A hybrid monkey search algorithm for clustering analysis.

PubMed

Chen, Xin; Zhou, Yongquan; Luo, Qifang

2014-01-01

Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

Neural Generalized Predictive Control: A Newton-Raphson Implementation

NASA Technical Reports Server (NTRS)

Soloway, Donald; Haley, Pamela J.

1997-01-01

An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant's nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm's implementation are also included.

Hybrid employment recommendation algorithm based on Spark

NASA Astrophysics Data System (ADS)

Li, Zuoquan; Lin, Yubei; Zhang, Xingming

2017-08-01

Aiming at the real-time application of collaborative filtering employment recommendation algorithm (CF), a clustering collaborative filtering recommendation algorithm (CCF) is developed, which applies hierarchical clustering to CF and narrows the query range of neighbour items. In addition, to solve the cold-start problem of content-based recommendation algorithm (CB), a content-based algorithm with users’ information (CBUI) is introduced for job recommendation. Furthermore, a hybrid recommendation algorithm (HRA) which combines CCF and CBUI algorithms is proposed, and implemented on Spark platform. The experimental results show that HRA can overcome the problems of cold start and data sparsity, and achieve good recommendation accuracy and scalability for employment recommendation.

The application of mixed recommendation algorithm with user clustering in the microblog advertisements promotion

NASA Astrophysics Data System (ADS)

Gong, Lina; Xu, Tao; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2017-03-01

The traditional microblog recommendation algorithm has the problems of low efficiency and modest effect in the era of big data. In the aim of solving these issues, this paper proposed a mixed recommendation algorithm with user clustering. This paper first introduced the situation of microblog marketing industry. Then, this paper elaborates the user interest modeling process and detailed advertisement recommendation methods. Finally, this paper compared the mixed recommendation algorithm with the traditional classification algorithm and mixed recommendation algorithm without user clustering. The results show that the mixed recommendation algorithm with user clustering has good accuracy and recall rate in the microblog advertisements promotion.

Scan-Line Methods in Spatial Data Systems

DTIC Science & Technology

1990-09-04

algorithms in detail to show some of the implementation issues. Data Compression Storage and transmission times can be reduced by using compression ...goes through the data . Luckily, there are good one-directional compression algorithms , such as run-length coding 13 in which each scan line can be...independently compressed . These are the algorithms to use in a parallel scan-line system. Data compression is usually only used for long-term storage of

A scalable and practical one-pass clustering algorithm for recommender system

NASA Astrophysics Data System (ADS)

Khalid, Asra; Ghazanfar, Mustansar Ali; Azam, Awais; Alahmari, Saad Ali

2015-12-01

KMeans clustering-based recommendation algorithms have been proposed claiming to increase the scalability of recommender systems. One potential drawback of these algorithms is that they perform training offline and hence cannot accommodate the incremental updates with the arrival of new data, making them unsuitable for the dynamic environments. From this line of research, a new clustering algorithm called One-Pass is proposed, which is a simple, fast, and accurate. We show empirically that the proposed algorithm outperforms K-Means in terms of recommendation and training time while maintaining a good level of accuracy.

Fast perceptual image hash based on cascade algorithm

NASA Astrophysics Data System (ADS)

Ruchay, Alexey; Kober, Vitaly; Yavtushenko, Evgeniya

2017-09-01

In this paper, we propose a perceptual image hash algorithm based on cascade algorithm, which can be applied in image authentication, retrieval, and indexing. Image perceptual hash uses for image retrieval in sense of human perception against distortions caused by compression, noise, common signal processing and geometrical modifications. The main disadvantage of perceptual hash is high time expenses. In the proposed cascade algorithm of image retrieval initializes with short hashes, and then a full hash is applied to the processed results. Computer simulation results show that the proposed hash algorithm yields a good performance in terms of robustness, discriminability, and time expenses.

An EEG blind source separation algorithm based on a weak exclusion principle.

PubMed

Lan Ma; Blu, Thierry; Wang, William S-Y

2016-08-01

The question of how to separate individual brain and non-brain signals, mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings, is a significant problem in contemporary neuroscience. This study proposes and evaluates a novel EEG Blind Source Separation (BSS) algorithm based on a weak exclusion principle (WEP). The chief point in which it differs from most previous EEG BSS algorithms is that the proposed algorithm is not based upon the hypothesis that the sources are statistically independent. Our first step was to investigate algorithm performance on simulated signals which have ground truth. The purpose of this simulation is to illustrate the proposed algorithm's efficacy. The results show that the proposed algorithm has good separation performance. Then, we used the proposed algorithm to separate real EEG signals from a memory study using a revised version of Sternberg Task. The results show that the proposed algorithm can effectively separate the non-brain and brain sources.

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.

Automatic target detection using binary template matching

NASA Astrophysics Data System (ADS)

Jun, Dong-San; Sun, Sun-Gu; Park, HyunWook

2005-03-01

This paper presents a new automatic target detection (ATD) algorithm to detect targets such as battle tanks and armored personal carriers in ground-to-ground scenarios. Whereas most ATD algorithms were developed for forward-looking infrared (FLIR) images, we have developed an ATD algorithm for charge-coupled device (CCD) images, which have superior quality to FLIR images in daylight. The proposed algorithm uses fast binary template matching with an adaptive binarization, which is robust to various light conditions in CCD images and saves computation time. Experimental results show that the proposed method has good detection performance.

Changes to the COS Extraction Algorithm for Lifetime Position 3

NASA Astrophysics Data System (ADS)

Proffitt, Charles R.; Bostroem, K. Azalee; Ely, Justin; Foster, Deatrick; Hernandez, Svea; Hodge, Philip; Jedrzejewski, Robert I.; Lockwood, Sean A.; Massa, Derck; Peeples, Molly S.; Oliveira, Cristina M.; Penton, Steven V.; Plesha, Rachel; Roman-Duval, Julia; Sana, Hugues; Sahnow, David J.; Sonnentrucker, Paule; Taylor, Joanna M.

2015-09-01

The COS FUV Detector Lifetime Position 3 (LP3) has been placed only 2.5" below the original lifetime position (LP1). This is sufficiently close to gain-sagged regions at LP1 that a revised extraction algorithm is needed to ensure good spectral quality. We provide an overview of this new "TWOZONE" extraction algorithm, discuss its strengths and limitations, describe new output columns in the X1D files that show the boundaries of the new extraction regions, and provide some advice on how to manually tune the algorithm for specialized applications.

An Image Encryption Algorithm Based on Information Hiding

NASA Astrophysics Data System (ADS)

Ge, Xin; Lu, Bin; Liu, Fenlin; Gong, Daofu

Aiming at resolving the conflict between security and efficiency in the design of chaotic image encryption algorithms, an image encryption algorithm based on information hiding is proposed based on the “one-time pad” idea. A random parameter is introduced to ensure a different keystream for each encryption, which has the characteristics of “one-time pad”, improving the security of the algorithm rapidly without significant increase in algorithm complexity. The random parameter is embedded into the ciphered image with information hiding technology, which avoids negotiation for its transport and makes the application of the algorithm easier. Algorithm analysis and experiments show that the algorithm is secure against chosen plaintext attack, differential attack and divide-and-conquer attack, and has good statistical properties in ciphered images.

Low-energy electron inelastic mean free paths for liquid water

NASA Astrophysics Data System (ADS)

Nguyen-Truong, Hieu T.

2018-04-01

We improve the Mermin–Penn algorithm (MPA) for determining the energy loss function (ELF) within the dielectric formalism. The present algorithm is applicable not only to real metals, but also to materials that have an energy gap in the excitation spectrum. Applying the improved MPA to liquid water, we show that the present algorithm is able to address the ELF overestimation at the energy gap, and the calculated results are in good agreement with experimental data.

Using Collision Cones to Asses Biological Deconiction Methods

NASA Astrophysics Data System (ADS)

Brace, Natalie

For autonomous vehicles to navigate the world as efficiently and effectively as biological species, improvements are needed in terms of control strategies and estimation algorithms. Reactive collision avoidance is one specific area where biological systems outperform engineered algorithms. To better understand the discrepancy between engineered and biological systems, a collision avoidance algorithm was applied to frames of trajectory data from three biological species (Myotis velifer, Hirundo rustica, and Danio aequipinnatus). The algorithm uses information that can be sensed through visual cues (relative position and velocity) to define collision cones which are used to determine if agents are on a collision course and if so, to find a safe velocity that requires minimal deviation from the original velocity for each individual agent. Two- and three-dimensional versions of the algorithm with constant speed and maximum speed velocity requirements were considered. The obstacles provided to the algorithm were determined by the sensing range in terms of either metric or topological distance. The calculated velocities showed good correlation with observed velocities over the range of sensing parameters, indicating that the algorithm is a good basis for comparison and could potentially be improved with further study.

Accuracy and Reliability Assessment of CT and MR Perfusion Analysis Software Using a Digital Phantom

PubMed Central

Christensen, Soren; Sasaki, Makoto; Østergaard, Leif; Shirato, Hiroki; Ogasawara, Kuniaki; Wintermark, Max; Warach, Steven

2013-01-01

Purpose: To design a digital phantom data set for computed tomography (CT) perfusion and perfusion-weighted imaging on the basis of the widely accepted tracer kinetic theory in which the true values of cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and tracer arrival delay are known and to evaluate the accuracy and reliability of postprocessing programs using this digital phantom. Materials and Methods: A phantom data set was created by generating concentration-time curves reflecting true values for CBF (2.5–87.5 mL/100 g per minute), CBV (1.0–5.0 mL/100 g), MTT (3.4–24 seconds), and tracer delays (0–3.0 seconds). These curves were embedded in human brain images. The data were analyzed by using 13 algorithms each for CT and magnetic resonance (MR), including five commercial vendors and five academic programs. Accuracy was assessed by using the Pearson correlation coefficient (r) for true values. Delay-, MTT-, or CBV-dependent errors and correlations between time to maximum of residue function (Tmax) were also evaluated. Results: In CT, CBV was generally well reproduced (r > 0.9 in 12 algorithms), but not CBF and MTT (r > 0.9 in seven and four algorithms, respectively). In MR, good correlation (r > 0.9) was observed in one-half of commercial programs, while all academic algorithms showed good correlations for all parameters. Most algorithms had delay-dependent errors, especially for commercial software, as well as CBV dependency for CBF or MTT calculation and MTT dependency for CBV calculation. Correlation was good in Tmax except for one algorithm. Conclusion: The digital phantom readily evaluated the accuracy and characteristics of the CT and MR perfusion analysis software. All commercial programs had delay-induced errors and/or insufficient correlations with true values, while academic programs for MR showed good correlations with true values. © RSNA, 2012 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12112618/-/DC1 PMID:23220899

Text image authenticating algorithm based on MD5-hash function and Henon map

NASA Astrophysics Data System (ADS)

Wei, Jinqiao; Wang, Ying; Ma, Xiaoxue

2017-07-01

In order to cater to the evidentiary requirements of the text image, this paper proposes a fragile watermarking algorithm based on Hash function and Henon map. The algorithm is to divide a text image into parts, get flippable pixels and nonflippable pixels of every lump according to PSD, generate watermark of non-flippable pixels with MD5-Hash, encrypt watermark with Henon map and select embedded blocks. The simulation results show that the algorithm with a good ability in tampering localization can be used to authenticate and forensics the authenticity and integrity of text images

Design and implementation of three-dimension texture mapping algorithm for panoramic system based on smart platform

NASA Astrophysics Data System (ADS)

Liu, Zhi; Zhou, Baotong; Zhang, Changnian

2017-03-01

Vehicle-mounted panoramic system is important safety assistant equipment for driving. However, traditional systems only render fixed top-down perspective view of limited view field, which may have potential safety hazard. In this paper, a texture mapping algorithm for 3D vehicle-mounted panoramic system is introduced, and an implementation of the algorithm utilizing OpenGL ES library based on Android smart platform is presented. Initial experiment results show that the proposed algorithm can render a good 3D panorama, and has the ability to change view point freely.

The Classification of Diabetes Mellitus Using Kernel k-means

NASA Astrophysics Data System (ADS)

Alamsyah, M.; Nafisah, Z.; Prayitno, E.; Afida, A. M.; Imah, E. M.

2018-01-01

Diabetes Mellitus is a metabolic disorder which is characterized by chronicle hypertensive glucose. Automatics detection of diabetes mellitus is still challenging. This study detected diabetes mellitus by using kernel k-Means algorithm. Kernel k-means is an algorithm which was developed from k-means algorithm. Kernel k-means used kernel learning that is able to handle non linear separable data; where it differs with a common k-means. The performance of kernel k-means in detecting diabetes mellitus is also compared with SOM algorithms. The experiment result shows that kernel k-means has good performance and a way much better than SOM.

Quantification of choroidal neovascularization vessel length using optical coherence tomography angiography

NASA Astrophysics Data System (ADS)

Gao, Simon S.; Liu, Li; Bailey, Steven T.; Flaxel, Christina J.; Huang, David; Li, Dengwang; Jia, Yali

2016-07-01

Quantification of choroidal neovascularization (CNV) as visualized by optical coherence tomography angiography (OCTA) may have importance clinically when diagnosing or tracking disease. Here, we present an automated algorithm to quantify the vessel skeleton of CNV as vessel length. Initial segmentation of the CNV on en face angiograms was achieved using saliency-based detection and thresholding. A level set method was then used to refine vessel edges. Finally, a skeleton algorithm was applied to identify vessel centerlines. The algorithm was tested on nine OCTA scans from participants with CNV and comparisons of the algorithm's output to manual delineation showed good agreement.

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

Digital watermarking algorithm research of color images based on quaternion Fourier transform

NASA Astrophysics Data System (ADS)

An, Mali; Wang, Weijiang; Zhao, Zhen

2013-10-01

A watermarking algorithm of color images based on the quaternion Fourier Transform (QFFT) and improved quantization index algorithm (QIM) is proposed in this paper. The original image is transformed by QFFT, the watermark image is processed by compression and quantization coding, and then the processed watermark image is embedded into the components of the transformed original image. It achieves embedding and blind extraction of the watermark image. The experimental results show that the watermarking algorithm based on the improved QIM algorithm with distortion compensation achieves a good tradeoff between invisibility and robustness, and better robustness for the attacks of Gaussian noises, salt and pepper noises, JPEG compression, cropping, filtering and image enhancement than the traditional QIM algorithm.

AMOBH: Adaptive Multiobjective Black Hole Algorithm.

PubMed

Wu, Chong; Wu, Tao; Fu, Kaiyuan; Zhu, Yuan; Li, Yongbo; He, Wangyong; Tang, Shengwen

2017-01-01

This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called "adaptive multiobjective black hole algorithm" (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.

Reproducibility and Variability of I/O Performance on BG/Q: Lessons Learned from a Data Aggregation Algorithm

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

Tessier, Francois; Vishwanath, Venkatram

2017-11-28

Reading and writing data efficiently from different tiers of storage is necessary for most scientific simulations to achieve good performance at scale. Many software solutions have been developed to decrease the I/O bottleneck. One wellknown strategy, in the context of collective I/O operations, is the two-phase I/O scheme. This strategy consists of selecting a subset of processes to aggregate contiguous pieces of data before performing reads/writes. In our previous work, we implemented the two-phase I/O scheme with a MPI-based topology-aware algorithm. Our algorithm showed very good performance at scale compared to the standard I/O libraries such as POSIX I/O andmore » MPI I/O. However, the algorithm had several limitations hindering a satisfying reproducibility of our experiments. In this paper, we extend our work by 1) identifying the obstacles we face to reproduce our experiments and 2) discovering solutions that reduce the unpredictability of our results.« less

Research on intelligent recommendation algorithm of e-commerce based on association rules

NASA Astrophysics Data System (ADS)

Shen, Jiajie; Cheng, Xianyi

2017-09-01

As the commodities of e-commerce are more and more rich, more and more consumers are willing to choose online shopping, because of these rich varieties of commodity information, customers will often appear aesthetic fatigue. Therefore, we need a recommendation algorithm according to the recent behavior of customers including browsing and consuming to predicate and intelligently recommend goods which the customers need, thus to improve the satisfaction of customers and to increase the profit of e-commerce. This paper first discusses recommendation algorithm, then improves Apriori. Finally, using R language realizes a recommendation algorithm of commodities. The result shows that this algorithm provides a certain decision-making role for customers to buy commodities.

Cloud Model Bat Algorithm

PubMed Central

Zhou, Yongquan; Xie, Jian; Li, Liangliang; Ma, Mingzhi

2014-01-01

Bat algorithm (BA) is a novel stochastic global optimization algorithm. Cloud model is an effective tool in transforming between qualitative concepts and their quantitative representation. Based on the bat echolocation mechanism and excellent characteristics of cloud model on uncertainty knowledge representation, a new cloud model bat algorithm (CBA) is proposed. This paper focuses on remodeling echolocation model based on living and preying characteristics of bats, utilizing the transformation theory of cloud model to depict the qualitative concept: “bats approach their prey.” Furthermore, Lévy flight mode and population information communication mechanism of bats are introduced to balance the advantage between exploration and exploitation. The simulation results show that the cloud model bat algorithm has good performance on functions optimization. PMID:24967425

Fuzzy PID control algorithm based on PSO and application in BLDC motor

NASA Astrophysics Data System (ADS)

Lin, Sen; Wang, Guanglong

2017-06-01

A fuzzy PID control algorithm is studied based on improved particle swarm optimization (PSO) to perform Brushless DC (BLDC) motor control which has high accuracy, good anti-jamming capability and steady state accuracy compared with traditional PID control. The mathematical and simulation model is established for BLDC motor by simulink software, and the speed loop of the fuzzy PID controller is designed. The simulation results show that the fuzzy PID control algorithm based on PSO has higher stability, high control precision and faster dynamic response speed.

Research on autonomous identification of airport targets based on Gabor filtering and Radon transform

NASA Astrophysics Data System (ADS)

Yi, Juan; Du, Qingyu; Zhang, Hong jiang; Zhang, Yao lei

2017-11-01

Target recognition is a leading key technology in intelligent image processing and application development at present, with the enhancement of computer processing ability, autonomous target recognition algorithm, gradually improve intelligence, and showed good adaptability. Taking the airport target as the research object, analysis the airport layout characteristics, construction of knowledge model, Gabor filter and Radon transform based on the target recognition algorithm of independent design, image processing and feature extraction of the airport, the algorithm was verified, and achieved better recognition results.

Learning classification trees

NASA Technical Reports Server (NTRS)

Buntine, Wray

1991-01-01

Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. How a tree learning algorithm can be derived from Bayesian decision theory is outlined. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule turns out to be similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 and Breiman et al. Cart show the full Bayesian algorithm is consistently as good, or more accurate than these other approaches though at a computational price.

The Basic Principles and Methods of the System Approach to Compression of Telemetry Data

NASA Astrophysics Data System (ADS)

Levenets, A. V.

2018-01-01

The task of data compressing of measurement data is still urgent for information-measurement systems. In paper the basic principles necessary for designing of highly effective systems of compression of telemetric information are offered. A basis of the offered principles is representation of a telemetric frame as whole information space where we can find of existing correlation. The methods of data transformation and compressing algorithms realizing the offered principles are described. The compression ratio for offered compression algorithm is about 1.8 times higher, than for a classic algorithm. Thus, results of a research of methods and algorithms showing their good perspectives.

A Novel Image Encryption Algorithm Based on DNA Subsequence Operation

PubMed Central

Zhang, Qiang; Xue, Xianglian; Wei, Xiaopeng

2012-01-01

We present a novel image encryption algorithm based on DNA subsequence operation. Different from the traditional DNA encryption methods, our algorithm does not use complex biological operation but just uses the idea of DNA subsequence operations (such as elongation operation, truncation operation, deletion operation, etc.) combining with the logistic chaotic map to scramble the location and the value of pixel points from the image. The experimental results and security analysis show that the proposed algorithm is easy to be implemented, can get good encryption effect, has a wide secret key's space, strong sensitivity to secret key, and has the abilities of resisting exhaustive attack and statistic attack. PMID:23093912

Symbiotic organisms search algorithm for dynamic economic dispatch with valve-point effects

NASA Astrophysics Data System (ADS)

Sonmez, Yusuf; Kahraman, H. Tolga; Dosoglu, M. Kenan; Guvenc, Ugur; Duman, Serhat

2017-05-01

In this study, symbiotic organisms search (SOS) algorithm is proposed to solve the dynamic economic dispatch with valve-point effects problem, which is one of the most important problems of the modern power system. Some practical constraints like valve-point effects, ramp rate limits and prohibited operating zones have been considered as solutions. Proposed algorithm was tested on five different test cases in 5 units, 10 units and 13 units systems. The obtained results have been compared with other well-known metaheuristic methods reported before. Results show that proposed algorithm has a good convergence and produces better results than other methods.

Comparing barrier algorithms

NASA Technical Reports Server (NTRS)

Arenstorf, Norbert S.; Jordan, Harry F.

1987-01-01

A barrier is a method for synchronizing a large number of concurrent computer processes. After considering some basic synchronization mechanisms, a collection of barrier algorithms with either linear or logarithmic depth are presented. A graphical model is described that profiles the execution of the barriers and other parallel programming constructs. This model shows how the interaction between the barrier algorithms and the work that they synchronize can impact their performance. One result is that logarithmic tree structured barriers show good performance when synchronizing fixed length work, while linear self-scheduled barriers show better performance when synchronizing fixed length work with an imbedded critical section. The linear barriers are better able to exploit the process skew associated with critical sections. Timing experiments, performed on an eighteen processor Flex/32 shared memory multiprocessor, that support these conclusions are detailed.

Active control of impulsive noise with symmetric α-stable distribution based on an improved step-size normalized adaptive algorithm

NASA Astrophysics Data System (ADS)

Zhou, Yali; Zhang, Qizhi; Yin, Yixin

2015-05-01

In this paper, active control of impulsive noise with symmetric α-stable (SαS) distribution is studied. A general step-size normalized filtered-x Least Mean Square (FxLMS) algorithm is developed based on the analysis of existing algorithms, and the Gaussian distribution function is used to normalize the step size. Compared with existing algorithms, the proposed algorithm needs neither the parameter selection and thresholds estimation nor the process of cost function selection and complex gradient computation. Computer simulations have been carried out to suggest that the proposed algorithm is effective for attenuating SαS impulsive noise, and then the proposed algorithm has been implemented in an experimental ANC system. Experimental results show that the proposed scheme has good performance for SαS impulsive noise attenuation.

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.

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.

Finite grade pheromone ant colony optimization for image segmentation

NASA Astrophysics Data System (ADS)

Yuanjing, F.; Li, Y.; Liangjun, K.

2008-06-01

By combining the decision process of ant colony optimization (ACO) with the multistage decision process of image segmentation based on active contour model (ACM), an algorithm called finite grade ACO (FACO) for image segmentation is proposed. This algorithm classifies pheromone into finite grades and updating of the pheromone is achieved by changing the grades and the updated quantity of pheromone is independent from the objective function. The algorithm that provides a new approach to obtain precise contour is proved to converge to the global optimal solutions linearly by means of finite Markov chains. The segmentation experiments with ultrasound heart image show the effectiveness of the algorithm. Comparing the results for segmentation of left ventricle images shows that the ACO for image segmentation is more effective than the GA approach and the new pheromone updating strategy appears good time performance in optimization process.

Fast noise level estimation algorithm based on principal component analysis transform and nonlinear rectification

NASA Astrophysics Data System (ADS)

Xu, Shaoping; Zeng, Xiaoxia; Jiang, Yinnan; Tang, Yiling

2018-01-01

We proposed a noniterative principal component analysis (PCA)-based noise level estimation (NLE) algorithm that addresses the problem of estimating the noise level with a two-step scheme. First, we randomly extracted a number of raw patches from a given noisy image and took the smallest eigenvalue of the covariance matrix of the raw patches as the preliminary estimation of the noise level. Next, the final estimation was directly obtained with a nonlinear mapping (rectification) function that was trained on some representative noisy images corrupted with different known noise levels. Compared with the state-of-art NLE algorithms, the experiment results show that the proposed NLE algorithm can reliably infer the noise level and has robust performance over a wide range of image contents and noise levels, showing a good compromise between speed and accuracy in general.

Parallel integer sorting with medium and fine-scale parallelism

NASA Technical Reports Server (NTRS)

Dagum, Leonardo

1993-01-01

Two new parallel integer sorting algorithms, queue-sort and barrel-sort, are presented and analyzed in detail. These algorithms do not have optimal parallel complexity, yet they show very good performance in practice. Queue-sort designed for fine-scale parallel architectures which allow the queueing of multiple messages to the same destination. Barrel-sort is designed for medium-scale parallel architectures with a high message passing overhead. The performance results from the implementation of queue-sort on a Connection Machine CM-2 and barrel-sort on a 128 processor iPSC/860 are given. The two implementations are found to be comparable in performance but not as good as a fully vectorized bucket sort on the Cray YMP.

Controlling for Frailty in Pharmacoepidemiologic Studies of Older Adults: Validation of an Existing Medicare Claims-based Algorithm.

PubMed

Cuthbertson, Carmen C; Kucharska-Newton, Anna; Faurot, Keturah R; Stürmer, Til; Jonsson Funk, Michele; Palta, Priya; Windham, B Gwen; Thai, Sydney; Lund, Jennifer L

2018-07-01

Frailty is a geriatric syndrome characterized by weakness and weight loss and is associated with adverse health outcomes. It is often an unmeasured confounder in pharmacoepidemiologic and comparative effectiveness studies using administrative claims data. Among the Atherosclerosis Risk in Communities (ARIC) Study Visit 5 participants (2011-2013; n = 3,146), we conducted a validation study to compare a Medicare claims-based algorithm of dependency in activities of daily living (or dependency) developed as a proxy for frailty with a reference standard measure of phenotypic frailty. We applied the algorithm to the ARIC participants' claims data to generate a predicted probability of dependency. Using the claims-based algorithm, we estimated the C-statistic for predicting phenotypic frailty. We further categorized participants by their predicted probability of dependency (<5%, 5% to <20%, and ≥20%) and estimated associations with difficulties in physical abilities, falls, and mortality. The claims-based algorithm showed good discrimination of phenotypic frailty (C-statistic = 0.71; 95% confidence interval [CI] = 0.67, 0.74). Participants classified with a high predicted probability of dependency (≥20%) had higher prevalence of falls and difficulty in physical ability, and a greater risk of 1-year all-cause mortality (hazard ratio = 5.7 [95% CI = 2.5, 13]) than participants classified with a low predicted probability (<5%). Sensitivity and specificity varied across predicted probability of dependency thresholds. The Medicare claims-based algorithm showed good discrimination of phenotypic frailty and high predictive ability with adverse health outcomes. This algorithm can be used in future Medicare claims analyses to reduce confounding by frailty and improve study validity.

Selection method of terrain matching area for TERCOM algorithm

NASA Astrophysics Data System (ADS)

Zhang, Qieqie; Zhao, Long

2017-10-01

The performance of terrain aided navigation is closely related to the selection of terrain matching area. The different matching algorithms have different adaptability to terrain. This paper mainly studies the adaptability to terrain of TERCOM algorithm, analyze the relation between terrain feature and terrain characteristic parameters by qualitative and quantitative methods, and then research the relation between matching probability and terrain characteristic parameters by the Monte Carlo method. After that, we propose a selection method of terrain matching area for TERCOM algorithm, and verify the method correctness with real terrain data by simulation experiment. Experimental results show that the matching area obtained by the method in this paper has the good navigation performance and the matching probability of TERCOM algorithm is great than 90%

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

Ridge-branch-based blood vessel detection algorithm for multimodal retinal images

NASA Astrophysics Data System (ADS)

Li, Y.; Hutchings, N.; Knighton, R. W.; Gregori, G.; Lujan, B. J.; Flanagan, J. G.

2009-02-01

Automatic detection of retinal blood vessels is important to medical diagnoses and imaging. With the development of imaging technologies, various modals of retinal images are available. Few of currently published algorithms are applied to multimodal retinal images. Besides, the performance of algorithms with pathologies is expected to be improved. The purpose of this paper is to propose an automatic Ridge-Branch-Based (RBB) detection algorithm of blood vessel centerlines and blood vessels for multimodal retinal images (color fundus photographs, fluorescein angiograms, fundus autofluorescence images, SLO fundus images and OCT fundus images, for example). Ridges, which can be considered as centerlines of vessel-like patterns, are first extracted. The method uses the connective branching information of image ridges: if ridge pixels are connected, they are more likely to be in the same class, vessel ridge pixels or non-vessel ridge pixels. Thanks to the good distinguishing ability of the designed "Segment-Based Ridge Features", the classifier and its parameters can be easily adapted to multimodal retinal images without ground truth training. We present thorough experimental results on SLO images, color fundus photograph database and other multimodal retinal images, as well as comparison between other published algorithms. Results showed that the RBB algorithm achieved a good performance.

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

PubMed

Wang, Xingmei; Hao, Wenqian; Li, Qiming

2017-12-18

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

PCA-based artifact removal algorithm for stroke detection using UWB radar imaging.

PubMed

Ricci, Elisa; di Domenico, Simone; Cianca, Ernestina; Rossi, Tommaso; Diomedi, Marina

2017-06-01

Stroke patients should be dispatched at the highest level of care available in the shortest time. In this context, a transportable system in specialized ambulances, able to evaluate the presence of an acute brain lesion in a short time interval (i.e., few minutes), could shorten delay of treatment. UWB radar imaging is an emerging diagnostic branch that has great potential for the implementation of a transportable and low-cost device. Transportability, low cost and short response time pose challenges to the signal processing algorithms of the backscattered signals as they should guarantee good performance with a reasonably low number of antennas and low computational complexity, tightly related to the response time of the device. The paper shows that a PCA-based preprocessing algorithm can: (1) achieve good performance already with a computationally simple beamforming algorithm; (2) outperform state-of-the-art preprocessing algorithms; (3) enable a further improvement in the performance (and/or decrease in the number of antennas) by using a multistatic approach with just a modest increase in computational complexity. This is an important result toward the implementation of such a diagnostic device that could play an important role in emergency scenario.

Color enhancement and image defogging in HSI based on Retinex model

NASA Astrophysics Data System (ADS)

Gao, Han; Wei, Ping; Ke, Jun

2015-08-01

Retinex is a luminance perceptual algorithm based on color consistency. It has a good performance in color enhancement. But in some cases, the traditional Retinex algorithms, both Single-Scale Retinex(SSR) and Multi-Scale Retinex(MSR) in RGB color space, do not work well and will cause color deviation. To solve this problem, we present improved SSR and MSR algorithms. Compared to other Retinex algorithms, we implement Retinex algorithms in HSI(Hue, Saturation, Intensity) color space, and use a parameter αto improve quality of the image. Moreover, the algorithms presented in this paper has a good performance in image defogging. Contrasted with traditional Retinex algorithms, we use intensity channel to obtain reflection information of an image. The intensity channel is processed using a Gaussian center-surround image filter to get light information, which should be removed from intensity channel. After that, we subtract the light information from intensity channel to obtain the reflection image, which only includes the attribute of the objects in image. Using the reflection image and a parameter α, which is an arbitrary scale factor set manually, we improve the intensity channel, and complete the color enhancement. Our experiments show that this approach works well compared with existing methods for color enhancement. Besides a better performance in color deviation problem and image defogging, a visible improvement in the image quality for human contrast perception is also observed.

Change Detection Algorithms for Surveillance in Visual IoT: A Comparative Study

NASA Astrophysics Data System (ADS)

Akram, Beenish Ayesha; Zafar, Amna; Akbar, Ali Hammad; Wajid, Bilal; Chaudhry, Shafique Ahmad

2018-01-01

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule's Coefficient) and JC (Jaccard's Coefficient), execution time and memory consumption. Experimental results showed that Kapur's algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes.

A detection method for X-ray images based on wavelet transforms: the case of the ROSAT PSPC.

NASA Astrophysics Data System (ADS)

Damiani, F.; Maggio, A.; Micela, G.; Sciortino, S.

1996-02-01

The authors have developed a method based on wavelet transforms (WT) to detect efficiently sources in PSPC X-ray images. The multiscale approach typical of WT can be used to detect sources with a large range of sizes, and to estimate their size and count rate. Significance thresholds for candidate detections (found as local WT maxima) have been derived from a detailed study of the probability distribution of the WT of a locally uniform background. The use of the exposure map allows good detection efficiency to be retained even near PSPC ribs and edges. The algorithm may also be used to get upper limits to the count rate of undetected objects. Simulations of realistic PSPC images containing either pure background or background+sources were used to test the overall algorithm performances, and to assess the frequency of spurious detections (vs. detection threshold) and the algorithm sensitivity. Actual PSPC images of galaxies and star clusters show the algorithm to have good performance even in cases of extended sources and crowded fields.

The new and computationally efficient MIL-SOM algorithm: potential benefits for visualization and analysis of a large-scale high-dimensional clinically acquired geographic data.

PubMed

Oyana, Tonny J; Achenie, Luke E K; Heo, Joon

2012-01-01

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM.

The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data

PubMed Central

Oyana, Tonny J.; Achenie, Luke E. K.; Heo, Joon

2012-01-01

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM. PMID:22481977

The application of immune genetic algorithm in main steam temperature of PID control of BP network

NASA Astrophysics Data System (ADS)

Li, Han; Zhen-yu, Zhang

In order to overcome the uncertainties, large delay, large inertia and nonlinear property of the main steam temperature controlled object in the power plant, a neural network intelligent PID control system based on immune genetic algorithm and BP neural network is designed. Using the immune genetic algorithm global search optimization ability and good convergence, optimize the weights of the neural network, meanwhile adjusting PID parameters using BP network. The simulation result shows that the system is superior to conventional PID control system in the control of quality and robustness.

Superiorization-based multi-energy CT image reconstruction

PubMed Central

Yang, Q; Cong, W; Wang, G

2017-01-01

The recently-developed superiorization approach is efficient and robust for solving various constrained optimization problems. This methodology can be applied to multi-energy CT image reconstruction with the regularization in terms of the prior rank, intensity and sparsity model (PRISM). In this paper, we propose a superiorized version of the simultaneous algebraic reconstruction technique (SART) based on the PRISM model. Then, we compare the proposed superiorized algorithm with the Split-Bregman algorithm in numerical experiments. The results show that both the Superiorized-SART and the Split-Bregman algorithms generate good results with weak noise and reduced artefacts. PMID:28983142

Algorithm for identifying and separating beats from arterial pulse records

PubMed Central

Treo, Ernesto F; Herrera, Myriam C; Valentinuzzi, Max E

2005-01-01

Background This project was designed as an epidemiological aid-selecting tool for a small country health center with the general objective of screening out possible coronary patients. Peripheral artery function can be non-invasively evaluated by impedance plethysmography. Changes in these vessels appear as good predictors of future coronary behavior. Impedance plethysmography detects volume variations after simple occlusive maneuvers that may show indicative modifications in arterial/venous responses. Averaging of a series of pulses is needed and this, in turn, requires proper determination of the beginning and end of each beat. Thus, the objective here is to describe an algorithm to identify and separate out beats from a plethysmographic record. A secondary objective was to compare the output given by human operators against the algorithm. Methods The identification algorithm detected the beat's onset and end on the basis of the maximum rising phase, the choice of possible ventricular systolic starting points considering cardiac frequency, and the adjustment of some tolerance values to optimize the behavior. Out of 800 patients in the study, 40 occlusive records (supradiastolic- subsystolic) were randomly selected without any preliminary diagnosis. Radial impedance plethysmographic pulse and standard ECG were recorded digitizing and storing the data. Cardiac frequency was estimated with the Power Density Function and, thereafter, the signal was derived twice, followed by binarization of the first derivative and rectification of the second derivative. The product of the two latter results led to a weighing signal from which the cycles' onsets and ends were established. Weighed and frequency filters are needed along with the pre-establishment of their respective tolerances. Out of the 40 records, 30 seconds strands were randomly chosen to be analyzed by the algorithm and by two operators. Sensitivity and accuracy were calculated by means of the true/false and positive/negative criteria. Synchronization ability was measured through the coefficient of variation and the median value of correlation for each patient. These parameters were assessed by means of Friedman's ANOVA and Kendall Concordance test. Results Sensitivity was 97% and 91% for the two operators, respectively, while accuracy was cero for both of them. The synchronism variability analysis was significant (p < 0.01) for the two statistics, showing that the algorithm produced the best result. Conclusion The proposed algorithm showed good performance as expressed by its high sensitivity. The correlation analysis demonstrated that, from the synchronism point of view, the algorithm performed the best detection. Patients with marked arrhythmic processes are not good candidates for this kind of analysis. At most, they would be singled out by the algorithm and, thereafter, to be checked by an operator. PMID:16095532

An Algorithm for Automatically Modifying Train Crew Schedule

NASA Astrophysics Data System (ADS)

Takahashi, Satoru; Kataoka, Kenji; Kojima, Teruhito; Asami, Masayuki

Once the break-down of the train schedule occurs, the crew schedule as well as the train schedule has to be modified as quickly as possible to restore them. In this paper, we propose an algorithm for automatically modifying a crew schedule that takes all constraints into consideration, presenting a model of the combined problem of crews and trains. The proposed algorithm builds an initial solution by relaxing some of the constraint conditions, and then uses a Taboo-search method to revise this solution in order to minimize the degree of constraint violation resulting from these relaxed conditions. Then we show not only that the algorithm can generate a constraint satisfaction solution, but also that the solution will satisfy the experts. That is, we show the proposed algorithm is capable of producing a usable solution in a short time by applying to actual cases of train-schedule break-down, and that the solution is at least as good as those produced manually, by comparing the both solutions with several point of view.

Parallel, stochastic measurement of molecular surface area.

PubMed

Juba, Derek; Varshney, Amitabh

2008-08-01

Biochemists often wish to compute surface areas of proteins. A variety of algorithms have been developed for this task, but they are designed for traditional single-processor architectures. The current trend in computer hardware is towards increasingly parallel architectures for which these algorithms are not well suited. We describe a parallel, stochastic algorithm for molecular surface area computation that maps well to the emerging multi-core architectures. Our algorithm is also progressive, providing a rough estimate of surface area immediately and refining this estimate as time goes on. Furthermore, the algorithm generates points on the molecular surface which can be used for point-based rendering. We demonstrate a GPU implementation of our algorithm and show that it compares favorably with several existing molecular surface computation programs, giving fast estimates of the molecular surface area with good accuracy.

An improved affine projection algorithm for active noise cancellation

NASA Astrophysics Data System (ADS)

Zhang, Congyan; Wang, Mingjiang; Han, Yufei; Sun, Yunzhuo

2017-08-01

Affine projection algorithm is a signal reuse algorithm, and it has a good convergence rate compared to other traditional adaptive filtering algorithm. There are two factors that affect the performance of the algorithm, which are step factor and the projection length. In the paper, we propose a new variable step size affine projection algorithm (VSS-APA). It dynamically changes the step size according to certain rules, so that it can get smaller steady-state error and faster convergence speed. Simulation results can prove that its performance is superior to the traditional affine projection algorithm and in the active noise control (ANC) applications, the new algorithm can get very good results.

Comparative performance of conventional OPC concrete and HPC designed by densified mixture design algorithm

NASA Astrophysics Data System (ADS)

Huynh, Trong-Phuoc; Hwang, Chao-Lung; Yang, Shu-Ti

2017-12-01

This experimental study evaluated the performance of normal ordinary Portland cement (OPC) concrete and high-performance concrete (HPC) that were designed by the conventional method (ACI) and densified mixture design algorithm (DMDA) method, respectively. Engineering properties and durability performance of both the OPC and HPC samples were studied using the tests of workability, compressive strength, water absorption, ultrasonic pulse velocity, and electrical surface resistivity. Test results show that the HPC performed good fresh property and further showed better performance in terms of strength and durability as compared to the OPC.

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

NASA Astrophysics Data System (ADS)

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

2012-11-01

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

Lane detection based on color probability model and fuzzy clustering

NASA Astrophysics Data System (ADS)

Yu, Yang; Jo, Kang-Hyun

2018-04-01

In the vehicle driver assistance systems, the accuracy and speed of lane line detection are the most important. This paper is based on color probability model and Fuzzy Local Information C-Means (FLICM) clustering algorithm. The Hough transform and the constraints of structural road are used to detect the lane line accurately. The global map of the lane line is drawn by the lane curve fitting equation. The experimental results show that the algorithm has good robustness.

Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

PubMed

Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

2015-08-01

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.

An algorithm for testing the efficient market hypothesis.

PubMed

Boboc, Ioana-Andreea; Dinică, Mihai-Cristian

2013-01-01

The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH).

An Algorithm for Testing the Efficient Market Hypothesis

PubMed Central

Boboc, Ioana-Andreea; Dinică, Mihai-Cristian

2013-01-01

The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH). PMID:24205148

A matrix-algebraic formulation of distributed-memory maximal cardinality matching algorithms in bipartite graphs

DOE PAGES

Azad, Ariful; Buluç, Aydın

2016-05-16

We describe parallel algorithms for computing maximal cardinality matching in a bipartite graph on distributed-memory systems. Unlike traditional algorithms that match one vertex at a time, our algorithms process many unmatched vertices simultaneously using a matrix-algebraic formulation of maximal matching. This generic matrix-algebraic framework is used to develop three efficient maximal matching algorithms with minimal changes. The newly developed algorithms have two benefits over existing graph-based algorithms. First, unlike existing parallel algorithms, cardinality of matching obtained by the new algorithms stays constant with increasing processor counts, which is important for predictable and reproducible performance. Second, relying on bulk-synchronous matrix operations,more » these algorithms expose a higher degree of parallelism on distributed-memory platforms than existing graph-based algorithms. We report high-performance implementations of three maximal matching algorithms using hybrid OpenMP-MPI and evaluate the performance of these algorithm using more than 35 real and randomly generated graphs. On real instances, our algorithms achieve up to 200 × speedup on 2048 cores of a Cray XC30 supercomputer. Even higher speedups are obtained on larger synthetically generated graphs where our algorithms show good scaling on up to 16,384 cores.« less

SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems

DOE PAGES

Krityakierne, Tipaluck; Akhtar, Taimoor; Shoemaker, Christine A.

2016-02-02

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

Underwater Sensor Network Redeployment Algorithm Based on Wolf Search

PubMed Central

Jiang, Peng; Feng, Yang; Wu, Feng

2016-01-01

This study addresses the optimization of node redeployment coverage in underwater wireless sensor networks. Given that nodes could easily become invalid under a poor environment and the large scale of underwater wireless sensor networks, an underwater sensor network redeployment algorithm was developed based on wolf search. This study is to apply the wolf search algorithm combined with crowded degree control in the deployment of underwater wireless sensor networks. The proposed algorithm uses nodes to ensure coverage of the events, and it avoids the prematurity of the nodes. The algorithm has good coverage effects. In addition, considering that obstacles exist in the underwater environment, nodes are prevented from being invalid by imitating the mechanism of avoiding predators. Thus, the energy consumption of the network is reduced. Comparative analysis shows that the algorithm is simple and effective in wireless sensor network deployment. Compared with the optimized artificial fish swarm algorithm, the proposed algorithm exhibits advantages in network coverage, energy conservation, and obstacle avoidance. PMID:27775659

Nonuniformity correction based on focal plane array temperature in uncooled long-wave infrared cameras without a shutter.

PubMed

Liang, Kun; Yang, Cailan; Peng, Li; Zhou, Bo

2017-02-01

In uncooled long-wave IR camera systems, the temperature of a focal plane array (FPA) is variable along with the environmental temperature as well as the operating time. The spatial nonuniformity of the FPA, which is partly affected by the FPA temperature, obviously changes as well, resulting in reduced image quality. This study presents a real-time nonuniformity correction algorithm based on FPA temperature to compensate for nonuniformity caused by FPA temperature fluctuation. First, gain coefficients are calculated using a two-point correction technique. Then offset parameters at different FPA temperatures are obtained and stored in tables. When the camera operates, the offset tables are called to update the current offset parameters via a temperature-dependent interpolation. Finally, the gain coefficients and offset parameters are used to correct the output of the IR camera in real time. The proposed algorithm is evaluated and compared with two representative shutterless algorithms [minimizing the sum of the squares of errors algorithm (MSSE), template-based solution algorithm (TBS)] using IR images captured by a 384×288 pixel uncooled IR camera with a 17 μm pitch. Experimental results show that this method can quickly trace the response drift of the detector units when the FPA temperature changes. The quality of the proposed algorithm is as good as MSSE, while the processing time is as short as TBS, which means the proposed algorithm is good for real-time control and at the same time has a high correction effect.

Advancing X-ray scattering metrology using inverse genetic algorithms.

PubMed

Hannon, Adam F; Sunday, Daniel F; Windover, Donald; Kline, R Joseph

2016-01-01

We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real space structure in periodic gratings measured using critical dimension small angle X-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real space structure of our nanogratings. The study shows that for X-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.

Determining open cluster membership. A Bayesian framework for quantitative member classification

NASA Astrophysics Data System (ADS)

Stott, Jonathan J.

