature. A main advantage of the proposed strategy is that the control actions are obtained by solving a system of linear equations. The optimal controller parameters are selected through Monte Carlo Randomized Algorithm in order to minimize a proposed cost index. The controller performance is evaluated through several tests, and compared with other controller reported in the literature. Finally, a Monte Carlo Randomized Algorithm is conducted to assess the performance of the proposed controller. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A vertical handoff decision algorithm based on ARMA prediction model

NASA Astrophysics Data System (ADS)

Li, Ru; Shen, Jiao; Chen, Jun; Liu, Qiuhuan

2012-01-01

With the development of computer technology and the increasing demand for mobile communications, the next generation wireless networks will be composed of various wireless networks (e.g., WiMAX and WiFi). Vertical handoff is a key technology of next generation wireless networks. During the vertical handoff procedure, handoff decision is a crucial issue for an efficient mobility. Based on auto regression moving average (ARMA) prediction model, we propose a vertical handoff decision algorithm, which aims to improve the performance of vertical handoff and avoid unnecessary handoff. Based on the current received signal strength (RSS) and the previous RSS, the proposed approach adopt ARMA model to predict the next RSS. And then according to the predicted RSS to determine whether trigger the link layer triggering event and complete vertical handoff. The simulation results indicate that the proposed algorithm outperforms the RSS-based scheme with a threshold in the performance of handoff and the number of handoff.

Image quality classification for DR screening using deep learning.

PubMed

FengLi Yu; Jing Sun; Annan Li; Jun Cheng; Cheng Wan; Jiang Liu

2017-07-01

The quality of input images significantly affects the outcome of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that only consider simple low-level features such as hand-crafted geometric and structural features, in this paper we propose a novel method for retinal image quality classification (IQC) that performs computational algorithms imitating the working of the human visual system. The proposed algorithm combines unsupervised features from saliency map and supervised features coming from convolutional neural networks (CNN), which are fed to an SVM to automatically detect high quality vs poor quality retinal fundus images. We demonstrate the superior performance of our proposed algorithm on a large retinal fundus image dataset and the method could achieve higher accuracy than other methods. Although retinal images are used in this study, the methodology is applicable to the image quality assessment and enhancement of other types of medical images.

Video-based depression detection using local Curvelet binary patterns in pairwise orthogonal planes.

PubMed

Pampouchidou, Anastasia; Marias, Kostas; Tsiknakis, Manolis; Simos, Panagiotis; Fan Yang; Lemaitre, Guillaume; Meriaudeau, Fabrice

2016-08-01

Depression is an increasingly prevalent mood disorder. This is the reason why the field of computer-based depression assessment has been gaining the attention of the research community during the past couple of years. The present work proposes two algorithms for depression detection, one Frame-based and the second Video-based, both employing Curvelet transform and Local Binary Patterns. The main advantage of these methods is that they have significantly lower computational requirements, as the extracted features are of very low dimensionality. This is achieved by modifying the previously proposed algorithm which considers Three-Orthogonal-Planes, to only Pairwise-Orthogonal-Planes. Performance of the algorithms was tested on the benchmark dataset provided by the Audio/Visual Emotion Challenge 2014, with the person-specific system achieving 97.6% classification accuracy, and the person-independed one yielding promising preliminary results of 74.5% accuracy. The paper concludes with open issues, proposed solutions, and future plans.

A parallelizable real-time motion tracking algorithm with applications to ultrasonic strain imaging.

PubMed

Jiang, J; Hall, T J

2007-07-07

Ultrasound-based mechanical strain imaging systems utilize signals from conventional diagnostic ultrasound systems to image tissue elasticity contrast that provides new diagnostically valuable information. Previous works (Hall et al 2003 Ultrasound Med. Biol. 29 427, Zhu and Hall 2002 Ultrason. Imaging 24 161) demonstrated that uniaxial deformation with minimal elevation motion is preferred for breast strain imaging and real-time strain image feedback to operators is important to accomplish this goal. The work reported here enhances the real-time speckle tracking algorithm with two significant modifications. One fundamental change is that the proposed algorithm is a column-based algorithm (a column is defined by a line of data parallel to the ultrasound beam direction, i.e. an A-line), as opposed to a row-based algorithm (a row is defined by a line of data perpendicular to the ultrasound beam direction). Then, displacement estimates from its adjacent columns provide good guidance for motion tracking in a significantly reduced search region to reduce computational cost. Consequently, the process of displacement estimation can be naturally split into at least two separated tasks, computed in parallel, propagating outward from the center of the region of interest (ROI). The proposed algorithm has been implemented and optimized in a Windows system as a stand-alone ANSI C++ program. Results of preliminary tests, using numerical and tissue-mimicking phantoms, and in vivo tissue data, suggest that high contrast strain images can be consistently obtained with frame rates (10 frames s(-1)) that exceed our previous methods.

Arrhythmia Evaluation in Wearable ECG Devices

PubMed Central

Sadrawi, Muammar; Lin, Chien-Hung; Hsieh, Yita; Kuo, Chia-Chun; Chien, Jen Chien; Haraikawa, Koichi; Abbod, Maysam F.; Shieh, Jiann-Shing

2017-01-01

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation. PMID:29068369

An efficient CU partition algorithm for HEVC based on improved Sobel operator

NASA Astrophysics Data System (ADS)

Sun, Xuebin; Chen, Xiaodong; Xu, Yong; Sun, Gang; Yang, Yunsheng

2018-04-01

As the latest video coding standard, High Efficiency Video Coding (HEVC) achieves over 50% bit rate reduction with similar video quality compared with previous standards H.264/AVC. However, the higher compression efficiency is attained at the cost of significantly increasing computational load. In order to reduce the complexity, this paper proposes a fast coding unit (CU) partition technique to speed up the process. To detect the edge features of each CU, a more accurate improved Sobel filtering is developed and performed By analyzing the textural features of CU, an early CU splitting termination is proposed to decide whether a CU should be decomposed into four lower-dimensions CUs or not. Compared with the reference software HM16.7, experimental results indicate the proposed algorithm can lessen the encoding time up to 44.09% on average, with a negligible bit rate increase of 0.24%, and quality losses lower 0.03 dB, respectively. In addition, the proposed algorithm gets a better trade-off between complexity and rate-distortion among the other proposed works.

Robust rotational-velocity-Verlet integration methods.

PubMed

Rozmanov, Dmitri; Kusalik, Peter G

2010-05-01

Two rotational integration algorithms for rigid-body dynamics are proposed in velocity-Verlet formulation. The first method uses quaternion dynamics and was derived from the original rotational leap-frog method by Svanberg [Mol. Phys. 92, 1085 (1997)]; it produces time consistent positions and momenta. The second method is also formulated in terms of quaternions but it is not quaternion specific and can be easily adapted for any other orientational representation. Both the methods are tested extensively and compared to existing rotational integrators. The proposed integrators demonstrated performance at least at the level of previously reported rotational algorithms. The choice of simulation parameters is also discussed.

Robust rotational-velocity-Verlet integration methods

NASA Astrophysics Data System (ADS)

Rozmanov, Dmitri; Kusalik, Peter G.

2010-05-01

Two rotational integration algorithms for rigid-body dynamics are proposed in velocity-Verlet formulation. The first method uses quaternion dynamics and was derived from the original rotational leap-frog method by Svanberg [Mol. Phys. 92, 1085 (1997)]; it produces time consistent positions and momenta. The second method is also formulated in terms of quaternions but it is not quaternion specific and can be easily adapted for any other orientational representation. Both the methods are tested extensively and compared to existing rotational integrators. The proposed integrators demonstrated performance at least at the level of previously reported rotational algorithms. The choice of simulation parameters is also discussed.

An Emergency Packet Forwarding Scheme for V2V Communication Networks

PubMed Central

2014-01-01

This paper proposes an effective warning message forwarding scheme for cooperative collision avoidance. In an emergency situation, an emergency-detecting vehicle warns the neighbor vehicles via an emergency warning message. Since the transmission range is limited, the warning message is broadcast in a multihop manner. Broadcast packets lead two challenges to forward the warning message in the vehicular network: redundancy of warning messages and competition with nonemergency transmissions. In this paper, we study and address the two major challenges to achieve low latency in delivery of the warning message. To reduce the intervehicle latency and end-to-end latency, which cause chain collisions, we propose a two-way intelligent broadcasting method with an adaptable distance-dependent backoff algorithm. Considering locations of vehicles, the proposed algorithm controls the broadcast of a warning message to reduce redundant EWM messages and adaptively chooses the contention window to compete with nonemergency transmission. Via simulations, we show that our proposed algorithm reduces the probability of rear-end crashes by 70% compared to previous algorithms by reducing the intervehicle delay. We also show that the end-to-end propagation delay of the warning message is reduced by 55%. PMID:25054181

An improved genetic algorithm for multidimensional optimization of precedence-constrained production planning and scheduling

NASA Astrophysics Data System (ADS)

Dao, Son Duy; Abhary, Kazem; Marian, Romeo

2017-06-01

Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial, NP-hard problem, for which no polynomial time algorithm is known to produce an optimal result on a random graph. In this paper, the further development of Genetic Algorithm (GA) for this integrated optimization is presented. Because of the dynamic nature of the problem, the size of its solution is variable. To deal with this variability and find an optimal solution to the problem, GA with new features in chromosome encoding, crossover, mutation, selection as well as algorithm structure is developed herein. With the proposed structure, the proposed GA is able to "learn" from its experience. Robustness of the proposed GA is demonstrated by a complex numerical example in which performance of the proposed GA is compared with those of three commercial optimization solvers.

Extraction and classification of 3D objects from volumetric CT data

NASA Astrophysics Data System (ADS)

Song, Samuel M.; Kwon, Junghyun; Ely, Austin; Enyeart, John; Johnson, Chad; Lee, Jongkyu; Kim, Namho; Boyd, Douglas P.

2016-05-01

We propose an Automatic Threat Detection (ATD) algorithm for Explosive Detection System (EDS) using our multistage Segmentation Carving (SC) followed by Support Vector Machine (SVM) classifier. The multi-stage Segmentation and Carving (SC) step extracts all suspect 3-D objects. The feature vector is then constructed for all extracted objects and the feature vector is classified by the Support Vector Machine (SVM) previously learned using a set of ground truth threat and benign objects. The learned SVM classifier has shown to be effective in classification of different types of threat materials. The proposed ATD algorithm robustly deals with CT data that are prone to artifacts due to scatter, beam hardening as well as other systematic idiosyncrasies of the CT data. Furthermore, the proposed ATD algorithm is amenable for including newly emerging threat materials as well as for accommodating data from newly developing sensor technologies. Efficacy of the proposed ATD algorithm with the SVM classifier is demonstrated by the Receiver Operating Characteristics (ROC) curve that relates Probability of Detection (PD) as a function of Probability of False Alarm (PFA). The tests performed using CT data of passenger bags shows excellent performance characteristics.

Small target detection using objectness and saliency

NASA Astrophysics Data System (ADS)

Zhang, Naiwen; Xiao, Yang; Fang, Zhiwen; Yang, Jian; Wang, Li; Li, Tao

2017-10-01

We are motived by the need for generic object detection algorithm which achieves high recall for small targets in complex scenes with acceptable computational efficiency. We propose a novel object detection algorithm, which has high localization quality with acceptable computational cost. Firstly, we obtain the objectness map as in BING[1] and use NMS to get the top N points. Then, k-means algorithm is used to cluster them into K classes according to their location. We set the center points of the K classes as seed points. For each seed point, an object potential region is extracted. Finally, a fast salient object detection algorithm[2] is applied to the object potential regions to highlight objectlike pixels, and a series of efficient post-processing operations are proposed to locate the targets. Our method runs at 5 FPS on 1000*1000 images, and significantly outperforms previous methods on small targets in cluttered background.

Differential sampling for fast frequency acquisition via adaptive extended least squares algorithm

NASA Technical Reports Server (NTRS)

Kumar, Rajendra

1987-01-01

This paper presents a differential signal model along with appropriate sampling techinques for least squares estimation of the frequency and frequency derivatives and possibly the phase and amplitude of a sinusoid received in the presence of noise. The proposed algorithm is recursive in mesurements and thus the computational requirement increases only linearly with the number of measurements. The dimension of the state vector in the proposed algorithm does not depend upon the number of measurements and is quite small, typically around four. This is an advantage when compared to previous algorithms wherein the dimension of the state vector increases monotonically with the product of the frequency uncertainty and the observation period. Such a computational simplification may possibly result in some loss of optimality. However, by applying the sampling techniques of the paper such a possible loss in optimality can made small.

Underground localization using dual magnetic field sequence measurement and pose graph SLAM for directional drilling

NASA Astrophysics Data System (ADS)

Park, Byeolteo; Myung, Hyun

2014-12-01

With the development of unconventional gas, the technology of directional drilling has become more advanced. Underground localization is the key technique of directional drilling for real-time path following and system control. However, there are problems such as vibration, disconnection with external infrastructure, and magnetic field distortion. Conventional methods cannot solve these problems in real time or in various environments. In this paper, a novel underground localization algorithm using a re-measurement of the sequence of the magnetic field and pose graph SLAM (simultaneous localization and mapping) is introduced. The proposed algorithm exploits the property of the drilling system that the body passes through the previous pass. By comparing the recorded measurement from one magnetic sensor and the current re-measurement from another magnetic sensor, the proposed algorithm predicts the pose of the drilling system. The performance of the algorithm is validated through simulations and experiments.

Algorithm for AEEG data selection leading to wireless and long term epilepsy monitoring.

PubMed

Casson, Alexander J; Yates, David C; Patel, Shyam; Rodriguez-Villegas, Esther

2007-01-01

High quality, wireless ambulatory EEG (AEEG) systems that can operate over extended periods of time are not currently feasible due to the high power consumption of wireless transmitters. Previous work has thus proposed data reduction by only transmitting sections of data that contain candidate epileptic activity. This paper investigates algorithms by which this data selection can be carried out. It is essential that the algorithm is low power and that all possible features are identified, even at the expense of more false detections. Given this, a brief review of spike detection algorithms is carried out with a view to using these algorithms to drive the data reduction process. A CWT based algorithm is deemed most suitable for use and an algorithm is described in detail and its performance tested. It is found that over 90% of expert marked spikes are identified whilst giving a 40% reduction in the amount of data to be transmitted and analysed. The performance varies with the recording duration in response to each detection and this effect is also investigated. The proposed algorithm will form the basis of new a AEEG system that allows wireless and longer term epilepsy monitoring.

An Adaptive Reputation-Based Algorithm for Grid Virtual Organization Formation

NASA Astrophysics Data System (ADS)

Cui, Yongrui; Li, Mingchu; Ren, Yizhi; Sakurai, Kouichi

A novel adaptive reputation-based virtual organization formation is proposed. It restrains the bad performers effectively based on the consideration of the global experience of the evaluator and evaluates the direct trust relation between two grid nodes accurately by consulting the previous trust value rationally. It also consults and improves the reputation evaluation process in PathTrust model by taking account of the inter-organizational trust relationship and combines it with direct and recommended trust in a weighted way, which makes the algorithm more robust against collusion attacks. Additionally, the proposed algorithm considers the perspective of the VO creator and takes required VO services as one of the most important fine-grained evaluation criterion, which makes the algorithm more suitable for constructing VOs in grid environments that include autonomous organizations. Simulation results show that our algorithm restrains the bad performers and resists against fake transaction attacks and badmouth attacks effectively. It provides a clear advantage in the design of a VO infrastructure.

Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering.

PubMed

Chah, E; Hok, V; Della-Chiesa, A; Miller, J J H; O'Mara, S M; Reilly, R B

2011-02-01

This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.A correction was made to this article on 22 February 2011. The spacing of the title was amended on the abstract page. No changes were made to the article PDF and the print version was unaffected.

Using landscape topology to compare continuous metaheuristics: a framework and case study on EDAs and ridge structure.

PubMed

Morgan, R; Gallagher, M

2012-01-01

In this paper we extend a previously proposed randomized landscape generator in combination with a comparative experimental methodology to study the behavior of continuous metaheuristic optimization algorithms. In particular, we generate two-dimensional landscapes with parameterized, linear ridge structure, and perform pairwise comparisons of algorithms to gain insight into what kind of problems are easy and difficult for one algorithm instance relative to another. We apply this methodology to investigate the specific issue of explicit dependency modeling in simple continuous estimation of distribution algorithms. Experimental results reveal specific examples of landscapes (with certain identifiable features) where dependency modeling is useful, harmful, or has little impact on mean algorithm performance. Heat maps are used to compare algorithm performance over a large number of landscape instances and algorithm trials. Finally, we perform a meta-search in the landscape parameter space to find landscapes which maximize the performance between algorithms. The results are related to some previous intuition about the behavior of these algorithms, but at the same time lead to new insights into the relationship between dependency modeling in EDAs and the structure of the problem landscape. The landscape generator and overall methodology are quite general and extendable and can be used to examine specific features of other algorithms.

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

A fast and automatic fusion algorithm for unregistered multi-exposure image sequence

NASA Astrophysics Data System (ADS)

Liu, Yan; Yu, Feihong

2014-09-01

Human visual system (HVS) can visualize all the brightness levels of the scene through visual adaptation. However, the dynamic range of most commercial digital cameras and display devices are smaller than the dynamic range of human eye. This implies low dynamic range (LDR) images captured by normal digital camera may lose image details. We propose an efficient approach to high dynamic (HDR) image fusion that copes with image displacement and image blur degradation in a computationally efficient manner, which is suitable for implementation on mobile devices. The various image registration algorithms proposed in the previous literatures are unable to meet the efficiency and performance requirements in the application of mobile devices. In this paper, we selected Oriented Brief (ORB) detector to extract local image structures. The descriptor selected in multi-exposure image fusion algorithm has to be fast and robust to illumination variations and geometric deformations. ORB descriptor is the best candidate in our algorithm. Further, we perform an improved RANdom Sample Consensus (RANSAC) algorithm to reject incorrect matches. For the fusion of images, a new approach based on Stationary Wavelet Transform (SWT) is used. The experimental results demonstrate that the proposed algorithm generates high quality images at low computational cost. Comparisons with a number of other feature matching methods show that our method gets better performance.

Noncontact Sleep Study by Multi-Modal Sensor Fusion.

PubMed

Chung, Ku-Young; Song, Kwangsub; Shin, Kangsoo; Sohn, Jinho; Cho, Seok Hyun; Chang, Joon-Hyuk

2017-07-21

Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner.

Noncontact Sleep Study by Multi-Modal Sensor Fusion

PubMed Central

Chung, Ku-young; Song, Kwangsub; Shin, Kangsoo; Sohn, Jinho; Cho, Seok Hyun; Chang, Joon-Hyuk

2017-01-01

Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner. PMID:28753994

Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm.

PubMed

Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

2016-10-01

Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.

Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm

PubMed Central

Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

2016-01-01

Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses. PMID:27706086

3D Protein structure prediction with genetic tabu search algorithm

PubMed Central

2010-01-01

Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256

Utility-preserving anonymization for health data publishing.

PubMed

Lee, Hyukki; Kim, Soohyung; Kim, Jong Wook; Chung, Yon Dohn

2017-07-11

Publishing raw electronic health records (EHRs) may be considered as a breach of the privacy of individuals because they usually contain sensitive information. A common practice for the privacy-preserving data publishing is to anonymize the data before publishing, and thus satisfy privacy models such as k-anonymity. Among various anonymization techniques, generalization is the most commonly used in medical/health data processing. Generalization inevitably causes information loss, and thus, various methods have been proposed to reduce information loss. However, existing generalization-based data anonymization methods cannot avoid excessive information loss and preserve data utility. We propose a utility-preserving anonymization for privacy preserving data publishing (PPDP). To preserve data utility, the proposed method comprises three parts: (1) utility-preserving model, (2) counterfeit record insertion, (3) catalog of the counterfeit records. We also propose an anonymization algorithm using the proposed method. Our anonymization algorithm applies full-domain generalization algorithm. We evaluate our method in comparison with existence method on two aspects, information loss measured through various quality metrics and error rate of analysis result. With all different types of quality metrics, our proposed method show the lower information loss than the existing method. In the real-world EHRs analysis, analysis results show small portion of error between the anonymized data through the proposed method and original data. We propose a new utility-preserving anonymization method and an anonymization algorithm using the proposed method. Through experiments on various datasets, we show that the utility of EHRs anonymized by the proposed method is significantly better than those anonymized by previous approaches.

Use of a New Set of Linguistic Features to Improve Automatic Assessment of Text Readability

ERIC Educational Resources Information Center

Yoshimi, Takehiko; Kotani, Katsunori; Isahara, Hitoshi

2012-01-01

The present paper proposes and evaluates a readability assessment method designed for Japanese learners of EFL (English as a foreign language). The proposed readability assessment method is constructed by a regression algorithm using a new set of linguistic features that were employed separately in previous studies. The results showed that the…

Geographic Gossip: Efficient Averaging for Sensor Networks

NASA Astrophysics Data System (ADS)

Dimakis, Alexandros D. G.; Sarwate, Anand D.; Wainwright, Martin J.

Gossip algorithms for distributed computation are attractive due to their simplicity, distributed nature, and robustness in noisy and uncertain environments. However, using standard gossip algorithms can lead to a significant waste in energy by repeatedly recirculating redundant information. For realistic sensor network model topologies like grids and random geometric graphs, the inefficiency of gossip schemes is related to the slow mixing times of random walks on the communication graph. We propose and analyze an alternative gossiping scheme that exploits geographic information. By utilizing geographic routing combined with a simple resampling method, we demonstrate substantial gains over previously proposed gossip protocols. For regular graphs such as the ring or grid, our algorithm improves standard gossip by factors of $n$ and $\\sqrt{n}$ respectively. For the more challenging case of random geometric graphs, our algorithm computes the true average to accuracy $\\epsilon$ using $O(\\frac{n^{1.5}}{\\sqrt{\\log n}} \\log \\epsilon^{-1})$ radio transmissions, which yields a $\\sqrt{\\frac{n}{\\log n}}$ factor improvement over standard gossip algorithms. We illustrate these theoretical results with experimental comparisons between our algorithm and standard methods as applied to various classes of random fields.

Efficient Deterministic Finite Automata Minimization Based on Backward Depth Information.

PubMed

Liu, Desheng; Huang, Zhiping; Zhang, Yimeng; Guo, Xiaojun; Su, Shaojing

2016-01-01

Obtaining a minimal automaton is a fundamental issue in the theory and practical implementation of deterministic finite automatons (DFAs). A minimization algorithm is presented in this paper that consists of two main phases. In the first phase, the backward depth information is built, and the state set of the DFA is partitioned into many blocks. In the second phase, the state set is refined using a hash table. The minimization algorithm has a lower time complexity O(n) than a naive comparison of transitions O(n2). Few states need to be refined by the hash table, because most states have been partitioned by the backward depth information in the coarse partition. This method achieves greater generality than previous methods because building the backward depth information is independent of the topological complexity of the DFA. The proposed algorithm can be applied not only to the minimization of acyclic automata or simple cyclic automata, but also to automata with high topological complexity. Overall, the proposal has three advantages: lower time complexity, greater generality, and scalability. A comparison to Hopcroft's algorithm demonstrates experimentally that the algorithm runs faster than traditional algorithms.

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

Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation.

PubMed

al-Rifaie, Mohammad Majid; Aber, Ahmed; Hemanth, Duraiswamy Jude

2015-12-01

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence-learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration-dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.

Motion and positional error correction for cone beam 3D-reconstruction with mobile C-arms.

PubMed

Bodensteiner, C; Darolti, C; Schumacher, H; Matthäus, L; Schweikard, A

2007-01-01

CT-images acquired by mobile C-arm devices can contain artefacts caused by positioning errors. We propose a data driven method based on iterative 3D-reconstruction and 2D/3D-registration to correct projection data inconsistencies. With a 2D/3D-registration algorithm, transformations are computed to align the acquired projection images to a previously reconstructed volume. In an iterative procedure, the reconstruction algorithm uses the results of the registration step. This algorithm also reduces small motion artefacts within 3D-reconstructions. Experiments with simulated projections from real patient data show the feasibility of the proposed method. In addition, experiments with real projection data acquired with an experimental robotised C-arm device have been performed with promising results.

Improving the efficiency of molecular replacement by utilizing a new iterative transform phasing algorithm

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

He, Hongxing; Fang, Hengrui; Miller, Mitchell D.

2016-07-15

An iterative transform algorithm is proposed to improve the conventional molecular-replacement method for solving the phase problem in X-ray crystallography. Several examples of successful trial calculations carried out with real diffraction data are presented. An iterative transform method proposed previously for direct phasing of high-solvent-content protein crystals is employed for enhancing the molecular-replacement (MR) algorithm in protein crystallography. Target structures that are resistant to conventional MR due to insufficient similarity between the template and target structures might be tractable with this modified phasing method. Trial calculations involving three different structures are described to test and illustrate the methodology. The relationshipmore » of the approach to PHENIX Phaser-MR and MR-Rosetta is discussed.« less

Bayesian reconstruction of projection reconstruction NMR (PR-NMR).

PubMed

Yoon, Ji Won

2014-11-01

Projection reconstruction nuclear magnetic resonance (PR-NMR) is a technique for generating multidimensional NMR spectra. A small number of projections from lower-dimensional NMR spectra are used to reconstruct the multidimensional NMR spectra. In our previous work, it was shown that multidimensional NMR spectra are efficiently reconstructed using peak-by-peak based reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. We propose an extended and generalized RJMCMC algorithm replacing a simple linear model with a linear mixed model to reconstruct close NMR spectra into true spectra. This statistical method generates samples in a Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of a protein HasA. Copyright © 2014 Elsevier Ltd. All rights reserved.

Contourlet domain multiband deblurring based on color correlation for fluid lens cameras.

PubMed

Tzeng, Jack; Liu, Chun-Chen; Nguyen, Truong Q

2010-10-01

Due to the novel fluid optics, unique image processing challenges are presented by the fluidic lens camera system. Developed for surgical applications, unique properties, such as no moving parts while zooming and better miniaturization than traditional glass optics, are advantages of the fluid lens. Despite these abilities, sharp color planes and blurred color planes are created by the nonuniform reaction of the liquid lens to different color wavelengths. Severe axial color aberrations are caused by this reaction. In order to deblur color images without estimating a point spread function, a contourlet filter bank system is proposed. Information from sharp color planes is used by this multiband deblurring method to improve blurred color planes. Compared to traditional Lucy-Richardson and Wiener deconvolution algorithms, significantly improved sharpness and reduced ghosting artifacts are produced by a previous wavelet-based method. Directional filtering is used by the proposed contourlet-based system to adjust to the contours of the image. An image is produced by the proposed method which has a similar level of sharpness to the previous wavelet-based method and has fewer ghosting artifacts. Conditions for when this algorithm will reduce the mean squared error are analyzed. While improving the blue color plane by using information from the green color plane is the primary focus of this paper, these methods could be adjusted to improve the red color plane. Many multiband systems such as global mapping, infrared imaging, and computer assisted surgery are natural extensions of this work. This information sharing algorithm is beneficial to any image set with high edge correlation. Improved results in the areas of deblurring, noise reduction, and resolution enhancement can be produced by the proposed algorithm.

Hierarchical minutiae matching for fingerprint and palmprint identification.

PubMed

Chen, Fanglin; Huang, Xiaolin; Zhou, Jie

2013-12-01

Fingerprints and palmprints are the most common authentic biometrics for personal identification, especially for forensic security. Previous research have been proposed to speed up the searching process in fingerprint and palmprint identification systems, such as those based on classification or indexing, in which the deterioration of identification accuracy is hard to avert. In this paper, a novel hierarchical minutiae matching algorithm for fingerprint and palmprint identification systems is proposed. This method decomposes the matching step into several stages and rejects many false fingerprints or palmprints on different stages, thus it can save much time while preserving a high identification rate. Experimental results show that the proposed algorithm can save almost 50% searching time compared with traditional methods and illustrate its effectiveness.

Spectral-Spatial Classification of Hyperspectral Images Using Hierarchical Optimization

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2011-01-01

A new spectral-spatial method for hyperspectral data classification is proposed. For a given hyperspectral image, probabilistic pixelwise classification is first applied. Then, hierarchical step-wise optimization algorithm is performed, by iteratively merging neighboring regions with the smallest Dissimilarity Criterion (DC) and recomputing class labels for new regions. The DC is computed by comparing region mean vectors, class labels and a number of pixels in the two regions under consideration. The algorithm is converged when all the pixels get involved in the region merging procedure. Experimental results are presented on two remote sensing hyperspectral images acquired by the AVIRIS and ROSIS sensors. The proposed approach improves classification accuracies and provides maps with more homogeneous regions, when compared to previously proposed classification techniques.

A distributed scheduling algorithm for heterogeneous real-time systems

NASA Technical Reports Server (NTRS)

Zeineldine, Osman; El-Toweissy, Mohamed; Mukkamala, Ravi

1991-01-01

Much of the previous work on load balancing and scheduling in distributed environments was concerned with homogeneous systems and homogeneous loads. Several of the results indicated that random policies are as effective as other more complex load allocation policies. The effects of heterogeneity on scheduling algorithms for hard real time systems is examined. A distributed scheduler specifically to handle heterogeneities in both nodes and node traffic is proposed. The performance of the algorithm is measured in terms of the percentage of jobs discarded. While a random task allocation is very sensitive to heterogeneities, the algorithm is shown to be robust to such non-uniformities in system components and load.

Increasing Prediction the Original Final Year Project of Student Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Saragih, Rijois Iboy Erwin; Turnip, Mardi; Sitanggang, Delima; Aritonang, Mendarissan; Harianja, Eva

2018-04-01

Final year project is very important forgraduation study of a student. Unfortunately, many students are not seriouslydidtheir final projects. Many of studentsask for someone to do it for them. In this paper, an application of genetic algorithms to predict the original final year project of a studentis proposed. In the simulation, the data of the final project for the last 5 years is collected. The genetic algorithm has several operators namely population, selection, crossover, and mutation. The result suggest that genetic algorithm can do better prediction than other comparable model. Experimental results of predicting showed that 70% was more accurate than the previous researched.

Research on improved edge extraction algorithm of rectangular piece

NASA Astrophysics Data System (ADS)

He, Yi-Bin; Zeng, Ya-Jun; Chen, Han-Xin; Xiao, San-Xia; Wang, Yan-Wei; Huang, Si-Yu

Traditional edge detection operators such as Prewitt operator, LOG operator and Canny operator, etc. cannot meet the requirements of the modern industrial measurement. This paper proposes a kind of image edge detection algorithm based on improved morphological gradient. It can be detect the image using structural elements, which deals with the characteristic information of the image directly. Choosing different shapes and sizes of structural elements to use together, the ideal image edge information can be detected. The experimental result shows that the algorithm can well extract image edge with noise, which is clearer, and has more detailed edges compared with the previous edge detection algorithm.

Erratum: Erratum: Denoising Phase Unwrapping Algorithm for Precise Phase Shifting Interferometry

NASA Astrophysics Data System (ADS)

Phuc, Phan Huy; Rhee, Hyug-Gyo; Ghim, Young-Sik

2018-06-01

This is a revision of the reference list reported in the original article. In order to clear the contribution of the previous work on the incremental breadth-first search (IBFS) method applied to the PUMA algorithm, we add one more reference to the existing reference list, as in this erratum. Page 83 : In this paper, we propose an algorithm that modifies the Boykov-Kolmogorov (BK) algorithm using the incremental breadth-first search (IBFS) method [27, 28] to find paths from the source to the sink of a graph. [28] S. Ali, H. Khan, I. Shaik and F. Ali, Int. J. Eng. and Technol. 7, 254 (2015).

Reversible Data Hiding Based on DNA Computing

PubMed Central

Xie, Yingjie

2017-01-01

Biocomputing, especially DNA, computing has got great development. It is widely used in information security. In this paper, a novel algorithm of reversible data hiding based on DNA computing is proposed. Inspired by the algorithm of histogram modification, which is a classical algorithm for reversible data hiding, we combine it with DNA computing to realize this algorithm based on biological technology. Compared with previous results, our experimental results have significantly improved the ER (Embedding Rate). Furthermore, some PSNR (peak signal-to-noise ratios) of test images are also improved. Experimental results show that it is suitable for protecting the copyright of cover image in DNA-based information security. PMID:28280504

Self calibrating monocular camera measurement of traffic parameters.

DOT National Transportation Integrated Search

2009-12-01

This proposed project will extend the work of previous projects that have developed algorithms and software : to measure traffic speed under adverse conditions using un-calibrated cameras. The present implementation : uses the WSDOT CCTV cameras moun...

Scalability of a Methodology for Generating Technical Trading Rules with GAPs Based on Risk-Return Adjustment and Incremental Training

NASA Astrophysics Data System (ADS)

de La Cal, E. A.; Fernández, E. M.; Quiroga, R.; Villar, J. R.; Sedano, J.

In previous works a methodology was defined, based on the design of a genetic algorithm GAP and an incremental training technique adapted to the learning of series of stock market values. The GAP technique consists in a fusion of GP and GA. The GAP algorithm implements the automatic search for crisp trading rules taking as objectives of the training both the optimization of the return obtained and the minimization of the assumed risk. Applying the proposed methodology, rules have been obtained for a period of eight years of the S&P500 index. The achieved adjustment of the relation return-risk has generated rules with returns very superior in the testing period to those obtained applying habitual methodologies and even clearly superior to Buy&Hold. This work probes that the proposed methodology is valid for different assets in a different market than previous work.

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

PubMed

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

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

Object Tracking and Target Reacquisition Based on 3-D Range Data for Moving Vehicles

PubMed Central

Lee, Jehoon; Lankton, Shawn; Tannenbaum, Allen

2013-01-01

In this paper, we propose an approach for tracking an object of interest based on 3-D range data. We employ particle filtering and active contours to simultaneously estimate the global motion of the object and its local deformations. The proposed algorithm takes advantage of range information to deal with the challenging (but common) situation in which the tracked object disappears from the image domain entirely and reappears later. To cope with this problem, a method based on principle component analysis (PCA) of shape information is proposed. In the proposed method, if the target disappears out of frame, shape similarity energy is used to detect target candidates that match a template shape learned online from previously observed frames. Thus, we require no a priori knowledge of the target’s shape. Experimental results show the practical applicability and robustness of the proposed algorithm in realistic tracking scenarios. PMID:21486717

Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps.

PubMed

Moya, José M; Araujo, Alvaro; Banković, Zorana; de Goyeneche, Juan-Mariano; Vallejo, Juan Carlos; Malagón, Pedro; Villanueva, Daniel; Fraga, David; Romero, Elena; Blesa, Javier

2009-01-01

The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.

Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps

PubMed Central

Moya, José M.; Araujo, Álvaro; Banković, Zorana; de Goyeneche, Juan-Mariano; Vallejo, Juan Carlos; Malagón, Pedro; Villanueva, Daniel; Fraga, David; Romero, Elena; Blesa, Javier

2009-01-01

The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals. PMID:22291569

LBP-based penalized weighted least-squares approach to low-dose cone-beam computed tomography reconstruction

NASA Astrophysics Data System (ADS)

Ma, Ming; Wang, Huafeng; Liu, Yan; Zhang, Hao; Gu, Xianfeng; Liang, Zhengrong

2014-03-01

Cone-beam computed tomography (CBCT) has attracted growing interest of researchers in image reconstruction. The mAs level of the X-ray tube current, in practical application of CBCT, is mitigated in order to reduce the CBCT dose. The lowering of the X-ray tube current, however, results in the degradation of image quality. Thus, low-dose CBCT image reconstruction is in effect a noise problem. To acquire clinically acceptable quality of image, and keep the X-ray tube current as low as achievable in the meanwhile, some penalized weighted least-squares (PWLS)-based image reconstruction algorithms have been developed. One representative strategy in previous work is to model the prior information for solution regularization using an anisotropic penalty term. To enhance the edge preserving and noise suppressing in a finer scale, a novel algorithm combining the local binary pattern (LBP) with penalized weighted leastsquares (PWLS), called LBP-PWLS-based image reconstruction algorithm, is proposed in this work. The proposed LBP-PWLS-based algorithm adaptively encourages strong diffusion on the local spot/flat region around a voxel and less diffusion on edge/corner ones by adjusting the penalty for cost function, after the LBP is utilized to detect the region around the voxel as spot, flat and edge ones. The LBP-PWLS-based reconstruction algorithm was evaluated using the sinogram data acquired by a clinical CT scanner from the CatPhan® 600 phantom. Experimental results on the noiseresolution tradeoff measurement and other quantitative measurements demonstrated its feasibility and effectiveness in edge preserving and noise suppressing in comparison with a previous PWLS reconstruction algorithm.

A parallelizable real-time motion tracking algorithm with applications to ultrasonic strain imaging

NASA Astrophysics Data System (ADS)

Jiang, J.; Hall, T. J.

2007-07-01

Ultrasound-based mechanical strain imaging systems utilize signals from conventional diagnostic ultrasound systems to image tissue elasticity contrast that provides new diagnostically valuable information. Previous works (Hall et al 2003 Ultrasound Med. Biol. 29 427, Zhu and Hall 2002 Ultrason. Imaging 24 161) demonstrated that uniaxial deformation with minimal elevation motion is preferred for breast strain imaging and real-time strain image feedback to operators is important to accomplish this goal. The work reported here enhances the real-time speckle tracking algorithm with two significant modifications. One fundamental change is that the proposed algorithm is a column-based algorithm (a column is defined by a line of data parallel to the ultrasound beam direction, i.e. an A-line), as opposed to a row-based algorithm (a row is defined by a line of data perpendicular to the ultrasound beam direction). Then, displacement estimates from its adjacent columns provide good guidance for motion tracking in a significantly reduced search region to reduce computational cost. Consequently, the process of displacement estimation can be naturally split into at least two separated tasks, computed in parallel, propagating outward from the center of the region of interest (ROI). The proposed algorithm has been implemented and optimized in a Windows® system as a stand-alone ANSI C++ program. Results of preliminary tests, using numerical and tissue-mimicking phantoms, and in vivo tissue data, suggest that high contrast strain images can be consistently obtained with frame rates (10 frames s-1) that exceed our previous methods.

Scene text recognition in mobile applications by character descriptor and structure configuration.

PubMed

Yi, Chucai; Tian, Yingli

2014-07-01

Text characters and strings in natural scene can provide valuable information for many applications. Extracting text directly from natural scene images or videos is a challenging task because of diverse text patterns and variant background interferences. This paper proposes a method of scene text recognition from detected text regions. In text detection, our previously proposed algorithms are applied to obtain text regions from scene image. First, we design a discriminative character descriptor by combining several state-of-the-art feature detectors and descriptors. Second, we model character structure at each character class by designing stroke configuration maps. Our algorithm design is compatible with the application of scene text extraction in smart mobile devices. An Android-based demo system is developed to show the effectiveness of our proposed method on scene text information extraction from nearby objects. The demo system also provides us some insight into algorithm design and performance improvement of scene text extraction. The evaluation results on benchmark data sets demonstrate that our proposed scheme of text recognition is comparable with the best existing methods.

Multicore and GPU algorithms for Nussinov RNA folding

PubMed Central

2014-01-01

Background One segment of a RNA sequence might be paired with another segment of the same RNA sequence due to the force of hydrogen bonds. This two-dimensional structure is called the RNA sequence's secondary structure. Several algorithms have been proposed to predict an RNA sequence's secondary structure. These algorithms are referred to as RNA folding algorithms. Results We develop cache efficient, multicore, and GPU algorithms for RNA folding using Nussinov's algorithm. Conclusions Our cache efficient algorithm provides a speedup between 1.6 and 3.0 relative to a naive straightforward single core code. The multicore version of the cache efficient single core algorithm provides a speedup, relative to the naive single core algorithm, between 7.5 and 14.0 on a 6 core hyperthreaded CPU. Our GPU algorithm for the NVIDIA C2050 is up to 1582 times as fast as the naive single core algorithm and between 5.1 and 11.2 times as fast as the fastest previously known GPU algorithm for Nussinov RNA folding. PMID:25082539

Runtime support for parallelizing data mining algorithms

NASA Astrophysics Data System (ADS)

Jin, Ruoming; Agrawal, Gagan

2002-03-01

With recent technological advances, shared memory parallel machines have become more scalable, and offer large main memories and high bus bandwidths. They are emerging as good platforms for data warehousing and data mining. In this paper, we focus on shared memory parallelization of data mining algorithms. We have developed a series of techniques for parallelization of data mining algorithms, including full replication, full locking, fixed locking, optimized full locking, and cache-sensitive locking. Unlike previous work on shared memory parallelization of specific data mining algorithms, all of our techniques apply to a large number of common data mining algorithms. In addition, we propose a reduction-object based interface for specifying a data mining algorithm. We show how our runtime system can apply any of the technique we have developed starting from a common specification of the algorithm.

Using 3D spatial correlations to improve the noise robustness of multi component analysis of 3D multi echo quantitative T2 relaxometry data.

PubMed

Kumar, Dushyant; Hariharan, Hari; Faizy, Tobias D; Borchert, Patrick; Siemonsen, Susanne; Fiehler, Jens; Reddy, Ravinder; Sedlacik, Jan

2018-05-12

We present a computationally feasible and iterative multi-voxel spatially regularized algorithm for myelin water fraction (MWF) reconstruction. This method utilizes 3D spatial correlations present in anatomical/pathological tissues and underlying B1 + -inhomogeneity or flip angle inhomogeneity to enhance the noise robustness of the reconstruction while intrinsically accounting for stimulated echo contributions using T2-distribution data alone. Simulated data and in vivo data acquired using 3D non-selective multi-echo spin echo (3DNS-MESE) were used to compare the reconstruction quality of the proposed approach against those of the popular algorithm (the method by Prasloski et al.) and our previously proposed 2D multi-slice spatial regularization spatial regularization approach. We also investigated whether the inter-sequence correlations and agreements improved as a result of the proposed approach. MWF-quantifications from two sequences, 3DNS-MESE vs 3DNS-gradient and spin echo (3DNS-GRASE), were compared for both reconstruction approaches to assess correlations and agreements between inter-sequence MWF-value pairs. MWF values from whole-brain data of six volunteers and two multiple sclerosis patients are being reported as well. In comparison with competing approaches such as Prasloski's method or our previously proposed 2D multi-slice spatial regularization method, the proposed method showed better agreements with simulated truths using regression analyses and Bland-Altman analyses. For 3DNS-MESE data, MWF-maps reconstructed using the proposed algorithm provided better depictions of white matter structures in subcortical areas adjoining gray matter which agreed more closely with corresponding contrasts on T2-weighted images than MWF-maps reconstructed with the method by Prasloski et al. We also achieved a higher level of correlations and agreements between inter-sequence (3DNS-MESE vs 3DNS-GRASE) MWF-value pairs. The proposed algorithm provides more noise-robust fits to T2-decay data and improves MWF-quantifications in white matter structures especially in the sub-cortical white matter and major white matter tract regions. Copyright © 2018 Elsevier Inc. All rights reserved.

Extending birthday paradox theory to estimate the number of tags in RFID systems.

PubMed

Shakiba, Masoud; Singh, Mandeep Jit; Sundararajan, Elankovan; Zavvari, Azam; Islam, Mohammad Tariqul

2014-01-01

The main objective of Radio Frequency Identification systems is to provide fast identification for tagged objects. However, there is always a chance of collision, when tags transmit their data to the reader simultaneously. Collision is a time-consuming event that reduces the performance of RFID systems. Consequently, several anti-collision algorithms have been proposed in the literature. Dynamic Framed Slotted ALOHA (DFSA) is one of the most popular of these algorithms. DFSA dynamically modifies the frame size based on the number of tags. Since the real number of tags is unknown, it needs to be estimated. Therefore, an accurate tag estimation method has an important role in increasing the efficiency and overall performance of the tag identification process. In this paper, we propose a novel estimation technique for DFSA anti-collision algorithms that applies birthday paradox theory to estimate the number of tags accurately. The analytical discussion and simulation results prove that the proposed method increases the accuracy of tag estimation and, consequently, outperforms previous schemes.