2018-01-01

Aims: My goal is to develop a quantitative algorithm for assessing open cluster membership probabilities. The algorithm is designed to work with single-epoch observations. In its simplest form, only one set of program images and one set of reference images are required. Methods: The algorithm is based on a two-stage joint astrometric and photometric assessment of cluster membership probabilities. The probabilities were computed within a Bayesian framework using any available prior information. Where possible, the algorithm emphasizes simplicity over mathematical sophistication. Results: The algorithm was implemented and tested against three observational fields using published survey data. M 67 and NGC 654 were selected as cluster examples while a third, cluster-free, field was used for the final test data set. The algorithm shows good quantitative agreement with the existing surveys and has a false-positive rate significantly lower than the astrometric or photometric methods used individually.

Adaboost multi-view face detection based on YCgCr skin color model

NASA Astrophysics Data System (ADS)

Lan, Qi; Xu, Zhiyong

2016-09-01

Traditional Adaboost face detection algorithm uses Haar-like features training face classifiers, whose detection error rate is low in the face region. While under the complex background, the classifiers will make wrong detection easily to the background regions with the similar faces gray level distribution, which leads to the error detection rate of traditional Adaboost algorithm is high. As one of the most important features of a face, skin in YCgCr color space has good clustering. We can fast exclude the non-face areas through the skin color model. Therefore, combining with the advantages of the Adaboost algorithm and skin color detection algorithm, this paper proposes Adaboost face detection algorithm method that bases on YCgCr skin color model. Experiments show that, compared with traditional algorithm, the method we proposed has improved significantly in the detection accuracy and errors.

An efficient group multicast routing for multimedia communication

NASA Astrophysics Data System (ADS)

Wang, Yanlin; Sun, Yugen; Yan, Xinfang

2004-04-01

Group multicasting is a kind of communication mechanism whereby each member of a group sends messages to all the other members of the same group. Group multicast routing algorithms capable of satisfying quality of service (QoS) requirements of multimedia applications are essential for high-speed networks. We present a heuristic algorithm for group multicast routing with end to end delay constraint. Source-specific routing trees for each member are generated in our algorithm, which satisfy member"s bandwidth and end to end delay requirements. Simulations over random network were carried out to compare proposed algorithm performance with Low and Song"s. The experimental results show that our proposed algorithm performs better in terms of network cost and ability in constructing feasible multicast trees for group members. Moreover, our algorithm achieves good performance in balancing traffic, which can avoid link blocking and enhance the network behavior efficiently.

Multi-Objective Control Optimization for Greenhouse Environment Using Evolutionary Algorithms

PubMed Central

Hu, Haigen; Xu, Lihong; Wei, Ruihua; Zhu, Bingkun

2011-01-01

This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an Evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production. PMID:22163927

Development of a stereo analysis algorithm for generating topographic maps using interactive techniques of the MPP

NASA Technical Reports Server (NTRS)

Strong, James P.

1987-01-01

A local area matching algorithm was developed on the Massively Parallel Processor (MPP). It is an iterative technique that first matches coarse or low resolution areas and at each iteration performs matches of higher resolution. Results so far show that when good matches are possible in the two images, the MPP algorithm matches corresponding areas as well as a human observer. To aid in developing this algorithm, a control or shell program was developed for the MPP that allows interactive experimentation with various parameters and procedures to be used in the matching process. (This would not be possible without the high speed of the MPP). With the system, optimal techniques can be developed for different types of matching problems.

[Research on non-rigid registration of multi-modal medical image based on Demons algorithm].

PubMed

Hao, Peibo; Chen, Zhen; Jiang, Shaofeng; Wang, Yang

2014-02-01

Non-rigid medical image registration is a popular subject in the research areas of the medical image and has an important clinical value. In this paper we put forward an improved algorithm of Demons, together with the conservation of gray model and local structure tensor conservation model, to construct a new energy function processing multi-modal registration problem. We then applied the L-BFGS algorithm to optimize the energy function and solve complex three-dimensional data optimization problem. And finally we used the multi-scale hierarchical refinement ideas to solve large deformation registration. The experimental results showed that the proposed algorithm for large de formation and multi-modal three-dimensional medical image registration had good effects.

Cloud computing task scheduling strategy based on improved differential evolution algorithm

NASA Astrophysics Data System (ADS)

Ge, Junwei; He, Qian; Fang, Yiqiu

2017-04-01

In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks.

Image compression using quad-tree coding with morphological dilation

NASA Astrophysics Data System (ADS)

Wu, Jiaji; Jiang, Weiwei; Jiao, Licheng; Wang, Lei

2007-11-01

In this paper, we propose a new algorithm which integrates morphological dilation operation to quad-tree coding, the purpose of doing this is to compensate each other's drawback by using quad-tree coding and morphological dilation operation respectively. New algorithm can not only quickly find the seed significant coefficient of dilation but also break the limit of block boundary of quad-tree coding. We also make a full use of both within-subband and cross-subband correlation to avoid the expensive cost of representing insignificant coefficients. Experimental results show that our algorithm outperforms SPECK and SPIHT. Without using any arithmetic coding, our algorithm can achieve good performance with low computational cost and it's more suitable to mobile devices or scenarios with a strict real-time requirement.

Compressed sensing based missing nodes prediction in temporal communication network

NASA Astrophysics Data System (ADS)

Cheng, Guangquan; Ma, Yang; Liu, Zhong; Xie, Fuli

2018-02-01

The reconstruction of complex network topology is of great theoretical and practical significance. Most research so far focuses on the prediction of missing links. There are many mature algorithms for link prediction which have achieved good results, but research on the prediction of missing nodes has just begun. In this paper, we propose an algorithm for missing node prediction in complex networks. We detect the position of missing nodes based on their neighbor nodes under the theory of compressed sensing, and extend the algorithm to the case of multiple missing nodes using spectral clustering. Experiments on real public network datasets and simulated datasets show that our algorithm can detect the locations of hidden nodes effectively with high precision.

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.

A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors

PubMed Central

Tian, Xiaochun; Chen, Jiabin; Han, Yongqiang; Shang, Jianyu; Li, Nan

2016-01-01

Zero velocity update (ZUPT) plays an important role in pedestrian navigation algorithms with the premise that the zero velocity interval (ZVI) should be detected accurately and effectively. A novel adaptive ZVI detection algorithm based on a smoothed pseudo Wigner–Ville distribution to remove multiple frequencies intelligently (SPWVD-RMFI) is proposed in this paper. The novel algorithm adopts the SPWVD-RMFI method to extract the pedestrian gait frequency and to calculate the optimal ZVI detection threshold in real time by establishing the function relationships between the thresholds and the gait frequency; then, the adaptive adjustment of thresholds with gait frequency is realized and improves the ZVI detection precision. To put it into practice, a ZVI detection experiment is carried out; the result shows that compared with the traditional fixed threshold ZVI detection method, the adaptive ZVI detection algorithm can effectively reduce the false and missed detection rate of ZVI; this indicates that the novel algorithm has high detection precision and good robustness. Furthermore, pedestrian trajectory positioning experiments at different walking speeds are carried out to evaluate the influence of the novel algorithm on positioning precision. The results show that the ZVI detected by the adaptive ZVI detection algorithm for pedestrian trajectory calculation can achieve better performance. PMID:27669266

[Fluorescent signal detection of chromatographic chip by algorithms of pyramid connection and Gaussian mixture model].

PubMed

Hu, Beibei; Zhang, Xueqing; Chen, Haopeng; Cui, Daxiang

2011-03-01

We proposed a new algorithm for automatic identification of fluorescent signal. Based on the features of chromatographic chips, mathematic morphology in RGB color space was used to filter and enhance the images, pyramid connection was used to segment the areas of fluorescent signal, and then the method of Gaussian Mixture Model was used to detect the fluorescent signal. Finally we calculated the average fluorescent intensity in obtained fluorescent areas. Our results show that the algorithm has a good efficacy to segment the fluorescent areas, can detect the fluorescent signal quickly and accurately, and finally realize the quantitative detection of fluorescent signal in chromatographic chip.

A New Algorithm for Detecting Cloud Height using OMPS/LP Measurements

NASA Technical Reports Server (NTRS)

Chen, Zhong; DeLand, Matthew; Bhartia, Pawan K.

2016-01-01

The Ozone Mapping and Profiler Suite Limb Profiler (OMPS/LP) ozone product requires the determination of cloud height for each event to establish the lower boundary of the profile for the retrieval algorithm. We have created a revised cloud detection algorithm for LP measurements that uses the spectral dependence of the vertical gradient in radiance between two wavelengths in the visible and near-IR spectral regions. This approach provides better discrimination between clouds and aerosols than results obtained using a single wavelength. Observed LP cloud height values show good agreement with coincident Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements.

An efficient hybrid method for stochastic reaction-diffusion biochemical systems with delay

NASA Astrophysics Data System (ADS)

Sayyidmousavi, Alireza; Ilie, Silvana

2017-12-01

Many chemical reactions, such as gene transcription and translation in living cells, need a certain time to finish once they are initiated. Simulating stochastic models of reaction-diffusion systems with delay can be computationally expensive. In the present paper, a novel hybrid algorithm is proposed to accelerate the stochastic simulation of delayed reaction-diffusion systems. The delayed reactions may be of consuming or non-consuming delay type. The algorithm is designed for moderately stiff systems in which the events can be partitioned into slow and fast subsets according to their propensities. The proposed algorithm is applied to three benchmark problems and the results are compared with those of the delayed Inhomogeneous Stochastic Simulation Algorithm. The numerical results show that the new hybrid algorithm achieves considerable speed-up in the run time and very good accuracy.

Modified artificial bee colony for the vehicle routing problems with time windows.

PubMed

Alzaqebah, Malek; Abdullah, Salwani; Jawarneh, Sana

2016-01-01

The natural behaviour of the honeybee has attracted the attention of researchers in recent years and several algorithms have been developed that mimic swarm behaviour to solve optimisation problems. This paper introduces an artificial bee colony (ABC) algorithm for the vehicle routing problem with time windows (VRPTW). A Modified ABC algorithm is proposed to improve the solution quality of the original ABC. The high exploration ability of the ABC slows-down its convergence speed, which may due to the mechanism used by scout bees in replacing abandoned (unimproved) solutions with new ones. In the Modified ABC a list of abandoned solutions is used by the scout bees to memorise the abandoned solutions, then the scout bees select a solution from the list based on roulette wheel selection and replace by a new solution with random routs selected from the best solution. The performance of the Modified ABC is evaluated on Solomon benchmark datasets and compared with the original ABC. The computational results demonstrate that the Modified ABC outperforms the original ABC also produce good solutions when compared with the best-known results in the literature. Computational investigations show that the proposed algorithm is a good and promising approach for the VRPTW.

Cosmology with the cosmic web

NASA Astrophysics Data System (ADS)

Forero-Romero, J. E.

2017-07-01

This talk summarizes different algorithms that can be used to trace the cosmic web both in simulations and observations. We present different applications in galaxy formation and cosmology. To finalize, we show how the Dark Energy Spectroscopic Instrument (DESI) could be a good place to apply these techniques.

Hyperspectral data discrimination methods

NASA Astrophysics Data System (ADS)

Casasent, David P.; Chen, Xuewen

2000-12-01

Hyperspectral data provides spectral response information that provides detailed chemical, moisture, and other description of constituent parts of an item. These new sensor data are useful in USDA product inspection. However, such data introduce problems such as the curse of dimensionality, the need to reduce the number of features used to accommodate realistic small training set sizes, and the need to employ discriminatory features and still achieve good generalization (comparable training and test set performance). Several two-step methods are compared to a new and preferable single-step spectral decomposition algorithm. Initial results on hyperspectral data for good/bad almonds and for good/bad (aflatoxin infested) corn kernels are presented. The hyperspectral application addressed differs greatly from prior USDA work (PLS) in which the level of a specific channel constituent in food was estimated. A validation set (separate from the test set) is used in selecting algorithm parameters. Threshold parameters are varied to select the best Pc operating point. Initial results show that nonlinear features yield improved performance.

Implementation of digital image encryption algorithm using logistic function and DNA encoding

NASA Astrophysics Data System (ADS)

Suryadi, MT; Satria, Yudi; Fauzi, Muhammad

2018-03-01

Cryptography is a method to secure information that might be in form of digital image. Based on past research, in order to increase security level of chaos based encryption algorithm and DNA based encryption algorithm, encryption algorithm using logistic function and DNA encoding was proposed. Digital image encryption algorithm using logistic function and DNA encoding use DNA encoding to scramble the pixel values into DNA base and scramble it in DNA addition, DNA complement, and XOR operation. The logistic function in this algorithm used as random number generator needed in DNA complement and XOR operation. The result of the test show that the PSNR values of cipher images are 7.98-7.99 bits, the entropy values are close to 8, the histogram of cipher images are uniformly distributed and the correlation coefficient of cipher images are near 0. Thus, the cipher image can be decrypted perfectly and the encryption algorithm has good resistance to entropy attack and statistical attack.

Image reconstruction algorithms for electrical capacitance tomography based on ROF model using new numerical techniques

NASA Astrophysics Data System (ADS)

Chen, Jiaoxuan; Zhang, Maomao; Liu, Yinyan; Chen, Jiaoliao; Li, Yi

2017-03-01

Electrical capacitance tomography (ECT) is a promising technique applied in many fields. However, the solutions for ECT are not unique and highly sensitive to the measurement noise. To remain a good shape of reconstructed object and endure a noisy data, a Rudin-Osher-Fatemi (ROF) model with total variation regularization is applied to image reconstruction in ECT. Two numerical methods, which are simplified augmented Lagrangian (SAL) and accelerated alternating direction method of multipliers (AADMM), are innovatively introduced to try to solve the above mentioned problems in ECT. The effect of the parameters and the number of iterations for different algorithms, and the noise level in capacitance data are discussed. Both simulation and experimental tests were carried out to validate the feasibility of the proposed algorithms, compared to the Landweber iteration (LI) algorithm. The results show that the SAL and AADMM algorithms can handle a high level of noise and the AADMM algorithm outperforms other algorithms in identifying the object from its background.

A linear recurrent kernel online learning algorithm with sparse updates.

PubMed

Fan, Haijin; Song, Qing

2014-02-01

In this paper, we propose a recurrent kernel algorithm with selectively sparse updates for online learning. The algorithm introduces a linear recurrent term in the estimation of the current output. This makes the past information reusable for updating of the algorithm in the form of a recurrent gradient term. To ensure that the reuse of this recurrent gradient indeed accelerates the convergence speed, a novel hybrid recurrent training is proposed to switch on or off learning the recurrent information according to the magnitude of the current training error. Furthermore, the algorithm includes a data-dependent adaptive learning rate which can provide guaranteed system weight convergence at each training iteration. The learning rate is set as zero when the training violates the derived convergence conditions, which makes the algorithm updating process sparse. Theoretical analyses of the weight convergence are presented and experimental results show the good performance of the proposed algorithm in terms of convergence speed and estimation accuracy. Copyright © 2013 Elsevier Ltd. All rights reserved.

On Parallel Push-Relabel based Algorithms for Bipartite Maximum Matching

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

Langguth, Johannes; Azad, Md Ariful; Halappanavar, Mahantesh

2014-07-01

We study multithreaded push-relabel based algorithms for computing maximum cardinality matching in bipartite graphs. Matching is a fundamental combinatorial (graph) problem with applications in a wide variety of problems in science and engineering. We are motivated by its use in the context of sparse linear solvers for computing maximum transversal of a matrix. We implement and test our algorithms on several multi-socket multicore systems and compare their performance to state-of-the-art augmenting path-based serial and parallel algorithms using a testset comprised of a wide range of real-world instances. Building on several heuristics for enhancing performance, we demonstrate good scaling for themore » parallel push-relabel algorithm. We show that it is comparable to the best augmenting path-based algorithms for bipartite matching. To the best of our knowledge, this is the first extensive study of multithreaded push-relabel based algorithms. In addition to a direct impact on the applications using matching, the proposed algorithmic techniques can be extended to preflow-push based algorithms for computing maximum flow in graphs.« less

Improved artificial bee colony algorithm for wavefront sensor-less system in free space optical communication

NASA Astrophysics Data System (ADS)

Niu, Chaojun; Han, Xiang'e.

2015-10-01

Adaptive optics (AO) technology is an effective way to alleviate the effect of turbulence on free space optical communication (FSO). A new adaptive compensation method can be used without a wave-front sensor. Artificial bee colony algorithm (ABC) is a population-based heuristic evolutionary algorithm inspired by the intelligent foraging behaviour of the honeybee swarm with the advantage of simple, good convergence rate, robust and less parameter setting. In this paper, we simulate the application of the improved ABC to correct the distorted wavefront and proved its effectiveness. Then we simulate the application of ABC algorithm, differential evolution (DE) algorithm and stochastic parallel gradient descent (SPGD) algorithm to the FSO system and analyze the wavefront correction capabilities by comparison of the coupling efficiency, the error rate and the intensity fluctuation in different turbulence before and after the correction. The results show that the ABC algorithm has much faster correction speed than DE algorithm and better correct ability for strong turbulence than SPGD algorithm. Intensity fluctuation can be effectively reduced in strong turbulence, but not so effective in week turbulence.

A Rapid Convergent Low Complexity Interference Alignment Algorithm for Wireless Sensor Networks.

PubMed

Jiang, Lihui; Wu, Zhilu; Ren, Guanghui; Wang, Gangyi; Zhao, Nan

2015-07-29

Interference alignment (IA) is a novel technique that can effectively eliminate the interference and approach the sum capacity of wireless sensor networks (WSNs) when the signal-to-noise ratio (SNR) is high, by casting the desired signal and interference into different signal subspaces. The traditional alternating minimization interference leakage (AMIL) algorithm for IA shows good performance in high SNR regimes, however, the complexity of the AMIL algorithm increases dramatically as the number of users and antennas increases, posing limits to its applications in the practical systems. In this paper, a novel IA algorithm, called directional quartic optimal (DQO) algorithm, is proposed to minimize the interference leakage with rapid convergence and low complexity. The properties of the AMIL algorithm are investigated, and it is discovered that the difference between the two consecutive iteration results of the AMIL algorithm will approximately point to the convergence solution when the precoding and decoding matrices obtained from the intermediate iterations are sufficiently close to their convergence values. Based on this important property, the proposed DQO algorithm employs the line search procedure so that it can converge to the destination directly. In addition, the optimal step size can be determined analytically by optimizing a quartic function. Numerical results show that the proposed DQO algorithm can suppress the interference leakage more rapidly than the traditional AMIL algorithm, and can achieve the same level of sum rate as that of AMIL algorithm with far less iterations and execution time.

Convergence issues in domain decomposition parallel computation of hovering rotor

NASA Astrophysics Data System (ADS)

Xiao, Zhongyun; Liu, Gang; Mou, Bin; Jiang, Xiong

2018-05-01

Implicit LU-SGS time integration algorithm has been widely used in parallel computation in spite of its lack of information from adjacent domains. When applied to parallel computation of hovering rotor flows in a rotating frame, it brings about convergence issues. To remedy the problem, three LU factorization-based implicit schemes (consisting of LU-SGS, DP-LUR and HLU-SGS) are investigated comparatively. A test case of pure grid rotation is designed to verify these algorithms, which show that LU-SGS algorithm introduces errors on boundary cells. When partition boundaries are circumferential, errors arise in proportion to grid speed, accumulating along with the rotation, and leading to computational failure in the end. Meanwhile, DP-LUR and HLU-SGS methods show good convergence owing to boundary treatment which are desirable in domain decomposition parallel computations.

Improved parallel data partitioning by nested dissection with applications to information retrieval.

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

Wolf, Michael M.; Chevalier, Cedric; Boman, Erik Gunnar

The computational work in many information retrieval and analysis algorithms is based on sparse linear algebra. Sparse matrix-vector multiplication is a common kernel in many of these computations. Thus, an important related combinatorial problem in parallel computing is how to distribute the matrix and the vectors among processors so as to minimize the communication cost. We focus on minimizing the total communication volume while keeping the computation balanced across processes. In [1], the first two authors presented a new 2D partitioning method, the nested dissection partitioning algorithm. In this paper, we improve on that algorithm and show that it ismore » a good option for data partitioning in information retrieval. We also show partitioning time can be substantially reduced by using the SCOTCH software, and quality improves in some cases, too.« less

Adaptive mechanism-based congestion control for networked systems

NASA Astrophysics Data System (ADS)

Liu, Zhi; Zhang, Yun; Chen, C. L. Philip

2013-03-01

In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol-AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.

Reducing noise component on medical images

NASA Astrophysics Data System (ADS)

Semenishchev, Evgeny; Voronin, Viacheslav; Dub, Vladimir; Balabaeva, Oksana

2018-04-01

Medical visualization and analysis of medical data is an actual direction. Medical images are used in microbiology, genetics, roentgenology, oncology, surgery, ophthalmology, etc. Initial data processing is a major step towards obtaining a good diagnostic result. The paper considers the approach allows an image filtering with preservation of objects borders. The algorithm proposed in this paper is based on sequential data processing. At the first stage, local areas are determined, for this purpose the method of threshold processing, as well as the classical ICI algorithm, is applied. The second stage uses a method based on based on two criteria, namely, L2 norm and the first order square difference. To preserve the boundaries of objects, we will process the transition boundary and local neighborhood the filtering algorithm with a fixed-coefficient. For example, reconstructed images of CT, x-ray, and microbiological studies are shown. The test images show the effectiveness of the proposed algorithm. This shows the applicability of analysis many medical imaging applications.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Bouc-Wen hysteresis model identification using Modified Firefly Algorithm

NASA Astrophysics Data System (ADS)

Zaman, Mohammad Asif; Sikder, Urmita

2015-12-01

The parameters of Bouc-Wen hysteresis model are identified using a Modified Firefly Algorithm. The proposed algorithm uses dynamic process control parameters to improve its performance. The algorithm is used to find the model parameter values that results in the least amount of error between a set of given data points and points obtained from the Bouc-Wen model. The performance of the algorithm is compared with the performance of conventional Firefly Algorithm, Genetic Algorithm and Differential Evolution algorithm in terms of convergence rate and accuracy. Compared to the other three optimization algorithms, the proposed algorithm is found to have good convergence rate with high degree of accuracy in identifying Bouc-Wen model parameters. Finally, the proposed method is used to find the Bouc-Wen model parameters from experimental data. The obtained model is found to be in good agreement with measured data.

Stokes space modulation format classification based on non-iterative clustering algorithm for coherent optical receivers.

PubMed

Mai, Xiaofeng; Liu, Jie; Wu, Xiong; Zhang, Qun; Guo, Changjian; Yang, Yanfu; Li, Zhaohui

2017-02-06

A Stokes-space modulation format classification (MFC) technique is proposed for coherent optical receivers by using a non-iterative clustering algorithm. In the clustering algorithm, two simple parameters are calculated to help find the density peaks of the data points in Stokes space and no iteration is required. Correct MFC can be realized in numerical simulations among PM-QPSK, PM-8QAM, PM-16QAM, PM-32QAM and PM-64QAM signals within practical optical signal-to-noise ratio (OSNR) ranges. The performance of the proposed MFC algorithm is also compared with those of other schemes based on clustering algorithms. The simulation results show that good classification performance can be achieved using the proposed MFC scheme with moderate time complexity. Proof-of-concept experiments are finally implemented to demonstrate MFC among PM-QPSK/16QAM/64QAM signals, which confirm the feasibility of our proposed MFC scheme.

Cross-media color reproduction using the frequency-based spatial gamut mapping algorithm based on human color vision

NASA Astrophysics Data System (ADS)

Wu, Guangyuan; Niu, Shijun; Li, Xiaozhou; Hu, Guichun

2018-04-01

Due to the increasing globalization of printing industry, remoting proofing will become the inevitable development trend. Cross-media color reproduction will occur in different color gamuts using remote proofing technologies, which usually leads to the problem of incompatible color gamut. In this paper, to achieve equivalent color reproduction between a monitor and a printer, a frequency-based spatial gamut mapping algorithm is proposed for decreasing the loss of visual color information. The design of algorithm is based on the contrast sensitivity functions (CSF), which exploited CSF spatial filter to preserve luminance of the high spatial frequencies and chrominance of the low frequencies. First we show a general framework for how to apply CSF spatial filter in retention of relevant visual information. Then we compare the proposed framework with HPMINDE, CUSP, Bala's algorithm. The psychophysical experimental results indicated the good performance of the proposed algorithm.

Research on Image Encryption Based on DNA Sequence and Chaos Theory

NASA Astrophysics Data System (ADS)

Tian Zhang, Tian; Yan, Shan Jun; Gu, Cheng Yan; Ren, Ran; Liao, Kai Xin

2018-04-01

Nowadays encryption is a common technique to protect image data from unauthorized access. In recent years, many scientists have proposed various encryption algorithms based on DNA sequence to provide a new idea for the design of image encryption algorithm. Therefore, a new method of image encryption based on DNA computing technology is proposed in this paper, whose original image is encrypted by DNA coding and 1-D logistic chaotic mapping. First, the algorithm uses two modules as the encryption key. The first module uses the real DNA sequence, and the second module is made by one-dimensional logistic chaos mapping. Secondly, the algorithm uses DNA complementary rules to encode original image, and uses the key and DNA computing technology to compute each pixel value of the original image, so as to realize the encryption of the whole image. Simulation results show that the algorithm has good encryption effect and security.

Arctic Sea Ice Parameters from AMSR-E Data using Two Techniques, and Comparisons with Sea Ice from SSM

NASA Technical Reports Server (NTRS)

Comiso, Josefino C.; Parkinson, Claire L.

2007-01-01

We use two algorithms to process AMSR-E data in order to determine algorithm dependence, if any, on the estimates of sea ice concentration, ice extent and area, and trends and to evaluate how AMSR-E data compare with historical SSM/I data. The monthly ice concentrations derived from the two algorithms from AMSR-E data (the AMSR-E Bootstrap Algorithm, or ABA, and the enhanced NASA Team algorithm, or NT2) differ on average by about 1 to 3%, with data from the consolidated ice region being generally comparable for ABA and NT2 retrievals while data in the marginal ice zones and thin ice regions show higher values when the NT2 algorithm is used. The ice extents and areas derived separately from AMSR-E using these two algorithms are, however, in good agreement, with the differences (ABA-NT2) being about 6.6 x 10(exp 4) square kilometers on average for ice extents and -6.6 x 10(exp 4) square kilometers for ice area which are small compared to mean seasonal values of 10.5 x 10(exp 6) and 9.8 x 10(exp 6) for ice extent and area: respectively. Likewise, extents and areas derived from the same algorithm but from AMSR-E and SSM/I data are consistent but differ by about -24.4 x 10(exp 4) square kilometers and -13.9 x 10(exp 4) square kilometers, respectively. The discrepancies are larger with the estimates of extents than area mainly because of differences in channel selection and sensor resolutions. Trends in extent during the AMSR-E era were also estimated and results from all three data sets are shown to be in good agreement (within errors).

Development and validation of an algorithm for laser application in wound treatment 1

PubMed Central

da Cunha, Diequison Rite; Salomé, Geraldo Magela; Massahud, Marcelo Renato; Mendes, Bruno; Ferreira, Lydia Masako

2017-01-01

ABSTRACT Objective: To develop and validate an algorithm for laser wound therapy. Method: Methodological study and literature review. For the development of the algorithm, a review was performed in the Health Sciences databases of the past ten years. The algorithm evaluation was performed by 24 participants, nurses, physiotherapists, and physicians. For data analysis, the Cronbach’s alpha coefficient and the chi-square test for independence was used. The level of significance of the statistical test was established at 5% (p<0.05). Results: The professionals’ responses regarding the facility to read the algorithm indicated: 41.70%, great; 41.70%, good; 16.70%, regular. With regard the algorithm being sufficient for supporting decisions related to wound evaluation and wound cleaning, 87.5% said yes to both questions. Regarding the participants’ opinion that the algorithm contained enough information to support their decision regarding the choice of laser parameters, 91.7% said yes. The questionnaire presented reliability using the Cronbach’s alpha coefficient test (α = 0.962). Conclusion: The developed and validated algorithm showed reliability for evaluation, wound cleaning, and use of laser therapy in wounds. PMID:29211197

Research on registration algorithm for check seal verification

NASA Astrophysics Data System (ADS)

Wang, Shuang; Liu, Tiegen

2008-03-01

Nowadays seals play an important role in China. With the development of social economy, the traditional method of manual check seal identification can't meet the need s of banking transactions badly. This paper focus on pre-processing and registration algorithm for check seal verification using theory of image processing and pattern recognition. First of all, analyze the complex characteristics of check seals. To eliminate the difference of producing conditions and the disturbance caused by background and writing in check image, many methods are used in the pre-processing of check seal verification, such as color components transformation, linearity transform to gray-scale image, medium value filter, Otsu, close calculations and labeling algorithm of mathematical morphology. After the processes above, the good binary seal image can be obtained. On the basis of traditional registration algorithm, a double-level registration method including rough and precise registration method is proposed. The deflection angle of precise registration method can be precise to 0.1°. This paper introduces the concepts of difference inside and difference outside and use the percent of difference inside and difference outside to judge whether the seal is real or fake. The experimental results of a mass of check seals are satisfied. It shows that the methods and algorithmic presented have good robustness to noise sealing conditions and satisfactory tolerance of difference within class.

Research on Method of Interactive Segmentation Based on Remote Sensing Images

NASA Astrophysics Data System (ADS)

Yang, Y.; Li, H.; Han, Y.; Yu, F.

2017-09-01

In this paper, we aim to solve the object extraction problem in remote sensing images using interactive segmentation tools. Firstly, an overview of the interactive segmentation algorithm is proposed. Then, our detailed implementation of intelligent scissors and GrabCut for remote sensing images is described. Finally, several experiments on different typical features (water area, vegetation) in remote sensing images are performed respectively. Compared with the manual result, it indicates that our tools maintain good feature boundaries and show good performance.

New clinical grading scales and objective measurement for conjunctival injection.

PubMed

Park, In Ki; Chun, Yeoun Sook; Kim, Kwang Gi; Yang, Hee Kyung; Hwang, Jeong-Min

2013-08-05

To establish a new clinical grading scale and objective measurement method to evaluate conjunctival injection. Photographs of conjunctival injection with variable ocular diseases in 429 eyes were reviewed. Seventy-three images with concordance by three ophthalmologists were classified into a 4-step and 10-step subjective grading scale, and used as standard photographs. Each image was quantified in four ways: the relative magnitude of the redness component of each red-green-blue (RGB) pixel; two different algorithms based on the occupied area by blood vessels (K-means clustering with LAB color model and contrast-limited adaptive histogram equalization [CLAHE] algorithm); and the presence of blood vessel edges, based on the Canny edge-detection algorithm. Area under the receiver operating characteristic curves (AUCs) were calculated to summarize diagnostic accuracies of the four algorithms. The RGB color model, K-means clustering with LAB color model, and CLAHE algorithm showed good correlation with the clinical 10-step grading scale (R = 0.741, 0.784, 0.919, respectively) and with the clinical 4-step grading scale (R = 0.645, 0.702, 0.838, respectively). The CLAHE method showed the largest AUC, best distinction power (P < 0.001, ANOVA, Bonferroni multiple comparison test), and high reproducibility (R = 0.996). CLAHE algorithm showed the best correlation with the 10-step and 4-step subjective clinical grading scales together with high distinction power and reproducibility. CLAHE algorithm can be a useful for method for assessment of conjunctival injection.

Research on cross - Project software defect prediction based on transfer learning

NASA Astrophysics Data System (ADS)

Chen, Ya; Ding, Xiaoming

2018-04-01

According to the two challenges in the prediction of cross-project software defects, the distribution differences between the source project and the target project dataset and the class imbalance in the dataset, proposing a cross-project software defect prediction method based on transfer learning, named NTrA. Firstly, solving the source project data's class imbalance based on the Augmented Neighborhood Cleaning Algorithm. Secondly, the data gravity method is used to give different weights on the basis of the attribute similarity of source project and target project data. Finally, a defect prediction model is constructed by using Trad boost algorithm. Experiments were conducted using data, come from NASA and SOFTLAB respectively, from a published PROMISE dataset. The results show that the method has achieved good values of recall and F-measure, and achieved good prediction results.

Pre-Launch Performance Testing of the ICESat-2/ATLAS Flight Science Receiver Algorithms

NASA Astrophysics Data System (ADS)

Mcgarry, J.; Carabajal, C. C.; Saba, J. L.; Rackley, A.; Holland, S.

2016-12-01

NASA's Advanced Topographic Laser Altimeter System (ATLAS) will be the single instrument on the ICESat-2 spacecraft which is expected to launch in late 2017 with a 3 year mission lifetime. The ICESat-2 planned orbital altitude is 500 km with a 92 degree inclination and 91-day repeat tracks. ATLAS is a single-photon detection system transmitting at 532nm with a laser repetition rate of 10 kHz and a 6 spot pattern on the Earth's surface. Without some method of reducing the received data, the volume of ATLAS telemetry would far exceed the normal X-band downlink capability. To reduce the data volume to an acceptable level a set of onboard Receiver Algorithms has been developed. These Algorithms limit the daily data volume by distinguishing surface echoes from the background noise and allowing the instrument to telemeter data from only a small vertical region about the signal. This is accomplished through the use of an onboard Digital Elevation Model (DEM), signal processing techniques, and onboard relief and surface reference maps. The ATLAS Receiver Algorithms have been completed and have been verified during Instrument testing in the spacecraft assembly area at the Goddard Space Flight Center in late 2015 and early 2016. Testing has been performed at ambient temperature with a pressure of one atmosphere as well as at the expected hot and cold temperatures in a vacuum. Results from testing to date show the Receiver Algorithms have the ability to handle a wide range of signal and noise levels with a very good sensitivity at relatively low signal to noise ratios. Testing with the ATLAS instrument and flight software shows very good agreement with previous Simulator testing and all of the requirements for ATLAS Receiver Algorithms were successfully verified during Run for the Record Testing in December 2015. This poster will describe the performance of the ATLAS Flight Science Receiver Algorithms during the Run for Record and Comprehensive Performance Testing performed at Goddard, which will give insight into the future on-orbit performance of the Algorithms. See the companion poster (Carabajal, et al) in this session.

Long-range prediction of Indian summer monsoon rainfall using data mining and statistical approaches

NASA Astrophysics Data System (ADS)

H, Vathsala; Koolagudi, Shashidhar G.

2017-10-01

This paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969-2005).

Novel Image Encryption Scheme Based on Chebyshev Polynomial and Duffing Map

PubMed Central

2014-01-01

We present a novel image encryption algorithm using Chebyshev polynomial based on permutation and substitution and Duffing map based on substitution. Comprehensive security analysis has been performed on the designed scheme using key space analysis, visual testing, histogram analysis, information entropy calculation, correlation coefficient analysis, differential analysis, key sensitivity test, and speed test. The study demonstrates that the proposed image encryption algorithm shows advantages of more than 10113 key space and desirable level of security based on the good statistical results and theoretical arguments. PMID:25143970

An integrated algorithm for hypersonic fluid-thermal-structural numerical simulation

NASA Astrophysics Data System (ADS)

Li, Jia-Wei; Wang, Jiang-Feng

2018-05-01

In this paper, a fluid-structural-thermal integrated method is presented based on finite volume method. A unified integral equations system is developed as the control equations for physical process of aero-heating and structural heat transfer. The whole physical field is discretized by using an up-wind finite volume method. To demonstrate its capability, the numerical simulation of Mach 6.47 flow over stainless steel cylinder shows a good agreement with measured values, and this method dynamically simulates the objective physical processes. Thus, the integrated algorithm proves to be efficient and reliable.

An Upwind Multigrid Algorithm for Calculating Flows on Unstructured Grids

NASA Technical Reports Server (NTRS)

Bonhaus, Daryl L.

1993-01-01

An algorithm is described that calculates inviscid, laminar, and turbulent flows on triangular meshes with an upwind discretization. A brief description of the base solver and the multigrid implementation is given, followed by results that consist mainly of convergence rates for inviscid and viscous flows over a NACA four-digit airfoil section. The results show that multigrid does accelerate convergence when the same relaxation parameters that yield good single-grid performance are used; however, larger gains in performance can be realized by doing less work in the relaxation scheme.

Design and Implementation of Pointer-Type Multi Meters Intelligent Recognition Device Based on ARM Platform

NASA Astrophysics Data System (ADS)

Cui, Yang; Luo, Wang; Fan, Qiang; Peng, Qiwei; Cai, Yiting; Yao, Yiyang; Xu, Changfu

2018-01-01

This paper adopts a low power consumption ARM Hisilicon mobile processing platform and OV4689 camera, combined with a new skeleton extraction based on distance transform algorithm and the improved Hough algorithm for multi meters real-time reading. The design and implementation of the device were completed. Experimental results shows that The average error of measurement was 0.005MPa, and the average reading time was 5s. The device had good stability and high accuracy which meets the needs of practical application.

UAV Control on the Basis of 3D Landmark Bearing-Only Observations.

PubMed

Karpenko, Simon; Konovalenko, Ivan; Miller, Alexander; Miller, Boris; Nikolaev, Dmitry

2015-11-27

The article presents an approach to the control of a UAV on the basis of 3D landmark observations. The novelty of the work is the usage of the 3D RANSAC algorithm developed on the basis of the landmarks' position prediction with the aid of a modified Kalman-type filter. Modification of the filter based on the pseudo-measurements approach permits obtaining unbiased UAV position estimation with quadratic error characteristics. Modeling of UAV flight on the basis of the suggested algorithm shows good performance, even under significant external perturbations.

Efficient clustering aggregation based on data fragments.

PubMed

Wu, Ou; Hu, Weiming; Maybank, Stephen J; Zhu, Mingliang; Li, Bing

2012-06-01

Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy.

Sort-Mid tasks scheduling algorithm in grid computing.

PubMed

Reda, Naglaa M; Tawfik, A; Marzok, Mohamed A; Khamis, Soheir M

2015-11-01

Scheduling tasks on heterogeneous resources distributed over a grid computing system is an NP-complete problem. The main aim for several researchers is to develop variant scheduling algorithms for achieving optimality, and they have shown a good performance for tasks scheduling regarding resources selection. However, using of the full power of resources is still a challenge. In this paper, a new heuristic algorithm called Sort-Mid is proposed. It aims to maximizing the utilization and minimizing the makespan. The new strategy of Sort-Mid algorithm is to find appropriate resources. The base step is to get the average value via sorting list of completion time of each task. Then, the maximum average is obtained. Finally, the task has the maximum average is allocated to the machine that has the minimum completion time. The allocated task is deleted and then, these steps are repeated until all tasks are allocated. Experimental tests show that the proposed algorithm outperforms almost other algorithms in terms of resources utilization and makespan.

Sort-Mid tasks scheduling algorithm in grid computing

PubMed Central

Reda, Naglaa M.; Tawfik, A.; Marzok, Mohamed A.; Khamis, Soheir M.

2014-01-01

Scheduling tasks on heterogeneous resources distributed over a grid computing system is an NP-complete problem. The main aim for several researchers is to develop variant scheduling algorithms for achieving optimality, and they have shown a good performance for tasks scheduling regarding resources selection. However, using of the full power of resources is still a challenge. In this paper, a new heuristic algorithm called Sort-Mid is proposed. It aims to maximizing the utilization and minimizing the makespan. The new strategy of Sort-Mid algorithm is to find appropriate resources. The base step is to get the average value via sorting list of completion time of each task. Then, the maximum average is obtained. Finally, the task has the maximum average is allocated to the machine that has the minimum completion time. The allocated task is deleted and then, these steps are repeated until all tasks are allocated. Experimental tests show that the proposed algorithm outperforms almost other algorithms in terms of resources utilization and makespan. PMID:26644937

Fast and robust wavelet-based dynamic range compression and contrast enhancement model with color restoration

NASA Astrophysics Data System (ADS)

Unaldi, Numan; Asari, Vijayan K.; Rahman, Zia-ur

2009-05-01

Recently we proposed a wavelet-based dynamic range compression algorithm to improve the visual quality of digital images captured from high dynamic range scenes with non-uniform lighting conditions. The fast image enhancement algorithm that provides dynamic range compression, while preserving the local contrast and tonal rendition, is also a good candidate for real time video processing applications. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some "pathological" scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for the final color restoration process. In this paper the latest version of the proposed algorithm, which deals with this issue is presented. The results obtained by applying the algorithm to numerous natural images show strong robustness and high image quality.

A vectorized Lanczos eigensolver for high-performance computers

NASA Technical Reports Server (NTRS)

Bostic, Susan W.

1990-01-01

The computational strategies used to implement a Lanczos-based-method eigensolver on the latest generation of supercomputers are described. Several examples of structural vibration and buckling problems are presented that show the effects of using optimization techniques to increase the vectorization of the computational steps. The data storage and access schemes and the tools and strategies that best exploit the computer resources are presented. The method is implemented on the Convex C220, the Cray 2, and the Cray Y-MP computers. Results show that very good computation rates are achieved for the most computationally intensive steps of the Lanczos algorithm and that the Lanczos algorithm is many times faster than other methods extensively used in the past.

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

PubMed Central

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

2011-01-01

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

Measuring the visual salience of alignments by their non-accidentalness.

PubMed

Blusseau, S; Carboni, A; Maiche, A; Morel, J M; Grompone von Gioi, R

2016-09-01

Quantitative approaches are part of the understanding of contour integration and the Gestalt law of good continuation. The present study introduces a new quantitative approach based on the a contrario theory, which formalizes the non-accidentalness principle for good continuation. This model yields an ideal observer algorithm, able to detect non-accidental alignments in Gabor patterns. More precisely, this parameterless algorithm associates with each candidate percept a measure, the Number of False Alarms (NFA), quantifying its degree of masking. To evaluate the approach, we compared this ideal observer with the human attentive performance on three experiments of straight contours detection in arrays of Gabor patches. The experiments showed a strong correlation between the detectability of the target stimuli and their degree of non-accidentalness, as measured by our model. What is more, the algorithm's detection curves were very similar to the ones of human subjects. This fact seems to validate our proposed measurement method as a convenient way to predict the visibility of alignments. This framework could be generalized to other Gestalts. Copyright © 2015 Elsevier Ltd. All rights reserved.

Microcontroller-based real-time QRS detection.

PubMed

Sun, Y; Suppappola, S; Wrublewski, T A

1992-01-01

The authors describe the design of a system for real-time detection of QRS complexes in the electrocardiogram based on a single-chip microcontroller (Motorola 68HC811). A systematic analysis of the instrumentation requirements for QRS detection and of the various design techniques is also given. Detection algorithms using different nonlinear transforms for the enhancement of QRS complexes are evaluated by using the ECG database of the American Heart Association. The results show that the nonlinear transform involving multiplication of three adjacent, sign-consistent differences in the time domain gives a good performance and a quick response. When implemented with an appropriate sampling rate, this algorithm is also capable of rejecting pacemaker spikes. The eight-bit single-chip microcontroller provides sufficient throughput and shows a satisfactory performance. Implementation of multiple detection algorithms in the same system improves flexibility and reliability. The low chip count in the design also favors maintainability and cost-effectiveness.

Ensembles of satellite aerosol retrievals based on three AATSR algorithms within aerosol_cci

NASA Astrophysics Data System (ADS)

Kosmale, Miriam; Popp, Thomas

2016-04-01

Ensemble techniques are widely used in the modelling community, combining different modelling results in order to reduce uncertainties. This approach could be also adapted to satellite measurements. Aerosol_cci is an ESA funded project, where most of the European aerosol retrieval groups work together. The different algorithms are homogenized as far as it makes sense, but remain essentially different. Datasets are compared with ground based measurements and between each other. Three AATSR algorithms (Swansea university aerosol retrieval, ADV aerosol retrieval by FMI and Oxford aerosol retrieval ORAC) provide within this project 17 year global aerosol records. Each of these algorithms provides also uncertainty information on pixel level. Within the presented work, an ensembles of the three AATSR algorithms is performed. The advantage over each single algorithm is the higher spatial coverage due to more measurement pixels per gridbox. A validation to ground based AERONET measurements shows still a good correlation of the ensemble, compared to the single algorithms. Annual mean maps show the global aerosol distribution, based on a combination of the three aerosol algorithms. In addition, pixel level uncertainties of each algorithm are used for weighting the contributions, in order to reduce the uncertainty of the ensemble. Results of different versions of the ensembles for aerosol optical depth will be presented and discussed. The results are validated against ground based AERONET measurements. A higher spatial coverage on daily basis allows better results in annual mean maps. The benefit of using pixel level uncertainties is analysed.

A Fast Exact k-Nearest Neighbors Algorithm for High Dimensional Search Using k-Means Clustering and Triangle Inequality.

PubMed

Wang, Xueyi

2012-02-08

The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The kMkNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, kd-trees, or ball-tree, kMkNN uses a simple k-means clustering method to preprocess the training dataset. In the searching stage, given a query object, kMkNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of kMkNN is surprisingly good compared to the traditional k-NN algorithm and tree-based k-NN algorithms such as kd-trees and ball-trees. On a collection of 20 datasets with up to 10(6) records and 10(4) dimensions, kMkNN shows a 2-to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional k-NN algorithm for 16 datasets. Furthermore, kMkNN performs significant better than a kd-tree based k-NN algorithm for all datasets and performs better than a ball-tree based k-NN algorithm for most datasets. The results show that kMkNN is effective for searching nearest neighbors in high dimensional spaces.

Active Control of Wind Tunnel Noise

NASA Technical Reports Server (NTRS)

Hollis, Patrick (Principal Investigator)

1991-01-01

The need for an adaptive active control system was realized, since a wind tunnel is subjected to variations in air velocity, temperature, air turbulence, and some other factors such as nonlinearity. Among many adaptive algorithms, the Least Mean Squares (LMS) algorithm, which is the simplest one, has been used in an Active Noise Control (ANC) system by some researchers. However, Eriksson's results, Eriksson (1985), showed instability in the ANC system with an ER filter for random noise input. The Restricted Least Squares (RLS) algorithm, although computationally more complex than the LMS algorithm, has better convergence and stability properties. The ANC system in the present work was simulated by using an FIR filter with an RLS algorithm for different inputs and for a number of plant models. Simulation results for the ANC system with acoustic feedback showed better robustness when used with the RLS algorithm than with the LMS algorithm for all types of inputs. Overall attenuation in the frequency domain was better in the case of the RLS adaptive algorithm. Simulation results with a more realistic plant model and an RLS adaptive algorithm showed a slower convergence rate than the case with an acoustic plant as a delay plant. However, the attenuation properties were satisfactory for the simulated system with the modified plant. The effect of filter length on the rate of convergence and attenuation was studied. It was found that the rate of convergence decreases with increase in filter length, whereas the attenuation increases with increase in filter length. The final design of the ANC system was simulated and found to have a reasonable convergence rate and good attenuation properties for an input containing discrete frequencies and random noise.

Time-frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation

NASA Astrophysics Data System (ADS)

Feng, Zhipeng; Chu, Fulei; Zuo, Ming J.

2011-03-01

Energy separation algorithm is good at tracking instantaneous changes in frequency and amplitude of modulated signals, but it is subject to the constraints of mono-component and narrow band. In most cases, time-varying modulated vibration signals of machinery consist of multiple components, and have so complicated instantaneous frequency trajectories on time-frequency plane that they overlap in frequency domain. For such signals, conventional filters fail to obtain mono-components of narrow band, and their rectangular decomposition of time-frequency plane may split instantaneous frequency trajectories thus resulting in information loss. Regarding the advantage of generalized demodulation method in decomposing multi-component signals into mono-components, an iterative generalized demodulation method is used as a preprocessing tool to separate signals into mono-components, so as to satisfy the requirements by energy separation algorithm. By this improvement, energy separation algorithm can be generalized to a broad range of signals, as long as the instantaneous frequency trajectories of signal components do not intersect on time-frequency plane. Due to the good adaptability of energy separation algorithm to instantaneous changes in signals and the mono-component decomposition nature of generalized demodulation, the derived time-frequency energy distribution has fine resolution and is free from cross term interferences. The good performance of the proposed time-frequency analysis is illustrated by analyses of a simulated signal and the on-site recorded nonstationary vibration signal of a hydroturbine rotor during a shut-down transient process, showing that it has potential to analyze time-varying modulated signals of multi-components.

A hybrid localization technique for patient tracking.

PubMed

Rodionov, Denis; Kolev, George; Bushminkin, Kirill

2013-01-01

Nowadays numerous technologies are employed for tracking patients and assets in hospitals or nursing homes. Each of them has advantages and drawbacks. For example, WiFi localization has relatively good accuracy but cannot be used in case of power outage or in the areas with poor WiFi coverage. Magnetometer positioning or cellular network does not have such problems but they are not as accurate as localization with WiFi. This paper describes technique that simultaneously employs different localization technologies for enhancing stability and average accuracy of localization. The proposed algorithm is based on fingerprinting method paired with data fusion and prediction algorithms for estimating the object location. The core idea of the algorithm is technology fusion using error estimation methods. For testing accuracy and performance of the algorithm testing simulation environment has been implemented. Significant accuracy improvement was showed in practical scenarios.

Personalized recommendation based on unbiased consistence

NASA Astrophysics Data System (ADS)

Zhu, Xuzhen; Tian, Hui; Zhang, Ping; Hu, Zheng; Zhou, Tao

2015-08-01

Recently, in physical dynamics, mass-diffusion-based recommendation algorithms on bipartite network provide an efficient solution by automatically pushing possible relevant items to users according to their past preferences. However, traditional mass-diffusion-based algorithms just focus on unidirectional mass diffusion from objects having been collected to those which should be recommended, resulting in a biased causal similarity estimation and not-so-good performance. In this letter, we argue that in many cases, a user's interests are stable, and thus bidirectional mass diffusion abilities, no matter originated from objects having been collected or from those which should be recommended, should be consistently powerful, showing unbiased consistence. We further propose a consistence-based mass diffusion algorithm via bidirectional diffusion against biased causality, outperforming the state-of-the-art recommendation algorithms in disparate real data sets, including Netflix, MovieLens, Amazon and Rate Your Music.

Scattering properties of electromagnetic waves from metal object in the lower terahertz region

NASA Astrophysics Data System (ADS)

Chen, Gang; Dang, H. X.; Hu, T. Y.; Su, Xiang; Lv, R. C.; Li, Hao; Tan, X. M.; Cui, T. J.

2018-01-01

An efficient hybrid algorithm is proposed to analyze the electromagnetic scattering properties of metal objects in the lower terahertz (THz) frequency. The metal object can be viewed as perfectly electrical conducting object with a slightly rough surface in the lower THz region. Hence the THz scattered field from metal object can be divided into coherent and incoherent parts. The physical optics and truncated-wedge incremental-length diffraction coefficients methods are combined to compute the coherent part; while the small perturbation method is used for the incoherent part. With the MonteCarlo method, the radar cross section of the rough metal surface is computed by the multilevel fast multipole algorithm and the proposed hybrid algorithm, respectively. The numerical results show that the proposed algorithm has good accuracy to simulate the scattering properties rapidly in the lower THz region.

Experimental and simulation studies of multivariable adaptive optimization of continuous bioreactors using bilevel forgetting factors.

PubMed

Chang, Y K; Lim, H C

1989-08-20

A multivariable on-line adaptive optimization algorithm using a bilevel forgetting factor method was developed and applied to a continuous baker's yeast culture in simulation and experimental studies to maximize the cellular productivity by manipulating the dilution rate and the temperature. The algorithm showed a good optimization speed and a good adaptability and reoptimization capability. The algorithm was able to stably maintain the process around the optimum point for an extended period of time. Two cases were investigated: an unconstrained and a constrained optimization. In the constrained optimization the ethanol concentration was used as an index for the baking quality of yeast cells. An equality constraint with a quadratic penalty was imposed on the ethanol concentration to keep its level close to a hypothetical "optimum" value. The developed algorithm was experimentally applied to a baker's yeast culture to demonstrate its validity. Only unconstrained optimization was carried out experimentally. A set of tuning parameter values was suggested after evaluating the results from several experimental runs. With those tuning parameter values the optimization took 50-90 h. At the attained steady state the dilution rate was 0.310 h(-1) the temperature 32.8 degrees C, and the cellular productivity 1.50 g/L/h.

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

PubMed

Lin, Lanny; Goodrich, Michael A

2014-12-01

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

Rough sets and Laplacian score based cost-sensitive feature selection.

PubMed

Yu, Shenglong; Zhao, Hong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of "good" features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel; Ershov, Maksim; Strotov, Valery

2016-10-01

This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking

NASA Astrophysics Data System (ADS)

Han, Yu-Yan; Gong, Dunwei; Sun, Xiaoyan

2015-07-01

A flow-shop scheduling problem with blocking has important applications in a variety of industrial systems but is underrepresented in the research literature. In this study, a novel discrete artificial bee colony (ABC) algorithm is presented to solve the above scheduling problem with a makespan criterion by incorporating the ABC with differential evolution (DE). The proposed algorithm (DE-ABC) contains three key operators. One is related to the employed bee operator (i.e. adopting mutation and crossover operators of discrete DE to generate solutions with good quality); the second is concerned with the onlooker bee operator, which modifies the selected solutions using insert or swap operators based on the self-adaptive strategy; and the last is for the local search, that is, the insert-neighbourhood-based local search with a small probability is adopted to improve the algorithm's capability in exploitation. The performance of the proposed DE-ABC algorithm is empirically evaluated by applying it to well-known benchmark problems. The experimental results show that the proposed algorithm is superior to the compared algorithms in minimizing the makespan criterion.

Self-recovery fragile watermarking algorithm based on SPHIT

NASA Astrophysics Data System (ADS)

Xin, Li Ping

2015-12-01

A fragile watermark algorithm is proposed, based on SPIHT coding, which can recover the primary image itself. The novelty of the algorithm is that it can tamper location and Self-restoration. The recovery has been very good effect. The first, utilizing the zero-tree structure, the algorithm compresses and encodes the image itself, and then gained self correlative watermark data, so as to greatly reduce the quantity of embedding watermark. Then the watermark data is encoded by error correcting code, and the check bits and watermark bits are scrambled and embedded to enhance the recovery ability. At the same time, by embedding watermark into the latter two bit place of gray level image's bit-plane code, the image after embedded watermark can gain nicer visual effect. The experiment results show that the proposed algorithm may not only detect various processing such as noise adding, cropping, and filtering, but also recover tampered image and realize blind-detection. Peak signal-to-noise ratios of the watermark image were higher than other similar algorithm. The attack capability of the algorithm was enhanced.

A quantum algorithm for obtaining the lowest eigenstate of a Hamiltonian assisted with an ancillary qubit system

NASA Astrophysics Data System (ADS)

Bang, Jeongho; Lee, Seung-Woo; Lee, Chang-Woo; Jeong, Hyunseok

2015-01-01

We propose a quantum algorithm to obtain the lowest eigenstate of any Hamiltonian simulated by a quantum computer. The proposed algorithm begins with an arbitrary initial state of the simulated system. A finite series of transforms is iteratively applied to the initial state assisted with an ancillary qubit. The fraction of the lowest eigenstate in the initial state is then amplified up to 1. We prove that our algorithm can faithfully work for any arbitrary Hamiltonian in the theoretical analysis. Numerical analyses are also carried out. We firstly provide a numerical proof-of-principle demonstration with a simple Hamiltonian in order to compare our scheme with the so-called "Demon-like algorithmic cooling (DLAC)", recently proposed in Xu (Nat Photonics 8:113, 2014). The result shows a good agreement with our theoretical analysis, exhibiting the comparable behavior to the best `cooling' with the DLAC method. We then consider a random Hamiltonian model for further analysis of our algorithm. By numerical simulations, we show that the total number of iterations is proportional to , where is the difference between the two lowest eigenvalues and is an error defined as the probability that the finally obtained system state is in an unexpected (i.e., not the lowest) eigenstate.

In vivo verification of radiation dose delivered to healthy tissue during radiotherapy for breast cancer

NASA Astrophysics Data System (ADS)

Lonski, P.; Taylor, M. L.; Hackworth, W.; Phipps, A.; Franich, R. D.; Kron, T.

2014-03-01

Different treatment planning system (TPS) algorithms calculate radiation dose in different ways. This work compares measurements made in vivo to the dose calculated at out-of-field locations using three different commercially available algorithms in the Eclipse treatment planning system. LiF: Mg, Cu, P thermoluminescent dosimeter (TLD) chips were placed with 1 cm build-up at six locations on the contralateral side of 5 patients undergoing radiotherapy for breast cancer. TLD readings were compared to calculations of Pencil Beam Convolution (PBC), Anisotropic Analytical Algorithm (AAA) and Acuros XB (XB). AAA predicted zero dose at points beyond 16 cm from the field edge. In the same region PBC returned an unrealistically constant result independent of distance and XB showed good agreement to measured data although consistently underestimated by ~0.1 % of the prescription dose. At points closer to the field edge XB was the superior algorithm, exhibiting agreement with TLD results to within 15 % of measured dose. Both AAA and PBC showed mixed agreement, with overall discrepancies considerably greater than XB. While XB is certainly the preferable algorithm, it should be noted that TPS algorithms in general are not designed to calculate dose at peripheral locations and calculation results in such regions should be treated with caution.

Spectral Regularization Algorithms for Learning Large Incomplete Matrices.

PubMed

Mazumder, Rahul; Hastie, Trevor; Tibshirani, Robert

2010-03-01

We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we provide a simple and very efficient convex algorithm for minimizing the reconstruction error subject to a bound on the nuclear norm. Our algorithm Soft-Impute iteratively replaces the missing elements with those obtained from a soft-thresholded SVD. With warm starts this allows us to efficiently compute an entire regularization path of solutions on a grid of values of the regularization parameter. The computationally intensive part of our algorithm is in computing a low-rank SVD of a dense matrix. Exploiting the problem structure, we show that the task can be performed with a complexity linear in the matrix dimensions. Our semidefinite-programming algorithm is readily scalable to large matrices: for example it can obtain a rank-80 approximation of a 10(6) × 10(6) incomplete matrix with 10(5) observed entries in 2.5 hours, and can fit a rank 40 approximation to the full Netflix training set in 6.6 hours. Our methods show very good performance both in training and test error when compared to other competitive state-of-the art techniques.

Spectral Regularization Algorithms for Learning Large Incomplete Matrices

PubMed Central

Mazumder, Rahul; Hastie, Trevor; Tibshirani, Robert

2010-01-01

We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we provide a simple and very efficient convex algorithm for minimizing the reconstruction error subject to a bound on the nuclear norm. Our algorithm Soft-Impute iteratively replaces the missing elements with those obtained from a soft-thresholded SVD. With warm starts this allows us to efficiently compute an entire regularization path of solutions on a grid of values of the regularization parameter. The computationally intensive part of our algorithm is in computing a low-rank SVD of a dense matrix. Exploiting the problem structure, we show that the task can be performed with a complexity linear in the matrix dimensions. Our semidefinite-programming algorithm is readily scalable to large matrices: for example it can obtain a rank-80 approximation of a 106 × 106 incomplete matrix with 105 observed entries in 2.5 hours, and can fit a rank 40 approximation to the full Netflix training set in 6.6 hours. Our methods show very good performance both in training and test error when compared to other competitive state-of-the art techniques. PMID:21552465

Combining constraint satisfaction and local improvement algorithms to construct anaesthetists' rotas

NASA Technical Reports Server (NTRS)

Smith, Barbara M.; Bennett, Sean

1992-01-01

A system is described which was built to compile weekly rotas for the anaesthetists in a large hospital. The rota compilation problem is an optimization problem (the number of tasks which cannot be assigned to an anaesthetist must be minimized) and was formulated as a constraint satisfaction problem (CSP). The forward checking algorithm is used to find a feasible rota, but because of the size of the problem, it cannot find an optimal (or even a good enough) solution in an acceptable time. Instead, an algorithm was devised which makes local improvements to a feasible solution. The algorithm makes use of the constraints as expressed in the CSP to ensure that feasibility is maintained, and produces very good rotas which are being used by the hospital involved in the project. It is argued that formulation as a constraint satisfaction problem may be a good approach to solving discrete optimization problems, even if the resulting CSP is too large to be solved exactly in an acceptable time. A CSP algorithm may be able to produce a feasible solution which can then be improved, giving a good, if not provably optimal, solution.

Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data.

PubMed

Kim, Eun Young; Magnotta, Vincent A; Liu, Dawei; Johnson, Hans J

2014-09-01

Machine learning (ML)-based segmentation methods are a common technique in the medical image processing field. In spite of numerous research groups that have investigated ML-based segmentation frameworks, there remains unanswered aspects of performance variability for the choice of two key components: ML algorithm and intensity normalization. This investigation reveals that the choice of those elements plays a major part in determining segmentation accuracy and generalizability. The approach we have used in this study aims to evaluate relative benefits of the two elements within a subcortical MRI segmentation framework. Experiments were conducted to contrast eight machine-learning algorithm configurations and 11 normalization strategies for our brain MR segmentation framework. For the intensity normalization, a Stable Atlas-based Mapped Prior (STAMP) was utilized to take better account of contrast along boundaries of structures. Comparing eight machine learning algorithms on down-sampled segmentation MR data, it was obvious that a significant improvement was obtained using ensemble-based ML algorithms (i.e., random forest) or ANN algorithms. Further investigation between these two algorithms also revealed that the random forest results provided exceptionally good agreement with manual delineations by experts. Additional experiments showed that the effect of STAMP-based intensity normalization also improved the robustness of segmentation for multicenter data sets. The constructed framework obtained good multicenter reliability and was successfully applied on a large multicenter MR data set (n>3000). Less than 10% of automated segmentations were recommended for minimal expert intervention. These results demonstrate the feasibility of using the ML-based segmentation tools for processing large amount of multicenter MR images. We demonstrated dramatically different result profiles in segmentation accuracy according to the choice of ML algorithm and intensity normalization chosen. Copyright © 2014 Elsevier Inc. All rights reserved.

Resonance assignment of the NMR spectra of disordered proteins using a multi-objective non-dominated sorting genetic algorithm.

PubMed

Yang, Yu; Fritzsching, Keith J; Hong, Mei

2013-11-01

A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra ("good connections"), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra ("bad connections"), and minimizing the number of assigned peaks that have no matching peaks in the other spectra ("edges"). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct assignment for a larger number of residues. On the other hand, when there are multiple equally good assignments that are significantly different from each other, the modified NSGA-II is less efficient than MC/SA in finding all the solutions. This problem is solved by a combined NSGA-II/MC algorithm, which appears to have the advantages of both NSGA-II and MC/SA. This combination algorithm is robust for the three most difficult chemical shift datasets examined here and is expected to give the highest-quality de novo assignment of challenging protein NMR spectra.

Dense-HOG-based drift-reduced 3D face tracking for infant pain monitoring

NASA Astrophysics Data System (ADS)

Saeijs, Ronald W. J. J.; Tjon A Ten, Walther E.; de With, Peter H. N.

2017-03-01

This paper presents a new algorithm for 3D face tracking intended for clinical infant pain monitoring. The algorithm uses a cylinder head model and 3D head pose recovery by alignment of dynamically extracted templates based on dense-HOG features. The algorithm includes extensions for drift reduction, using re-registration in combination with multi-pose state estimation by means of a square-root unscented Kalman filter. The paper reports experimental results on videos of moving infants in hospital who are relaxed or in pain. Results show good tracking behavior for poses up to 50 degrees from upright-frontal. In terms of eye location error relative to inter-ocular distance, the mean tracking error is below 9%.

An Approach to the Use of Depth Cameras for Weed Volume Estimation

PubMed Central

Andújar, Dionisio; Dorado, José; Fernández-Quintanilla, César; Ribeiro, Angela

2016-01-01

The use of depth cameras in precision agriculture is increasing day by day. This type of sensor has been used for the plant structure characterization of several crops. However, the discrimination of small plants, such as weeds, is still a challenge within agricultural fields. Improvements in the new Microsoft Kinect v2 sensor can capture the details of plants. The use of a dual methodology using height selection and RGB (Red, Green, Blue) segmentation can separate crops, weeds, and soil. This paper explores the possibilities of this sensor by using Kinect Fusion algorithms to reconstruct 3D point clouds of weed-infested maize crops under real field conditions. The processed models showed good consistency among the 3D depth images and soil measurements obtained from the actual structural parameters. Maize plants were identified in the samples by height selection of the connected faces and showed a correlation of 0.77 with maize biomass. The lower height of the weeds made RGB recognition necessary to separate them from the soil microrelief of the samples, achieving a good correlation of 0.83 with weed biomass. In addition, weed density showed good correlation with volumetric measurements. The canonical discriminant analysis showed promising results for classification into monocots and dictos. These results suggest that estimating volume using the Kinect methodology can be a highly accurate method for crop status determination and weed detection. It offers several possibilities for the automation of agricultural processes by the construction of a new system integrating these sensors and the development of algorithms to properly process the information provided by them. PMID:27347972

An Approach to the Use of Depth Cameras for Weed Volume Estimation.

PubMed

Andújar, Dionisio; Dorado, José; Fernández-Quintanilla, César; Ribeiro, Angela

2016-06-25

The use of depth cameras in precision agriculture is increasing day by day. This type of sensor has been used for the plant structure characterization of several crops. However, the discrimination of small plants, such as weeds, is still a challenge within agricultural fields. Improvements in the new Microsoft Kinect v2 sensor can capture the details of plants. The use of a dual methodology using height selection and RGB (Red, Green, Blue) segmentation can separate crops, weeds, and soil. This paper explores the possibilities of this sensor by using Kinect Fusion algorithms to reconstruct 3D point clouds of weed-infested maize crops under real field conditions. The processed models showed good consistency among the 3D depth images and soil measurements obtained from the actual structural parameters. Maize plants were identified in the samples by height selection of the connected faces and showed a correlation of 0.77 with maize biomass. The lower height of the weeds made RGB recognition necessary to separate them from the soil microrelief of the samples, achieving a good correlation of 0.83 with weed biomass. In addition, weed density showed good correlation with volumetric measurements. The canonical discriminant analysis showed promising results for classification into monocots and dictos. These results suggest that estimating volume using the Kinect methodology can be a highly accurate method for crop status determination and weed detection. It offers several possibilities for the automation of agricultural processes by the construction of a new system integrating these sensors and the development of algorithms to properly process the information provided by them.

Detection of cracks in shafts with the Approximated Entropy algorithm

NASA Astrophysics Data System (ADS)

Sampaio, Diego Luchesi; Nicoletti, Rodrigo

2016-05-01

The Approximate Entropy is a statistical calculus used primarily in the fields of Medicine, Biology, and Telecommunication for classifying and identifying complex signal data. In this work, an Approximate Entropy algorithm is used to detect cracks in a rotating shaft. The signals of the cracked shaft are obtained from numerical simulations of a de Laval rotor with breathing cracks modelled by the Fracture Mechanics. In this case, one analysed the vertical displacements of the rotor during run-up transients. The results show the feasibility of detecting cracks from 5% depth, irrespective of the unbalance of the rotating system and crack orientation in the shaft. The results also show that the algorithm can differentiate the occurrence of crack only, misalignment only, and crack + misalignment in the system. However, the algorithm is sensitive to intrinsic parameters p (number of data points in a sample vector) and f (fraction of the standard deviation that defines the minimum distance between two sample vectors), and good results are only obtained by appropriately choosing their values according to the sampling rate of the signal.

Experimental validation of an OSEM-type iterative reconstruction algorithm for inverse geometry computed tomography

NASA Astrophysics Data System (ADS)

David, Sabrina; Burion, Steve; Tepe, Alan; Wilfley, Brian; Menig, Daniel; Funk, Tobias

2012-03-01

Iterative reconstruction methods have emerged as a promising avenue to reduce dose in CT imaging. Another, perhaps less well-known, advance has been the development of inverse geometry CT (IGCT) imaging systems, which can significantly reduce the radiation dose delivered to a patient during a CT scan compared to conventional CT systems. Here we show that IGCT data can be reconstructed using iterative methods, thereby combining two novel methods for CT dose reduction. A prototype IGCT scanner was developed using a scanning beam digital X-ray system - an inverse geometry fluoroscopy system with a 9,000 focal spot x-ray source and small photon counting detector. 90 fluoroscopic projections or "superviews" spanning an angle of 360 degrees were acquired of an anthropomorphic phantom mimicking a 1 year-old boy. The superviews were reconstructed with a custom iterative reconstruction algorithm, based on the maximum-likelihood algorithm for transmission tomography (ML-TR). The normalization term was calculated based on flat-field data acquired without a phantom. 15 subsets were used, and a total of 10 complete iterations were performed. Initial reconstructed images showed faithful reconstruction of anatomical details. Good edge resolution and good contrast-to-noise properties were observed. Overall, ML-TR reconstruction of IGCT data collected by a bench-top prototype was shown to be viable, which may be an important milestone in the further development of inverse geometry CT.

A Double-function Digital Watermarking Algorithm Based on Chaotic System and LWT

NASA Astrophysics Data System (ADS)

Yuxia, Zhao; Jingbo, Fan

A double- function digital watermarking technology is studied and a double-function digital watermarking algorithm of colored image is presented based on chaotic system and the lifting wavelet transformation (LWT).The algorithm has realized the double aims of the copyright protection and the integrity authentication of image content. Making use of feature of human visual system (HVS), the watermark image is embedded into the color image's low frequency component and middle frequency components by different means. The algorithm has great security by using two kinds chaotic mappings and Arnold to scramble the watermark image at the same time. The algorithm has good efficiency by using LWT. The emulation experiment indicates the algorithm has great efficiency and security, and the effect of concealing is really good.

A novel artificial bee colony algorithm based on modified search equation and orthogonal learning.

PubMed

Gao, Wei-feng; Liu, San-yang; Huang, Ling-ling

2013-06-01

The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, ABC has an insufficiency regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose an improved ABC method called as CABC where a modified search equation is applied to generate a candidate solution to improve the search ability of ABC. Furthermore, we use the orthogonal experimental design (OED) to form an orthogonal learning (OL) strategy for variant ABCs to discover more useful information from the search experiences. Owing to OED's good character of sampling a small number of well representative combinations for testing, the OL strategy can construct a more promising and efficient candidate solution. In this paper, the OL strategy is applied to three versions of ABC, i.e., the standard ABC, global-best-guided ABC (GABC), and CABC, which yields OABC, OGABC, and OCABC, respectively. The experimental results on a set of 22 benchmark functions demonstrate the effectiveness and efficiency of the modified search equation and the OL strategy. The comparisons with some other ABCs and several state-of-the-art algorithms show that the proposed algorithms significantly improve the performance of ABC. Moreover, OCABC offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all the test functions.

Vehicle routing problem with time windows using natural inspired algorithms

NASA Astrophysics Data System (ADS)

Pratiwi, A. B.; Pratama, A.; Sa’diyah, I.; Suprajitno, H.

2018-03-01

Process of distribution of goods needs a strategy to make the total cost spent for operational activities minimized. But there are several constrains have to be satisfied which are the capacity of the vehicles and the service time of the customers. This Vehicle Routing Problem with Time Windows (VRPTW) gives complex constrains problem. This paper proposes natural inspired algorithms for dealing with constrains of VRPTW which involves Bat Algorithm and Cat Swarm Optimization. Bat Algorithm is being hybrid with Simulated Annealing, the worst solution of Bat Algorithm is replaced by the solution from Simulated Annealing. Algorithm which is based on behavior of cats, Cat Swarm Optimization, is improved using Crow Search Algorithm to make simplier and faster convergence. From the computational result, these algorithms give good performances in finding the minimized total distance. Higher number of population causes better computational performance. The improved Cat Swarm Optimization with Crow Search gives better performance than the hybridization of Bat Algorithm and Simulated Annealing in dealing with big data.

SU-F-T-619: Dose Evaluation of Specific Patient Plans Based On Monte Carlo Algorithm for a CyberKnife Stereotactic Radiosurgery System

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

Piao, J; PLA 302 Hospital, Beijing; Xu, S

2016-06-15

Purpose: This study will use Monte Carlo to simulate the Cyberknife system, and intend to develop the third-party tool to evaluate the dose verification of specific patient plans in TPS. Methods: By simulating the treatment head using the BEAMnrc and DOSXYZnrc software, the comparison between the calculated and measured data will be done to determine the beam parameters. The dose distribution calculated in the Raytracing, Monte Carlo algorithms of TPS (Multiplan Ver4.0.2) and in-house Monte Carlo simulation method for 30 patient plans, which included 10 head, lung and liver cases in each, were analyzed. The γ analysis with the combinedmore » 3mm/3% criteria would be introduced to quantitatively evaluate the difference of the accuracy between three algorithms. Results: More than 90% of the global error points were less than 2% for the comparison of the PDD and OAR curves after determining the mean energy and FWHM.The relative ideal Monte Carlo beam model had been established. Based on the quantitative evaluation of dose accuracy for three algorithms, the results of γ analysis shows that the passing rates (84.88±9.67% for head,98.83±1.05% for liver,98.26±1.87% for lung) of PTV in 30 plans between Monte Carlo simulation and TPS Monte Carlo algorithms were good. And the passing rates (95.93±3.12%,99.84±0.33% in each) of PTV in head and liver plans between Monte Carlo simulation and TPS Ray-tracing algorithms were also good. But the difference of DVHs in lung plans between Monte Carlo simulation and Ray-tracing algorithms was obvious, and the passing rate (51.263±38.964%) of γ criteria was not good. It is feasible that Monte Carlo simulation was used for verifying the dose distribution of patient plans. Conclusion: Monte Carlo simulation algorithm developed in the CyberKnife system of this study can be used as a reference tool for the third-party tool, which plays an important role in dose verification of patient plans. This work was supported in part by the grant from Chinese Natural Science Foundation (Grant No. 11275105). Thanks for the support from Accuray Corp.« less

A Comparison of Three PML Treatments for CAA (and CFD)

NASA Technical Reports Server (NTRS)

Goodrich, John W.

2008-01-01

In this paper we compare three Perfectly Matched Layer (PML) treatments by means of a series of numerical experiments, using common numerical algorithms, computational grids, and code implementations. These comparisons are with the Linearized Euler Equations, for base uniform base flow. We see that there are two very good PML candidates, and that can both control the introduced error. Furthermore, we also show that corners can be handled with essentially no increase in the introduced error, and that with a good PML, the outer boundary is the most significant source of err

Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN

PubMed Central

2017-01-01

Computational scientists have designed many useful algorithms by exploring a biological process or imitating natural evolution. These algorithms can be used to solve engineering optimization problems. Inspired by the change of matter state, we proposed a novel optimization algorithm called differential cloud particles evolution algorithm based on data-driven mechanism (CPDD). In the proposed algorithm, the optimization process is divided into two stages, namely, fluid stage and solid stage. The algorithm carries out the strategy of integrating global exploration with local exploitation in fluid stage. Furthermore, local exploitation is carried out mainly in solid stage. The quality of the solution and the efficiency of the search are influenced greatly by the control parameters. Therefore, the data-driven mechanism is designed for obtaining better control parameters to ensure good performance on numerical benchmark problems. In order to verify the effectiveness of CPDD, numerical experiments are carried out on all the CEC2014 contest benchmark functions. Finally, two application problems of artificial neural network are examined. The experimental results show that CPDD is competitive with respect to other eight state-of-the-art intelligent optimization algorithms. PMID:28761438

The implementation of aerial object recognition algorithm based on contour descriptor in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Babayan, Pavel; Smirnov, Sergey; Strotov, Valery

2017-10-01

This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

MOLA II Laser Transmitter Calibration and Performance. 1.2

NASA Technical Reports Server (NTRS)

Afzal, Robert S.; Smith, David E. (Technical Monitor)

1997-01-01

The goal of the document is to explain the algorithm for determining the laser output energy from the telemetry data within the return packets from MOLA II. A simple algorithm is developed to convert the raw start detector data into laser energy, measured in millijoules. This conversion is dependent on three variables, start detector counts, array heat sink temperature and start detector temperature. All these values are contained within the return packets. The conversion is applied to the GSFC Thermal Vacuum data as well as the in-space data to date and shows good correlation.

Optimization of hydraulic turbine governor parameters based on WPA

NASA Astrophysics Data System (ADS)

Gao, Chunyang; Yu, Xiangyang; Zhu, Yong; Feng, Baohao

2018-01-01

The parameters of hydraulic turbine governor directly affect the dynamic characteristics of the hydraulic unit, thus affecting the regulation capacity and the power quality of power grid. The governor of conventional hydropower unit is mainly PID governor with three adjustable parameters, which are difficult to set up. In order to optimize the hydraulic turbine governor, this paper proposes wolf pack algorithm (WPA) for intelligent tuning since the good global optimization capability of WPA. Compared with the traditional optimization method and PSO algorithm, the results show that the PID controller designed by WPA achieves a dynamic quality of hydraulic system and inhibits overshoot.

Link prediction based on local community properties

NASA Astrophysics Data System (ADS)

Yang, Xu-Hua; Zhang, Hai-Feng; Ling, Fei; Cheng, Zhi; Weng, Guo-Qing; Huang, Yu-Jiao

2016-09-01

The link prediction algorithm is one of the key technologies to reveal the inherent rule of network evolution. This paper proposes a novel link prediction algorithm based on the properties of the local community, which is composed of the common neighbor nodes of any two nodes in the network and the links between these nodes. By referring to the node degree and the condition of assortativity or disassortativity in a network, we comprehensively consider the effect of the shortest path and edge clustering coefficient within the local community on node similarity. We numerically show the proposed method provide good link prediction results.

An experimental SMI adaptive antenna array simulator for weak interfering signals

NASA Technical Reports Server (NTRS)

Dilsavor, Ronald S.; Gupta, Inder J.

1991-01-01

An experimental sample matrix inversion (SMI) adaptive antenna array for suppressing weak interfering signals is described. The experimental adaptive array uses a modified SMI algorithm to increase the interference suppression. In the modified SMI algorithm, the sample covariance matrix is redefined to reduce the effect of thermal noise on the weights of an adaptive array. This is accomplished by subtracting a fraction of the smallest eigenvalue of the original covariance matrix from its diagonal entries. The test results obtained using the experimental system are compared with theoretical results. The two show a good agreement.

UAV Control on the Basis of 3D Landmark Bearing-Only Observations

PubMed Central

Karpenko, Simon; Konovalenko, Ivan; Miller, Alexander; Miller, Boris; Nikolaev, Dmitry

2015-01-01

The article presents an approach to the control of a UAV on the basis of 3D landmark observations. The novelty of the work is the usage of the 3D RANSAC algorithm developed on the basis of the landmarks’ position prediction with the aid of a modified Kalman-type filter. Modification of the filter based on the pseudo-measurements approach permits obtaining unbiased UAV position estimation with quadratic error characteristics. Modeling of UAV flight on the basis of the suggested algorithm shows good performance, even under significant external perturbations. PMID:26633394

Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems

NASA Astrophysics Data System (ADS)

Guo, Weian; Li, Wuzhao; Zhang, Qun; Wang, Lei; Wu, Qidi; Ren, Hongliang

2014-11-01

In evolutionary algorithms, elites are crucial to maintain good features in solutions. However, too many elites can make the evolutionary process stagnate and cannot enhance the performance. This article employs particle swarm optimization (PSO) and biogeography-based optimization (BBO) to propose a hybrid algorithm termed biogeography-based particle swarm optimization (BPSO) which could make a large number of elites effective in searching optima. In this algorithm, the whole population is split into several subgroups; BBO is employed to search within each subgroup and PSO for the global search. Since not all the population is used in PSO, this structure overcomes the premature convergence in the original PSO. Time complexity analysis shows that the novel algorithm does not increase the time consumption. Fourteen numerical benchmarks and four engineering problems with constraints are used to test the BPSO. To better deal with constraints, a fuzzy strategy for the number of elites is investigated. The simulation results validate the feasibility and effectiveness of the proposed algorithm.

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.

Computing and analyzing the sensitivity of MLP due to the errors of the i.i.d. inputs and weights based on CLT.

PubMed

Yang, Sheng-Sung; Ho, Chia-Lu; Siu, Sammy

2010-12-01

In this paper, we propose an algorithm based on the central limit theorem to compute the sensitivity of the multilayer perceptron (MLP) due to the errors of the inputs and weights. For simplicity and practicality, all inputs and weights studied here are independently identically distributed (i.i.d.). The theoretical results derived from the proposed algorithm show that the sensitivity of the MLP is affected by the number of layers and the number of neurons adopted in each layer. To prove the reliability of the proposed algorithm, some experimental results of the sensitivity are also presented, and they match the theoretical ones. The good agreement between the theoretical results and the experimental results verifies the reliability and feasibility of the proposed algorithm. Furthermore, the proposed algorithm can also be applied to compute precisely the sensitivity of the MLP with any available activation functions and any types of i.i.d. inputs and weights.

Integrated scheduling of a container handling system with simultaneous loading and discharging operations

NASA Astrophysics Data System (ADS)

Li, Chen; Lu, Zhiqiang; Han, Xiaole; Zhang, Yuejun; Wang, Li

2016-03-01

The integrated scheduling of container handling systems aims to optimize the coordination and overall utilization of all handling equipment, so as to minimize the makespan of a given set of container tasks. A modified disjunctive graph is proposed and a mixed 0-1 programming model is formulated. A heuristic algorithm is presented, in which the original problem is divided into two subproblems. In the first subproblem, contiguous bay crane operations are applied to obtain a good quay crane schedule. In the second subproblem, proper internal truck and yard crane schedules are generated to match the given quay crane schedule. Furthermore, a genetic algorithm based on the heuristic algorithm is developed to search for better solutions. The computational results show that the proposed algorithm can efficiently find high-quality solutions. They also indicate the effectiveness of simultaneous loading and discharging operations compared with separate ones.

Evaluation of an automated spike-and-wave complex detection algorithm in the EEG from a rat model of absence epilepsy.

PubMed

Bauquier, Sebastien H; Lai, Alan; Jiang, Jonathan L; Sui, Yi; Cook, Mark J

2015-10-01

The aim of this prospective blinded study was to evaluate an automated algorithm for spike-and-wave discharge (SWD) detection applied to EEGs from genetic absence epilepsy rats from Strasbourg (GAERS). Five GAERS underwent four sessions of 20-min EEG recording. Each EEG was manually analyzed for SWDs longer than one second by two investigators and automatically using an algorithm developed in MATLAB®. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the manual (reference) versus the automatic (test) methods. The results showed that the algorithm had specificity, sensitivity, PPV and NPV >94%, comparable to published methods that are based on analyzing EEG changes in the frequency domain. This provides a good alternative as a method designed to mimic human manual marking in the time domain.

A novel speech watermarking algorithm by line spectrum pair modification

NASA Astrophysics Data System (ADS)

Zhang, Qian; Yang, Senbin; Chen, Guang; Zhou, Jun

2011-10-01

To explore digital watermarking specifically suitable for the speech domain, this paper experimentally investigates the properties of line spectrum pair (LSP) parameters firstly. The results show that the differences between contiguous LSPs are robust against common signal processing operations and small modifications of LSPs are imperceptible to the human auditory system (HAS). According to these conclusions, three contiguous LSPs of a speech frame are selected to embed a watermark bit. The middle LSP is slightly altered to modify the differences of these LSPs when embedding watermark. Correspondingly, the watermark is extracted by comparing these differences. The proposed algorithm's transparency is adjustable to meet the needs of different applications. The algorithm has good robustness against additive noise, quantization, amplitude scale and MP3 compression attacks, for the bit error rate (BER) is less than 5%. In addition, the algorithm allows a relatively low capacity, which approximates to 50 bps.

Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT-BIH databases.

PubMed

Carrara, Marta; Carozzi, Luca; Moss, Travis J; de Pasquale, Marco; Cerutti, Sergio; Lake, Douglas E; Moorman, J Randall; Ferrario, Manuela

2015-01-01

Identification of atrial fibrillation (AF) is a clinical imperative. Heartbeat interval time series are increasingly available from personal monitors, allowing new opportunity for AF diagnosis. Previously, we devised numerical algorithms for identification of normal sinus rhythm (NSR), AF, and SR with frequent ectopy using dynamical measures of heart rate. Here, we wished to validate them in the canonical MIT-BIH ECG databases. We tested algorithms on the NSR, AF and arrhythmia databases. When the databases were combined, the positive predictive value of the new algorithms exceeded 95% for NSR and AF, and was 40% for SR with ectopy. Further, dynamical measures did not distinguish atrial from ventricular ectopy. Inspection of individual 24hour records showed good correlation of observed and predicted rhythms. Heart rate dynamical measures are effective ingredients in numerical algorithms to classify cardiac rhythm from the heartbeat intervals time series alone. Copyright © 2015 Elsevier Inc. All rights reserved.

Modified reactive tabu search for the symmetric traveling salesman problems

NASA Astrophysics Data System (ADS)

Lim, Yai-Fung; Hong, Pei-Yee; Ramli, Razamin; Khalid, Ruzelan

2013-09-01

Reactive tabu search (RTS) is an improved method of tabu search (TS) and it dynamically adjusts tabu list size based on how the search is performed. RTS can avoid disadvantage of TS which is in the parameter tuning in tabu list size. In this paper, we proposed a modified RTS approach for solving symmetric traveling salesman problems (TSP). The tabu list size of the proposed algorithm depends on the number of iterations when the solutions do not override the aspiration level to achieve a good balance between diversification and intensification. The proposed algorithm was tested on seven chosen benchmarked problems of symmetric TSP. The performance of the proposed algorithm is compared with that of the TS by using empirical testing, benchmark solution and simple probabilistic analysis in order to validate the quality of solution. The computational results and comparisons show that the proposed algorithm provides a better quality solution than that of the TS.

Particle Swarm Optimization

NASA Technical Reports Server (NTRS)

Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw

2002-01-01

The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.

A statistical framework for evaluating neural networks to predict recurrent events in breast cancer

NASA Astrophysics Data System (ADS)

Gorunescu, Florin; Gorunescu, Marina; El-Darzi, Elia; Gorunescu, Smaranda

2010-07-01

Breast cancer is the second leading cause of cancer deaths in women today. Sometimes, breast cancer can return after primary treatment. A medical diagnosis of recurrent cancer is often a more challenging task than the initial one. In this paper, we investigate the potential contribution of neural networks (NNs) to support health professionals in diagnosing such events. The NN algorithms are tested and applied to two different datasets. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perceptron and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and, finally, the classification performances of all algorithms are statistically robust. Moreover, we have shown that the best performing algorithm will strongly depend on the features of the datasets, and hence, there is not necessarily a single best classifier.

A family of variable step-size affine projection adaptive filter algorithms using statistics of channel impulse response

NASA Astrophysics Data System (ADS)

Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar

2011-12-01

This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.

Analysis of the Dryden Wet Bulb GLobe Temperature Algorithm for White Sands Missile Range

NASA Technical Reports Server (NTRS)

LaQuay, Ryan Matthew

2011-01-01

In locations where workforce is exposed to high relative humidity and light winds, heat stress is a significant concern. Such is the case at the White Sands Missile Range in New Mexico. Heat stress is depicted by the wet bulb globe temperature, which is the official measurement used by the American Conference of Governmental Industrial Hygienists. The wet bulb globe temperature is measured by an instrument which was designed to be portable and needing routine maintenance. As an alternative form for measuring the wet bulb globe temperature, algorithms have been created to calculate the wet bulb globe temperature from basic meteorological observations. The algorithms are location dependent; therefore a specific algorithm is usually not suitable for multiple locations. Due to climatology similarities, the algorithm developed for use at the Dryden Flight Research Center was applied to data from the White Sands Missile Range. A study was performed that compared a wet bulb globe instrument to data from two Surface Atmospheric Measurement Systems that was applied to the Dryden wet bulb globe temperature algorithm. The period of study was from June to September of2009, with focus being applied from 0900 to 1800, local time. Analysis showed that the algorithm worked well, with a few exceptions. The algorithm becomes less accurate to the measurement when the dew point temperature is over 10 Celsius. Cloud cover also has a significant effect on the measured wet bulb globe temperature. The algorithm does not show red and black heat stress flags well due to shorter time scales of such events. The results of this study show that it is plausible that the Dryden Flight Research wet bulb globe temperature algorithm is compatible with the White Sands Missile Range, except for when there are increased dew point temperatures and cloud cover or precipitation. During such occasions, the wet bulb globe temperature instrument would be the preferred method of measurement. Out of the 30 dates examined, 23 fell under the category of having good accuracy.

Adaptive algorithm of magnetic heading detection

NASA Astrophysics Data System (ADS)

Liu, Gong-Xu; Shi, Ling-Feng

2017-11-01

Magnetic data obtained from a magnetic sensor usually fluctuate in a certain range, which makes it difficult to estimate the magnetic heading accurately. In fact, magnetic heading information is usually submerged in noise because of all kinds of electromagnetic interference and the diversity of the pedestrian’s motion states. In order to solve this problem, a new adaptive algorithm based on the (typically) right-angled corridors of a building or residential buildings is put forward to process heading information. First, a 3D indoor localization platform is set up based on MPU9250. Then, several groups of data are measured by changing the experimental environment and pedestrian’s motion pace. The raw data from the attached inertial measurement unit are calibrated and arranged into a time-stamped array and written to a data file. Later, the data file is imported into MATLAB for processing and analysis using the proposed adaptive algorithm. Finally, the algorithm is verified by comparison with the existing algorithm. The experimental results show that the algorithm has strong robustness and good fault tolerance, which can detect the heading information accurately and in real-time.

An imperialist competitive algorithm for virtual machine placement in cloud computing

NASA Astrophysics Data System (ADS)

Jamali, Shahram; Malektaji, Sepideh; Analoui, Morteza

2017-05-01

Cloud computing, the recently emerged revolution in IT industry, is empowered by virtualisation technology. In this paradigm, the user's applications run over some virtual machines (VMs). The process of selecting proper physical machines to host these virtual machines is called virtual machine placement. It plays an important role on resource utilisation and power efficiency of cloud computing environment. In this paper, we propose an imperialist competitive-based algorithm for the virtual machine placement problem called ICA-VMPLC. The base optimisation algorithm is chosen to be ICA because of its ease in neighbourhood movement, good convergence rate and suitable terminology. The proposed algorithm investigates search space in a unique manner to efficiently obtain optimal placement solution that simultaneously minimises power consumption and total resource wastage. Its final solution performance is compared with several existing methods such as grouping genetic and ant colony-based algorithms as well as bin packing heuristic. The simulation results show that the proposed method is superior to other tested algorithms in terms of power consumption, resource wastage, CPU usage efficiency and memory usage efficiency.