Extending Birthday Paradox Theory to Estimate the Number of Tags in RFID Systems

PubMed Central

Shakiba, Masoud; Singh, Mandeep Jit; Sundararajan, Elankovan; Zavvari, Azam; Islam, Mohammad Tariqul

2014-01-01

The main objective of Radio Frequency Identification systems is to provide fast identification for tagged objects. However, there is always a chance of collision, when tags transmit their data to the reader simultaneously. Collision is a time-consuming event that reduces the performance of RFID systems. Consequently, several anti-collision algorithms have been proposed in the literature. Dynamic Framed Slotted ALOHA (DFSA) is one of the most popular of these algorithms. DFSA dynamically modifies the frame size based on the number of tags. Since the real number of tags is unknown, it needs to be estimated. Therefore, an accurate tag estimation method has an important role in increasing the efficiency and overall performance of the tag identification process. In this paper, we propose a novel estimation technique for DFSA anti-collision algorithms that applies birthday paradox theory to estimate the number of tags accurately. The analytical discussion and simulation results prove that the proposed method increases the accuracy of tag estimation and, consequently, outperforms previous schemes. PMID:24752285

Evolvable Neuronal Paths: A Novel Basis for Information and Search in the Brain

PubMed Central

Fernando, Chrisantha; Vasas, Vera; Szathmáry, Eörs; Husbands, Phil

2011-01-01

We propose a previously unrecognized kind of informational entity in the brain that is capable of acting as the basis for unlimited hereditary variation in neuronal networks. This unit is a path of activity through a network of neurons, analogous to a path taken through a hidden Markov model. To prove in principle the capabilities of this new kind of informational substrate, we show how a population of paths can be used as the hereditary material for a neuronally implemented genetic algorithm, (the swiss-army knife of black-box optimization techniques) which we have proposed elsewhere could operate at somatic timescales in the brain. We compare this to the same genetic algorithm that uses a standard ‘genetic’ informational substrate, i.e. non-overlapping discrete genotypes, on a range of optimization problems. A path evolution algorithm (PEA) is defined as any algorithm that implements natural selection of paths in a network substrate. A PEA is a previously unrecognized type of natural selection that is well suited for implementation by biological neuronal networks with structural plasticity. The important similarities and differences between a standard genetic algorithm and a PEA are considered. Whilst most experiments are conducted on an abstract network model, at the conclusion of the paper a slightly more realistic neuronal implementation of a PEA is outlined based on Izhikevich spiking neurons. Finally, experimental predictions are made for the identification of such informational paths in the brain. PMID:21887266

Proposing a new iterative learning control algorithm based on a non-linear least square formulation - Minimising draw-in errors

NASA Astrophysics Data System (ADS)

Endelt, B.

2017-09-01

Forming operation are subject to external disturbances and changing operating conditions e.g. new material batch, increasing tool temperature due to plastic work, material properties and lubrication is sensitive to tool temperature. It is generally accepted that forming operations are not stable over time and it is not uncommon to adjust the process parameters during the first half hour production, indicating that process instability is gradually developing over time. Thus, in-process feedback control scheme might not-be necessary to stabilize the process and an alternative approach is to apply an iterative learning algorithm, which can learn from previously produced parts i.e. a self learning system which gradually reduces error based on historical process information. What is proposed in the paper is a simple algorithm which can be applied to a wide range of sheet-metal forming processes. The input to the algorithm is the final flange edge geometry and the basic idea is to reduce the least-square error between the current flange geometry and a reference geometry using a non-linear least square algorithm. The ILC scheme is applied to a square deep-drawing and the Numisheet’08 S-rail benchmark problem, the numerical tests shows that the proposed control scheme is able control and stabilise both processes.

Multifeature-based high-resolution palmprint recognition.

PubMed

Dai, Jifeng; Zhou, Jie

2011-05-01

Palmprint is a promising biometric feature for use in access control and forensic applications. Previous research on palmprint recognition mainly concentrates on low-resolution (about 100 ppi) palmprints. But for high-security applications (e.g., forensic usage), high-resolution palmprints (500 ppi or higher) are required from which more useful information can be extracted. In this paper, we propose a novel recognition algorithm for high-resolution palmprint. The main contributions of the proposed algorithm include the following: 1) use of multiple features, namely, minutiae, density, orientation, and principal lines, for palmprint recognition to significantly improve the matching performance of the conventional algorithm. 2) Design of a quality-based and adaptive orientation field estimation algorithm which performs better than the existing algorithm in case of regions with a large number of creases. 3) Use of a novel fusion scheme for an identification application which performs better than conventional fusion methods, e.g., weighted sum rule, SVMs, or Neyman-Pearson rule. Besides, we analyze the discriminative power of different feature combinations and find that density is very useful for palmprint recognition. Experimental results on the database containing 14,576 full palmprints show that the proposed algorithm has achieved a good performance. In the case of verification, the recognition system's False Rejection Rate (FRR) is 16 percent, which is 17 percent lower than the best existing algorithm at a False Acceptance Rate (FAR) of 10(-5), while in the identification experiment, the rank-1 live-scan partial palmprint recognition rate is improved from 82.0 to 91.7 percent.

High performance reconciliation for continuous-variable quantum key distribution with LDPC code

NASA Astrophysics Data System (ADS)

Lin, Dakai; Huang, Duan; Huang, Peng; Peng, Jinye; Zeng, Guihua

2015-03-01

Reconciliation is a significant procedure in a continuous-variable quantum key distribution (CV-QKD) system. It is employed to extract secure secret key from the resulted string through quantum channel between two users. However, the efficiency and the speed of previous reconciliation algorithms are low. These problems limit the secure communication distance and the secure key rate of CV-QKD systems. In this paper, we proposed a high-speed reconciliation algorithm through employing a well-structured decoding scheme based on low density parity-check (LDPC) code. The complexity of the proposed algorithm is reduced obviously. By using a graphics processing unit (GPU) device, our method may reach a reconciliation speed of 25 Mb/s for a CV-QKD system, which is currently the highest level and paves the way to high-speed CV-QKD.

Robust automatic line scratch detection in films.

PubMed

Newson, Alasdair; Almansa, Andrés; Gousseau, Yann; Pérez, Patrick

2014-03-01

Line scratch detection in old films is a particularly challenging problem due to the variable spatiotemporal characteristics of this defect. Some of the main problems include sensitivity to noise and texture, and false detections due to thin vertical structures belonging to the scene. We propose a robust and automatic algorithm for frame-by-frame line scratch detection in old films, as well as a temporal algorithm for the filtering of false detections. In the frame-by-frame algorithm, we relax some of the hypotheses used in previous algorithms in order to detect a wider variety of scratches. This step's robustness and lack of external parameters is ensured by the combined use of an a contrario methodology and local statistical estimation. In this manner, over-detection in textured or cluttered areas is greatly reduced. The temporal filtering algorithm eliminates false detections due to thin vertical structures by exploiting the coherence of their motion with that of the underlying scene. Experiments demonstrate the ability of the resulting detection procedure to deal with difficult situations, in particular in the presence of noise, texture, and slanted or partial scratches. Comparisons show significant advantages over previous work.

Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images.

PubMed

Arslan, Salim; Ersahin, Tulin; Cetin-Atalay, Rengul; Gunduz-Demir, Cigdem

2013-06-01

More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms.

A New Soft Computing Method for K-Harmonic Means Clustering.

PubMed

Yeh, Wei-Chang; Jiang, Yunzhi; Chen, Yee-Fen; Chen, Zhe

2016-01-01

The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of the utility of the proposed iSSO-KHM, we present extensive computational results on eight benchmark problems. From the computational results, the comparison appears to support the superiority of the proposed iSSO-KHM over previously developed algorithms for all experiments in the literature.

Generalized image contrast enhancement technique based on Heinemann contrast discrimination model

NASA Astrophysics Data System (ADS)

Liu, Hong; Nodine, Calvin F.

1994-03-01

This paper presents a generalized image contrast enhancement technique which equalizes perceived brightness based on the Heinemann contrast discrimination model. This is a modified algorithm which presents an improvement over the previous study by Mokrane in its mathematically proven existence of a unique solution and in its easily tunable parameterization. The model uses a log-log representation of contrast luminosity between targets and the surround in a fixed luminosity background setting. The algorithm consists of two nonlinear gray-scale mapping functions which have seven parameters, two of which are adjustable Heinemann constants. Another parameter is the background gray level. The remaining four parameters are nonlinear functions of gray scale distribution of the image, and can be uniquely determined once the previous three are given. Tests have been carried out to examine the effectiveness of the algorithm for increasing the overall contrast of images. It can be demonstrated that the generalized algorithm provides better contrast enhancement than histogram equalization. In fact, the histogram equalization technique is a special case of the proposed mapping.

Road detection in SAR images using a tensor voting algorithm

NASA Astrophysics Data System (ADS)

Shen, Dajiang; Hu, Chun; Yang, Bing; Tian, Jinwen; Liu, Jian

2007-11-01

In this paper, the problem of the detection of road networks in Synthetic Aperture Radar (SAR) images is addressed. Most of the previous methods extract the road by detecting lines and network reconstruction. Traditional algorithms such as MRFs, GA, Level Set, used in the progress of reconstruction are iterative. The tensor voting methodology we proposed is non-iterative, and non-sensitive to initialization. Furthermore, the only free parameter is the size of the neighborhood, related to the scale. The algorithm we present is verified to be effective when it's applied to the road extraction using the real Radarsat Image.

Symmetry compression method for discovering network motifs.

PubMed

Wang, Jianxin; Huang, Yuannan; Wu, Fang-Xiang; Pan, Yi

2012-01-01

Discovering network motifs could provide a significant insight into systems biology. Interestingly, many biological networks have been found to have a high degree of symmetry (automorphism), which is inherent in biological network topologies. The symmetry due to the large number of basic symmetric subgraphs (BSSs) causes a certain redundant calculation in discovering network motifs. Therefore, we compress all basic symmetric subgraphs before extracting compressed subgraphs and propose an efficient decompression algorithm to decompress all compressed subgraphs without loss of any information. In contrast to previous approaches, the novel Symmetry Compression method for Motif Detection, named as SCMD, eliminates most redundant calculations caused by widespread symmetry of biological networks. We use SCMD to improve three notable exact algorithms and two efficient sampling algorithms. Results of all exact algorithms with SCMD are the same as those of the original algorithms, since SCMD is a lossless method. The sampling results show that the use of SCMD almost does not affect the quality of sampling results. For highly symmetric networks, we find that SCMD used in both exact and sampling algorithms can help get a remarkable speedup. Furthermore, SCMD enables us to find larger motifs in biological networks with notable symmetry than previously possible.

New Hybrid Algorithms for Estimating Tree Stem Diameters at Breast Height Using a Two Dimensional Terrestrial Laser Scanner

PubMed Central

Kong, Jianlei; Ding, Xiaokang; Liu, Jinhao; Yan, Lei; Wang, Jianli

2015-01-01

In this paper, a new algorithm to improve the accuracy of estimating diameter at breast height (DBH) for tree trunks in forest areas is proposed. First, the information is collected by a two-dimensional terrestrial laser scanner (2DTLS), which emits laser pulses to generate a point cloud. After extraction and filtration, the laser point clusters of the trunks are obtained, which are optimized by an arithmetic means method. Then, an algebraic circle fitting algorithm in polar form is non-linearly optimized by the Levenberg-Marquardt method to form a new hybrid algorithm, which is used to acquire the diameters and positions of the trees. Compared with previous works, this proposed method improves the accuracy of diameter estimation of trees significantly and effectively reduces the calculation time. Moreover, the experimental results indicate that this method is stable and suitable for the most challenging conditions, which has practical significance in improving the operating efficiency of forest harvester and reducing the risk of causing accidents. PMID:26147726

Low-Complexity User Selection for Rate Maximization in MIMO Broadcast Channels with Downlink Beamforming

PubMed Central

Silva, Adão; Gameiro, Atílio

2014-01-01

We present in this work a low-complexity algorithm to solve the sum rate maximization problem in multiuser MIMO broadcast channels with downlink beamforming. Our approach decouples the user selection problem from the resource allocation problem and its main goal is to create a set of quasiorthogonal users. The proposed algorithm exploits physical metrics of the wireless channels that can be easily computed in such a way that a null space projection power can be approximated efficiently. Based on the derived metrics we present a mathematical model that describes the dynamics of the user selection process which renders the user selection problem into an integer linear program. Numerical results show that our approach is highly efficient to form groups of quasiorthogonal users when compared to previously proposed algorithms in the literature. Our user selection algorithm achieves a large portion of the optimum user selection sum rate (90%) for a moderate number of active users. PMID:24574928

Validation of simultaneous reverse optimization reconstruction algorithm in a practical circular subaperture stitching interferometer

NASA Astrophysics Data System (ADS)

Zhang, Lei; Li, Dong; Liu, Yu; Liu, Jingxiao; Li, Jingsong; Yu, Benli

2017-11-01

We demonstrate the validity of the simultaneous reverse optimization reconstruction (SROR) algorithm in circular subaperture stitching interferometry (CSSI), which is previously proposed for non-null aspheric annular subaperture stitching interferometry (ASSI). The merits of the modified SROR algorithm in CSSI, such as auto retrace error correction, no need of overlap and even permission of missed coverage, are analyzed in detail in simulations and experiments. Meanwhile, a practical CSSI system is proposed for this demonstration. An optical wedge is employed to deflect the incident beam for subaperture scanning by its rotation and shift instead of the six-axis motion-control system. Also the reference path can provide variable Zernike defocus for each subaperture test, which would decrease the fringe density. Experiments validating the SROR algorithm in this CSSI is implemented with cross validation by testing of paraboloidal mirror, flat mirror and astigmatism mirror. It is an indispensable supplement in SROR application in general subaperture stitching interferometry.

Color analysis and image rendering of woodblock prints with oil-based ink

NASA Astrophysics Data System (ADS)

Horiuchi, Takahiko; Tanimoto, Tetsushi; Tominaga, Shoji

2012-01-01

This paper proposes a method for analyzing the color characteristics of woodblock prints having oil-based ink and rendering realistic images based on camera data. The analysis results of woodblock prints show some characteristic features in comparison with oil paintings: 1) A woodblock print can be divided into several cluster areas, each with similar surface spectral reflectance; and 2) strong specular reflection from the influence of overlapping paints arises only in specific cluster areas. By considering these properties, we develop an effective rendering algorithm by modifying our previous algorithm for oil paintings. A set of surface spectral reflectances of a woodblock print is represented by using only a small number of average surface spectral reflectances and the registered scaling coefficients, whereas the previous algorithm for oil paintings required surface spectral reflectances of high dimension at all pixels. In the rendering process, in order to reproduce the strong specular reflection in specific cluster areas, we use two sets of parameters in the Torrance-Sparrow model for cluster areas with or without strong specular reflection. An experiment on a woodblock printing with oil-based ink was performed to demonstrate the feasibility of the proposed method.

Multi-Robot Coalitions Formation with Deadlines: Complexity Analysis and Solutions

PubMed Central

2017-01-01

Multi-robot task allocation is one of the main problems to address in order to design a multi-robot system, very especially when robots form coalitions that must carry out tasks before a deadline. A lot of factors affect the performance of these systems and among them, this paper is focused on the physical interference effect, produced when two or more robots want to access the same point simultaneously. To our best knowledge, this paper presents the first formal description of multi-robot task allocation that includes a model of interference. Thanks to this description, the complexity of the allocation problem is analyzed. Moreover, the main contribution of this paper is to provide the conditions under which the optimal solution of the aforementioned allocation problem can be obtained solving an integer linear problem. The optimal results are compared to previous allocation algorithms already proposed by the first two authors of this paper and with a new method proposed in this paper. The results obtained show how the new task allocation algorithms reach up more than an 80% of the median of the optimal solution, outperforming previous auction algorithms with a huge reduction of the execution time. PMID:28118384

Multi-Robot Coalitions Formation with Deadlines: Complexity Analysis and Solutions.

PubMed

Guerrero, Jose; Oliver, Gabriel; Valero, Oscar

2017-01-01

Multi-robot task allocation is one of the main problems to address in order to design a multi-robot system, very especially when robots form coalitions that must carry out tasks before a deadline. A lot of factors affect the performance of these systems and among them, this paper is focused on the physical interference effect, produced when two or more robots want to access the same point simultaneously. To our best knowledge, this paper presents the first formal description of multi-robot task allocation that includes a model of interference. Thanks to this description, the complexity of the allocation problem is analyzed. Moreover, the main contribution of this paper is to provide the conditions under which the optimal solution of the aforementioned allocation problem can be obtained solving an integer linear problem. The optimal results are compared to previous allocation algorithms already proposed by the first two authors of this paper and with a new method proposed in this paper. The results obtained show how the new task allocation algorithms reach up more than an 80% of the median of the optimal solution, outperforming previous auction algorithms with a huge reduction of the execution time.

Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm

PubMed Central

2014-01-01

Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme. PMID:24744773

UV Reconstruction Algorithm And Diurnal Cycle Variability

NASA Astrophysics Data System (ADS)

Curylo, Aleksander; Litynska, Zenobia; Krzyscin, Janusz; Bogdanska, Barbara

2009-03-01

UV reconstruction is a method of estimation of surface UV with the use of available actinometrical and aerological measurements. UV reconstruction is necessary for the study of long-term UV change. A typical series of UV measurements is not longer than 15 years, which is too short for trend estimation. The essential problem in the reconstruction algorithm is the good parameterization of clouds. In our previous algorithm we used an empirical relation between Cloud Modification Factor (CMF) in global radiation and CMF in UV. The CMF is defined as the ratio between measured and modelled irradiances. Clear sky irradiance was calculated with a solar radiative transfer model. In the proposed algorithm, the time variability of global radiation during the diurnal cycle is used as an additional source of information. For elaborating an improved reconstruction algorithm relevant data from Legionowo [52.4 N, 21.0 E, 96 m a.s.l], Poland were collected with the following instruments: NILU-UV multi channel radiometer, Kipp&Zonen pyranometer, radiosonde profiles of ozone, humidity and temperature. The proposed algorithm has been used for reconstruction of UV at four Polish sites: Mikolajki, Kolobrzeg, Warszawa-Bielany and Zakopane since the early 1960s. Krzyscin's reconstruction of total ozone has been used in the calculations.

Efficient Deterministic Finite Automata Minimization Based on Backward Depth Information

PubMed Central

Liu, Desheng; Huang, Zhiping; Zhang, Yimeng; Guo, Xiaojun; Su, Shaojing

2016-01-01

Obtaining a minimal automaton is a fundamental issue in the theory and practical implementation of deterministic finite automatons (DFAs). A minimization algorithm is presented in this paper that consists of two main phases. In the first phase, the backward depth information is built, and the state set of the DFA is partitioned into many blocks. In the second phase, the state set is refined using a hash table. The minimization algorithm has a lower time complexity O(n) than a naive comparison of transitions O(n2). Few states need to be refined by the hash table, because most states have been partitioned by the backward depth information in the coarse partition. This method achieves greater generality than previous methods because building the backward depth information is independent of the topological complexity of the DFA. The proposed algorithm can be applied not only to the minimization of acyclic automata or simple cyclic automata, but also to automata with high topological complexity. Overall, the proposal has three advantages: lower time complexity, greater generality, and scalability. A comparison to Hopcroft’s algorithm demonstrates experimentally that the algorithm runs faster than traditional algorithms. PMID:27806102

BPF-type region-of-interest reconstruction for parallel translational computed tomography.

PubMed

Wu, Weiwen; Yu, Hengyong; Wang, Shaoyu; Liu, Fenglin

2017-01-01

The objective of this study is to present and test a new ultra-low-cost linear scan based tomography architecture. Similar to linear tomosynthesis, the source and detector are translated in opposite directions and the data acquisition system targets on a region-of-interest (ROI) to acquire data for image reconstruction. This kind of tomographic architecture was named parallel translational computed tomography (PTCT). In previous studies, filtered backprojection (FBP)-type algorithms were developed to reconstruct images from PTCT. However, the reconstructed ROI images from truncated projections have severe truncation artefact. In order to overcome this limitation, we in this study proposed two backprojection filtering (BPF)-type algorithms named MP-BPF and MZ-BPF to reconstruct ROI images from truncated PTCT data. A weight function is constructed to deal with data redundancy for multi-linear translations modes. Extensive numerical simulations are performed to evaluate the proposed MP-BPF and MZ-BPF algorithms for PTCT in fan-beam geometry. Qualitative and quantitative results demonstrate that the proposed BPF-type algorithms cannot only more accurately reconstruct ROI images from truncated projections but also generate high-quality images for the entire image support in some circumstances.

Spatial Statistics for Tumor Cell Counting and Classification

NASA Astrophysics Data System (ADS)

Wirjadi, Oliver; Kim, Yoo-Jin; Breuel, Thomas

To count and classify cells in histological sections is a standard task in histology. One example is the grading of meningiomas, benign tumors of the meninges, which requires to assess the fraction of proliferating cells in an image. As this process is very time consuming when performed manually, automation is required. To address such problems, we propose a novel application of Markov point process methods in computer vision, leading to algorithms for computing the locations of circular objects in images. In contrast to previous algorithms using such spatial statistics methods in image analysis, the present one is fully trainable. This is achieved by combining point process methods with statistical classifiers. Using simulated data, the method proposed in this paper will be shown to be more accurate and more robust to noise than standard image processing methods. On the publicly available SIMCEP benchmark for cell image analysis algorithms, the cell count performance of the present paper is significantly more accurate than results published elsewhere, especially when cells form dense clusters. Furthermore, the proposed system performs as well as a state-of-the-art algorithm for the computer-aided histological grading of meningiomas when combined with a simple k-nearest neighbor classifier for identifying proliferating cells.

A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem

PubMed Central

Liu, Dong-sheng; Fan, Shu-jiang

2014-01-01

In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389

Bladed wheels damage detection through Non-Harmonic Fourier Analysis improved algorithm

NASA Astrophysics Data System (ADS)

Neri, P.

2017-05-01

Recent papers introduced the Non-Harmonic Fourier Analysis for bladed wheels damage detection. This technique showed its potential in estimating the frequency of sinusoidal signals even when the acquisition time is short with respect to the vibration period, provided that some hypothesis are fulfilled. Anyway, previously proposed algorithms showed severe limitations in cracks detection at their early stage. The present paper proposes an improved algorithm which allows to detect a blade vibration frequency shift due to a crack whose size is really small compared to the blade width. Such a technique could be implemented for condition-based maintenance, allowing to use non-contact methods for vibration measurements. A stator-fixed laser sensor could monitor all the blades as they pass in front of the spot, giving precious information about the wheel health. This configuration determines an acquisition time for each blade which become shorter as the machine rotational speed increases. In this situation, traditional Discrete Fourier Transform analysis results in poor frequency resolution, being not suitable for small frequency shift detection. Non-Harmonic Fourier Analysis instead showed high reliability in vibration frequency estimation even with data samples collected in a short time range. A description of the improved algorithm is provided in the paper, along with a comparison with the previous one. Finally, a validation of the method is presented, based on finite element simulations results.

Early Obstacle Detection and Avoidance for All to All Traffic Pattern in Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Huc, Florian; Jarry, Aubin; Leone, Pierre; Moraru, Luminita; Nikoletseas, Sotiris; Rolim, Jose

This paper deals with early obstacles recognition in wireless sensor networks under various traffic patterns. In the presence of obstacles, the efficiency of routing algorithms is increased by voluntarily avoiding some regions in the vicinity of obstacles, areas which we call dead-ends. In this paper, we first propose a fast convergent routing algorithm with proactive dead-end detection together with a formal definition and description of dead-ends. Secondly, we present a generalization of this algorithm which improves performances in all to many and all to all traffic patterns. In a third part we prove that this algorithm produces paths that are optimal up to a constant factor of 2π + 1. In a fourth part we consider the reactive version of the algorithm which is an extension of a previously known early obstacle detection algorithm. Finally we give experimental results to illustrate the efficiency of our algorithms in different scenarios.

Novel techniques for data decomposition and load balancing for parallel processing of vision systems: Implementation and evaluation using a motion estimation system

NASA Technical Reports Server (NTRS)

Choudhary, Alok Nidhi; Leung, Mun K.; Huang, Thomas S.; Patel, Janak H.

1989-01-01

Computer vision systems employ a sequence of vision algorithms in which the output of an algorithm is the input of the next algorithm in the sequence. Algorithms that constitute such systems exhibit vastly different computational characteristics, and therefore, require different data decomposition techniques and efficient load balancing techniques for parallel implementation. However, since the input data for a task is produced as the output data of the previous task, this information can be exploited to perform knowledge based data decomposition and load balancing. Presented here are algorithms for a motion estimation system. The motion estimation is based on the point correspondence between the involved images which are a sequence of stereo image pairs. Researchers propose algorithms to obtain point correspondences by matching feature points among stereo image pairs at any two consecutive time instants. Furthermore, the proposed algorithms employ non-iterative procedures, which results in saving considerable amounts of computation time. The system consists of the following steps: (1) extraction of features; (2) stereo match of images in one time instant; (3) time match of images from consecutive time instants; (4) stereo match to compute final unambiguous points; and (5) computation of motion parameters.

An iterative sinogram gap-filling method with object- and scanner-dedicated discrete cosine transform (DCT)-domain filters for high resolution PET scanners.

PubMed

Kim, Kwangdon; Lee, Kisung; Lee, Hakjae; Joo, Sungkwan; Kang, Jungwon

2018-01-01

We aimed to develop a gap-filling algorithm, in particular the filter mask design method of the algorithm, which optimizes the filter to the imaging object by an adaptive and iterative process, rather than by manual means. Two numerical phantoms (Shepp-Logan and Jaszczak) were used for sinogram generation. The algorithm works iteratively, not only on the gap-filling iteration but also on the mask generation, to identify the object-dedicated low frequency area in the DCT-domain that is to be preserved. We redefine the low frequency preserving region of the filter mask at every gap-filling iteration, and the region verges on the property of the original image in the DCT domain. The previous DCT2 mask for each phantom case had been manually well optimized, and the results show little difference from the reference image and sinogram. We observed little or no difference between the results of the manually optimized DCT2 algorithm and those of the proposed algorithm. The proposed algorithm works well for various types of scanning object and shows results that compare to those of the manually optimized DCT2 algorithm without perfect or full information of the imaging object.

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.

[Optimization of the parameters of microcirculatory structural adaptation model based on improved quantum-behaved particle swarm optimization algorithm].

PubMed

Pan, Qing; Yao, Jialiang; Wang, Ruofan; Cao, Ping; Ning, Gangmin; Fang, Luping

2017-08-01

The vessels in the microcirculation keep adjusting their structure to meet the functional requirements of the different tissues. A previously developed theoretical model can reproduce the process of vascular structural adaptation to help the study of the microcirculatory physiology. However, until now, such model lacks the appropriate methods for its parameter settings with subsequent limitation of further applications. This study proposed an improved quantum-behaved particle swarm optimization (QPSO) algorithm for setting the parameter values in this model. The optimization was performed on a real mesenteric microvascular network of rat. The results showed that the improved QPSO was superior to the standard particle swarm optimization, the standard QPSO and the previously reported Downhill algorithm. We conclude that the improved QPSO leads to a better agreement between mathematical simulation and animal experiment, rendering the model more reliable in future physiological studies.

MCSCF wave functions for excited states of polar molecules - Application to BeO. [Multi-Configuration Self-Consistent Field

NASA Technical Reports Server (NTRS)

Bauschlicher, C. W., Jr.; Yarkony, D. R.

1980-01-01

A previously reported multi-configuration self-consistent field (MCSCF) algorithm based on the generalized Brillouin theorem is extended in order to treat the excited states of polar molecules. In particular, the algorithm takes into account the proper treatment of nonorthogonality in the space of single excitations and invokes, when necessary, a constrained optimization procedure to prevent the variational collapse of excited states. In addition, a configuration selection scheme (suitable for use in conjunction with extended configuration interaction methods) is proposed for the MCSCF procedure. The algorithm is used to study the low-lying singlet states of BeO, a system which has not previously been studied using an MCSCF procedure. MCSCF wave functions are obtained for three 1 Sigma + and two 1 Pi states. The 1 Sigma + results are juxtaposed with comparable results for MgO in order to assess the generality of the description presented here.

Duality quantum algorithm efficiently simulates open quantum systems

PubMed Central

Wei, Shi-Jie; Ruan, Dong; Long, Gui-Lu

2016-01-01

Because of inevitable coupling with the environment, nearly all practical quantum systems are open system, where the evolution is not necessarily unitary. In this paper, we propose a duality quantum algorithm for simulating Hamiltonian evolution of an open quantum system. In contrast to unitary evolution in a usual quantum computer, the evolution operator in a duality quantum computer is a linear combination of unitary operators. In this duality quantum algorithm, the time evolution of the open quantum system is realized by using Kraus operators which is naturally implemented in duality quantum computer. This duality quantum algorithm has two distinct advantages compared to existing quantum simulation algorithms with unitary evolution operations. Firstly, the query complexity of the algorithm is O(d3) in contrast to O(d4) in existing unitary simulation algorithm, where d is the dimension of the open quantum system. Secondly, By using a truncated Taylor series of the evolution operators, this duality quantum algorithm provides an exponential improvement in precision compared with previous unitary simulation algorithm. PMID:27464855

Generation algorithm of craniofacial structure contour in cephalometric images

NASA Astrophysics Data System (ADS)

Mondal, Tanmoy; Jain, Ashish; Sardana, H. K.

2010-02-01

Anatomical structure tracing on cephalograms is a significant way to obtain cephalometric analysis. Computerized cephalometric analysis involves both manual and automatic approaches. The manual approach is limited in accuracy and repeatability. In this paper we have attempted to develop and test a novel method for automatic localization of craniofacial structure based on the detected edges on the region of interest. According to the grey scale feature at the different region of the cephalometric images, an algorithm for obtaining tissue contour is put forward. Using edge detection with specific threshold an improved bidirectional contour tracing approach is proposed by an interactive selection of the starting edge pixels, the tracking process searches repetitively for an edge pixel at the neighborhood of previously searched edge pixel to segment images, and then craniofacial structures are obtained. The effectiveness of the algorithm is demonstrated by the preliminary experimental results obtained with the proposed method.

Segmenting human from photo images based on a coarse-to-fine scheme.

PubMed

Lu, Huchuan; Fang, Guoliang; Shao, Xinqing; Li, Xuelong

2012-06-01

Human segmentation in photo images is a challenging and important problem that finds numerous applications ranging from album making and photo classification to image retrieval. Previous works on human segmentation usually demand a time-consuming training phase for complex shape-matching processes. In this paper, we propose a straightforward framework to automatically recover human bodies from color photos. Employing a coarse-to-fine strategy, we first detect a coarse torso (CT) using the multicue CT detection algorithm and then extract the accurate region of the upper body. Then, an iterative multiple oblique histogram algorithm is presented to accurately recover the lower body based on human kinematics. The performance of our algorithm is evaluated on our own data set (contains 197 images with human body region ground truth data), VOC 2006, and the 2010 data set. Experimental results demonstrate the merits of the proposed method in segmenting a person with various poses.

RISMA: A Rule-based Interval State Machine Algorithm for Alerts Generation, Performance Analysis and Monitoring Real-Time Data Processing

NASA Astrophysics Data System (ADS)

Laban, Shaban; El-Desouky, Aly

2013-04-01

The monitoring of real-time systems is a challenging and complicated process. So, there is a continuous need to improve the monitoring process through the use of new intelligent techniques and algorithms for detecting exceptions, anomalous behaviours and generating the necessary alerts during the workflow monitoring of such systems. The interval-based or period-based theorems have been discussed, analysed, and used by many researches in Artificial Intelligence (AI), philosophy, and linguistics. As explained by Allen, there are 13 relations between any two intervals. Also, there have also been many studies of interval-based temporal reasoning and logics over the past decades. Interval-based theorems can be used for monitoring real-time interval-based data processing. However, increasing the number of processed intervals makes the implementation of such theorems a complex and time consuming process as the relationships between such intervals are increasing exponentially. To overcome the previous problem, this paper presents a Rule-based Interval State Machine Algorithm (RISMA) for processing, monitoring, and analysing the behaviour of interval-based data, received from real-time sensors. The proposed intelligent algorithm uses the Interval State Machine (ISM) approach to model any number of interval-based data into well-defined states as well as inferring them. An interval-based state transition model and methodology are presented to identify the relationships between the different states of the proposed algorithm. By using such model, the unlimited number of relationships between similar large numbers of intervals can be reduced to only 18 direct relationships using the proposed well-defined states. For testing the proposed algorithm, necessary inference rules and code have been designed and applied to the continuous data received in near real-time from the stations of International Monitoring System (IMS) by the International Data Centre (IDC) of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). The CLIPS expert system shell has been used as the main rule engine for implementing the algorithm rules. Python programming language and the module "PyCLIPS" are used for building the necessary code for algorithm implementation. More than 1.7 million intervals constitute the Concise List of Frames (CLF) from 20 different seismic stations have been used for evaluating the proposed algorithm and evaluating stations behaviour and performance. The initial results showed that proposed algorithm can help in better understanding of the operation and performance of those stations. Different important information, such as alerts and some station performance parameters, can be derived from the proposed algorithm. For IMS interval-based data and at any period of time it is possible to analyze station behavior, determine the missing data, generate necessary alerts, and to measure some of station performance attributes. The details of the proposed algorithm, methodology, implementation, experimental results, advantages, and limitations of this research are presented. Finally, future directions and recommendations are discussed.

Fast dictionary-based reconstruction for diffusion spectrum imaging.

PubMed

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F; Yendiki, Anastasia; Wald, Lawrence L; Adalsteinsson, Elfar

2013-11-01

Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using MATLAB running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using principal component analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm.

Fast Dictionary-Based Reconstruction for Diffusion Spectrum Imaging

PubMed Central

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F.; Yendiki, Anastasia; Wald, Lawrence L.; Adalsteinsson, Elfar

2015-01-01

Diffusion Spectrum Imaging (DSI) reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation (TV) transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using Matlab running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using Principal Component Analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm. PMID:23846466

Deinterlacing using modular neural network

NASA Astrophysics Data System (ADS)

Woo, Dong H.; Eom, Il K.; Kim, Yoo S.

2004-05-01

Deinterlacing is the conversion process from the interlaced scan to progressive one. While many previous algorithms that are based on weighted-sum cause blurring in edge region, deinterlacing using neural network can reduce the blurring through recovering of high frequency component by learning process, and is found robust to noise. In proposed algorithm, input image is divided into edge and smooth region, and then, to each region, one neural network is assigned. Through this process, each neural network learns only patterns that are similar, therefore it makes learning more effective and estimation more accurate. But even within each region, there are various patterns such as long edge and texture in edge region. To solve this problem, modular neural network is proposed. In proposed modular neural network, two modules are combined in output node. One is for low frequency feature of local area of input image, and the other is for high frequency feature. With this structure, each modular neural network can learn different patterns with compensating for drawback of counterpart. Therefore it can adapt to various patterns within each region effectively. In simulation, the proposed algorithm shows better performance compared with conventional deinterlacing methods and single neural network method.

A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT

PubMed Central

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A.; Pan, Xiaochuan

2010-01-01

Purpose: Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. Methods: The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack–Noo-formula-based filteredbackprojection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. Results: The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. Conclusions: They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories. PMID:20175463

A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT.

PubMed

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A; Pan, Xiaochuan

2010-01-01

Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack-Noo-formula-based filteredback-projection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories.

Processor core for real time background identification of HD video based on OpenCV Gaussian mixture model algorithm

NASA Astrophysics Data System (ADS)

Genovese, Mariangela; Napoli, Ettore

2013-05-01

The identification of moving objects is a fundamental step in computer vision processing chains. The development of low cost and lightweight smart cameras steadily increases the request of efficient and high performance circuits able to process high definition video in real time. The paper proposes two processor cores aimed to perform the real time background identification on High Definition (HD, 1920 1080 pixel) video streams. The implemented algorithm is the OpenCV version of the Gaussian Mixture Model (GMM), an high performance probabilistic algorithm for the segmentation of the background that is however computationally intensive and impossible to implement on general purpose CPU with the constraint of real time processing. In the proposed paper, the equations of the OpenCV GMM algorithm are optimized in such a way that a lightweight and low power implementation of the algorithm is obtained. The reported performances are also the result of the use of state of the art truncated binary multipliers and ROM compression techniques for the implementation of the non-linear functions. The first circuit has commercial FPGA devices as a target and provides speed and logic resource occupation that overcome previously proposed implementations. The second circuit is oriented to an ASIC (UMC-90nm) standard cell implementation. Both implementations are able to process more than 60 frames per second in 1080p format, a frame rate compatible with HD television.

A Combined Independent Source Separation and Quality Index Optimization Method for Fetal ECG Extraction from Abdominal Maternal Leads

PubMed Central

Billeci, Lucia; Varanini, Maurizio

2017-01-01

The non-invasive fetal electrocardiogram (fECG) technique has recently received considerable interest in monitoring fetal health. The aim of our paper is to propose a novel fECG algorithm based on the combination of the criteria of independent source separation and of a quality index optimization (ICAQIO-based). The algorithm was compared with two methods applying the two different criteria independently—the ICA-based and the QIO-based methods—which were previously developed by our group. All three methods were tested on the recently implemented Fetal ECG Synthetic Database (FECGSYNDB). Moreover, the performance of the algorithm was tested on real data from the PhysioNet fetal ECG Challenge 2013 Database. The proposed combined method outperformed the other two algorithms on the FECGSYNDB (ICAQIO-based: 98.78%, QIO-based: 97.77%, ICA-based: 97.61%). Significant differences were obtained in particular in the conditions when uterine contractions and maternal and fetal ectopic beats occurred. On the real data, all three methods obtained very high performances, with the QIO-based method proving slightly better than the other two (ICAQIO-based: 99.38%, QIO-based: 99.76%, ICA-based: 99.37%). The findings from this study suggest that the proposed method could potentially be applied as a novel algorithm for accurate extraction of fECG, especially in critical recording conditions. PMID:28509860

Autonomous reinforcement learning with experience replay.

PubMed

Wawrzyński, Paweł; Tanwani, Ajay Kumar

2013-05-01

This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based on the actor-critic with experience replay whose step-sizes are determined on-line by an enhanced fixed point algorithm for on-line neural network training. An experimental study with simulated octopus arm and half-cheetah demonstrates the feasibility of the proposed algorithm to solve difficult learning control problems in an autonomous way within reasonably short time. Copyright © 2012 Elsevier Ltd. All rights reserved.

Using the Detectability Index to Predict P300 Speller Performance

PubMed Central

Mainsah, B.O.; Collins, L.M.; Throckmorton, C.S.

2017-01-01

Objective The P300 speller is a popular brain-computer interface (BCI) system that has been investigated as a potential communication alternative for individuals with severe neuromuscular limitations. To achieve acceptable accuracy levels for communication, the system requires repeated data measurements in a given signal condition to enhance the signal-to-noise ratio of elicited brain responses. These elicited brain responses, which are used as control signals, are embedded in noisy electroencephalography (EEG) data. The discriminability between target and non-target EEG responses defines a user’s performance with the system. A previous P300 speller model has been proposed to estimate system accuracy given a certain amount of data collection. However, the approach was limited to a static stopping algorithm, i.e. averaging over a fixed number of measurements, and the row-column paradigm. A generalized method that is also applicable to dynamic stopping algorithms and other stimulus paradigms is desirable. Approach We developed a new probabilistic model-based approach to predicting BCI performance, where performance functions can be derived analytically or via Monte Carlo methods. Within this framework, we introduce a new model for the P300 speller with the Bayesian dynamic stopping (DS) algorithm, by simplifying a multi-hypothesis to a binary hypothesis problem using the likelihood ratio test. Under a normality assumption, the performance functions for the Bayesian algorithm can be parameterized with the detectability index, a measure which quantifies the discriminability between target and non-target EEG responses. Main results Simulations with synthetic and empirical data provided initial verification of the proposed method of estimating performance with Bayesian DS using the detectability index. Analysis of results from previous online studies validated the proposed method. Significance The proposed method could serve as a useful tool to initially asses BCI performance without extensive online testing, in order to estimate the amount of data required to achieve a desired accuracy level. PMID:27705956

Using the detectability index to predict P300 speller performance

NASA Astrophysics Data System (ADS)

Mainsah, B. O.; Collins, L. M.; Throckmorton, C. S.

2016-12-01

Objective. The P300 speller is a popular brain-computer interface (BCI) system that has been investigated as a potential communication alternative for individuals with severe neuromuscular limitations. To achieve acceptable accuracy levels for communication, the system requires repeated data measurements in a given signal condition to enhance the signal-to-noise ratio of elicited brain responses. These elicited brain responses, which are used as control signals, are embedded in noisy electroencephalography (EEG) data. The discriminability between target and non-target EEG responses defines a user’s performance with the system. A previous P300 speller model has been proposed to estimate system accuracy given a certain amount of data collection. However, the approach was limited to a static stopping algorithm, i.e. averaging over a fixed number of measurements, and the row-column paradigm. A generalized method that is also applicable to dynamic stopping (DS) algorithms and other stimulus paradigms is desirable. Approach. We developed a new probabilistic model-based approach to predicting BCI performance, where performance functions can be derived analytically or via Monte Carlo methods. Within this framework, we introduce a new model for the P300 speller with the Bayesian DS algorithm, by simplifying a multi-hypothesis to a binary hypothesis problem using the likelihood ratio test. Under a normality assumption, the performance functions for the Bayesian algorithm can be parameterized with the detectability index, a measure which quantifies the discriminability between target and non-target EEG responses. Main results. Simulations with synthetic and empirical data provided initial verification of the proposed method of estimating performance with Bayesian DS using the detectability index. Analysis of results from previous online studies validated the proposed method. Significance. The proposed method could serve as a useful tool to initially assess BCI performance without extensive online testing, in order to estimate the amount of data required to achieve a desired accuracy level.

A 3D reconstruction algorithm for magneto-acoustic tomography with magnetic induction based on ultrasound transducer characteristics.

PubMed

Ma, Ren; Zhou, Xiaoqing; Zhang, Shunqi; Yin, Tao; Liu, Zhipeng

2016-12-21

In this study we present a three-dimensional (3D) reconstruction algorithm for magneto-acoustic tomography with magnetic induction (MAT-MI) based on the characteristics of the ultrasound transducer. The algorithm is investigated to solve the blur problem of the MAT-MI acoustic source image, which is caused by the ultrasound transducer and the scanning geometry. First, we established a transducer model matrix using measured data from the real transducer. With reference to the S-L model used in the computed tomography algorithm, a 3D phantom model of electrical conductivity is set up. Both sphere scanning and cylinder scanning geometries are adopted in the computer simulation. Then, using finite element analysis, the distribution of the eddy current and the acoustic source as well as the acoustic pressure can be obtained with the transducer model matrix. Next, using singular value decomposition, the inverse transducer model matrix together with the reconstruction algorithm are worked out. The acoustic source and the conductivity images are reconstructed using the proposed algorithm. Comparisons between an ideal point transducer and the realistic transducer are made to evaluate the algorithms. Finally, an experiment is performed using a graphite phantom. We found that images of the acoustic source reconstructed using the proposed algorithm are a better match than those using the previous one, the correlation coefficient of sphere scanning geometry is 98.49% and that of cylinder scanning geometry is 94.96%. Comparison between the ideal point transducer and the realistic transducer shows that the correlation coefficients are 90.2% in sphere scanning geometry and 86.35% in cylinder scanning geometry. The reconstruction of the graphite phantom experiment also shows a higher resolution using the proposed algorithm. We conclude that the proposed reconstruction algorithm, which considers the characteristics of the transducer, can obviously improve the resolution of the reconstructed image. This study can be applied to analyse the effect of the position of the transducer and the scanning geometry on imaging. It may provide a more precise method to reconstruct the conductivity distribution in MAT-MI.