Peak-Seeking Optimization of Trim for Reduced Fuel Consumption: Flight-Test Results

NASA Technical Reports Server (NTRS)

Brown, Nelson Andrew; Schaefer, Jacob Robert

2013-01-01

A peak-seeking control algorithm for real-time trim optimization for reduced fuel consumption has been developed by researchers at the National Aeronautics and Space Administration (NASA) Dryden Flight Research Center to address the goals of the NASA Environmentally Responsible Aviation project to reduce fuel burn and emissions. The peak-seeking control algorithm is based on a steepest-descent algorithm using a time-varying Kalman filter to estimate the gradient of a performance function of fuel flow versus control surface positions. In real-time operation, deflections of symmetric ailerons, trailing-edge flaps, and leading-edge flaps of an F/A-18 airplane (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) are used for optimization of fuel flow. Results from six research flights are presented herein. The optimization algorithm found a trim configuration that required approximately 3 percent less fuel flow than the baseline trim at the same flight condition. The algorithm consistently rediscovered the solution from several initial conditions. These results show that the algorithm has good performance in a relevant environment.

An image hiding method based on cascaded iterative Fourier transform and public-key encryption algorithm

NASA Astrophysics Data System (ADS)

Zhang, B.; Sang, Jun; Alam, Mohammad S.

2013-03-01

An image hiding method based on cascaded iterative Fourier transform and public-key encryption algorithm was proposed. Firstly, the original secret image was encrypted into two phase-only masks M1 and M2 via cascaded iterative Fourier transform (CIFT) algorithm. Then, the public-key encryption algorithm RSA was adopted to encrypt M2 into M2' . Finally, a host image was enlarged by extending one pixel into 2×2 pixels and each element in M1 and M2' was multiplied with a superimposition coefficient and added to or subtracted from two different elements in the 2×2 pixels of the enlarged host image. To recover the secret image from the stego-image, the two masks were extracted from the stego-image without the original host image. By applying public-key encryption algorithm, the key distribution was facilitated, and also compared with the image hiding method based on optical interference, the proposed method may reach higher robustness by employing the characteristics of the CIFT algorithm. Computer simulations show that this method has good robustness against image processing.

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.

Peak-Seeking Optimization of Trim for Reduced Fuel Consumption: Flight-test Results

NASA Technical Reports Server (NTRS)

Brown, Nelson Andrew; Schaefer, Jacob Robert

2013-01-01

A peak-seeking control algorithm for real-time trim optimization for reduced fuel consumption has been developed by researchers at the National Aeronautics and Space Administration (NASA) Dryden Flight Research Center to address the goals of the NASA Environmentally Responsible Aviation project to reduce fuel burn and emissions. The peak-seeking control algorithm is based on a steepest-descent algorithm using a time-varying Kalman filter to estimate the gradient of a performance function of fuel flow versus control surface positions. In real-time operation, deflections of symmetric ailerons, trailing-edge flaps, and leading-edge flaps of an F/A-18 airplane (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) are used for optimization of fuel flow. Results from six research flights are presented herein. The optimization algorithm found a trim configuration that required approximately 3 percent less fuel flow than the baseline trim at the same flight condition. The algorithm consistently rediscovered the solution from several initial conditions. These results show that the algorithm has good performance in a relevant environment.

Reduction from cost-sensitive ordinal ranking to weighted binary classification.

PubMed

Lin, Hsuan-Tien; Li, Ling

2012-05-01

We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary classifier upper-bounds the mislabeling cost of the ranker, both error-wise and regret-wise. Our framework allows not only the design of good ordinal ranking algorithms based on well-tuned binary classification approaches, but also the derivation of new generalization bounds for ordinal ranking from known bounds for binary classification. In addition, our framework unifies many existing ordinal ranking algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms. In addition, the newly designed algorithms lead to better cost-sensitive ordinal ranking performance, as well as improved listwise ranking performance.

Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors

NASA Astrophysics Data System (ADS)

Chen, Xiang; Li, Jingchao; Han, Hui; Ying, Yulong

2018-05-01

Because of the limitations of the traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm is proposed. First, the radiation source signal was preprocessed, and a Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of the signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without the Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors formed the multi-dimensional eigenvectors as signal subtle features, which were used for radiation source signal recognition by the grey relation algorithm. The experimental results show that, compared with the traditional fractal box-counting dimension algorithm and the single improved fractal box-counting dimension algorithm, the proposed dual improved fractal box-counting dimension algorithm can better extract the signal subtle distribution characteristics under different reconstruction phase space, and has a better recognition effect with good real-time performance.

A Comparison of Three Algorithms for Orion Drogue Parachute Release

NASA Technical Reports Server (NTRS)

Matz, Daniel A.; Braun, Robert D.

2015-01-01

The Orion Multi-Purpose Crew Vehicle is susceptible to ipping apex forward between drogue parachute release and main parachute in ation. A smart drogue release algorithm is required to select a drogue release condition that will not result in an apex forward main parachute deployment. The baseline algorithm is simple and elegant, but does not perform as well as desired in drogue failure cases. A simple modi cation to the baseline algorithm can improve performance, but can also sometimes fail to identify a good release condition. A new algorithm employing simpli ed rotational dynamics and a numeric predictor to minimize a rotational energy metric is proposed. A Monte Carlo analysis of a drogue failure scenario is used to compare the performance of the algorithms. The numeric predictor prevents more of the cases from ipping apex forward, and also results in an improvement in the capsule attitude at main bag extraction. The sensitivity of the numeric predictor to aerodynamic dispersions, errors in the navigated state, and execution rate is investigated, showing little degradation in performance.

Edge-oriented dual-dictionary guided enrichment (EDGE) for MRI-CT image reconstruction.

PubMed

Li, Liang; Wang, Bigong; Wang, Ge

2016-01-01

In this paper, we formulate the joint/simultaneous X-ray CT and MRI image reconstruction. In particular, a novel algorithm is proposed for MRI image reconstruction from highly under-sampled MRI data and CT images. It consists of two steps. First, a training dataset is generated from a series of well-registered MRI and CT images on the same patients. Then, an initial MRI image of a patient can be reconstructed via edge-oriented dual-dictionary guided enrichment (EDGE) based on the training dataset and a CT image of the patient. Second, an MRI image is reconstructed using the dictionary learning (DL) algorithm from highly under-sampled k-space data and the initial MRI image. Our algorithm can establish a one-to-one correspondence between the two imaging modalities, and obtain a good initial MRI estimation. Both noise-free and noisy simulation studies were performed to evaluate and validate the proposed algorithm. The results with different under-sampling factors show that the proposed algorithm performed significantly better than those reconstructed using the DL algorithm from MRI data alone.

Research on the Improved Image Dodging Algorithm Based on Mask Technique

NASA Astrophysics Data System (ADS)

Yao, F.; Hu, H.; Wan, Y.

2012-08-01

The remote sensing image dodging algorithm based on Mask technique is a good method for removing the uneven lightness within a single image. However, there are some problems with this algorithm, such as how to set an appropriate filter size, for which there is no good solution. In order to solve these problems, an improved algorithm is proposed. In this improved algorithm, the original image is divided into blocks, and then the image blocks with different definitions are smoothed using the low-pass filters with different cut-off frequencies to get the background image; for the image after subtraction, the regions with different lightness are processed using different linear transformation models. The improved algorithm can get a better dodging result than the original one, and can make the contrast of the whole image more consistent.

Chaotic CDMA watermarking algorithm for digital image in FRFT domain

NASA Astrophysics Data System (ADS)

Liu, Weizhong; Yang, Wentao; Feng, Zhuoming; Zou, Xuecheng

2007-11-01

A digital image-watermarking algorithm based on fractional Fourier transform (FRFT) domain is presented by utilizing chaotic CDMA technique in this paper. As a popular and typical transmission technique, CDMA has many advantages such as privacy, anti-jamming and low power spectral density, which can provide robustness against image distortions and malicious attempts to remove or tamper with the watermark. A super-hybrid chaotic map, with good auto-correlation and cross-correlation characteristics, is adopted to produce many quasi-orthogonal codes (QOC) that can replace the periodic PN-code used in traditional CDAM system. The watermarking data is divided into a lot of segments that correspond to different chaotic QOC respectively and are modulated into the CDMA watermarking data embedded into low-frequency amplitude coefficients of FRFT domain of the cover image. During watermark detection, each chaotic QOC extracts its corresponding watermarking segment by calculating correlation coefficients between chaotic QOC and watermarked data of the detected image. The CDMA technique not only can enhance the robustness of watermark but also can compress the data of the modulated watermark. Experimental results show that the watermarking algorithm has good performances in three aspects: better imperceptibility, anti-attack robustness and security.

Solution Path for Pin-SVM Classifiers With Positive and Negative $\\tau $ Values.

PubMed

Huang, Xiaolin; Shi, Lei; Suykens, Johan A K

2017-07-01

Applying the pinball loss in a support vector machine (SVM) classifier results in pin-SVM. The pinball loss is characterized by a parameter τ . Its value is related to the quantile level and different τ values are suitable for different problems. In this paper, we establish an algorithm to find the entire solution path for pin-SVM with different τ values. This algorithm is based on the fact that the optimal solution to pin-SVM is continuous and piecewise linear with respect to τ . We also show that the nonnegativity constraint on τ is not necessary, i.e., τ can be extended to negative values. First, in some applications, a negative τ leads to better accuracy. Second, τ = -1 corresponds to a simple solution that links SVM and the classical kernel rule. The solution for τ = -1 can be obtained directly and then be used as a starting point of the solution path. The proposed method efficiently traverses τ values through the solution path, and then achieves good performance by a suitable τ . In particular, τ = 0 corresponds to C-SVM, meaning that the traversal algorithm can output a result at least as good as C-SVM with respect to validation error.

Node Redeployment Algorithm Based on Stratified Connected Tree for Underwater Sensor Networks

PubMed Central

Liu, Jun; Jiang, Peng; Wu, Feng; Yu, Shanen; Song, Chunyue

2016-01-01

During the underwater sensor networks (UWSNs) operation, node drift with water environment causes network topology changes. Periodic node location examination and adjustment are needed to maintain good network monitoring quality as long as possible. In this paper, a node redeployment algorithm based on stratified connected tree for UWSNs is proposed. At every network adjustment moment, self-examination and adjustment on node locations are performed firstly. If a node is outside the monitored space, it returns to the last location recorded in its memory along straight line. Later, the network topology is stratified into a connected tree that takes the sink node as the root node by broadcasting ready information level by level, which can improve the network connectivity rate. Finally, with synthetically considering network coverage and connectivity rates, and node movement distance, the sink node performs centralized optimization on locations of leaf nodes in the stratified connected tree. Simulation results show that the proposed redeployment algorithm can not only keep the number of nodes in the monitored space as much as possible and maintain good network coverage and connectivity rates during network operation, but also reduce node movement distance during node redeployment and prolong the network lifetime. PMID:28029124

Evaluation of automatic cloud removal method for high elevation areas in Landsat 8 OLI images to improve environmental indexes computation

NASA Astrophysics Data System (ADS)

Alvarez, César I.; Teodoro, Ana; Tierra, Alfonso

2017-10-01

Thin clouds in the optical remote sensing data are frequent and in most of the cases don't allow to have a pure surface data in order to calculate some indexes as Normalized Difference Vegetation Index (NDVI). This paper aims to evaluate the Automatic Cloud Removal Method (ACRM) algorithm over a high elevation city like Quito (Ecuador), with an altitude of 2800 meters above sea level, where the clouds are presented all the year. The ACRM is an algorithm that considers a linear regression between each Landsat 8 OLI band and the Cirrus band using the slope obtained with the linear regression established. This algorithm was employed without any reference image or mask to try to remove the clouds. The results of the application of the ACRM algorithm over Quito didn't show a good performance. Therefore, was considered improving this algorithm using a different slope value data (ACMR Improved). After, the NDVI computation was compared with a reference NDVI MODIS data (MOD13Q1). The ACMR Improved algorithm had a successful result when compared with the original ACRM algorithm. In the future, this Improved ACRM algorithm needs to be tested in different regions of the world with different conditions to evaluate if the algorithm works successfully for all conditions.

A new bio-optical algorithm for the remote sensing of algal blooms in complex ocean waters

NASA Astrophysics Data System (ADS)

Shanmugam, Palanisamy

2011-04-01

A new bio-optical algorithm has been developed to provide accurate assessments of chlorophyll a (Chl a) concentration for detection and mapping of algal blooms from satellite data in optically complex waters, where the presence of suspended sediments and dissolved substances can interfere with phytoplankton signal and thus confound conventional band ratio algorithms. A global data set of concurrent measurements of pigment concentration and radiometric reflectance was compiled and used to develop this algorithm that uses the normalized water-leaving radiance ratios along with an algal bloom index (ABI) between three visible bands to determine Chl a concentrations. The algorithm is derived using Sea-viewing Wide Field-of-view Sensor bands, and it is subsequently tuned to be applicable to Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua data. When compared with large in situ data sets and satellite matchups in a variety of coastal and ocean waters the present algorithm makes good retrievals of the Chl a concentration and shows statistically significant improvement over current global algorithms (e.g., OC3 and OC4v4). An examination of the performance of these algorithms on several MODIS/Aqua images in complex waters of the Arabian Sea and west Florida shelf shows that the new algorithm provides a better means for detecting and differentiating algal blooms from other turbid features, whereas the OC3 algorithm has significant errors although yielding relatively consistent results in clear waters. These findings imply that, provided that an accurate atmospheric correction scheme is available to deal with complex waters, the current MODIS/Aqua, MERIS and OCM data could be extensively used for quantitative and operational monitoring of algal blooms in various regional and global waters.

A Double Perturbation Method for Reducing Dynamical Degradation of the Digital Baker Map

NASA Astrophysics Data System (ADS)

Liu, Lingfeng; Lin, Jun; Miao, Suoxia; Liu, Bocheng

2017-06-01

The digital Baker map is widely used in different kinds of cryptosystems, especially for image encryption. However, any chaotic map which is realized on the finite precision device (e.g. computer) will suffer from dynamical degradation, which refers to short cycle lengths, low complexity and strong correlations. In this paper, a novel double perturbation method is proposed for reducing the dynamical degradation of the digital Baker map. Both state variables and system parameters are perturbed by the digital logistic map. Numerical experiments show that the perturbed Baker map can achieve good statistical and cryptographic properties. Furthermore, a new image encryption algorithm is provided as a simple application. With a rather simple algorithm, the encrypted image can achieve high security, which is competitive to the recently proposed image encryption algorithms.

An unsupervised classification scheme for improving predictions of prokaryotic TIS.

PubMed

Tech, Maike; Meinicke, Peter

2006-03-09

Although it is not difficult for state-of-the-art gene finders to identify coding regions in prokaryotic genomes, exact prediction of the corresponding translation initiation sites (TIS) is still a challenging problem. Recently a number of post-processing tools have been proposed for improving the annotation of prokaryotic TIS. However, inherent difficulties of these approaches arise from the considerable variation of TIS characteristics across different species. Therefore prior assumptions about the properties of prokaryotic gene starts may cause suboptimal predictions for newly sequenced genomes with TIS signals differing from those of well-investigated genomes. We introduce a clustering algorithm for completely unsupervised scoring of potential TIS, based on positionally smoothed probability matrices. The algorithm requires an initial gene prediction and the genomic sequence of the organism to perform the reannotation. As compared with other methods for improving predictions of gene starts in bacterial genomes, our approach is not based on any specific assumptions about prokaryotic TIS. Despite the generality of the underlying algorithm, the prediction rate of our method is competitive on experimentally verified test data from E. coli and B. subtilis. Regarding genomes with high G+C content, in contrast to some previously proposed methods, our algorithm also provides good performance on P. aeruginosa, B. pseudomallei and R. solanacearum. On reliable test data we showed that our method provides good results in post-processing the predictions of the widely-used program GLIMMER. The underlying clustering algorithm is robust with respect to variations in the initial TIS annotation and does not require specific assumptions about prokaryotic gene starts. These features are particularly useful on genomes with high G+C content. The algorithm has been implemented in the tool "TICO" (TIs COrrector) which is publicly available from our web site.

Mapping Snow Grain Size over Greenland from MODIS

NASA Technical Reports Server (NTRS)

Lyapustin, Alexei; Tedesco, Marco; Wang, Yujie; Kokhanovsky, Alexander

2008-01-01

This paper presents a new automatic algorithm to derive optical snow grain size (SGS) at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Differently from previous approaches, snow grains are not assumed to be spherical but a fractal approach is used to account for their irregular shape. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived SGS shows a good sensitivity to snow metamorphism, including melting and snow precipitation events. Preprocessing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask (CM) is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust performance of CM algorithm in discrimination of clouds over bright snow and ice. As part of the validation analysis, SGS derived from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM\\I radiometer, which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS SGS was compared with predictions of the snow model CROCUS driven by measurements of the automatic whether stations of the Greenland Climate Network. We found that CROCUS grain size is on average a factor of two larger than MODIS-derived SGS. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of SGS for four permanent sites, the paper presents SGS maps over the Greenland ice sheet for the March-September period of 2004.

Data quality system using reference dictionaries and edit distance algorithms

NASA Astrophysics Data System (ADS)

Karbarz, Radosław; Mulawka, Jan

2015-09-01

The real art of management it is important to make smart decisions, what in most of the cases is not a trivial task. Those decisions may lead to determination of production level, funds allocation for investments etc. Most of the parameters in decision-making process such as: interest rate, goods value or exchange rate may change. It is well know that these parameters in the decision-making are based on the data contained in datamarts or data warehouse. However, if the information derived from the processed data sets is the basis for the most important management decisions, it is required that the data is accurate, complete and current. In order to achieve high quality data and to gain from them measurable business benefits, data quality system should be used. The article describes the approach to the problem, shows the algorithms in details and their usage. Finally the test results are provide. Test results show the best algorithms (in terms of quality and quantity) for different parameters and data distribution.

Studies of the Antarctic Sea Ice Edges and Ice Extents from Satellite and Ship Observations

NASA Technical Reports Server (NTRS)

Worby, Anthony P.; Comiso, Josefino C.

2003-01-01

Passive-microwave derived ice edge locations in Antarctica are assessed against other satellite data as well as in situ observations of ice edge location made between 1989 and 2000. The passive microwave data generally agree with satellite and ship data but the ice concentration at the observed ice edge varies greatly with averages of 14% for the TEAM algorithm and 19% for the Bootstrap algorithm. The comparisons of passive microwave with the field data show that in the ice growth season (March - October) the agreement is extremely good, with r(sup 2) values of 0.9967 and 0.9797 for the Bootstrap and TEAM algorithms respectively. In the melt season however (November - February) the passive microwave ice edge is typically 1-2 degrees south of the observations due to the low concentration and saturated nature of the ice. Sensitivity studies show that these results can have significant impact on trend and mass balance studies of the sea ice cover in the Southern Ocean.

Single snapshot DOA estimation

NASA Astrophysics Data System (ADS)

Häcker, P.; Yang, B.

2010-10-01

In array signal processing, direction of arrival (DOA) estimation has been studied for decades. Many algorithms have been proposed and their performance has been studied thoroughly. Yet, most of these works are focused on the asymptotic case of a large number of snapshots. In automotive radar applications like driver assistance systems, however, only a small number of snapshots of the radar sensor array or, in the worst case, a single snapshot is available for DOA estimation. In this paper, we investigate and compare different DOA estimators with respect to their single snapshot performance. The main focus is on the estimation accuracy and the angular resolution in multi-target scenarios including difficult situations like correlated targets and large target power differences. We will show that some algorithms lose their ability to resolve targets or do not work properly at all. Other sophisticated algorithms do not show a superior performance as expected. It turns out that the deterministic maximum likelihood estimator is a good choice under these hard conditions.

The research of automatic speed control algorithm based on Green CBTC

NASA Astrophysics Data System (ADS)

Lin, Ying; Xiong, Hui; Wang, Xiaoliang; Wu, Youyou; Zhang, Chuanqi

2017-06-01

Automatic speed control algorithm is one of the core technologies of train operation control system. It’s a typical multi-objective optimization control algorithm, which achieve the train speed control for timing, comfort, energy-saving and precise parking. At present, the train speed automatic control technology is widely used in metro and inter-city railways. It has been found that the automatic speed control technology can effectively reduce the driver’s intensity, and improve the operation quality. However, the current used algorithm is poor at energy-saving, even not as good as manual driving. In order to solve the problem of energy-saving, this paper proposes an automatic speed control algorithm based on Green CBTC system. Based on the Green CBTC system, the algorithm can adjust the operation status of the train to improve the efficient using rate of regenerative braking feedback energy while ensuring the timing, comfort and precise parking targets. Due to the reason, the energy-using of Green CBTC system is lower than traditional CBTC system. The simulation results show that the algorithm based on Green CBTC system can effectively reduce the energy-using due to the improvement of the using rate of regenerative braking feedback energy.

A semi-active suspension control algorithm for vehicle comprehensive vertical dynamics performance

NASA Astrophysics Data System (ADS)

Nie, Shida; Zhuang, Ye; Liu, Weiping; Chen, Fan

2017-08-01

Comprehensive performance of the vehicle, including ride qualities and road-holding, is essentially of great value in practice. Many up-to-date semi-active control algorithms improve vehicle dynamics performance effectively. However, it is hard to improve comprehensive performance for the conflict between ride qualities and road-holding around the second-order resonance. Hence, a new control algorithm is proposed to achieve a good trade-off between ride qualities and road-holding. In this paper, the properties of the invariant points are analysed, which gives an insight into the performance conflicting around the second-order resonance. Based on it, a new control algorithm is proposed. The algorithm employs a novel frequency selector to balance suspension ride and handling performance by adopting a medium damping around the second-order resonance. The results of this study show that the proposed control algorithm could improve the performance of ride qualities and suspension working space up to 18.3% and 8.2%, respectively, with little loss of road-holding compared to the passive suspension. Consequently, the comprehensive performance can be improved by 6.6%. Hence, the proposed algorithm is of great potential to be implemented in practice.

[Surgical treatment of gynecomastia: an algorithm].

PubMed

Wolter, A; Scholz, T; Diedrichson, J; Liebau, J

2013-04-01

Gynecomastia is a persistent benign uni- or bilateral enlargement of the male breast ranging from small to excessive findings with marked skin redundancy. In this paper we introduce an algorithm to facilitate the selection of the appropriate surgical technique according to the presented morphological aspects. The records of 118 patients (217 breasts) with gynecomastia from 01/2009 to 08/2012 were retrospectively reviewed. The authors conducted three different surgical techniques depending on four severity grades. The outcome parameters complication rate, patient satisfaction with the aesthetic result, nipple sensitivity and the need to re-operate were observed and related to the employed technique. In 167 (77%) breasts with moderate breast enlargement without skin redundancy (Grade I-IIa by Simon's classification) a subcutaneous semicircular periareolar mastectomy was performed in combination with water-jet assisted liposuction. In 40 (18%) breasts with skin redundancy (Grade IIb) a circumferential mastopexy was performed additionally. An inferior pedicled mammaplasty was used in 10 (5%) severe cases (Grade III). Complication rate was 4.1%. Surgical corrections were necessary in 17 breasts (7.8%). The patient survey revealed a high satisfaction level: 88% of the patients rated the aesthetic results as "very good" or "good", nipple sensitivity was rated as "very good" or "good" by 83%. Surgical treatment of gynecomastia should ensure minimal scarring while respecting the aesthetic unit. The selection of the appropriate surgical method depends on the severity grade, the presence of skin redundancy and the volume of the male breast glandular tissue. The presented algorithm rarely leads to complications, is simple to perform and shows a high satisfaction rate and a preservation of the nipple sensitivity. © Georg Thieme Verlag KG Stuttgart · New York.

Markov Dynamics as a Zooming Lens for Multiscale Community Detection: Non Clique-Like Communities and the Field-of-View Limit

PubMed Central

Schaub, Michael T.; Delvenne, Jean-Charles; Yaliraki, Sophia N.; Barahona, Mauricio

2012-01-01

In recent years, there has been a surge of interest in community detection algorithms for complex networks. A variety of computational heuristics, some with a long history, have been proposed for the identification of communities or, alternatively, of good graph partitions. In most cases, the algorithms maximize a particular objective function, thereby finding the ‘right’ split into communities. Although a thorough comparison of algorithms is still lacking, there has been an effort to design benchmarks, i.e., random graph models with known community structure against which algorithms can be evaluated. However, popular community detection methods and benchmarks normally assume an implicit notion of community based on clique-like subgraphs, a form of community structure that is not always characteristic of real networks. Specifically, networks that emerge from geometric constraints can have natural non clique-like substructures with large effective diameters, which can be interpreted as long-range communities. In this work, we show that long-range communities escape detection by popular methods, which are blinded by a restricted ‘field-of-view’ limit, an intrinsic upper scale on the communities they can detect. The field-of-view limit means that long-range communities tend to be overpartitioned. We show how by adopting a dynamical perspective towards community detection [1], [2], in which the evolution of a Markov process on the graph is used as a zooming lens over the structure of the network at all scales, one can detect both clique- or non clique-like communities without imposing an upper scale to the detection. Consequently, the performance of algorithms on inherently low-diameter, clique-like benchmarks may not always be indicative of equally good results in real networks with local, sparser connectivity. We illustrate our ideas with constructive examples and through the analysis of real-world networks from imaging, protein structures and the power grid, where a multiscale structure of non clique-like communities is revealed. PMID:22384178

SU-E-T-516: Dosimetric Validation of AcurosXB Algorithm in Comparison with AAA & CCC Algorithms for VMAT Technique.

PubMed

Kathirvel, M; Subramanian, V Sai; Arun, G; Thirumalaiswamy, S; Ramalingam, K; Kumar, S Ashok; Jagadeesh, K

2012-06-01

To dosimetrically validate AcurosXB algorithm for Volumetric Modulated Arc Therapy (VMAT) in comparison with standard clinical Anisotropic Analytic Algorithm(AAA) and Collapsed Cone Convolution(CCC) dose calculation algorithms. AcurosXB dose calculation algorithm is available with Varian Eclipse treatment planning system (V10). It uses grid-based Boltzmann equation solver to predict dose precisely in lesser time. This study was made to realize algorithms ability to predict dose accurately as its delivery for which five clinical cases each of Brain, Head&Neck, Thoracic, Pelvic and SBRT were taken. Verification plans were created on multicube phantom with iMatrixx-2D detector array and then dose prediction was done with AcurosXB, AAA & CCC (COMPASS System) algorithm and the same were delivered onto CLINAC-iX treatment machine. Delivered dose was captured in iMatrixx plane for all 25 plans. Measured dose was taken as reference to quantify the agreement between AcurosXB calculation algorithm against previously validated AAA and CCC algorithm. Gamma evaluation was performed with clinical criteria distance-to-agreement 3&2mm and dose difference 3&2% in omnipro-I'MRT software. Plans were evaluated in terms of correlation coefficient, quantitative area gamma and average gamma. Study shows good agreement between mean correlation 0.9979±0.0012, 0.9984±0.0009 & 0.9979±0.0011 for AAA, CCC & Acuros respectively. Mean area gamma for criteria 3mm/3% was found to be 98.80±1.04, 98.14±2.31, 98.08±2.01 and 2mm/2% was found to be 93.94±3.83, 87.17±10.54 & 92.36±5.46 for AAA, CCC & Acuros respectively. Mean average gamma for 3mm/3% was 0.26±0.07, 0.42±0.08, 0.28±0.09 and 2mm/2% was found to be 0.39±0.10, 0.64±0.11, 0.42±0.13 for AAA, CCC & Acuros respectively. This study demonstrated that the AcurosXB algorithm had a good agreement with the AAA & CCC in terms of dose prediction. In conclusion AcurosXB algorithm provides a valid, accurate and speedy alternative to AAA and CCC algorithms in a busy clinical environment. © 2012 American Association of Physicists in Medicine.

Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM

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

Lin, Jian; Hamidouche, Khaled; Zheng, Jie

2015-08-05

Machine Learning algorithms are benefiting from the continuous improvement of programming models, including MPI, MapReduce and PGAS. k-Nearest Neighbors (k-NN) algorithm is a widely used machine learning algorithm, applied to supervised learning tasks such as classification. Several parallel implementations of k-NN have been proposed in the literature and practice. However, on high-performance computing systems with high-speed interconnects, it is important to further accelerate existing designs of the k-NN algorithm through taking advantage of scalable programming models. To improve the performance of k-NN on large-scale environment with InfiniBand network, this paper proposes several alternative hybrid MPI+OpenSHMEM designs and performs a systemicmore » evaluation and analysis on typical workloads. The hybrid designs leverage the one-sided memory access to better overlap communication with computation than the existing pure MPI design, and propose better schemes for efficient buffer management. The implementation based on k-NN program from MaTEx with MVAPICH2-X (Unified MPI+PGAS Communication Runtime over InfiniBand) shows up to 9.0% time reduction for training KDD Cup 2010 workload over 512 cores, and 27.6% time reduction for small workload with balanced communication and computation. Experiments of running with varied number of cores show that our design can maintain good scalability.« less

Reverse engineering a gene network using an asynchronous parallel evolution strategy

PubMed Central

2010-01-01

Background The use of reverse engineering methods to infer gene regulatory networks by fitting mathematical models to gene expression data is becoming increasingly popular and successful. However, increasing model complexity means that more powerful global optimisation techniques are required for model fitting. The parallel Lam Simulated Annealing (pLSA) algorithm has been used in such approaches, but recent research has shown that island Evolutionary Strategies can produce faster, more reliable results. However, no parallel island Evolutionary Strategy (piES) has yet been demonstrated to be effective for this task. Results Here, we present synchronous and asynchronous versions of the piES algorithm, and apply them to a real reverse engineering problem: inferring parameters in the gap gene network. We find that the asynchronous piES exhibits very little communication overhead, and shows significant speed-up for up to 50 nodes: the piES running on 50 nodes is nearly 10 times faster than the best serial algorithm. We compare the asynchronous piES to pLSA on the same test problem, measuring the time required to reach particular levels of residual error, and show that it shows much faster convergence than pLSA across all optimisation conditions tested. Conclusions Our results demonstrate that the piES is consistently faster and more reliable than the pLSA algorithm on this problem, and scales better with increasing numbers of nodes. In addition, the piES is especially well suited to further improvements and adaptations: Firstly, the algorithm's fast initial descent speed and high reliability make it a good candidate for being used as part of a global/local search hybrid algorithm. Secondly, it has the potential to be used as part of a hierarchical evolutionary algorithm, which takes advantage of modern multi-core computing architectures. PMID:20196855

A set-covering based heuristic algorithm for the periodic vehicle routing problem.

PubMed

Cacchiani, V; Hemmelmayr, V C; Tricoire, F

2014-01-30

We present a hybrid optimization algorithm for mixed-integer linear programming, embedding both heuristic and exact components. In order to validate it we use the periodic vehicle routing problem (PVRP) as a case study. This problem consists of determining a set of minimum cost routes for each day of a given planning horizon, with the constraints that each customer must be visited a required number of times (chosen among a set of valid day combinations), must receive every time the required quantity of product, and that the number of routes per day (each respecting the capacity of the vehicle) does not exceed the total number of available vehicles. This is a generalization of the well-known vehicle routing problem (VRP). Our algorithm is based on the linear programming (LP) relaxation of a set-covering-like integer linear programming formulation of the problem, with additional constraints. The LP-relaxation is solved by column generation, where columns are generated heuristically by an iterated local search algorithm. The whole solution method takes advantage of the LP-solution and applies techniques of fixing and releasing of the columns as a local search, making use of a tabu list to avoid cycling. We show the results of the proposed algorithm on benchmark instances from the literature and compare them to the state-of-the-art algorithms, showing the effectiveness of our approach in producing good quality solutions. In addition, we report the results on realistic instances of the PVRP introduced in Pacheco et al. (2011)  [24] and on benchmark instances of the periodic traveling salesman problem (PTSP), showing the efficacy of the proposed algorithm on these as well. Finally, we report the new best known solutions found for all the tested problems.

A set-covering based heuristic algorithm for the periodic vehicle routing problem

PubMed Central

Cacchiani, V.; Hemmelmayr, V.C.; Tricoire, F.

2014-01-01

We present a hybrid optimization algorithm for mixed-integer linear programming, embedding both heuristic and exact components. In order to validate it we use the periodic vehicle routing problem (PVRP) as a case study. This problem consists of determining a set of minimum cost routes for each day of a given planning horizon, with the constraints that each customer must be visited a required number of times (chosen among a set of valid day combinations), must receive every time the required quantity of product, and that the number of routes per day (each respecting the capacity of the vehicle) does not exceed the total number of available vehicles. This is a generalization of the well-known vehicle routing problem (VRP). Our algorithm is based on the linear programming (LP) relaxation of a set-covering-like integer linear programming formulation of the problem, with additional constraints. The LP-relaxation is solved by column generation, where columns are generated heuristically by an iterated local search algorithm. The whole solution method takes advantage of the LP-solution and applies techniques of fixing and releasing of the columns as a local search, making use of a tabu list to avoid cycling. We show the results of the proposed algorithm on benchmark instances from the literature and compare them to the state-of-the-art algorithms, showing the effectiveness of our approach in producing good quality solutions. In addition, we report the results on realistic instances of the PVRP introduced in Pacheco et al. (2011)  [24] and on benchmark instances of the periodic traveling salesman problem (PTSP), showing the efficacy of the proposed algorithm on these as well. Finally, we report the new best known solutions found for all the tested problems. PMID:24748696

Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem.

PubMed

Contreras-Bolton, Carlos; Parada, Victor

2015-01-01

Genetic algorithms are powerful search methods inspired by Darwinian evolution. To date, they have been applied to the solution of many optimization problems because of the easy use of their properties and their robustness in finding good solutions to difficult problems. The good operation of genetic algorithms is due in part to its two main variation operators, namely, crossover and mutation operators. Typically, in the literature, we find the use of a single crossover and mutation operator. However, there are studies that have shown that using multi-operators produces synergy and that the operators are mutually complementary. Using multi-operators is not a simple task because which operators to use and how to combine them must be determined, which in itself is an optimization problem. In this paper, it is proposed that the task of exploring the different combinations of the crossover and mutation operators can be carried out by evolutionary computing. The crossover and mutation operators used are those typically used for solving the traveling salesman problem. The process of searching for good combinations was effective, yielding appropriate and synergic combinations of the crossover and mutation operators. The numerical results show that the use of the combination of operators obtained by evolutionary computing is better than the use of a single operator and the use of multi-operators combined in the standard way. The results were also better than those of the last operators reported in the literature.

Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem

PubMed Central

2015-01-01

Genetic algorithms are powerful search methods inspired by Darwinian evolution. To date, they have been applied to the solution of many optimization problems because of the easy use of their properties and their robustness in finding good solutions to difficult problems. The good operation of genetic algorithms is due in part to its two main variation operators, namely, crossover and mutation operators. Typically, in the literature, we find the use of a single crossover and mutation operator. However, there are studies that have shown that using multi-operators produces synergy and that the operators are mutually complementary. Using multi-operators is not a simple task because which operators to use and how to combine them must be determined, which in itself is an optimization problem. In this paper, it is proposed that the task of exploring the different combinations of the crossover and mutation operators can be carried out by evolutionary computing. The crossover and mutation operators used are those typically used for solving the traveling salesman problem. The process of searching for good combinations was effective, yielding appropriate and synergic combinations of the crossover and mutation operators. The numerical results show that the use of the combination of operators obtained by evolutionary computing is better than the use of a single operator and the use of multi-operators combined in the standard way. The results were also better than those of the last operators reported in the literature. PMID:26367182

Comparison of ANN and RKS approaches to model SCC strength

NASA Astrophysics Data System (ADS)

Prakash, Aravind J.; Sathyan, Dhanya; Anand, K. B.; Aravind, N. R.

2018-02-01

Self compacting concrete (SCC) is a high performance concrete that has high flowability and can be used in heavily reinforced concrete members with minimal compaction segregation and bleeding. The mix proportioning of SCC is highly complex and large number of trials are required to get the mix with the desired properties resulting in the wastage of materials and time. The research on SCC has been highly empirical and no theoretical relationships have been developed between the mixture proportioning and engineering properties of SCC. In this work effectiveness of artificial neural network (ANN) and random kitchen sink algorithm(RKS) with regularized least square algorithm(RLS) in predicting the split tensile strength of the SCC is analysed. Random kitchen sink algorithm is used for mapping data to higher dimension and classification of this data is done using Regularized least square algorithm. The training and testing data for the algorithm was obtained experimentally using standard test procedures and materials available. Total of 40 trials were done which were used as the training and testing data. Trials were performed by varying the amount of fine aggregate, coarse aggregate, dosage and type of super plasticizer and water. Prediction accuracy of the ANN and RKS model is checked by comparing the RMSE value of both ANN and RKS. Analysis shows that eventhough the RKS model is good for large data set, its prediction accuracy is as good as conventional prediction method like ANN so the split tensile strength model developed by RKS can be used in industries for the proportioning of SCC with tailor made property.

A multi-sensor data-driven methodology for all-sky passive microwave inundation retrieval

NASA Astrophysics Data System (ADS)

Takbiri, Zeinab; Ebtehaj, Ardeshir M.; Foufoula-Georgiou, Efi

2017-06-01

We present a multi-sensor Bayesian passive microwave retrieval algorithm for flood inundation mapping at high spatial and temporal resolutions. The algorithm takes advantage of observations from multiple sensors in optical, short-infrared, and microwave bands, thereby allowing for detection and mapping of the sub-pixel fraction of inundated areas under almost all-sky conditions. The method relies on a nearest-neighbor search and a modern sparsity-promoting inversion method that make use of an a priori dataset in the form of two joint dictionaries. These dictionaries contain almost overlapping observations by the Special Sensor Microwave Imager and Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) F17 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua and Terra satellites. Evaluation of the retrieval algorithm over the Mekong Delta shows that it is capable of capturing to a good degree the inundation diurnal variability due to localized convective precipitation. At longer timescales, the results demonstrate consistency with the ground-based water level observations, denoting that the method is properly capturing inundation seasonal patterns in response to regional monsoonal rain. The calculated Euclidean distance, rank-correlation, and also copula quantile analysis demonstrate a good agreement between the outputs of the algorithm and the observed water levels at monthly and daily timescales. The current inundation products are at a resolution of 12.5 km and taken twice per day, but a higher resolution (order of 5 km and every 3 h) can be achieved using the same algorithm with the dictionary populated by the Global Precipitation Mission (GPM) Microwave Imager (GMI) products.

A Sustainable City Planning Algorithm Based on TLBO and Local Search

NASA Astrophysics Data System (ADS)

Zhang, Ke; Lin, Li; Huang, Xuanxuan; Liu, Yiming; Zhang, Yonggang

2017-09-01

Nowadays, how to design a city with more sustainable features has become a center problem in the field of social development, meanwhile it has provided a broad stage for the application of artificial intelligence theories and methods. Because the design of sustainable city is essentially a constraint optimization problem, the swarm intelligence algorithm of extensive research has become a natural candidate for solving the problem. TLBO (Teaching-Learning-Based Optimization) algorithm is a new swarm intelligence algorithm. Its inspiration comes from the “teaching” and “learning” behavior of teaching class in the life. The evolution of the population is realized by simulating the “teaching” of the teacher and the student “learning” from each other, with features of less parameters, efficient, simple thinking, easy to achieve and so on. It has been successfully applied to scheduling, planning, configuration and other fields, which achieved a good effect and has been paid more and more attention by artificial intelligence researchers. Based on the classical TLBO algorithm, we propose a TLBO_LS algorithm combined with local search. We design and implement the random generation algorithm and evaluation model of urban planning problem. The experiments on the small and medium-sized random generation problem showed that our proposed algorithm has obvious advantages over DE algorithm and classical TLBO algorithm in terms of convergence speed and solution quality.

Sensor fusion methods for reducing false alarms in heart rate monitoring.

PubMed

Borges, Gabriel; Brusamarello, Valner

2016-12-01

Automatic patient monitoring is an essential resource in hospitals for good health care management. While alarms caused by abnormal physiological conditions are important for the delivery of fast treatment, they can be also a source of unnecessary noise because of false alarms caused by electromagnetic interference or motion artifacts. One significant source of false alarms is related to heart rate, which is triggered when the heart rhythm of the patient is too fast or too slow. In this work, the fusion of different physiological sensors is explored in order to create a robust heart rate estimation. A set of algorithms using heart rate variability index, Bayesian inference, neural networks, fuzzy logic and majority voting is proposed to fuse the information from the electrocardiogram, arterial blood pressure and photoplethysmogram. Three kinds of information are extracted from each source, namely, heart rate variability, the heart rate difference between sensors and the spectral analysis of low and high noise of each sensor. This information is used as input to the algorithms. Twenty recordings selected from the MIMIC database were used to validate the system. The results showed that neural networks fusion had the best false alarm reduction of 92.5 %, while the Bayesian technique had a reduction of 84.3 %, fuzzy logic 80.6 %, majority voter 72.5 % and the heart rate variability index 67.5 %. Therefore, the proposed algorithms showed good performance and could be useful in bedside monitors.

A multiagent evolutionary algorithm for constraint satisfaction problems.

PubMed

Liu, Jing; Zhong, Weicai; Jiao, Licheng

2006-02-01

With the intrinsic properties of constraint satisfaction problems (CSPs) in mind, we divide CSPs into two types, namely, permutation CSPs and nonpermutation CSPs. According to their characteristics, several behaviors are designed for agents by making use of the ability of agents to sense and act on the environment. These behaviors are controlled by means of evolution, so that the multiagent evolutionary algorithm for constraint satisfaction problems (MAEA-CSPs) results. To overcome the disadvantages of the general encoding methods, the minimum conflict encoding is also proposed. Theoretical analyzes show that MAEA-CSPs has a linear space complexity and converges to the global optimum. The first part of the experiments uses 250 benchmark binary CSPs and 79 graph coloring problems from the DIMACS challenge to test the performance of MAEA-CSPs for nonpermutation CSPs. MAEA-CSPs is compared with six well-defined algorithms and the effect of the parameters is analyzed systematically. The second part of the experiments uses a classical CSP, n-queen problems, and a more practical case, job-shop scheduling problems (JSPs), to test the performance of MAEA-CSPs for permutation CSPs. The scalability of MAEA-CSPs along n for n-queen problems is studied with great care. The results show that MAEA-CSPs achieves good performance when n increases from 10(4) to 10(7), and has a linear time complexity. Even for 10(7)-queen problems, MAEA-CSPs finds the solutions by only 150 seconds. For JSPs, 59 benchmark problems are used, and good performance is also obtained.

Stochastic resonance algorithm applied to quantitative analysis for weak chromatographic signals of alkyl halides and alkyl benzenes in water samples.

PubMed

Xiang, Suyun; Wang, Wei; Xia, Jia; Xiang, Bingren; Ouyang, Pingkai

2009-09-01

The stochastic resonance algorithm is applied to the trace analysis of alkyl halides and alkyl benzenes in water samples. Compared to encountering a single signal when applying the algorithm, the optimization of system parameters for a multicomponent is more complex. In this article, the resolution of adjacent chromatographic peaks is first involved in the optimization of parameters. With the optimized parameters, the algorithm gave an ideal output with good resolution as well as enhanced signal-to-noise ratio. Applying the enhanced signals, the method extended the limit of detection and exhibited good linearity, which ensures accurate determination of the multicomponent.

Topology for efficient information dissemination in ad-hoc networking

NASA Technical Reports Server (NTRS)

Jennings, E.; Okino, C. M.

2002-01-01

In this paper, we explore the information dissemination problem in ad-hoc wirless networks. First, we analyze the probability of successful broadcast, assuming: the nodes are uniformly distributed, the available area has a lower bould relative to the total number of nodes, and there is zero knowledge of the overall topology of the network. By showing that the probability of such events is small, we are motivated to extract good graph topologies to minimize the overall transmissions. Three algorithms are used to generate topologies of the network with guaranteed connectivity. These are the minimum radius graph, the relative neighborhood graph and the minimum spanning tree. Our simulation shows that the relative neighborhood graph has certain good graph properties, which makes it suitable for efficient information dissemination.

A hardware-oriented concurrent TZ search algorithm for High-Efficiency Video Coding

NASA Astrophysics Data System (ADS)

Doan, Nghia; Kim, Tae Sung; Rhee, Chae Eun; Lee, Hyuk-Jae

2017-12-01

High-Efficiency Video Coding (HEVC) is the latest video coding standard, in which the compression performance is double that of its predecessor, the H.264/AVC standard, while the video quality remains unchanged. In HEVC, the test zone (TZ) search algorithm is widely used for integer motion estimation because it effectively searches the good-quality motion vector with a relatively small amount of computation. However, the complex computation structure of the TZ search algorithm makes it difficult to implement it in the hardware. This paper proposes a new integer motion estimation algorithm which is designed for hardware execution by modifying the conventional TZ search to allow parallel motion estimations of all prediction unit (PU) partitions. The algorithm consists of the three phases of zonal, raster, and refinement searches. At the beginning of each phase, the algorithm obtains the search points required by the original TZ search for all PU partitions in a coding unit (CU). Then, all redundant search points are removed prior to the estimation of the motion costs, and the best search points are then selected for all PUs. Compared to the conventional TZ search algorithm, experimental results show that the proposed algorithm significantly decreases the Bjøntegaard Delta bitrate (BD-BR) by 0.84%, and it also reduces the computational complexity by 54.54%.

Morphological decomposition of 2-D binary shapes into convex polygons: a heuristic algorithm.

PubMed

Xu, J

2001-01-01

In many morphological shape decomposition algorithms, either a shape can only be decomposed into shape components of extremely simple forms or a time consuming search process is employed to determine a decomposition. In this paper, we present a morphological shape decomposition algorithm that decomposes a two-dimensional (2-D) binary shape into a collection of convex polygonal components. A single convex polygonal approximation for a given image is first identified. This first component is determined incrementally by selecting a sequence of basic shape primitives. These shape primitives are chosen based on shape information extracted from the given shape at different scale levels. Additional shape components are identified recursively from the difference image between the given image and the first component. Simple operations are used to repair certain concavities caused by the set difference operation. The resulting hierarchical structure provides descriptions for the given shape at different detail levels. The experiments show that the decomposition results produced by the algorithm seem to be in good agreement with the natural structures of the given shapes. The computational cost of the algorithm is significantly lower than that of an earlier search-based convex decomposition algorithm. Compared to nonconvex decomposition algorithms, our algorithm allows accurate approximations for the given shapes at low coding costs.

Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

NASA Astrophysics Data System (ADS)

Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

2015-12-01

In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

Real-time scheduling using minimum search

NASA Technical Reports Server (NTRS)

Tadepalli, Prasad; Joshi, Varad

1992-01-01

In this paper we consider a simple model of real-time scheduling. We present a real-time scheduling system called RTS which is based on Korf's Minimin algorithm. Experimental results show that the schedule quality initially improves with the amount of look-ahead search and tapers off quickly. So it sppears that reasonably good schedules can be produced with a relatively shallow search.

Surface net solar radiation estimated from satellite measurements - Comparisons with tower observations

NASA Technical Reports Server (NTRS)

Li, Zhanqing; Leighton, H. G.; Cess, Robert D.

1993-01-01

A parameterization that relates the reflected solar flux at the top of the atmosphere to the net solar flux at the surface in terms of only the column water vapor amount and the solar zenith angle was tested against surface observations. Net surface fluxes deduced from coincidental collocated satellite-measured radiances and from measurements from towers in Boulder during summer and near Saskatoon in winter have mean differences of about 2 W/sq m, regardless of whether the sky is clear or cloudy. Furthermore, comparisons between the net fluxes deduced from the parameterization and from surface measurements showed equally good agreement when the data were partitioned into morning and afternoon observations. This is in contrast to results from an empirical clear-sky algorithm that is unable to account adequately for the effects of clouds and that shows, at Boulder, a distinct morning to afternoon variation. It is also demonstrated that the parameterization may be applied to irradiances at the top of the atmosphere that have been temporally averaged. The good agreement between the results of the parameterization and surface measurements suggests that the algorithm is a useful tool for a variety of climate studies.

Kalman filter tracking on parallel architectures

NASA Astrophysics Data System (ADS)

Cerati, G.; Elmer, P.; Krutelyov, S.; Lantz, S.; Lefebvre, M.; McDermott, K.; Riley, D.; Tadel, M.; Wittich, P.; Wurthwein, F.; Yagil, A.

2017-10-01

We report on the progress of our studies towards a Kalman filter track reconstruction algorithm with optimal performance on manycore architectures. The combinatorial structure of these algorithms is not immediately compatible with an efficient SIMD (or SIMT) implementation; the challenge for us is to recast the existing software so it can readily generate hundreds of shared-memory threads that exploit the underlying instruction set of modern processors. We show how the data and associated tasks can be organized in a way that is conducive to both multithreading and vectorization. We demonstrate very good performance on Intel Xeon and Xeon Phi architectures, as well as promising first results on Nvidia GPUs.

Learning polynomial feedforward neural networks by genetic programming and backpropagation.

PubMed

Nikolaev, N Y; Iba, H

2003-01-01

This paper presents an approach to learning polynomial feedforward neural networks (PFNNs). The approach suggests, first, finding the polynomial network structure by means of a population-based search technique relying on the genetic programming paradigm, and second, further adjustment of the best discovered network weights by an especially derived backpropagation algorithm for higher order networks with polynomial activation functions. These two stages of the PFNN learning process enable us to identify networks with good training as well as generalization performance. Empirical results show that this approach finds PFNN which outperform considerably some previous constructive polynomial network algorithms on processing benchmark time series.

Genetic algorithm and graph theory based matrix factorization method for online friend recommendation.

PubMed

Li, Qu; Yao, Min; Yang, Jianhua; Xu, Ning

2014-01-01

Online friend recommendation is a fast developing topic in web mining. In this paper, we used SVD matrix factorization to model user and item feature vector and used stochastic gradient descent to amend parameter and improve accuracy. To tackle cold start problem and data sparsity, we used KNN model to influence user feature vector. At the same time, we used graph theory to partition communities with fairly low time and space complexity. What is more, matrix factorization can combine online and offline recommendation. Experiments showed that the hybrid recommendation algorithm is able to recommend online friends with good accuracy.

A low-power and high-quality implementation of the discrete cosine transformation

NASA Astrophysics Data System (ADS)

Heyne, B.; Götze, J.

2007-06-01

In this paper a computationally efficient and high-quality preserving DCT architecture is presented. It is obtained by optimizing the Loeffler DCT based on the Cordic algorithm. The computational complexity is reduced from 11 multiply and 29 add operations (Loeffler DCT) to 38 add and 16 shift operations (which is similar to the complexity of the binDCT). The experimental results show that the proposed DCT algorithm not only reduces the computational complexity significantly, but also retains the good transformation quality of the Loeffler DCT. Therefore, the proposed Cordic based Loeffler DCT is especially suited for low-power and high-quality CODECs in battery-based systems.

Fast Transformation of Temporal Plans for Efficient Execution

NASA Technical Reports Server (NTRS)

Tsamardinos, Ioannis; Muscettola, Nicola; Morris, Paul

1998-01-01

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

Choice of crystal surface finishing for a dual-ended readout depth-of-interaction (DOI) detector.

PubMed

Fan, Peng; Ma, Tianyu; Wei, Qingyang; Yao, Rutao; Liu, Yaqiang; Wang, Shi

2016-02-07

The objective of this study was to choose the crystal surface finishing for a dual-ended readout (DER) DOI detector. Through Monte Carlo simulations and experimental studies, we evaluated 4 crystal surface finishing options as combinations of crystal surface polishing (diffuse or specular) and reflector (diffuse or specular) options on a DER detector. We also tested one linear and one logarithm DOI calculation algorithm. The figures of merit used were DOI resolution, DOI positioning error, and energy resolution. Both the simulation and experimental results show that (1) choosing a diffuse type in either surface polishing or reflector would improve DOI resolution but degrade energy resolution; (2) crystal surface finishing with a diffuse polishing combined with a specular reflector appears a favorable candidate with a good balance of DOI and energy resolution; and (3) the linear and logarithm DOI calculation algorithms show overall comparable DOI error, and the linear algorithm was better for photon interactions near the ends of the crystal while the logarithm algorithm was better near the center. These results provide useful guidance in DER DOI detector design in choosing the crystal surface finishing and DOI calculation methods.

Active Solution Space and Search on Job-shop Scheduling Problem

NASA Astrophysics Data System (ADS)

Watanabe, Masato; Ida, Kenichi; Gen, Mitsuo

In this paper we propose a new searching method of Genetic Algorithm for Job-shop scheduling problem (JSP). The coding method that represent job number in order to decide a priority to arrange a job to Gannt Chart (called the ordinal representation with a priority) in JSP, an active schedule is created by using left shift. We define an active solution at first. It is solution which can create an active schedule without using left shift, and set of its defined an active solution space. Next, we propose an algorithm named Genetic Algorithm with active solution space search (GA-asol) which can create an active solution while solution is evaluated, in order to search the active solution space effectively. We applied it for some benchmark problems to compare with other method. The experimental results show good performance.

Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

NASA Astrophysics Data System (ADS)

Veerakachen, Watcharee; Raksapatcharawong, Mongkol

2015-09-01

Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

Contour-based object orientation estimation

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel

2016-04-01

Real-time object orientation estimation is an actual problem of computer vision nowadays. In this paper we propose an approach to estimate an orientation of objects lacking axial symmetry. Proposed algorithm is intended to estimate orientation of a specific known 3D object, so 3D model is required for learning. The proposed orientation estimation algorithm consists of 2 stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. It minimizes the training image set. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy (mean error value less than 6°) in all case studies. The real-time performance of the algorithm was also demonstrated.

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

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

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

2011-09-15

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

Neighbor Discovery Algorithm in Wireless Local Area Networks Using Multi-beam Directional Antennas

NASA Astrophysics Data System (ADS)

Wang, Jin; Peng, Wei; Liu, Song

2017-10-01

Neighbor discovery is an important step for Wireless Local Area Networks (WLAN) and the use of multi-beam directional antennas can greatly improve the network performance. However, most neighbor discovery algorithms in WLAN, based on multi-beam directional antennas, can only work effectively in synchronous system but not in asynchro-nous system. And collisions at AP remain a bottleneck for neighbor discovery. In this paper, we propose two asynchrono-us neighbor discovery algorithms: asynchronous hierarchical scanning (AHS) and asynchronous directional scanning (ADS) algorithm. Both of them are based on three-way handshaking mechanism. AHS and ADS reduce collisions at AP to have a good performance in a hierarchical way and directional way respectively. In the end, the performance of the AHS and ADS are tested on OMNeT++. Moreover, it is analyzed that different application scenarios and the factors how to affect the performance of these algorithms. The simulation results show that AHS is suitable for the densely populated scenes around AP while ADS is suitable for that most of the neighborhood nodes are far from AP.

Noninvasive evaluation of mental stress using by a refined rough set technique based on biomedical signals.

PubMed

Liu, Tung-Kuan; Chen, Yeh-Peng; Hou, Zone-Yuan; Wang, Chao-Chih; Chou, Jyh-Horng

2014-06-01

Evaluating and treating of stress can substantially benefits to people with health problems. Currently, mental stress evaluated using medical questionnaires. However, the accuracy of this evaluation method is questionable because of variations caused by factors such as cultural differences and individual subjectivity. Measuring of biomedical signals is an effective method for estimating mental stress that enables this problem to be overcome. However, the relationship between the levels of mental stress and biomedical signals remain poorly understood. A refined rough set algorithm is proposed to determine the relationship between mental stress and biomedical signals, this algorithm combines rough set theory with a hybrid Taguchi-genetic algorithm, called RS-HTGA. Two parameters were used for evaluating the performance of the proposed RS-HTGA method. A dataset obtained from a practice clinic comprising 362 cases (196 male, 166 female) was adopted to evaluate the performance of the proposed approach. The empirical results indicate that the proposed method can achieve acceptable accuracy in medical practice. Furthermore, the proposed method was successfully used to identify the relationship between mental stress levels and bio-medical signals. In addition, the comparison between the RS-HTGA and a support vector machine (SVM) method indicated that both methods yield good results. The total averages for sensitivity, specificity, and precision were greater than 96%, the results indicated that both algorithms produced highly accurate results, but a substantial difference in discrimination existed among people with Phase 0 stress. The SVM algorithm shows 89% and the RS-HTGA shows 96%. Therefore, the RS-HTGA is superior to the SVM algorithm. The kappa test results for both algorithms were greater than 0.936, indicating high accuracy and consistency. The area under receiver operating characteristic curve for both the RS-HTGA and a SVM method were greater than 0.77, indicating a good discrimination capability. In this study, crucial attributes in stress evaluation were successfully recognized using biomedical signals, thereby enabling the conservation of medical resources and elucidating the mapping relationship between levels of mental stress and candidate attributes. In addition, we developed a prototype system for mental stress evaluation that can be used to provide benefits in medical practice. Copyright © 2014. Published by Elsevier B.V.

A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem

PubMed Central

Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah

2016-01-01

The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them. PMID:26819585

Can Dissimilar Users Contribute to Accuracy and Diversity of Personalized Recommendation?

NASA Astrophysics Data System (ADS)

Zeng, Wei; Shang, Ming-Sheng; Zhang, Qian-Ming; Lü, Linyuan; Zhou, Tao

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem.

PubMed

Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah

2016-01-01

The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them.

Development of a data-processing method based on Bayesian k-means clustering to discriminate aneugens and clastogens in a high-content micronucleus assay.

PubMed

Huang, Z H; Li, N; Rao, K F; Liu, C T; Huang, Y; Ma, M; Wang, Z J

2018-03-01

Genotoxicants can be identified as aneugens and clastogens through a micronucleus (MN) assay. The current high-content screening-based MN assays usually discriminate an aneugen from a clastogen based on only one parameter, such as the MN size, intensity, or morphology, which yields low accuracies (70-84%) because each of these parameters may contribute to the results. Therefore, the development of an algorithm that can synthesize high-dimensionality data to attain comparative results is important. To improve the automation and accuracy of detection using the current parameter-based mode of action (MoA), the MN MoA signatures of 20 chemicals were systematically recruited in this study to develop an algorithm. The results of the algorithm showed very good agreement (93.58%) between the prediction and reality, indicating that the proposed algorithm is a validated analytical platform for the rapid and objective acquisition of genotoxic MoA messages.

Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

PubMed

Xia, Youshen; Wang, Jun

2015-07-01

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Gait Planning and Stability Control of a Quadruped Robot

PubMed Central

Li, Junmin; Wang, Jinge; Yang, Simon X.; Zhou, Kedong; Tang, Huijuan

2016-01-01

In order to realize smooth gait planning and stability control of a quadruped robot, a new controller algorithm based on CPG-ZMP (central pattern generator-zero moment point) is put forward in this paper. To generate smooth gait and shorten the adjusting time of the model oscillation system, a new CPG model controller and its gait switching strategy based on Wilson-Cowan model are presented in the paper. The control signals of knee-hip joints are obtained by the improved multi-DOF reduced order control theory. To realize stability control, the adaptive speed adjustment and gait switch are completed by the real-time computing of ZMP. Experiment results show that the quadruped robot's gaits are efficiently generated and the gait switch is smooth in the CPG control algorithm. Meanwhile, the stability of robot's movement is improved greatly with the CPG-ZMP algorithm. The algorithm in this paper has good practicability, which lays a foundation for the production of the robot prototype. PMID:27143959

Gait Planning and Stability Control of a Quadruped Robot.

PubMed

Li, Junmin; Wang, Jinge; Yang, Simon X; Zhou, Kedong; Tang, Huijuan

2016-01-01

In order to realize smooth gait planning and stability control of a quadruped robot, a new controller algorithm based on CPG-ZMP (central pattern generator-zero moment point) is put forward in this paper. To generate smooth gait and shorten the adjusting time of the model oscillation system, a new CPG model controller and its gait switching strategy based on Wilson-Cowan model are presented in the paper. The control signals of knee-hip joints are obtained by the improved multi-DOF reduced order control theory. To realize stability control, the adaptive speed adjustment and gait switch are completed by the real-time computing of ZMP. Experiment results show that the quadruped robot's gaits are efficiently generated and the gait switch is smooth in the CPG control algorithm. Meanwhile, the stability of robot's movement is improved greatly with the CPG-ZMP algorithm. The algorithm in this paper has good practicability, which lays a foundation for the production of the robot prototype.

Memetic Algorithm-Based Multi-Objective Coverage Optimization for Wireless Sensor Networks

PubMed Central

Chen, Zhi; Li, Shuai; Yue, Wenjing

2014-01-01

Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms. PMID:25360579

Memetic algorithm-based multi-objective coverage optimization for wireless sensor networks.

PubMed

Chen, Zhi; Li, Shuai; Yue, Wenjing

2014-10-30

Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms.

Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm.

PubMed

Sinha, S K; Karray, F

2002-01-01

Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

A combined reconstruction-classification method for diffuse optical tomography.

PubMed

Hiltunen, P; Prince, S J D; Arridge, S

2009-11-07

We present a combined classification and reconstruction algorithm for diffuse optical tomography (DOT). DOT is a nonlinear ill-posed inverse problem. Therefore, some regularization is needed. We present a mixture of Gaussians prior, which regularizes the DOT reconstruction step. During each iteration, the parameters of a mixture model are estimated. These associate each reconstructed pixel with one of several classes based on the current estimate of the optical parameters. This classification is exploited to form a new prior distribution to regularize the reconstruction step and update the optical parameters. The algorithm can be described as an iteration between an optimization scheme with zeroth-order variable mean and variance Tikhonov regularization and an expectation-maximization scheme for estimation of the model parameters. We describe the algorithm in a general Bayesian framework. Results from simulated test cases and phantom measurements show that the algorithm enhances the contrast of the reconstructed images with good spatial accuracy. The probabilistic classifications of each image contain only a few misclassified pixels.

Research on robust optimization of emergency logistics network considering the time dependence characteristic

NASA Astrophysics Data System (ADS)

WANG, Qingrong; ZHU, Changfeng; LI, Ying; ZHANG, Zhengkun

2017-06-01

Considering the time dependence of emergency logistic network and complexity of the environment that the network exists in, in this paper the time dependent network optimization theory and robust discrete optimization theory are combined, and the emergency logistics dynamic network optimization model with characteristics of robustness is built to maximize the timeliness of emergency logistics. On this basis, considering the complexity of dynamic network and the time dependence of edge weight, an improved ant colony algorithm is proposed to realize the coupling of the optimization algorithm and the network time dependence and robustness. Finally, a case study has been carried out in order to testify validity of this robustness optimization model and its algorithm, and the value of different regulation factors was analyzed considering the importance of the value of the control factor in solving the optimal path. Analysis results show that this model and its algorithm above-mentioned have good timeliness and strong robustness.

Effective application of improved profit-mining algorithm for the interday trading model.

PubMed

Hsieh, Yu-Lung; Yang, Don-Lin; Wu, Jungpin

2014-01-01

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets.

Effective Application of Improved Profit-Mining Algorithm for the Interday Trading Model

PubMed Central

Wu, Jungpin

2014-01-01

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets. PMID:24688442

Study on store-space assignment based on logistic AGV in e-commerce goods to person picking pattern

NASA Astrophysics Data System (ADS)

Xu, Lijuan; Zhu, Jie

2017-10-01

This paper studied on the store-space assignment based on logistic AGV in E-commerce goods to person picking pattern, and established the store-space assignment model based on the lowest picking cost, and design for store-space assignment algorithm after the cluster analysis based on similarity coefficient. And then through the example analysis, compared the picking cost between store-space assignment algorithm this paper design and according to item number and storage according to ABC classification allocation, and verified the effectiveness of the design of the store-space assignment algorithm.

Relevance feedback for CBIR: a new approach based on probabilistic feature weighting with positive and negative examples.

PubMed

Kherfi, Mohammed Lamine; Ziou, Djemel

2006-04-01

In content-based image retrieval, understanding the user's needs is a challenging task that requires integrating him in the process of retrieval. Relevance feedback (RF) has proven to be an effective tool for taking the user's judgement into account. In this paper, we present a new RF framework based on a feature selection algorithm that nicely combines the advantages of a probabilistic formulation with those of using both the positive example (PE) and the negative example (NE). Through interaction with the user, our algorithm learns the importance he assigns to image features, and then applies the results obtained to define similarity measures that correspond better to his judgement. The use of the NE allows images undesired by the user to be discarded, thereby improving retrieval accuracy. As for the probabilistic formulation of the problem, it presents a multitude of advantages and opens the door to more modeling possibilities that achieve a good feature selection. It makes it possible to cluster the query data into classes, choose the probability law that best models each class, model missing data, and support queries with multiple PE and/or NE classes. The basic principle of our algorithm is to assign more importance to features with a high likelihood and those which distinguish well between PE classes and NE classes. The proposed algorithm was validated separately and in image retrieval context, and the experiments show that it performs a good feature selection and contributes to improving retrieval effectiveness.

Simultaneous phylogeny reconstruction and multiple sequence alignment

PubMed Central

Yue, Feng; Shi, Jian; Tang, Jijun

2009-01-01

Background A phylogeny is the evolutionary history of a group of organisms. To date, sequence data is still the most used data type for phylogenetic reconstruction. Before any sequences can be used for phylogeny reconstruction, they must be aligned, and the quality of the multiple sequence alignment has been shown to affect the quality of the inferred phylogeny. At the same time, all the current multiple sequence alignment programs use a guide tree to produce the alignment and experiments showed that good guide trees can significantly improve the multiple alignment quality. Results We devise a new algorithm to simultaneously align multiple sequences and search for the phylogenetic tree that leads to the best alignment. We also implemented the algorithm as a C program package, which can handle both DNA and protein data and can take simple cost model as well as complex substitution matrices, such as PAM250 or BLOSUM62. The performance of the new method are compared with those from other popular multiple sequence alignment tools, including the widely used programs such as ClustalW and T-Coffee. Experimental results suggest that this method has good performance in terms of both phylogeny accuracy and alignment quality. Conclusion We present an algorithm to align multiple sequences and reconstruct the phylogenies that minimize the alignment score, which is based on an efficient algorithm to solve the median problems for three sequences. Our extensive experiments suggest that this method is very promising and can produce high quality phylogenies and alignments. PMID:19208110

Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

NASA Astrophysics Data System (ADS)

Anam, S.

2017-10-01

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.

SU-F-T-431: Dosimetric Validation of Acuros XB Algorithm for Photon Dose Calculation in Water

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

Kumar, L; Yadav, G; Kishore, V

2016-06-15

Purpose: To validate the Acuros XB algorithm implemented in Eclipse Treatment planning system version 11 (Varian Medical System, Inc., Palo Alto, CA, USA) for photon dose calculation. Methods: Acuros XB is a Linear Boltzmann transport equation (LBTE) solver that solves LBTE equation explicitly and gives result equivalent to Monte Carlo. 6MV photon beam from Varian Clinac-iX (2300CD) was used for dosimetric validation of Acuros XB. Percentage depth dose (PDD) and profiles (at dmax, 5, 10, 20 and 30 cm) measurements were performed in water for field size ranging from 2×2,4×4, 6×6, 10×10, 20×20, 30×30 and 40×40 cm{sup 2}. Acuros XBmore » results were compared against measurements and anisotropic analytical algorithm (AAA) algorithm. Results: Acuros XB result shows good agreement with measurements, and were comparable to AAA algorithm. Result for PDD and profiles shows less than one percent difference from measurements, and from calculated PDD and profiles by AAA algorithm for all field size. TPS calculated Gamma error histogram values, average gamma errors in PDD curves before dmax and after dmax were 0.28, 0.15 for Acuros XB and 0.24, 0.17 for AAA respectively, average gamma error in profile curves in central region, penumbra region and outside field region were 0.17, 0.21, 0.42 for Acuros XB and 0.10, 0.22, 0.35 for AAA respectively. Conclusion: The dosimetric validation of Acuros XB algorithms in water medium was satisfactory. Acuros XB algorithm has potential to perform photon dose calculation with high accuracy, which is more desirable for modern radiotherapy environment.« less

Atmospheric correction over coastal waters using multilayer neural networks

NASA Astrophysics Data System (ADS)

Fan, Y.; Li, W.; Charles, G.; Jamet, C.; Zibordi, G.; Schroeder, T.; Stamnes, K. H.

2017-12-01

Standard atmospheric correction (AC) algorithms work well in open ocean areas where the water inherent optical properties (IOPs) are correlated with pigmented particles. However, the IOPs of turbid coastal waters may independently vary with pigmented particles, suspended inorganic particles, and colored dissolved organic matter (CDOM). In turbid coastal waters standard AC algorithms often exhibit large inaccuracies that may lead to negative water-leaving radiances (Lw) or remote sensing reflectance (Rrs). We introduce a new atmospheric correction algorithm for coastal waters based on a multilayer neural network (MLNN) machine learning method. We use a coupled atmosphere-ocean radiative transfer model to simulate the Rayleigh-corrected radiance (Lrc) at the top of the atmosphere (TOA) and the Rrs just above the surface simultaneously, and train a MLNN to derive the aerosol optical depth (AOD) and Rrs directly from the TOA Lrc. The SeaDAS NIR algorithm, the SeaDAS NIR/SWIR algorithm, and the MODIS version of the Case 2 regional water - CoastColour (C2RCC) algorithm are included in the comparison with AERONET-OC measurements. The results show that the MLNN algorithm significantly improves retrieval of normalized Lw in blue bands (412 nm and 443 nm) and yields minor improvements in green and red bands. These results indicate that the MLNN algorithm is suitable for application in turbid coastal waters. Application of the MLNN algorithm to MODIS Aqua images in several coastal areas also shows that it is robust and resilient to contamination due to sunglint or adjacency effects of land and cloud edges. The MLNN algorithm is very fast once the neural network has been properly trained and is therefore suitable for operational use. A significant advantage of the MLNN algorithm is that it does not need SWIR bands, which implies significant cost reduction for dedicated OC missions. A recent effort has been made to extend the MLNN AC algorithm to extreme atmospheric conditions (i.e. heavy polluted continental aerosols) over coastal areas by including additional aerosol and ocean models to generate the training dataset. Preliminary tests show very good results. Results of applying the extended MLNN algorithm to VIIRS images over the Yellow Sea and East China Sea areas with extreme atmospheric and marine conditions will be provided.

Estimation of atmospheric columnar organic matter (OM) mass concentration from remote sensing measurements of aerosol spectral refractive indices

NASA Astrophysics Data System (ADS)

Zhang, Ying; Li, Zhengqiang; Sun, Yele; Lv, Yang; Xie, Yisong

2018-04-01

Aerosols have adverse effects on human health and air quality, changing Earth's energy balance and lead to climate change. The components of aerosol are important because of the different spectral characteristics. Based on the low hygroscopic and high scattering properties of organic matter (OM) in fine modal atmospheric aerosols, we develop an inversion algorithm using remote sensing to obtain aerosol components including black carbon (BC), organic matter (OM), ammonium nitrate-like (AN), dust-like (DU) components and aerosol water content (AW). In the algorithm, the microphysical characteristics (i.e. volume distribution and complex refractive index) of particulates are preliminarily separated to fine and coarse modes, and then aerosol components are retrieved using bimodal parameters. We execute the algorithm using remote sensing measurements of sun-sky radiometer at AERONET site (Beijing RADI) in a period from October of 2014 to January of 2015. The results show a reasonable distribution of aerosol components and a good fit for spectral feature calculations. The mean OM mass concentration in atmospheric column is account for 14.93% of the total and 56.34% of dry and fine-mode aerosol, being a fairly good correlation (R = 0.56) with the in situ observations near the surface layer.

Automatic selection of landmarks in T1-weighted head MRI with regression forests for image registration initialization.

PubMed

Wang, Jianing; Liu, Yuan; Noble, Jack H; Dawant, Benoit M

2017-10-01

Medical image registration establishes a correspondence between images of biological structures, and it is at the core of many applications. Commonly used deformable image registration methods depend on a good preregistration initialization. We develop a learning-based method to automatically find a set of robust landmarks in three-dimensional MR image volumes of the head. These landmarks are then used to compute a thin plate spline-based initialization transformation. The process involves two steps: (1) identifying a set of landmarks that can be reliably localized in the images and (2) selecting among them the subset that leads to a good initial transformation. To validate our method, we use it to initialize five well-established deformable registration algorithms that are subsequently used to register an atlas to MR images of the head. We compare our proposed initialization method with a standard approach that involves estimating an affine transformation with an intensity-based approach. We show that for all five registration algorithms the final registration results are statistically better when they are initialized with the method that we propose than when a standard approach is used. The technique that we propose is generic and could be used to initialize nonrigid registration algorithms for other applications.

A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images.

PubMed

Du, Xiaogang; Dang, Jianwu; Wang, Yangping; Wang, Song; Lei, Tao

2016-01-01

The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU).

The Normalized-Rate Iterative Algorithm: A Practical Dynamic Spectrum Management Method for DSL

NASA Astrophysics Data System (ADS)

Statovci, Driton; Nordström, Tomas; Nilsson, Rickard

2006-12-01

We present a practical solution for dynamic spectrum management (DSM) in digital subscriber line systems: the normalized-rate iterative algorithm (NRIA). Supported by a novel optimization problem formulation, the NRIA is the only DSM algorithm that jointly addresses spectrum balancing for frequency division duplexing systems and power allocation for the users sharing a common cable bundle. With a focus on being implementable rather than obtaining the highest possible theoretical performance, the NRIA is designed to efficiently solve the DSM optimization problem with the operators' business models in mind. This is achieved with the help of two types of parameters: the desired network asymmetry and the desired user priorities. The NRIA is a centralized DSM algorithm based on the iterative water-filling algorithm (IWFA) for finding efficient power allocations, but extends the IWFA by finding the achievable bitrates and by optimizing the bandplan. It is compared with three other DSM proposals: the IWFA, the optimal spectrum balancing algorithm (OSBA), and the bidirectional IWFA (bi-IWFA). We show that the NRIA achieves better bitrate performance than the IWFA and the bi-IWFA. It can even achieve performance almost as good as the OSBA, but with dramatically lower requirements on complexity. Additionally, the NRIA can achieve bitrate combinations that cannot be supported by any other DSM algorithm.

Dosimetry audit simulation of treatment planning system in multicenters radiotherapy

NASA Astrophysics Data System (ADS)

Kasmuri, S.; Pawiro, S. A.

2017-07-01

Treatment Planning System (TPS) is an important modality that determines radiotherapy outcome. TPS requires input data obtained through commissioning and the potentially error occurred. Error in this stage may result in the systematic error. The aim of this study to verify the TPS dosimetry to know deviation range between calculated and measurement dose. This study used CIRS phantom 002LFC representing the human thorax and simulated all external beam radiotherapy stages. The phantom was scanned using CT Scanner and planned 8 test cases that were similar to those in clinical practice situation were made, tested in four radiotherapy centers. Dose measurement using 0.6 cc ionization chamber. The results of this study showed that generally, deviation of all test cases in four centers was within agreement criteria with average deviation about -0.17±1.59 %, -1.64±1.92 %, 0.34±1.34 % and 0.13±1.81 %. The conclusion of this study was all TPS involved in this study showed good performance. The superposition algorithm showed rather poor performance than either analytic anisotropic algorithm (AAA) and convolution algorithm with average deviation about -1.64±1.92 %, -0.17±1.59 % and -0.27±1.51 % respectively.

Visual feature extraction and establishment of visual tags in the intelligent visual internet of things

NASA Astrophysics Data System (ADS)

Zhao, Yiqun; Wang, Zhihui

2015-12-01

The Internet of things (IOT) is a kind of intelligent networks which can be used to locate, track, identify and supervise people and objects. One of important core technologies of intelligent visual internet of things ( IVIOT) is the intelligent visual tag system. In this paper, a research is done into visual feature extraction and establishment of visual tags of the human face based on ORL face database. Firstly, we use the principal component analysis (PCA) algorithm for face feature extraction, then adopt the support vector machine (SVM) for classifying and face recognition, finally establish a visual tag for face which is already classified. We conducted a experiment focused on a group of people face images, the result show that the proposed algorithm have good performance, and can show the visual tag of objects conveniently.

An algorithm for the kinetics of tire pyrolysis under different heating rates.

PubMed

Quek, Augustine; Balasubramanian, Rajashekhar

2009-07-15

Tires exhibit different kinetic behaviors when pyrolyzed under different heating rates. A new algorithm has been developed to investigate pyrolysis behavior of scrap tires. The algorithm includes heat and mass transfer equations to account for the different extents of thermal lag as the tire is heated at different heating rates. The algorithm uses an iterative approach to fit model equations to experimental data to obtain quantitative values of kinetic parameters. These parameters describe the pyrolysis process well, with good agreement (r(2)>0.96) between the model and experimental data when the model is applied to three different brands of automobile tires heated under five different heating rates in a pure nitrogen atmosphere. The model agrees with other researchers' results that frequencies factors increased and time constants decreased with increasing heating rates. The model also shows the change in the behavior of individual tire components when the heating rates are increased above 30 K min(-1). This result indicates that heating rates, rather than temperature, can significantly affect pyrolysis reactions. This algorithm is simple in structure and yet accurate in describing tire pyrolysis under a wide range of heating rates (10-50 K min(-1)). It improves our understanding of the tire pyrolysis process by showing the relationship between the heating rate and the many components in a tire that depolymerize as parallel reactions.

Building of Reusable Reverse Logistics Model and its Optimization Considering the Decision of Backorder or Next Arrival of Goods

NASA Astrophysics Data System (ADS)

Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu; Lee, Hee-Hyol

This paper deals with the building of the reusable reverse logistics model considering the decision of the backorder or the next arrival of goods. The optimization method to minimize the transportation cost and to minimize the volume of the backorder or the next arrival of goods occurred by the Just in Time delivery of the final delivery stage between the manufacturer and the processing center is proposed. Through the optimization algorithms using the priority-based genetic algorithm and the hybrid genetic algorithm, the sub-optimal delivery routes are determined. Based on the case study of a distilling and sale company in Busan in Korea, the new model of the reusable reverse logistics of empty bottles is built and the effectiveness of the proposed method is verified.

Exact and heuristic algorithms for Space Information Flow.

PubMed

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

2018-01-01

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

Stochastic approach to data analysis in fluorescence correlation spectroscopy.

PubMed

Rao, Ramachandra; Langoju, Rajesh; Gösch, Michael; Rigler, Per; Serov, Alexandre; Lasser, Theo

2006-09-21

Fluorescence correlation spectroscopy (FCS) has emerged as a powerful technique for measuring low concentrations of fluorescent molecules and their diffusion constants. In FCS, the experimental data is conventionally fit using standard local search techniques, for example, the Marquardt-Levenberg (ML) algorithm. A prerequisite for these categories of algorithms is the sound knowledge of the behavior of fit parameters and in most cases good initial guesses for accurate fitting, otherwise leading to fitting artifacts. For known fit models and with user experience about the behavior of fit parameters, these local search algorithms work extremely well. However, for heterogeneous systems or where automated data analysis is a prerequisite, there is a need to apply a procedure, which treats FCS data fitting as a black box and generates reliable fit parameters with accuracy for the chosen model in hand. We present a computational approach to analyze FCS data by means of a stochastic algorithm for global search called PGSL, an acronym for Probabilistic Global Search Lausanne. This algorithm does not require any initial guesses and does the fitting in terms of searching for solutions by global sampling. It is flexible as well as computationally faster at the same time for multiparameter evaluations. We present the performance study of PGSL for two-component with triplet fits. The statistical study and the goodness of fit criterion for PGSL are also presented. The robustness of PGSL on noisy experimental data for parameter estimation is also verified. We further extend the scope of PGSL by a hybrid analysis wherein the output of PGSL is fed as initial guesses to ML. Reliability studies show that PGSL and the hybrid combination of both perform better than ML for various thresholds of the mean-squared error (MSE).

Microarray missing data imputation based on a set theoretic framework and biological knowledge.

PubMed

Gan, Xiangchao; Liew, Alan Wee-Chung; Yan, Hong

2006-01-01

Gene expressions measured using microarrays usually suffer from the missing value problem. However, in many data analysis methods, a complete data matrix is required. Although existing missing value imputation algorithms have shown good performance to deal with missing values, they also have their limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by global structure. In addition, these algorithms do not take into account any biological constraint in their imputation. In this paper, we propose a set theoretic framework based on projection onto convex sets (POCS) for missing data imputation. POCS allows us to incorporate different types of a priori knowledge about missing values into the estimation process. The main idea of POCS is to formulate every piece of prior knowledge into a corresponding convex set and then use a convergence-guaranteed iterative procedure to obtain a solution in the intersection of all these sets. In this work, we design several convex sets, taking into consideration the biological characteristic of the data: the first set mainly exploit the local correlation structure among genes in microarray data, while the second set captures the global correlation structure among arrays. The third set (actually a series of sets) exploits the biological phenomenon of synchronization loss in microarray experiments. In cyclic systems, synchronization loss is a common phenomenon and we construct a series of sets based on this phenomenon for our POCS imputation algorithm. Experiments show that our algorithm can achieve a significant reduction of error compared to the KNNimpute, SVDimpute and LSimpute methods.

Research on Classification of Chinese Text Data Based on SVM

NASA Astrophysics Data System (ADS)

Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

2017-09-01

Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

Optimization of IBF parameters based on adaptive tool-path algorithm

NASA Astrophysics Data System (ADS)

Deng, Wen Hui; Chen, Xian Hua; Jin, Hui Liang; Zhong, Bo; Hou, Jin; Li, An Qi

2018-03-01

As a kind of Computer Controlled Optical Surfacing(CCOS) technology. Ion Beam Figuring(IBF) has obvious advantages in the control of surface accuracy, surface roughness and subsurface damage. The superiority and characteristics of IBF in optical component processing are analyzed from the point of view of removal mechanism. For getting more effective and automatic tool path with the information of dwell time, a novel algorithm is proposed in this thesis. Based on the removal functions made through our IBF equipment and the adaptive tool-path, optimized parameters are obtained through analysis the residual error that would be created in the polishing process. A Φ600 mm plane reflector element was used to be a simulation instance. The simulation result shows that after four combinations of processing, the surface accuracy of PV (Peak Valley) value and the RMS (Root Mean Square) value was reduced to 4.81 nm and 0.495 nm from 110.22 nm and 13.998 nm respectively in the 98% aperture. The result shows that the algorithm and optimized parameters provide a good theoretical for high precision processing of IBF.

Peak Seeking Control for Reduced Fuel Consumption with Preliminary Flight Test Results

NASA Technical Reports Server (NTRS)

Brown, Nelson

2012-01-01

The Environmentally Responsible Aviation project seeks to accomplish the simultaneous reduction of fuel burn, noise, and emissions. A project at NASA Dryden Flight Research Center is contributing to ERAs goals by exploring the practical application of real-time trim configuration optimization for enhanced performance and reduced fuel consumption. This peak-seeking control approach is based on Newton-Raphson algorithm using a time-varying Kalman filter to estimate the gradient of the performance function. In real-time operation, deflection of symmetric ailerons, trailing-edge flaps, and leading-edge flaps of a modified F-18 are directly optimized, and the horizontal stabilators and angle of attack are indirectly optimized. Preliminary results from three research flights are presented herein. The optimization system found a trim configuration that required approximately 3.5% less fuel flow than the baseline trim at the given flight condition. The algorithm consistently rediscovered the solution from several initial conditions. These preliminary results show the algorithm has good performance and is expected to show similar results at other flight conditions and aircraft configurations.

Solving a combinatorial problem via self-organizing process: an application of the Kohonen algorithm to the traveling salesman problem.

PubMed

Fort, J C

1988-01-01

We present an application of the Kohonen algorithm to the traveling salesman problem: Using only this algorithm, without energy function nor any parameter chosen "ad hoc", we found good suboptimal tours. We give a neural model version of this algorithm, closer to classical neural networks. This is illustrated with various numerical examples.

Combining Computational and Social Effort for Collaborative Problem Solving

PubMed Central

Wagy, Mark D.; Bongard, Josh C.

2015-01-01

Rather than replacing human labor, there is growing evidence that networked computers create opportunities for collaborations of people and algorithms to solve problems beyond either of them. In this study, we demonstrate the conditions under which such synergy can arise. We show that, for a design task, three elements are sufficient: humans apply intuitions to the problem, algorithms automatically determine and report back on the quality of designs, and humans observe and innovate on others’ designs to focus creative and computational effort on good designs. This study suggests how such collaborations should be composed for other domains, as well as how social and computational dynamics mutually influence one another during collaborative problem solving. PMID:26544199

Satellite image processing for precision agriculture and agroindustry using convolutional neural network and genetic algorithm

NASA Astrophysics Data System (ADS)

Firdaus; Arkeman, Y.; Buono, A.; Hermadi, I.

2017-01-01

Translating satellite imagery to a useful data for decision making during this time are usually done manually by human. In this research, we are going to translate satellite imagery by using artificial intelligence method specifically using convolutional neural network and genetic algorithm to become a useful data for decision making, especially for precision agriculture and agroindustry. In this research, we are focused on how to made a sustainable land use planning with 3 objectives. The first is maximizing economic factor. Second is minimizing CO2 emission and the last is minimizing land degradation. Results show that by using artificial intelligence method, can produced a good pareto optimum solutions in a short time.

CP decomposition approach to blind separation for DS-CDMA system using a new performance index

NASA Astrophysics Data System (ADS)

Rouijel, Awatif; Minaoui, Khalid; Comon, Pierre; Aboutajdine, Driss

2014-12-01

In this paper, we present a canonical polyadic (CP) tensor decomposition isolating the scaling matrix. This has two major implications: (i) the problem conditioning shows up explicitly and could be controlled through a constraint on the so-called coherences and (ii) a performance criterion concerning the factor matrices can be exactly calculated and is more realistic than performance metrics used in the literature. Two new algorithms optimizing the CP decomposition based on gradient descent are proposed. This decomposition is illustrated by an application to direct-sequence code division multiplexing access (DS-CDMA) systems; computer simulations are provided and demonstrate the good behavior of these algorithms, compared to others in the literature.

Localization for robotic capsule looped by axially magnetized permanent-magnet ring based on hybrid strategy.

PubMed

Yang, Wanan; Li, Yan; Qin, Fengqing

2015-01-01

To actively maneuver a robotic capsule for interactive diagnosis in the gastrointestinal tract, visualizing accurate position and orientation of the capsule when it moves in the gastrointestinal tract is essential. A possible method that encloses the circuits, batteries, imaging device, etc into the capsule looped by an axially magnetized permanent-magnet ring is proposed. Based on expression of the axially magnetized permanent-magnet ring's magnetic fields, a localization and orientation model was established. An improved hybrid strategy that combines the advantages of particle-swarm optimization, clone algorithm, and the Levenberg-Marquardt algorithm was found to solve the model. Experiments showed that the hybrid strategy has good accuracy, convergence, and real time performance.

An Automatic Networking and Routing Algorithm for Mesh Network in PLC System

NASA Astrophysics Data System (ADS)

Liu, Xiaosheng; Liu, Hao; Liu, Jiasheng; Xu, Dianguo

2017-05-01

Power line communication (PLC) is considered to be one of the best communication technologies in smart grid. However, the topology of low voltage distribution network is complex, meanwhile power line channel has characteristics of time varying and attenuation, which lead to the unreliability of power line communication. In this paper, an automatic networking and routing algorithm is introduced which can be adapted to the "blind state" topology. The results of simulation and test show that the scheme is feasible, the routing overhead is small, and the load balance performance is good, which can achieve the establishment and maintenance of network quickly and effectively. The scheme is of great significance to improve the reliability of PLC.

A Comparison of LBG and ADPCM Speech Compression Techniques

NASA Astrophysics Data System (ADS)

Bachu, Rajesh G.; Patel, Jignasa; Barkana, Buket D.

Speech compression is the technology of converting human speech into an efficiently encoded representation that can later be decoded to produce a close approximation of the original signal. In all speech there is a degree of predictability and speech coding techniques exploit this to reduce bit rates yet still maintain a suitable level of quality. This paper is a study and implementation of Linde-Buzo-Gray Algorithm (LBG) and Adaptive Differential Pulse Code Modulation (ADPCM) algorithms to compress speech signals. In here we implemented the methods using MATLAB 7.0. The methods we used in this study gave good results and performance in compressing the speech and listening tests showed that efficient and high quality coding is achieved.

Efficient Cache use for Stencil Operations on Structured Discretization Grids

NASA Technical Reports Server (NTRS)

Frumkin, Michael; VanderWijngaart, Rob F.

2001-01-01

We derive tight bounds on the cache misses for evaluation of explicit stencil operators on structured grids. Our lower bound is based on the isoperimetrical property of the discrete octahedron. Our upper bound is based on a good surface to volume ratio of a parallelepiped spanned by a reduced basis of the interference lattice of a grid. Measurements show that our algorithm typically reduces the number of cache misses by a factor of three, relative to a compiler optimized code. We show that stencil calculations on grids whose interference lattice have a short vector feature abnormally high numbers of cache misses. We call such grids unfavorable and suggest to avoid these in computations by appropriate padding. By direct measurements on a MIPS R10000 processor we show a good correlation between abnormally high numbers of cache misses and unfavorable three-dimensional grids.

Removing Visual Bias in Filament Identification: A New Goodness-of-fit Measure

NASA Astrophysics Data System (ADS)

Green, C.-E.; Cunningham, M. R.; Dawson, J. R.; Jones, P. A.; Novak, G.; Fissel, L. M.