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

PubMed Central

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

2014-01-01

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

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

PubMed

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

2014-01-01

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

Left ventricle segmentation via graph cut distribution matching.

PubMed

Ben Ayed, Ismail; Punithakumar, Kumaradevan; Li, Shuo; Islam, Ali; Chong, Jaron

2009-01-01

We present a discrete kernel density matching energy for segmenting the left ventricle cavity in cardiac magnetic resonance sequences. The energy and its graph cut optimization based on an original first-order approximation of the Bhattacharyya measure have not been proposed previously, and yield competitive results in nearly real-time. The algorithm seeks a region within each frame by optimization of two priors, one geometric (distance-based) and the other photometric, each measuring a distribution similarity between the region and a model learned from the first frame. Based on global rather than pixelwise information, the proposed algorithm does not require complex training and optimization with respect to geometric transformations. Unlike related active contour methods, it does not compute iterative updates of computationally expensive kernel densities. Furthermore, the proposed first-order analysis can be used for other intractable energies and, therefore, can lead to segmentation algorithms which share the flexibility of active contours and computational advantages of graph cuts. Quantitative evaluations over 2280 images acquired from 20 subjects demonstrated that the results correlate well with independent manual segmentations by an expert.

Efficient nonlinear equalizer for intra-channel nonlinearity compensation for next generation agile and dynamically reconfigurable optical networks.

PubMed

Malekiha, Mahdi; Tselniker, Igor; Plant, David V

2016-02-22

In this work, we propose and experimentally demonstrate a novel low-complexity technique for fiber nonlinearity compensation. We achieved a transmission distance of 2818 km for a 32-GBaud dual-polarization 16QAM signal. For efficient implantation, and to facilitate integration with conventional digital signal processing (DSP) approaches, we independently compensate fiber nonlinearities after linear impairment equalization. Therefore this algorithm can be easily implemented in currently deployed transmission systems after using linear DSP. The proposed equalizer operates at one sample per symbol and requires only one computation step. The structure of the algorithm is based on a first-order perturbation model with quantized perturbation coefficients. Also, it does not require any prior calculation or detailed knowledge of the transmission system. We identified common symmetries between perturbation coefficients to avoid duplicate and unnecessary operations. In addition, we use only a few adaptive filter coefficients by grouping multiple nonlinear terms and dedicating only one adaptive nonlinear filter coefficient to each group. Finally, the complexity of the proposed algorithm is lower than previously studied nonlinear equalizers by more than one order of magnitude.

Hair and bare skin discrimination for laser-assisted hair removal systems.

PubMed

Cayir, Sercan; Yetik, Imam Samil

2017-07-01

Laser-assisted hair removal devices aim to remove body hair permanently. In most cases, these devices irradiate the whole area of the skin with a homogenous power density. Thus, a significant portion of the skin, where hair is not present, is burnt unnecessarily causing health risks. Therefore, methods that can distinguish hair regions automatically would be very helpful avoiding these unnecessary applications of laser. This study proposes a new system of algorithms to detect hair regions with the help of a digital camera. Unlike previous limited number of studies, our methods are very fast allowing for real-time application. Proposed methods are based on certain features derived from histograms of hair and skin regions. We compare our algorithm with competing methods in terms of localization performance and computation time and show that a much faster real-time accurate localization of hair regions is possible with the proposed method. Our results show that the algorithm we have developed is extremely fast (around 45 milliseconds) allowing for real-time application with high accuracy hair localization ( 96.48 %).

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

Zhang, Jing, E-mail: jing.zhang2@duke.edu; Ghate, Sujata V.; Yoon, Sora C.

Purpose: Mammography is the most widely accepted and utilized screening modality for early breast cancer detection. Providing high quality mammography education to radiology trainees is essential, since excellent interpretation skills are needed to ensure the highest benefit of screening mammography for patients. The authors have previously proposed a computer-aided education system based on trainee models. Those models relate human-assessed image characteristics to trainee error. In this study, the authors propose to build trainee models that utilize features automatically extracted from images using computer vision algorithms to predict likelihood of missing each mass by the trainee. This computer vision-based approach tomore » trainee modeling will allow for automatically searching large databases of mammograms in order to identify challenging cases for each trainee. Methods: The authors’ algorithm for predicting the likelihood of missing a mass consists of three steps. First, a mammogram is segmented into air, pectoral muscle, fatty tissue, dense tissue, and mass using automated segmentation algorithms. Second, 43 features are extracted using computer vision algorithms for each abnormality identified by experts. Third, error-making models (classifiers) are applied to predict the likelihood of trainees missing the abnormality based on the extracted features. The models are developed individually for each trainee using his/her previous reading data. The authors evaluated the predictive performance of the proposed algorithm using data from a reader study in which 10 subjects (7 residents and 3 novices) and 3 experts read 100 mammographic cases. Receiver operating characteristic (ROC) methodology was applied for the evaluation. Results: The average area under the ROC curve (AUC) of the error-making models for the task of predicting which masses will be detected and which will be missed was 0.607 (95% CI,0.564-0.650). This value was statistically significantly different from 0.5 (p < 0.0001). For the 7 residents only, the AUC performance of the models was 0.590 (95% CI,0.537-0.642) and was also significantly higher than 0.5 (p = 0.0009). Therefore, generally the authors’ models were able to predict which masses were detected and which were missed better than chance. Conclusions: The authors proposed an algorithm that was able to predict which masses will be detected and which will be missed by each individual trainee. This confirms existence of error-making patterns in the detection of masses among radiology trainees. Furthermore, the proposed methodology will allow for the optimized selection of difficult cases for the trainees in an automatic and efficient manner.« less

Forest height estimation from mountain forest areas using general model-based decomposition for polarimetric interferometric synthetic aperture radar images

NASA Astrophysics Data System (ADS)

Minh, Nghia Pham; Zou, Bin; Cai, Hongjun; Wang, Chengyi

2014-01-01

The estimation of forest parameters over mountain forest areas using polarimetric interferometric synthetic aperture radar (PolInSAR) images is one of the greatest interests in remote sensing applications. For mountain forest areas, scattering mechanisms are strongly affected by the ground topography variations. Most of the previous studies in modeling microwave backscattering signatures of forest area have been carried out over relatively flat areas. Therefore, a new algorithm for the forest height estimation from mountain forest areas using the general model-based decomposition (GMBD) for PolInSAR image is proposed. This algorithm enables the retrieval of not only the forest parameters, but also the magnitude associated with each mechanism. In addition, general double- and single-bounce scattering models are proposed to fit for the cross-polarization and off-diagonal term by separating their independent orientation angle, which remains unachieved in the previous model-based decompositions. The efficiency of the proposed approach is demonstrated with simulated data from PolSARProSim software and ALOS-PALSAR spaceborne PolInSAR datasets over the Kalimantan areas, Indonesia. Experimental results indicate that forest height could be effectively estimated by GMBD.

Correlation Filter Learning Toward Peak Strength for Visual Tracking.

PubMed

Sui, Yao; Wang, Guanghui; Zhang, Li

2018-04-01

This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering. A new peak strength metric is proposed to measure the discriminative capability of the learned correlation filter. It is demonstrated that the proposed approach effectively strengthens the peak of the correlation response, leading to more discriminative performance than previous methods. Extensive experiments on a challenging visual tracking benchmark demonstrate that the proposed tracker outperforms most state-of-the-art methods.

Feature extraction and classification algorithms for high dimensional data

NASA Technical Reports Server (NTRS)

Lee, Chulhee; Landgrebe, David

1993-01-01

Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.

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

Stassi, D.; Ma, H.; Schmidt, T. G., E-mail: taly.gilat-schmidt@marquette.edu

Purpose: Reconstructing a low-motion cardiac phase is expected to improve coronary artery visualization in coronary computed tomography angiography (CCTA) exams. This study developed an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. The algorithm uses prospectively gated, single-beat, multiphase data made possible by wide cone-beam imaging. The proposed algorithm differs from previous approaches because the optimal phase is identified based on vessel image quality (IQ) directly, compared to previous approaches that included motion estimation and interphase processing. Because there is no processing of interphase information, the algorithm can be applied to any sampling of image phases, makingmore » it suited for prospectively gated studies where only a subset of phases are available. Methods: An automated algorithm was developed to select the optimal phase based on quantitative IQ metrics. For each reconstructed slice at each reconstructed phase, an image quality metric was calculated based on measures of circularity and edge strength of through-plane vessels. The image quality metric was aggregated across slices, while a metric of vessel-location consistency was used to ignore slices that did not contain through-plane vessels. The algorithm performance was evaluated using two observer studies. Fourteen single-beat cardiac CT exams (Revolution CT, GE Healthcare, Chalfont St. Giles, UK) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Pairwise inter-reader and reader-algorithm agreement was evaluated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC) between the reader and algorithm-selected phases. A reader-consensus best phase was determined and compared to the algorithm selected phase. In cases where the algorithm and consensus best phases differed by more than 2%, IQ was scored by three readers using a five point Likert scale. Results: There was no statistically significant difference between inter-reader and reader-algorithm agreement for either MAD or CCC metrics (p > 0.1). The algorithm phase was within 2% of the consensus phase in 15/21 of cases. The average absolute difference between consensus and algorithm best phases was 2.29% ± 2.47%, with a maximum difference of 8%. Average image quality scores for the algorithm chosen best phase were 4.01 ± 0.65 overall, 3.33 ± 1.27 for right coronary artery (RCA), 4.50 ± 0.35 for left anterior descending (LAD) artery, and 4.50 ± 0.35 for left circumflex artery (LCX). Average image quality scores for the consensus best phase were 4.11 ± 0.54 overall, 3.44 ± 1.03 for RCA, 4.39 ± 0.39 for LAD, and 4.50 ± 0.18 for LCX. There was no statistically significant difference (p > 0.1) between the image quality scores of the algorithm phase and the consensus phase. Conclusions: The proposed algorithm was statistically equivalent to a reader in selecting an optimal cardiac phase for CCTA exams. When reader and algorithm phases differed by >2%, image quality as rated by blinded readers was statistically equivalent. By detecting the optimal phase for CCTA reconstruction, the proposed algorithm is expected to improve coronary artery visualization in CCTA exams.« less

Parallel algorithms for placement and routing in VLSI design. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Brouwer, Randall Jay

1991-01-01

The computational requirements for high quality synthesis, analysis, and verification of very large scale integration (VLSI) designs have rapidly increased with the fast growing complexity of these designs. Research in the past has focused on the development of heuristic algorithms, special purpose hardware accelerators, or parallel algorithms for the numerous design tasks to decrease the time required for solution. Two new parallel algorithms are proposed for two VLSI synthesis tasks, standard cell placement and global routing. The first algorithm, a parallel algorithm for global routing, uses hierarchical techniques to decompose the routing problem into independent routing subproblems that are solved in parallel. Results are then presented which compare the routing quality to the results of other published global routers and which evaluate the speedups attained. The second algorithm, a parallel algorithm for cell placement and global routing, hierarchically integrates a quadrisection placement algorithm, a bisection placement algorithm, and the previous global routing algorithm. Unique partitioning techniques are used to decompose the various stages of the algorithm into independent tasks which can be evaluated in parallel. Finally, results are presented which evaluate the various algorithm alternatives and compare the algorithm performance to other placement programs. Measurements are presented on the parallel speedups available.

Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms.

PubMed

Rashno, Abdolreza; Koozekanani, Dara D; Drayna, Paul M; Nazari, Behzad; Sadri, Saeed; Rabbani, Hossein; Parhi, Keshab K

2018-05-01

This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image is transformed into three sets: (true), (indeterminate) that represents noise, and (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set , and a new -correction operation is introduced to compute the set in neutrosophic domain. Second, a graph shortest-path method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights . Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively.

Optimal erasure protection for scalably compressed video streams with limited retransmission.

PubMed

Taubman, David; Thie, Johnson

2005-08-01

This paper shows how the priority encoding transmission (PET) framework may be leveraged to exploit both unequal error protection and limited retransmission for RD-optimized delivery of streaming media. Previous work on scalable media protection with PET has largely ignored the possibility of retransmission. Conversely, the PET framework has not been harnessed by the substantial body of previous work on RD optimized hybrid forward error correction/automatic repeat request schemes. We limit our attention to sources which can be modeled as independently compressed frames (e.g., video frames), where each element in the scalable representation of each frame can be transmitted in one or both of two transmission slots. An optimization algorithm determines the level of protection which should be assigned to each element in each slot, subject to transmission bandwidth constraints. To balance the protection assigned to elements which are being transmitted for the first time with those which are being retransmitted, the proposed algorithm formulates a collection of hypotheses concerning its own behavior in future transmission slots. We show how the PET framework allows for a decoupled optimization algorithm with only modest complexity. Experimental results obtained with Motion JPEG2000 compressed video demonstrate that substantial performance benefits can be obtained using the proposed framework.

A Probability-Based Algorithm Using Image Sensors to Track the LED in a Vehicle Visible Light Communication System.

PubMed

Huynh, Phat; Do, Trong-Hop; Yoo, Myungsik

2017-02-10

This paper proposes a probability-based algorithm to track the LED in vehicle visible light communication systems using a camera. In this system, the transmitters are the vehicles' front and rear LED lights. The receivers are high speed cameras that take a series of images of the LEDs. ThedataembeddedinthelightisextractedbyfirstdetectingthepositionoftheLEDsintheseimages. Traditionally, LEDs are detected according to pixel intensity. However, when the vehicle is moving, motion blur occurs in the LED images, making it difficult to detect the LEDs. Particularly at high speeds, some frames are blurred at a high degree, which makes it impossible to detect the LED as well as extract the information embedded in these frames. The proposed algorithm relies not only on the pixel intensity, but also on the optical flow of the LEDs and on statistical information obtained from previous frames. Based on this information, the conditional probability that a pixel belongs to a LED is calculated. Then, the position of LED is determined based on this probability. To verify the suitability of the proposed algorithm, simulations are conducted by considering the incidents that can happen in a real-world situation, including a change in the position of the LEDs at each frame, as well as motion blur due to the vehicle speed.

Probabilistic n/γ discrimination with robustness against outliers for use in neutron profile monitors

NASA Astrophysics Data System (ADS)

Uchida, Y.; Takada, E.; Fujisaki, A.; Kikuchi, T.; Ogawa, K.; Isobe, M.

2017-08-01

A method to stochastically discriminate neutron and γ-ray signals measured with a stilbene organic scintillator is proposed. Each pulse signal was stochastically categorized into two groups: neutron and γ-ray. In previous work, the Expectation Maximization (EM) algorithm was used with the assumption that the measured data followed a Gaussian mixture distribution. It was shown that probabilistic discrimination between these groups is possible. Moreover, by setting the initial parameters for the Gaussian mixture distribution with a k-means algorithm, the possibility of automatic discrimination was demonstrated. In this study, the Student's t-mixture distribution was used as a probabilistic distribution with the EM algorithm to improve the robustness against the effect of outliers caused by pileup of the signals. To validate the proposed method, the figures of merit (FOMs) were compared for the EM algorithm assuming a t-mixture distribution and a Gaussian mixture distribution. The t-mixture distribution resulted in an improvement of the FOMs compared with the Gaussian mixture distribution. The proposed data processing technique is a promising tool not only for neutron and γ-ray discrimination in fusion experiments but also in other fields, for example, homeland security, cancer therapy with high energy particles, nuclear reactor decommissioning, pattern recognition, and so on.

Curvature correction of retinal OCTs using graph-based geometry detection

NASA Astrophysics Data System (ADS)

Kafieh, Raheleh; Rabbani, Hossein; Abramoff, Michael D.; Sonka, Milan

2013-05-01

In this paper, we present a new algorithm as an enhancement and preprocessing step for acquired optical coherence tomography (OCT) images of the retina. The proposed method is composed of two steps, first of which is a denoising algorithm with wavelet diffusion based on a circular symmetric Laplacian model, and the second part can be described in terms of graph-based geometry detection and curvature correction according to the hyper-reflective complex layer in the retina. The proposed denoising algorithm showed an improvement of contrast-to-noise ratio from 0.89 to 1.49 and an increase of signal-to-noise ratio (OCT image SNR) from 18.27 to 30.43 dB. By applying the proposed method for estimation of the interpolated curve using a full automatic method, the mean ± SD unsigned border positioning error was calculated for normal and abnormal cases. The error values of 2.19 ± 1.25 and 8.53 ± 3.76 µm were detected for 200 randomly selected slices without pathological curvature and 50 randomly selected slices with pathological curvature, respectively. The important aspect of this algorithm is its ability in detection of curvature in strongly pathological images that surpasses previously introduced methods; the method is also fast, compared to the relatively low speed of similar methods.

Quantitative analysis of terahertz spectra for illicit drugs using adaptive-range micro-genetic algorithm

NASA Astrophysics Data System (ADS)

Chen, Yi; Ma, Yong; Lu, Zheng; Peng, Bei; Chen, Qin

2011-08-01

In the field of anti-illicit drug applications, many suspicious mixture samples might consist of various drug components—for example, a mixture of methamphetamine, heroin, and amoxicillin—which makes spectral identification very difficult. A terahertz spectroscopic quantitative analysis method using an adaptive range micro-genetic algorithm with a variable internal population (ARVIPɛμGA) has been proposed. Five mixture cases are discussed using ARVIPɛμGA driven quantitative terahertz spectroscopic analysis in this paper. The devised simulation results show agreement with the previous experimental results, which suggested that the proposed technique has potential applications for terahertz spectral identifications of drug mixture components. The results show agreement with the results obtained using other experimental and numerical techniques.

An improved K-means clustering method for cDNA microarray image segmentation.

PubMed

Wang, T N; Li, T J; Shao, G F; Wu, S X

2015-07-14

Microarray technology is a powerful tool for human genetic research and other biomedical applications. Numerous improvements to the standard K-means algorithm have been carried out to complete the image segmentation step. However, most of the previous studies classify the image into two clusters. In this paper, we propose a novel K-means algorithm, which first classifies the image into three clusters, and then one of the three clusters is divided as the background region and the other two clusters, as the foreground region. The proposed method was evaluated on six different data sets. The analyses of accuracy, efficiency, expression values, special gene spots, and noise images demonstrate the effectiveness of our method in improving the segmentation quality.

Joint polarization tracking and channel equalization based on radius-directed linear Kalman filter

NASA Astrophysics Data System (ADS)

Zhang, Qun; Yang, Yanfu; Zhong, Kangping; Liu, Jie; Wu, Xiong; Yao, Yong

2018-01-01

We propose a joint polarization tracking and channel equalization scheme based on radius-directed linear Kalman filter (RD-LKF) by introducing the butterfly finite-impulse-response (FIR) filter in our previously proposed RD-LKF method. Along with the fast polarization tracking, it can also simultaneously compensate the inter-symbol interference (ISI) effects including residual chromatic dispersion and polarization mode dispersion. Compared with the conventional radius-directed equalizer (RDE) algorithm, it is demonstrated experimentally that three times faster convergence speed, one order of magnitude better tracking capability, and better BER performance is obtained in polarization division multiplexing 16 quadrature amplitude modulation system. Besides, the influences of the algorithm parameters on the convergence and the tracking performance are investigated by numerical simulation.

Analysis, calculation and utilization of the k-balance attribute in interdependent networks

NASA Astrophysics Data System (ADS)

Liu, Zheng; Li, Qing; Wang, Dan; Xu, Mingwei

2018-05-01

Interdependent networks, where two networks depend on each other, are becoming more and more significant in modern systems. From previous work, it can be concluded that interdependent networks are more vulnerable than a single network. The robustness in interdependent networks deserves special attention. In this paper, we propose a metric of robustness from a new perspective-the balance. First, we define the balance-coefficient of the interdependent system. Based on precise analysis and derivation, we prove some significant theories and provide an efficient algorithm to compute the balance-coefficient. Finally, we propose an optimal solution to reduce the balance-coefficient to enhance the robustness of the given system. Comprehensive experiments confirm the efficiency of our algorithms.

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

PubMed

Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

2013-05-01

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs).

PubMed

Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

2014-01-01

Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

Models of performance of evolutionary program induction algorithms based on indicators of problem difficulty.

PubMed

Graff, Mario; Poli, Riccardo; Flores, Juan J

2013-01-01

Modeling the behavior of algorithms is the realm of evolutionary algorithm theory. From a practitioner's point of view, theory must provide some guidelines regarding which algorithm/parameters to use in order to solve a particular problem. Unfortunately, most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. However, in recent work (Graff and Poli, 2008, 2010), where we developed a method to practically estimate the performance of evolutionary program-induction algorithms (EPAs), we started addressing this issue. The method was quite general; however, it suffered from some limitations: it required the identification of a set of reference problems, it required hand picking a distance measure in each particular domain, and the resulting models were opaque, typically being linear combinations of 100 features or more. In this paper, we propose a significant improvement of this technique that overcomes the three limitations of our previous method. We achieve this through the use of a novel set of features for assessing problem difficulty for EPAs which are very general, essentially based on the notion of finite difference. To show the capabilities or our technique and to compare it with our previous performance models, we create models for the same two important classes of problems-symbolic regression on rational functions and Boolean function induction-used in our previous work. We model a variety of EPAs. The comparison showed that for the majority of the algorithms and problem classes, the new method produced much simpler and more accurate models than before. To further illustrate the practicality of the technique and its generality (beyond EPAs), we have also used it to predict the performance of both autoregressive models and EPAs on the problem of wind speed forecasting, obtaining simpler and more accurate models that outperform in all cases our previous performance models.

Real-time generation of infrared ocean scene based on GPU

NASA Astrophysics Data System (ADS)

Jiang, Zhaoyi; Wang, Xun; Lin, Yun; Jin, Jianqiu

2007-12-01

Infrared (IR) image synthesis for ocean scene has become more and more important nowadays, especially for remote sensing and military application. Although a number of works present ready-to-use simulations, those techniques cover only a few possible ways of water interacting with the environment. And the detail calculation of ocean temperature is rarely considered by previous investigators. With the advance of programmable features of graphic card, many algorithms previously limited to offline processing have become feasible for real-time usage. In this paper, we propose an efficient algorithm for real-time rendering of infrared ocean scene using the newest features of programmable graphics processors (GPU). It differs from previous works in three aspects: adaptive GPU-based ocean surface tessellation, sophisticated balance equation of thermal balance for ocean surface, and GPU-based rendering for infrared ocean scene. Finally some results of infrared image are shown, which are in good accordance with real images.

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Guo, Yiqing; Jia, Xiuping; Paull, David

2018-06-01

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

Tracking tumor boundary in MV-EPID images without implanted markers: A feasibility study.

PubMed

Zhang, Xiaoyong; Homma, Noriyasu; Ichiji, Kei; Takai, Yoshihiro; Yoshizawa, Makoto

2015-05-01

To develop a markerless tracking algorithm to track the tumor boundary in megavoltage (MV)-electronic portal imaging device (EPID) images for image-guided radiation therapy. A level set method (LSM)-based algorithm is developed to track tumor boundary in EPID image sequences. Given an EPID image sequence, an initial curve is manually specified in the first frame. Driven by a region-scalable energy fitting function, the initial curve automatically evolves toward the tumor boundary and stops on the desired boundary while the energy function reaches its minimum. For the subsequent frames, the tracking algorithm updates the initial curve by using the tracking result in the previous frame and reuses the LSM to detect the tumor boundary in the subsequent frame so that the tracking processing can be continued without user intervention. The tracking algorithm is tested on three image datasets, including a 4-D phantom EPID image sequence, four digitally deformable phantom image sequences with different noise levels, and four clinical EPID image sequences acquired in lung cancer treatment. The tracking accuracy is evaluated based on two metrics: centroid localization error (CLE) and volume overlap index (VOI) between the tracking result and the ground truth. For the 4-D phantom image sequence, the CLE is 0.23 ± 0.20 mm, and VOI is 95.6% ± 0.2%. For the digital phantom image sequences, the total CLE and VOI are 0.11 ± 0.08 mm and 96.7% ± 0.7%, respectively. In addition, for the clinical EPID image sequences, the proposed algorithm achieves 0.32 ± 0.77 mm in the CLE and 72.1% ± 5.5% in the VOI. These results demonstrate the effectiveness of the authors' proposed method both in tumor localization and boundary tracking in EPID images. In addition, compared with two existing tracking algorithms, the proposed method achieves a higher accuracy in tumor localization. In this paper, the authors presented a feasibility study of tracking tumor boundary in EPID images by using a LSM-based algorithm. Experimental results conducted on phantom and clinical EPID images demonstrated the effectiveness of the tracking algorithm for visible tumor target. Compared with previous tracking methods, the authors' algorithm has the potential to improve the tracking accuracy in radiation therapy. In addition, real-time tumor boundary information within the irradiation field will be potentially useful for further applications, such as adaptive beam delivery, dose evaluation.

Tracking tumor boundary in MV-EPID images without implanted markers: A feasibility study

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

Zhang, Xiaoyong, E-mail: xiaoyong@ieee.org; Homma, Noriyasu, E-mail: homma@ieee.org; Ichiji, Kei, E-mail: ichiji@yoshizawa.ecei.tohoku.ac.jp

2015-05-15

Purpose: To develop a markerless tracking algorithm to track the tumor boundary in megavoltage (MV)-electronic portal imaging device (EPID) images for image-guided radiation therapy. Methods: A level set method (LSM)-based algorithm is developed to track tumor boundary in EPID image sequences. Given an EPID image sequence, an initial curve is manually specified in the first frame. Driven by a region-scalable energy fitting function, the initial curve automatically evolves toward the tumor boundary and stops on the desired boundary while the energy function reaches its minimum. For the subsequent frames, the tracking algorithm updates the initial curve by using the trackingmore » result in the previous frame and reuses the LSM to detect the tumor boundary in the subsequent frame so that the tracking processing can be continued without user intervention. The tracking algorithm is tested on three image datasets, including a 4-D phantom EPID image sequence, four digitally deformable phantom image sequences with different noise levels, and four clinical EPID image sequences acquired in lung cancer treatment. The tracking accuracy is evaluated based on two metrics: centroid localization error (CLE) and volume overlap index (VOI) between the tracking result and the ground truth. Results: For the 4-D phantom image sequence, the CLE is 0.23 ± 0.20 mm, and VOI is 95.6% ± 0.2%. For the digital phantom image sequences, the total CLE and VOI are 0.11 ± 0.08 mm and 96.7% ± 0.7%, respectively. In addition, for the clinical EPID image sequences, the proposed algorithm achieves 0.32 ± 0.77 mm in the CLE and 72.1% ± 5.5% in the VOI. These results demonstrate the effectiveness of the authors’ proposed method both in tumor localization and boundary tracking in EPID images. In addition, compared with two existing tracking algorithms, the proposed method achieves a higher accuracy in tumor localization. Conclusions: In this paper, the authors presented a feasibility study of tracking tumor boundary in EPID images by using a LSM-based algorithm. Experimental results conducted on phantom and clinical EPID images demonstrated the effectiveness of the tracking algorithm for visible tumor target. Compared with previous tracking methods, the authors’ algorithm has the potential to improve the tracking accuracy in radiation therapy. In addition, real-time tumor boundary information within the irradiation field will be potentially useful for further applications, such as adaptive beam delivery, dose evaluation.« less

Energy minimization of mobile video devices with a hardware H.264/AVC encoder based on energy-rate-distortion optimization

NASA Astrophysics Data System (ADS)

Kang, Donghun; Lee, Jungeon; Jung, Jongpil; Lee, Chul-Hee; Kyung, Chong-Min

2014-09-01

In mobile video systems powered by battery, reducing the encoder's compression energy consumption is critical to prolong its lifetime. Previous Energy-rate-distortion (E-R-D) optimization methods based on a software codec is not suitable for practical mobile camera systems because the energy consumption is too large and encoding rate is too low. In this paper, we propose an E-R-D model for the hardware codec based on the gate-level simulation framework to measure the switching activity and the energy consumption. From the proposed E-R-D model, an energy minimizing algorithm for mobile video camera sensor have been developed with the GOP (Group of Pictures) size and QP(Quantization Parameter) as run-time control variables. Our experimental results show that the proposed algorithm provides up to 31.76% of energy consumption saving while satisfying the rate and distortion constraints.

Threshold secret sharing scheme based on phase-shifting interferometry.

PubMed

Deng, Xiaopeng; Shi, Zhengang; Wen, Wei

2016-11-01

We propose a new method for secret image sharing with the (3,N) threshold scheme based on phase-shifting interferometry. The secret image, which is multiplied with an encryption key in advance, is first encrypted by using Fourier transformation. Then, the encoded image is shared into N shadow images based on the recording principle of phase-shifting interferometry. Based on the reconstruction principle of phase-shifting interferometry, any three or more shadow images can retrieve the secret image, while any two or fewer shadow images cannot obtain any information of the secret image. Thus, a (3,N) threshold secret sharing scheme can be implemented. Compared with our previously reported method, the algorithm of this paper is suited for not only a binary image but also a gray-scale image. Moreover, the proposed algorithm can obtain a larger threshold value t. Simulation results are presented to demonstrate the feasibility of the proposed method.

Localized Dictionaries Based Orientation Field Estimation for Latent Fingerprints.

PubMed

Xiao Yang; Jianjiang Feng; Jie Zhou

2014-05-01

Dictionary based orientation field estimation approach has shown promising performance for latent fingerprints. In this paper, we seek to exploit stronger prior knowledge of fingerprints in order to further improve the performance. Realizing that ridge orientations at different locations of fingerprints have different characteristics, we propose a localized dictionaries-based orientation field estimation algorithm, in which noisy orientation patch at a location output by a local estimation approach is replaced by real orientation patch in the local dictionary at the same location. The precondition of applying localized dictionaries is that the pose of the latent fingerprint needs to be estimated. We propose a Hough transform-based fingerprint pose estimation algorithm, in which the predictions about fingerprint pose made by all orientation patches in the latent fingerprint are accumulated. Experimental results on challenging latent fingerprint datasets show the proposed method outperforms previous ones markedly.

A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors

PubMed Central

Han, Manhyung; Bang, Jae Hun; Nugent, Chris; McClean, Sally; Lee, Sungyoung

2014-01-01

Activity recognition for the purposes of recognizing a user's intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user's activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%. PMID:25184486

3D segmentations of neuronal nuclei from confocal microscope image stacks

PubMed Central

LaTorre, Antonio; Alonso-Nanclares, Lidia; Muelas, Santiago; Peña, José-María; DeFelipe, Javier

2013-01-01

In this paper, we present an algorithm to create 3D segmentations of neuronal cells from stacks of previously segmented 2D images. The idea behind this proposal is to provide a general method to reconstruct 3D structures from 2D stacks, regardless of how these 2D stacks have been obtained. The algorithm not only reuses the information obtained in the 2D segmentation, but also attempts to correct some typical mistakes made by the 2D segmentation algorithms (for example, under segmentation of tightly-coupled clusters of cells). We have tested our algorithm in a real scenario—the segmentation of the neuronal nuclei in different layers of the rat cerebral cortex. Several representative images from different layers of the cerebral cortex have been considered and several 2D segmentation algorithms have been compared. Furthermore, the algorithm has also been compared with the traditional 3D Watershed algorithm and the results obtained here show better performance in terms of correctly identified neuronal nuclei. PMID:24409123

3D segmentations of neuronal nuclei from confocal microscope image stacks.

PubMed

Latorre, Antonio; Alonso-Nanclares, Lidia; Muelas, Santiago; Peña, José-María; Defelipe, Javier

2013-01-01

In this paper, we present an algorithm to create 3D segmentations of neuronal cells from stacks of previously segmented 2D images. The idea behind this proposal is to provide a general method to reconstruct 3D structures from 2D stacks, regardless of how these 2D stacks have been obtained. The algorithm not only reuses the information obtained in the 2D segmentation, but also attempts to correct some typical mistakes made by the 2D segmentation algorithms (for example, under segmentation of tightly-coupled clusters of cells). We have tested our algorithm in a real scenario-the segmentation of the neuronal nuclei in different layers of the rat cerebral cortex. Several representative images from different layers of the cerebral cortex have been considered and several 2D segmentation algorithms have been compared. Furthermore, the algorithm has also been compared with the traditional 3D Watershed algorithm and the results obtained here show better performance in terms of correctly identified neuronal nuclei.

Analysis and improvement of the quantum image matching

NASA Astrophysics Data System (ADS)

Dang, Yijie; Jiang, Nan; Hu, Hao; Zhang, Wenyin

2017-11-01

We investigate the quantum image matching algorithm proposed by Jiang et al. (Quantum Inf Process 15(9):3543-3572, 2016). Although the complexity of this algorithm is much better than the classical exhaustive algorithm, there may be an error in it: After matching the area between two images, only the pixel at the upper left corner of the matched area played part in following steps. That is to say, the paper only matched one pixel, instead of an area. If more than one pixels in the big image are the same as the one at the upper left corner of the small image, the algorithm will randomly measure one of them, which causes the error. In this paper, an improved version is presented which takes full advantage of the whole matched area to locate a small image in a big image. The theoretical analysis indicates that the network complexity is higher than the previous algorithm, but it is still far lower than the classical algorithm. Hence, this algorithm is still efficient.

Autofocus algorithm using one-dimensional Fourier transform and Pearson correlation

NASA Astrophysics Data System (ADS)

Bueno Mario, A.; Alvarez-Borrego, Josue; Acho, L.

2004-10-01

A new autofocus algorithm based on one-dimensional Fourier transform and Pearson correlation for Z automatized microscope is proposed. Our goal is to determine in fast response time and accuracy, the best focused plane through an algorithm. We capture in bright and dark field several images set at different Z distances from biological organism sample. The algorithm uses the one-dimensional Fourier transform to obtain the image frequency content of a vectors pattern previously defined comparing the Pearson correlation of these frequency vectors versus the reference image frequency vector, the most out of focus image, we find the best focusing. Experimental results showed the algorithm has fast response time and accuracy in getting the best focus plane from captured images. In conclusions, the algorithm can be implemented in real time systems due fast response time, accuracy and robustness. The algorithm can be used to get focused images in bright and dark field and it can be extended to include fusion techniques to construct multifocus final images beyond of this paper.

Selective epidemic vaccination under the performant routing algorithms

NASA Astrophysics Data System (ADS)

Bamaarouf, O.; Alweimine, A. Ould Baba; Rachadi, A.; EZ-Zahraouy, H.

2018-04-01

Despite the extensive research on traffic dynamics and epidemic spreading, the effect of the routing algorithms strategies on the traffic-driven epidemic spreading has not received an adequate attention. It is well known that more performant routing algorithm strategies are used to overcome the congestion problem. However, our main result shows unexpectedly that these algorithms favor the virus spreading more than the case where the shortest path based algorithm is used. In this work, we studied the virus spreading in a complex network using the efficient path and the global dynamic routing algorithms as compared to shortest path strategy. Some previous studies have tried to modify the routing rules to limit the virus spreading, but at the expense of reducing the traffic transport efficiency. This work proposed a solution to overcome this drawback by using a selective vaccination procedure instead of a random vaccination used often in the literature. We found that the selective vaccination succeeded in eradicating the virus better than a pure random intervention for the performant routing algorithm strategies.

Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning.

PubMed

Li, Bing; Yuan, Chunfeng; Xiong, Weihua; Hu, Weiming; Peng, Houwen; Ding, Xinmiao; Maybank, Steve

2017-12-01

In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (MIL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse -graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the MIL. Experiments and analyses in many practical applications prove the effectiveness of the M IL.

Forward collision warning based on kernelized correlation filters

NASA Astrophysics Data System (ADS)

Pu, Jinchuan; Liu, Jun; Zhao, Yong

2017-07-01

A vehicle detection and tracking system is one of the indispensable methods to reduce the occurrence of traffic accidents. The nearest vehicle is the most likely to cause harm to us. So, this paper will do more research on about the nearest vehicle in the region of interest (ROI). For this system, high accuracy, real-time and intelligence are the basic requirement. In this paper, we set up a system that combines the advanced KCF tracking algorithm with the HaarAdaBoost detection algorithm. The KCF algorithm reduces computation time and increase the speed through the cyclic shift and diagonalization. This algorithm satisfies the real-time requirement. At the same time, Haar features also have the same advantage of simple operation and high speed for detection. The combination of this two algorithm contribute to an obvious improvement of the system running rate comparing with previous works. The detection result of the HaarAdaBoost classifier provides the initial value for the KCF algorithm. This fact optimizes KCF algorithm flaws that manual car marking in the initial phase, which is more scientific and more intelligent. Haar detection and KCF tracking with Histogram of Oriented Gradient (HOG) ensures the accuracy of the system. We evaluate the performance of framework on dataset that were self-collected. The experimental results demonstrate that the proposed method is robust and real-time. The algorithm can effectively adapt to illumination variation, even in the night it can meet the detection and tracking requirements, which is an improvement compared with the previous work.

Taboo search algorithm for item assignment in synchronized zone automated order picking system

NASA Astrophysics Data System (ADS)

Wu, Yingying; Wu, Yaohua

2014-07-01

The idle time which is part of the order fulfillment time is decided by the number of items in the zone; therefore the item assignment method affects the picking efficiency. Whereas previous studies only focus on the balance of number of kinds of items between different zones but not the number of items and the idle time in each zone. In this paper, an idle factor is proposed to measure the idle time exactly. The idle factor is proven to obey the same vary trend with the idle time, so the object of this problem can be simplified from minimizing idle time to minimizing idle factor. Based on this, the model of item assignment problem in synchronized zone automated order picking system is built. The model is a form of relaxation of parallel machine scheduling problem which had been proven to be NP-complete. To solve the model, a taboo search algorithm is proposed. The main idea of the algorithm is minimizing the greatest idle factor of zones with the 2-exchange algorithm. Finally, the simulation which applies the data collected from a tobacco distribution center is conducted to evaluate the performance of the algorithm. The result verifies the model and shows the algorithm can do a steady work to reduce idle time and the idle time can be reduced by 45.63% on average. This research proposed an approach to measure the idle time in synchronized zone automated order picking system. The approach can improve the picking efficiency significantly and can be seen as theoretical basis when optimizing the synchronized automated order picking systems.

Adaptive kernel regression for freehand 3D ultrasound reconstruction

NASA Astrophysics Data System (ADS)

Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen

2017-03-01

Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.

Approximated mutual information training for speech recognition using myoelectric signals.

PubMed

Guo, Hua J; Chan, A D C

2006-01-01

A new training algorithm called the approximated maximum mutual information (AMMI) is proposed to improve the accuracy of myoelectric speech recognition using hidden Markov models (HMMs). Previous studies have demonstrated that automatic speech recognition can be performed using myoelectric signals from articulatory muscles of the face. Classification of facial myoelectric signals can be performed using HMMs that are trained using the maximum likelihood (ML) algorithm; however, this algorithm maximizes the likelihood of the observations in the training sequence, which is not directly associated with optimal classification accuracy. The AMMI training algorithm attempts to maximize the mutual information, thereby training the HMMs to optimize their parameters for discrimination. Our results show that AMMI training consistently reduces the error rates compared to these by the ML training, increasing the accuracy by approximately 3% on average.

Initialization of a fractional order identification algorithm applied for Lithium-ion battery modeling in time domain

NASA Astrophysics Data System (ADS)

Nasser Eddine, Achraf; Huard, Benoît; Gabano, Jean-Denis; Poinot, Thierry

2018-06-01

This paper deals with the initialization of a non linear identification algorithm used to accurately estimate the physical parameters of Lithium-ion battery. A Randles electric equivalent circuit is used to describe the internal impedance of the battery. The diffusion phenomenon related to this modeling is presented using a fractional order method. The battery model is thus reformulated into a transfer function which can be identified through Levenberg-Marquardt algorithm to ensure the algorithm's convergence to the physical parameters. An initialization method is proposed in this paper by taking into account previously acquired information about the static and dynamic system behavior. The method is validated using noisy voltage response, while precision of the final identification results is evaluated using Monte-Carlo method.

Global Linking of Cell Tracks Using the Viterbi Algorithm

PubMed Central

Jaldén, Joakim; Gilbert, Penney M.; Blau, Helen M.

2016-01-01

Automated tracking of living cells in microscopy image sequences is an important and challenging problem. With this application in mind, we propose a global track linking algorithm, which links cell outlines generated by a segmentation algorithm into tracks. The algorithm adds tracks to the image sequence one at a time, in a way which uses information from the complete image sequence in every linking decision. This is achieved by finding the tracks which give the largest possible increases to a probabilistically motivated scoring function, using the Viterbi algorithm. We also present a novel way to alter previously created tracks when new tracks are created, thus mitigating the effects of error propagation. The algorithm can handle mitosis, apoptosis, and migration in and out of the imaged area, and can also deal with false positives, missed detections, and clusters of jointly segmented cells. The algorithm performance is demonstrated on two challenging datasets acquired using bright-field microscopy, but in principle, the algorithm can be used with any cell type and any imaging technique, presuming there is a suitable segmentation algorithm. PMID:25415983

Kernel-based least squares policy iteration for reinforcement learning.

PubMed

Xu, Xin; Hu, Dewen; Lu, Xicheng

2007-07-01

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online 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.

Reference-free automatic quality assessment of tracheoesophageal speech.

PubMed

Huang, Andy; Falk, Tiago H; Chan, Wai-Yip; Parsa, Vijay; Doyle, Philip

2009-01-01

Evaluation of the quality of tracheoesophageal (TE) speech using machines instead of human experts can enhance the voice rehabilitation process for patients who have undergone total laryngectomy and voice restoration. Towards the goal of devising a reference-free TE speech quality estimation algorithm, we investigate the efficacy of speech signal features that are used in standard telephone-speech quality assessment algorithms, in conjunction with a recently introduced speech modulation spectrum measure. Tests performed on two TE speech databases demonstrate that the modulation spectral measure and a subset of features in the standard ITU-T P.563 algorithm estimate TE speech quality with better correlation (up to 0.9) than previously proposed features.

A Systolic Array-Based FPGA Parallel Architecture for the BLAST Algorithm

PubMed Central

Guo, Xinyu; Wang, Hong; Devabhaktuni, Vijay

2012-01-01

A design of systolic array-based Field Programmable Gate Array (FPGA) parallel architecture for Basic Local Alignment Search Tool (BLAST) Algorithm is proposed. BLAST is a heuristic biological sequence alignment algorithm which has been used by bioinformatics experts. In contrast to other designs that detect at most one hit in one-clock-cycle, our design applies a Multiple Hits Detection Module which is a pipelining systolic array to search multiple hits in a single-clock-cycle. Further, we designed a Hits Combination Block which combines overlapping hits from systolic array into one hit. These implementations completed the first and second step of BLAST architecture and achieved significant speedup comparing with previously published architectures. PMID:25969747

Improving Vector Evaluated Particle Swarm Optimisation Using Multiple Nondominated Leaders

PubMed Central

Lim, Kian Sheng; Buyamin, Salinda; Ahmad, Anita; Shapiai, Mohd Ibrahim; Naim, Faradila; Mubin, Marizan; Kim, Dong Hwa

2014-01-01

The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorporated to further improve the VEPSO algorithm. Hence, multiple nondominated solutions that are best at a respective objective function are used to guide particles in finding optimal solutions. The improved VEPSO is measured by the number of nondominated solutions found, generational distance, spread, and hypervolume. The results from the conducted experiments show that the proposed VEPSO significantly improved the existing VEPSO algorithms. PMID:24883386

Real-time stereo matching using orthogonal reliability-based dynamic programming.