2017-05-01

Different combinations of input parameters to filament identification algorithms, such as disperse and filfinder, produce numerous different output skeletons. The skeletons are a one-pixel-wide representation of the filamentary structure in the original input image. However, these output skeletons may not necessarily be a good representation of that structure. Furthermore, a given skeleton may not be as good of a representation as another. Previously, there has been no mathematical “goodness-of-fit” measure to compare output skeletons to the input image. Thus far this has been assessed visually, introducing visual bias. We propose the application of the mean structural similarity index (MSSIM) as a mathematical goodness-of-fit measure. We describe the use of the MSSIM to find the output skeletons that are the most mathematically similar to the original input image (the optimum, or “best,” skeletons) for a given algorithm, and independently of the algorithm. This measure makes possible systematic parameter studies, aimed at finding the subset of input parameter values returning optimum skeletons. It can also be applied to the output of non-skeleton-based filament identification algorithms, such as the Hessian matrix method. The MSSIM removes the need to visually examine thousands of output skeletons, and eliminates the visual bias, subjectivity, and limited reproducibility inherent in that process, representing a major improvement upon existing techniques. Importantly, it also allows further automation in the post-processing of output skeletons, which is crucial in this era of “big data.”

Symmetric Stream Cipher using Triple Transposition Key Method and Base64 Algorithm for Security Improvement

NASA Astrophysics Data System (ADS)

Nurdiyanto, Heri; Rahim, Robbi; Wulan, Nur

2017-12-01

Symmetric type cryptography algorithm is known many weaknesses in encryption process compared with asymmetric type algorithm, symmetric stream cipher are algorithm that works on XOR process between plaintext and key, to improve the security of symmetric stream cipher algorithm done improvisation by using Triple Transposition Key which developed from Transposition Cipher and also use Base64 algorithm for encryption ending process, and from experiment the ciphertext that produced good enough and very random.

Preventing the Flu: Good Health Habits Can Help Stop Germs

MedlinePlus

... UPDATE: Parotitis and Influenza FAQ Parotitis and Influenza Algorithm: Interpreting Influenza Testing Results When Influenza is Circulating Algorithm: Interpreting Influenza Testing Results When Influenza is NOT ...

Gyro and accelerometer failure detection and identification in redundant sensor systems

NASA Technical Reports Server (NTRS)

Potter, J. E.; Deckert, J. C.

1972-01-01

Algorithms for failure detection and identification for redundant noncolinear arrays of single degree of freedom gyros and accelerometers are described. These algorithms are optimum in the sense that detection occurs as soon as it is no longer possible to account for the instrument outputs as the outputs of good instruments operating within their noise tolerances, and identification occurs as soon as it is true that only a particular instrument failure could account for the actual instrument outputs within the noise tolerance of good instruments. An estimation algorithm is described which minimizes the maximum possible estimation error magnitude for the given set of instrument outputs. Monte Carlo simulation results are presented for the application of the algorithms to an inertial reference unit consisting of six gyros and six accelerometers in two alternate configurations.

Conception of discrete systems decomposition algorithm using p-invariants and hypergraphs

NASA Astrophysics Data System (ADS)

Stefanowicz, Ł.

2016-09-01

In the article author presents an idea of decomposition algorithm of discrete systems described by Petri Nets using pinvariants. Decomposition process is significant from the point of view of discrete systems design, because it allows separation of the smaller sequential parts. Proposed algorithm uses modified Martinez-Silva method as well as author's selection algorithm. The developed method is a good complement of classical decomposition algorithms using graphs and hypergraphs.

Novel permutation measures for image encryption algorithms

NASA Astrophysics Data System (ADS)

Abd-El-Hafiz, Salwa K.; AbdElHaleem, Sherif H.; Radwan, Ahmed G.

2016-10-01

This paper proposes two measures for the evaluation of permutation techniques used in image encryption. First, a general mathematical framework for describing the permutation phase used in image encryption is presented. Using this framework, six different permutation techniques, based on chaotic and non-chaotic generators, are described. The two new measures are, then, introduced to evaluate the effectiveness of permutation techniques. These measures are (1) Percentage of Adjacent Pixels Count (PAPC) and (2) Distance Between Adjacent Pixels (DBAP). The proposed measures are used to evaluate and compare the six permutation techniques in different scenarios. The permutation techniques are applied on several standard images and the resulting scrambled images are analyzed. Moreover, the new measures are used to compare the permutation algorithms on different matrix sizes irrespective of the actual parameters used in each algorithm. The analysis results show that the proposed measures are good indicators of the effectiveness of the permutation technique.

Fundamental Algorithms of the Goddard Battery Model

NASA Technical Reports Server (NTRS)

Jagielski, J. M.

1985-01-01

The Goddard Space Flight Center (GSFC) is currently producing a computer model to predict Nickel Cadmium (NiCd) performance in a Low Earth Orbit (LEO) cycling regime. The model proper is currently still in development, but the inherent, fundamental algorithms (or methodologies) of the model are defined. At present, the model is closely dependent on empirical data and the data base currently used is of questionable accuracy. Even so, very good correlations have been determined between model predictions and actual cycling data. A more accurate and encompassing data base has been generated to serve dual functions: show the limitations of the current data base, and be inbred in the model properly for more accurate predictions. The fundamental algorithms of the model, and the present data base and its limitations, are described and a brief preliminary analysis of the new data base and its verification of the model's methodology are presented.

A New SAR Image Segmentation Algorithm for the Detection of Target and Shadow Regions

PubMed Central

Huang, Shiqi; Huang, Wenzhun; Zhang, Ting

2016-01-01

The most distinctive characteristic of synthetic aperture radar (SAR) is that it can acquire data under all weather conditions and at all times. However, its coherent imaging mechanism introduces a great deal of speckle noise into SAR images, which makes the segmentation of target and shadow regions in SAR images very difficult. This paper proposes a new SAR image segmentation method based on wavelet decomposition and a constant false alarm rate (WD-CFAR). The WD-CFAR algorithm not only is insensitive to the speckle noise in SAR images but also can segment target and shadow regions simultaneously, and it is also able to effectively segment SAR images with a low signal-to-clutter ratio (SCR). Experiments were performed to assess the performance of the new algorithm on various SAR images. The experimental results show that the proposed method is effective and feasible and possesses good characteristics for general application. PMID:27924935

A frequency dependent preconditioned wavelet method for atmospheric tomography

NASA Astrophysics Data System (ADS)

Yudytskiy, Mykhaylo; Helin, Tapio; Ramlau, Ronny

2013-12-01

Atmospheric tomography, i.e. the reconstruction of the turbulence in the atmosphere, is a main task for the adaptive optics systems of the next generation telescopes. For extremely large telescopes, such as the European Extremely Large Telescope, this problem becomes overly complex and an efficient algorithm is needed to reduce numerical costs. Recently, a conjugate gradient method based on wavelet parametrization of turbulence layers was introduced [5]. An iterative algorithm can only be numerically efficient when the number of iterations required for a sufficient reconstruction is low. A way to achieve this is to design an efficient preconditioner. In this paper we propose a new frequency-dependent preconditioner for the wavelet method. In the context of a multi conjugate adaptive optics (MCAO) system simulated on the official end-to-end simulation tool OCTOPUS of the European Southern Observatory we demonstrate robustness and speed of the preconditioned algorithm. We show that three iterations are sufficient for a good reconstruction.

Automatic cortical segmentation in the developing brain.

PubMed

Xue, Hui; Srinivasan, Latha; Jiang, Shuzhou; Rutherford, Mary; Edwards, A David; Rueckert, Daniel; Hajnal, Jo V

2007-01-01

The segmentation of neonatal cortex from magnetic resonance (MR) images is much more challenging than the segmentation of cortex in adults. The main reason is the inverted contrast between grey matter (GM) and white matter (WM) that occurs when myelination is incomplete. This causes mislabeled partial volume voxels, especially at the interface between GM and cerebrospinal fluid (CSF). We propose a fully automatic cortical segmentation algorithm, detecting these mislabeled voxels using a knowledge-based approach and correcting errors by adjusting local priors to favor the correct classification. Our results show that the proposed algorithm corrects errors in the segmentation of both GM and WM compared to the classic EM scheme. The segmentation algorithm has been tested on 25 neonates with the gestational ages ranging from approximately 27 to 45 weeks. Quantitative comparison to the manual segmentation demonstrates good performance of the method (mean Dice similarity: 0.758 +/- 0.037 for GM and 0.794 +/- 0.078 for WM).

Unsymmetric ordering using a constrained Markowitz scheme

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

Amestoy, Patrick R.; Xiaoye S.; Pralet, Stephane

2005-01-18

We present a family of ordering algorithms that can be used as a preprocessing step prior to performing sparse LU factorization. The ordering algorithms simultaneously achieve the objectives of selecting numerically good pivots and preserving the sparsity. We describe the algorithmic properties and challenges in their implementation. By mixing the two objectives we show that we can reduce the amount of fill-in in the factors and reduce the number of numerical problems during factorization. On a set of large unsymmetric real problems, we obtained the median reductions of 12% in the factorization time, of 13% in the size of themore » LU factors, of 20% in the number of operations performed during the factorization phase, and of 11% in the memory needed by the multifrontal solver MA41-UNS. A byproduct of this ordering strategy is an incomplete LU-factored matrix that can be used as a preconditioner in an iterative solver.« less

Semi-automated segmentation of the prostate gland boundary in ultrasound images using a machine learning approach

NASA Astrophysics Data System (ADS)

Diaz, Kristians; Castaneda, Benjamin

2008-03-01

This paper presents a semi-automated algorithm for prostate boundary segmentation from three-dimensional (3D) ultrasound (US) images. The US volume is sampled into 72 slices which go through the center of the prostate gland and are separated at a uniform angular spacing of 2.5 degrees. The approach requires the user to select four points from slices (at 0, 45, 90 and 135 degrees) which are used to initialize a discrete dynamic contour (DDC) algorithm. 4 Support Vector Machines (SVMs) are trained over the output of the DDC and classify the rest of the slices. The output of the SVMs is refined using binary morphological operations and DDC to produce the final result. The algorithm was tested on seven ex vivo 3D US images of prostate glands embedded in an agar mold. Results show good agreement with manual segmentation.

Scattering of targets over layered half space using a semi-analytic method in conjunction with FDTD algorithm.

PubMed

Cao, Le; Wei, Bing

2014-08-25

Finite-difference time-domain (FDTD) algorithm with a new method of plane wave excitation is used to investigate the RCS (Radar Cross Section) characteristics of targets over layered half space. Compare with the traditional excitation plane wave method, the calculation memory and time requirement is greatly decreased. The FDTD calculation is performed with a plane wave incidence, and the RCS of far field is obtained by extrapolating the currently calculated data on the output boundary. However, methods available for extrapolating have to evaluate the half space Green function. In this paper, a new method which avoids using the complex and time-consuming half space Green function is proposed. Numerical results show that this method is in good agreement with classic algorithm and it can be used in the fast calculation of scattering and radiation of targets over layered half space.

An arc tangent function demodulation method of fiber-optic Fabry-Perot high-temperature pressure sensor

NASA Astrophysics Data System (ADS)

Ren, Qianyu; Li, Junhong; Hong, Yingping; Jia, Pinggang; Xiong, Jijun

2017-09-01

A new demodulation algorithm of the fiber-optic Fabry-Perot cavity length based on the phase generated carrier (PGC) is proposed in this paper, which can be applied in the high-temperature pressure sensor. This new algorithm based on arc tangent function outputs two orthogonal signals by utilizing an optical system, which is designed based on the field-programmable gate array (FPGA) to overcome the range limit of the original PGC arc tangent function demodulation algorithm. The simulation and analysis are also carried on. According to the analysis of demodulation speed and precision, the simulation of different numbers of sampling points, and measurement results of the pressure sensor, the arc tangent function demodulation method has good demodulation results: 1 MHz processing speed of single data and less than 1% error showing practical feasibility in the fiber-optic Fabry-Perot cavity length demodulation of the Fabry-Perot high-temperature pressure sensor.

Further optimization of SeDDaRA blind image deconvolution algorithm and its DSP implementation

NASA Astrophysics Data System (ADS)

Wen, Bo; Zhang, Qiheng; Zhang, Jianlin

2011-11-01

Efficient algorithm for blind image deconvolution and its high-speed implementation is of great value in practice. Further optimization of SeDDaRA is developed, from algorithm structure to numerical calculation methods. The main optimization covers that, the structure's modularization for good implementation feasibility, reducing the data computation and dependency of 2D-FFT/IFFT, and acceleration of power operation by segmented look-up table. Then the Fast SeDDaRA is proposed and specialized for low complexity. As the final implementation, a hardware system of image restoration is conducted by using the multi-DSP parallel processing. Experimental results show that, the processing time and memory demand of Fast SeDDaRA decreases 50% at least; the data throughput of image restoration system is over 7.8Msps. The optimization is proved efficient and feasible, and the Fast SeDDaRA is able to support the real-time application.

Exact and Approximate Probabilistic Symbolic Execution

NASA Technical Reports Server (NTRS)

Luckow, Kasper; Pasareanu, Corina S.; Dwyer, Matthew B.; Filieri, Antonio; Visser, Willem

2014-01-01

Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also introduce approximate algorithms to search for good schedulers, speeding up established random sampling and reinforcement learning results through the quantification of path probabilities based on symbolic execution. We implemented the techniques in Symbolic PathFinder and evaluated them on nondeterministic Java programs. We show that our algorithms significantly improve upon a state-of- the-art statistical model checking algorithm, originally developed for Markov Decision Processes.

A New SAR Image Segmentation Algorithm for the Detection of Target and Shadow Regions.

PubMed

Huang, Shiqi; Huang, Wenzhun; Zhang, Ting

2016-12-07

The most distinctive characteristic of synthetic aperture radar (SAR) is that it can acquire data under all weather conditions and at all times. However, its coherent imaging mechanism introduces a great deal of speckle noise into SAR images, which makes the segmentation of target and shadow regions in SAR images very difficult. This paper proposes a new SAR image segmentation method based on wavelet decomposition and a constant false alarm rate (WD-CFAR). The WD-CFAR algorithm not only is insensitive to the speckle noise in SAR images but also can segment target and shadow regions simultaneously, and it is also able to effectively segment SAR images with a low signal-to-clutter ratio (SCR). Experiments were performed to assess the performance of the new algorithm on various SAR images. The experimental results show that the proposed method is effective and feasible and possesses good characteristics for general application.

An improved algorithm of mask image dodging for aerial image

NASA Astrophysics Data System (ADS)

Zhang, Zuxun; Zou, Songbai; Zuo, Zhiqi

2011-12-01

The technology of Mask image dodging based on Fourier transform is a good algorithm in removing the uneven luminance within a single image. At present, the difference method and the ratio method are the methods in common use, but they both have their own defects .For example, the difference method can keep the brightness uniformity of the whole image, but it is deficient in local contrast; meanwhile the ratio method can work better in local contrast, but sometimes it makes the dark areas of the original image too bright. In order to remove the defects of the two methods effectively, this paper on the basis of research of the two methods proposes a balance solution. Experiments show that the scheme not only can combine the advantages of the difference method and the ratio method, but also can avoid the deficiencies of the two algorithms.

Rule-Based vs. Behavior-Based Self-Deployment for Mobile Wireless Sensor Networks

PubMed Central

Urdiales, Cristina; Aguilera, Francisco; González-Parada, Eva; Cano-García, Jose; Sandoval, Francisco

2016-01-01

In mobile wireless sensor networks (MWSN), nodes are allowed to move autonomously for deployment. This process is meant: (i) to achieve good coverage; and (ii) to distribute the communication load as homogeneously as possible. Rather than optimizing deployment, reactive algorithms are based on a set of rules or behaviors, so nodes can determine when to move. This paper presents an experimental evaluation of both reactive deployment approaches: rule-based and behavior-based ones. Specifically, we compare a backbone dispersion algorithm with a social potential fields algorithm. Most tests are done under simulation for a large number of nodes in environments with and without obstacles. Results are validated using a small robot network in the real world. Our results show that behavior-based deployment tends to provide better coverage and communication balance, especially for a large number of nodes in areas with obstacles. PMID:27399709

The challenge of computer mathematics.

PubMed

Barendregt, Henk; Wiedijk, Freek

2005-10-15

Progress in the foundations of mathematics has made it possible to formulate all thinkable mathematical concepts, algorithms and proofs in one language and in an impeccable way. This is not in spite of, but partially based on the famous results of Gödel and Turing. In this way statements are about mathematical objects and algorithms, proofs show the correctness of statements and computations, and computations are dealing with objects and proofs. Interactive computer systems for a full integration of defining, computing and proving are based on this. The human defines concepts, constructs algorithms and provides proofs, while the machine checks that the definitions are well formed and the proofs and computations are correct. Results formalized so far demonstrate the feasibility of this 'computer mathematics'. Also there are very good applications. The challenge is to make the systems more mathematician-friendly, by building libraries and tools. The eventual goal is to help humans to learn, develop, communicate, referee and apply mathematics.

A new procedure for dynamic adaption of three-dimensional unstructured grids

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Strawn, Roger

1993-01-01

A new procedure is presented for the simultaneous coarsening and refinement of three-dimensional unstructured tetrahedral meshes. This algorithm allows for localized grid adaption that is used to capture aerodynamic flow features such as vortices and shock waves in helicopter flowfield simulations. The mesh-adaption algorithm is implemented in the C programming language and uses a data structure consisting of a series of dynamically-allocated linked lists. These lists allow the mesh connectivity to be rapidly reconstructed when individual mesh points are added and/or deleted. The algorithm allows the mesh to change in an anisotropic manner in order to efficiently resolve directional flow features. The procedure has been successfully implemented on a single processor of a Cray Y-MP computer. Two sample cases are presented involving three-dimensional transonic flow. Computed results show good agreement with conventional structured-grid solutions for the Euler equations.

Comparison between extreme learning machine and wavelet neural networks in data classification

NASA Astrophysics Data System (ADS)

Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

2017-03-01

Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

Good-enough linguistic representations and online cognitive equilibrium in language processing.

PubMed

Karimi, Hossein; Ferreira, Fernanda

2016-01-01

We review previous research showing that representations formed during language processing are sometimes just "good enough" for the task at hand and propose the "online cognitive equilibrium" hypothesis as the driving force behind the formation of good-enough representations in language processing. Based on this view, we assume that the language comprehension system by default prefers to achieve as early as possible and remain as long as possible in a state of cognitive equilibrium where linguistic representations are successfully incorporated with existing knowledge structures (i.e., schemata) so that a meaningful and coherent overall representation is formed, and uncertainty is resolved or at least minimized. We also argue that the online equilibrium hypothesis is consistent with current theories of language processing, which maintain that linguistic representations are formed through a complex interplay between simple heuristics and deep syntactic algorithms and also theories that hold that linguistic representations are often incomplete and lacking in detail. We also propose a model of language processing that makes use of both heuristic and algorithmic processing, is sensitive to online cognitive equilibrium, and, we argue, is capable of explaining the formation of underspecified representations. We review previous findings providing evidence for underspecification in relation to this hypothesis and the associated language processing model and argue that most of these findings are compatible with them.

The serial message-passing schedule for LDPC decoding algorithms

NASA Astrophysics Data System (ADS)

Liu, Mingshan; Liu, Shanshan; Zhou, Yuan; Jiang, Xue

2015-12-01

The conventional message-passing schedule for LDPC decoding algorithms is the so-called flooding schedule. It has the disadvantage that the updated messages cannot be used until next iteration, thus reducing the convergence speed . In this case, the Layered Decoding algorithm (LBP) based on serial message-passing schedule is proposed. In this paper the decoding principle of LBP algorithm is briefly introduced, and then proposed its two improved algorithms, the grouped serial decoding algorithm (Grouped LBP) and the semi-serial decoding algorithm .They can improve LBP algorithm's decoding speed while maintaining a good decoding performance.

Hybrid feature selection algorithm using symmetrical uncertainty and a harmony search algorithm

NASA Astrophysics Data System (ADS)

Salameh Shreem, Salam; Abdullah, Salwani; Nazri, Mohd Zakree Ahmad

2016-04-01

Microarray technology can be used as an efficient diagnostic system to recognise diseases such as tumours or to discriminate between different types of cancers in normal tissues. This technology has received increasing attention from the bioinformatics community because of its potential in designing powerful decision-making tools for cancer diagnosis. However, the presence of thousands or tens of thousands of genes affects the predictive accuracy of this technology from the perspective of classification. Thus, a key issue in microarray data is identifying or selecting the smallest possible set of genes from the input data that can achieve good predictive accuracy for classification. In this work, we propose a two-stage selection algorithm for gene selection problems in microarray data-sets called the symmetrical uncertainty filter and harmony search algorithm wrapper (SU-HSA). Experimental results show that the SU-HSA is better than HSA in isolation for all data-sets in terms of the accuracy and achieves a lower number of genes on 6 out of 10 instances. Furthermore, the comparison with state-of-the-art methods shows that our proposed approach is able to obtain 5 (out of 10) new best results in terms of the number of selected genes and competitive results in terms of the classification accuracy.

High spatial resolution technique for SPECT using a fan-beam collimator

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

Ichihar, T.; Nambu, K.; Motomura, N.

1993-08-01

The physical characteristics of the collimator cause degradation of resolution with increasing distance from the collimator surface. A new convolutional backprojection algorithm has been derived for fanbeam SPECT data without rebinding into parallel beam geometry. The projections are filtered and then backprojected into the area within an isosceles triangle whose vertex is the focal point of the fan-beam and whose base is the fan-beam collimator face, and outside of the circle whose center is located midway between the focal point and the center of rotation and whose diameter is the distance between the focal point and the center of rotation.more » Consequently the backprojected area is close to the collimator surface. This algorithm has been implemented on a GCA-9300A SPECT system showing good results with both phantom and patient studies. The SPECT transaxial resolution was 4.6mm FWHM (reconstructed image matrix size of 256x256) at the center of SPECT FOV using UHR (ultra-high-resolution) fan beam collimators for brain study. Clinically, Tc-99m HMPAO and Tc-99m ECD brain data were reconstructed using this algorithm. The reconstruction results were compared with MRI images of the same slice position and showed significantly improved over results obtained with standard reconstruction algorithms.« less

A Fast Superpixel Segmentation Algorithm for PolSAR Images Based on Edge Refinement and Revised Wishart Distance

PubMed Central

Zhang, Yue; Zou, Huanxin; Luo, Tiancheng; Qin, Xianxiang; Zhou, Shilin; Ji, Kefeng

2016-01-01

The superpixel segmentation algorithm, as a preprocessing technique, should show good performance in fast segmentation speed, accurate boundary adherence and homogeneous regularity. A fast superpixel segmentation algorithm by iterative edge refinement (IER) works well on optical images. However, it may generate poor superpixels for Polarimetric synthetic aperture radar (PolSAR) images due to the influence of strong speckle noise and many small-sized or slim regions. To solve these problems, we utilized a fast revised Wishart distance instead of Euclidean distance in the local relabeling of unstable pixels, and initialized unstable pixels as all the pixels substituted for the initial grid edge pixels in the initialization step. Then, postprocessing with the dissimilarity measure is employed to remove the generated small isolated regions as well as to preserve strong point targets. Finally, the superiority of the proposed algorithm is validated with extensive experiments on four simulated and two real-world PolSAR images from Experimental Synthetic Aperture Radar (ESAR) and Airborne Synthetic Aperture Radar (AirSAR) data sets, which demonstrate that the proposed method shows better performance with respect to several commonly used evaluation measures, even with about nine times higher computational efficiency, as well as fine boundary adherence and strong point targets preservation, compared with three state-of-the-art methods. PMID:27754385

Segmentation of risk structures for otologic surgery using the Probabilistic Active Shape Model (PASM)

NASA Astrophysics Data System (ADS)

Becker, Meike; Kirschner, Matthias; Sakas, Georgios

2014-03-01

Our research project investigates a multi-port approach for minimally-invasive otologic surgery. For planning such a surgery, an accurate segmentation of the risk structures is crucial. However, the segmentation of these risk structures is a challenging task: The anatomical structures are very small and some have a complex shape, low contrast and vary both in shape and appearance. Therefore, prior knowledge is needed which is why we apply model-based approaches. In the present work, we use the Probabilistic Active Shape Model (PASM), which is a more flexible and specific variant of the Active Shape Model (ASM), to segment the following risk structures: cochlea, semicircular canals, facial nerve, chorda tympani, ossicles, internal auditory canal, external auditory canal and internal carotid artery. For the evaluation we trained and tested the algorithm on 42 computed tomography data sets using leave-one-out tests. Visual assessment of the results shows in general a good agreement of manual and algorithmic segmentations. Further, we achieve a good Average Symmetric Surface Distance while the maximum error is comparatively large due to low contrast at start and end points. Last, we compare the PASM to the standard ASM and show that the PASM leads to a higher accuracy.

Node Deployment Algorithm Based on Connected Tree for Underwater Sensor Networks

PubMed Central

Jiang, Peng; Wang, Xingmin; Jiang, Lurong

2015-01-01

Designing an efficient deployment method to guarantee optimal monitoring quality is one of the key topics in underwater sensor networks. At present, a realistic approach of deployment involves adjusting the depths of nodes in water. One of the typical algorithms used in such process is the self-deployment depth adjustment algorithm (SDDA). This algorithm mainly focuses on maximizing network coverage by constantly adjusting node depths to reduce coverage overlaps between two neighboring nodes, and thus, achieves good performance. However, the connectivity performance of SDDA is irresolute. In this paper, we propose a depth adjustment algorithm based on connected tree (CTDA). In CTDA, the sink node is used as the first root node to start building a connected tree. Finally, the network can be organized as a forest to maintain network connectivity. Coverage overlaps between the parent node and the child node are then reduced within each sub-tree to optimize coverage. The hierarchical strategy is used to adjust the distance between the parent node and the child node to reduce node movement. Furthermore, the silent mode is adopted to reduce communication cost. Simulations show that compared with SDDA, CTDA can achieve high connectivity with various communication ranges and different numbers of nodes. Moreover, it can realize coverage as high as that of SDDA with various sensing ranges and numbers of nodes but with less energy consumption. Simulations under sparse environments show that the connectivity and energy consumption performances of CTDA are considerably better than those of SDDA. Meanwhile, the connectivity and coverage performances of CTDA are close to those depth adjustment algorithms base on connected dominating set (CDA), which is an algorithm similar to CTDA. However, the energy consumption of CTDA is less than that of CDA, particularly in sparse underwater environments. PMID:26184209

A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography.

PubMed

Yang, Xiaogang; De Carlo, Francesco; Phatak, Charudatta; Gürsoy, Dogˇa

2017-03-01

This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.

A collaborative scheduling model for the supply-hub with multiple suppliers and multiple manufacturers.

PubMed

Li, Guo; Lv, Fei; Guan, Xu

2014-01-01

This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment.

A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers

PubMed Central

Lv, Fei; Guan, Xu

2014-01-01

This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment. PMID:24892104

Enhancing the Selection of Backoff Interval Using Fuzzy Logic over Wireless Ad Hoc Networks

PubMed Central

Ranganathan, Radha; Kannan, Kathiravan

2015-01-01

IEEE 802.11 is the de facto standard for medium access over wireless ad hoc network. The collision avoidance mechanism (i.e., random binary exponential backoff—BEB) of IEEE 802.11 DCF (distributed coordination function) is inefficient and unfair especially under heavy load. In the literature, many algorithms have been proposed to tune the contention window (CW) size. However, these algorithms make every node select its backoff interval between [0, CW] in a random and uniform manner. This randomness is incorporated to avoid collisions among the nodes. But this random backoff interval can change the optimal order and frequency of channel access among competing nodes which results in unfairness and increased delay. In this paper, we propose an algorithm that schedules the medium access in a fair and effective manner. This algorithm enhances IEEE 802.11 DCF with additional level of contention resolution that prioritizes the contending nodes according to its queue length and waiting time. Each node computes its unique backoff interval using fuzzy logic based on the input parameters collected from contending nodes through overhearing. We evaluate our algorithm against IEEE 802.11, GDCF (gentle distributed coordination function) protocols using ns-2.35 simulator and show that our algorithm achieves good performance. PMID:25879066

Image defog algorithm based on open close filter and gradient domain recursive bilateral filter

NASA Astrophysics Data System (ADS)

Liu, Daqian; Liu, Wanjun; Zhao, Qingguo; Fei, Bowen

2017-11-01

To solve the problems of fuzzy details, color distortion, low brightness of the image obtained by the dark channel prior defog algorithm, an image defog algorithm based on open close filter and gradient domain recursive bilateral filter, referred to as OCRBF, was put forward. The algorithm named OCRBF firstly makes use of weighted quad tree to obtain more accurate the global atmospheric value, then exploits multiple-structure element morphological open and close filter towards the minimum channel map to obtain a rough scattering map by dark channel prior, makes use of variogram to correct the transmittance map,and uses gradient domain recursive bilateral filter for the smooth operation, finally gets recovery images by image degradation model, and makes contrast adjustment to get bright, clear and no fog image. A large number of experimental results show that the proposed defog method in this paper can be good to remove the fog , recover color and definition of the fog image containing close range image, image perspective, the image including the bright areas very well, compared with other image defog algorithms,obtain more clear and natural fog free images with details of higher visibility, what's more, the relationship between the time complexity of SIDA algorithm and the number of image pixels is a linear correlation.

Local linear discriminant analysis framework using sample neighbors.

PubMed

Fan, Zizhu; Xu, Yong; Zhang, David

2011-07-01

The linear discriminant analysis (LDA) is a very popular linear feature extraction approach. The algorithms of LDA usually perform well under the following two assumptions. The first assumption is that the global data structure is consistent with the local data structure. The second assumption is that the input data classes are Gaussian distributions. However, in real-world applications, these assumptions are not always satisfied. In this paper, we propose an improved LDA framework, the local LDA (LLDA), which can perform well without needing to satisfy the above two assumptions. Our LLDA framework can effectively capture the local structure of samples. According to different types of local data structure, our LLDA framework incorporates several different forms of linear feature extraction approaches, such as the classical LDA and principal component analysis. The proposed framework includes two LLDA algorithms: a vector-based LLDA algorithm and a matrix-based LLDA (MLLDA) algorithm. MLLDA is directly applicable to image recognition, such as face recognition. Our algorithms need to train only a small portion of the whole training set before testing a sample. They are suitable for learning large-scale databases especially when the input data dimensions are very high and can achieve high classification accuracy. Extensive experiments show that the proposed algorithms can obtain good classification results.

Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach

NASA Astrophysics Data System (ADS)

Koeppen, W. C.; Pilger, E.; Wright, R.

2011-07-01

We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.

Mining the National Career Assessment Examination Result Using Clustering Algorithm

NASA Astrophysics Data System (ADS)

Pagudpud, M. V.; Palaoag, T. T.; Padirayon, L. M.

2018-03-01

Education is an essential process today which elicits authorities to discover and establish innovative strategies for educational improvement. This study applied data mining using clustering technique for knowledge extraction from the National Career Assessment Examination (NCAE) result in the Division of Quirino. The NCAE is an examination given to all grade 9 students in the Philippines to assess their aptitudes in the different domains. Clustering the students is helpful in identifying students’ learning considerations. With the use of the RapidMiner tool, clustering algorithms such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), k-means, k-medoid, expectation maximization clustering, and support vector clustering algorithms were analyzed. The silhouette indexes of the said clustering algorithms were compared, and the result showed that the k-means algorithm with k = 3 and silhouette index equal to 0.196 is the most appropriate clustering algorithm to group the students. Three groups were formed having 477 students in the determined group (cluster 0), 310 proficient students (cluster 1) and 396 developing students (cluster 2). The data mining technique used in this study is essential in extracting useful information from the NCAE result to better understand the abilities of students which in turn is a good basis for adopting teaching strategies.

Channel coding for underwater acoustic single-carrier CDMA communication system

NASA Astrophysics Data System (ADS)

Liu, Lanjun; Zhang, Yonglei; Zhang, Pengcheng; Zhou, Lin; Niu, Jiong

2017-01-01

CDMA is an effective multiple access protocol for underwater acoustic networks, and channel coding can effectively reduce the bit error rate (BER) of the underwater acoustic communication system. For the requirements of underwater acoustic mobile networks based on CDMA, an underwater acoustic single-carrier CDMA communication system (UWA/SCCDMA) based on the direct-sequence spread spectrum is proposed, and its channel coding scheme is studied based on convolution, RA, Turbo and LDPC coding respectively. The implementation steps of the Viterbi algorithm of convolutional coding, BP and minimum sum algorithms of RA coding, Log-MAP and SOVA algorithms of Turbo coding, and sum-product algorithm of LDPC coding are given. An UWA/SCCDMA simulation system based on Matlab is designed. Simulation results show that the UWA/SCCDMA based on RA, Turbo and LDPC coding have good performance such that the communication BER is all less than 10-6 in the underwater acoustic channel with low signal to noise ratio (SNR) from -12 dB to -10dB, which is about 2 orders of magnitude lower than that of the convolutional coding. The system based on Turbo coding with Log-MAP algorithm has the best performance.

A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization

PubMed Central

Zhu, Wenyong; Liu, Zijuan; Duan, Qingyan; Cao, Long

2016-01-01

This paper proposes a novel quantum-behaved bat algorithm with the direction of mean best position (QMBA). In QMBA, the position of each bat is mainly updated by the current optimal solution in the early stage of searching and in the late search it also depends on the mean best position which can enhance the convergence speed of the algorithm. During the process of searching, quantum behavior of bats is introduced which is beneficial to jump out of local optimal solution and make the quantum-behaved bats not easily fall into local optimal solution, and it has better ability to adapt complex environment. Meanwhile, QMBA makes good use of statistical information of best position which bats had experienced to generate better quality solutions. This approach not only inherits the characteristic of quick convergence, simplicity, and easy implementation of original bat algorithm, but also increases the diversity of population and improves the accuracy of solution. Twenty-four benchmark test functions are tested and compared with other variant bat algorithms for numerical optimization the simulation results show that this approach is simple and efficient and can achieve a more accurate solution. PMID:27293424

How Crossover Speeds up Building Block Assembly in Genetic Algorithms.

PubMed

Sudholt, Dirk

2017-01-01

We reinvestigate a fundamental question: How effective is crossover in genetic algorithms in combining building blocks of good solutions? Although this has been discussed controversially for decades, we are still lacking a rigorous and intuitive answer. We provide such answers for royal road functions and OneMax, where every bit is a building block. For the latter, we show that using crossover makes every ([Formula: see text]+[Formula: see text]) genetic algorithm at least twice as fast as the fastest evolutionary algorithm using only standard bit mutation, up to small-order terms and for moderate [Formula: see text] and [Formula: see text]. Crossover is beneficial because it can capitalize on mutations that have both beneficial and disruptive effects on building blocks: crossover is able to repair the disruptive effects of mutation in later generations. Compared to mutation-based evolutionary algorithms, this makes multibit mutations more useful. Introducing crossover changes the optimal mutation rate on OneMax from [Formula: see text] to [Formula: see text]. This holds both for uniform crossover and k-point crossover. Experiments and statistical tests confirm that our findings apply to a broad class of building block functions.

Investigation of optical current transformer signal processing method based on an improved Kalman algorithm

NASA Astrophysics Data System (ADS)

Shen, Yan; Ge, Jin-ming; Zhang, Guo-qing; Yu, Wen-bin; Liu, Rui-tong; Fan, Wei; Yang, Ying-xuan

2018-01-01

This paper explores the problem of signal processing in optical current transformers (OCTs). Based on the noise characteristics of OCTs, such as overlapping signals, noise frequency bands, low signal-to-noise ratios, and difficulties in acquiring statistical features of noise power, an improved standard Kalman filtering algorithm was proposed for direct current (DC) signal processing. The state-space model of the OCT DC measurement system is first established, and then mixed noise can be processed by adding mixed noise into measurement and state parameters. According to the minimum mean squared error criterion, state predictions and update equations of the improved Kalman algorithm could be deduced based on the established model. An improved central difference Kalman filter was proposed for alternating current (AC) signal processing, which improved the sampling strategy and noise processing of colored noise. Real-time estimation and correction of noise were achieved by designing AC and DC noise recursive filters. Experimental results show that the improved signal processing algorithms had a good filtering effect on the AC and DC signals with mixed noise of OCT. Furthermore, the proposed algorithm was able to achieve real-time correction of noise during the OCT filtering process.

Algorithm for Automatic Behavior Quantification of Laboratory Mice Using High-Frame-Rate Videos

NASA Astrophysics Data System (ADS)

Nie, Yuman; Takaki, Takeshi; Ishii, Idaku; Matsuda, Hiroshi

In this paper, we propose an algorithm for automatic behavior quantification in laboratory mice to quantify several model behaviors. The algorithm can detect repetitive motions of the fore- or hind-limbs at several or dozens of hertz, which are too rapid for the naked eye, from high-frame-rate video images. Multiple repetitive motions can always be identified from periodic frame-differential image features in four segmented regions — the head, left side, right side, and tail. Even when a mouse changes its posture and orientation relative to the camera, these features can still be extracted from the shift- and orientation-invariant shape of the mouse silhouette by using the polar coordinate system and adjusting the angle coordinate according to the head and tail positions. The effectiveness of the algorithm is evaluated by analyzing long-term 240-fps videos of four laboratory mice for six typical model behaviors: moving, rearing, immobility, head grooming, left-side scratching, and right-side scratching. The time durations for the model behaviors determined by the algorithm have detection/correction ratios greater than 80% for all the model behaviors. This shows good quantification results for actual animal testing.

Lymph node segmentation on CT images by a shape model guided deformable surface methodh

NASA Astrophysics Data System (ADS)

Maleike, Daniel; Fabel, Michael; Tetzlaff, Ralf; von Tengg-Kobligk, Hendrik; Heimann, Tobias; Meinzer, Hans-Peter; Wolf, Ivo

2008-03-01

With many tumor entities, quantitative assessment of lymph node growth over time is important to make therapy choices or to evaluate new therapies. The clinical standard is to document diameters on transversal slices, which is not the best measure for a volume. We present a new algorithm to segment (metastatic) lymph nodes and evaluate the algorithm with 29 lymph nodes in clinical CT images. The algorithm is based on a deformable surface search, which uses statistical shape models to restrict free deformation. To model lymph nodes, we construct an ellipsoid shape model, which strives for a surface with strong gradients and user-defined gray values. The algorithm is integrated into an application, which also allows interactive correction of the segmentation results. The evaluation shows that the algorithm gives good results in the majority of cases and is comparable to time-consuming manual segmentation. The median volume error was 10.1% of the reference volume before and 6.1% after manual correction. Integrated into an application, it is possible to perform lymph node volumetry for a whole patient within the 10 to 15 minutes time limit imposed by clinical routine.

Development of Viscoelastic Multi-Body Simulation and Impact Response Analysis of a Ballasted Railway Track under Cyclic Loading

PubMed Central

Nishiura, Daisuke; Sakaguchi, Hide; Aikawa, Akira

2017-01-01

Simulation of a large number of deformable bodies is often difficult because complex high-level modeling is required to address both multi-body contact and viscoelastic deformation. This necessitates the combined use of a discrete element method (DEM) and a finite element method (FEM). In this study, a quadruple discrete element method (QDEM) was developed for dynamic analysis of viscoelastic materials using a simpler algorithm compared to the standard FEM. QDEM easily incorporates the contact algorithm used in DEM. As the first step toward multi-body simulation, the fundamental performance of QDEM was investigated for viscoelastic analysis. The amplitude and frequency of cantilever elastic vibration were nearly equal to those obtained by the standard FEM. A comparison of creep recovery tests with an analytical solution showed good agreement between them. In addition, good correlation between the attenuation degree and the real physical viscosity was confirmed for viscoelastic vibration analysis. Therefore, the high accuracy of QDEM in the fundamental analysis of infinitesimal viscoelastic deformations was verified. Finally, the impact response of a ballast and sleeper under cyclic loading on a railway track was analyzed using QDEM as an application of deformable multi-body dynamics. The results showed that the vibration of the ballasted track was qualitatively in good agreement with the actual measurements. Moreover, the ballast layer with high friction reduced the ballasted track deterioration. This study suggests that QDEM, as an alternative to DEM and FEM, can provide deeper insights into the contact dynamics of a large number of deformable bodies. PMID:28772974

Development of Viscoelastic Multi-Body Simulation and Impact Response Analysis of a Ballasted Railway Track under Cyclic Loading.

PubMed

Nishiura, Daisuke; Sakaguchi, Hide; Aikawa, Akira

2017-06-03

Simulation of a large number of deformable bodies is often difficult because complex high-level modeling is required to address both multi-body contact and viscoelastic deformation. This necessitates the combined use of a discrete element method (DEM) and a finite element method (FEM). In this study, a quadruple discrete element method (QDEM) was developed for dynamic analysis of viscoelastic materials using a simpler algorithm compared to the standard FEM. QDEM easily incorporates the contact algorithm used in DEM. As the first step toward multi-body simulation, the fundamental performance of QDEM was investigated for viscoelastic analysis. The amplitude and frequency of cantilever elastic vibration were nearly equal to those obtained by the standard FEM. A comparison of creep recovery tests with an analytical solution showed good agreement between them. In addition, good correlation between the attenuation degree and the real physical viscosity was confirmed for viscoelastic vibration analysis. Therefore, the high accuracy of QDEM in the fundamental analysis of infinitesimal viscoelastic deformations was verified. Finally, the impact response of a ballast and sleeper under cyclic loading on a railway track was analyzed using QDEM as an application of deformable multi-body dynamics. The results showed that the vibration of the ballasted track was qualitatively in good agreement with the actual measurements. Moreover, the ballast layer with high friction reduced the ballasted track deterioration. This study suggests that QDEM, as an alternative to DEM and FEM, can provide deeper insights into the contact dynamics of a large number of deformable bodies.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems.

PubMed

Huang, Shuqiang; Tao, Ming

2017-01-22

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K -center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.

Optimal solution for travelling salesman problem using heuristic shortest path algorithm with imprecise arc length

NASA Astrophysics Data System (ADS)

Bakar, Sumarni Abu; Ibrahim, Milbah

2017-08-01

The shortest path problem is a popular problem in graph theory. It is about finding a path with minimum length between a specified pair of vertices. In any network the weight of each edge is usually represented in a form of crisp real number and subsequently the weight is used in the calculation of shortest path problem using deterministic algorithms. However, due to failure, uncertainty is always encountered in practice whereby the weight of edge of the network is uncertain and imprecise. In this paper, a modified algorithm which utilized heuristic shortest path method and fuzzy approach is proposed for solving a network with imprecise arc length. Here, interval number and triangular fuzzy number in representing arc length of the network are considered. The modified algorithm is then applied to a specific example of the Travelling Salesman Problem (TSP). Total shortest distance obtained from this algorithm is then compared with the total distance obtained from traditional nearest neighbour heuristic algorithm. The result shows that the modified algorithm can provide not only on the sequence of visited cities which shown to be similar with traditional approach but it also provides a good measurement of total shortest distance which is lesser as compared to the total shortest distance calculated using traditional approach. Hence, this research could contribute to the enrichment of methods used in solving TSP.

A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images

PubMed Central

Wang, Yangping; Wang, Song

2016-01-01

The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU). PMID:28053653

Support vector machine incremental learning triggered by wrongly predicted samples

NASA Astrophysics Data System (ADS)

Tang, Ting-long; Guan, Qiu; Wu, Yi-rong

2018-05-01

According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.

Genome-scale fluxes predicted under the guidance of enzyme abundance using a novel hyper-cube shrink algorithm.

PubMed

Xie, Zhengwei; Zhang, Tianyu; Ouyang, Qi

2018-02-01

One of the long-expected goals of genome-scale metabolic modelling is to evaluate the influence of the perturbed enzymes on flux distribution. Both ordinary differential equation (ODE) models and constraint-based models, like Flux balance analysis (FBA), lack the capacity to perform metabolic control analysis (MCA) for large-scale networks. In this study, we developed a hyper-cube shrink algorithm (HCSA) to incorporate the enzymatic properties into the FBA model by introducing a pseudo reaction V constrained by enzymatic parameters. Our algorithm uses the enzymatic information quantitatively rather than qualitatively. We first demonstrate the concept by applying HCSA to a simple three-node network, whereby we obtained a good correlation between flux and enzyme abundance. We then validate its prediction by comparison with ODE and with a synthetic network producing voilacein and analogues in Saccharomyces cerevisiae. We show that HCSA can mimic the state-state results of ODE. Finally, we show its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast. We show the ability of HCSA to operate without biomass flux and perform MCA to determine rate-limiting reactions. Algorithm was implemented by Matlab and C ++. The code is available at https://github.com/kekegg/HCSA. xiezhengwei@hsc.pku.edu.cn or qi@pku.edu.cn. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

The MAP Spacecraft Angular State Estimation After Sensor Failure

NASA Technical Reports Server (NTRS)

Bar-Itzhack, Itzhack Y.; Harman, Richard R.