PubMed

Gong, Minglun; Yang, Yee-Hong

2007-03-01

A novel algorithm is presented in this paper for estimating reliable stereo matches in real time. Based on the dynamic programming-based technique we previously proposed, the new algorithm can generate semi-dense disparity maps using as few as two dynamic programming passes. The iterative best path tracing process used in traditional dynamic programming is replaced by a local minimum searching process, making the algorithm suitable for parallel execution. Most computations are implemented on programmable graphics hardware, which improves the processing speed and makes real-time estimation possible. The experiments on the four new Middlebury stereo datasets show that, on an ATI Radeon X800 card, the presented algorithm can produce reliable matches for 60% approximately 80% of pixels at the rate of 10 approximately 20 frames per second. If needed, the algorithm can be configured for generating full density disparity maps.

A two-stage linear discriminant analysis via QR-decomposition.

PubMed

Ye, Jieping; Li, Qi

2005-06-01

Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional data, such as image and text classification. An intrinsic limitation of classical LDA is the so-called singularity problems; that is, it fails when all scatter matrices are singular. Many LDA extensions were proposed in the past to overcome the singularity problems. Among these extensions, PCA+LDA, a two-stage method, received relatively more attention. In PCA+LDA, the LDA stage is preceded by an intermediate dimension reduction stage using Principal Component Analysis (PCA). Most previous LDA extensions are computationally expensive, and not scalable, due to the use of Singular Value Decomposition or Generalized Singular Value Decomposition. In this paper, we propose a two-stage LDA method, namely LDA/QR, which aims to overcome the singularity problems of classical LDA, while achieving efficiency and scalability simultaneously. The key difference between LDA/QR and PCA+LDA lies in the first stage, where LDA/QR applies QR decomposition to a small matrix involving the class centroids, while PCA+LDA applies PCA to the total scatter matrix involving all training data points. We further justify the proposed algorithm by showing the relationship among LDA/QR and previous LDA methods. Extensive experiments on face images and text documents are presented to show the effectiveness of the proposed algorithm.

A new modified conjugate gradient coefficient for solving system of linear equations

NASA Astrophysics Data System (ADS)

Hajar, N.; ‘Aini, N.; Shapiee, N.; Abidin, Z. Z.; Khadijah, W.; Rivaie, M.; Mamat, M.

2017-09-01

Conjugate gradient (CG) method is an evolution of computational method in solving unconstrained optimization problems. This approach is easy to implement due to its simplicity and has been proven to be effective in solving real-life application. Although this field has received copious amount of attentions in recent years, some of the new approaches of CG algorithm cannot surpass the efficiency of the previous versions. Therefore, in this paper, a new CG coefficient which retains the sufficient descent and global convergence properties of the original CG methods is proposed. This new CG is tested on a set of test functions under exact line search. Its performance is then compared to that of some of the well-known previous CG methods based on number of iterations and CPU time. The results show that the new CG algorithm has the best efficiency amongst all the methods tested. This paper also includes an application of the new CG algorithm for solving large system of linear equations

Automated discovery of local search heuristics for satisfiability testing.

PubMed

Fukunaga, Alex S

2008-01-01

The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.

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

PubMed Central

Zhang, Pan; Moore, Cristopher

2014-01-01

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

Bayesian inference of interaction properties of noisy dynamical systems with time-varying coupling: capabilities and limitations

NASA Astrophysics Data System (ADS)

Wilting, Jens; Lehnertz, Klaus

2015-08-01

We investigate a recently published analysis framework based on Bayesian inference for the time-resolved characterization of interaction properties of noisy, coupled dynamical systems. It promises wide applicability and a better time resolution than well-established methods. At the example of representative model systems, we show that the analysis framework has the same weaknesses as previous methods, particularly when investigating interacting, structurally different non-linear oscillators. We also inspect the tracking of time-varying interaction properties and propose a further modification of the algorithm, which improves the reliability of obtained results. We exemplarily investigate the suitability of this algorithm to infer strength and direction of interactions between various regions of the human brain during an epileptic seizure. Within the limitations of the applicability of this analysis tool, we show that the modified algorithm indeed allows a better time resolution through Bayesian inference when compared to previous methods based on least square fits.

A Novel Deployment Method for Communication-Intensive Applications in Service Clouds

PubMed Central

Liu, Chuanchang; Yang, Jingqi

2014-01-01

The service platforms are migrating to clouds for reasonably solving long construction periods, low resource utilizations, and isolated constructions of service platforms. However, when the migration is conducted in service clouds, there is a little focus of deploying communication-intensive applications in previous deployment methods. To address this problem, this paper proposed the combination of the online deployment and the offline deployment for deploying communication-intensive applications in service clouds. Firstly, the system architecture was designed for implementing the communication-aware deployment method for communication-intensive applications in service clouds. Secondly, in the online-deployment algorithm and the offline-deployment algorithm, service instances were deployed in an optimal cloud node based on the communication overhead which is determined by the communication traffic between services, as well as the communication performance between cloud nodes. Finally, the experimental results demonstrated that the proposed methods deployed communication-intensive applications effectively with lower latency and lower load compared with existing algorithms. PMID:25140331

A Hybrid alldifferent-Tabu Search Algorithm for Solving Sudoku Puzzles

PubMed Central

Crawford, Broderick; Paredes, Fernando; Norero, Enrique

2015-01-01

The Sudoku problem is a well-known logic-based puzzle of combinatorial number-placement. It consists in filling a n 2 × n 2 grid, composed of n columns, n rows, and n subgrids, each one containing distinct integers from 1 to n 2. Such a puzzle belongs to the NP-complete collection of problems, to which there exist diverse exact and approximate methods able to solve it. In this paper, we propose a new hybrid algorithm that smartly combines a classic tabu search procedure with the alldifferent global constraint from the constraint programming world. The alldifferent constraint is known to be efficient for domain filtering in the presence of constraints that must be pairwise different, which are exactly the kind of constraints that Sudokus own. This ability clearly alleviates the work of the tabu search, resulting in a faster and more robust approach for solving Sudokus. We illustrate interesting experimental results where our proposed algorithm outperforms the best results previously reported by hybrids and approximate methods. PMID:26078751

Automatic Tracking Algorithm in Coaxial Near-Infrared Laser Ablation Endoscope for Fetus Surgery

NASA Astrophysics Data System (ADS)

Hu, Yan; Yamanaka, Noriaki; Masamune, Ken

2014-07-01

This article reports a stable vessel object tracking method for the treatment of twin-to-twin transfusion syndrome based on our previous 2 DOF endoscope. During the treatment of laser coagulation, it is necessary to focus on the exact position of the target object, however it moves by the mother's respiratory motion and still remains a challenge to obtain and track the position precisely. In this article, an algorithm which uses features from accelerated segment test (FAST) to extract the features and optical flow as the object tracking method, is proposed to deal with above problem. Further, we experimentally simulate the movement due to the mother's respiration, and the results of position errors and similarity verify the effectiveness of the proposed tracking algorithm for laser ablation endoscopy in-vitro and under water considering two influential factors. At average, the errors are about 10 pixels and the similarity over 0.92 are obtained in the experiments.

A novel deployment method for communication-intensive applications in service clouds.

PubMed

Liu, Chuanchang; Yang, Jingqi

2014-01-01

The service platforms are migrating to clouds for reasonably solving long construction periods, low resource utilizations, and isolated constructions of service platforms. However, when the migration is conducted in service clouds, there is a little focus of deploying communication-intensive applications in previous deployment methods. To address this problem, this paper proposed the combination of the online deployment and the offline deployment for deploying communication-intensive applications in service clouds. Firstly, the system architecture was designed for implementing the communication-aware deployment method for communication-intensive applications in service clouds. Secondly, in the online-deployment algorithm and the offline-deployment algorithm, service instances were deployed in an optimal cloud node based on the communication overhead which is determined by the communication traffic between services, as well as the communication performance between cloud nodes. Finally, the experimental results demonstrated that the proposed methods deployed communication-intensive applications effectively with lower latency and lower load compared with existing algorithms.

A Hybrid alldifferent-Tabu Search Algorithm for Solving Sudoku Puzzles.

PubMed

Soto, Ricardo; Crawford, Broderick; Galleguillos, Cristian; Paredes, Fernando; Norero, Enrique

2015-01-01

The Sudoku problem is a well-known logic-based puzzle of combinatorial number-placement. It consists in filling a n(2) × n(2) grid, composed of n columns, n rows, and n subgrids, each one containing distinct integers from 1 to n(2). Such a puzzle belongs to the NP-complete collection of problems, to which there exist diverse exact and approximate methods able to solve it. In this paper, we propose a new hybrid algorithm that smartly combines a classic tabu search procedure with the alldifferent global constraint from the constraint programming world. The alldifferent constraint is known to be efficient for domain filtering in the presence of constraints that must be pairwise different, which are exactly the kind of constraints that Sudokus own. This ability clearly alleviates the work of the tabu search, resulting in a faster and more robust approach for solving Sudokus. We illustrate interesting experimental results where our proposed algorithm outperforms the best results previously reported by hybrids and approximate methods.

Automated selection of the optimal cardiac phase for single-beat coronary CT angiography reconstruction.

PubMed

Stassi, D; Dutta, S; Ma, H; Soderman, A; Pazzani, D; Gros, E; Okerlund, D; Schmidt, T G

2016-01-01

Reconstructing a low-motion cardiac phase is expected to improve coronary artery visualization in coronary computed tomography angiography (CCTA) exams. This study developed an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. The algorithm uses prospectively gated, single-beat, multiphase data made possible by wide cone-beam imaging. The proposed algorithm differs from previous approaches because the optimal phase is identified based on vessel image quality (IQ) directly, compared to previous approaches that included motion estimation and interphase processing. Because there is no processing of interphase information, the algorithm can be applied to any sampling of image phases, making it suited for prospectively gated studies where only a subset of phases are available. An automated algorithm was developed to select the optimal phase based on quantitative IQ metrics. For each reconstructed slice at each reconstructed phase, an image quality metric was calculated based on measures of circularity and edge strength of through-plane vessels. The image quality metric was aggregated across slices, while a metric of vessel-location consistency was used to ignore slices that did not contain through-plane vessels. The algorithm performance was evaluated using two observer studies. Fourteen single-beat cardiac CT exams (Revolution CT, GE Healthcare, Chalfont St. Giles, UK) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Pairwise inter-reader and reader-algorithm agreement was evaluated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC) between the reader and algorithm-selected phases. A reader-consensus best phase was determined and compared to the algorithm selected phase. In cases where the algorithm and consensus best phases differed by more than 2%, IQ was scored by three readers using a five point Likert scale. There was no statistically significant difference between inter-reader and reader-algorithm agreement for either MAD or CCC metrics (p > 0.1). The algorithm phase was within 2% of the consensus phase in 15/21 of cases. The average absolute difference between consensus and algorithm best phases was 2.29% ± 2.47%, with a maximum difference of 8%. Average image quality scores for the algorithm chosen best phase were 4.01 ± 0.65 overall, 3.33 ± 1.27 for right coronary artery (RCA), 4.50 ± 0.35 for left anterior descending (LAD) artery, and 4.50 ± 0.35 for left circumflex artery (LCX). Average image quality scores for the consensus best phase were 4.11 ± 0.54 overall, 3.44 ± 1.03 for RCA, 4.39 ± 0.39 for LAD, and 4.50 ± 0.18 for LCX. There was no statistically significant difference (p > 0.1) between the image quality scores of the algorithm phase and the consensus phase. The proposed algorithm was statistically equivalent to a reader in selecting an optimal cardiac phase for CCTA exams. When reader and algorithm phases differed by >2%, image quality as rated by blinded readers was statistically equivalent. By detecting the optimal phase for CCTA reconstruction, the proposed algorithm is expected to improve coronary artery visualization in CCTA exams.

Abel inversion using fast Fourier transforms.

PubMed

Kalal, M; Nugent, K A

1988-05-15

A fast Fourier transform based Abel inversion technique is proposed. The method is faster than previously used techniques, potentially very accurate (even for a relatively small number of points), and capable of handling large data sets. The technique is discussed in the context of its use with 2-D digital interferogram analysis algorithms. Several examples are given.

Use of ATR FT-IR spectroscopy in non-destructive and rapid assessment of developmental cotton fibers

USDA-ARS?s Scientific Manuscript database

The knowledge of chemical and compositional components in cotton fibers is of value to cotton breeders and growers for cotton enhancement and to textile processors for quality control. In this work, we applied the previously proposed simple algorithms to analyze the attenuated total reflection Fouri...

GraphStore: A Distributed Graph Storage System for Big Data Networks

ERIC Educational Resources Information Center

Martha, VenkataSwamy

2013-01-01

Networks, such as social networks, are a universal solution for modeling complex problems in real time, especially in the Big Data community. While previous studies have attempted to enhance network processing algorithms, none have paved a path for the development of a persistent storage system. The proposed solution, GraphStore, provides an…

Robust information encryption diffractive-imaging-based scheme with special phase retrieval algorithm for a customized data container

NASA Astrophysics Data System (ADS)

Qin, Yi; Wang, Zhipeng; Wang, Hongjuan; Gong, Qiong; Zhou, Nanrun

2018-06-01

The diffractive-imaging-based encryption (DIBE) scheme has aroused wide interesting due to its compact architecture and low requirement of conditions. Nevertheless, the primary information can hardly be recovered exactly in the real applications when considering the speckle noise and potential occlusion imposed on the ciphertext. To deal with this issue, the customized data container (CDC) into DIBE is introduced and a new phase retrieval algorithm (PRA) for plaintext retrieval is proposed. The PRA, designed according to the peculiarity of the CDC, combines two key techniques from previous approaches, i.e., input-support-constraint and median-filtering. The proposed scheme can guarantee totally the reconstruction of the primary information despite heavy noise or occlusion and its effectiveness and feasibility have been demonstrated with simulation results.

Robust non-rigid registration algorithm based on local affine registration

NASA Astrophysics Data System (ADS)

Wu, Liyang; Xiong, Lei; Du, Shaoyi; Bi, Duyan; Fang, Ting; Liu, Kun; Wu, Dongpeng

2018-04-01

Aiming at the problem that the traditional point set non-rigid registration algorithm has low precision and slow convergence speed for complex local deformation data, this paper proposes a robust non-rigid registration algorithm based on local affine registration. The algorithm uses a hierarchical iterative method to complete the point set non-rigid registration from coarse to fine. In each iteration, the sub data point sets and sub model point sets are divided and the shape control points of each sub point set are updated. Then we use the control point guided affine ICP algorithm to solve the local affine transformation between the corresponding sub point sets. Next, the local affine transformation obtained by the previous step is used to update the sub data point sets and their shape control point sets. When the algorithm reaches the maximum iteration layer K, the loop ends and outputs the updated sub data point sets. Experimental results demonstrate that the accuracy and convergence of our algorithm are greatly improved compared with the traditional point set non-rigid registration algorithms.

Sensor integration of multiple tripolar concentric ring electrodes improves pentylenetetrazole-induced seizure onset detection in rats.

PubMed

Makeyev, Oleksandr; Ding, Quan; Kay, Steven M; Besio, Walter G

2012-01-01

As epilepsy affects approximately one percent of the world population, electrical stimulation of the brain has recently shown potential for additive seizure control therapy. Previously, we applied noninvasive transcranial focal stimulation via tripolar concentric ring electrodes on the scalp of rats after inducing seizures with pentylenetetrazole. We developed a system to detect seizures and automatically trigger the stimulation and evaluated the system on the electrographic activity from rats. In this preliminary study we propose and validate a novel seizure onset detection algorithm based on exponentially embedded family. Unlike the previously proposed approach it integrates the data from multiple electrodes allowing an improvement of the detector performance.

Theoretical Bounds of Direct Binary Search Halftoning.

PubMed

Liao, Jan-Ray

2015-11-01

Direct binary search (DBS) produces the images of the best quality among half-toning algorithms. The reason is that it minimizes the total squared perceived error instead of using heuristic approaches. The search for the optimal solution involves two operations: (1) toggle and (2) swap. Both operations try to find the binary states for each pixel to minimize the total squared perceived error. This error energy minimization leads to a conjecture that the absolute value of the filtered error after DBS converges is bounded by half of the peak value of the autocorrelation filter. However, a proof of the bound's existence has not yet been found. In this paper, we present a proof that shows the bound existed as conjectured under the condition that at least one swap occurs after toggle converges. The theoretical analysis also indicates that a swap with a pixel further away from the center of the autocorrelation filter results in a tighter bound. Therefore, we propose a new DBS algorithm which considers toggle and swap separately, and the swap operations are considered in the order from the edge to the center of the filter. Experimental results show that the new algorithm is more efficient than the previous algorithm and can produce half-toned images of the same quality as the previous algorithm.

Increasing feasibility of the field-programmable gate array implementation of an iterative image registration using a kernel-warping algorithm

NASA Astrophysics Data System (ADS)

Nguyen, An Hung; Guillemette, Thomas; Lambert, Andrew J.; Pickering, Mark R.; Garratt, Matthew A.

2017-09-01

Image registration is a fundamental image processing technique. It is used to spatially align two or more images that have been captured at different times, from different sensors, or from different viewpoints. There have been many algorithms proposed for this task. The most common of these being the well-known Lucas-Kanade (LK) and Horn-Schunck approaches. However, the main limitation of these approaches is the computational complexity required to implement the large number of iterations necessary for successful alignment of the images. Previously, a multi-pass image interpolation algorithm (MP-I2A) was developed to considerably reduce the number of iterations required for successful registration compared with the LK algorithm. This paper develops a kernel-warping algorithm (KWA), a modified version of the MP-I2A, which requires fewer iterations to successfully register two images and less memory space for the field-programmable gate array (FPGA) implementation than the MP-I2A. These reductions increase feasibility of the implementation of the proposed algorithm on FPGAs with very limited memory space and other hardware resources. A two-FPGA system rather than single FPGA system is successfully developed to implement the KWA in order to compensate insufficiency of hardware resources supported by one FPGA, and increase parallel processing ability and scalability of the system.

Gradient Evolution-based Support Vector Machine Algorithm for Classification

NASA Astrophysics Data System (ADS)

Zulvia, Ferani E.; Kuo, R. J.

2018-03-01

This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

A Real-time Spectrum Handoff Algorithm for VoIP based Cognitive Radio Networks: Design and Performance Analysis

NASA Astrophysics Data System (ADS)

Chakraborty, Tamal; Saha Misra, Iti

2016-03-01

Secondary Users (SUs) in a Cognitive Radio Network (CRN) face unpredictable interruptions in transmission due to the random arrival of Primary Users (PUs), leading to spectrum handoff or dropping instances. An efficient spectrum handoff algorithm, thus, becomes one of the indispensable components in CRN, especially for real-time communication like Voice over IP (VoIP). In this regard, this paper investigates the effects of spectrum handoff on the Quality of Service (QoS) for VoIP traffic in CRN, and proposes a real-time spectrum handoff algorithm in two phases. The first phase (VAST-VoIP based Adaptive Sensing and Transmission) adaptively varies the channel sensing and transmission durations to perform intelligent dropping decisions. The second phase (ProReact-Proactive and Reactive Handoff) deploys efficient channel selection mechanisms during spectrum handoff for resuming communication. Extensive performance analysis in analytical and simulation models confirms a decrease in spectrum handoff delay for VoIP SUs by more than 40% and 60%, compared to existing proactive and reactive algorithms, respectively and ensures a minimum 10% reduction in call-dropping probability with respect to the previous works in this domain. The effective SU transmission duration is also maximized under the proposed algorithm, thereby making it suitable for successful VoIP communication.

A multi-pattern hash-binary hybrid algorithm for URL matching in the HTTP protocol.

PubMed

Zeng, Ping; Tan, Qingping; Meng, Xiankai; Shao, Zeming; Xie, Qinzheng; Yan, Ying; Cao, Wei; Xu, Jianjun

2017-01-01

In this paper, based on our previous multi-pattern uniform resource locator (URL) binary-matching algorithm called HEM, we propose an improved multi-pattern matching algorithm called MH that is based on hash tables and binary tables. The MH algorithm can be applied to the fields of network security, data analysis, load balancing, cloud robotic communications, and so on-all of which require string matching from a fixed starting position. Our approach effectively solves the performance problems of the classical multi-pattern matching algorithms. This paper explores ways to improve string matching performance under the HTTP protocol by using a hash method combined with a binary method that transforms the symbol-space matching problem into a digital-space numerical-size comparison and hashing problem. The MH approach has a fast matching speed, requires little memory, performs better than both the classical algorithms and HEM for matching fields in an HTTP stream, and it has great promise for use in real-world applications.

A multi-pattern hash-binary hybrid algorithm for URL matching in the HTTP protocol

PubMed Central

Tan, Qingping; Meng, Xiankai; Shao, Zeming; Xie, Qinzheng; Yan, Ying; Cao, Wei; Xu, Jianjun

2017-01-01

In this paper, based on our previous multi-pattern uniform resource locator (URL) binary-matching algorithm called HEM, we propose an improved multi-pattern matching algorithm called MH that is based on hash tables and binary tables. The MH algorithm can be applied to the fields of network security, data analysis, load balancing, cloud robotic communications, and so on—all of which require string matching from a fixed starting position. Our approach effectively solves the performance problems of the classical multi-pattern matching algorithms. This paper explores ways to improve string matching performance under the HTTP protocol by using a hash method combined with a binary method that transforms the symbol-space matching problem into a digital-space numerical-size comparison and hashing problem. The MH approach has a fast matching speed, requires little memory, performs better than both the classical algorithms and HEM for matching fields in an HTTP stream, and it has great promise for use in real-world applications. PMID:28399157

Parameter optimization of differential evolution algorithm for automatic playlist generation problem

NASA Astrophysics Data System (ADS)

Alamag, Kaye Melina Natividad B.; Addawe, Joel M.

2017-11-01

With the digitalization of music, the number of collection of music increased largely and there is a need to create lists of music that filter the collection according to user preferences, thus giving rise to the Automatic Playlist Generation Problem (APGP). Previous attempts to solve this problem include the use of search and optimization algorithms. If a music database is very large, the algorithm to be used must be able to search the lists thoroughly taking into account the quality of the playlist given a set of user constraints. In this paper we perform an evolutionary meta-heuristic optimization algorithm, Differential Evolution (DE) using different combination of parameter values and select the best performing set when used to solve four standard test functions. Performance of the proposed algorithm is then compared with normal Genetic Algorithm (GA) and a hybrid GA with Tabu Search. Numerical simulations are carried out to show better results from Differential Evolution approach with the optimized parameter values.

A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification.

PubMed

Zhengming Li; Zhihui Lai; Yong Xu; Jian Yang; Zhang, David

2017-02-01

Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.

Study on data compression algorithm and its implementation in portable electronic device for Internet of Things applications

NASA Astrophysics Data System (ADS)

Asilah Khairi, Nor; Bahari Jambek, Asral

2017-11-01

An Internet of Things (IoT) device is usually powered by a small battery, which does not last long. As a result, saving energy in IoT devices has become an important issue when it comes to this subject. Since power consumption is the primary cause of radio communication, some researchers have proposed several compression algorithms with the purpose of overcoming this particular problem. Several data compression algorithms from previous reference papers are discussed in this paper. The description of the compression algorithm in the reference papers was collected and summarized in a table form. From the analysis, MAS compression algorithm was selected as a project prototype due to its high potential for meeting the project requirements. Besides that, it also produced better performance regarding energy-saving, better memory usage, and data transmission efficiency. This method is also suitable to be implemented in WSN. MAS compression algorithm will be prototyped and applied in portable electronic devices for Internet of Things applications.

Fuzzy CMAC With incremental Bayesian Ying-Yang learning and dynamic rule construction.

PubMed

Nguyen, M N

2010-04-01

Inspired by the philosophy of ancient Chinese Taoism, Xu's Bayesian ying-yang (BYY) learning technique performs clustering by harmonizing the training data (yang) with the solution (ying). In our previous work, the BYY learning technique was applied to a fuzzy cerebellar model articulation controller (FCMAC) to find the optimal fuzzy sets; however, this is not suitable for time series data analysis. To address this problem, we propose an incremental BYY learning technique in this paper, with the idea of sliding window and rule structure dynamic algorithms. Three contributions are made as a result of this research. First, an online expectation-maximization algorithm incorporated with the sliding window is proposed for the fuzzification phase. Second, the memory requirement is greatly reduced since the entire data set no longer needs to be obtained during the prediction process. Third, the rule structure dynamic algorithm with dynamically initializing, recruiting, and pruning rules relieves the "curse of dimensionality" problem that is inherent in the FCMAC. Because of these features, the experimental results of the benchmark data sets of currency exchange rates and Mackey-Glass show that the proposed model is more suitable for real-time streaming data analysis.

Community-aware task allocation for social networked multiagent systems.

PubMed

Wang, Wanyuan; Jiang, Yichuan

2014-09-01

In this paper, we propose a novel community-aware task allocation model for social networked multiagent systems (SN-MASs), where the agent' cooperation domain is constrained in community and each agent can negotiate only with its intracommunity member agents. Under such community-aware scenarios, we prove that it remains NP-hard to maximize system overall profit. To solve this problem effectively, we present a heuristic algorithm that is composed of three phases: 1) task selection: select the desirable task to be allocated preferentially; 2) allocation to community: allocate the selected task to communities based on a significant task-first heuristics; and 3) allocation to agent: negotiate resources for the selected task based on a nonoverlap agent-first and breadth-first resource negotiation mechanism. Through the theoretical analyses and experiments, the advantages of our presented heuristic algorithm and community-aware task allocation model are validated. 1) Our presented heuristic algorithm performs very closely to the benchmark exponential brute-force optimal algorithm and the network flow-based greedy algorithm in terms of system overall profit in small-scale applications. Moreover, in the large-scale applications, the presented heuristic algorithm achieves approximately the same overall system profit, but significantly reduces the computational load compared with the greedy algorithm. 2) Our presented community-aware task allocation model reduces the system communication cost compared with the previous global-aware task allocation model and improves the system overall profit greatly compared with the previous local neighbor-aware task allocation model.

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

NASA Astrophysics Data System (ADS)

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

2011-06-01

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

Beef quality parameters estimation using ultrasound and color images

PubMed Central

2015-01-01

Background Beef quality measurement is a complex task with high economic impact. There is high interest in obtaining an automatic quality parameters estimation in live cattle or post mortem. In this paper we set out to obtain beef quality estimates from the analysis of ultrasound (in vivo) and color images (post mortem), with the measurement of various parameters related to tenderness and amount of meat: rib eye area, percentage of intramuscular fat and backfat thickness or subcutaneous fat. Proposal An algorithm based on curve evolution is implemented to calculate the rib eye area. The backfat thickness is estimated from the profile of distances between two curves that limit the steak and the rib eye, previously detected. A model base in Support Vector Regression (SVR) is trained to estimate the intramuscular fat percentage. A series of features extracted on a region of interest, previously detected in both ultrasound and color images, were proposed. In all cases, a complete evaluation was performed with different databases including: color and ultrasound images acquired by a beef industry expert, intramuscular fat estimation obtained by an expert using a commercial software, and chemical analysis. Conclusions The proposed algorithms show good results to calculate the rib eye area and the backfat thickness measure and profile. They are also promising in predicting the percentage of intramuscular fat. PMID:25734452

Segmentation of cortical bone using fast level sets

NASA Astrophysics Data System (ADS)

Chowdhury, Manish; Jörgens, Daniel; Wang, Chunliang; Smedby, Årjan; Moreno, Rodrigo

2017-02-01

Cortical bone plays a big role in the mechanical competence of bone. The analysis of cortical bone requires accurate segmentation methods. Level set methods are usually in the state-of-the-art for segmenting medical images. However, traditional implementations of this method are computationally expensive. This drawback was recently tackled through the so-called coherent propagation extension of the classical algorithm which has decreased computation times dramatically. In this study, we assess the potential of this technique for segmenting cortical bone in interactive time in 3D images acquired through High Resolution peripheral Quantitative Computed Tomography (HR-pQCT). The obtained segmentations are used to estimate cortical thickness and cortical porosity of the investigated images. Cortical thickness and Cortical porosity is computed using sphere fitting and mathematical morphological operations respectively. Qualitative comparison between the segmentations of our proposed algorithm and a previously published approach on six images volumes reveals superior smoothness properties of the level set approach. While the proposed method yields similar results to previous approaches in regions where the boundary between trabecular and cortical bone is well defined, it yields more stable segmentations in challenging regions. This results in more stable estimation of parameters of cortical bone. The proposed technique takes few seconds to compute, which makes it suitable for clinical settings.

Speeding Up the Bilateral Filter: A Joint Acceleration Way.

PubMed

Dai, Longquan; Yuan, Mengke; Zhang, Xiaopeng

2016-06-01

Computational complexity of the brute-force implementation of the bilateral filter (BF) depends on its filter kernel size. To achieve the constant-time BF whose complexity is irrelevant to the kernel size, many techniques have been proposed, such as 2D box filtering, dimension promotion, and shiftability property. Although each of the above techniques suffers from accuracy and efficiency problems, previous algorithm designers were used to take only one of them to assemble fast implementations due to the hardness of combining them together. Hence, no joint exploitation of these techniques has been proposed to construct a new cutting edge implementation that solves these problems. Jointly employing five techniques: kernel truncation, best N-term approximation as well as previous 2D box filtering, dimension promotion, and shiftability property, we propose a unified framework to transform BF with arbitrary spatial and range kernels into a set of 3D box filters that can be computed in linear time. To the best of our knowledge, our algorithm is the first method that can integrate all these acceleration techniques and, therefore, can draw upon one another's strong point to overcome deficiencies. The strength of our method has been corroborated by several carefully designed experiments. In particular, the filtering accuracy is significantly improved without sacrificing the efficiency at running time.

Automatic estimation of heart boundaries and cardiothoracic ratio from chest x-ray images

NASA Astrophysics Data System (ADS)

Dallal, Ahmed H.; Agarwal, Chirag; Arbabshirani, Mohammad R.; Patel, Aalpen; Moore, Gregory

2017-03-01

Cardiothoracic ratio (CTR) is a widely used radiographic index to assess heart size on chest X-rays (CXRs). Recent studies have suggested that also two-dimensional CTR might contain clinical information about the heart function. However, manual measurement of such indices is both subjective and time consuming. This study proposes a fast algorithm to automatically estimate CTR indices based on CXRs. The algorithm has three main steps: 1) model based lung segmentation, 2) estimation of heart boundaries from lung contours, and 3) computation of cardiothoracic indices from the estimated boundaries. We extended a previously employed lung detection algorithm to automatically estimate heart boundaries without using ground truth heart markings. We used two datasets: a publicly available dataset with 247 images as well as clinical dataset with 167 studies from Geisinger Health System. The models of lung fields are learned from both datasets. The lung regions in a given test image are estimated by registering the learned models to patient CXRs. Then, heart region is estimated by applying Harris operator on segmented lung fields to detect the corner points corresponding to the heart boundaries. The algorithm calculates three indices, CTR1D, CTR2D, and cardiothoracic area ratio (CTAR). The method was tested on 103 clinical CXRs and average error rates of 7.9%, 25.5%, and 26.4% (for CTR1D, CTR2D, and CTAR respectively) were achieved. The proposed method outperforms previous CTR estimation methods without using any heart templates. This method can have important clinical implications as it can provide fast and accurate estimate of cardiothoracic indices.

A Systematic Investigation of Computation Models for Predicting Adverse Drug Reactions (ADRs)

PubMed Central

Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

2014-01-01

Background Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. Principal Findings In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Conclusion Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms. PMID:25180585

Real-time video streaming using H.264 scalable video coding (SVC) in multihomed mobile networks: a testbed approach

NASA Astrophysics Data System (ADS)

Nightingale, James; Wang, Qi; Grecos, Christos

2011-03-01

Users of the next generation wireless paradigm known as multihomed mobile networks expect satisfactory quality of service (QoS) when accessing streamed multimedia content. The recent H.264 Scalable Video Coding (SVC) extension to the Advanced Video Coding standard (AVC), offers the facility to adapt real-time video streams in response to the dynamic conditions of multiple network paths encountered in multihomed wireless mobile networks. Nevertheless, preexisting streaming algorithms were mainly proposed for AVC delivery over multipath wired networks and were evaluated by software simulation. This paper introduces a practical, hardware-based testbed upon which we implement and evaluate real-time H.264 SVC streaming algorithms in a realistic multihomed wireless mobile networks environment. We propose an optimised streaming algorithm with multi-fold technical contributions. Firstly, we extended the AVC packet prioritisation schemes to reflect the three-dimensional granularity of SVC. Secondly, we designed a mechanism for evaluating the effects of different streamer 'read ahead window' sizes on real-time performance. Thirdly, we took account of the previously unconsidered path switching and mobile networks tunnelling overheads encountered in real-world deployments. Finally, we implemented a path condition monitoring and reporting scheme to facilitate the intelligent path switching. The proposed system has been experimentally shown to offer a significant improvement in PSNR of the received stream compared with representative existing algorithms.

Knee cartilage extraction and bone-cartilage interface analysis from 3D MRI data sets

NASA Astrophysics Data System (ADS)

Tamez-Pena, Jose G.; Barbu-McInnis, Monica; Totterman, Saara

2004-05-01

This works presents a robust methodology for the analysis of the knee joint cartilage and the knee bone-cartilage interface from fused MRI sets. The proposed approach starts by fusing a set of two 3D MR images the knee. Although the proposed method is not pulse sequence dependent, the first sequence should be programmed to achieve good contrast between bone and cartilage. The recommended second pulse sequence is one that maximizes the contrast between cartilage and surrounding soft tissues. Once both pulse sequences are fused, the proposed bone-cartilage analysis is done in four major steps. First, an unsupervised segmentation algorithm is used to extract the femur, the tibia, and the patella. Second, a knowledge based feature extraction algorithm is used to extract the femoral, tibia and patellar cartilages. Third, a trained user corrects cartilage miss-classifications done by the automated extracted cartilage. Finally, the final segmentation is the revisited using an unsupervised MAP voxel relaxation algorithm. This final segmentation has the property that includes the extracted bone tissue as well as all the cartilage tissue. This is an improvement over previous approaches where only the cartilage was segmented. Furthermore, this approach yields very reproducible segmentation results in a set of scan-rescan experiments. When these segmentations were coupled with a partial volume compensated surface extraction algorithm the volume, area, thickness measurements shows precisions around 2.6%

General Metropolis-Hastings jump diffusions for automatic target recognition in infrared scenes

NASA Astrophysics Data System (ADS)

Lanterman, Aaron D.; Miller, Michael I.; Snyder, Donald L.

1997-04-01

To locate and recognize ground-based targets in forward- looking IR (FLIR) images, 3D faceted models with associated pose parameters are formulated to accommodate the variability found in FLIR imagery. Taking a Bayesian approach, scenes are simulated from the emissive characteristics of the CAD models and compared with the collected data by a likelihood function based on sensor statistics. This likelihood is combined with a prior distribution defined over the set of possible scenes to form a posterior distribution. To accommodate scenes with variable numbers of targets, the posterior distribution is defined over parameter vectors of varying dimension. An inference algorithm based on Metropolis-Hastings jump- diffusion processes empirically samples from the posterior distribution, generating configurations of templates and transformations that match the collected sensor data with high probability. The jumps accommodate the addition and deletion of targets and the estimation of target identities; diffusions refine the hypotheses by drifting along the gradient of the posterior distribution with respect to the orientation and position parameters. Previous results on jumps strategies analogous to the Metropolis acceptance/rejection algorithm, with proposals drawn from the prior and accepted based on the likelihood, are extended to encompass general Metropolis-Hastings proposal densities. In particular, the algorithm proposes moves by drawing from the posterior distribution over computationally tractible subsets of the parameter space. The algorithm is illustrated by an implementation on a Silicon Graphics Onyx/Reality Engine.

Unsupervised classification of variable stars

NASA Astrophysics Data System (ADS)

Valenzuela, Lucas; Pichara, Karim

2018-03-01

During the past 10 years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric data sets where objects are represented as light curves. Classifiers require training sets to learn the underlying patterns that allow the separation among classes. Unfortunately, building training sets is an expensive process that demands a lot of human efforts. Every time data come from new surveys; the only available training instances are the ones that have a cross-match with previously labelled objects, consequently generating insufficient training sets compared with the large amounts of unlabelled sources. In this work, we present an algorithm that performs unsupervised classification of variable stars, relying only on the similarity among light curves. We tackle the unsupervised classification problem by proposing an untraditional approach. Instead of trying to match classes of stars with clusters found by a clustering algorithm, we propose a query-based method where astronomers can find groups of variable stars ranked by similarity. We also develop a fast similarity function specific for light curves, based on a novel data structure that allows scaling the search over the entire data set of unlabelled objects. Experiments show that our unsupervised model achieves high accuracy in the classification of different types of variable stars and that the proposed algorithm scales up to massive amounts of light curves.

RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms

NASA Astrophysics Data System (ADS)

Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay

The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.

Online Phase Detection Using Wearable Sensors for Walking with a Robotic Prosthesis

PubMed Central

Goršič, Maja; Kamnik, Roman; Ambrožič, Luka; Vitiello, Nicola; Lefeber, Dirk; Pasquini, Guido; Munih, Marko

2014-01-01

This paper presents a gait phase detection algorithm for providing feedback in walking with a robotic prosthesis. The algorithm utilizes the output signals of a wearable wireless sensory system incorporating sensorized shoe insoles and inertial measurement units attached to body segments. The principle of detecting transitions between gait phases is based on heuristic threshold rules, dividing a steady-state walking stride into four phases. For the evaluation of the algorithm, experiments with three amputees, walking with the robotic prosthesis and wearable sensors, were performed. Results show a high rate of successful detection for all four phases (the average success rate across all subjects >90%). A comparison of the proposed method to an off-line trained algorithm using hidden Markov models reveals a similar performance achieved without the need for learning dataset acquisition and previous model training. PMID:24521944

cOSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design

PubMed Central

Pan, Yuchao; Dong, Yuxi; Zhou, Jingtian; Hallen, Mark; Donald, Bruce R.; Xu, Wei

2016-01-01

Abstract Finding the global minimum energy conformation (GMEC) of a huge combinatorial search space is the key challenge in computational protein design (CPD) problems. Traditional algorithms lack a scalable and efficient distributed design scheme, preventing researchers from taking full advantage of current cloud infrastructures. We design cloud OSPREY (cOSPREY), an extension to a widely used protein design software OSPREY, to allow the original design framework to scale to the commercial cloud infrastructures. We propose several novel designs to integrate both algorithm and system optimizations, such as GMEC-specific pruning, state search partitioning, asynchronous algorithm state sharing, and fault tolerance. We evaluate cOSPREY on three different cloud platforms using different technologies and show that it can solve a number of large-scale protein design problems that have not been possible with previous approaches. PMID:27154509

General form of a cooperative gradual maximal covering location problem

NASA Astrophysics Data System (ADS)

Bagherinejad, Jafar; Bashiri, Mahdi; Nikzad, Hamideh

2018-07-01

Cooperative and gradual covering are two new methods for developing covering location models. In this paper, a cooperative maximal covering location-allocation model is developed (CMCLAP). In addition, both cooperative and gradual covering concepts are applied to the maximal covering location simultaneously (CGMCLP). Then, we develop an integrated form of a cooperative gradual maximal covering location problem, which is called a general CGMCLP. By setting the model parameters, the proposed general model can easily be transformed into other existing models, facilitating general comparisons. The proposed models are developed without allocation for physical signals and with allocation for non-physical signals in discrete location space. Comparison of the previously introduced gradual maximal covering location problem (GMCLP) and cooperative maximal covering location problem (CMCLP) models with our proposed CGMCLP model in similar data sets shows that the proposed model can cover more demands and acts more efficiently. Sensitivity analyses are performed to show the effect of related parameters and the model's validity. Simulated annealing (SA) and a tabu search (TS) are proposed as solution algorithms for the developed models for large-sized instances. The results show that the proposed algorithms are efficient solution approaches, considering solution quality and running time.

A low-rank matrix recovery approach for energy efficient EEG acquisition for a wireless body area network.

PubMed

Majumdar, Angshul; Gogna, Anupriya; Ward, Rabab

2014-08-25

We address the problem of acquiring and transmitting EEG signals in Wireless Body Area Networks (WBAN) in an energy efficient fashion. In WBANs, the energy is consumed by three operations: sensing (sampling), processing and transmission. Previous studies only addressed the problem of reducing the transmission energy. For the first time, in this work, we propose a technique to reduce sensing and processing energy as well: this is achieved by randomly under-sampling the EEG signal. We depart from previous Compressed Sensing based approaches and formulate signal recovery (from under-sampled measurements) as a matrix completion problem. A new algorithm to solve the matrix completion problem is derived here. We test our proposed method and find that the reconstruction accuracy of our method is significantly better than state-of-the-art techniques; and we achieve this while saving sensing, processing and transmission energy. Simple power analysis shows that our proposed methodology consumes considerably less power compared to previous CS based techniques.

A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT

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

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A.

2010-01-15

Purpose: Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. Methods: The proposed approach comprises of two reconstruction steps. In the first step, amore » chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack-Noo-formula-based filteredbackprojection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. Results: The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. Conclusions: They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories.« less

Resolution recovery for Compton camera using origin ensemble algorithm.

PubMed

Andreyev, A; Celler, A; Ozsahin, I; Sitek, A

2016-08-01

Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. To validate the proposed algorithm we used Monte Carlo simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background. Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2-3 orders of magnitude per iteration. The results of our tests demonstrate the improvement of image resolution provided by the OE reconstructions with resolution recovery. The quality of images and their contrast are similar to those obtained from the OE reconstructions from scans simulated with perfect energy and spatial resolutions.

Using a Search Engine-Based Mutually Reinforcing Approach to Assess the Semantic Relatedness of Biomedical Terms

PubMed Central

Hsu, Yi-Yu; Chen, Hung-Yu; Kao, Hung-Yu

2013-01-01

Background Determining the semantic relatedness of two biomedical terms is an important task for many text-mining applications in the biomedical field. Previous studies, such as those using ontology-based and corpus-based approaches, measured semantic relatedness by using information from the structure of biomedical literature, but these methods are limited by the small size of training resources. To increase the size of training datasets, the outputs of search engines have been used extensively to analyze the lexical patterns of biomedical terms. Methodology/Principal Findings In this work, we propose the Mutually Reinforcing Lexical Pattern Ranking (ReLPR) algorithm for learning and exploring the lexical patterns of synonym pairs in biomedical text. ReLPR employs lexical patterns and their pattern containers to assess the semantic relatedness of biomedical terms. By combining sentence structures and the linking activities between containers and lexical patterns, our algorithm can explore the correlation between two biomedical terms. Conclusions/Significance The average correlation coefficient of the ReLPR algorithm was 0.82 for various datasets. The results of the ReLPR algorithm were significantly superior to those of previous methods. PMID:24348899

A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems

NASA Astrophysics Data System (ADS)

Abtahi, Amir-Reza; Bijari, Afsane

2017-03-01

In this paper, a hybrid meta-heuristic algorithm, based on imperialistic competition algorithm (ICA), harmony search (HS), and simulated annealing (SA) is presented. The body of the proposed hybrid algorithm is based on ICA. The proposed hybrid algorithm inherits the advantages of the process of harmony creation in HS algorithm to improve the exploitation phase of the ICA algorithm. In addition, the proposed hybrid algorithm uses SA to make a balance between exploration and exploitation phases. The proposed hybrid algorithm is compared with several meta-heuristic methods, including genetic algorithm (GA), HS, and ICA on several well-known benchmark instances. The comprehensive experiments and statistical analysis on standard benchmark functions certify the superiority of the proposed method over the other algorithms. The efficacy of the proposed hybrid algorithm is promising and can be used in several real-life engineering and management problems.

Optimization-based scatter estimation using primary modulation for computed tomography

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

Chen, Yi; Ma, Jingchen; Zhao, Jun, E-mail: junzhao

Purpose: Scatter reduces the image quality in computed tomography (CT), but scatter correction remains a challenge. A previously proposed primary modulation method simultaneously obtains the primary and scatter in a single scan. However, separating the scatter and primary in primary modulation is challenging because it is an underdetermined problem. In this study, an optimization-based scatter estimation (OSE) algorithm is proposed to estimate and correct scatter. Methods: In the concept of primary modulation, the primary is modulated, but the scatter remains smooth by inserting a modulator between the x-ray source and the object. In the proposed algorithm, an objective function ismore » designed for separating the scatter and primary. Prior knowledge is incorporated in the optimization-based framework to improve the accuracy of the estimation: (1) the primary is always positive; (2) the primary is locally smooth and the scatter is smooth; (3) the location of penumbra can be determined; and (4) the scatter-contaminated data provide knowledge about which part is smooth. Results: The simulation study shows that the edge-preserving weighting in OSE improves the estimation accuracy near the object boundary. Simulation study also demonstrates that OSE outperforms the two existing primary modulation algorithms for most regions of interest in terms of the CT number accuracy and noise. The proposed method was tested on a clinical cone beam CT, demonstrating that OSE corrects the scatter even when the modulator is not accurately registered. Conclusions: The proposed OSE algorithm improves the robustness and accuracy in scatter estimation and correction. This method is promising for scatter correction of various kinds of x-ray imaging modalities, such as x-ray radiography, cone beam CT, and the fourth-generation CT.« less

DTFP-Growth: Dynamic Threshold-Based FP-Growth Rule Mining Algorithm Through Integrating Gene Expression, Methylation, and Protein-Protein Interaction Profiles.

PubMed

Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan

2018-04-01

Association rule mining is an important technique for identifying interesting relationships between gene pairs in a biological data set. Earlier methods basically work for a single biological data set, and, in maximum cases, a single minimum support cutoff can be applied globally, i.e., across all genesets/itemsets. To overcome this limitation, in this paper, we propose dynamic threshold-based FP-growth rule mining algorithm that integrates gene expression, methylation and protein-protein interaction profiles based on weighted shortest distance to find the novel associations among different pairs of genes in multi-view data sets. For this purpose, we introduce three new thresholds, namely, Distance-based Variable/Dynamic Supports (DVS), Distance-based Variable Confidences (DVC), and Distance-based Variable Lifts (DVL) for each rule by integrating co-expression, co-methylation, and protein-protein interactions existed in the multi-omics data set. We develop the proposed algorithm utilizing these three novel multiple threshold measures. In the proposed algorithm, the values of , , and are computed for each rule separately, and subsequently it is verified whether the support, confidence, and lift of each evolved rule are greater than or equal to the corresponding individual , , and values, respectively, or not. If all these three conditions for a rule are found to be true, the rule is treated as a resultant rule. One of the major advantages of the proposed method compared with other related state-of-the-art methods is that it considers both the quantitative and interactive significance among all pairwise genes belonging to each rule. Moreover, the proposed method generates fewer rules, takes less running time, and provides greater biological significance for the resultant top-ranking rules compared to previous methods.

The impact of initialization procedures on unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization

NASA Astrophysics Data System (ADS)

Masalmah, Yahya M.; Vélez-Reyes, Miguel

2007-04-01

The authors proposed in previous papers the use of the constrained Positive Matrix Factorization (cPMF) to perform unsupervised unmixing of hyperspectral imagery. Two iterative algorithms were proposed to compute the cPMF based on the Gauss-Seidel and penalty approaches to solve optimization problems. Results presented in previous papers have shown the potential of the proposed method to perform unsupervised unmixing in HYPERION and AVIRIS imagery. The performance of iterative methods is highly dependent on the initialization scheme. Good initialization schemes can improve convergence speed, whether or not a global minimum is found, and whether or not spectra with physical relevance are retrieved as endmembers. In this paper, different initializations using random selection, longest norm pixels, and standard endmembers selection routines are studied and compared using simulated and real data.

A Fast, Automatic Segmentation Algorithm for Locating and Delineating Touching Cell Boundaries in Imaged Histopathology

PubMed Central

Qi, Xin; Xing, Fuyong; Foran, David J.; Yang, Lin

2013-01-01

Summary Background Automated analysis of imaged histopathology specimens could potentially provide support for improved reliability in detection and classification in a range of investigative and clinical cancer applications. Automated segmentation of cells in the digitized tissue microarray (TMA) is often the prerequisite for quantitative analysis. However overlapping cells usually bring significant challenges for traditional segmentation algorithms. Objectives In this paper, we propose a novel, automatic algorithm to separate overlapping cells in stained histology specimens acquired using bright-field RGB imaging. Methods It starts by systematically identifying salient regions of interest throughout the image based upon their underlying visual content. The segmentation algorithm subsequently performs a quick, voting based seed detection. Finally, the contour of each cell is obtained using a repulsive level set deformable model using the seeds generated in the previous step. We compared the experimental results with the most current literature, and the pixel wise accuracy between human experts' annotation and those generated using the automatic segmentation algorithm. Results The method is tested with 100 image patches which contain more than 1000 overlapping cells. The overall precision and recall of the developed algorithm is 90% and 78%, respectively. We also implement the algorithm on GPU. The parallel implementation is 22 times faster than its C/C++ sequential implementation. Conclusion The proposed overlapping cell segmentation algorithm can accurately detect the center of each overlapping cell and effectively separate each of the overlapping cells. GPU is proven to be an efficient parallel platform for overlapping cell segmentation. PMID:22526139

Adapting an Ant Colony Metaphor for Multi-Robot Chemical Plume Tracing

PubMed Central

Meng, Qing-Hao; Yang, Wei-Xing; Wang, Yang; Li, Fei; Zeng, Ming

2012-01-01

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments. PMID:22666056

Adapting an ant colony metaphor for multi-robot chemical plume tracing.

PubMed

Meng, Qing-Hao; Yang, Wei-Xing; Wang, Yang; Li, Fei; Zeng, Ming

2012-01-01

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments.

LOD-based clustering techniques for efficient large-scale terrain storage and visualization

NASA Astrophysics Data System (ADS)

Bao, Xiaohong; Pajarola, Renato

2003-05-01

Large multi-resolution terrain data sets are usually stored out-of-core. To visualize terrain data at interactive frame rates, the data needs to be organized on disk, loaded into main memory part by part, then rendered efficiently. Many main-memory algorithms have been proposed for efficient vertex selection and mesh construction. Organization of terrain data on disk is quite difficult because the error, the triangulation dependency and the spatial location of each vertex all need to be considered. Previous terrain clustering algorithms did not consider the per-vertex approximation error of individual terrain data sets. Therefore, the vertex sequences on disk are exactly the same for any terrain. In this paper, we propose a novel clustering algorithm which introduces the level-of-detail (LOD) information to terrain data organization to map multi-resolution terrain data to external memory. In our approach the LOD parameters of the terrain elevation points are reflected during clustering. The experiments show that dynamic loading and paging of terrain data at varying LOD is very efficient and minimizes page faults. Additionally, the preprocessing of this algorithm is very fast and works from out-of-core.

Curvature Continuous and Bounded Path Planning for Fixed-Wing UAVs

PubMed Central

Jiang, Peng; Li, Deshi; Sun, Tao

2017-01-01

Unmanned Aerial Vehicles (UAVs) play an important role in applications such as data collection and target reconnaissance. An accurate and optimal path can effectively increase the mission success rate in the case of small UAVs. Although path planning for UAVs is similar to that for traditional mobile robots, the special kinematic characteristics of UAVs (such as their minimum turning radius) have not been taken into account in previous studies. In this paper, we propose a locally-adjustable, continuous-curvature, bounded path-planning algorithm for fixed-wing UAVs. To deal with the curvature discontinuity problem, an optimal interpolation algorithm and a key-point shift algorithm are proposed based on the derivation of a curvature continuity condition. To meet the upper bound for curvature and to render the curvature extrema controllable, a local replanning scheme is designed by combining arcs and Bezier curves with monotonic curvature. In particular, a path transition mechanism is built for the replanning phase using minimum curvature circles for a planning philosophy. Numerical results demonstrate that the analytical planning algorithm can effectively generate continuous-curvature paths, while satisfying the curvature upper bound constraint and allowing UAVs to pass through all predefined waypoints in the desired mission region. PMID:28925960

Curvature Continuous and Bounded Path Planning for Fixed-Wing UAVs.

PubMed

Wang, Xiaoliang; Jiang, Peng; Li, Deshi; Sun, Tao

2017-09-19

Unmanned Aerial Vehicles (UAVs) play an important role in applications such as data collection and target reconnaissance. An accurate and optimal path can effectively increase the mission success rate in the case of small UAVs. Although path planning for UAVs is similar to that for traditional mobile robots, the special kinematic characteristics of UAVs (such as their minimum turning radius) have not been taken into account in previous studies. In this paper, we propose a locally-adjustable, continuous-curvature, bounded path-planning algorithm for fixed-wing UAVs. To deal with the curvature discontinuity problem, an optimal interpolation algorithm and a key-point shift algorithm are proposed based on the derivation of a curvature continuity condition. To meet the upper bound for curvature and to render the curvature extrema controllable, a local replanning scheme is designed by combining arcs and Bezier curves with monotonic curvature. In particular, a path transition mechanism is built for the replanning phase using minimum curvature circles for a planning philosophy. Numerical results demonstrate that the analytical planning algorithm can effectively generate continuous-curvature paths, while satisfying the curvature upper bound constraint and allowing UAVs to pass through all predefined waypoints in the desired mission region.

Measurement of signal use and vehicle turns as indication of driver cognition.

PubMed

Wallace, Bruce; Goubran, Rafik; Knoefel, Frank

2014-01-01

This paper uses data analytics to provide a method for the measurement of a key driving task, turn signal usage as a measure of an automatic over-learned cognitive function drivers. The paper augments previously reported more complex executive function cognition measures by proposing an algorithm that analyzes dashboard video to detect turn indicator use with 100% accuracy without any false positives. The paper proposes two algorithms that determine the actual turns made on a trip. The first through analysis of GPS location traces for the vehicle, locating 73% of the turns made with a very low false positive rate of 3%. A second algorithm uses GIS tools to retroactively create turn by turn directions. Fusion of GIS and GPS information raises performance to 77%. The paper presents the algorithm required to measure signal use for actual turns by realigning the 0.2Hz GPS data, 30fps video and GIS turn events. The result is a measure that can be tracked over time and changes in the driver's performance can result in alerts to the driver, caregivers or clinicians as indication of cognitive change. A lack of decline can also be shared as reassurance.

Etracker: A Mobile Gaze-Tracking System with Near-Eye Display Based on a Combined Gaze-Tracking Algorithm.

PubMed

Li, Bin; Fu, Hong; Wen, Desheng; Lo, WaiLun

2018-05-19

Eye tracking technology has become increasingly important for psychological analysis, medical diagnosis, driver assistance systems, and many other applications. Various gaze-tracking models have been established by previous researchers. However, there is currently no near-eye display system with accurate gaze-tracking performance and a convenient user experience. In this paper, we constructed a complete prototype of the mobile gaze-tracking system ' Etracker ' with a near-eye viewing device for human gaze tracking. We proposed a combined gaze-tracking algorithm. In this algorithm, the convolutional neural network is used to remove blinking images and predict coarse gaze position, and then a geometric model is defined for accurate human gaze tracking. Moreover, we proposed using the mean value of gazes to resolve pupil center changes caused by nystagmus in calibration algorithms, so that an individual user only needs to calibrate it the first time, which makes our system more convenient. The experiments on gaze data from 26 participants show that the eye center detection accuracy is 98% and Etracker can provide an average gaze accuracy of 0.53° at a rate of 30⁻60 Hz.

A localized Richardson-Lucy algorithm for fiber orientation estimation in high angular resolution diffusion imaging.

PubMed

Liu, Xiaozheng; Yuan, Zhenming; Guo, Zhongwei; Xu, Dongrong

2015-05-01

Diffusion tensor imaging is widely used for studying neural fiber trajectories in white matter and for quantifying changes in tissue using diffusion properties at each voxel in the brain. To better model the nature of crossing fibers within complex architectures, rather than using a simplified tensor model that assumes only a single fiber direction at each image voxel, a model mixing multiple diffusion tensors is used to profile diffusion signals from high angular resolution diffusion imaging (HARDI) data. Based on the HARDI signal and a multiple tensors model, spherical deconvolution methods have been developed to overcome the limitations of the diffusion tensor model when resolving crossing fibers. The Richardson-Lucy algorithm is a popular spherical deconvolution method used in previous work. However, it is based on a Gaussian distribution, while HARDI data are always very noisy, and the distribution of HARDI data follows a Rician distribution. This current work aims to present a novel solution to address these issues. By simultaneously considering both the Rician bias and neighbor correlation in HARDI data, the authors propose a localized Richardson-Lucy (LRL) algorithm to estimate fiber orientations for HARDI data. The proposed method can simultaneously reduce noise and correct the Rician bias. Mean angular error (MAE) between the estimated Fiber orientation distribution (FOD) field and the reference FOD field was computed to examine whether the proposed LRL algorithm offered any advantage over the conventional RL algorithm at various levels of noise. Normalized mean squared error (NMSE) was also computed to measure the similarity between the true FOD field and the estimated FOD filed. For MAE comparisons, the proposed LRL approach obtained the best results in most of the cases at different levels of SNR and b-values. For NMSE comparisons, the proposed LRL approach obtained the best results in most of the cases at b-value = 3000 s/mm(2), which is the recommended schema for HARDI data acquisition. In addition, the FOD fields estimated by the proposed LRL approach in regions of fiber crossing regions using real data sets also showed similar fiber structures which agreed with common acknowledge in these regions. The novel spherical deconvolution method for improved accuracy in investigating crossing fibers can simultaneously reduce noise and correct Rician bias. With the noise smoothed and bias corrected, this algorithm is especially suitable for estimation of fiber orientations in HARDI data. Experimental results using both synthetic and real imaging data demonstrated the success and effectiveness of the proposed LRL algorithm.

Formulation and implementation of a practical algorithm for parameter estimation with process and measurement noise

NASA Technical Reports Server (NTRS)

Maine, R. E.; Iliff, K. W.

1980-01-01

A new formulation is proposed for the problem of parameter estimation of dynamic systems with both process and measurement noise. The formulation gives estimates that are maximum likelihood asymptotically in time. The means used to overcome the difficulties encountered by previous formulations are discussed. It is then shown how the proposed formulation can be efficiently implemented in a computer program. A computer program using the proposed formulation is available in a form suitable for routine application. Examples with simulated and real data are given to illustrate that the program works well.

Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip.

PubMed

Paesani, S; Gentile, A A; Santagati, R; Wang, J; Wiebe, N; Tew, D P; O'Brien, J L; Thompson, M G

2017-03-10

Quantum phase estimation is a fundamental subroutine in many quantum algorithms, including Shor's factorization algorithm and quantum simulation. However, so far results have cast doubt on its practicability for near-term, nonfault tolerant, quantum devices. Here we report experimental results demonstrating that this intuition need not be true. We implement a recently proposed adaptive Bayesian approach to quantum phase estimation and use it to simulate molecular energies on a silicon quantum photonic device. The approach is verified to be well suited for prethreshold quantum processors by investigating its superior robustness to noise and decoherence compared to the iterative phase estimation algorithm. This shows a promising route to unlock the power of quantum phase estimation much sooner than previously believed.

Towards a computational- and algorithmic-level account of concept blending using analogies and amalgams

NASA Astrophysics Data System (ADS)

Besold, Tarek R.; Kühnberger, Kai-Uwe; Plaza, Enric

2017-10-01

Concept blending - a cognitive process which allows for the combination of certain elements (and their relations) from originally distinct conceptual spaces into a new unified space combining these previously separate elements, and enables reasoning and inference over the combination - is taken as a key element of creative thought and combinatorial creativity. In this article, we summarise our work towards the development of a computational-level and algorithmic-level account of concept blending, combining approaches from computational analogy-making and case-based reasoning (CBR). We present the theoretical background, as well as an algorithmic proposal integrating higher-order anti-unification matching and generalisation from analogy with amalgams from CBR. The feasibility of the approach is then exemplified in two case studies.

Optimal Parameter Design of Coarse Alignment for Fiber Optic Gyro Inertial Navigation System.

PubMed

Lu, Baofeng; Wang, Qiuying; Yu, Chunmei; Gao, Wei

2015-06-25

Two different coarse alignment algorithms for Fiber Optic Gyro (FOG) Inertial Navigation System (INS) based on inertial reference frame are discussed in this paper. Both of them are based on gravity vector integration, therefore, the performance of these algorithms is determined by integration time. In previous works, integration time is selected by experience. In order to give a criterion for the selection process, and make the selection of the integration time more accurate, optimal parameter design of these algorithms for FOG INS is performed in this paper. The design process is accomplished based on the analysis of the error characteristics of these two coarse alignment algorithms. Moreover, this analysis and optimal parameter design allow us to make an adequate selection of the most accurate algorithm for FOG INS according to the actual operational conditions. The analysis and simulation results show that the parameter provided by this work is the optimal value, and indicate that in different operational conditions, the coarse alignment algorithms adopted for FOG INS are different in order to achieve better performance. Lastly, the experiment results validate the effectiveness of the proposed algorithm.

Comparative intelligibility investigation of single-channel noise-reduction algorithms for Chinese, Japanese, and English.

PubMed

Li, Junfeng; Yang, Lin; Zhang, Jianping; Yan, Yonghong; Hu, Yi; Akagi, Masato; Loizou, Philipos C

2011-05-01

A large number of single-channel noise-reduction algorithms have been proposed based largely on mathematical principles. Most of these algorithms, however, have been evaluated with English speech. Given the different perceptual cues used by native listeners of different languages including tonal languages, it is of interest to examine whether there are any language effects when the same noise-reduction algorithm is used to process noisy speech in different languages. A comparative evaluation and investigation is taken in this study of various single-channel noise-reduction algorithms applied to noisy speech taken from three languages: Chinese, Japanese, and English. Clean speech signals (Chinese words and Japanese words) were first corrupted by three types of noise at two signal-to-noise ratios and then processed by five single-channel noise-reduction algorithms. The processed signals were finally presented to normal-hearing listeners for recognition. Intelligibility evaluation showed that the majority of noise-reduction algorithms did not improve speech intelligibility. Consistent with a previous study with the English language, the Wiener filtering algorithm produced small, but statistically significant, improvements in intelligibility for car and white noise conditions. Significant differences between the performances of noise-reduction algorithms across the three languages were observed.

ECS: efficient communication scheduling for underwater sensor networks.

PubMed

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

2011-01-01

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

Mesh Denoising based on Normal Voting Tensor and Binary Optimization.

PubMed

Yadav, Sunil Kumar; Reitebuch, Ulrich; Polthier, Konrad

2017-08-17

This paper presents a two-stage mesh denoising algorithm. Unlike other traditional averaging approaches, our approach uses an element-based normal voting tensor to compute smooth surfaces. By introducing a binary optimization on the proposed tensor together with a local binary neighborhood concept, our algorithm better retains sharp features and produces smoother umbilical regions than previous approaches. On top of that, we provide a stochastic analysis on the different kinds of noise based on the average edge length. The quantitative results demonstrate that the performance of our method is better compared to state-of-the-art smoothing approaches.

Improving the efficiency of a user-driven learning system with reconfigurable hardware. Application to DNA splicing.

PubMed

Lemoine, E; Merceron, D; Sallantin, J; Nguifo, E M

1999-01-01

This paper describes a new approach to problem solving by splitting up problem component parts between software and hardware. Our main idea arises from the combination of two previously published works. The first one proposed a conceptual environment of concept modelling in which the machine and the human expert interact. The second one reported an algorithm based on reconfigurable hardware system which outperforms any kind of previously published genetic data base scanning hardware or algorithms. Here we show how efficient the interaction between the machine and the expert is when the concept modelling is based on reconfigurable hardware system. Their cooperation is thus achieved with an real time interaction speed. The designed system has been partially applied to the recognition of primate splice junctions sites in genetic sequences.

Fast conjugate phase image reconstruction based on a Chebyshev approximation to correct for B0 field inhomogeneity and concomitant gradients.

PubMed

Chen, Weitian; Sica, Christopher T; Meyer, Craig H

2008-11-01

Off-resonance effects can cause image blurring in spiral scanning and various forms of image degradation in other MRI methods. Off-resonance effects can be caused by both B0 inhomogeneity and concomitant gradient fields. Previously developed off-resonance correction methods focus on the correction of a single source of off-resonance. This work introduces a computationally efficient method of correcting for B0 inhomogeneity and concomitant gradients simultaneously. The method is a fast alternative to conjugate phase reconstruction, with the off-resonance phase term approximated by Chebyshev polynomials. The proposed algorithm is well suited for semiautomatic off-resonance correction, which works well even with an inaccurate or low-resolution field map. The proposed algorithm is demonstrated using phantom and in vivo data sets acquired by spiral scanning. Semiautomatic off-resonance correction alone is shown to provide a moderate amount of correction for concomitant gradient field effects, in addition to B0 imhomogeneity effects. However, better correction is provided by the proposed combined method. The best results were produced using the semiautomatic version of the proposed combined method.

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.

A Distributed Signature Detection Method for Detecting Intrusions in Sensor Systems

PubMed Central

Kim, Ilkyu; Oh, Doohwan; Yoon, Myung Kuk; Yi, Kyueun; Ro, Won Woo

2013-01-01

Sensor nodes in wireless sensor networks are easily exposed to open and unprotected regions. A security solution is strongly recommended to prevent networks against malicious attacks. Although many intrusion detection systems have been developed, most systems are difficult to implement for the sensor nodes owing to limited computation resources. To address this problem, we develop a novel distributed network intrusion detection system based on the Wu–Manber algorithm. In the proposed system, the algorithm is divided into two steps; the first step is dedicated to a sensor node, and the second step is assigned to a base station. In addition, the first step is modified to achieve efficient performance under limited computation resources. We conduct evaluations with random string sets and actual intrusion signatures to show the performance improvement of the proposed method. The proposed method achieves a speedup factor of 25.96 and reduces 43.94% of packet transmissions to the base station compared with the previously proposed method. The system achieves efficient utilization of the sensor nodes and provides a structural basis of cooperative systems among the sensors. PMID:23529146

A distributed signature detection method for detecting intrusions in sensor systems.

PubMed

Kim, Ilkyu; Oh, Doohwan; Yoon, Myung Kuk; Yi, Kyueun; Ro, Won Woo

2013-03-25

Sensor nodes in wireless sensor networks are easily exposed to open and unprotected regions. A security solution is strongly recommended to prevent networks against malicious attacks. Although many intrusion detection systems have been developed, most systems are difficult to implement for the sensor nodes owing to limited computation resources. To address this problem, we develop a novel distributed network intrusion detection system based on the Wu-Manber algorithm. In the proposed system, the algorithm is divided into two steps; the first step is dedicated to a sensor node, and the second step is assigned to a base station. In addition, the first step is modified to achieve efficient performance under limited computation resources. We conduct evaluations with random string sets and actual intrusion signatures to show the performance improvement of the proposed method. The proposed method achieves a speedup factor of 25.96 and reduces 43.94% of packet transmissions to the base station compared with the previously proposed method. The system achieves efficient utilization of the sensor nodes and provides a structural basis of cooperative systems among the sensors.

Survey data and metadata modelling using document-oriented NoSQL

NASA Astrophysics Data System (ADS)

Rahmatuti Maghfiroh, Lutfi; Gusti Bagus Baskara Nugraha, I.

2018-03-01

Survey data that are collected from year to year have metadata change. However it need to be stored integratedly to get statistical data faster and easier. Data warehouse (DW) can be used to solve this limitation. However there is a change of variables in every period that can not be accommodated by DW. Traditional DW can not handle variable change via Slowly Changing Dimension (SCD). Previous research handle the change of variables in DW to manage metadata by using multiversion DW (MVDW). MVDW is designed using relational model. Some researches also found that developing nonrelational model in NoSQL database has reading time faster than the relational model. Therefore, we propose changes to metadata management by using NoSQL. This study proposes a model DW to manage change and algorithms to retrieve data with metadata changes. Evaluation of the proposed models and algorithms result in that database with the proposed design can retrieve data with metadata changes properly. This paper has contribution in comprehensive data analysis with metadata changes (especially data survey) in integrated storage.

Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning

PubMed Central

Gönen, Mehmet

2014-01-01

Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F1, and micro F1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks. PMID:24532862

Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning.

PubMed

Gönen, Mehmet

2014-03-01

Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F 1 , and micro F 1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks.

Discriminating between intentional and unintentional gaze fixation using multimodal-based fuzzy logic algorithm for gaze tracking system with NIR camera sensor

NASA Astrophysics Data System (ADS)

Naqvi, Rizwan Ali; Park, Kang Ryoung

2016-06-01

Gaze tracking systems are widely used in human-computer interfaces, interfaces for the disabled, game interfaces, and for controlling home appliances. Most studies on gaze detection have focused on enhancing its accuracy, whereas few have considered the discrimination of intentional gaze fixation (looking at a target to activate or select it) from unintentional fixation while using gaze detection systems. Previous research methods based on the use of a keyboard or mouse button, eye blinking, and the dwell time of gaze position have various limitations. Therefore, we propose a method for discriminating between intentional and unintentional gaze fixation using a multimodal fuzzy logic algorithm applied to a gaze tracking system with a near-infrared camera sensor. Experimental results show that the proposed method outperforms the conventional method for determining gaze fixation.

Vehicle Counting and Moving Direction Identification Based on Small-Aperture Microphone Array.

PubMed

Zu, Xingshui; Zhang, Shaojie; Guo, Feng; Zhao, Qin; Zhang, Xin; You, Xing; Liu, Huawei; Li, Baoqing; Yuan, Xiaobing

2017-05-10

The varying trend of a moving vehicle's angles provides much important intelligence for an unattended ground sensor (UGS) monitoring system. The present study investigates the capabilities of a small-aperture microphone array (SAMA) based system to identify the number and moving direction of vehicles travelling on a previously established route. In this paper, a SAMA-based acoustic monitoring system, including the system hardware architecture and algorithm mechanism, is designed as a single node sensor for the application of UGS. The algorithm is built on the varying trend of a vehicle's bearing angles around the closest point of approach (CPA). We demonstrate the effectiveness of our proposed method with our designed SAMA-based monitoring system in various experimental sites. The experimental results in harsh conditions validate the usefulness of our proposed UGS monitoring system.

Real-time driver fatigue detection based on face alignment

NASA Astrophysics Data System (ADS)

Tao, Huanhuan; Zhang, Guiying; Zhao, Yong; Zhou, Yi

2017-07-01

The performance and robustness of fatigue detection largely decrease if the driver with glasses. To address this issue, this paper proposes a practical driver fatigue detection method based on face alignment at 3000 FPS algorithm. Firstly, the eye regions of the driver are localized by exploiting 6 landmarks surrounding each eye. Secondly, the HOG features of the extracted eye regions are calculated and put into SVM classifier to recognize the eye state. Finally, the value of PERCLOS is calculated to determine whether the driver is drowsy or not. An alarm will be generated if the eye is closed for a specified period of time. The accuracy and real-time on testing videos with different drivers demonstrate that the proposed algorithm is robust and obtain better accuracy for driver fatigue detection compared with some previous method.

Multi-camera sensor system for 3D segmentation and localization of multiple mobile robots.

PubMed

Losada, Cristina; Mazo, Manuel; Palazuelos, Sira; Pizarro, Daniel; Marrón, Marta

2010-01-01

This paper presents a method for obtaining the motion segmentation and 3D localization of multiple mobile robots in an intelligent space using a multi-camera sensor system. The set of calibrated and synchronized cameras are placed in fixed positions within the environment (intelligent space). The proposed algorithm for motion segmentation and 3D localization is based on the minimization of an objective function. This function includes information from all the cameras, and it does not rely on previous knowledge or invasive landmarks on board the robots. The proposed objective function depends on three groups of variables: the segmentation boundaries, the motion parameters and the depth. For the objective function minimization, we use a greedy iterative algorithm with three steps that, after initialization of segmentation boundaries and depth, are repeated until convergence.

A Moving Mesh Finite Element Algorithm for Singular Problems in Two and Three Space Dimensions

NASA Astrophysics Data System (ADS)

Li, Ruo; Tang, Tao; Zhang, Pingwen

2002-04-01

A framework for adaptive meshes based on the Hamilton-Schoen-Yau theory was proposed by Dvinsky. In a recent work (2001, J. Comput. Phys.170, 562-588), we extended Dvinsky's method to provide an efficient moving mesh algorithm which compared favorably with the previously proposed schemes in terms of simplicity and reliability. In this work, we will further extend the moving mesh methods based on harmonic maps to deal with mesh adaptation in three space dimensions. In obtaining the variational mesh, we will solve an optimization problem with some appropriate constraints, which is in contrast to the traditional method of solving the Euler-Lagrange equation directly. The key idea of this approach is to update the interior and boundary grids simultaneously, rather than considering them separately. Application of the proposed moving mesh scheme is illustrated with some two- and three-dimensional problems with large solution gradients. The numerical experiments show that our methods can accurately resolve detail features of singular problems in 3D.

A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method.

PubMed

Liu, Xiao; Wang, Xiaoli; Su, Qiang; Zhang, Mo; Zhu, Yanhong; Wang, Qiugen; Wang, Qian

2017-01-01

Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques.

Economic analysis for transmission operation and planning

NASA Astrophysics Data System (ADS)

Zhou, Qun

2011-12-01

Restructuring of the electric power industry has caused dramatic changes in the use of transmission system. The increasing congestion conditions as well as the necessity of integrating renewable energy introduce new challenges and uncertainties to transmission operation and planning. Accurate short-term congestion forecasting facilitates market traders in bidding and trading activities. Cost sharing and recovery issue is a major impediment for long-term transmission investment to integrate renewable energy. In this research, a new short-term forecasting algorithm is proposed for predicting congestion, LMPs, and other power system variables based on the concept of system patterns. The advantage of this algorithm relative to standard statistical forecasting methods is that structural aspects underlying power market operations are exploited to reduce the forecasting error. The advantage relative to previously proposed structural forecasting methods is that data requirements are substantially reduced. Forecasting results based on a NYISO case study demonstrate the feasibility and accuracy of the proposed algorithm. Moreover, a negotiation methodology is developed to guide transmission investment for integrating renewable energy. Built on Nash Bargaining theory, the negotiation of investment plans and payment rate can proceed between renewable generation and transmission companies for cost sharing and recovery. The proposed approach is applied to Garver's six bus system. The numerical results demonstrate fairness and efficiency of the approach, and hence can be used as guidelines for renewable energy investors. The results also shed light on policy-making of renewable energy subsidies.

A high performance long-reach passive optical network with a novel excess bandwidth distribution scheme

NASA Astrophysics Data System (ADS)

Chao, I.-Fen; Zhang, Tsung-Min

2015-06-01

Long-reach passive optical networks (LR-PONs) have been considered to be promising solutions for future access networks. In this paper, we propose a distributed medium access control (MAC) scheme over an advantageous LR-PON network architecture that reroutes the control information from and back to all ONUs through an (N + 1) × (N + 1) star coupler (SC) deployed near the ONUs, thereby overwhelming the extremely long propagation delay problem in LR-PONs. In the network, the control slot is designed to contain all bandwidth requirements of all ONUs and is in-band time-division-multiplexed with a number of data slots within a cycle. In the proposed MAC scheme, a novel profit-weight-based dynamic bandwidth allocation (P-DBA) scheme is presented. The algorithm is designed to efficiently and fairly distribute the amount of excess bandwidth based on a profit value derived from the excess bandwidth usage of each ONU, which resolves the problems of previously reported DBA schemes that are either unfair or inefficient. The simulation results show that the proposed decentralized algorithms exhibit a nearly three-order-of-magnitude improvement in delay performance compared to the centralized algorithms over LR-PONs. Moreover, the newly proposed P-DBA scheme guarantees low delay performance and fairness even when under attack by the malevolent ONU irrespective of traffic loads and burstiness.

Development of an inverse distance weighted active infrared stealth scheme using the repulsive particle swarm optimization algorithm.

PubMed

Han, Kuk-Il; Kim, Do-Hwi; Choi, Jun-Hyuk; Kim, Tae-Kuk

2018-04-20

Treatments for detection by infrared (IR) signals are higher than for other signals such as radar or sonar because an object detected by the IR sensor cannot easily recognize its detection status. Recently, research for actively reducing IR signal has been conducted to control the IR signal by adjusting the surface temperature of the object. In this paper, we propose an active IR stealth algorithm to synchronize IR signals from the object and the background around the object. The proposed method includes the repulsive particle swarm optimization statistical optimization algorithm to estimate the IR stealth surface temperature, which will result in a synchronization between the IR signals from the object and the surrounding background by setting the inverse distance weighted contrast radiant intensity (CRI) equal to zero. We tested the IR stealth performance in mid wavelength infrared (MWIR) and long wavelength infrared (LWIR) bands for a test plate located at three different positions on a forest scene to verify the proposed method. Our results show that the inverse distance weighted active IR stealth technique proposed in this study is proved to be an effective method for reducing the contrast radiant intensity between the object and background up to 32% as compared to the previous method using the CRI determined as the simple signal difference between the object and the background.

An artificial bioindicator system for network intrusion detection.

PubMed

Blum, Christian; Lozano, José A; Davidson, Pedro Pinacho

An artificial bioindicator system is developed in order to solve a network intrusion detection problem. The system, inspired by an ecological approach to biological immune systems, evolves a population of agents that learn to survive in their environment. An adaptation process allows the transformation of the agent population into a bioindicator that is capable of reacting to system anomalies. Two characteristics stand out in our proposal. On the one hand, it is able to discover new, previously unseen attacks, and on the other hand, contrary to most of the existing systems for network intrusion detection, it does not need any previous training. We experimentally compare our proposal with three state-of-the-art algorithms and show that it outperforms the competing approaches on widely used benchmark data.

A UWB Radar Signal Processing Platform for Real-Time Human Respiratory Feature Extraction Based on Four-Segment Linear Waveform Model.

PubMed

Hsieh, Chi-Hsuan; Chiu, Yu-Fang; Shen, Yi-Hsiang; Chu, Ta-Shun; Huang, Yuan-Hao

2016-02-01

This paper presents an ultra-wideband (UWB) impulse-radio radar signal processing platform used to analyze human respiratory features. Conventional radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory signal information is available that has not been explored using radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB signal processing platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB radar signal processing platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.

Improved dynamic MRI reconstruction by exploiting sparsity and rank-deficiency.

PubMed

Majumdar, Angshul

2013-06-01

In this paper we address the problem of dynamic MRI reconstruction from partially sampled K-space data. Our work is motivated by previous studies in this area that proposed exploiting the spatiotemporal correlation of the dynamic MRI sequence by posing the reconstruction problem as a least squares minimization regularized by sparsity and low-rank penalties. Ideally the sparsity and low-rank penalties should be represented by the l(0)-norm and the rank of a matrix; however both are NP hard penalties. The previous studies used the convex l(1)-norm as a surrogate for the l(0)-norm and the non-convex Schatten-q norm (0

Efficient image enhancement using sparse source separation in the Retinex theory

NASA Astrophysics Data System (ADS)

Yoon, Jongsu; Choi, Jangwon; Choe, Yoonsik

2017-11-01

Color constancy is the feature of the human vision system (HVS) that ensures the relative constancy of the perceived color of objects under varying illumination conditions. The Retinex theory of machine vision systems is based on the HVS. Among Retinex algorithms, the physics-based algorithms are efficient; however, they generally do not satisfy the local characteristics of the original Retinex theory because they eliminate global illumination from their optimization. We apply the sparse source separation technique to the Retinex theory to present a physics-based algorithm that satisfies the locality characteristic of the original Retinex theory. Previous Retinex algorithms have limited use in image enhancement because the total variation Retinex results in an overly enhanced image and the sparse source separation Retinex cannot completely restore the original image. In contrast, our proposed method preserves the image edge and can very nearly replicate the original image without any special operation.

Constrained independent component analysis approach to nonobtrusive pulse rate measurements

NASA Astrophysics Data System (ADS)

Tsouri, Gill R.; Kyal, Survi; Dianat, Sohail; Mestha, Lalit K.

2012-07-01

Nonobtrusive pulse rate measurement using a webcam is considered. We demonstrate how state-of-the-art algorithms based on independent component analysis suffer from a sorting problem which hinders their performance, and propose a novel algorithm based on constrained independent component analysis to improve performance. We present how the proposed algorithm extracts a photoplethysmography signal and resolves the sorting problem. In addition, we perform a comparative study between the proposed algorithm and state-of-the-art algorithms over 45 video streams using a finger probe oxymeter for reference measurements. The proposed algorithm provides improved accuracy: the root mean square error is decreased from 20.6 and 9.5 beats per minute (bpm) for existing algorithms to 3.5 bpm for the proposed algorithm. An error of 3.5 bpm is within the inaccuracy expected from the reference measurements. This implies that the proposed algorithm provided performance of equal accuracy to the finger probe oximeter.

Constrained independent component analysis approach to nonobtrusive pulse rate measurements.

PubMed

Tsouri, Gill R; Kyal, Survi; Dianat, Sohail; Mestha, Lalit K

2012-07-01

Nonobtrusive pulse rate measurement using a webcam is considered. We demonstrate how state-of-the-art algorithms based on independent component analysis suffer from a sorting problem which hinders their performance, and propose a novel algorithm based on constrained independent component analysis to improve performance. We present how the proposed algorithm extracts a photoplethysmography signal and resolves the sorting problem. In addition, we perform a comparative study between the proposed algorithm and state-of-the-art algorithms over 45 video streams using a finger probe oxymeter for reference measurements. The proposed algorithm provides improved accuracy: the root mean square error is decreased from 20.6 and 9.5 beats per minute (bpm) for existing algorithms to 3.5 bpm for the proposed algorithm. An error of 3.5 bpm is within the inaccuracy expected from the reference measurements. This implies that the proposed algorithm provided performance of equal accuracy to the finger probe oximeter.

SU-E-J-88: Deformable Registration Using Multi-Resolution Demons Algorithm for 4DCT.

PubMed

Li, Dengwang; Yin, Yong

2012-06-01

In order to register 4DCT efficiently, we propose an improved deformable registration algorithm based on improved multi-resolution demons strategy to improve the efficiency of the algorithm. 4DCT images of lung cancer patients are collected from a General Electric Discovery ST CT scanner from our cancer hospital. All of the images are sorted into groups and reconstructed according to their phases, and eachrespiratory cycle is divided into 10 phases with the time interval of 10%. Firstly, in our improved demons algorithm we use gradients of both reference and floating images as deformation forces and also redistribute the forces according to the proportion of the two forces. Furthermore, we introduce intermediate variable to cost function for decreasing the noise in registration process. At the same time, Gaussian multi-resolution strategy and BFGS method for optimization are used to improve speed and accuracy of the registration. To validate the performance of the algorithm, we register the previous 10 phase-images. We compared the difference of floating and reference images before and after registered where two landmarks are decided by experienced clinician. We registered 10 phase-images of 4D-CT which is lung cancer patient from cancer hospital and choose images in exhalationas the reference images, and all other images were registered into the reference images. This method has a good accuracy demonstrated by a higher similarity measure for registration of 4D-CT and it can register a large deformation precisely. Finally, we obtain the tumor target achieved by the deformation fields using proposed method, which is more accurately than the internal margin (IM) expanded by the Gross Tumor Volume (GTV). Furthermore, we achieve tumor and normal tissue tracking and dose accumulation using 4DCT data. An efficient deformable registration algorithm was proposed by using multi-resolution demons algorithm for 4DCT. © 2012 American Association of Physicists in Medicine.

Using the Multiplicative Schwarz Alternating Algorithm (MSAA) for Solving the Large Linear System of Equations Related to Global Gravity Field Recovery up to Degree and Order 120

NASA Astrophysics Data System (ADS)

Safari, A.; Sharifi, M. A.; Amjadiparvar, B.

2010-05-01

The GRACE mission has substantiated the low-low satellite-to-satellite tracking (LL-SST) concept. The LL-SST configuration can be combined with the previously realized high-low SST concept in the CHAMP mission to provide a much higher accuracy. The line of sight (LOS) acceleration difference between the GRACE satellite pair is the mostly used observable for mapping the global gravity field of the Earth in terms of spherical harmonic coefficients. In this paper, mathematical formulae for LOS acceleration difference observations have been derived and the corresponding linear system of equations has been set up for spherical harmonic up to degree and order 120. The total number of unknowns is 14641. Such a linear equation system can be solved with iterative solvers or direct solvers. However, the runtime of direct methods or that of iterative solvers without a suitable preconditioner increases tremendously. This is the reason why we need a more sophisticated method to solve the linear system of problems with a large number of unknowns. Multiplicative variant of the Schwarz alternating algorithm is a domain decomposition method, which allows it to split the normal matrix of the system into several smaller overlaped submatrices. In each iteration step the multiplicative variant of the Schwarz alternating algorithm solves linear systems with the matrices obtained from the splitting successively. It reduces both runtime and memory requirements drastically. In this paper we propose the Multiplicative Schwarz Alternating Algorithm (MSAA) for solving the large linear system of gravity field recovery. The proposed algorithm has been tested on the International Association of Geodesy (IAG)-simulated data of the GRACE mission. The achieved results indicate the validity and efficiency of the proposed algorithm in solving the linear system of equations from accuracy and runtime points of view. Keywords: Gravity field recovery, Multiplicative Schwarz Alternating Algorithm, Low-Low Satellite-to-Satellite Tracking

An analysis of the effect of defect structures on catalytic surfaces by the boundary element technique

NASA Astrophysics Data System (ADS)

Peirce, Anthony P.; Rabitz, Herschel

1988-08-01

The boundary element (BE) technique is used to analyze the effect of defects on one-dimensional chemically active surfaces. The standard BE algorithm for diffusion is modified to include the effects of bulk desorption by making use of an asymptotic expansion technique to evaluate influences near boundaries and defect sites. An explicit time evolution scheme is proposed to treat the non-linear equations associated with defect sites. The proposed BE algorithm is shown to provide an efficient and convergent algorithm for modelling localized non-linear behavior. Since it exploits the actual Green's function of the linear diffusion-desorption process that takes place on the surface, the BE algorithm is extremely stable. The BE algorithm is applied to a number of interesting physical problems in which non-linear reactions occur at localized defects. The Lotka-Volterra system is considered in which the source, sink and predator-prey interaction terms are distributed at different defect sites in the domain and in which the defects are coupled by diffusion. This example provides a stringent test of the stability of the numerical algorithm. Marginal stability oscillations are analyzed for the Prigogine-Lefever reaction that occurs on a lattice of defects. Dissipative effects are observed for large perturbations to the marginal stability state, and rapid spatial reorganization of uniformly distributed initial perturbations is seen to take place. In another series of examples the effect of defect locations on the balance between desorptive processes on chemically active surfaces is considered. The effect of dynamic pulsing at various time-scales is considered for a one species reactive trapping model. Similar competitive behavior between neighboring defects previously observed for static adsorption levels is shown to persist for dynamic loading of the surface. The analysis of a more complex three species reaction process also provides evidence of competitive behavior between neighboring defect sites. The proposed BE algorithm is shown to provide a useful technique for analyzing the effect of defect sites on chemically active surfaces.

Efficient Controls for Finitely Convergent Sequential Algorithms

PubMed Central

Chen, Wei; Herman, Gabor T.