2003-01-01

This work describes two algorithms for computing the angular rate and attitude in case of a gyro and a Star Tracker failure in the Microwave Anisotropy Probe (MAP) satellite, which was placed in the L2 parking point from where it collects data to determine the origin of the universe. The nature of the problem is described, two algorithms are suggested, an observability study is carried out and real MAP data are used to determine the merit of the algorithms. It is shown that one of the algorithms yields a good estimate of the rates but not of the attitude whereas the other algorithm yields a good estimate of the rate as well as two of the three attitude angles. The estimation of the third angle depends on the initial state estimate. There is a contradiction between this result and the outcome of the observability analysis. An explanation of this contradiction is given in the paper. Although this work treats a particular spacecraft, the conclusions have a far reaching consequence.

The Effect of Sensor Failure on the Attitude and Rate Estimation of MAP Spacecraft

NASA Technical Reports Server (NTRS)

Bar-Itzhack, Itzhack Y.; Harman, Richard R.

2003-01-01

This work describes two algorithms for computing the angular rate and attitude in case of a gyro and a Star Tracker failure in the Microwave Anisotropy Probe (MAP) satellite, which was placed in the L2 parking point from where it collects data to determine the origin of the universe. The nature of the problem is described, two algorithms are suggested, an observability study is carried out and real MAP data are used to determine the merit of the algorithms. It is shown that one of the algorithms yields a good estimate of the rates but not of the attitude whereas the other algorithm yields a good estimate of the rate as well as two of the three attitude angles. The estimation of the third angle depends on the initial state estimate. There is a contradiction between this result and the outcome of the observability analysis. An explanation of this contradiction is given in the paper. Although this work treats a particular spacecraft, its conclusions are more general.

VES/TEM 1D joint inversion by using Controlled Random Search (CRS) algorithm

NASA Astrophysics Data System (ADS)

Bortolozo, Cassiano Antonio; Porsani, Jorge Luís; Santos, Fernando Acácio Monteiro dos; Almeida, Emerson Rodrigo

2015-01-01

Electrical (DC) and Transient Electromagnetic (TEM) soundings are used in a great number of environmental, hydrological, and mining exploration studies. Usually, data interpretation is accomplished by individual 1D models resulting often in ambiguous models. This fact can be explained by the way as the two different methodologies sample the medium beneath surface. Vertical Electrical Sounding (VES) is good in marking resistive structures, while Transient Electromagnetic sounding (TEM) is very sensitive to conductive structures. Another difference is VES is better to detect shallow structures, while TEM soundings can reach deeper layers. A Matlab program for 1D joint inversion of VES and TEM soundings was developed aiming at exploring the best of both methods. The program uses CRS - Controlled Random Search - algorithm for both single and 1D joint inversions. Usually inversion programs use Marquadt type algorithms but for electrical and electromagnetic methods, these algorithms may find a local minimum or not converge. Initially, the algorithm was tested with synthetic data, and then it was used to invert experimental data from two places in Paraná sedimentary basin (Bebedouro and Pirassununga cities), both located in São Paulo State, Brazil. Geoelectric model obtained from VES and TEM data 1D joint inversion is similar to the real geological condition, and ambiguities were minimized. Results with synthetic and real data show that 1D VES/TEM joint inversion better recovers simulated models and shows a great potential in geological studies, especially in hydrogeological studies.

Denoising of gravitational wave signals via dictionary learning algorithms

NASA Astrophysics Data System (ADS)

Torres-Forné, Alejandro; Marquina, Antonio; Font, José A.; Ibáñez, José M.

2016-12-01

Gravitational wave astronomy has become a reality after the historical detections accomplished during the first observing run of the two advanced LIGO detectors. In the following years, the number of detections is expected to increase significantly with the full commissioning of the advanced LIGO, advanced Virgo and KAGRA detectors. The development of sophisticated data analysis techniques to improve the opportunities of detection for low signal-to-noise-ratio events is, hence, a most crucial effort. In this paper, we present one such technique, dictionary-learning algorithms, which have been extensively developed in the last few years and successfully applied mostly in the context of image processing. However, to the best of our knowledge, such algorithms have not yet been employed to denoise gravitational wave signals. By building dictionaries from numerical relativity templates of both binary black holes mergers and bursts of rotational core collapse, we show how machine-learning algorithms based on dictionaries can also be successfully applied for gravitational wave denoising. We use a subset of signals from both catalogs, embedded in nonwhite Gaussian noise, to assess our techniques with a large sample of tests and to find the best model parameters. The application of our method to the actual signal GW150914 shows promising results. Dictionary-learning algorithms could be a complementary addition to the gravitational wave data analysis toolkit. They may be used to extract signals from noise and to infer physical parameters if the data are in good enough agreement with the morphology of the dictionary atoms.

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

Experimental scheme and restoration algorithm of block compression sensing

NASA Astrophysics Data System (ADS)

Zhang, Linxia; Zhou, Qun; Ke, Jun

2018-01-01

Compressed Sensing (CS) can use the sparseness of a target to obtain its image with much less data than that defined by the Nyquist sampling theorem. In this paper, we study the hardware implementation of a block compression sensing system and its reconstruction algorithms. Different block sizes are used. Two algorithms, the orthogonal matching algorithm (OMP) and the full variation minimum algorithm (TV) are used to obtain good reconstructions. The influence of block size on reconstruction is also discussed.

A genetic-algorithm approach for assessing the liquefaction potential of sandy soils

NASA Astrophysics Data System (ADS)

Sen, G.; Akyol, E.

2010-04-01

The determination of liquefaction potential is required to take into account a large number of parameters, which creates a complex nonlinear structure of the liquefaction phenomenon. The conventional methods rely on simple statistical and empirical relations or charts. However, they cannot characterise these complexities. Genetic algorithms are suited to solve these types of problems. A genetic algorithm-based model has been developed to determine the liquefaction potential by confirming Cone Penetration Test datasets derived from case studies of sandy soils. Software has been developed that uses genetic algorithms for the parameter selection and assessment of liquefaction potential. Then several estimation functions for the assessment of a Liquefaction Index have been generated from the dataset. The generated Liquefaction Index estimation functions were evaluated by assessing the training and test data. The suggested formulation estimates the liquefaction occurrence with significant accuracy. Besides, the parametric study on the liquefaction index curves shows a good relation with the physical behaviour. The total number of misestimated cases was only 7.8% for the proposed method, which is quite low when compared to another commonly used method.

Complex motion measurement using genetic algorithm

NASA Astrophysics Data System (ADS)

Shen, Jianjun; Tu, Dan; Shen, Zhenkang

1997-12-01

Genetic algorithm (GA) is an optimization technique that provides an untraditional approach to deal with many nonlinear, complicated problems. The notion of motion measurement using genetic algorithm arises from the fact that the motion measurement is virtually an optimization process based on some criterions. In the paper, we propose a complex motion measurement method using genetic algorithm based on block-matching criterion. The following three problems are mainly discussed and solved in the paper: (1) apply an adaptive method to modify the control parameters of GA that are critical to itself, and offer an elitism strategy at the same time (2) derive an evaluate function of motion measurement for GA based on block-matching technique (3) employ hill-climbing (HC) method hybridly to assist GA's search for the global optimal solution. Some other related problems are also discussed. At the end of paper, experiments result is listed. We employ six motion parameters for measurement in our experiments. Experiments result shows that the performance of our GA is good. The GA can find the object motion accurately and rapidly.

Development of an Inverse Algorithm for Resonance Inspection

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

Lai, Canhai; Xu, Wei; Sun, Xin

2012-10-01

Resonance inspection (RI), which employs the natural frequency spectra shift between the good and the anomalous part populations to detect defects, is a non-destructive evaluation (NDE) technique with many advantages such as low inspection cost, high testing speed, and broad applicability to structures with complex geometry compared to other contemporary NDE methods. It has already been widely used in the automobile industry for quality inspections of safety critical parts. Unlike some conventionally used NDE methods, the current RI technology is unable to provide details, i.e. location, dimension, or types, of the flaws for the discrepant parts. Such limitation severely hindersmore » its wide spread applications and further development. In this study, an inverse RI algorithm based on maximum correlation function is proposed to quantify the location and size of flaws for a discrepant part. A dog-bone shaped stainless steel sample with and without controlled flaws are used for algorithm development and validation. The results show that multiple flaws can be accurately pinpointed back using the algorithms developed, and the prediction accuracy decreases with increasing flaw numbers and decreasing distance between flaws.« less

A Fault Recognition System for Gearboxes of Wind Turbines

NASA Astrophysics Data System (ADS)

Yang, Zhiling; Huang, Haiyue; Yin, Zidong

2017-12-01

Costs of maintenance and loss of power generation caused by the faults of wind turbines gearboxes are the main components of operation costs for a wind farm. Therefore, the technology of condition monitoring and fault recognition for wind turbines gearboxes is becoming a hot topic. A condition monitoring and fault recognition system (CMFRS) is presented for CBM of wind turbines gearboxes in this paper. The vibration signals from acceleration sensors at different locations of gearbox and the data from supervisory control and data acquisition (SCADA) system are collected to CMFRS. Then the feature extraction and optimization algorithm is applied to these operational data. Furthermore, to recognize the fault of gearboxes, the GSO-LSSVR algorithm is proposed, combining the least squares support vector regression machine (LSSVR) with the Glowworm Swarm Optimization (GSO) algorithm. Finally, the results show that the fault recognition system used in this paper has a high rate for identifying three states of wind turbines’ gears; besides, the combination of date features can affect the identifying rate and the selection optimization algorithm presented in this paper can get a pretty good date feature subset for the fault recognition.

Evaluation of Origin Ensemble algorithm for image reconstruction for pixelated solid-state detectors with large number of channels

NASA Astrophysics Data System (ADS)

Kolstein, M.; De Lorenzo, G.; Mikhaylova, E.; Chmeissani, M.; Ariño, G.; Calderón, Y.; Ozsahin, I.; Uzun, D.

2013-04-01

The Voxel Imaging PET (VIP) Pathfinder project intends to show the advantages of using pixelated solid-state technology for nuclear medicine applications. It proposes designs for Positron Emission Tomography (PET), Positron Emission Mammography (PEM) and Compton gamma camera detectors with a large number of signal channels (of the order of 106). For PET scanners, conventional algorithms like Filtered Back-Projection (FBP) and Ordered Subset Expectation Maximization (OSEM) are straightforward to use and give good results. However, FBP presents difficulties for detectors with limited angular coverage like PEM and Compton gamma cameras, whereas OSEM has an impractically large time and memory consumption for a Compton gamma camera with a large number of channels. In this article, the Origin Ensemble (OE) algorithm is evaluated as an alternative algorithm for image reconstruction. Monte Carlo simulations of the PET design are used to compare the performance of OE, FBP and OSEM in terms of the bias, variance and average mean squared error (MSE) image quality metrics. For the PEM and Compton camera designs, results obtained with OE are presented.

Automated segmentation and feature extraction of product inspection items

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1997-03-01

X-ray film and linescan images of pistachio nuts on conveyor trays for product inspection are considered. The final objective is the categorization of pistachios into good, blemished and infested nuts. A crucial step before classification is the separation of touching products and the extraction of features essential for classification. This paper addresses new detection and segmentation algorithms to isolate touching or overlapping items. These algorithms employ a new filter, a new watershed algorithm, and morphological processing to produce nutmeat-only images. Tests on a large database of x-ray film and real-time x-ray linescan images of around 2900 small, medium and large nuts showed excellent segmentation results. A new technique to detect and segment dark regions in nutmeat images is also presented and tested on approximately 300 x-ray film and approximately 300 real-time linescan x-ray images with 95-97 percent detection and correct segmentation. New algorithms are described that determine nutmeat fill ratio and locate splits in nutmeat. The techniques formulated in this paper are of general use in many different product inspection and computer vision problems.

Can genetic algorithms help virus writers reshape their creations and avoid detection?

NASA Astrophysics Data System (ADS)

Abu Doush, Iyad; Al-Saleh, Mohammed I.

2017-11-01

Different attack and defence techniques have been evolved over time as actions and reactions between black-hat and white-hat communities. Encryption, polymorphism, metamorphism and obfuscation are among the techniques used by the attackers to bypass security controls. On the other hand, pattern matching, algorithmic scanning, emulation and heuristic are used by the defence team. The Antivirus (AV) is a vital security control that is used against a variety of threats. The AV mainly scans data against its database of virus signatures. Basically, it claims a virus if a match is found. This paper seeks to find the minimal possible changes that can be made on the virus so that it will appear normal when scanned by the AV. Brute-force search through all possible changes can be a computationally expensive task. Alternatively, this paper tries to apply a Genetic Algorithm in solving such a problem. Our proposed algorithm is tested on seven different malware instances. The results show that in all the tested malware instances only a small change in each instance was good enough to bypass the AV.

Particle merging algorithm for PIC codes

NASA Astrophysics Data System (ADS)

Vranic, M.; Grismayer, T.; Martins, J. L.; Fonseca, R. A.; Silva, L. O.

2015-06-01

Particle-in-cell merging algorithms aim to resample dynamically the six-dimensional phase space occupied by particles without distorting substantially the physical description of the system. Whereas various approaches have been proposed in previous works, none of them seemed to be able to conserve fully charge, momentum, energy and their associated distributions. We describe here an alternative algorithm based on the coalescence of N massive or massless particles, considered to be close enough in phase space, into two new macro-particles. The local conservation of charge, momentum and energy are ensured by the resolution of a system of scalar equations. Various simulation comparisons have been carried out with and without the merging algorithm, from classical plasma physics problems to extreme scenarios where quantum electrodynamics is taken into account, showing in addition to the conservation of local quantities, the good reproducibility of the particle distributions. In case where the number of particles ought to increase exponentially in the simulation box, the dynamical merging permits a considerable speedup, and significant memory savings that otherwise would make the simulations impossible to perform.

Implementation of several mathematical algorithms to breast tissue density classification

NASA Astrophysics Data System (ADS)

Quintana, C.; Redondo, M.; Tirao, G.

2014-02-01

The accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories.

A simple and fast heuristic for protein structure comparison.

PubMed

Pelta, David A; González, Juan R; Moreno Vega, Marcos

2008-03-25

Protein structure comparison is a key problem in bioinformatics. There exist several methods for doing protein comparison, being the solution of the Maximum Contact Map Overlap problem (MAX-CMO) one of the alternatives available. Although this problem may be solved using exact algorithms, researchers require approximate algorithms that obtain good quality solutions using less computational resources than the formers. We propose a variable neighborhood search metaheuristic for solving MAX-CMO. We analyze this strategy in two aspects: 1) from an optimization point of view the strategy is tested on two different datasets, obtaining an error of 3.5%(over 2702 pairs) and 1.7% (over 161 pairs) with respect to optimal values; thus leading to high accurate solutions in a simpler and less expensive way than exact algorithms; 2) in terms of protein structure classification, we conduct experiments on three datasets and show that is feasible to detect structural similarities at SCOP's family and CATH's architecture levels using normalized overlap values. Some limitations and the role of normalization are outlined for doing classification at SCOP's fold level. We designed, implemented and tested.a new tool for solving MAX-CMO, based on a well-known metaheuristic technique. The good balance between solution's quality and computational effort makes it a valuable tool. Moreover, to the best of our knowledge, this is the first time the MAX-CMO measure is tested at SCOP's fold and CATH's architecture levels with encouraging results.

STREAMFINDER II: A possible fanning structure parallel to the GD-1 stream in Pan-STARRS1

NASA Astrophysics Data System (ADS)

Malhan, Khyati; Ibata, Rodrigo A.; Goldman, Bertrand; Martin, Nicolas F.; Magnier, Eugene; Chambers, Kenneth

2018-05-01

STREAMFINDER is a new algorithm that we have built to detect stellar streams in an automated and systematic way in astrophysical datasets that possess any combination of positional and kinematic information. In Paper I, we introduced the methodology and the workings of our algorithm and showed that it is capable of detecting ultra-faint and distant halo stream structures containing as few as ˜15 members (ΣG ˜ 33.6 mag arcsec-2) in the Gaia dataset. Here, we test the method with real proper motion data from the Pan-STARRS1 survey, and by selecting targets down to r0 = 18.5 mag we show that it is able to detect the GD-1 stellar stream, whereas the structure remains below a useful detection limit when using a Matched Filter technique. The radial velocity solutions provided by STREAMFINDER for GD-1 candidate members are found to be in good agreement with observations. Furthermore, our algorithm detects a ˜ {40}° long structure approximately parallel to GD-1, and which fans out from it, possibly a sign of stream-fanning due to the triaxiality of the Galactic potential. This analysis shows the promise of this method for detecting and analysing stellar streams in the upcoming Gaia DR2 catalogue.

Multiple sclerosis lesion segmentation using an automatic multimodal graph cuts.

PubMed

García-Lorenzo, Daniel; Lecoeur, Jeremy; Arnold, Douglas L; Collins, D Louis; Barillot, Christian

2009-01-01

Graph Cuts have been shown as a powerful interactive segmentation technique in several medical domains. We propose to automate the Graph Cuts in order to automatically segment Multiple Sclerosis (MS) lesions in MRI. We replace the manual interaction with a robust EM-based approach in order to discriminate between MS lesions and the Normal Appearing Brain Tissues (NABT). Evaluation is performed in synthetic and real images showing good agreement between the automatic segmentation and the target segmentation. We compare our algorithm with the state of the art techniques and with several manual segmentations. An advantage of our algorithm over previously published ones is the possibility to semi-automatically improve the segmentation due to the Graph Cuts interactive feature.

Clustering with Missing Values: No Imputation Required

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri

2004-01-01

Clustering algorithms can identify groups in large data sets, such as star catalogs and hyperspectral images. In general, clustering methods cannot analyze items that have missing data values. Common solutions either fill in the missing values (imputation) or ignore the missing data (marginalization). Imputed values are treated as just as reliable as the truly observed data, but they are only as good as the assumptions used to create them. In contrast, we present a method for encoding partially observed features as a set of supplemental soft constraints and introduce the KSC algorithm, which incorporates constraints into the clustering process. In experiments on artificial data and data from the Sloan Digital Sky Survey, we show that soft constraints are an effective way to enable clustering with missing values.

SCIAMACHY and FTS CO2 Retrievals Using the OCO Retrieval Algorithm

NASA Technical Reports Server (NTRS)

Boesch, Hartmut; Buchwitz, M.; Sen, Bhaswar; Toon, Geoffrey C.; Washenfelder, Rebecca A.; Wennberg, Paul O.

2005-01-01

The Orbiting Carbon Observatory (OCO) mission will make the first global, space-based measurements of atmospheric C02 with the precision and coverage needed to characterize C02 sources and sinks on regional scales. OCO will make spectrally and spatially highly resolved measurements of reflected sunlight in the 02A -band and two near-infrared C02 bands. To test the OCO retrieval algorithm, SCIAMACHY and ground-based Fourier Transform Spectrometer (FTS) measurements at Park Falls, Wisconsin have been analyzed. Good agreement between SCIAMACHY and FTS C02 columns has been found with SCIAMACHY showing a much larger scatter than FTS measurements. Both SCIAMACHY and FTS overestimate the surface pressure by a few percent which significantly impacts retrieved C02 columns.

Telemanipulation of cooperative robots: a case of study

NASA Astrophysics Data System (ADS)

Pliego-Jiménez, Javier; Arteaga-Pérez, Marco

2018-06-01

This article addresses the problem of dexterous robotic grasping by means of a telemanipulation system composed of a single master and two slave robot manipulators. The slave robots are analysed as a cooperative system where it is assumed that the robots can push but not pull the object. In order to achieve a stable rigid grasp, a centralised adaptive position-force control algorithm for the slave robots is proposed. On the other hand, a linear velocity observer for the master robot is developed to avoid numerical differentiation. A set of experiments with different human operators were carried out to show the good performance and capabilities of the proposed control-observer algorithm. In addition, the dynamic model and closed-loop dynamics of the telemanipulation is presented.

A multi-reference filtered-x-Newton narrowband algorithm for active isolation of vibration and experimental investigations

NASA Astrophysics Data System (ADS)

Wang, Chun-yu; He, Lin; Li, Yan; Shuai, Chang-geng

2018-01-01

In engineering applications, ship machinery vibration may be induced by multiple rotational machines sharing a common vibration isolation platform and operating at the same time, and multiple sinusoidal components may be excited. These components may be located at frequencies with large differences or at very close frequencies. A multi-reference filtered-x Newton narrowband (MRFx-Newton) algorithm is proposed to control these multiple sinusoidal components in an MIMO (multiple input and multiple output) system, especially for those located at very close frequencies. The proposed MRFx-Newton algorithm can decouple and suppress multiple sinusoidal components located in the same narrow frequency band even though such components cannot be separated from each other by a narrowband-pass filter. Like the Fx-Newton algorithm, good real-time performance is also achieved by the faster convergence speed brought by the 2nd-order inverse secondary-path filter in the time domain. Experiments are also conducted to verify the feasibility and test the performance of the proposed algorithm installed in an active-passive vibration isolation system in suppressing the vibration excited by an artificial source and air compressor/s. The results show that the proposed algorithm not only has comparable convergence rate as the Fx-Newton algorithm but also has better real-time performance and robustness than the Fx-Newton algorithm in active control of the vibration induced by multiple sound sources/rotational machines working on a shared platform.

Activity Recognition for Personal Time Management

NASA Astrophysics Data System (ADS)

Prekopcsák, Zoltán; Soha, Sugárka; Henk, Tamás; Gáspár-Papanek, Csaba

We describe an accelerometer based activity recognition system for mobile phones with a special focus on personal time management. We compare several data mining algorithms for the automatic recognition task in the case of single user and multiuser scenario, and improve accuracy with heuristics and advanced data mining methods. The results show that daily activities can be recognized with high accuracy and the integration with the RescueTime software can give good insights for personal time management.

Wearable Wireless Sensor for Multi-Scale Physiological Monitoring

DTIC Science & Technology

2015-10-01

clothes with different colors and patterns. The developed algorithm can still detect the chest movements even if single color clothes are worn...Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT One of the aims of Year 2 of the project was to complete development of a prototype multi...this aim, we have developed a prototype 6-photodetector reflectance-based pulse oximeter and results to date show that good signals can be obtained in

GOSAT CO2 retrieval results using TANSO-CAI aerosol information over East Asia

NASA Astrophysics Data System (ADS)

KIM, M.; Kim, W.; Jung, Y.; Lee, S.; Kim, J.; Lee, H.; Boesch, H.; Goo, T. Y.

2015-12-01

In the satellite remote sensing of CO2, incorrect aerosol information could induce large errors as previous studies suggested. Many factors, such as, aerosol type, wavelength dependency of AOD, aerosol polarization effect and etc. have been main error sources. Due to these aerosol effects, large number of data retrieved are screened out in quality control, or retrieval errors tend to increase if not screened out, especially in East Asia where aerosol concentrations are fairly high. To reduce these aerosol induced errors, a CO2 retrieval algorithm using the simultaneous TANSO-CAI aerosol information is developed. This algorithm adopts AOD and aerosol type information as a priori information from the CAI aerosol retrieval algorithm. The CO2 retrieval algorithm based on optimal estimation method and VLIDORT, a vector discrete ordinate radiative transfer model. The CO2 algorithm, developed with various state vectors to find accurate CO2 concentration, shows reasonable results when compared with other dataset. This study concentrates on the validation of retrieved results with the ground-based TCCON measurements in East Asia and the comparison with the previous retrieval from ACOS, NIES, and UoL. Although, the retrieved CO2 concentration is lower than previous results by ppm's, it shows similar trend and high correlation with previous results. Retrieved data and TCCON measurements data are compared at three stations of Tsukuba, Saga, Anmyeondo in East Asia, with the collocation criteria of ±2°in latitude/longitude and ±1 hours of GOSAT passing time. Compared results also show similar trend with good correlation. Based on the TCCON comparison results, bias correction equation is calculated and applied to the East Asia data.

Clustering algorithm for determining community structure in large networks

NASA Astrophysics Data System (ADS)

Pujol, Josep M.; Béjar, Javier; Delgado, Jordi

2006-07-01

We propose an algorithm to find the community structure in complex networks based on the combination of spectral analysis and modularity optimization. The clustering produced by our algorithm is as accurate as the best algorithms on the literature of modularity optimization; however, the main asset of the algorithm is its efficiency. The best match for our algorithm is Newman’s fast algorithm, which is the reference algorithm for clustering in large networks due to its efficiency. When both algorithms are compared, our algorithm outperforms the fast algorithm both in efficiency and accuracy of the clustering, in terms of modularity. Thus, the results suggest that the proposed algorithm is a good choice to analyze the community structure of medium and large networks in the range of tens and hundreds of thousand vertices.

Conservative multizonal interface algorithm for the 3-D Navier-Stokes equations

NASA Technical Reports Server (NTRS)

Klopfer, G. H.; Molvik, G. A.

1991-01-01

A conservative zonal interface algorithm using features of both structured and unstructured mesh CFD technology is presented. The flow solver within each of the zones is based on structured mesh CFD technology. The interface algorithm was implemented into two three-dimensional Navier-Stokes finite volume codes and was found to yield good results.

Testing algorithms for a passenger train braking performance model.

DOT National Transportation Integrated Search

2011-09-01

"The Federal Railroad Administrations Office of Research and Development funded a project to establish performance model to develop, analyze, and test positive train control (PTC) braking algorithms for passenger train operations. With a good brak...

Inverting the parameters of an earthquake-ruptured fault with a genetic algorithm

NASA Astrophysics Data System (ADS)

Yu, Ting-To; Fernàndez, Josè; Rundle, John B.

1998-03-01

Natural selection is the spirit of the genetic algorithm (GA): by keeping the good genes in the current generation, thereby producing better offspring during evolution. The crossover function ensures the heritage of good genes from parent to offspring. Meanwhile, the process of mutation creates a special gene, the character of which does not exist in the parent generation. A program based on genetic algorithms using C language is constructed to invert the parameters of an earthquake-ruptured fault. The verification and application of this code is shown to demonstrate its capabilities. It is determined that this code is able to find the global extreme and can be used to solve more practical problems with constraints gathered from other sources. It is shown that GA is superior to other inverting schema in many aspects. This easy handling and yet powerful algorithm should have many suitable applications in the field of geosciences.

Multidimensional incremental parsing for universal source coding.

PubMed

Bae, Soo Hyun; Juang, Biing-Hwang

2008-10-01

A multidimensional incremental parsing algorithm (MDIP) for multidimensional discrete sources, as a generalization of the Lempel-Ziv coding algorithm, is investigated. It consists of three essential component schemes, maximum decimation matching, hierarchical structure of multidimensional source coding, and dictionary augmentation. As a counterpart of the longest match search in the Lempel-Ziv algorithm, two classes of maximum decimation matching are studied. Also, an underlying behavior of the dictionary augmentation scheme for estimating the source statistics is examined. For an m-dimensional source, m augmentative patches are appended into the dictionary at each coding epoch, thus requiring the transmission of a substantial amount of information to the decoder. The property of the hierarchical structure of the source coding algorithm resolves this issue by successively incorporating lower dimensional coding procedures in the scheme. In regard to universal lossy source coders, we propose two distortion functions, the local average distortion and the local minimax distortion with a set of threshold levels for each source symbol. For performance evaluation, we implemented three image compression algorithms based upon the MDIP; one is lossless and the others are lossy. The lossless image compression algorithm does not perform better than the Lempel-Ziv-Welch coding, but experimentally shows efficiency in capturing the source structure. The two lossy image compression algorithms are implemented using the two distortion functions, respectively. The algorithm based on the local average distortion is efficient at minimizing the signal distortion, but the images by the one with the local minimax distortion have a good perceptual fidelity among other compression algorithms. Our insights inspire future research on feature extraction of multidimensional discrete sources.

An optimized BP neural network based on genetic algorithm for static decoupling of a six-axis force/torque sensor

NASA Astrophysics Data System (ADS)

Fu, Liyue; Song, Aiguo

2018-02-01

In order to improve the measurement precision of 6-axis force/torque sensor for robot, BP decoupling algorithm optimized by GA (GA-BP algorithm) is proposed in this paper. The weights and thresholds of a BP neural network with 6-10-6 topology are optimized by GA to develop decouple a six-axis force/torque sensor. By comparison with other traditional decoupling algorithm, calculating the pseudo-inverse matrix of calibration and classical BP algorithm, the decoupling results validate the good decoupling performance of GA-BP algorithm and the coupling errors are reduced.

Data decomposition method for parallel polygon rasterization considering load balancing

NASA Astrophysics Data System (ADS)

Zhou, Chen; Chen, Zhenjie; Liu, Yongxue; Li, Feixue; Cheng, Liang; Zhu, A.-xing; Li, Manchun

2015-12-01

It is essential to adopt parallel computing technology to rapidly rasterize massive polygon data. In parallel rasterization, it is difficult to design an effective data decomposition method. Conventional methods ignore load balancing of polygon complexity in parallel rasterization and thus fail to achieve high parallel efficiency. In this paper, a novel data decomposition method based on polygon complexity (DMPC) is proposed. First, four factors that possibly affect the rasterization efficiency were investigated. Then, a metric represented by the boundary number and raster pixel number in the minimum bounding rectangle was developed to calculate the complexity of each polygon. Using this metric, polygons were rationally allocated according to the polygon complexity, and each process could achieve balanced loads of polygon complexity. To validate the efficiency of DMPC, it was used to parallelize different polygon rasterization algorithms and tested on different datasets. Experimental results showed that DMPC could effectively parallelize polygon rasterization algorithms. Furthermore, the implemented parallel algorithms with DMPC could achieve good speedup ratios of at least 15.69 and generally outperformed conventional decomposition methods in terms of parallel efficiency and load balancing. In addition, the results showed that DMPC exhibited consistently better performance for different spatial distributions of polygons.

Astronomical algorithms for automated analysis of tissue protein expression in breast cancer

PubMed Central

Ali, H R; Irwin, M; Morris, L; Dawson, S-J; Blows, F M; Provenzano, E; Mahler-Araujo, B; Pharoah, P D; Walton, N A; Brenton, J D; Caldas, C

2013-01-01

Background: High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour transcriptomes, the bottleneck and imprecision of manually scoring TMAs has impeded progress. Methods: We report image analysis algorithms adapted from astronomy for the precise automated analysis of IHC in all subcellular compartments. The power of this technique is demonstrated using over 2000 breast tumours and comparing quantitative automated scores against manual assessment by pathologists. Results: All continuous automated scores showed good correlation with their corresponding ordinal manual scores. For oestrogen receptor (ER), the correlation was 0.82, P<0.0001, for BCL2 0.72, P<0.0001 and for HER2 0.62, P<0.0001. Automated scores showed excellent concordance with manual scores for the unsupervised assignment of cases to ‘positive' or ‘negative' categories with agreement rates of up to 96%. Conclusion: The adaptation of astronomical algorithms coupled with their application to large annotated study cohorts, constitutes a powerful tool for the realisation of the enormous potential of digital pathology. PMID:23329232

Hidden Markov random field model and Broyden-Fletcher-Goldfarb-Shanno algorithm for brain image segmentation

NASA Astrophysics Data System (ADS)

Guerrout, EL-Hachemi; Ait-Aoudia, Samy; Michelucci, Dominique; Mahiou, Ramdane

2018-05-01

Many routine medical examinations produce images of patients suffering from various pathologies. With the huge number of medical images, the manual analysis and interpretation became a tedious task. Thus, automatic image segmentation became essential for diagnosis assistance. Segmentation consists in dividing the image into homogeneous and significant regions. We focus on hidden Markov random fields referred to as HMRF to model the problem of segmentation. This modelisation leads to a classical function minimisation problem. Broyden-Fletcher-Goldfarb-Shanno algorithm referred to as BFGS is one of the most powerful methods to solve unconstrained optimisation problem. In this paper, we investigate the combination of HMRF and BFGS algorithm to perform the segmentation operation. The proposed method shows very good segmentation results comparing with well-known approaches. The tests are conducted on brain magnetic resonance image databases (BrainWeb and IBSR) largely used to objectively confront the results obtained. The well-known Dice coefficient (DC) was used as similarity metric. The experimental results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice Coefficient above .9. Moreover, it generally outperforms other methods in the tests conducted.

Estimation of Bridge Height over Water from Polarimetric SAR Image Data Using Mapping and Projection Algorithm and De-Orientation Theory

NASA Astrophysics Data System (ADS)

Wang, Haipeng; Xu, Feng; Jin, Ya-Qiu; Ouchi, Kazuo

An inversion method of bridge height over water by polarimetric synthetic aperture radar (SAR) is developed. A geometric ray description to illustrate scattering mechanism of a bridge over water surface is identified by polarimetric image analysis. Using the mapping and projecting algorithm, a polarimetric SAR image of a bridge model is first simulated and shows that scattering from a bridge over water can be identified by three strip lines corresponding to single-, double-, and triple-order scattering, respectively. A set of polarimetric parameters based on the de-orientation theory is applied to analysis of three types scattering, and the thinning-clustering algorithm and Hough transform are then employed to locate the image positions of these strip lines. These lines are used to invert the bridge height. Fully polarimetric image data of airborne Pi-SAR at X-band are applied to inversion of the height and width of the Naruto Bridge in Japan. Based on the same principle, this approach is also applicable to spaceborne ALOSPALSAR single-polarization data of the Eastern Ocean Bridge in China. The results show good feasibility to realize the bridge height inversion.

Ground based measurements on reflectance towards validating atmospheric correction algorithms on IRS-P6 AWiFS data

NASA Astrophysics Data System (ADS)

Rani Sharma, Anu; Kharol, Shailesh Kumar; Kvs, Badarinath; Roy, P. S.

In Earth observation, the atmosphere has a non-negligible influence on the visible and infrared radiation which is strong enough to modify the reflected electromagnetic signal and at-target reflectance. Scattering of solar irradiance by atmospheric molecules and aerosol generates path radiance, which increases the apparent surface reflectance over dark surfaces while absorption by aerosols and other molecules in the atmosphere causes loss of brightness to the scene, as recorded by the satellite sensor. In order to derive precise surface reflectance from satellite image data, it is indispensable to apply the atmospheric correction which serves to remove the effects of molecular and aerosol scattering. In the present study, we have implemented a fast atmospheric correction algorithm to IRS-P6 AWiFS satellite data which can effectively retrieve surface reflectance under different atmospheric and surface conditions. The algorithm is based on MODIS climatology products and simplified use of Second Simulation of Satellite Signal in Solar Spectrum (6S) radiative transfer code, which is used to generate look-up-tables (LUTs). The algorithm requires information on aerosol optical depth for correcting the satellite dataset. The proposed method is simple and easy to implement for estimating surface reflectance from the at sensor recorded signal, on a per pixel basis. The atmospheric correction algorithm has been tested for different IRS-P6 AWiFS False color composites (FCC) covering the ICRISAT Farm, Patancheru, Hyderabad, India under varying atmospheric conditions. Ground measurements of surface reflectance representing different land use/land cover, i.e., Red soil, Chick Pea crop, Groundnut crop and Pigeon Pea crop were conducted to validate the algorithm and found a very good match between surface reflectance and atmospherically corrected reflectance for all spectral bands. Further, we aggregated all datasets together and compared the retrieved AWiFS reflectance with aggregated ground measurements which showed a very good correlation of 0.96 in all four spectral bands (i.e. green, red, NIR and SWIR). In order to quantify the accuracy of the proposed method in the estimation of the surface reflectance, the root mean square error (RMSE) associated to the proposed method was evaluated. The analysis of the ground measured versus retrieved AWiFS reflectance yielded smaller RMSE values in case of all four spectral bands. EOS TERRA/AQUA MODIS derived AOD exhibited very good correlation of 0.92 and the data sets provides an effective means for carrying out atmospheric corrections in an operational way. Keywords: Atmospheric correction, 6S code, MODIS, Spectroradiometer, Sun-Photometer

Development of an aerobic capacity prediction model from one-mile run/walk performance in adolescents aged 13-16 years.

PubMed

Burns, Ryan D; Hannon, James C; Brusseau, Timothy A; Eisenman, Patricia A; Shultz, Barry B; Saint-Maurice, Pedro F; Welk, Gregory J; Mahar, Matthew T

2016-01-01

A popular algorithm to predict VO2Peak from the one-mile run/walk test (1MRW) includes body mass index (BMI), which manifests practical issues in school settings. The purpose of this study was to develop an aerobic capacity model from 1MRW in adolescents independent of BMI. Cardiorespiratory endurance data were collected on 90 adolescents aged 13-16 years. The 1MRW was administered on an outside track and a laboratory VO2Peak test was conducted using a maximal treadmill protocol. Multiple linear regression was employed to develop the prediction model. Results yielded the following algorithm: VO2Peak = 7.34 × (1MRW speed in m s(-1)) + 0.23 × (age × sex) + 17.75. The New Model displayed a multiple correlation and prediction error of R = 0.81, standard error of the estimate = 4.78 ml kg(-1) · min(-1), with measured VO2Peak and good criterion-referenced (CR) agreement into FITNESSGRAM's Healthy Fitness Zone (Kappa = 0.62; percentage agreement = 84.4%; Φ = 0.62). The New Model was validated using k-fold cross-validation and showed homoscedastic residuals across the range of predicted scores. The omission of BMI did not compromise accuracy of the model. In conclusion, the New Model displayed good predictive accuracy and good CR agreement with measured VO2Peak in adolescents aged 13-16 years.

Statistical alignment: computational properties, homology testing and goodness-of-fit.

PubMed

Hein, J; Wiuf, C; Knudsen, B; Møller, M B; Wibling, G

2000-09-08

The model of insertions and deletions in biological sequences, first formulated by Thorne, Kishino, and Felsenstein in 1991 (the TKF91 model), provides a basis for performing alignment within a statistical framework. Here we investigate this model.Firstly, we show how to accelerate the statistical alignment algorithms several orders of magnitude. The main innovations are to confine likelihood calculations to a band close to the similarity based alignment, to get good initial guesses of the evolutionary parameters and to apply an efficient numerical optimisation algorithm for finding the maximum likelihood estimate. In addition, the recursions originally presented by Thorne, Kishino and Felsenstein can be simplified. Two proteins, about 1500 amino acids long, can be analysed with this method in less than five seconds on a fast desktop computer, which makes this method practical for actual data analysis.Secondly, we propose a new homology test based on this model, where homology means that an ancestor to a sequence pair can be found finitely far back in time. This test has statistical advantages relative to the traditional shuffle test for proteins.Finally, we describe a goodness-of-fit test, that allows testing the proposed insertion-deletion (indel) process inherent to this model and find that real sequences (here globins) probably experience indels longer than one, contrary to what is assumed by the model. Copyright 2000 Academic Press.

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

PubMed Central

Liu, Yongxiang; Huo, Kai; Zhang, Zhongshuai

2018-01-01

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available. PMID:29320453

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine.

PubMed

Zhao, Feixiang; Liu, Yongxiang; Huo, Kai; Zhang, Shuanghui; Zhang, Zhongshuai

2018-01-10

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

A robust coding scheme for packet video

NASA Technical Reports Server (NTRS)

Chen, Y. C.; Sayood, Khalid; Nelson, D. J.

1991-01-01

We present a layered packet video coding algorithm based on a progressive transmission scheme. The algorithm provides good compression and can handle significant packet loss with graceful degradation in the reconstruction sequence. Simulation results for various conditions are presented.

A robust coding scheme for packet video

NASA Technical Reports Server (NTRS)

Chen, Yun-Chung; Sayood, Khalid; Nelson, Don J.

1992-01-01

A layered packet video coding algorithm based on a progressive transmission scheme is presented. The algorithm provides good compression and can handle significant packet loss with graceful degradation in the reconstruction sequence. Simulation results for various conditions are presented.

An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data

NASA Astrophysics Data System (ADS)

Zhuang, Wei; Mountrakis, Giorgos

2014-09-01

Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large footprint waveform is a significant bottleneck in exploring full usage of the waveform datasets. In the current study, an accurate and computationally efficient algorithm was developed for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation, and Ice Sensor (LVIS) waveform datasets acquired over Central NY. FICA incorporates a set of multi-scale second derivative filters and a k-means clustering algorithm in order to avoid detecting false ground peaks. FICA was tested in five different land cover types (deciduous trees, coniferous trees, shrub, grass and developed area) and showed more accurate results when compared to existing algorithms. More specifically, compared with Gaussian decomposition, the RMSE ground peak identification by FICA was 2.82 m (5.29 m for GD) in deciduous plots, 3.25 m (4.57 m for GD) in coniferous plots, 2.63 m (2.83 m for GD) in shrub plots, 0.82 m (0.93 m for GD) in grass plots, and 0.70 m (0.51 m for GD) in plots of developed areas. FICA performance was also relatively consistent under various slope and canopy coverage (CC) conditions. In addition, FICA showed better computational efficiency compared to existing methods. FICA's major computational and accuracy advantage is a result of the adopted multi-scale signal processing procedures that concentrate on local portions of the signal as opposed to the Gaussian decomposition that uses a curve-fitting strategy applied in the entire signal. The FICA algorithm is a good candidate for large-scale implementation on future space-borne waveform LiDAR sensors.

Improving Classification of Cancer and Mining Biomarkers from Gene Expression Profiles Using Hybrid Optimization Algorithms and Fuzzy Support Vector Machine

PubMed Central

Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Garshasbi, Masoud

2018-01-01

Background: Gene expression data are characteristically high dimensional with a small sample size in contrast to the feature size and variability inherent in biological processes that contribute to difficulties in analysis. Selection of highly discriminative features decreases the computational cost and complexity of the classifier and improves its reliability for prediction of a new class of samples. Methods: The present study used hybrid particle swarm optimization and genetic algorithms for gene selection and a fuzzy support vector machine (SVM) as the classifier. Fuzzy logic is used to infer the importance of each sample in the training phase and decrease the outlier sensitivity of the system to increase the ability to generalize the classifier. A decision-tree algorithm was applied to the most frequent genes to develop a set of rules for each type of cancer. This improved the abilities of the algorithm by finding the best parameters for the classifier during the training phase without the need for trial-and-error by the user. The proposed approach was tested on four benchmark gene expression profiles. Results: Good results have been demonstrated for the proposed algorithm. The classification accuracy for leukemia data is 100%, for colon cancer is 96.67% and for breast cancer is 98%. The results show that the best kernel used in training the SVM classifier is the radial basis function. Conclusions: The experimental results show that the proposed algorithm can decrease the dimensionality of the dataset, determine the most informative gene subset, and improve classification accuracy using the optimal parameters of the classifier with no user interface. PMID:29535919

ADS-B and multilateration sensor fusion algorithm for air traffic control

NASA Astrophysics Data System (ADS)

Liang, Mengchen

Air traffic is expected to increase rapidly in the next decade. But, the current Air Traffic Control (ATC) system does not meet the demand of the future safety and efficiency. The Next Generation Air Transportation System (NextGen) is a transformation program for the ATC system in the United States. The latest estimates by Federal Aviation Administration (FAA) show that by 2018 NextGen will reduce total delays in flight by 35 percent and provide 23 billion dollars in cumulative benefits. A satellite-based technology called the Automatic Dependent Surveillance-Broadcast (ADS-B) system is one of the most important elements in NextGen. FAA expects that ADS-B systems will be available in the National Airspace System (NAS) by 2020. However, an alternative surveillance system is needed due to vulnerabilities that exist in ADS-B systems. Multilateration has a high accuracy performance and is believed to be an ideal back-up strategy for ADS-B systems. Thus, in this study, we develop the ADS-B and multilateration sensor fusion algorithm for aircraft tracking applications in ATC. The algorithm contains a fault detection function for ADS-B information monitoring by using Trajectory Change Points reports from ADS-B and numerical vectors from a hybrid estimation algorithm. We consider two types of faults in the ADS-B measurement model to show that the algorithm is able to deal with the bad data from ADS-B systems and automatically select good data from multilateration systems. We apply fuzzy logic concepts and generate time variant parameters during the fusion process. The parameters play a role of weights for combining data from different sensors. The algorithm performance is validated through two aircraft tracking examples.

Study of image matching algorithm and sub-pixel fitting algorithm in target tracking

NASA Astrophysics Data System (ADS)

Yang, Ming-dong; Jia, Jianjun; Qiang, Jia; Wang, Jian-yu

2015-03-01

Image correlation matching is a tracking method that searched a region most approximate to the target template based on the correlation measure between two images. Because there is no need to segment the image, and the computation of this method is little. Image correlation matching is a basic method of target tracking. This paper mainly studies the image matching algorithm of gray scale image, which precision is at sub-pixel level. The matching algorithm used in this paper is SAD (Sum of Absolute Difference) method. This method excels in real-time systems because of its low computation complexity. The SAD method is introduced firstly and the most frequently used sub-pixel fitting algorithms are introduced at the meantime. These fitting algorithms can't be used in real-time systems because they are too complex. However, target tracking often requires high real-time performance, we put forward a fitting algorithm named paraboloidal fitting algorithm based on the consideration above, this algorithm is simple and realized easily in real-time system. The result of this algorithm is compared with that of surface fitting algorithm through image matching simulation. By comparison, the precision difference between these two algorithms is little, it's less than 0.01pixel. In order to research the influence of target rotation on precision of image matching, the experiment of camera rotation was carried on. The detector used in the camera is a CMOS detector. It is fixed to an arc pendulum table, take pictures when the camera rotated different angles. Choose a subarea in the original picture as the template, and search the best matching spot using image matching algorithm mentioned above. The result shows that the matching error is bigger when the target rotation angle is larger. It's an approximate linear relation. Finally, the influence of noise on matching precision was researched. Gaussian noise and pepper and salt noise were added in the image respectively, and the image was processed by mean filter and median filter, then image matching was processed. The result show that when the noise is little, mean filter and median filter can achieve a good result. But when the noise density of salt and pepper noise is bigger than 0.4, or the variance of Gaussian noise is bigger than 0.0015, the result of image matching will be wrong.