2010-01-01

Finding a feasible point that satisfies a set of constraints is a common task in scientific computing: examples are the linear feasibility problem and the convex feasibility problem. Finitely convergent sequential algorithms can be used for solving such problems; an example of such an algorithm is ART3, which is defined in such a way that its control is cyclic in the sense that during its execution it repeatedly cycles through the given constraints. Previously we found a variant of ART3 whose control is no longer cyclic, but which is still finitely convergent and in practice it usually converges faster than ART3 does. In this paper we propose a general methodology for automatic transformation of finitely convergent sequential algorithms in such a way that (i) finite convergence is retained and (ii) the speed of convergence is improved. The first of these two properties is proven by mathematical theorems, the second is illustrated by applying the algorithms to a practical problem. PMID:20953327

An End-to-End Loss Discrimination Scheme for Multimedia Transmission over Wireless IP Networks

NASA Astrophysics Data System (ADS)

Zhao, Hai-Tao; Dong, Yu-Ning; Li, Yang

As the rapid growth of wireless IP networks, wireless IP access networks have a lot of potential applications in a variety of fields in civilian and military environments. Many of these applications, such as realtime audio/video streaming, will require some form of end-to-end QoS assurance. In this paper, an algorithm WMPLD (Wireless Multimedia Packet Loss Discrimination) is proposed for multimedia transmission control over wired-wireless hybrid IP networks. The relationship between packet length and packet loss rate in the Gilbert wireless error model is investigated. Furthermore, the algorithm can detect the nature of packet losses by sending large and small packets alternately, and control the sending rate of nodes. In addition, by means of updating factor K, this algorithm can adapt to the changes of network states quickly. Simulation results show that, compared to previous algorithms, WMPLD algorithm can improve the networks throughput as well as reduce the congestion loss rate in various situations.

HPC-NMF: A High-Performance Parallel Algorithm for Nonnegative Matrix Factorization

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

Kannan, Ramakrishnan; Sukumar, Sreenivas R.; Ballard, Grey M.

NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient distributed algorithms to solve the problem for big data sets. We propose a high-performance distributed-memory parallel algorithm that computes the factorization by iteratively solving alternating non-negative least squares (NLS) subproblems formore » $$\\WW$$ and $$\\HH$$. It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). As opposed to previous implementation, our algorithm is also flexible: It performs well for both dense and sparse matrices, and allows the user to choose any one of the multiple algorithms for solving the updates to low rank factors $$\\WW$$ and $$\\HH$$ within the alternating iterations.« less

Numerical Conformal Mapping Using Cross-Ratios and Delaunay Triangulation

NASA Technical Reports Server (NTRS)

Driscoll, Tobin A.; Vavasis, Stephen A.

1996-01-01

We propose a new algorithm for computing the Riemann mapping of the unit disk to a polygon, also known as the Schwarz-Christoffel transformation. The new algorithm, CRDT, is based on cross-ratios of the prevertices, and also on cross-ratios of quadrilaterals in a Delaunay triangulation of the polygon. The CRDT algorithm produces an accurate representation of the Riemann mapping even in the presence of arbitrary long, thin regions in the polygon, unlike any previous conformal mapping algorithm. We believe that CRDT can never fail to converge to the correct Riemann mapping, but the correctness and convergence proof depend on conjectures that we have so far not been able to prove. We demonstrate convergence with computational experiments. The Riemann mapping has applications to problems in two-dimensional potential theory and to finite-difference mesh generation. We use CRDT to produce a mapping and solve a boundary value problem on long, thin regions for which no other algorithm can solve these problems.

Kernel Recursive Least-Squares Temporal Difference Algorithms with Sparsification and Regularization

PubMed Central

Zhu, Qingxin; Niu, Xinzheng

2016-01-01

By combining with sparse kernel methods, least-squares temporal difference (LSTD) algorithms can construct the feature dictionary automatically and obtain a better generalization ability. However, the previous kernel-based LSTD algorithms do not consider regularization and their sparsification processes are batch or offline, which hinder their widespread applications in online learning problems. In this paper, we combine the following five techniques and propose two novel kernel recursive LSTD algorithms: (i) online sparsification, which can cope with unknown state regions and be used for online learning, (ii) L 2 and L 1 regularization, which can avoid overfitting and eliminate the influence of noise, (iii) recursive least squares, which can eliminate matrix-inversion operations and reduce computational complexity, (iv) a sliding-window approach, which can avoid caching all history samples and reduce the computational cost, and (v) the fixed-point subiteration and online pruning, which can make L 1 regularization easy to implement. Finally, simulation results on two 50-state chain problems demonstrate the effectiveness of our algorithms. PMID:27436996

Kernel Recursive Least-Squares Temporal Difference Algorithms with Sparsification and Regularization.

PubMed

Zhang, Chunyuan; Zhu, Qingxin; Niu, Xinzheng

2016-01-01

By combining with sparse kernel methods, least-squares temporal difference (LSTD) algorithms can construct the feature dictionary automatically and obtain a better generalization ability. However, the previous kernel-based LSTD algorithms do not consider regularization and their sparsification processes are batch or offline, which hinder their widespread applications in online learning problems. In this paper, we combine the following five techniques and propose two novel kernel recursive LSTD algorithms: (i) online sparsification, which can cope with unknown state regions and be used for online learning, (ii) L 2 and L 1 regularization, which can avoid overfitting and eliminate the influence of noise, (iii) recursive least squares, which can eliminate matrix-inversion operations and reduce computational complexity, (iv) a sliding-window approach, which can avoid caching all history samples and reduce the computational cost, and (v) the fixed-point subiteration and online pruning, which can make L 1 regularization easy to implement. Finally, simulation results on two 50-state chain problems demonstrate the effectiveness of our algorithms.

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

PubMed

Berens, Philipp; Freeman, Jeremy; Deneux, Thomas; Chenkov, Nikolay; McColgan, Thomas; Speiser, Artur; Macke, Jakob H; Turaga, Srinivas C; Mineault, Patrick; Rupprecht, Peter; Gerhard, Stephan; Friedrich, Rainer W; Friedrich, Johannes; Paninski, Liam; Pachitariu, Marius; Harris, Kenneth D; Bolte, Ben; Machado, Timothy A; Ringach, Dario; Stone, Jasmine; Rogerson, Luke E; Sofroniew, Nicolas J; Reimer, Jacob; Froudarakis, Emmanouil; Euler, Thomas; Román Rosón, Miroslav; Theis, Lucas; Tolias, Andreas S; Bethge, Matthias

2018-05-01

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

Concurrent computation of attribute filters on shared memory parallel machines.

PubMed

Wilkinson, Michael H F; Gao, Hui; Hesselink, Wim H; Jonker, Jan-Eppo; Meijster, Arnold

2008-10-01

Morphological attribute filters have not previously been parallelized, mainly because they are both global and non-separable. We propose a parallel algorithm that achieves efficient parallelism for a large class of attribute filters, including attribute openings, closings, thinnings and thickenings, based on Salembier's Max-Trees and Min-trees. The image or volume is first partitioned in multiple slices. We then compute the Max-trees of each slice using any sequential Max-Tree algorithm. Subsequently, the Max-trees of the slices can be merged to obtain the Max-tree of the image. A C-implementation yielded good speed-ups on both a 16-processor MIPS 14000 parallel machine, and a dual-core Opteron-based machine. It is shown that the speed-up of the parallel algorithm is a direct measure of the gain with respect to the sequential algorithm used. Furthermore, the concurrent algorithm shows a speed gain of up to 72 percent on a single-core processor, due to reduced cache thrashing.

Maximizing the nurses' preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

NASA Astrophysics Data System (ADS)

Jafari, Hamed; Salmasi, Nasser

2015-09-01

The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital's demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses' preferences for working shifts and weekends off by considering several important factors such as hospital's policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.

Adaptive Gaussian mixture models for pre-screening in GPR data

NASA Astrophysics Data System (ADS)

Torrione, Peter; Morton, Kenneth, Jr.; Besaw, Lance E.

2011-06-01

Due to the large amount of data generated by vehicle-mounted ground penetrating radar (GPR) antennae arrays, advanced feature extraction and classification can only be performed on a small subset of data during real-time operation. As a result, most GPR based landmine detection systems implement "pre-screening" algorithms to processes all of the data generated by the antennae array and identify locations with anomalous signatures for more advanced processing. These pre-screening algorithms must be computationally efficient and obtain high probability of detection, but can permit a false alarm rate which might be higher than the total system requirements. Many approaches to prescreening have previously been proposed, including linear prediction coefficients, the LMS algorithm, and CFAR-based approaches. Similar pre-screening techniques have also been developed in the field of video processing to identify anomalous behavior or anomalous objects. One such algorithm, an online k-means approximation to an adaptive Gaussian mixture model (GMM), is particularly well-suited to application for pre-screening in GPR data due to its computational efficiency, non-linear nature, and relevance of the logic underlying the algorithm to GPR processing. In this work we explore the application of an adaptive GMM-based approach for anomaly detection from the video processing literature to pre-screening in GPR data. Results with the ARA Nemesis landmine detection system demonstrate significant pre-screening performance improvements compared to alternative approaches, and indicate that the proposed algorithm is a complimentary technique to existing methods.

AdaBoost-based on-line signature verifier

NASA Astrophysics Data System (ADS)

Hongo, Yasunori; Muramatsu, Daigo; Matsumoto, Takashi

2005-03-01

Authentication of individuals is rapidly becoming an important issue. The authors previously proposed a Pen-input online signature verification algorithm. The algorithm considers a writer"s signature as a trajectory of pen position, pen pressure, pen azimuth, and pen altitude that evolve over time, so that it is dynamic and biometric. Many algorithms have been proposed and reported to achieve accuracy for on-line signature verification, but setting the threshold value for these algorithms is a problem. In this paper, we introduce a user-generic model generated by AdaBoost, which resolves this problem. When user- specific models (one model for each user) are used for signature verification problems, we need to generate the models using only genuine signatures. Forged signatures are not available because imposters do not give forged signatures for training in advance. However, we can make use of another's forged signature in addition to the genuine signatures for learning by introducing a user generic model. And Adaboost is a well-known classification algorithm, making final decisions depending on the sign of the output value. Therefore, it is not necessary to set the threshold value. A preliminary experiment is performed on a database consisting of data from 50 individuals. This set consists of western-alphabet-based signatures provide by a European research group. In this experiment, our algorithm gives an FRR of 1.88% and an FAR of 1.60%. Since no fine-tuning was done, this preliminary result looks very promising.

Coevolving memetic algorithms: a review and progress report.

PubMed

Smith, Jim E

2007-02-01

Coevolving memetic algorithms are a family of metaheuristic search algorithms in which a rule-based representation of local search (LS) is coadapted alongside candidate solutions within a hybrid evolutionary system. Simple versions of these systems have been shown to outperform other nonadaptive memetic and evolutionary algorithms on a range of problems. This paper presents a rationale for such systems and places them in the context of other recent work on adaptive memetic algorithms. It then proposes a general structure within which a population of LS algorithms can be evolved in tandem with the solutions to which they are applied. Previous research started with a simple self-adaptive system before moving on to more complex models. Results showed that the algorithm was able to discover and exploit certain forms of structure and regularities within the problems. This "metalearning" of problem features provided a means of creating highly scalable algorithms. This work is briefly reviewed to highlight some of the important findings and behaviors exhibited. Based on this analysis, new results are then presented from systems with more flexible representations, which, again, show significant improvements. Finally, the current state of, and future directions for, research in this area is discussed.

Spiral bacterial foraging optimization method: Algorithm, evaluation and convergence analysis

NASA Astrophysics Data System (ADS)

Kasaiezadeh, Alireza; Khajepour, Amir; Waslander, Steven L.

2014-04-01

A biologically-inspired algorithm called Spiral Bacterial Foraging Optimization (SBFO) is investigated in this article. SBFO, previously proposed by the same authors, is a multi-agent, gradient-based algorithm that minimizes both the main objective function (local cost) and the distance between each agent and a temporary central point (global cost). A random jump is included normal to the connecting line of each agent to the central point, which produces a vortex around the temporary central point. This random jump is also suitable to cope with premature convergence, which is a feature of swarm-based optimization methods. The most important advantages of this algorithm are as follows: First, this algorithm involves a stochastic type of search with a deterministic convergence. Second, as gradient-based methods are employed, faster convergence is demonstrated over GA, DE, BFO, etc. Third, the algorithm can be implemented in a parallel fashion in order to decentralize large-scale computation. Fourth, the algorithm has a limited number of tunable parameters, and finally SBFO has a strong certainty of convergence which is rare in existing global optimization algorithms. A detailed convergence analysis of SBFO for continuously differentiable objective functions has also been investigated in this article.

Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks

NASA Astrophysics Data System (ADS)

Sun, Wei; Chang, K. C.

2005-05-01

Probabilistic inference for Bayesian networks is in general NP-hard using either exact algorithms or approximate methods. However, for very complex networks, only the approximate methods such as stochastic sampling could be used to provide a solution given any time constraint. There are several simulation methods currently available. They include logic sampling (the first proposed stochastic method for Bayesian networks, the likelihood weighting algorithm) the most commonly used simulation method because of its simplicity and efficiency, the Markov blanket scoring method, and the importance sampling algorithm. In this paper, we first briefly review and compare these available simulation methods, then we propose an improved importance sampling algorithm called linear Gaussian importance sampling algorithm for general hybrid model (LGIS). LGIS is aimed for hybrid Bayesian networks consisting of both discrete and continuous random variables with arbitrary distributions. It uses linear function and Gaussian additive noise to approximate the true conditional probability distribution for continuous variable given both its parents and evidence in a Bayesian network. One of the most important features of the newly developed method is that it can adaptively learn the optimal important function from the previous samples. We test the inference performance of LGIS using a 16-node linear Gaussian model and a 6-node general hybrid model. The performance comparison with other well-known methods such as Junction tree (JT) and likelihood weighting (LW) shows that LGIS-GHM is very promising.

Detection of Gait Modes Using an Artificial Neural Network during Walking with a Powered Ankle-Foot Orthosis

PubMed Central

2016-01-01

This paper presents an algorithm, for use with a Portable Powered Ankle-Foot Orthosis (i.e., PPAFO) that can automatically detect changes in gait modes (level ground, ascent and descent of stairs or ramps), thus allowing for appropriate ankle actuation control during swing phase. An artificial neural network (ANN) algorithm used input signals from an inertial measurement unit and foot switches, that is, vertical velocity and segment angle of the foot. Output from the ANN was filtered and adjusted to generate a final data set used to classify different gait modes. Five healthy male subjects walked with the PPAFO on the right leg for two test scenarios (walking over level ground and up and down stairs or a ramp; three trials per scenario). Success rate was quantified by the number of correctly classified steps with respect to the total number of steps. The results indicated that the proposed algorithm's success rate was high (99.3%, 100%, and 98.3% for level, ascent, and descent modes in the stairs scenario, respectively; 98.9%, 97.8%, and 100% in the ramp scenario). The proposed algorithm continuously detected each step's gait mode with faster timing and higher accuracy compared to a previous algorithm that used a decision tree based on maximizing the reliability of the mode recognition. PMID:28070188

Biplane reconstruction and visualization of virtual endoscopic and fluoroscopic views for interventional device navigation

NASA Astrophysics Data System (ADS)

Wagner, Martin G.; Strother, Charles M.; Schafer, Sebastian; Mistretta, Charles A.

2016-03-01

Biplane fluoroscopic imaging is an important tool for minimally invasive procedures for the treatment of cerebrovascular diseases. However, finding a good working angle for the C-arms of the angiography system as well as navigating based on the 2D projection images can be a difficult task. The purpose of this work is to propose a novel 4D reconstruction algorithm for interventional devices from biplane fluoroscopy images and to propose new techniques for a better visualization of the results. The proposed reconstruction methods binarizes the fluoroscopic images using a dedicated noise reduction algorithm for curvilinear structures and a global thresholding approach. A topology preserving thinning algorithm is then applied and a path search algorithm minimizing the curvature of the device is used to extract the 2D device centerlines. Finally, the 3D device path is reconstructed using epipolar geometry. The point correspondences are determined by a monotonic mapping function that minimizes the reconstruction error. The three dimensional reconstruction of the device path allows the rendering of virtual fluoroscopy images from arbitrary angles as well as 3D visualizations like virtual endoscopic views or glass pipe renderings, where the vessel wall is rendered with a semi-transparent material. This work also proposes a combination of different visualization techniques in order to increase the usability and spatial orientation for the user. A combination of synchronized endoscopic and glass pipe views is proposed, where the virtual endoscopic camera position is determined based on the device tip location as well as the previous camera position using a Kalman filter in order to create a smooth path. Additionally, vessel centerlines are displayed and the path to the target is highlighted. Finally, the virtual endoscopic camera position is also visualized in the glass pipe view to further improve the spatial orientation. The proposed techniques could considerably improve the workflow of minimally invasive procedures for the treatment of cerebrovascular diseases.

Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow

NASA Astrophysics Data System (ADS)

Peralta, Richard C.; Forghani, Ali; Fayad, Hala

2014-04-01

Many real water resources optimization problems involve conflicting objectives for which the main goal is to find a set of optimal solutions on, or near to the Pareto front. E-constraint and weighting multiobjective optimization techniques have shortcomings, especially as the number of objectives increases. Multiobjective Genetic Algorithms (MGA) have been previously proposed to overcome these difficulties. Here, an MGA derives a set of optimal solutions for multiobjective multiuser conjunctive use of reservoir, stream, and (un)confined groundwater resources. The proposed methodology is applied to a hydraulically and economically nonlinear system in which all significant flows, including stream-aquifer-reservoir-diversion-return flow interactions, are simulated and optimized simultaneously for multiple periods. Neural networks represent constrained state variables. The addressed objectives that can be optimized simultaneously in the coupled simulation-optimization model are: (1) maximizing water provided from sources, (2) maximizing hydropower production, and (3) minimizing operation costs of transporting water from sources to destinations. Results show the efficiency of multiobjective genetic algorithms for generating Pareto optimal sets for complex nonlinear multiobjective optimization problems.

Distributed Bees Algorithm Parameters Optimization for a Cost Efficient Target Allocation in Swarms of Robots

PubMed Central

Jevtić, Aleksandar; Gutiérrez, Álvaro

2011-01-01

Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce. PMID:22346677

Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm

NASA Astrophysics Data System (ADS)

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm.

PubMed

Beloufa, Fayssal; Chikh, M A

2013-10-01

In this study, diagnosis of diabetes disease, which is one of the most important diseases, is conducted with artificial intelligence techniques. We have proposed a novel Artificial Bee Colony (ABC) algorithm in which a mutation operator is added to an Artificial Bee Colony for improving its performance. When the current best solution cannot be updated, a blended crossover operator (BLX-α) of genetic algorithm is applied, in order to enhance the diversity of ABC, without compromising with the solution quality. This modified version of ABC is used as a new tool to create and optimize automatically the membership functions and rules base directly from data. We take the diabetes dataset used in our work from the UCI machine learning repository. The performances of the proposed method are evaluated through classification rate, sensitivity and specificity values using 10-fold cross-validation method. The obtained classification rate of our method is 84.21% and it is very promising when compared with the previous research in the literature for the same problem. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm.

PubMed

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

Application of Harmony Search algorithm to the solution of groundwater management models

NASA Astrophysics Data System (ADS)

Tamer Ayvaz, M.

2009-06-01

This study proposes a groundwater resources management model in which the solution is performed through a combined simulation-optimization model. A modular three-dimensional finite difference groundwater flow model, MODFLOW is used as the simulation model. This model is then combined with a Harmony Search (HS) optimization algorithm which is based on the musical process of searching for a perfect state of harmony. The performance of the proposed HS based management model is tested on three separate groundwater management problems: (i) maximization of total pumping from an aquifer (steady-state); (ii) minimization of the total pumping cost to satisfy the given demand (steady-state); and (iii) minimization of the pumping cost to satisfy the given demand for multiple management periods (transient). The sensitivity of HS algorithm is evaluated by performing a sensitivity analysis which aims to determine the impact of related solution parameters on convergence behavior. The results show that HS yields nearly same or better solutions than the previous solution methods and may be used to solve management problems in groundwater modeling.

A Functional-Genetic Scheme for Seizure Forecasting in Canine Epilepsy.

PubMed

Bou Assi, Elie; Nguyen, Dang K; Rihana, Sandy; Sawan, Mohamad

2018-06-01

The objective of this work is the development of an accurate seizure forecasting algorithm that considers brain's functional connectivity for electrode selection. We start by proposing Kmeans-directed transfer function, an adaptive functional connectivity method intended for seizure onset zone localization in bilateral intracranial EEG recordings. Electrodes identified as seizure activity sources and sinks are then used to implement a seizure-forecasting algorithm on long-term continuous recordings in dogs with naturally-occurring epilepsy. A precision-recall genetic algorithm is proposed for feature selection in line with a probabilistic support vector machine classifier. Epileptic activity generators were focal in all dogs confirming the diagnosis of focal epilepsy in these animals while sinks spanned both hemispheres in 2 of 3 dogs. Seizure forecasting results show performance improvement compared to previous studies, achieving average sensitivity of 84.82% and time in warning of 0.1. Achieved performances highlight the feasibility of seizure forecasting in canine epilepsy. The ability to improve seizure forecasting provides promise for the development of EEG-triggered closed-loop seizure intervention systems for ambulatory implantation in patients with refractory epilepsy.

RBoost: Label Noise-Robust Boosting Algorithm Based on a Nonconvex Loss Function and the Numerically Stable Base Learners.

PubMed

Miao, Qiguang; Cao, Ying; Xia, Ge; Gong, Maoguo; Liu, Jiachen; Song, Jianfeng

2016-11-01

AdaBoost has attracted much attention in the machine learning community because of its excellent performance in combining weak classifiers into strong classifiers. However, AdaBoost tends to overfit to the noisy data in many applications. Accordingly, improving the antinoise ability of AdaBoost plays an important role in many applications. The sensitiveness to the noisy data of AdaBoost stems from the exponential loss function, which puts unrestricted penalties to the misclassified samples with very large margins. In this paper, we propose two boosting algorithms, referred to as RBoost1 and RBoost2, which are more robust to the noisy data compared with AdaBoost. RBoost1 and RBoost2 optimize a nonconvex loss function of the classification margin. Because the penalties to the misclassified samples are restricted to an amount less than one, RBoost1 and RBoost2 do not overfocus on the samples that are always misclassified by the previous base learners. Besides the loss function, at each boosting iteration, RBoost1 and RBoost2 use numerically stable ways to compute the base learners. These two improvements contribute to the robustness of the proposed algorithms to the noisy training and testing samples. Experimental results on the synthetic Gaussian data set, the UCI data sets, and a real malware behavior data set illustrate that the proposed RBoost1 and RBoost2 algorithms perform better when the training data sets contain noisy data.

Reduction of artifacts in computer simulation of breast Cooper's ligaments

NASA Astrophysics Data System (ADS)

Pokrajac, David D.; Kuperavage, Adam; Maidment, Andrew D. A.; Bakic, Predrag R.

2016-03-01

Anthropomorphic software breast phantoms have been introduced as a tool for quantitative validation of breast imaging systems. Efficacy of the validation results depends on the realism of phantom images. The recursive partitioning algorithm based upon the octree simulation has been demonstrated as versatile and capable of efficiently generating large number of phantoms to support virtual clinical trials of breast imaging. Previously, we have observed specific artifacts, (here labeled "dents") on the boundaries of simulated Cooper's ligaments. In this work, we have demonstrated that these "dents" result from the approximate determination of the closest simulated ligament to an examined subvolume (i.e., octree node) of the phantom. We propose a modification of the algorithm that determines the closest ligament by considering a pre-specified number of neighboring ligaments selected based upon the functions that govern the shape of ligaments simulated in the subvolume. We have qualitatively and quantitatively demonstrated that the modified algorithm can lead to elimination or reduction of dent artifacts in software phantoms. In a proof-of concept example, we simulated a 450 ml phantom with 333 compartments at 100 micrometer resolution. After the proposed modification, we corrected 148,105 dents, with an average size of 5.27 voxels (5.27nl). We have also qualitatively analyzed the corresponding improvement in the appearance of simulated mammographic images. The proposed algorithm leads to reduction of linear and star-like artifacts in simulated phantom projections, which can be attributed to dents. Analysis of a larger number of phantoms is ongoing.

Accelerating Multiagent Reinforcement Learning by Equilibrium Transfer.

PubMed

Hu, Yujing; Gao, Yang; An, Bo

2015-07-01

An important approach in multiagent reinforcement learning (MARL) is equilibrium-based MARL, which adopts equilibrium solution concepts in game theory and requires agents to play equilibrium strategies at each state. However, most existing equilibrium-based MARL algorithms cannot scale due to a large number of computationally expensive equilibrium computations (e.g., computing Nash equilibria is PPAD-hard) during learning. For the first time, this paper finds that during the learning process of equilibrium-based MARL, the one-shot games corresponding to each state's successive visits often have the same or similar equilibria (for some states more than 90% of games corresponding to successive visits have similar equilibria). Inspired by this observation, this paper proposes to use equilibrium transfer to accelerate equilibrium-based MARL. The key idea of equilibrium transfer is to reuse previously computed equilibria when each agent has a small incentive to deviate. By introducing transfer loss and transfer condition, a novel framework called equilibrium transfer-based MARL is proposed. We prove that although equilibrium transfer brings transfer loss, equilibrium-based MARL algorithms can still converge to an equilibrium policy under certain assumptions. Experimental results in widely used benchmarks (e.g., grid world game, soccer game, and wall game) show that the proposed framework: 1) not only significantly accelerates equilibrium-based MARL (up to 96.7% reduction in learning time), but also achieves higher average rewards than algorithms without equilibrium transfer and 2) scales significantly better than algorithms without equilibrium transfer when the state/action space grows and the number of agents increases.

Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Azmi, Nur Iffah Mohamed; Arifin Mat Piah, Kamal; Yusoff, Wan Azhar Wan; Romlay, Fadhlur Rahman Mohd

2018-03-01

Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey- Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO- PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Furthermore, the physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning method responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a tuning method in the hydraulic positioning system.

Quantitative analysis of airway abnormalities in CT

NASA Astrophysics Data System (ADS)

Petersen, Jens; Lo, Pechin; Nielsen, Mads; Edula, Goutham; Ashraf, Haseem; Dirksen, Asger; de Bruijne, Marleen

2010-03-01

A coupled surface graph cut algorithm for airway wall segmentation from Computed Tomography (CT) images is presented. Using cost functions that highlight both inner and outer wall borders, the method combines the search for both borders into one graph cut. The proposed method is evaluated on 173 manually segmented images extracted from 15 different subjects and shown to give accurate results, with 37% less errors than the Full Width at Half Maximum (FWHM) algorithm and 62% less than a similar graph cut method without coupled surfaces. Common measures of airway wall thickness such as the Interior Area (IA) and Wall Area percentage (WA%) was measured by the proposed method on a total of 723 CT scans from a lung cancer screening study. These measures were significantly different for participants with Chronic Obstructive Pulmonary Disease (COPD) compared to asymptomatic participants. Furthermore, reproducibility was good as confirmed by repeat scans and the measures correlated well with the outcomes of pulmonary function tests, demonstrating the use of the algorithm as a COPD diagnostic tool. Additionally, a new measure of airway wall thickness is proposed, Normalized Wall Intensity Sum (NWIS). NWIS is shown to correlate better with lung function test values and to be more reproducible than previous measures IA, WA% and airway wall thickness at a lumen perimeter of 10 mm (PI10).

Performance evaluation of multi-stratum resources integrated resilience for software defined inter-data center interconnect.

PubMed

Yang, Hui; Zhang, Jie; Zhao, Yongli; Ji, Yuefeng; Wu, Jialin; Lin, Yi; Han, Jianrui; Lee, Young

2015-05-18

Inter-data center interconnect with IP over elastic optical network (EON) is a promising scenario to meet the high burstiness and high-bandwidth requirements of data center services. In our previous work, we implemented multi-stratum resources integration among IP networks, optical networks and application stratums resources that allows to accommodate data center services. In view of this, this study extends to consider the service resilience in case of edge optical node failure. We propose a novel multi-stratum resources integrated resilience (MSRIR) architecture for the services in software defined inter-data center interconnect based on IP over EON. A global resources integrated resilience (GRIR) algorithm is introduced based on the proposed architecture. The MSRIR can enable cross stratum optimization and provide resilience using the multiple stratums resources, and enhance the data center service resilience responsiveness to the dynamic end-to-end service demands. The overall feasibility and efficiency of the proposed architecture is experimentally verified on the control plane of our OpenFlow-based enhanced SDN (eSDN) testbed. The performance of GRIR algorithm under heavy traffic load scenario is also quantitatively evaluated based on MSRIR architecture in terms of path blocking probability, resilience latency and resource utilization, compared with other resilience algorithms.

Design Time Optimization for Hardware Watermarking Protection of HDL Designs

PubMed Central

Castillo, E.; Morales, D. P.; García, A.; Parrilla, L.; Todorovich, E.; Meyer-Baese, U.

2015-01-01

HDL-level design offers important advantages for the application of watermarking to IP cores, but its complexity also requires tools automating these watermarking algorithms. A new tool for signature distribution through combinational logic is proposed in this work. IPP@HDL, a previously proposed high-level watermarking technique, has been employed for evaluating the tool. IPP@HDL relies on spreading the bits of a digital signature at the HDL design level using combinational logic included within the original system. The development of this new tool for the signature distribution has not only extended and eased the applicability of this IPP technique, but it has also improved the signature hosting process itself. Three algorithms were studied in order to develop this automated tool. The selection of a cost function determines the best hosting solutions in terms of area and performance penalties on the IP core to protect. An 1D-DWT core and MD5 and SHA1 digital signatures were used in order to illustrate the benefits of the new tool and its optimization related to the extraction logic resources. Among the proposed algorithms, the alternative based on simulated annealing reduces the additional resources while maintaining an acceptable computation time and also saving designer effort and time. PMID:25861681

Conformational B-cell epitopes prediction from sequences using cost-sensitive ensemble classifiers and spatial clustering.

PubMed

Zhang, Jian; Zhao, Xiaowei; Sun, Pingping; Gao, Bo; Ma, Zhiqiang

2014-01-01

B-cell epitopes are regions of the antigen surface which can be recognized by certain antibodies and elicit the immune response. Identification of epitopes for a given antigen chain finds vital applications in vaccine and drug research. Experimental prediction of B-cell epitopes is time-consuming and resource intensive, which may benefit from the computational approaches to identify B-cell epitopes. In this paper, a novel cost-sensitive ensemble algorithm is proposed for predicting the antigenic determinant residues and then a spatial clustering algorithm is adopted to identify the potential epitopes. Firstly, we explore various discriminative features from primary sequences. Secondly, cost-sensitive ensemble scheme is introduced to deal with imbalanced learning problem. Thirdly, we adopt spatial algorithm to tell which residues may potentially form the epitopes. Based on the strategies mentioned above, a new predictor, called CBEP (conformational B-cell epitopes prediction), is proposed in this study. CBEP achieves good prediction performance with the mean AUC scores (AUCs) of 0.721 and 0.703 on two benchmark datasets (bound and unbound) using the leave-one-out cross-validation (LOOCV). When compared with previous prediction tools, CBEP produces higher sensitivity and comparable specificity values. A web server named CBEP which implements the proposed method is available for academic use.

Advanced Image Enhancement Method for Distant Vessels and Structures in Capsule Endoscopy

PubMed Central

Pedersen, Marius

2017-01-01

This paper proposes an advanced method for contrast enhancement of capsule endoscopic images, with the main objective to obtain sufficient information about the vessels and structures in more distant (or darker) parts of capsule endoscopic images. The proposed method (PM) combines two algorithms for the enhancement of darker and brighter areas of capsule endoscopic images, respectively. The half-unit weighted-bilinear algorithm (HWB) proposed in our previous work is used to enhance darker areas according to the darker map content of its HSV's component V. Enhancement of brighter areas is achieved thanks to the novel threshold weighted-bilinear algorithm (TWB) developed to avoid overexposure and enlargement of specular highlight spots while preserving the hue, in such areas. The TWB performs enhancement operations following a gradual increment of the brightness of the brighter map content of its HSV's component V. In other words, the TWB decreases its averaged weights as the intensity content of the component V increases. Extensive experimental demonstrations were conducted, and, based on evaluation of the reference and PM enhanced images, a gastroenterologist (Ø.H.) concluded that the PM enhanced images were the best ones based on the information about the vessels, contrast in the images, and the view or visibility of the structures in more distant parts of the capsule endoscopy images. PMID:29225668

Texture-preserved penalized weighted least-squares reconstruction of low-dose CT image via image segmentation and high-order MRF modeling

NASA Astrophysics Data System (ADS)

Han, Hao; Zhang, Hao; Wei, Xinzhou; Moore, William; Liang, Zhengrong

2016-03-01

In this paper, we proposed a low-dose computed tomography (LdCT) image reconstruction method with the help of prior knowledge learning from previous high-quality or normal-dose CT (NdCT) scans. The well-established statistical penalized weighted least squares (PWLS) algorithm was adopted for image reconstruction, where the penalty term was formulated by a texture-based Gaussian Markov random field (gMRF) model. The NdCT scan was firstly segmented into different tissue types by a feature vector quantization (FVQ) approach. Then for each tissue type, a set of tissue-specific coefficients for the gMRF penalty was statistically learnt from the NdCT image via multiple-linear regression analysis. We also proposed a scheme to adaptively select the order of gMRF model for coefficients prediction. The tissue-specific gMRF patterns learnt from the NdCT image were finally used to form an adaptive MRF penalty for the PWLS reconstruction of LdCT image. The proposed texture-adaptive PWLS image reconstruction algorithm was shown to be more effective to preserve image textures than the conventional PWLS image reconstruction algorithm, and we further demonstrated the gain of high-order MRF modeling for texture-preserved LdCT PWLS image reconstruction.

The successively temporal error concealment algorithm using error-adaptive block matching principle

NASA Astrophysics Data System (ADS)

Lee, Yu-Hsuan; Wu, Tsai-Hsing; Chen, Chao-Chyun

2014-09-01

Generally, the temporal error concealment (TEC) adopts the blocks around the corrupted block (CB) as the search pattern to find the best-match block in previous frame. Once the CB is recovered, it is referred to as the recovered block (RB). Although RB can be the search pattern to find the best-match block of another CB, RB is not the same as its original block (OB). The error between the RB and its OB limits the performance of TEC. The successively temporal error concealment (STEC) algorithm is proposed to alleviate this error. The STEC procedure consists of tier-1 and tier-2. The tier-1 divides a corrupted macroblock into four corrupted 8 × 8 blocks and generates a recovering order for them. The corrupted 8 × 8 block with the first place of recovering order is recovered in tier-1, and remaining 8 × 8 CBs are recovered in tier-2 along the recovering order. In tier-2, the error-adaptive block matching principle (EA-BMP) is proposed for the RB as the search pattern to recover remaining corrupted 8 × 8 blocks. The proposed STEC outperforms sophisticated TEC algorithms on average PSNR by 0.3 dB on the packet error rate of 20% at least.

Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts.

PubMed

Dashtban, M; Balafar, Mohammadali

2017-03-01

Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification. A filter method was first applied to reduce the dimensionality of feature space followed by employing an integer-coded genetic algorithm with dynamic-length genotype, intelligent parameter settings, and modified operators. The algorithmic behaviors including convergence trends, mutation and crossover rate changes, and running time were studied, conceptually discussed, and shown to be coherent with literature findings. Two well-known filter methods, Laplacian and Fisher score, were examined considering similarities, the quality of selected genes, and their influences on the evolutionary approach. Several statistical tests concerning choice of classifier, choice of dataset, and choice of filter method were performed, and they revealed some significant differences between the performance of different classifiers and filter methods over datasets. The proposed method was benchmarked upon five popular high-dimensional cancer datasets; for each, top explored genes were reported. Comparing the experimental results with several state-of-the-art methods revealed that the proposed method outperforms previous methods in DLBCL dataset. Copyright © 2017 Elsevier Inc. All rights reserved.

An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture.

PubMed

Doulamis, A; Doulamis, N; Ntalianis, K; Kollias, S

2003-01-01

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).

Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization

NASA Astrophysics Data System (ADS)

Lin, Chuang; Wang, Binghui; Jiang, Ning; Farina, Dario

2018-04-01

Objective. This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) myoelectric control method for active prostheses. Approach. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently. Main Results. In online testing based on target hitting, able-bodied subjects reached a greater throughput (TP) when using sparse NMF (SNMF) than with classic NMF or with linear regression (LR). Accordingly, the completion time (CT) was shorter for SNMF than NMF or LR. The same observations were made in two patients with unilateral limb deficiencies. Significance. The addition of sparseness constraints to NMF allows for a quasi-unsupervised approach to myoelectric control with superior results with respect to previous methods for the simultaneous and proportional control of multi-DOF. The proposed factorization algorithm allows robust simultaneous and proportional control, is superior to previous supervised algorithms, and, because of minimal supervision, paves the way to online adaptation in myoelectric control.

Adaptive enhanced sampling by force-biasing using neural networks

NASA Astrophysics Data System (ADS)

Guo, Ashley Z.; Sevgen, Emre; Sidky, Hythem; Whitmer, Jonathan K.; Hubbell, Jeffrey A.; de Pablo, Juan J.

2018-04-01

A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables (1) smooth estimates of generalized forces in sparsely sampled regions, (2) force estimates in previously unexplored regions, and (3) continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. The method is also found to provide improvements over previous implementations of neural network assisted algorithms.

Siamese convolutional networks for tracking the spine motion

NASA Astrophysics Data System (ADS)

Liu, Yuan; Sui, Xiubao; Sun, Yicheng; Liu, Chengwei; Hu, Yong

2017-09-01

Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.

Electrical conductivity imaging using gradient B, decomposition algorithm in magnetic resonance electrical impedance tomography (MREIT).

PubMed

Park, Chunjae; Kwon, Ohin; Woo, Eung Je; Seo, Jin Keun

2004-03-01

In magnetic resonance electrical impedance tomography (MREIT), we try to visualize cross-sectional conductivity (or resistivity) images of a subject. We inject electrical currents into the subject through surface electrodes and measure the z component Bz of the induced internal magnetic flux density using an MRI scanner. Here, z is the direction of the main magnetic field of the MRI scanner. We formulate the conductivity image reconstruction problem in MREIT from a careful analysis of the relationship between the injection current and the induced magnetic flux density Bz. Based on the novel mathematical formulation, we propose the gradient Bz decomposition algorithm to reconstruct conductivity images. This new algorithm needs to differentiate Bz only once in contrast to the previously developed harmonic Bz algorithm where the numerical computation of (inverted delta)2Bz is required. The new algorithm, therefore, has the important advantage of much improved noise tolerance. Numerical simulations with added random noise of realistic amounts show the feasibility of the algorithm in practical applications and also its robustness against measurement noise.

Improving personalized link prediction by hybrid diffusion

NASA Astrophysics Data System (ADS)

Liu, Jin-Hu; Zhu, Yu-Xiao; Zhou, Tao

2016-04-01

Inspired by traditional link prediction and to solve the problem of recommending friends in social networks, we introduce the personalized link prediction in this paper, in which each individual will get equal number of diversiform predictions. While the performances of many classical algorithms are not satisfactory under this framework, thus new algorithms are in urgent need. Motivated by previous researches in other fields, we generalize heat conduction process to the framework of personalized link prediction and find that this method outperforms many classical similarity-based algorithms, especially in the performance of diversity. In addition, we demonstrate that adding one ground node that is supposed to connect all the nodes in the system will greatly benefit the performance of heat conduction. Finally, better hybrid algorithms composed of local random walk and heat conduction have been proposed. Numerical results show that the hybrid algorithms can outperform other algorithms simultaneously in all four adopted metrics: AUC, precision, recall and hamming distance. In a word, this work may shed some light on the in-depth understanding of the effect of physical processes in personalized link prediction.

Harmony search algorithm: application to the redundancy optimization problem

NASA Astrophysics Data System (ADS)

Nahas, Nabil; Thien-My, Dao

2010-09-01

The redundancy optimization problem is a well known NP-hard problem which involves the selection of elements and redundancy levels to maximize system performance, given different system-level constraints. This article presents an efficient algorithm based on the harmony search algorithm (HSA) to solve this optimization problem. The HSA is a new nature-inspired algorithm which mimics the improvization process of music players. Two kinds of problems are considered in testing the proposed algorithm, with the first limited to the binary series-parallel system, where the problem consists of a selection of elements and redundancy levels used to maximize the system reliability given various system-level constraints; the second problem for its part concerns the multi-state series-parallel systems with performance levels ranging from perfect operation to complete failure, and in which identical redundant elements are included in order to achieve a desirable level of availability. Numerical results for test problems from previous research are reported and compared. The results of HSA showed that this algorithm could provide very good solutions when compared to those obtained through other approaches.

An Examination of New Paradigms for Spline Approximations.

PubMed

Witzgall, Christoph; Gilsinn, David E; McClain, Marjorie A

2006-01-01

Lavery splines are examined in the univariate and bivariate cases. In both instances relaxation based algorithms for approximate calculation of Lavery splines are proposed. Following previous work Gilsinn, et al. [7] addressing the bivariate case, a rotationally invariant functional is assumed. The version of bivariate splines proposed in this paper also aims at irregularly spaced data and uses Hseih-Clough-Tocher elements based on the triangulated irregular network (TIN) concept. In this paper, the univariate case, however, is investigated in greater detail so as to further the understanding of the bivariate case.

Comprehensive European dietary exposure model (CEDEM) for food additives.

PubMed

Tennant, David R

2016-05-01

European methods for assessing dietary exposures to nutrients, additives and other substances in food are limited by the availability of detailed food consumption data for all member states. A proposed comprehensive European dietary exposure model (CEDEM) applies summary data published by the European Food Safety Authority (EFSA) in a deterministic model based on an algorithm from the EFSA intake method for food additives. The proposed approach can predict estimates of food additive exposure provided in previous EFSA scientific opinions that were based on the full European food consumption database.

A voting-based star identification algorithm utilizing local and global distribution

NASA Astrophysics Data System (ADS)

Fan, Qiaoyun; Zhong, Xuyang; Sun, Junhua

2018-03-01

A novel star identification algorithm based on voting scheme is presented in this paper. In the proposed algorithm, the global distribution and local distribution of sensor stars are fully utilized, and the stratified voting scheme is adopted to obtain the candidates for sensor stars. The database optimization is employed to reduce its memory requirement and improve the robustness of the proposed algorithm. The simulation shows that the proposed algorithm exhibits 99.81% identification rate with 2-pixel standard deviations of positional noises and 0.322-Mv magnitude noises. Compared with two similar algorithms, the proposed algorithm is more robust towards noise, and the average identification time and required memory is less. Furthermore, the real sky test shows that the proposed algorithm performs well on the real star images.

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

PubMed Central

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

Parallel heterogeneous architectures for efficient OMP compressive sensing reconstruction

NASA Astrophysics Data System (ADS)

Kulkarni, Amey; Stanislaus, Jerome L.; Mohsenin, Tinoosh

2014-05-01

Compressive Sensing (CS) is a novel scheme, in which a signal that is sparse in a known transform domain can be reconstructed using fewer samples. The signal reconstruction techniques are computationally intensive and have sluggish performance, which make them impractical for real-time processing applications . The paper presents novel architectures for Orthogonal Matching Pursuit algorithm, one of the popular CS reconstruction algorithms. We show the implementation results of proposed architectures on FPGA, ASIC and on a custom many-core platform. For FPGA and ASIC implementation, a novel thresholding method is used to reduce the processing time for the optimization problem by at least 25%. Whereas, for the custom many-core platform, efficient parallelization techniques are applied, to reconstruct signals with variant signal lengths of N and sparsity of m. The algorithm is divided into three kernels. Each kernel is parallelized to reduce execution time, whereas efficient reuse of the matrix operators allows us to reduce area. Matrix operations are efficiently paralellized by taking advantage of blocked algorithms. For demonstration purpose, all architectures reconstruct a 256-length signal with maximum sparsity of 8 using 64 measurements. Implementation on Xilinx Virtex-5 FPGA, requires 27.14 μs to reconstruct the signal using basic OMP. Whereas, with thresholding method it requires 18 μs. ASIC implementation reconstructs the signal in 13 μs. However, our custom many-core, operating at 1.18 GHz, takes 18.28 μs to complete. Our results show that compared to the previous published work of the same algorithm and matrix size, proposed architectures for FPGA and ASIC implementations perform 1.3x and 1.8x respectively faster. Also, the proposed many-core implementation performs 3000x faster than the CPU and 2000x faster than the GPU.

Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III.

PubMed

Boon, K H; Khalil-Hani, M; Malarvili, M B

2018-01-01

This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity. Copyright © 2017 Elsevier B.V. All rights reserved.

The introduction of capillary structures in 4D simulated vascular tree for ART 3.5D algorithm further validation

NASA Astrophysics Data System (ADS)

Barra, Beatrice; El Hadji, Sara; De Momi, Elena; Ferrigno, Giancarlo; Cardinale, Francesco; Baselli, Giuseppe

2017-03-01

Several neurosurgical procedures, such as Artero Venous Malformations (AVMs), aneurysm embolizations and StereoElectroEncephaloGraphy (SEEG) require accurate reconstruction of the cerebral vascular tree, as well as the classification of arteries and veins, in order to increase the safety of the intervention. Segmentation of arteries and veins from 4D CT perfusion scans has already been proposed in different studies. Nonetheless, such procedures require long acquisition protocols and the radiation dose given to the patient is not negligible. Hence, space is open to approaches attempting to recover the dynamic information from standard Contrast Enhanced Cone Beam Computed Tomography (CE-CBCT) scans. The algorithm proposed by our team is called ART 3.5 D. It is a novel algorithm based on the postprocessing of both the angiogram and the raw data of a standard Digital Subtraction Angiography from a CBCT (DSACBCT) allowing arteries and veins segmentation and labeling without requiring any additional radiation exposure for the patient and neither lowering the resolution. In addition, while in previous versions of the algorithm just the distinction of arteries and veins was considered, here the capillary phase simulation and identification is introduced, in order to increase further information useful for more precise vasculature segmentation.

An Algorithm Enabling Blind Users to Find and Read Barcodes

PubMed Central

Tekin, Ender; Coughlan, James M.

2010-01-01

Most camera-based systems for finding and reading barcodes are designed to be used by sighted users (e.g. the Red Laser iPhone app), and assume the user carefully centers the barcode in the image before the barcode is read. Blind individuals could benefit greatly from such systems to identify packaged goods (such as canned goods in a supermarket), but unfortunately in their current form these systems are completely inaccessible because of their reliance on visual feedback from the user. To remedy this problem, we propose a computer vision algorithm that processes several frames of video per second to detect barcodes from a distance of several inches; the algorithm issues directional information with audio feedback (e.g. “left,” “right”) and thereby guides a blind user holding a webcam or other portable camera to locate and home in on a barcode. Once the barcode is detected at sufficiently close range, a barcode reading algorithm previously developed by the authors scans and reads aloud the barcode and the corresponding product information. We demonstrate encouraging experimental results of our proposed system implemented on a desktop computer with a webcam held by a blindfolded user; ultimately the system will be ported to a camera phone for use by visually impaired users. PMID:20617114

Empirical Testing of an Algorithm for Defining Somatization in Children

PubMed Central

Eisman, Howard D.; Fogel, Joshua; Lazarovich, Regina; Pustilnik, Inna

2007-01-01

Introduction A previous article proposed an algorithm for defining somatization in children by classifying them into three categories: well, medically ill, and somatizer; the authors suggested further empirical validation of the algorithm (Postilnik et al., 2006). We use the Child Behavior Checklist (CBCL) to provide this empirical validation. Method Parents of children seen in pediatric clinics completed the CBCL (n=126). The physicians of these children completed specially-designed questionnaires. The sample comprised of 62 boys and 64 girls (age range 2 to 15 years). Classification categories included: well (n=53), medically ill (n=55), and somatizer (n=18). Analysis of variance (ANOVA) was used for statistical comparisons. Discriminant function analysis was conducted with the CBCL subscales. Results There were significant differences between the classification categories for the somatic complaints (p=<0.001), social problems (p=0.004), thought problems (p=0.01), attention problems (0.006), and internalizing (p=0.003) subscales and also total (p=0.001), and total-t (p=0.001) scales of the CBCL. Discriminant function analysis showed that 78% of somatizers and 66% of well were accurately classified, while only 35% of medically ill were accurately classified. Conclusion The somatization classification algorithm proposed by Postilnik et al. (2006) shows promise for classification of children and adolescents with somatic symptoms. PMID:18421368

An efficient algorithm for sorting by block-interchanges and its application to the evolution of vibrio species.

PubMed

Lin, Ying Chih; Lu, Chin Lung; Chang, Hwan-You; Tang, Chuan Yi

2005-01-01

In the study of genome rearrangement, the block-interchanges have been proposed recently as a new kind of global rearrangement events affecting a genome by swapping two nonintersecting segments of any length. The so-called block-interchange distance problem, which is equivalent to the sorting-by-block-interchange problem, is to find a minimum series of block-interchanges for transforming one chromosome into another. In this paper, we study this problem by considering the circular chromosomes and propose a Omicron(deltan) time algorithm for solving it by making use of permutation groups in algebra, where n is the length of the circular chromosome and delta is the minimum number of block-interchanges required for the transformation, which can be calculated in Omicron(n) time in advance. Moreover, we obtain analogous results by extending our algorithm to linear chromosomes. Finally, we have implemented our algorithm and applied it to the circular genomic sequences of three human vibrio pathogens for predicting their evolutionary relationships. Consequently, our experimental results coincide with the previous ones obtained by others using a different comparative genomics approach, which implies that the block-interchange events seem to play a significant role in the evolution of vibrio species.

Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems.

PubMed

Ravishankar, Saiprasad; Nadakuditi, Raj Rao; Fessler, Jeffrey A

2017-12-01

The sparsity of signals in a transform domain or dictionary has been exploited in applications such as compression, denoising and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise compared to analytical dictionary models. However, dictionary learning problems are typically non-convex and NP-hard, and the usual alternating minimization approaches for these problems are often computationally expensive, with the computations dominated by the NP-hard synthesis sparse coding step. This paper exploits the ideas that drive algorithms such as K-SVD, and investigates in detail efficient methods for aggregate sparsity penalized dictionary learning by first approximating the data with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns. The resulting block coordinate descent algorithms involve efficient closed-form solutions. Furthermore, we consider the problem of dictionary-blind image reconstruction, and propose novel and efficient algorithms for adaptive image reconstruction using block coordinate descent and sum of outer products methodologies. We provide a convergence study of the algorithms for dictionary learning and dictionary-blind image reconstruction. Our numerical experiments show the promising performance and speedups provided by the proposed methods over previous schemes in sparse data representation and compressed sensing-based image reconstruction.

Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems

PubMed Central

Ravishankar, Saiprasad; Nadakuditi, Raj Rao; Fessler, Jeffrey A.

2017-01-01

The sparsity of signals in a transform domain or dictionary has been exploited in applications such as compression, denoising and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise compared to analytical dictionary models. However, dictionary learning problems are typically non-convex and NP-hard, and the usual alternating minimization approaches for these problems are often computationally expensive, with the computations dominated by the NP-hard synthesis sparse coding step. This paper exploits the ideas that drive algorithms such as K-SVD, and investigates in detail efficient methods for aggregate sparsity penalized dictionary learning by first approximating the data with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns. The resulting block coordinate descent algorithms involve efficient closed-form solutions. Furthermore, we consider the problem of dictionary-blind image reconstruction, and propose novel and efficient algorithms for adaptive image reconstruction using block coordinate descent and sum of outer products methodologies. We provide a convergence study of the algorithms for dictionary learning and dictionary-blind image reconstruction. Our numerical experiments show the promising performance and speedups provided by the proposed methods over previous schemes in sparse data representation and compressed sensing-based image reconstruction. PMID:29376111

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

Andreyev, A.

Purpose: Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. Methods: To validate the proposed algorithm we used Monte Carlomore » simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background. Results: Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2–3 orders of magnitude per iteration. Conclusions: The results of our tests demonstrate the improvement of image resolution provided by the OE reconstructions with resolution recovery. The quality of images and their contrast are similar to those obtained from the OE reconstructions from scans simulated with perfect energy and spatial resolutions.« less

An effective automatic procedure for testing parameter identifiability of HIV/AIDS models.

PubMed

Saccomani, Maria Pia

2011-08-01

Realistic HIV models tend to be rather complex and many recent models proposed in the literature could not yet be analyzed by traditional identifiability testing techniques. In this paper, we check a priori global identifiability of some of these nonlinear HIV models taken from the recent literature, by using a differential algebra algorithm based on previous work of the author. The algorithm is implemented in a software tool, called DAISY (Differential Algebra for Identifiability of SYstems), which has been recently released (DAISY is freely available on the web site http://www.dei.unipd.it/~pia/ ). The software can be used to automatically check global identifiability of (linear and) nonlinear models described by polynomial or rational differential equations, thus providing a general and reliable tool to test global identifiability of several HIV models proposed in the literature. It can be used by researchers with a minimum of mathematical background.

Solving Assembly Sequence Planning using Angle Modulated Simulated Kalman Filter

NASA Astrophysics Data System (ADS)

Mustapa, Ainizar; Yusof, Zulkifli Md.; Adam, Asrul; Muhammad, Badaruddin; Ibrahim, Zuwairie

2018-03-01

This paper presents an implementation of Simulated Kalman Filter (SKF) algorithm for optimizing an Assembly Sequence Planning (ASP) problem. The SKF search strategy contains three simple steps; predict-measure-estimate. The main objective of the ASP is to determine the sequence of component installation to shorten assembly time or save assembly costs. Initially, permutation sequence is generated to represent each agent. Each agent is then subjected to a precedence matrix constraint to produce feasible assembly sequence. Next, the Angle Modulated SKF (AMSKF) is proposed for solving ASP problem. The main idea of the angle modulated approach in solving combinatorial optimization problem is to use a function, g(x), to create a continuous signal. The performance of the proposed AMSKF is compared against previous works in solving ASP by applying BGSA, BPSO, and MSPSO. Using a case study of ASP, the results show that AMSKF outperformed all the algorithms in obtaining the best solution.

A Multi-Verse Optimizer with Levy Flights for Numerical Optimization and Its Application in Test Scheduling for Network-on-Chip.

PubMed

Hu, Cong; Li, Zhi; Zhou, Tian; Zhu, Aijun; Xu, Chuanpei

2016-01-01

We propose a new meta-heuristic algorithm named Levy flights multi-verse optimizer (LFMVO), which incorporates Levy flights into multi-verse optimizer (MVO) algorithm to solve numerical and engineering optimization problems. The Original MVO easily falls into stagnation when wormholes stochastically re-span a number of universes (solutions) around the best universe achieved over the course of iterations. Since Levy flights are superior in exploring unknown, large-scale search space, they are integrated into the previous best universe to force MVO out of stagnation. We test this method on three sets of 23 well-known benchmark test functions and an NP complete problem of test scheduling for Network-on-Chip (NoC). Experimental results prove that the proposed LFMVO is more competitive than its peers in both the quality of the resulting solutions and convergence speed.

A Multi-Verse Optimizer with Levy Flights for Numerical Optimization and Its Application in Test Scheduling for Network-on-Chip

PubMed Central

Hu, Cong; Li, Zhi; Zhou, Tian; Zhu, Aijun; Xu, Chuanpei

2016-01-01

We propose a new meta-heuristic algorithm named Levy flights multi-verse optimizer (LFMVO), which incorporates Levy flights into multi-verse optimizer (MVO) algorithm to solve numerical and engineering optimization problems. The Original MVO easily falls into stagnation when wormholes stochastically re-span a number of universes (solutions) around the best universe achieved over the course of iterations. Since Levy flights are superior in exploring unknown, large-scale search space, they are integrated into the previous best universe to force MVO out of stagnation. We test this method on three sets of 23 well-known benchmark test functions and an NP complete problem of test scheduling for Network-on-Chip (NoC). Experimental results prove that the proposed LFMVO is more competitive than its peers in both the quality of the resulting solutions and convergence speed. PMID:27926946

Radar Imaging of Non-Uniformly Rotating Targets via a Novel Approach for Multi-Component AM-FM Signal Parameter Estimation

PubMed Central

Wang, Yong

2015-01-01

A novel radar imaging approach for non-uniformly rotating targets is proposed in this study. It is assumed that the maneuverability of the non-cooperative target is severe, and the received signal in a range cell can be modeled as multi-component amplitude-modulated and frequency-modulated (AM-FM) signals after motion compensation. Then, the modified version of Chirplet decomposition (MCD) based on the integrated high order ambiguity function (IHAF) is presented for the parameter estimation of AM-FM signals, and the corresponding high quality instantaneous ISAR images can be obtained from the estimated parameters. Compared with the MCD algorithm based on the generalized cubic phase function (GCPF) in the authors’ previous paper, the novel algorithm presented in this paper is more accurate and efficient, and the results with simulated and real data demonstrate the superiority of the proposed method. PMID:25806870

Global Artificial Boundary Conditions for Computation of External Flow Problems with Propulsive Jets

NASA Technical Reports Server (NTRS)

Tsynkov, Semyon; Abarbanel, Saul; Nordstrom, Jan; Ryabenkii, Viktor; Vatsa, Veer

1998-01-01

We propose new global artificial boundary conditions (ABC's) for computation of flows with propulsive jets. The algorithm is based on application of the difference potentials method (DPM). Previously, similar boundary conditions have been implemented for calculation of external compressible viscous flows around finite bodies. The proposed modification substantially extends the applicability range of the DPM-based algorithm. In the paper, we present the general formulation of the problem, describe our numerical methodology, and discuss the corresponding computational results. The particular configuration that we analyze is a slender three-dimensional body with boat-tail geometry and supersonic jet exhaust in a subsonic external flow under zero angle of attack. Similarly to the results obtained earlier for the flows around airfoils and wings, current results for the jet flow case corroborate the superiority of the DPM-based ABC's over standard local methodologies from the standpoints of accuracy, overall numerical performance, and robustness.

Finding topological center of a geographic space via road network

NASA Astrophysics Data System (ADS)

Gao, Liang; Miao, Yanan; Qin, Yuhao; Zhao, Xiaomei; Gao, Zi-You

2015-02-01

Previous studies show that the center of a geographic space is of great importance in urban and regional studies, including study of population distribution, urban growth modeling, and scaling properties of urban systems, etc. But how to well define and how to efficiently extract the center of a geographic space are still largely unknown. Recently, Jiang et al. have presented a definition of topological center by their block detection (BD) algorithm. Despite the fact that they first introduced the definition and discovered the 'true center', in human minds, their algorithm left several redundancies in its traversal process. Here, we propose an alternative road-cycle detection (RCD) algorithm to find the topological center, which extracts the outmost road-cycle recursively. To foster the application of the topological center in related research fields, we first reproduce the BD algorithm in Python (pyBD), then implement the RCD algorithm in two ways: the ArcPy implementation (arcRCD) and the Python implementation (pyRCD). After the experiments on twenty-four typical road networks, we find that the results of our RCD algorithm are consistent with those of Jiang's BD algorithm. We also find that the RCD algorithm is at least seven times more efficient than the BD algorithm on all the ten typical road networks.

Statistical hadronization and microcanonical ensemble

DOE PAGES

Becattini, F.; Ferroni, L.

2004-01-01

We present a Monte Carlo calculation of the microcanonical ensemble of the of the ideal hadron-resonance gas including all known states up to a mass of 1. 8 GeV, taking into account quantum statistics. The computing method is a development of a previous one based on a Metropolis Monte Carlo algorithm, with a the grand-canonical limit of the multi-species multiplicity distribution as proposal matrix. The microcanonical average multiplicities of the various hadron species are found to converge to the canonical ones for moderately low values of the total energy. This algorithm opens the way for event generators based for themore » statistical hadronization model.« less

ECS: Efficient Communication Scheduling for Underwater Sensor Networks

PubMed Central

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

2011-01-01

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

The shaped pulses control and operation on the SG-III prototype facility

NASA Astrophysics Data System (ADS)

Ping, Li; Wei, Wang; Sai, Jin; Wanqing, Huang; Wenyi, Wang; Jingqin, Su; Runchang, Zhao

2018-04-01

The laser driven inertial confined fusion experiments require careful temporal shape control of the laser pulse. Two approaches are introduced to improve the accuracy and efficiency of the close loop feedback system for long term operation in TIL; the first one is a statistical model to analyze the variation of the parameters obtained from previous shots, the other is a matrix algorithm proposed to relate the electrical signal and the impulse amplitudes. With the model and algorithm applied in the pulse shaping in TIL, a variety of shaped pulses were produced with a 10% precision in half an hour for almost three years under different circumstance.

Hyperspectral feature mapping classification based on mathematical morphology

NASA Astrophysics Data System (ADS)

Liu, Chang; Li, Junwei; Wang, Guangping; Wu, Jingli

2016-03-01

This paper proposed a hyperspectral feature mapping classification algorithm based on mathematical morphology. Without the priori information such as spectral library etc., the spectral and spatial information can be used to realize the hyperspectral feature mapping classification. The mathematical morphological erosion and dilation operations are performed respectively to extract endmembers. The spectral feature mapping algorithm is used to carry on hyperspectral image classification. The hyperspectral image collected by AVIRIS is applied to evaluate the proposed algorithm. The proposed algorithm is compared with minimum Euclidean distance mapping algorithm, minimum Mahalanobis distance mapping algorithm, SAM algorithm and binary encoding mapping algorithm. From the results of the experiments, it is illuminated that the proposed algorithm's performance is better than that of the other algorithms under the same condition and has higher classification accuracy.

Novel cooperative neural fusion algorithms for image restoration and image fusion.

PubMed

Xia, Youshen; Kamel, Mohamed S

2007-02-01

To deal with the problem of restoring degraded images with non-Gaussian noise, this paper proposes a novel cooperative neural fusion regularization (CNFR) algorithm for image restoration. Compared with conventional regularization algorithms for image restoration, the proposed CNFR algorithm can relax need of the optimal regularization parameter to be estimated. Furthermore, to enhance the quality of restored images, this paper presents a cooperative neural fusion (CNF) algorithm for image fusion. Compared with existing signal-level image fusion algorithms, the proposed CNF algorithm can greatly reduce the loss of contrast information under blind Gaussian noise environments. The performance analysis shows that the proposed two neural fusion algorithms can converge globally to the robust and optimal image estimate. Simulation results confirm that in different noise environments, the proposed two neural fusion algorithms can obtain a better image estimate than several well known image restoration and image fusion methods.

Better ILP models for haplotype assembly.

PubMed

Etemadi, Maryam; Bagherian, Mehri; Chen, Zhi-Zhong; Wang, Lusheng

2018-02-19

The haplotype assembly problem for diploid is to find a pair of haplotypes from a given set of aligned Single Nucleotide Polymorphism (SNP) fragments (reads). It has many applications in association studies, drug design, and genetic research. Since this problem is computationally hard, both heuristic and exact algorithms have been designed for it. Although exact algorithms are much slower, they are still of great interest because they usually output significantly better solutions than heuristic algorithms in terms of popular measures such as the Minimum Error Correction (MEC) score, the number of switch errors, and the QAN50 score. Exact algorithms are also valuable because they can be used to witness how good a heuristic algorithm is. The best known exact algorithm is based on integer linear programming (ILP) and it is known that ILP can also be used to improve the output quality of every heuristic algorithm with a little decline in speed. Therefore, faster ILP models for the problem are highly demanded. As in previous studies, we consider not only the general case of the problem but also its all-heterozygous case where we assume that if a column of the input read matrix contains at least one 0 and one 1, then it corresponds to a heterozygous SNP site. For both cases, we design new ILP models for the haplotype assembly problem which aim at minimizing the MEC score. The new models are theoretically better because they contain significantly fewer constraints. More importantly, our experimental results show that for both simulated and real datasets, the new model for the all-heterozygous (respectively, general) case can usually be solved via CPLEX (an ILP solver) at least 5 times (respectively, twice) faster than the previous bests. Indeed, the running time can sometimes be 41 times better. This paper proposes a new ILP model for the haplotype assembly problem and its all-heterozygous case, respectively. Experiments with both real and simulated datasets show that the new models can be solved within much shorter time by CPLEX than the previous bests. We believe that the models can be used to improve heuristic algorithms as well.

Evaluation of the CDC proposed laboratory HIV testing algorithm among men who have sex with men (MSM) from five US metropolitan statistical areas using specimens collected in 2011.

PubMed

Masciotra, Silvina; Smith, Amanda J; Youngpairoj, Ae S; Sprinkle, Patrick; Miles, Isa; Sionean, Catlainn; Paz-Bailey, Gabriela; Johnson, Jeffrey A; Owen, S Michele

2013-12-01

Until recently most testing algorithms in the United States (US) utilized Western blot (WB) as the supplemental test. CDC has proposed an algorithm for HIV diagnosis which includes an initial screen with a Combo Antigen/Antibody 4th generation-immunoassay (IA), followed by an HIV-1/2 discriminatory IA of initially reactive-IA specimens. Discordant results in the proposed algorithm are resolved by nucleic acid-amplification testing (NAAT). Evaluate the results obtained with the CDC proposed laboratory-based algorithm using specimens from men who have sex with men (MSM) obtained in five metropolitan statistical areas (MSAs). Specimens from 992 MSM from five MSAs participating in the CDC's National HIV Behavioral Surveillance System in 2011 were tested at local facilities and CDC. The five MSAs utilized algorithms of various screening assays and specimen types, and WB as the supplemental test. At the CDC, serum/plasma specimens were screened with 4th generation-IA and the Multispot HIV-1/HIV-2 discriminatory assay was used as the supplemental test. NAAT was used to resolve discordant results and to further identify acute HIV infections from all screened-non-reactive missed by the proposed algorithm. Performance of the proposed algorithm was compared to site-specific WB-based algorithms. The proposed algorithm detected 254 infections. The WB-based algorithms detected 19 fewer infections; 4 by oral fluid (OF) rapid testing and 15 by WB supplemental testing (12 OF and 3 blood). One acute infection was identified by NAAT from all screened-non-reactive specimens. The proposed algorithm identified more infections than the WB-based algorithms in a high-risk MSM population. OF testing was associated with most of the discordant results between algorithms. HIV testing with the proposed algorithm can increase diagnosis of infected individuals, including early infections. Published by Elsevier B.V.

A GPU-Accelerated 3-D Coupled Subsample Estimation Algorithm for Volumetric Breast Strain Elastography.

PubMed

Peng, Bo; Wang, Yuqi; Hall, Timothy J; Jiang, Jingfeng

2017-04-01

Our primary objective of this paper was to extend a previously published 2-D coupled subsample tracking algorithm for 3-D speckle tracking in the framework of ultrasound breast strain elastography. In order to overcome heavy computational cost, we investigated the use of a graphic processing unit (GPU) to accelerate the 3-D coupled subsample speckle tracking method. The performance of the proposed GPU implementation was tested using a tissue-mimicking phantom and in vivo breast ultrasound data. The performance of this 3-D subsample tracking algorithm was compared with the conventional 3-D quadratic subsample estimation algorithm. On the basis of these evaluations, we concluded that the GPU implementation of this 3-D subsample estimation algorithm can provide high-quality strain data (i.e., high correlation between the predeformation and the motion-compensated postdeformation radio frequency echo data and high contrast-to-noise ratio strain images), as compared with the conventional 3-D quadratic subsample algorithm. Using the GPU implementation of the 3-D speckle tracking algorithm, volumetric strain data can be achieved relatively fast (approximately 20 s per volume [2.5 cm ×2.5 cm ×2.5 cm]).

Modeling and analysis of solar distributed generation

NASA Astrophysics Data System (ADS)

Ortiz Rivera, Eduardo Ivan

Recent changes in the global economy are creating a big impact in our daily life. The price of oil is increasing and the number of reserves are less every day. Also, dramatic demographic changes are impacting the viability of the electric infrastructure and ultimately the economic future of the industry. These are some of the reasons that many countries are looking for alternative energy to produce electric energy. The most common form of green energy in our daily life is solar energy. To convert solar energy into electrical energy is required solar panels, dc-dc converters, power control, sensors, and inverters. In this work, a photovoltaic module, PVM, model using the electrical characteristics provided by the manufacturer data sheet is presented for power system applications. Experimental results from testing are showed, verifying the proposed PVM model. Also in this work, three maximum power point tracker, MPPT, algorithms would be presented to obtain the maximum power from a PVM. The first MPPT algorithm is a method based on the Rolle's and Lagrange's Theorems and can provide at least an approximate answer to a family of transcendental functions that cannot be solved using differential calculus. The second MPPT algorithm is based on the approximation of the proposed PVM model using fractional polynomials where the shape, boundary conditions and performance of the proposed PVM model are satisfied. The third MPPT algorithm is based in the determination of the optimal duty cycle for a dc-dc converter and the previous knowledge of the load or load matching conditions. Also, four algorithms to calculate the effective irradiance level and temperature over a photovoltaic module are presented in this work. The main reasons to develop these algorithms are for monitoring climate conditions, the elimination of temperature and solar irradiance sensors, reductions in cost for a photovoltaic inverter system, and development of new algorithms to be integrated with maximum power point tracking algorithms. Finally, several PV power applications will be presented like circuit analysis for a load connected to two different PV arrays, speed control for a do motor connected to a PVM, and a novel single phase photovoltaic inverter system using the Z-source converter.

Power iteration ranking via hybrid diffusion for vital nodes identification

NASA Astrophysics Data System (ADS)

Wu, Tao; Xian, Xingping; Zhong, Linfeng; Xiong, Xi; Stanley, H. Eugene

2018-09-01

One of the most interesting challenges in network science is to understand the relation between network structure and dynamics on it, and many topological properties, including degree distribution, community strength and clustering coefficient, have been proposed in the last decade. Prominent in this context is the centrality measures, which aim at quantifying the relative importance of individual nodes in the overall topology with regard to network organization and function. However, most of the previous centrality measures have been proposed based on different concepts and each of them focuses on a specific structural feature of networks. Thus, the straightforward and standard methods may lead to some bias against node importance measure. In this paper, we introduce two physical processes with potential complementarity between them. Then we propose to combine them as an elegant integration with the classic eigenvector centrality framework to improve the accuracy of node ranking. To test the produced power iteration ranking (PIRank) algorithm, we apply it to the selection of attack targets in network optimal attack problem. Extensive experimental results on synthetic networks and real-world networks suggest that the proposed centrality performs better than other well-known measures. Moreover, comparing with the eigenvector centrality, the PIRank algorithm can achieve about thirty percent performance improvement while keeping similar running time. Our experiment on random networks also shows that PIRank algorithm can avoid the localization phenomenon of eigenvector centrality, in particular for the networks with high-degree hubs.

A Fast Method to Calculate the Spatial Impulse Response for 1-D Linear Ultrasonic Phased Array Transducers

PubMed Central

Zou, Cheng; Sun, Zhenguo; Cai, Dong; Muhammad, Salman; Zhang, Wenzeng; Chen, Qiang

2016-01-01

A method is developed to accurately determine the spatial impulse response at the specifically discretized observation points in the radiated field of 1-D linear ultrasonic phased array transducers with great efficiency. In contrast, the previously adopted solutions only optimize the calculation procedure for a single rectangular transducer and required approximation considerations or nonlinear calculation. In this research, an algorithm that follows an alternative approach to expedite the calculation of the spatial impulse response of a rectangular linear array is presented. The key assumption for this algorithm is that the transducer apertures are identical and linearly distributed on an infinite rigid plane baffled with the same pitch. Two points in the observation field, which have the same position relative to two transducer apertures, share the same spatial impulse response that contributed from corresponding transducer, respectively. The observation field is discretized specifically to meet the relationship of equality. The analytical expressions of the proposed algorithm, based on the specific selection of the observation points, are derived to remove redundant calculations. In order to measure the proposed methodology, the simulation results obtained from the proposed method and the classical summation method are compared. The outcomes demonstrate that the proposed strategy can speed up the calculation procedure since it accelerates the speed-up ratio which relies upon the number of discrete points and the number of the array transducers. This development will be valuable in the development of advanced and faster linear ultrasonic phased array systems. PMID:27834799

Natural Aggregation Approach based Home Energy Manage System with User Satisfaction Modelling

NASA Astrophysics Data System (ADS)

Luo, F. J.; Ranzi, G.; Dong, Z. Y.; Murata, J.

2017-07-01

With the prevalence of advanced sensing and two-way communication technologies, Home Energy Management System (HEMS) has attracted lots of attentions in recent years. This paper proposes a HEMS that optimally schedules the controllable Residential Energy Resources (RERs) in a Time-of-Use (TOU) pricing and high solar power penetrated environment. The HEMS aims to minimize the overall operational cost of the home, and the user’s satisfactions and requirements on the operation of different household appliances are modelled and considered in the HEMS. Further, a new biological self-aggregation intelligence based optimization technique previously proposed by the authors, i.e., Natural Aggregation Algorithm (NAA), is applied to solve the proposed HEMS optimization model. Simulations are conducted to validate the proposed method.

3-Dimensional stereo implementation of photoacoustic imaging based on a new image reconstruction algorithm without using discrete Fourier transform

NASA Astrophysics Data System (ADS)

Ham, Woonchul; Song, Chulgyu

2017-05-01

In this paper, we propose a new three-dimensional stereo image reconstruction algorithm for a photoacoustic medical imaging system. We also introduce and discuss a new theoretical algorithm by using the physical concept of Radon transform. The main key concept of proposed theoretical algorithm is to evaluate the existence possibility of the acoustic source within a searching region by using the geometric distance between each sensor element of acoustic detector and the corresponding searching region denoted by grid. We derive the mathematical equation for the magnitude of the existence possibility which can be used for implementing a new proposed algorithm. We handle and derive mathematical equations of proposed algorithm for the one-dimensional sensing array case as well as two dimensional sensing array case too. A mathematical k-wave simulation data are used for comparing the image quality of the proposed algorithm with that of general conventional algorithm in which the FFT should be necessarily used. From the k-wave Matlab simulation results, we can prove the effectiveness of the proposed reconstruction algorithm.

Fast conjugate phase image reconstruction based on a Chebyshev approximation to correct for B0 field inhomogeneity and concomitant gradients

PubMed Central

Chen, Weitian; Sica, Christopher T.; Meyer, Craig H.

2008-01-01

Off-resonance effects can cause image blurring in spiral scanning and various forms of image degradation in other MRI methods. Off-resonance effects can be caused by both B0 inhomogeneity and concomitant gradient fields. Previously developed off-resonance correction methods focus on the correction of a single source of off-resonance. This work introduces a computationally efficient method of correcting for B0 inhomogeneity and concomitant gradients simultaneously. The method is a fast alternative to conjugate phase reconstruction, with the off-resonance phase term approximated by Chebyshev polynomials. The proposed algorithm is well suited for semiautomatic off-resonance correction, which works well even with an inaccurate or low-resolution field map. The proposed algorithm is demonstrated using phantom and in vivo data sets acquired by spiral scanning. Semiautomatic off-resonance correction alone is shown to provide a moderate amount of correction for concomitant gradient field effects, in addition to B0 imhomogeneity effects. However, better correction is provided by the proposed combined method. The best results were produced using the semiautomatic version of the proposed combined method. PMID:18956462

Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets.

PubMed

Li, Jinyan; Fong, Simon; Wong, Raymond K; Millham, Richard; Wong, Kelvin K L

2017-06-28

Due to the high-dimensional characteristics of dataset, we propose a new method based on the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed approach uses the natural strategy established by Charles Darwin; that is, 'It is not the strongest of the species that survives, but the most adaptable'. This means that in the evolution of a swarm, the elitists are motivated to quickly obtain more and better resources. The memory function helps the proposed method to avoid repeat searches for the worst position in order to enhance the effectiveness of the search, while the binary strategy simplifies the feature selection problem into a similar problem of function optimisation. Furthermore, the wrapper strategy gathers these strengthened wolves with the classifier of extreme learning machine to find a sub-dataset with a reasonable number of features that offers the maximum correctness of global classification models. The experimental results from the six public high-dimensional bioinformatics datasets tested demonstrate that the proposed method can best some of the conventional feature selection methods up to 29% in classification accuracy, and outperform previous WSAs by up to 99.81% in computational time.

Localized contourlet features in vehicle make and model recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, B. S.

2009-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance.

A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm.

PubMed

Cuevas, Erik; Díaz, Margarita

2015-01-01

In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. The rules for the generation of candidate solutions (samples) are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. The method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the efficiency of the proposed method in terms of accuracy, speed, and robustness.

Interior search algorithm (ISA): a novel approach for global optimization.

PubMed

Gandomi, Amir H

2014-07-01

This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Cryptanalysis of "an improvement over an image encryption method based on total shuffling"

NASA Astrophysics Data System (ADS)

Akhavan, A.; Samsudin, A.; Akhshani, A.

2015-09-01

In the past two decades, several image encryption algorithms based on chaotic systems had been proposed. Many of the proposed algorithms are meant to improve other chaos based and conventional cryptographic algorithms. Whereas, many of the proposed improvement methods suffer from serious security problems. In this paper, the security of the recently proposed improvement method for a chaos-based image encryption algorithm is analyzed. The results indicate the weakness of the analyzed algorithm against chosen plain-text.

Ensemble method: Community detection based on game theory

NASA Astrophysics Data System (ADS)

Zhang, Xia; Xia, Zhengyou; Xu, Shengwu; Wang, J. D.

2014-08-01

Timely and cost-effective analytics over social network has emerged as a key ingredient for success in many businesses and government endeavors. Community detection is an active research area of relevance to analyze online social network. The problem of selecting a particular community detection algorithm is crucial if the aim is to unveil the community structure of a network. The choice of a given methodology could affect the outcome of the experiments because different algorithms have different advantages and depend on tuning specific parameters. In this paper, we propose a community division model based on the notion of game theory, which can combine advantages of previous algorithms effectively to get a better community classification result. By making experiments on some standard dataset, it verifies that our community detection model based on game theory is valid and better.

Inferring phenomenological models of Markov processes from data

NASA Astrophysics Data System (ADS)

Rivera, Catalina; Nemenman, Ilya

Microscopically accurate modeling of stochastic dynamics of biochemical networks is hard due to the extremely high dimensionality of the state space of such networks. Here we propose an algorithm for inference of phenomenological, coarse-grained models of Markov processes describing the network dynamics directly from data, without the intermediate step of microscopically accurate modeling. The approach relies on the linear nature of the Chemical Master Equation and uses Bayesian Model Selection for identification of parsimonious models that fit the data. When applied to synthetic data from the Kinetic Proofreading process (KPR), a common mechanism used by cells for increasing specificity of molecular assembly, the algorithm successfully uncovers the known coarse-grained description of the process. This phenomenological description has been notice previously, but this time it is derived in an automated manner by the algorithm. James S. McDonnell Foundation Grant No. 220020321.

The SARS algorithm: detrending CoRoT light curves with Sysrem using simultaneous external parameters

NASA Astrophysics Data System (ADS)

Ofir, Aviv; Alonso, Roi; Bonomo, Aldo Stefano; Carone, Ludmila; Carpano, Stefania; Samuel, Benjamin; Weingrill, Jörg; Aigrain, Suzanne; Auvergne, Michel; Baglin, Annie; Barge, Pierre; Borde, Pascal; Bouchy, Francois; Deeg, Hans J.; Deleuil, Magali; Dvorak, Rudolf; Erikson, Anders; Mello, Sylvio Ferraz; Fridlund, Malcolm; Gillon, Michel; Guillot, Tristan; Hatzes, Artie; Jorda, Laurent; Lammer, Helmut; Leger, Alain; Llebaria, Antoine; Moutou, Claire; Ollivier, Marc; Päetzold, Martin; Queloz, Didier; Rauer, Heike; Rouan, Daniel; Schneider, Jean; Wuchterl, Guenther

2010-05-01

Surveys for exoplanetary transits are usually limited not by photon noise but rather by the amount of red noise in their data. In particular, although the CoRoT space-based survey data are being carefully scrutinized, significant new sources of systematic noises are still being discovered. Recently, a magnitude-dependant systematic effect was discovered in the CoRoT data by Mazeh et al. and a phenomenological correction was proposed. Here we tie the observed effect to a particular type of effect, and in the process generalize the popular Sysrem algorithm to include external parameters in a simultaneous solution with the unknown effects. We show that a post-processing scheme based on this algorithm performs well and indeed allows for the detection of new transit-like signals that were not previously detected.

New Multi-objective Uncertainty-based Algorithm for Water Resource Models' Calibration

NASA Astrophysics Data System (ADS)

Keshavarz, Kasra; Alizadeh, Hossein

2017-04-01

Water resource models are powerful tools to support water management decision making process and are developed to deal with a broad range of issues including land use and climate change impacts analysis, water allocation, systems design and operation, waste load control and allocation, etc. These models are divided into two categories of simulation and optimization models whose calibration has been addressed in the literature where great relevant efforts in recent decades have led to two main categories of auto-calibration methods of uncertainty-based algorithms such as GLUE, MCMC and PEST and optimization-based algorithms including single-objective optimization such as SCE-UA and multi-objective optimization such as MOCOM-UA and MOSCEM-UA. Although algorithms which benefit from capabilities of both types, such as SUFI-2, were rather developed, this paper proposes a new auto-calibration algorithm which is capable of both finding optimal parameters values regarding multiple objectives like optimization-based algorithms and providing interval estimations of parameters like uncertainty-based algorithms. The algorithm is actually developed to improve quality of SUFI-2 results. Based on a single-objective, e.g. NSE and RMSE, SUFI-2 proposes a routine to find the best point and interval estimation of parameters and corresponding prediction intervals (95 PPU) of time series of interest. To assess the goodness of calibration, final results are presented using two uncertainty measures of p-factor quantifying percentage of observations covered by 95PPU and r-factor quantifying degree of uncertainty, and the analyst has to select the point and interval estimation of parameters which are actually non-dominated regarding both of the uncertainty measures. Based on the described properties of SUFI-2, two important questions are raised, answering of which are our research motivation: Given that in SUFI-2, final selection is based on the two measures or objectives and on the other hand, knowing that there is no multi-objective optimization mechanism in SUFI-2, are the final estimations Pareto-optimal? Can systematic methods be applied to select the final estimations? Dealing with these questions, a new auto-calibration algorithm was proposed where the uncertainty measures were considered as two objectives to find non-dominated interval estimations of parameters by means of coupling Monte Carlo simulation and Multi-Objective Particle Swarm Optimization. Both the proposed algorithm and SUFI-2 were applied to calibrate parameters of water resources planning model of Helleh river basin, Iran. The model is a comprehensive water quantity-quality model developed in the previous researches using WEAP software in order to analyze the impacts of different water resources management strategies including dam construction, increasing cultivation area, utilization of more efficient irrigation technologies, changing crop pattern, etc. Comparing the Pareto frontier resulted from the proposed auto-calibration algorithm with SUFI-2 results, it was revealed that the new algorithm leads to a better and also continuous Pareto frontier, even though it is more computationally expensive. Finally, Nash and Kalai-Smorodinsky bargaining methods were used to choose compromised interval estimation regarding Pareto frontier.

On the estimation algorithm used in adaptive performance optimization of turbofan engines

NASA Technical Reports Server (NTRS)

Espana, Martin D.; Gilyard, Glenn B.

1993-01-01

The performance seeking control algorithm is designed to continuously optimize the performance of propulsion systems. The performance seeking control algorithm uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters characterizing the engine deviations with respect to nominal conditions. In practice, because of measurement biases and/or model uncertainties, the estimated engine deviation parameters may not reflect the engine's actual off-nominal condition. This factor has a necessary impact on the overall performance seeking control scheme exacerbated by the open-loop character of the algorithm. The effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the performance seeking control algorithm to an F100 engine. An equivalence relation between the biases and engine deviation parameters stems from an observability study; therefore, it is undecided whether the estimated engine deviation parameters represent the actual engine deviation or whether they simply reflect the measurement biases. A new algorithm, based on the engine's (steady-state) optimization model, is proposed and tested with flight data. When compared with previous Kalman filter schemes, based on local engine dynamic models, the new algorithm is easier to design and tune and it reduces the computational burden of the onboard computer.

Fast flow-based algorithm for creating density-equalizing map projections

PubMed Central

Gastner, Michael T.; Seguy, Vivien; More, Pratyush

2018-01-01

Cartograms are maps that rescale geographic regions (e.g., countries, districts) such that their areas are proportional to quantitative demographic data (e.g., population size, gross domestic product). Unlike conventional bar or pie charts, cartograms can represent correctly which regions share common borders, resulting in insightful visualizations that can be the basis for further spatial statistical analysis. Computer programs can assist data scientists in preparing cartograms, but developing an algorithm that can quickly transform every coordinate on the map (including points that are not exactly on a border) while generating recognizable images has remained a challenge. Methods that translate the cartographic deformations into physics-inspired equations of motion have become popular, but solving these equations with sufficient accuracy can still take several minutes on current hardware. Here we introduce a flow-based algorithm whose equations of motion are numerically easier to solve compared with previous methods. The equations allow straightforward parallelization so that the calculation takes only a few seconds even for complex and detailed input. Despite the speedup, the proposed algorithm still keeps the advantages of previous techniques: With comparable quantitative measures of shape distortion, it accurately scales all areas, correctly fits the regions together, and generates a map projection for every point. We demonstrate the use of our algorithm with applications to the 2016 US election results, the gross domestic products of Indian states and Chinese provinces, and the spatial distribution of deaths in the London borough of Kensington and Chelsea between 2011 and 2014. PMID:29463721

Word-level language modeling for P300 spellers based on discriminative graphical models

NASA Astrophysics Data System (ADS)

Delgado Saa, Jaime F.; de Pesters, Adriana; McFarland, Dennis; Çetin, Müjdat

2015-04-01

Objective. In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers. Approach. This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based on a limited vocabulary, we propose a probabilistic graphical model framework and an associated classification algorithm that uses learned statistical models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate of the speller. Main results. Our experimental results demonstrate that the proposed approach offers several advantages over existing methods. Most importantly, it increases the classification accuracy while reducing the number of times the letters need to be flashed, increasing the communication rate of the system. Significance. The proposed approach models all the variables in the P300 speller in a unified framework and has the capability to correct errors in previous letters in a word, given the data for the current one. The structure of the model we propose allows the use of efficient inference algorithms, which in turn makes it possible to use this approach in real-time applications.

Efficient traffic grooming in SONET/WDM BLSR Networks

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

Awwal, A S; Billah, A B; Wang, B

2004-04-02

In this paper, we study traffic grooming in SONET/WDM BLSR networks under the uniform all-to-all traffic model with an objective to reduce total network costs (wavelength and electronic multiplexing costs), in particular, to minimize the number of ADMs while using the optimal number of wavelengths. We derive a new tighter lower bound for the number of wavelengths when the number of nodes is a multiple of 4. We show that this lower bound is achievable. All previous ADM lower bounds except perhaps that in were derived under the assumption that the magnitude of the traffic streams (r) is one unitmore » (r = 1) with respect to the wavelength capacity granularity g. We then derive new, more general and tighter lower bounds for the number of ADMs subject to that the optimal number of wavelengths is used, and propose heuristic algorithms (circle construction algorithm and circle grooming algorithm) that try to minimize the number of ADMs while using the optimal number of wavelengths in BLSR networks. Both the bounds and algorithms are applicable to any value of r and for different wavelength granularity g. Performance evaluation shows that wherever applicable, our lower bounds are at least as good as existing bounds and are much tighter than existing ones in many cases. Our proposed heuristic grooming algorithms perform very well with traffic streams of larger magnitude. The resulting number of ADMs required is very close to the corresponding lower bounds derived in this paper.« less

3D widefield light microscope image reconstruction without dyes

NASA Astrophysics Data System (ADS)

Larkin, S.; Larson, J.; Holmes, C.; Vaicik, M.; Turturro, M.; Jurkevich, A.; Sinha, S.; Ezashi, T.; Papavasiliou, G.; Brey, E.; Holmes, T.

2015-03-01

3D image reconstruction using light microscope modalities without exogenous contrast agents is proposed and investigated as an approach to produce 3D images of biological samples for live imaging applications. Multimodality and multispectral imaging, used in concert with this 3D optical sectioning approach is also proposed as a way to further produce contrast that could be specific to components in the sample. The methods avoid usage of contrast agents. Contrast agents, such as fluorescent or absorbing dyes, can be toxic to cells or alter cell behavior. Current modes of producing 3D image sets from a light microscope, such as 3D deconvolution algorithms and confocal microscopy generally require contrast agents. Zernike phase contrast (ZPC), transmitted light brightfield (TLB), darkfield microscopy and others can produce contrast without dyes. Some of these modalities have not previously benefitted from 3D image reconstruction algorithms, however. The 3D image reconstruction algorithm is based on an underlying physical model of scattering potential, expressed as the sample's 3D absorption and phase quantities. The algorithm is based upon optimizing an objective function - the I-divergence - while solving for the 3D absorption and phase quantities. Unlike typical deconvolution algorithms, each microscope modality, such as ZPC or TLB, produces two output image sets instead of one. Contrast in the displayed image and 3D renderings is further enabled by treating the multispectral/multimodal data as a feature set in a mathematical formulation that uses the principal component method of statistics.

Multicast routing for wavelength-routed WDM networks with dynamic membership

NASA Astrophysics Data System (ADS)

Huang, Nen-Fu; Liu, Te-Lung; Wang, Yao-Tzung; Li, Bo

2000-09-01

Future broadband networks must support integrated services and offer flexible bandwidth usage. In our previous work, we explore the optical link control layer on the top of optical layer that enables the possibility of bandwidth on-demand service directly over wavelength division multiplexed (WDM) networks. Today, more and more applications and services such as video-conferencing software and Virtual LAN service require multicast support over the underlying networks. Currently, it is difficult to provide wavelength multicast over the optical switches without optical/electronic conversions although the conversion takes extra cost. In this paper, based on the proposed wavelength router architecture (equipped with ATM switches to offer O/E and E/O conversions when necessary), a dynamic multicast routing algorithm is proposed to furnish multicast services over WDM networks. The goal is to joint a new group member into the multicast tree so that the cost, including the link cost and the optical/electronic conversion cost, is kept as less as possible. The effectiveness of the proposed wavelength router architecture as well as the dynamic multicast algorithm is evaluated by simulation.

Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique.

PubMed

Hao, Ming; Wang, Yanli; Bryant, Stephen H

2016-02-25

Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision-recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. Published by Elsevier B.V.

Unified framework for automated iris segmentation using distantly acquired face images.

PubMed

Tan, Chun-Wei; Kumar, Ajay

2012-09-01

Remote human identification using iris biometrics has high civilian and surveillance applications and its success requires the development of robust segmentation algorithm to automatically extract the iris region. This paper presents a new iris segmentation framework which can robustly segment the iris images acquired using near infrared or visible illumination. The proposed approach exploits multiple higher order local pixel dependencies to robustly classify the eye region pixels into iris or noniris regions. Face and eye detection modules have been incorporated in the unified framework to automatically provide the localized eye region from facial image for iris segmentation. We develop robust postprocessing operations algorithm to effectively mitigate the noisy pixels caused by the misclassification. Experimental results presented in this paper suggest significant improvement in the average segmentation errors over the previously proposed approaches, i.e., 47.5%, 34.1%, and 32.6% on UBIRIS.v2, FRGC, and CASIA.v4 at-a-distance databases, respectively. The usefulness of the proposed approach is also ascertained from recognition experiments on three different publicly available databases.

Biclustering Learning of Trading Rules.

PubMed

Huang, Qinghua; Wang, Ting; Tao, Dacheng; Li, Xuelong

2015-10-01

Technical analysis with numerous indicators and patterns has been regarded as important evidence for making trading decisions in financial markets. However, it is extremely difficult for investors to find useful trading rules based on numerous technical indicators. This paper innovatively proposes the use of biclustering mining to discover effective technical trading patterns that contain a combination of indicators from historical financial data series. This is the first attempt to use biclustering algorithm on trading data. The mined patterns are regarded as trading rules and can be classified as three trading actions (i.e., the buy, the sell, and no-action signals) with respect to the maximum support. A modified K nearest neighborhood ( K -NN) method is applied to classification of trading days in the testing period. The proposed method [called biclustering algorithm and the K nearest neighbor (BIC- K -NN)] was implemented on four historical datasets and the average performance was compared with the conventional buy-and-hold strategy and three previously reported intelligent trading systems. Experimental results demonstrate that the proposed trading system outperforms its counterparts and will be useful for investment in various financial markets.

L-split marker for augmented reality in aircraft assembly

NASA Astrophysics Data System (ADS)

Han, Pengfei; Zhao, Gang

2016-04-01

In order to improve the performance of conventional square markers widely used by marker-based augmented reality systems in aircraft assembly environments, an L-split marker is proposed. Every marker consists of four separate L-shaped parts and each of them contains partial information about the marker. Geometric features of the L-shape, which are more discriminate than the symmetrical square shape adopted by conventional markers, are used to detect proposed markers from the camera images effectively. The marker is split into four separate parts in order to improve the robustness to occlusion and curvature to some extent. The registration process can be successfully completed as long as three parts are detected (up to about 80% of the area could be occluded). Moreover, when we attach the marker on nonplanar surfaces, the curvature status of the marker can be roughly analyzed with every part's normal direction, which can be obtained since their six corners have been explicitly determined in the previous detection process. And based on the marker design, new detection and recognition algorithms are proposed and detailed. The experimental results show that the marker and the algorithms are effective.

A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems

NASA Astrophysics Data System (ADS)

Thammano, Arit; Teekeng, Wannaporn

2015-05-01

The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.

New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

PubMed Central

Li, Xiguang; Zhao, Liang; Gong, Changqing; Liu, Xiaojing

2017-01-01

Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA), is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM) for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent. PMID:29085425

An improved VSS NLMS algorithm for active noise cancellation

NASA Astrophysics Data System (ADS)

Sun, Yunzhuo; Wang, Mingjiang; Han, Yufei; Zhang, Congyan

2017-08-01

In this paper, an improved variable step size NLMS algorithm is proposed. NLMS has fast convergence rate and low steady state error compared to other traditional adaptive filtering algorithm. But there is a contradiction between the convergence speed and steady state error that affect the performance of the NLMS algorithm. Now, we propose a new variable step size NLMS algorithm. It dynamically changes the step size according to current error and iteration times. The proposed algorithm has simple formulation and easily setting parameters, and effectively solves the contradiction in NLMS. The simulation results show that the proposed algorithm has a good tracking ability, fast convergence rate and low steady state error simultaneously.

A Novel 2D Image Compression Algorithm Based on Two Levels DWT and DCT Transforms with Enhanced Minimize-Matrix-Size Algorithm for High Resolution Structured Light 3D Surface Reconstruction

NASA Astrophysics Data System (ADS)

Siddeq, M. M.; Rodrigues, M. A.

2015-09-01

Image compression techniques are widely used on 2D image 2D video 3D images and 3D video. There are many types of compression techniques and among the most popular are JPEG and JPEG2000. In this research, we introduce a new compression method based on applying a two level discrete cosine transform (DCT) and a two level discrete wavelet transform (DWT) in connection with novel compression steps for high-resolution images. The proposed image compression algorithm consists of four steps. (1) Transform an image by a two level DWT followed by a DCT to produce two matrices: DC- and AC-Matrix, or low and high frequency matrix, respectively, (2) apply a second level DCT on the DC-Matrix to generate two arrays, namely nonzero-array and zero-array, (3) apply the Minimize-Matrix-Size algorithm to the AC-Matrix and to the other high-frequencies generated by the second level DWT, (4) apply arithmetic coding to the output of previous steps. A novel decompression algorithm, Fast-Match-Search algorithm (FMS), is used to reconstruct all high-frequency matrices. The FMS-algorithm computes all compressed data probabilities by using a table of data, and then using a binary search algorithm for finding decompressed data inside the table. Thereafter, all decoded DC-values with the decoded AC-coefficients are combined in one matrix followed by inverse two levels DCT with two levels DWT. The technique is tested by compression and reconstruction of 3D surface patches. Additionally, this technique is compared with JPEG and JPEG2000 algorithm through 2D and 3D root-mean-square-error following reconstruction. The results demonstrate that the proposed compression method has better visual properties than JPEG and JPEG2000 and is able to more accurately reconstruct surface patches in 3D.

Priority and Negotiation Based Dynamic Spectrum Allocation Scheme for Multiple Radio Access Network Operators

NASA Astrophysics Data System (ADS)

Kim, Hoon; Hyon, Taein; Lee, Yeonwoo

Most of previous works have presented the dynamic spectrum allocation (DSA) gain achieved by utilizing the time or regional variations in traffic demand between multi-network operators (NOs). In this paper, we introduce the functionalities required for the entities related with the spectrum sharing and allocation and propose a spectrum allocation algorithm while considering the long-term priority between NOs, the priority between multiple class services, and the urgent bandwidth request. To take into account the priorities among the NOs and the priorities of multiple class services, a spectrum sharing metric (SSM) is proposed, while a negotiation procedure is proposed to treat the urgent bandwidth request.

A Diagonal-Steering-Based Binaural Beamforming Algorithm Incorporating a Diagonal Speech Localizer for Persons With Bilateral Hearing Impairment.

PubMed

Lee, Jun Chang; Nam, Kyoung Won; Jang, Dong Pyo; Kim, In Young

2015-12-01

Previously suggested diagonal-steering algorithms for binaural hearing support devices have commonly assumed that the direction of the speech signal is known in advance, which is not always the case in many real circumstances. In this study, a new diagonal-steering-based binaural speech localization (BSL) algorithm is proposed, and the performances of the BSL algorithm and the binaural beamforming algorithm, which integrates the BSL and diagonal-steering algorithms, were evaluated using actual speech-in-noise signals in several simulated listening scenarios. Testing sounds were recorded in a KEMAR mannequin setup and two objective indices, improvements in signal-to-noise ratio (SNRi ) and segmental SNR (segSNRi ), were utilized for performance evaluation. Experimental results demonstrated that the accuracy of the BSL was in the 90-100% range when input SNR was -10 to +5 dB range. The average differences between the γ-adjusted and γ-fixed diagonal-steering algorithms (for -15 to +5 dB input SNR) in the talking in the restaurant scenario were 0.203-0.937 dB for SNRi and 0.052-0.437 dB for segSNRi , and in the listening while car driving scenario, the differences were 0.387-0.835 dB for SNRi and 0.259-1.175 dB for segSNRi . In addition, the average difference between the BSL-turned-on and the BSL-turned-off cases for the binaural beamforming algorithm in the listening while car driving scenario was 1.631-4.246 dB for SNRi and 0.574-2.784 dB for segSNRi . In all testing conditions, the γ-adjusted diagonal-steering and BSL algorithm improved the values of the indices more than the conventional algorithms. The binaural beamforming algorithm, which integrates the proposed BSL and diagonal-steering algorithm, is expected to improve the performance of the binaural hearing support devices in noisy situations. Copyright © 2015 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

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.

Rapid Calculation of Max-Min Fair Rates for Multi-Commodity Flows in Fat-Tree Networks

DOE PAGES

Mollah, Md Atiqul; Yuan, Xin; Pakin, Scott; ...

2017-08-29

Max-min fairness is often used in the performance modeling of interconnection networks. Existing methods to compute max-min fair rates for multi-commodity flows have high complexity and are computationally infeasible for large networks. In this paper, we show that by considering topological features, this problem can be solved efficiently for the fat-tree topology that is widely used in data centers and high performance compute clusters. Several efficient new algorithms are developed for this problem, including a parallel algorithm that can take advantage of multi-core and shared-memory architectures. Using these algorithms, we demonstrate that it is possible to find the max-min fairmore » rate allocation for multi-commodity flows in fat-tree networks that support tens of thousands of nodes. We evaluate the run-time performance of the proposed algorithms and show improvement in orders of magnitude over the previously best known method. Finally, we further demonstrate a new application of max-min fair rate allocation that is only computationally feasible using our new algorithms.« less

Rapid Calculation of Max-Min Fair Rates for Multi-Commodity Flows in Fat-Tree Networks

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

Mollah, Md Atiqul; Yuan, Xin; Pakin, Scott

Max-min fairness is often used in the performance modeling of interconnection networks. Existing methods to compute max-min fair rates for multi-commodity flows have high complexity and are computationally infeasible for large networks. In this paper, we show that by considering topological features, this problem can be solved efficiently for the fat-tree topology that is widely used in data centers and high performance compute clusters. Several efficient new algorithms are developed for this problem, including a parallel algorithm that can take advantage of multi-core and shared-memory architectures. Using these algorithms, we demonstrate that it is possible to find the max-min fairmore » rate allocation for multi-commodity flows in fat-tree networks that support tens of thousands of nodes. We evaluate the run-time performance of the proposed algorithms and show improvement in orders of magnitude over the previously best known method. Finally, we further demonstrate a new application of max-min fair rate allocation that is only computationally feasible using our new algorithms.« less

A multifaceted independent performance analysis of facial subspace recognition algorithms.

PubMed

Bajwa, Usama Ijaz; Taj, Imtiaz Ahmad; Anwar, Muhammad Waqas; Wang, Xuan

2013-01-01

Face recognition has emerged as the fastest growing biometric technology and has expanded a lot in the last few years. Many new algorithms and commercial systems have been proposed and developed. Most of them use Principal Component Analysis (PCA) as a base for their techniques. Different and even conflicting results have been reported by researchers comparing these algorithms. The purpose of this study is to have an independent comparative analysis considering both performance and computational complexity of six appearance based face recognition algorithms namely PCA, 2DPCA, A2DPCA, (2D)(2)PCA, LPP and 2DLPP under equal working conditions. This study was motivated due to the lack of unbiased comprehensive comparative analysis of some recent subspace methods with diverse distance metric combinations. For comparison with other studies, FERET, ORL and YALE databases have been used with evaluation criteria as of FERET evaluations which closely simulate real life scenarios. A comparison of results with previous studies is performed and anomalies are reported. An important contribution of this study is that it presents the suitable performance conditions for each of the algorithms under consideration.

Formulating face verification with semidefinite programming.

PubMed

Yan, Shuicheng; Liu, Jianzhuang; Tang, Xiaoou; Huang, Thomas S

2007-11-01

This paper presents a unified solution to three unsolved problems existing in face verification with subspace learning techniques: selection of verification threshold, automatic determination of subspace dimension, and deducing feature fusing weights. In contrast to previous algorithms which search for the projection matrix directly, our new algorithm investigates a similarity metric matrix (SMM). With a certain verification threshold, this matrix is learned by a semidefinite programming approach, along with the constraints of the kindred pairs with similarity larger than the threshold, and inhomogeneous pairs with similarity smaller than the threshold. Then, the subspace dimension and the feature fusing weights are simultaneously inferred from the singular value decomposition of the derived SMM. In addition, the weighted and tensor extensions are proposed to further improve the algorithmic effectiveness and efficiency, respectively. Essentially, the verification is conducted within an affine subspace in this new algorithm and is, hence, called the affine subspace for verification (ASV). Extensive experiments show that the ASV can achieve encouraging face verification accuracy in comparison to other subspace algorithms, even without the need to explore any parameters.

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

Krityakierne, Tipaluck; Akhtar, Taimoor; Shoemaker, Christine A.

This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centersmore » from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases the efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors.« less

Control Allocation with Load Balancing

NASA Technical Reports Server (NTRS)

Bodson, Marc; Frost, Susan A.

2009-01-01

Next generation aircraft with a large number of actuators will require advanced control allocation methods to compute the actuator commands needed to follow desired trajectories while respecting system constraints. Previously, algorithms were proposed to minimize the l1 or l2 norms of the tracking error and of the actuator deflections. The paper discusses the alternative choice of the l(infinity) norm, or sup norm. Minimization of the control effort translates into the minimization of the maximum actuator deflection (min-max optimization). The paper shows how the problem can be solved effectively by converting it into a linear program and solving it using a simplex algorithm. Properties of the algorithm are also investigated through examples. In particular, the min-max criterion results in a type of load balancing, where the load is th desired command and the algorithm balances this load among various actuators. The solution using the l(infinity) norm also results in better robustness to failures and to lower sensitivity to nonlinearities in illustrative examples.

Equivalence of Szegedy's and coined quantum walks

NASA Astrophysics Data System (ADS)

Wong, Thomas G.

2017-09-01

Szegedy's quantum walk is a quantization of a classical random walk or Markov chain, where the walk occurs on the edges of the bipartite double cover of the original graph. To search, one can simply quantize a Markov chain with absorbing vertices. Recently, Santos proposed two alternative search algorithms that instead utilize the sign-flip oracle in Grover's algorithm rather than absorbing vertices. In this paper, we show that these two algorithms are exactly equivalent to two algorithms involving coined quantum walks, which are walks on the vertices of the original graph with an internal degree of freedom. The first scheme is equivalent to a coined quantum walk with one walk step per query of Grover's oracle, and the second is equivalent to a coined quantum walk with two walk steps per query of Grover's oracle. These equivalences lie outside the previously known equivalence of Szegedy's quantum walk with absorbing vertices and the coined quantum walk with the negative identity operator as the coin for marked vertices, whose precise relationships we also investigate.

A fuzzy discrete harmony search algorithm applied to annual cost reduction in radial distribution systems

NASA Astrophysics Data System (ADS)

Ameli, Kazem; Alfi, Alireza; Aghaebrahimi, Mohammadreza

2016-09-01

Similarly to other optimization algorithms, harmony search (HS) is quite sensitive to the tuning parameters. Several variants of the HS algorithm have been developed to decrease the parameter-dependency character of HS. This article proposes a novel version of the discrete harmony search (DHS) algorithm, namely fuzzy discrete harmony search (FDHS), for optimizing capacitor placement in distribution systems. In the FDHS, a fuzzy system is employed to dynamically adjust two parameter values, i.e. harmony memory considering rate and pitch adjusting rate, with respect to normalized mean fitness of the harmony memory. The key aspect of FDHS is that it needs substantially fewer iterations to reach convergence in comparison with classical discrete harmony search (CDHS). To the authors' knowledge, this is the first application of DHS to specify appropriate capacitor locations and their best amounts in the distribution systems. Simulations are provided for 10-, 34-, 85- and 141-bus distribution systems using CDHS and FDHS. The results show the effectiveness of FDHS over previous related studies.

Respiration-rate estimation of a moving target using impulse-based ultra wideband radars.

PubMed

Sharafi, Azadeh; Baboli, Mehran; Eshghi, Mohammad; Ahmadian, Alireza

2012-03-01

Recently, Ultra-wide band signals have become attractive for their particular advantage of having high spatial resolution and good penetration ability which makes them suitable in medical applications. One of these applications is wireless detection of heart rate and respiration rate. Two hypothesis of static environment and fixed patient are considered in the method presented in previous literatures which are not valid for long term monitoring of ambulant patients. In this article, a new method to detect the respiration rate of a moving target is presented. The first algorithm is applied to the simulated and experimental data for detecting respiration rate of a fixed target. Then, the second algorithm is developed to detect respiration rate of a moving target. The proposed algorithm uses correlation for body movement cancellation, and then detects the respiration rate based on energy in frequency domain. The results of algorithm prove an accuracy of 98.4 and 97% in simulated and experimental data, respectively.

A physics-based algorithm for real-time simulation of electrosurgery procedures in minimally invasive surgery.

PubMed

Lu, Zhonghua; Arikatla, Venkata S; Han, Zhongqing; Allen, Brian F; De, Suvranu

2014-12-01

High-frequency electricity is used in the majority of surgical interventions. However, modern computer-based training and simulation systems rely on physically unrealistic models that fail to capture the interplay of the electrical, mechanical and thermal properties of biological tissue. We present a real-time and physically realistic simulation of electrosurgery by modelling the electrical, thermal and mechanical properties as three iteratively solved finite element models. To provide subfinite-element graphical rendering of vaporized tissue, a dual-mesh dynamic triangulation algorithm based on isotherms is proposed. The block compressed row storage (BCRS) structure is shown to be critical in allowing computationally efficient changes in the tissue topology due to vaporization. We have demonstrated our physics-based electrosurgery cutting algorithm through various examples. Our matrix manipulation algorithms designed for topology changes have shown low computational cost. Our simulator offers substantially greater physical fidelity compared to previous simulators that use simple geometry-based heat characterization. Copyright © 2013 John Wiley & Sons, Ltd.

A splitting algorithm for a novel regularization of Perona-Malik and application to image restoration

NASA Astrophysics Data System (ADS)

Karami, Fahd; Ziad, Lamia; Sadik, Khadija

2017-12-01

In this paper, we focus on a numerical method of a problem called the Perona-Malik inequality which we use for image denoising. This model is obtained as the limit of the Perona-Malik model and the p-Laplacian operator with p→ ∞. In Atlas et al., (Nonlinear Anal. Real World Appl 18:57-68, 2014), the authors have proved the existence and uniqueness of the solution of the proposed model. However, in their work, they used the explicit numerical scheme for approximated problem which is strongly dependent to the parameter p. To overcome this, we use in this work an efficient algorithm which is a combination of the classical additive operator splitting and a nonlinear relaxation algorithm. At last, we have presented the experimental results in image filtering show, which demonstrate the efficiency and effectiveness of our algorithm and finally, we have compared it with the previous scheme presented in Atlas et al., (Nonlinear Anal. Real World Appl 18:57-68, 2014).

Super-resolution algorithm based on sparse representation and wavelet preprocessing for remote sensing imagery

NASA Astrophysics Data System (ADS)

Ren, Ruizhi; Gu, Lingjia; Fu, Haoyang; Sun, Chenglin

2017-04-01

An effective super-resolution (SR) algorithm is proposed for actual spectral remote sensing images based on sparse representation and wavelet preprocessing. The proposed SR algorithm mainly consists of dictionary training and image reconstruction. Wavelet preprocessing is used to establish four subbands, i.e., low frequency, horizontal, vertical, and diagonal high frequency, for an input image. As compared to the traditional approaches involving the direct training of image patches, the proposed approach focuses on the training of features derived from these four subbands. The proposed algorithm is verified using different spectral remote sensing images, e.g., moderate-resolution imaging spectroradiometer (MODIS) images with different bands, and the latest Chinese Jilin-1 satellite images with high spatial resolution. According to the visual experimental results obtained from the MODIS remote sensing data, the SR images using the proposed SR algorithm are superior to those using a conventional bicubic interpolation algorithm or traditional SR algorithms without preprocessing. Fusion algorithms, e.g., standard intensity-hue-saturation, principal component analysis, wavelet transform, and the proposed SR algorithms are utilized to merge the multispectral and panchromatic images acquired by the Jilin-1 satellite. The effectiveness of the proposed SR algorithm is assessed by parameters such as peak signal-to-noise ratio, structural similarity index, correlation coefficient, root-mean-square error, relative dimensionless global error in synthesis, relative average spectral error, spectral angle mapper, and the quality index Q4, and its performance is better than that of the standard image fusion algorithms.

Statically determined slip-line field solution for the axial forming force estimation in the radial-axial ring rolling process

NASA Astrophysics Data System (ADS)

Quagliato, Luca; Berti, Guido A.

2017-10-01

In this paper, a statically determined slip-line solution algorithm is proposed for the calculation of the axial forming force in the radial-axial ring rolling process of flat rings. The developed solution is implemented in an Excel spreadsheet for the construction of the slip-line field and the calculation of the pressure factor to be used in the force model. The comparison between analytical solution and authors' FE simulation allows stating that the developed model supersedes the previous literature ones and proves the reliability of the proposed approach.

Self-Tuning Threshold Method for Real-Time Gait Phase Detection Based on Ground Contact Forces Using FSRs.

PubMed

Tang, Jing; Zheng, Jianbin; Wang, Yang; Yu, Lie; Zhan, Enqi; Song, Qiuzhi

2018-02-06

This paper presents a novel methodology for detecting the gait phase of human walking on level ground. The previous threshold method (TM) sets a threshold to divide the ground contact forces (GCFs) into on-ground and off-ground states. However, the previous methods for gait phase detection demonstrate no adaptability to different people and different walking speeds. Therefore, this paper presents a self-tuning triple threshold algorithm (STTTA) that calculates adjustable thresholds to adapt to human walking. Two force sensitive resistors (FSRs) were placed on the ball and heel to measure GCFs. Three thresholds (i.e., high-threshold, middle-threshold andlow-threshold) were used to search out the maximum and minimum GCFs for the self-adjustments of thresholds. The high-threshold was the main threshold used to divide the GCFs into on-ground and off-ground statuses. Then, the gait phases were obtained through the gait phase detection algorithm (GPDA), which provides the rules that determine calculations for STTTA. Finally, the STTTA reliability is determined by comparing the results between STTTA and Mariani method referenced as the timing analysis module (TAM) and Lopez-Meyer methods. Experimental results show that the proposed method can be used to detect gait phases in real time and obtain high reliability when compared with the previous methods in the literature. In addition, the proposed method exhibits strong adaptability to different wearers walking at different walking speeds.

A bio-inspired swarm robot coordination algorithm for multiple target searching

NASA Astrophysics Data System (ADS)

Meng, Yan; Gan, Jing; Desai, Sachi

2008-04-01

The coordination of a multi-robot system searching for multi targets is challenging under dynamic environment since the multi-robot system demands group coherence (agents need to have the incentive to work together faithfully) and group competence (agents need to know how to work together well). In our previous proposed bio-inspired coordination method, Local Interaction through Virtual Stigmergy (LIVS), one problem is the considerable randomness of the robot movement during coordination, which may lead to more power consumption and longer searching time. To address these issues, an adaptive LIVS (ALIVS) method is proposed in this paper, which not only considers the travel cost and target weight, but also predicting the target/robot ratio and potential robot redundancy with respect to the detected targets. Furthermore, a dynamic weight adjustment is also applied to improve the searching performance. This new method a truly distributed method where each robot makes its own decision based on its local sensing information and the information from its neighbors. Basically, each robot only communicates with its neighbors through a virtual stigmergy mechanism and makes its local movement decision based on a Particle Swarm Optimization (PSO) algorithm. The proposed ALIVS algorithm has been implemented on the embodied robot simulator, Player/Stage, in a searching target. The simulation results demonstrate the efficiency and robustness in a power-efficient manner with the real-world constraints.

A learning approach to the bandwidth multicolouring problem

NASA Astrophysics Data System (ADS)

Akbari Torkestani, Javad

2016-05-01

In this article, a generalisation of the vertex colouring problem known as bandwidth multicolouring problem (BMCP), in which a set of colours is assigned to each vertex such that the difference between the colours, assigned to each vertex and its neighbours, is by no means less than a predefined threshold, is considered. It is shown that the proposed method can be applied to solve the bandwidth colouring problem (BCP) as well. BMCP is known to be NP-hard in graph theory, and so a large number of approximation solutions, as well as exact algorithms, have been proposed to solve it. In this article, two learning automata-based approximation algorithms are proposed for estimating a near-optimal solution to the BMCP. We show, for the first proposed algorithm, that by choosing a proper learning rate, the algorithm finds the optimal solution with a probability close enough to unity. Moreover, we compute the worst-case time complexity of the first algorithm for finding a 1/(1-ɛ) optimal solution to the given problem. The main advantage of this method is that a trade-off between the running time of algorithm and the colour set size (colouring optimality) can be made, by a proper choice of the learning rate also. Finally, it is shown that the running time of the proposed algorithm is independent of the graph size, and so it is a scalable algorithm for large graphs. The second proposed algorithm is compared with some well-known colouring algorithms and the results show the efficiency of the proposed algorithm in terms of the colour set size and running time of algorithm.

Efficient global fiber tracking on multi-dimensional diffusion direction maps

NASA Astrophysics Data System (ADS)

Klein, Jan; Köhler, Benjamin; Hahn, Horst K.

2012-02-01

Global fiber tracking algorithms have recently been proposed which were able to compute results of unprecedented quality. They account for avoiding accumulation errors by a global optimization process at the cost of a high computation time of several hours or even days. In this paper, we introduce a novel global fiber tracking algorithm which, for the first time, globally optimizes the underlying diffusion direction map obtained from DTI or HARDI data, instead of single fiber segments. As a consequence, the number of iterations in the optimization process can drastically be reduced by about three orders of magnitude. Furthermore, in contrast to all previous algorithms, the density of the tracked fibers can be adjusted after the optimization within a few seconds. We evaluated our method for diffusion-weighted images obtained from software phantoms, healthy volunteers, and tumor patients. We show that difficult fiber bundles, e.g., the visual pathways or tracts for different motor functions can be determined and separated in an excellent quality. Furthermore, crossing and kissing bundles are correctly resolved. On current standard hardware, a dense fiber tracking result of a whole brain can be determined in less than half an hour which is a strong improvement compared to previous work.

Detection of sub-kilometer craters in high resolution planetary images using shape and texture features

NASA Astrophysics Data System (ADS)

Bandeira, Lourenço; Ding, Wei; Stepinski, Tomasz F.

2012-01-01

Counting craters is a paramount tool of planetary analysis because it provides relative dating of planetary surfaces. Dating surfaces with high spatial resolution requires counting a very large number of small, sub-kilometer size craters. Exhaustive manual surveys of such craters over extensive regions are impractical, sparking interest in designing crater detection algorithms (CDAs). As a part of our effort to design a CDA, which is robust and practical for planetary research analysis, we propose a crater detection approach that utilizes both shape and texture features to identify efficiently sub-kilometer craters in high resolution panchromatic images. First, a mathematical morphology-based shape analysis is used to identify regions in an image that may contain craters; only those regions - crater candidates - are the subject of further processing. Second, image texture features in combination with the boosting ensemble supervised learning algorithm are used to accurately classify previously identified candidates into craters and non-craters. The design of the proposed CDA is described and its performance is evaluated using a high resolution image of Mars for which sub-kilometer craters have been manually identified. The overall detection rate of the proposed CDA is 81%, the branching factor is 0.14, and the overall quality factor is 72%. This performance is a significant improvement over the previous CDA based exclusively on the shape features. The combination of performance level and computational efficiency offered by this CDA makes it attractive for practical application.

The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis.

PubMed

Kavitha, Muthu Subash; Asano, Akira; Taguchi, Akira; Heo, Min-Suk

2013-09-01

To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

Weighted graph cuts without eigenvectors a multilevel approach.

PubMed

Dhillon, Inderjit S; Guan, Yuqiang; Kulis, Brian

2007-11-01

A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods. In this paper, we discuss an equivalence between the objective functions used in these seemingly different methods--in particular, a general weighted kernel k-means objective is mathematically equivalent to a weighted graph clustering objective. We exploit this equivalence to develop a fast, high-quality multilevel algorithm that directly optimizes various weighted graph clustering objectives, such as the popular ratio cut, normalized cut, and ratio association criteria. This eliminates the need for any eigenvector computation for graph clustering problems, which can be prohibitive for very large graphs. Previous multilevel graph partitioning methods, such as Metis, have suffered from the restriction of equal-sized clusters; our multilevel algorithm removes this restriction by using kernel k-means to optimize weighted graph cuts. Experimental results show that our multilevel algorithm outperforms a state-of-the-art spectral clustering algorithm in terms of speed, memory usage, and quality. We demonstrate that our algorithm is applicable to large-scale clustering tasks such as image segmentation, social network analysis and gene network analysis.

An algorithm for the detection and characterisation of volcanic plumes using thermal camera imagery

NASA Astrophysics Data System (ADS)

Bombrun, Maxime; Jessop, David; Harris, Andrew; Barra, Vincent

2018-02-01

Volcanic plumes are turbulent mixtures of particles and gas which are injected into the atmosphere during a volcanic eruption. Depending on the intensity of the eruption, plumes can rise from a few tens of metres up to many tens of kilometres above the vent and thus, present a major hazard for the surrounding population. Currently, however, few if any algorithms are available for automated plume tracking and assessment. Here, we present a new image processing algorithm for segmentation, tracking and parameters extraction of convective plume recorded with thermal cameras. We used thermal video of two volcanic eruptions and two plumes simulated in laboratory to develop and test an efficient technique for analysis of volcanic plumes. We validated our method by two different approaches. First, we compare our segmentation method to previously published algorithms. Next, we computed plume parameters, such as height, width and spreading angle at regular intervals of time. These parameters allowed us to calculate an entrainment coefficient and obtain information about the entrainment efficiency in Strombolian eruptions. Our proposed algorithm is rapid, automated while producing better visual outlines compared to the other segmentation algorithms, and provides output that is at least as accurate as manual measurements of plumes.

PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting

PubMed Central

Jiang, Yexi; Tang, Mingjie; Yuan, Changan; Tang, Changjie

2013-01-01

Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream. PMID:23956693

PRESEE: an MDL/MML algorithm to time-series stream segmenting.

PubMed

Xu, Kaikuo; Jiang, Yexi; Tang, Mingjie; Yuan, Changan; Tang, Changjie

2013-01-01

Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream.

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

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 Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

PubMed Central

Jiang, Feng; Han, Ji-zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods. PMID:29623088

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

PubMed

Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

Identification of informative features for predicting proinflammatory potentials of engine exhausts.

PubMed

Wang, Chia-Chi; Lin, Ying-Chi; Lin, Yuan-Chung; Jhang, Syu-Ruei; Tung, Chun-Wei

2017-08-18

The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

A novel frame-level constant-distortion bit allocation for smooth H.264/AVC video quality

NASA Astrophysics Data System (ADS)

Liu, Li; Zhuang, Xinhua

2009-01-01

It is known that quality fluctuation has a major negative effect on visual perception. In previous work, we introduced a constant-distortion bit allocation method [1] for H.263+ encoder. However, the method in [1] can not be adapted to the newest H.264/AVC encoder directly as the well-known chicken-egg dilemma resulted from the rate-distortion optimization (RDO) decision process. To solve this problem, we propose a new two stage constant-distortion bit allocation (CDBA) algorithm with enhanced rate control for H.264/AVC encoder. In stage-1, the algorithm performs RD optimization process with a constant quantization QP. Based on prediction residual signals from stage-1 and target distortion for smooth video quality purpose, the frame-level bit target is allocated by using a close-form approximations of ratedistortion relationship similar to [1], and a fast stage-2 encoding process is performed with enhanced basic unit rate control. Experimental results show that, compared with original rate control algorithm provided by H.264/AVC reference software JM12.1, the proposed constant-distortion frame-level bit allocation scheme reduces quality fluctuation and delivers much smoother PSNR on all testing sequences.

Detecting Solenoid Valve Deterioration in In-Use Electronic Diesel Fuel Injection Control Systems

PubMed Central

Tsai, Hsun-Heng; Tseng, Chyuan-Yow

2010-01-01

The diesel engine is the main power source for most agricultural vehicles. The control of diesel engine emissions is an important global issue. Fuel injection control systems directly affect fuel efficiency and emissions of diesel engines. Deterioration faults, such as rack deformation, solenoid valve failure, and rack-travel sensor malfunction, are possibly in the fuel injection module of electronic diesel control (EDC) systems. Among these faults, solenoid valve failure is most likely to occur for in-use diesel engines. According to the previous studies, this failure is a result of the wear of the plunger and sleeve, based on a long period of usage, lubricant degradation, or engine overheating. Due to the difficulty in identifying solenoid valve deterioration, this study focuses on developing a sensor identification algorithm that can clearly classify the usability of the solenoid valve, without disassembling the fuel pump of an EDC system for in-use agricultural vehicles. A diagnostic algorithm is proposed, including a feedback controller, a parameter identifier, a linear variable differential transformer (LVDT) sensor, and a neural network classifier. Experimental results show that the proposed algorithm can accurately identify the usability of solenoid valves. PMID:22163597

Design of Neural Networks for Fast Convergence and Accuracy: Dynamics and Control

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Sparks, Dean W., Jr.

1997-01-01

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Detecting solenoid valve deterioration in in-use electronic diesel fuel injection control systems.

PubMed

Tsai, Hsun-Heng; Tseng, Chyuan-Yow

2010-01-01

The diesel engine is the main power source for most agricultural vehicles. The control of diesel engine emissions is an important global issue. Fuel injection control systems directly affect fuel efficiency and emissions of diesel engines. Deterioration faults, such as rack deformation, solenoid valve failure, and rack-travel sensor malfunction, are possibly in the fuel injection module of electronic diesel control (EDC) systems. Among these faults, solenoid valve failure is most likely to occur for in-use diesel engines. According to the previous studies, this failure is a result of the wear of the plunger and sleeve, based on a long period of usage, lubricant degradation, or engine overheating. Due to the difficulty in identifying solenoid valve deterioration, this study focuses on developing a sensor identification algorithm that can clearly classify the usability of the solenoid valve, without disassembling the fuel pump of an EDC system for in-use agricultural vehicles. A diagnostic algorithm is proposed, including a feedback controller, a parameter identifier, a linear variable differential transformer (LVDT) sensor, and a neural network classifier. Experimental results show that the proposed algorithm can accurately identify the usability of solenoid valves.

Design of neural networks for fast convergence and accuracy: dynamics and control.

PubMed

Maghami, P G; Sparks, D R

2000-01-01

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

FPGA Implementation of an Efficient Algorithm for the Calculation of Charged Particle Trajectories in Cosmic Ray Detectors

NASA Astrophysics Data System (ADS)

Villar, Xabier; Piso, Daniel; Bruguera, Javier D.

2014-02-01

This paper presents an FPGA implementation of an algorithm, previously published, for the the reconstruction of cosmic rays' trajectories and the determination of the time of arrival and velocity of the particles. The accuracy and precision issues of the algorithm have been analyzed to propose a suitable implementation. Thus, a 32-bit fixed-point format has been used for the representation of the data values. Moreover, the dependencies among the different operations have been taken into account to obtain a highly parallel and efficient hardware implementation. The final hardware architecture requires 18 cycles to process every particle, and has been exhaustively simulated to validate all the design decisions. The architecture has been mapped over different commercial FPGAs, with a frequency of operation ranging from 300 MHz to 1.3 GHz, depending on the FPGA being used. Consequently, the number of particle trajectories processed per second is between 16 million and 72 million. The high number of particle trajectories calculated per second shows that the proposed FPGA implementation might be used also in high rate environments such as those found in particle and nuclear physics experiments.

An improved non-uniformity correction algorithm and its hardware implementation on FPGA

NASA Astrophysics Data System (ADS)

Rong, Shenghui; Zhou, Huixin; Wen, Zhigang; Qin, Hanlin; Qian, Kun; Cheng, Kuanhong

2017-09-01

The Non-uniformity of Infrared Focal Plane Arrays (IRFPA) severely degrades the infrared image quality. An effective non-uniformity correction (NUC) algorithm is necessary for an IRFPA imaging and application system. However traditional scene-based NUC algorithm suffers the image blurring and artificial ghosting. In addition, few effective hardware platforms have been proposed to implement corresponding NUC algorithms. Thus, this paper proposed an improved neural-network based NUC algorithm by the guided image filter and the projection-based motion detection algorithm. First, the guided image filter is utilized to achieve the accurate desired image to decrease the artificial ghosting. Then a projection-based moving detection algorithm is utilized to determine whether the correction coefficients should be updated or not. In this way the problem of image blurring can be overcome. At last, an FPGA-based hardware design is introduced to realize the proposed NUC algorithm. A real and a simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. Experimental results indicated that the proposed NUC algorithm can effectively eliminate the fix pattern noise with less image blurring and artificial ghosting. The proposed hardware design takes less logic elements in FPGA and spends less clock cycles to process one frame of image.

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors

PubMed Central

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-01-01

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms. PMID:26198233

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors.

PubMed

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-07-07

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms.

Stochastic control approaches for sensor management in search and exploitation

NASA Astrophysics Data System (ADS)

Hitchings, Darin Chester

Recent improvements in the capabilities of autonomous vehicles have motivated their increased use in such applications as defense, homeland security, environmental monitoring, and surveillance. To enhance performance in these applications, new algorithms are required to control teams of robots autonomously and through limited interactions with human operators. In this dissertation we develop new algorithms for control of robots performing information-seeking missions in unknown environments. These missions require robots to control their sensors in order to discover the presence of objects, keep track of the objects, and learn what these objects are, given a fixed sensing budget. Initially, we investigate control of multiple sensors, with a finite set of sensing options and finite-valued measurements, to locate and classify objects given a limited resource budget. The control problem is formulated as a Partially Observed Markov Decision Problem (POMDP), but its exact solution requires excessive computation. Under the assumption that sensor error statistics are independent and time-invariant, we develop a class of algorithms using Lagrangian Relaxation techniques to obtain optimal mixed strategies using performance bounds developed in previous research. We investigate alternative Receding Horizon (RH) controllers to convert the mixed strategies to feasible adaptive-sensing strategies and evaluate the relative performance of these controllers in simulation. The resulting controllers provide superior performance to alternative algorithms proposed in the literature and obtain solutions to large-scale POMDP problems several orders of magnitude faster than optimal Dynamic Programming (DP) approaches with comparable performance quality. We extend our results for finite action, finite measurement sensor control to scenarios with moving objects. We use Hidden Markov Models (HMMs) for the evolution of objects, according to the dynamics of a birth-death process. We develop a new lower bound on the performance of adaptive controllers in these scenarios, develop algorithms for computing solutions to this lower bound, and use these algorithms as part of a RH controller for sensor allocation in the presence of moving objects We also consider an adaptive Search problem where sensing actions are continuous and the underlying measurement space is also continuous. We extend our previous hierarchical decomposition approach based on performance bounds to this problem and develop novel implementations of Stochastic Dynamic Programming (SDP) techniques to solve this problem. Our algorithms are nearly two orders of magnitude faster than previously proposed approaches and yield solutions of comparable quality. For supervisory control, we discuss how human operators can work with and augment robotic teams performing these tasks. Our focus is on how tasks are partitioned among teams of robots and how a human operator can make intelligent decisions for task partitioning. We explore these questions through the design of a game that involves robot automata controlled by our algorithms and a human supervisor that partitions tasks based on different levels of support information. This game can be used with human subject experiments to explore the effect of information on quality of supervisory control.

Modified artificial bee colony algorithm for reactive power optimization

NASA Astrophysics Data System (ADS)

Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani

2015-05-01

Bio-inspired algorithms (BIAs) implemented to solve various optimization problems have shown promising results which are very important in this severely complex real-world. Artificial Bee Colony (ABC) algorithm, a kind of BIAs has demonstrated tremendous results as compared to other optimization algorithms. This paper presents a new modified ABC algorithm referred to as JA-ABC3 with the aim to enhance convergence speed and avoid premature convergence. The proposed algorithm has been simulated on ten commonly used benchmarks functions. Its performance has also been compared with other existing ABC variants. To justify its robust applicability, the proposed algorithm has been tested to solve Reactive Power Optimization problem. The results have shown that the proposed algorithm has superior performance to other existing ABC variants e.g. GABC, BABC1, BABC2, BsfABC dan IABC in terms of convergence speed. Furthermore, the proposed algorithm has also demonstrated excellence performance in solving Reactive Power Optimization problem.