Parallel Algorithms for Switching Edges in Heterogeneous Graphs.

PubMed

Bhuiyan, Hasanuzzaman; Khan, Maleq; Chen, Jiangzhuo; Marathe, Madhav

2017-06-01

An edge switch is an operation on a graph (or network) where two edges are selected randomly and one of their end vertices are swapped with each other. Edge switch operations have important applications in graph theory and network analysis, such as in generating random networks with a given degree sequence, modeling and analyzing dynamic networks, and in studying various dynamic phenomena over a network. The recent growth of real-world networks motivates the need for efficient parallel algorithms. The dependencies among successive edge switch operations and the requirement to keep the graph simple (i.e., no self-loops or parallel edges) as the edges are switched lead to significant challenges in designing a parallel algorithm. Addressing these challenges requires complex synchronization and communication among the processors leading to difficulties in achieving a good speedup by parallelization. In this paper, we present distributed memory parallel algorithms for switching edges in massive networks. These algorithms provide good speedup and scale well to a large number of processors. A harmonic mean speedup of 73.25 is achieved on eight different networks with 1024 processors. One of the steps in our edge switch algorithms requires the computation of multinomial random variables in parallel. This paper presents the first non-trivial parallel algorithm for the problem, achieving a speedup of 925 using 1024 processors.

Parallel Algorithms for Switching Edges in Heterogeneous Graphs☆

PubMed Central

Khan, Maleq; Chen, Jiangzhuo; Marathe, Madhav

2017-01-01

An edge switch is an operation on a graph (or network) where two edges are selected randomly and one of their end vertices are swapped with each other. Edge switch operations have important applications in graph theory and network analysis, such as in generating random networks with a given degree sequence, modeling and analyzing dynamic networks, and in studying various dynamic phenomena over a network. The recent growth of real-world networks motivates the need for efficient parallel algorithms. The dependencies among successive edge switch operations and the requirement to keep the graph simple (i.e., no self-loops or parallel edges) as the edges are switched lead to significant challenges in designing a parallel algorithm. Addressing these challenges requires complex synchronization and communication among the processors leading to difficulties in achieving a good speedup by parallelization. In this paper, we present distributed memory parallel algorithms for switching edges in massive networks. These algorithms provide good speedup and scale well to a large number of processors. A harmonic mean speedup of 73.25 is achieved on eight different networks with 1024 processors. One of the steps in our edge switch algorithms requires the computation of multinomial random variables in parallel. This paper presents the first non-trivial parallel algorithm for the problem, achieving a speedup of 925 using 1024 processors. PMID:28757680

A second-order accurate parabolized Navier-Stokes algorithm for internal flows

NASA Technical Reports Server (NTRS)

Chitsomboon, T.; Tiwari, S. N.

1984-01-01

A parabolized implicit Navier-Stokes algorithm which is of second-order accuracy in both the cross flow and marching directions is presented. The algorithm is used to analyze three model supersonic flow problems (the flow over a 10-degree edge). The results are found to be in good agreement with the results of other techniques available in the literature.

Is It that Difficult to Find a Good Preference Order for the Incremental Algorithm?

ERIC Educational Resources Information Center

Krahmer, Emiel; Koolen, Ruud; Theune, Mariet

2012-01-01

In a recent article published in this journal (van Deemter, Gatt, van der Sluis, & Power, 2012), the authors criticize the Incremental Algorithm (a well-known algorithm for the generation of referring expressions due to Dale & Reiter, 1995, also in this journal) because of its strong reliance on a pre-determined, domain-dependent Preference Order.…

Transition Manifolds of Complex Metastable Systems: Theory and Data-Driven Computation of Effective Dynamics.

PubMed

Bittracher, Andreas; Koltai, Péter; Klus, Stefan; Banisch, Ralf; Dellnitz, Michael; Schütte, Christof

2018-01-01

We consider complex dynamical systems showing metastable behavior, but no local separation of fast and slow time scales. The article raises the question of whether such systems exhibit a low-dimensional manifold supporting its effective dynamics. For answering this question, we aim at finding nonlinear coordinates, called reaction coordinates, such that the projection of the dynamics onto these coordinates preserves the dominant time scales of the dynamics. We show that, based on a specific reducibility property, the existence of good low-dimensional reaction coordinates preserving the dominant time scales is guaranteed. Based on this theoretical framework, we develop and test a novel numerical approach for computing good reaction coordinates. The proposed algorithmic approach is fully local and thus not prone to the curse of dimension with respect to the state space of the dynamics. Hence, it is a promising method for data-based model reduction of complex dynamical systems such as molecular dynamics.

Unsupervised analysis of small animal dynamic Cerenkov luminescence imaging

NASA Astrophysics Data System (ADS)

Spinelli, Antonello E.; Boschi, Federico

2011-12-01

Clustering analysis (CA) and principal component analysis (PCA) were applied to dynamic Cerenkov luminescence images (dCLI). In order to investigate the performances of the proposed approaches, two distinct dynamic data sets obtained by injecting mice with 32P-ATP and 18F-FDG were acquired using the IVIS 200 optical imager. The k-means clustering algorithm has been applied to dCLI and was implemented using interactive data language 8.1. We show that cluster analysis allows us to obtain good agreement between the clustered and the corresponding emission regions like the bladder, the liver, and the tumor. We also show a good correspondence between the time activity curves of the different regions obtained by using CA and manual region of interest analysis on dCLIT and PCA images. We conclude that CA provides an automatic unsupervised method for the analysis of preclinical dynamic Cerenkov luminescence image data.

Modeling Selection and Extinction Mechanisms of Biological Systems

NASA Astrophysics Data System (ADS)

Amirjanov, Adil

In this paper, the behavior of a genetic algorithm is modeled to enhance its applicability as a modeling tool of biological systems. A new description model for selection mechanism is introduced which operates on a portion of individuals of population. The extinction and recolonization mechanism is modeled, and solving the dynamics analytically shows that the genetic drift in the population with extinction/recolonization is doubled. The mathematical analysis of the interaction between selection and extinction/recolonization processes is carried out to assess the dynamics of motion of the macroscopic statistical properties of population. Computer simulations confirm that the theoretical predictions of described models are in good approximations. A mathematical model of GA dynamics was also examined, which describes the anti-predator vigilance in an animal group with respect to a known analytical solution of the problem, and showed a good agreement between them to find the evolutionarily stable strategies.

Transition Manifolds of Complex Metastable Systems

NASA Astrophysics Data System (ADS)

Bittracher, Andreas; Koltai, Péter; Klus, Stefan; Banisch, Ralf; Dellnitz, Michael; Schütte, Christof

2018-04-01

We consider complex dynamical systems showing metastable behavior, but no local separation of fast and slow time scales. The article raises the question of whether such systems exhibit a low-dimensional manifold supporting its effective dynamics. For answering this question, we aim at finding nonlinear coordinates, called reaction coordinates, such that the projection of the dynamics onto these coordinates preserves the dominant time scales of the dynamics. We show that, based on a specific reducibility property, the existence of good low-dimensional reaction coordinates preserving the dominant time scales is guaranteed. Based on this theoretical framework, we develop and test a novel numerical approach for computing good reaction coordinates. The proposed algorithmic approach is fully local and thus not prone to the curse of dimension with respect to the state space of the dynamics. Hence, it is a promising method for data-based model reduction of complex dynamical systems such as molecular dynamics.

Efficient super-resolution image reconstruction applied to surveillance video captured by small unmanned aircraft systems

NASA Astrophysics Data System (ADS)

He, Qiang; Schultz, Richard R.; Chu, Chee-Hung Henry

2008-04-01

The concept surrounding super-resolution image reconstruction is to recover a highly-resolved image from a series of low-resolution images via between-frame subpixel image registration. In this paper, we propose a novel and efficient super-resolution algorithm, and then apply it to the reconstruction of real video data captured by a small Unmanned Aircraft System (UAS). Small UAS aircraft generally have a wingspan of less than four meters, so that these vehicles and their payloads can be buffeted by even light winds, resulting in potentially unstable video. This algorithm is based on a coarse-to-fine strategy, in which a coarsely super-resolved image sequence is first built from the original video data by image registration and bi-cubic interpolation between a fixed reference frame and every additional frame. It is well known that the median filter is robust to outliers. If we calculate pixel-wise medians in the coarsely super-resolved image sequence, we can restore a refined super-resolved image. The primary advantage is that this is a noniterative algorithm, unlike traditional approaches based on highly-computational iterative algorithms. Experimental results show that our coarse-to-fine super-resolution algorithm is not only robust, but also very efficient. In comparison with five well-known super-resolution algorithms, namely the robust super-resolution algorithm, bi-cubic interpolation, projection onto convex sets (POCS), the Papoulis-Gerchberg algorithm, and the iterated back projection algorithm, our proposed algorithm gives both strong efficiency and robustness, as well as good visual performance. This is particularly useful for the application of super-resolution to UAS surveillance video, where real-time processing is highly desired.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems

PubMed Central

Huang, Shuqiang; Tao, Ming

2017-01-01

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms. PMID:28117735

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

Serin, E.; Codel, G.; Mabhouti, H.

Purpose: In small field geometries, the electronic equilibrium can be lost, making it challenging for the dose-calculation algorithm to accurately predict the dose, especially in the presence of tissue heterogeneities. In this study, dosimetric accuracy of Monte Carlo (MC) advanced dose calculation and sequential algorithms of Multiplan treatment planning system were investigated for small radiation fields incident on homogeneous and heterogeneous geometries. Methods: Small open fields of fixed cones of Cyberknife M6 unit 100 to 500 mm2 were used for this study. The fields were incident on in house phantom containing lung, air, and bone inhomogeneities and also homogeneous phantom.more » Using the same film batch, the net OD to dose calibration curve was obtained using CK with the 60 mm fixed cone by delivering 0- 800 cGy. Films were scanned 48 hours after irradiation using an Epson 1000XL flatbed scanner. The dosimetric accuracy of MC and sequential algorithms in the presence of the inhomogeneities was compared against EBT3 film dosimetry Results: Open field tests in a homogeneous phantom showed good agreement between two algorithms and film measurement For MC algorithm, the minimum gamma analysis passing rates between measured and calculated dose distributions were 99.7% and 98.3% for homogeneous and inhomogeneous fields in the case of lung and bone respectively. For sequential algorithm, the minimum gamma analysis passing rates were 98.9% and 92.5% for for homogeneous and inhomogeneous fields respectively for used all cone sizes. In the case of the air heterogeneity, the differences were larger for both calculation algorithms. Overall, when compared to measurement, the MC had better agreement than sequential algorithm. Conclusion: The Monte Carlo calculation algorithm in the Multiplan treatment planning system is an improvement over the existing sequential algorithm. Dose discrepancies were observed for in the presence of air inhomogeneities.« less

Effective side length formula for resonant frequency of equilateral triangular microstrip antenna

NASA Astrophysics Data System (ADS)

Guney, Kerim; Kurt, Erhan

2016-02-01

A novel and accurate expression is obtained by employing the differential evolution algorithm for the effective side length (ESL) of the equilateral triangular microstrip antenna (ETMA). This useful formula allows the antenna engineers to accurately calculate the ESL of the ETMA. The computed resonant frequencies (RFs) show very good agreement with the experimental RFs when this accurate ESL formula is utilised for the computation of the RFs for the first five modes.

Parameter estimation techniques based on optimizing goodness-of-fit statistics for structural reliability

NASA Technical Reports Server (NTRS)

Starlinger, Alois; Duffy, Stephen F.; Palko, Joseph L.

1993-01-01

New methods are presented that utilize the optimization of goodness-of-fit statistics in order to estimate Weibull parameters from failure data. It is assumed that the underlying population is characterized by a three-parameter Weibull distribution. Goodness-of-fit tests are based on the empirical distribution function (EDF). The EDF is a step function, calculated using failure data, and represents an approximation of the cumulative distribution function for the underlying population. Statistics (such as the Kolmogorov-Smirnov statistic and the Anderson-Darling statistic) measure the discrepancy between the EDF and the cumulative distribution function (CDF). These statistics are minimized with respect to the three Weibull parameters. Due to nonlinearities encountered in the minimization process, Powell's numerical optimization procedure is applied to obtain the optimum value of the EDF. Numerical examples show the applicability of these new estimation methods. The results are compared to the estimates obtained with Cooper's nonlinear regression algorithm.

Surface Net Solar Radiation Estimated from Satellite Measurements: Comparisons with Tower Observations

NASA Technical Reports Server (NTRS)

Li, Zhanqing; Leighton, H. G.; Cess, Robert D.

1993-01-01

A parameterization that relates the reflected solar flux at the top of the atmosphere to the net solar flux at the surface in terms of only the column water vapor amount and the solar zenith angle was tested against surface observations. Net surface fluxes deduced from coincidental collocated satellite-measured radiances and from measurements from towers in Boulder during summer and near Saskatoon in winter have mean differences of about 2 W/sq m, regardless of whether the sky is clear or cloudy. Furthermore, comparisons between the net fluxes deduced from the parameterization and from surface measurements showed equally good agreement when the data were partitioned into morning and afternoon observations. This is in contrast to results from an empirical clear-sky algorithm that is unable to account adequately for the effects of clouds and that shows, at Boulder, a distinct morning to afternoon variation, which is presumably due to the predominance of different cloud types throughout the day. It is also demonstrated that the parameterization may be applied to irradiances at the top of the atmosphere that have been temporally averaged by using the temporally averaged column water vapor amount and the temporally averaged cosine of the solar zenith angle. The good agreement between the results of the parameterization and surface measurements suggests that the algorithm is a useful tool for a variety of climate studies.

Vehicle active steering control research based on two-DOF robust internal model control

NASA Astrophysics Data System (ADS)

Wu, Jian; Liu, Yahui; Wang, Fengbo; Bao, Chunjiang; Sun, Qun; Zhao, Youqun

2016-07-01

Because of vehicle's external disturbances and model uncertainties, robust control algorithms have obtained popularity in vehicle stability control. The robust control usually gives up performance in order to guarantee the robustness of the control algorithm, therefore an improved robust internal model control(IMC) algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness. The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties. In order to separate the design process of model tracking from the robustness design process, the improved 2 degree of freedom(DOF) robust internal model controller structure is given from the standard Youla parameterization. Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm, on the basis of a nonlinear vehicle simulation model with a magic tyre model. Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance, which can enhance the vehicle stability and handling, regardless of variations of the vehicle model parameters and the external crosswind interferences. Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

Solving the Traveling Salesman's Problem Using the African Buffalo Optimization.

PubMed

Odili, Julius Beneoluchi; Mohmad Kahar, Mohd Nizam

2016-01-01

This paper proposes the African Buffalo Optimization (ABO) which is a new metaheuristic algorithm that is derived from careful observation of the African buffalos, a species of wild cows, in the African forests and savannahs. This animal displays uncommon intelligence, strategic organizational skills, and exceptional navigational ingenuity in its traversal of the African landscape in search for food. The African Buffalo Optimization builds a mathematical model from the behavior of this animal and uses the model to solve 33 benchmark symmetric Traveling Salesman's Problem and six difficult asymmetric instances from the TSPLIB. This study shows that buffalos are able to ensure excellent exploration and exploitation of the search space through regular communication, cooperation, and good memory of its previous personal exploits as well as tapping from the herd's collective exploits. The results obtained by using the ABO to solve these TSP cases were benchmarked against the results obtained by using other popular algorithms. The results obtained using the African Buffalo Optimization algorithm are very competitive.

Chaotic Image Encryption of Regions of Interest

NASA Astrophysics Data System (ADS)

Xiao, Di; Fu, Qingqing; Xiang, Tao; Zhang, Yushu

Since different regions of an image have different importance, therefore only the important information of the image regions, which the users are really interested in, needs to be encrypted and protected emphatically in some special multimedia applications. However, the regions of interest (ROI) are always some irregular parts, such as the face and the eyes. Assuming the bulk data in transmission without being damaged, we propose a chaotic image encryption algorithm for ROI. ROI with irregular shapes are chosen and detected arbitrarily. Then the chaos-based image encryption algorithm with scrambling, S-box and diffusion parts is used to encrypt the ROI. Further, the whole image is compressed with Huffman coding. At last, a message authentication code (MAC) of the compressed image is generated based on chaotic maps. The simulation results show that the encryption algorithm has a good security level and can resist various attacks. Moreover, the compression method improves the storage and transmission efficiency to some extent, and the MAC ensures the integrity of the transmission data.

Stochastic parallel gradient descent based adaptive optics used for a high contrast imaging coronagraph

NASA Astrophysics Data System (ADS)

Dong, Bing; Ren, De-Qing; Zhang, Xi

2011-08-01

An adaptive optics (AO) system based on a stochastic parallel gradient descent (SPGD) algorithm is proposed to reduce the speckle noises in the optical system of a stellar coronagraph in order to further improve the contrast. The principle of the SPGD algorithm is described briefly and a metric suitable for point source imaging optimization is given. The feasibility and good performance of the SPGD algorithm is demonstrated by an experimental system featured with a 140-actuator deformable mirror and a Hartmann-Shark wavefront sensor. Then the SPGD based AO is applied to a liquid crystal array (LCA) based coronagraph to improve the contrast. The LCA can modulate the incoming light to generate a pupil apodization mask of any pattern. A circular stepped pattern is used in our preliminary experiment and the image contrast shows improvement from 10-3 to 10-4.5 at an angular distance of 2λ/D after being corrected by SPGD based AO.

Image quality evaluation of full reference algorithm

NASA Astrophysics Data System (ADS)

He, Nannan; Xie, Kai; Li, Tong; Ye, Yushan

2018-03-01

Image quality evaluation is a classic research topic, the goal is to design the algorithm, given the subjective feelings consistent with the evaluation value. This paper mainly introduces several typical reference methods of Mean Squared Error(MSE), Peak Signal to Noise Rate(PSNR), Structural Similarity Image Metric(SSIM) and feature similarity(FSIM) of objective evaluation methods. The different evaluation methods are tested by Matlab, and the advantages and disadvantages of these methods are obtained by analyzing and comparing them.MSE and PSNR are simple, but they are not considered to introduce HVS characteristics into image quality evaluation. The evaluation result is not ideal. SSIM has a good correlation and simple calculation ,because it is considered to the human visual effect into image quality evaluation,However the SSIM method is based on a hypothesis,The evaluation result is limited. The FSIM method can be used for test of gray image and color image test, and the result is better. Experimental results show that the new image quality evaluation algorithm based on FSIM is more accurate.

Efficient Compressed Sensing Based MRI Reconstruction using Nonconvex Total Variation Penalties

NASA Astrophysics Data System (ADS)

Lazzaro, D.; Loli Piccolomini, E.; Zama, F.

2016-10-01

This work addresses the problem of Magnetic Resonance Image Reconstruction from highly sub-sampled measurements in the Fourier domain. It is modeled as a constrained minimization problem, where the objective function is a non-convex function of the gradient of the unknown image and the constraints are given by the data fidelity term. We propose an algorithm, Fast Non Convex Reweighted (FNCR), where the constrained problem is solved by a reweighting scheme, as a strategy to overcome the non-convexity of the objective function, with an adaptive adjustment of the penalization parameter. We propose a fast iterative algorithm and we can prove that it converges to a local minimum because the constrained problem satisfies the Kurdyka-Lojasiewicz property. Moreover the adaptation of non convex l0 approximation and penalization parameters, by means of a continuation technique, allows us to obtain good quality solutions, avoiding to get stuck in unwanted local minima. Some numerical experiments performed on MRI sub-sampled data show the efficiency of the algorithm and the accuracy of the solution.

Automatic updating and 3D modeling of airport information from high resolution images using GIS and LIDAR data

NASA Astrophysics Data System (ADS)

Lv, Zheng; Sui, Haigang; Zhang, Xilin; Huang, Xianfeng

2007-11-01

As one of the most important geo-spatial objects and military establishment, airport is always a key target in fields of transportation and military affairs. Therefore, automatic recognition and extraction of airport from remote sensing images is very important and urgent for updating of civil aviation and military application. In this paper, a new multi-source data fusion approach on automatic airport information extraction, updating and 3D modeling is addressed. Corresponding key technologies including feature extraction of airport information based on a modified Ostu algorithm, automatic change detection based on new parallel lines-based buffer detection algorithm, 3D modeling based on gradual elimination of non-building points algorithm, 3D change detecting between old airport model and LIDAR data, typical CAD models imported and so on are discussed in detail. At last, based on these technologies, we develop a prototype system and the results show our method can achieve good effects.

A new method of Quickbird own image fusion

NASA Astrophysics Data System (ADS)

Han, Ying; Jiang, Hong; Zhang, Xiuying

2009-10-01

With the rapid development of remote sensing technology, the means of accessing to remote sensing data become increasingly abundant, thus the same area can form a large number of multi-temporal, different resolution image sequence. At present, the fusion methods are mainly: HPF, IHS transform method, PCA method, Brovey, Mallat algorithm and wavelet transform and so on. There exists a serious distortion of the spectrums in the IHS transform, Mallat algorithm omits low-frequency information of the high spatial resolution images, the integration results of which has obvious blocking effects. Wavelet multi-scale decomposition for different sizes, the directions, details and the edges can have achieved very good results, but different fusion rules and algorithms can achieve different effects. This article takes the Quickbird own image fusion as an example, basing on wavelet transform and HVS, wavelet transform and IHS integration. The result shows that the former better. This paper introduces the correlation coefficient, the relative average spectral error index and usual index to evaluate the quality of image.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Solving the Traveling Salesman's Problem Using the African Buffalo Optimization

PubMed Central

Odili, Julius Beneoluchi; Mohmad Kahar, Mohd Nizam

2016-01-01

This paper proposes the African Buffalo Optimization (ABO) which is a new metaheuristic algorithm that is derived from careful observation of the African buffalos, a species of wild cows, in the African forests and savannahs. This animal displays uncommon intelligence, strategic organizational skills, and exceptional navigational ingenuity in its traversal of the African landscape in search for food. The African Buffalo Optimization builds a mathematical model from the behavior of this animal and uses the model to solve 33 benchmark symmetric Traveling Salesman's Problem and six difficult asymmetric instances from the TSPLIB. This study shows that buffalos are able to ensure excellent exploration and exploitation of the search space through regular communication, cooperation, and good memory of its previous personal exploits as well as tapping from the herd's collective exploits. The results obtained by using the ABO to solve these TSP cases were benchmarked against the results obtained by using other popular algorithms. The results obtained using the African Buffalo Optimization algorithm are very competitive. PMID:26880872

Concurrent design of an RTP chamber and advanced control system

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

Spence, P.; Schaper, C.; Kermani, A.

1995-12-31

A concurrent-engineering approach is applied to the development of an axisymmetric rapid-thermal-processing (RTP) reactor and its associated temperature controller. Using a detailed finite-element thermal model as a surrogate for actual hardware, the authors have developed and tested a multi-input multi-output (MIMO) controller. Closed-loop simulations are performed by linking the control algorithm with the finite-element code. Simulations show that good temperature uniformity is maintained on the wafer during both steady and transient conditions. A numerical study shows the effect of ramp rate, feedback gain, sensor placement, and wafer-emissivity patterns on system performance.

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.

Gauge properties of the guiding center variational symplectic integrator

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

Squire, J.; Tang, W. M.; Qin, H.

Variational symplectic algorithms have recently been developed for carrying out long-time simulation of charged particles in magnetic fields [H. Qin and X. Guan, Phys. Rev. Lett. 100, 035006 (2008); H. Qin, X. Guan, and W. Tang, Phys. Plasmas (2009); J. Li, H. Qin, Z. Pu, L. Xie, and S. Fu, Phys. Plasmas 18, 052902 (2011)]. As a direct consequence of their derivation from a discrete variational principle, these algorithms have very good long-time energy conservation, as well as exactly preserving discrete momenta. We present stability results for these algorithms, focusing on understanding how explicit variational integrators can be designed formore » this type of system. It is found that for explicit algorithms, an instability arises because the discrete symplectic structure does not become the continuous structure in the t{yields}0 limit. We examine how a generalized gauge transformation can be used to put the Lagrangian in the 'antisymmetric discretization gauge,' in which the discrete symplectic structure has the correct form, thus eliminating the numerical instability. Finally, it is noted that the variational guiding center algorithms are not electromagnetically gauge invariant. By designing a model discrete Lagrangian, we show that the algorithms are approximately gauge invariant as long as A and {phi} are relatively smooth. A gauge invariant discrete Lagrangian is very important in a variational particle-in-cell algorithm where it ensures current continuity and preservation of Gauss's law [J. Squire, H. Qin, and W. Tang (to be published)].« less

When Machines Think: Radiology's Next Frontier.

PubMed

Dreyer, Keith J; Geis, J Raymond

2017-12-01

Artificial intelligence (AI), machine learning, and deep learning are terms now seen frequently, all of which refer to computer algorithms that change as they are exposed to more data. Many of these algorithms are surprisingly good at recognizing objects in images. The combination of large amounts of machine-consumable digital data, increased and cheaper computing power, and increasingly sophisticated statistical models combine to enable machines to find patterns in data in ways that are not only cost-effective but also potentially beyond humans' abilities. Building an AI algorithm can be surprisingly easy. Understanding the associated data structures and statistics, on the other hand, is often difficult and obscure. Converting the algorithm into a sophisticated product that works consistently in broad, general clinical use is complex and incompletely understood. To show how these AI products reduce costs and improve outcomes will require clinical translation and industrial-grade integration into routine workflow. Radiology has the chance to leverage AI to become a center of intelligently aggregated, quantitative, diagnostic information. Centaur radiologists, formed as a synergy of human plus computer, will provide interpretations using data extracted from images by humans and image-analysis computer algorithms, as well as the electronic health record, genomics, and other disparate sources. These interpretations will form the foundation of precision health care, or care customized to an individual patient. © RSNA, 2017.

Segmenting texts from outdoor images taken by mobile phones using color features

NASA Astrophysics Data System (ADS)

Liu, Zongyi; Zhou, Hanning

2011-01-01

Recognizing texts from images taken by mobile phones with low resolution has wide applications. It has been shown that a good image binarization can substantially improve the performances of OCR engines. In this paper, we present a framework to segment texts from outdoor images taken by mobile phones using color features. The framework consists of three steps: (i) the initial process including image enhancement, binarization and noise filtering, where we binarize the input images in each RGB channel, and apply component level noise filtering; (ii) grouping components into blocks using color features, where we compute the component similarities by dynamically adjusting the weights of RGB channels, and merge groups hierachically, and (iii) blocks selection, where we use the run-length features and choose the Support Vector Machine (SVM) as the classifier. We tested the algorithm using 13 outdoor images taken by an old-style LG-64693 mobile phone with 640x480 resolution. We compared the segmentation results with Tsar's algorithm, a state-of-the-art camera text detection algorithm, and show that our algorithm is more robust, particularly in terms of the false alarm rates. In addition, we also evaluated the impacts of our algorithm on the Abbyy's FineReader, one of the most popular commercial OCR engines in the market.

Hybrid spiral-dynamic bacteria-chemotaxis algorithm with application to control two-wheeled machines.

PubMed

Goher, K M; Almeshal, A M; Agouri, S A; Nasir, A N K; Tokhi, M O; Alenezi, M R; Al Zanki, T; Fadlallah, S O

2017-01-01

This paper presents the implementation of the hybrid spiral-dynamic bacteria-chemotaxis (HSDBC) approach to control two different configurations of a two-wheeled vehicle. The HSDBC is a combination of bacterial chemotaxis used in bacterial forging algorithm (BFA) and the spiral-dynamic algorithm (SDA). BFA provides a good exploration strategy due to the chemotaxis approach. However, it endures an oscillation problem near the end of the search process when using a large step size. Conversely; for a small step size, it affords better exploitation and accuracy with slower convergence. SDA provides better stability when approaching an optimum point and has faster convergence speed. This may cause the search agents to get trapped into local optima which results in low accurate solution. HSDBC exploits the chemotactic strategy of BFA and fitness accuracy and convergence speed of SDA so as to overcome the problems associated with both the SDA and BFA algorithms alone. The HSDBC thus developed is evaluated in optimizing the performance and energy consumption of two highly nonlinear platforms, namely single and double inverted pendulum-like vehicles with an extended rod. Comparative results with BFA and SDA show that the proposed algorithm is able to result in better performance of the highly nonlinear systems.

Rational approximations to rational models: alternative algorithms for category learning.

PubMed

Sanborn, Adam N; Griffiths, Thomas L; Navarro, Daniel J

2010-10-01

Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of rational process models that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson's (1990, 1991) rational model of categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose 2 alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure appropriate when all stimuli are presented simultaneously, and particle filters, which sequentially approximate the posterior distribution with a small number of samples that are updated as new data become available. Applying these algorithms to several existing datasets shows that a particle filter with a single particle provides a good description of human inferences.

The Effect of Photon Statistics and Pulse Shaping on the Performance of the Wiener Filter Crystal Identification Algorithm Applied to LabPET Phoswich Detectors

NASA Astrophysics Data System (ADS)

Yousefzadeh, Hoorvash Camilia; Lecomte, Roger; Fontaine, Réjean

2012-06-01

A fast Wiener filter-based crystal identification (WFCI) algorithm was recently developed to discriminate crystals with close scintillation decay times in phoswich detectors. Despite the promising performance of WFCI, the influence of various physical factors and electrical noise sources of the data acquisition chain (DAQ) on the crystal identification process was not fully investigated. This paper examines the effect of different noise sources, such as photon statistics, avalanche photodiode (APD) excess multiplication noise, and front-end electronic noise, as well as the influence of different shaping filters on the performance of the WFCI algorithm. To this end, a PET-like signal simulator based on a model of the LabPET DAQ, a small animal APD-based digital PET scanner, was developed. Simulated signals were generated under various noise conditions with CR-RC shapers of order 1, 3, and 5 having different time constants (τ). Applying the WFCI algorithm to these simulated signals showed that the non-stationary Poisson photon statistics is the main contributor to the identification error of WFCI algorithm. A shaping filter of order 1 with τ = 50 ns yielded the best WFCI performance (error 1%), while a longer shaping time of τ = 100 ns slightly degraded the WFCI performance (error 3%). Filters of higher orders with fast shaping time constants (10-33 ns) also produced good WFCI results (error 1.4% to 1.6%). This study shows the advantage of the pulse simulator in evaluating various DAQ conditions and confirms the influence of the detection chain on the WFCI performance.

Comprehensive evaluation of an image segmentation technique for measuring tumor volume from CT images

NASA Astrophysics Data System (ADS)

Deng, Xiang; Huang, Haibin; Zhu, Lei; Du, Guangwei; Xu, Xiaodong; Sun, Yiyong; Xu, Chenyang; Jolly, Marie-Pierre; Chen, Jiuhong; Xiao, Jie; Merges, Reto; Suehling, Michael; Rinck, Daniel; Song, Lan; Jin, Zhengyu; Jiang, Zhaoxia; Wu, Bin; Wang, Xiaohong; Zhang, Shuai; Peng, Weijun

2008-03-01

Comprehensive quantitative evaluation of tumor segmentation technique on large scale clinical data sets is crucial for routine clinical use of CT based tumor volumetry for cancer diagnosis and treatment response evaluation. In this paper, we present a systematic validation study of a semi-automatic image segmentation technique for measuring tumor volume from CT images. The segmentation algorithm was tested using clinical data of 200 tumors in 107 patients with liver, lung, lymphoma and other types of cancer. The performance was evaluated using both accuracy and reproducibility. The accuracy was assessed using 7 commonly used metrics that can provide complementary information regarding the quality of the segmentation results. The reproducibility was measured by the variation of the volume measurements from 10 independent segmentations. The effect of disease type, lesion size and slice thickness of image data on the accuracy measures were also analyzed. Our results demonstrate that the tumor segmentation algorithm showed good correlation with ground truth for all four lesion types (r = 0.97, 0.99, 0.97, 0.98, p < 0.0001 for liver, lung, lymphoma and other respectively). The segmentation algorithm can produce relatively reproducible volume measurements on all lesion types (coefficient of variation in the range of 10-20%). Our results show that the algorithm is insensitive to lesion size (coefficient of determination close to 0) and slice thickness of image data(p > 0.90). The validation framework used in this study has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale evaluation of segmentation techniques for other clinical applications.

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

NASA Astrophysics Data System (ADS)

Xiao, Xiaoxu

2018-04-01

In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

Cooperative optimization and their application in LDPC codes

NASA Astrophysics Data System (ADS)

Chen, Ke; Rong, Jian; Zhong, Xiaochun

2008-10-01

Cooperative optimization is a new way for finding global optima of complicated functions of many variables. The proposed algorithm is a class of message passing algorithms and has solid theory foundations. It can achieve good coding gains over the sum-product algorithm for LDPC codes. For (6561, 4096) LDPC codes, the proposed algorithm can achieve 2.0 dB gains over the sum-product algorithm at BER of 4×10-7. The decoding complexity of the proposed algorithm is lower than the sum-product algorithm can do; furthermore, the former can achieve much lower error floor than the latter can do after the Eb / No is higher than 1.8 dB.

Real time chromametry measurement for food quality detection using mobile device

NASA Astrophysics Data System (ADS)

Witjaksono, Gunawan; Mohamad Hussin, Nur Haziqah Farah Binti; Abdelkreem Saeed Rabih, Almur; Alfa, Sagir

2017-09-01

Freshness of the food is the main factor in determining the quality and safety of the consumed food and hence consumers satisfaction. Current technologies for food quality determination depend on colour changing labels to indicate the freshness level, which is subjective to human eyes. The goal of this paper is to design and develop chromatic algorithm based on RGB colour reading and correlation with pH values for real time determination of freshness level of shrimp. The results show that the developed algorithm is able to measure, analyse and display the freshness level of food directly on the screen of a mobile app technology. The mobile app is developed on Android platform and is tested in the shrimp freshness range by stating whether it is “fresh, good or spoiled”.

A novel artificial fish swarm algorithm for solving large-scale reliability-redundancy application problem.

PubMed

He, Qiang; Hu, Xiangtao; Ren, Hong; Zhang, Hongqi

2015-11-01

A novel artificial fish swarm algorithm (NAFSA) is proposed for solving large-scale reliability-redundancy allocation problem (RAP). In NAFSA, the social behaviors of fish swarm are classified in three ways: foraging behavior, reproductive behavior, and random behavior. The foraging behavior designs two position-updating strategies. And, the selection and crossover operators are applied to define the reproductive ability of an artificial fish. For the random behavior, which is essentially a mutation strategy, the basic cloud generator is used as the mutation operator. Finally, numerical results of four benchmark problems and a large-scale RAP are reported and compared. NAFSA shows good performance in terms of computational accuracy and computational efficiency for large scale RAP. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Survey of new vector computers: The CRAY 1S from CRAY research; the CYBER 205 from CDC and the parallel computer from ICL - architecture and programming

NASA Technical Reports Server (NTRS)

Gentzsch, W.

1982-01-01

Problems which can arise with vector and parallel computers are discussed in a user oriented context. Emphasis is placed on the algorithms used and the programming techniques adopted. Three recently developed supercomputers are examined and typical application examples are given in CRAY FORTRAN, CYBER 205 FORTRAN and DAP (distributed array processor) FORTRAN. The systems performance is compared. The addition of parts of two N x N arrays is considered. The influence of the architecture on the algorithms and programming language is demonstrated. Numerical analysis of magnetohydrodynamic differential equations by an explicit difference method is illustrated, showing very good results for all three systems. The prognosis for supercomputer development is assessed.

Satellite-based retrieval of particulate matter concentrations over the United Arab Emirates (UAE)

NASA Astrophysics Data System (ADS)

Zhao, Jun; Temimi, Marouane; Hareb, Fahad; Eibedingil, Iyasu

2016-04-01

In this study, an empirical algorithm was established to retrieve particulate matter (PM) concentrations (PM2.5 and PM10) using satellite-derived aerosol optical depth (AOD) over the United Arab Emirates (UAE). Validation of the proposed algorithm using ground truth data demonstrates its good accuracy. Time series of in situ measured PM concentrations between 2014 and 2015 showed high values in summer and low values in winter. Estimated and in situ measured PM concentrations were higher in 2015 than 2014. Remote sensing is an essential tool to reveal and back track the seasonality and inter-annual variations of PM concentrations and provide valuable information on the protection of human health and the response of air quality to anthropogenic activities and climate change.

Introduction to Blueweb: A Decentralized Scatternet Formation Algorithm for Bluetooth Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Yu, Chih-Min; Huang, Chia-Chi

In this letter, a decentralized scatternet formation algorithm called Bluelayer is proposed. First, Bluelayer uses a designated root to construct a tree-shaped subnet and propagates an integer variable k1 called counter limit as well as a constant k in its downstream direction to determine new roots. Then each new root asks its upstream master to start a return connection procedure to convert the tree-shaped subnet into a web-shaped subnet for its immediate upstream root. At the same time, each new root repeats the same procedure as the root to build its own subnet until the whole scatternet is formed. Simulation results show that Bluelayer achieves good network scalability and generates an efficient scatternet configuration for various sizes of Bluetooth ad hoc network.

Deformation Invariant Attribute Vector for Deformable Registration of Longitudinal Brain MR Images

PubMed Central

Li, Gang; Guo, Lei; Liu, Tianming

2009-01-01

This paper presents a novel approach to define deformation invariant attribute vector (DIAV) for each voxel in 3D brain image for the purpose of anatomic correspondence detection. The DIAV method is validated by using synthesized deformation in 3D brain MRI images. Both theoretic analysis and experimental studies demonstrate that the proposed DIAV is invariant to general nonlinear deformation. Moreover, our experimental results show that the DIAV is able to capture rich anatomic information around the voxels and exhibit strong discriminative ability. The DIAV has been integrated into a deformable registration algorithm for longitudinal brain MR images, and the results on both simulated and real brain images are provided to demonstrate the good performance of the proposed registration algorithm based on matching of DIAVs. PMID:19369031

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

A second order derivative scheme based on Bregman algorithm class

NASA Astrophysics Data System (ADS)

Campagna, Rosanna; Crisci, Serena; Cuomo, Salvatore; Galletti, Ardelio; Marcellino, Livia

2016-10-01

The algorithms based on the Bregman iterative regularization are known for efficiently solving convex constraint optimization problems. In this paper, we introduce a second order derivative scheme for the class of Bregman algorithms. Its properties of convergence and stability are investigated by means of numerical evidences. Moreover, we apply the proposed scheme to an isotropic Total Variation (TV) problem arising out of the Magnetic Resonance Image (MRI) denoising. Experimental results confirm that our algorithm has good performance in terms of denoising quality, effectiveness and robustness.

Partitioning sparse matrices with eigenvectors of graphs

NASA Technical Reports Server (NTRS)

Pothen, Alex; Simon, Horst D.; Liou, Kang-Pu

1990-01-01

The problem of computing a small vertex separator in a graph arises in the context of computing a good ordering for the parallel factorization of sparse, symmetric matrices. An algebraic approach for computing vertex separators is considered in this paper. It is shown that lower bounds on separator sizes can be obtained in terms of the eigenvalues of the Laplacian matrix associated with a graph. The Laplacian eigenvectors of grid graphs can be computed from Kronecker products involving the eigenvectors of path graphs, and these eigenvectors can be used to compute good separators in grid graphs. A heuristic algorithm is designed to compute a vertex separator in a general graph by first computing an edge separator in the graph from an eigenvector of the Laplacian matrix, and then using a maximum matching in a subgraph to compute the vertex separator. Results on the quality of the separators computed by the spectral algorithm are presented, and these are compared with separators obtained from other algorithms for computing separators. Finally, the time required to compute the Laplacian eigenvector is reported, and the accuracy with which the eigenvector must be computed to obtain good separators is considered. The spectral algorithm has the advantage that it can be implemented on a medium-size multiprocessor in a straightforward manner.

Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement

PubMed Central

Negri, Lucas; Nied, Ademir; Kalinowski, Hypolito; Paterno, Aleksander

2011-01-01

This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented. PMID:22163806

A simple and fast heuristic for protein structure comparison

PubMed Central

Pelta, David A; González, Juan R; Moreno Vega, Marcos

2008-01-01

Background Protein structure comparison is a key problem in bioinformatics. There exist several methods for doing protein comparison, being the solution of the Maximum Contact Map Overlap problem (MAX-CMO) one of the alternatives available. Although this problem may be solved using exact algorithms, researchers require approximate algorithms that obtain good quality solutions using less computational resources than the formers. Results We propose a variable neighborhood search metaheuristic for solving MAX-CMO. We analyze this strategy in two aspects: 1) from an optimization point of view the strategy is tested on two different datasets, obtaining an error of 3.5%(over 2702 pairs) and 1.7% (over 161 pairs) with respect to optimal values; thus leading to high accurate solutions in a simpler and less expensive way than exact algorithms; 2) in terms of protein structure classification, we conduct experiments on three datasets and show that is feasible to detect structural similarities at SCOP's family and CATH's architecture levels using normalized overlap values. Some limitations and the role of normalization are outlined for doing classification at SCOP's fold level. Conclusion We designed, implemented and tested.a new tool for solving MAX-CMO, based on a well-known metaheuristic technique. The good balance between solution's quality and computational effort makes it a valuable tool. Moreover, to the best of our knowledge, this is the first time the MAX-CMO measure is tested at SCOP's fold and CATH's architecture levels with encouraging results. Software is available for download at . PMID:18366735

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

Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variablemore » objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.« less

Clustering-based Feature Learning on Variable Stars

NASA Astrophysics Data System (ADS)

Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

2016-04-01

The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

PubMed Central

Zhu, Zhichuan; Zhao, Qingdong; Liu, Liwei; Zhang, Lijuan

2018-01-01

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified. PMID:29853983

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO.

PubMed

Li, Yang; Zhu, Zhichuan; Hou, Alin; Zhao, Qingdong; Liu, Liwei; Zhang, Lijuan

2018-01-01

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

BootGraph: probabilistic fiber tractography using bootstrap algorithms and graph theory.

PubMed

Vorburger, Robert S; Reischauer, Carolin; Boesiger, Peter

2013-02-01

Bootstrap methods have recently been introduced to diffusion-weighted magnetic resonance imaging to estimate the measurement uncertainty of ensuing diffusion parameters directly from the acquired data without the necessity to assume a noise model. These methods have been previously combined with deterministic streamline tractography algorithms to allow for the assessment of connection probabilities in the human brain. Thereby, the local noise induced disturbance in the diffusion data is accumulated additively due to the incremental progression of streamline tractography algorithms. Graph based approaches have been proposed to overcome this drawback of streamline techniques. For this reason, the bootstrap method is in the present work incorporated into a graph setup to derive a new probabilistic fiber tractography method, called BootGraph. The acquired data set is thereby converted into a weighted, undirected graph by defining a vertex in each voxel and edges between adjacent vertices. By means of the cone of uncertainty, which is derived using the wild bootstrap, a weight is thereafter assigned to each edge. Two path finding algorithms are subsequently applied to derive connection probabilities. While the first algorithm is based on the shortest path approach, the second algorithm takes all existing paths between two vertices into consideration. Tracking results are compared to an established algorithm based on the bootstrap method in combination with streamline fiber tractography and to another graph based algorithm. The BootGraph shows a very good performance in crossing situations with respect to false negatives and permits incorporating additional constraints, such as a curvature threshold. By inheriting the advantages of the bootstrap method and graph theory, the BootGraph method provides a computationally efficient and flexible probabilistic tractography setup to compute connection probability maps and virtual fiber pathways without the drawbacks of streamline tractography algorithms or the assumption of a noise distribution. Moreover, the BootGraph can be applied to common DTI data sets without further modifications and shows a high repeatability. Thus, it is very well suited for longitudinal studies and meta-studies based on DTI. Copyright © 2012 Elsevier Inc. All rights reserved.

Nuclear Potential Clustering As a New Tool to Detect Patterns in High Dimensional Datasets

NASA Astrophysics Data System (ADS)

Tonkova, V.; Paulus, D.; Neeb, H.

2013-02-01

We present a new approach for the clustering of high dimensional data without prior assumptions about the structure of the underlying distribution. The proposed algorithm is based on a concept adapted from nuclear physics. To partition the data, we model the dynamic behaviour of nucleons interacting in an N-dimensional space. An adaptive nuclear potential, comprised of a short-range attractive (strong interaction) and a long-range repulsive term (Coulomb force) is assigned to each data point. By modelling the dynamics, nucleons that are densely distributed in space fuse to build nuclei (clusters) whereas single point clusters repel each other. The formation of clusters is completed when the system reaches the state of minimal potential energy. The data are then grouped according to the particles' final effective potential energy level. The performance of the algorithm is tested with several synthetic datasets showing that the proposed method can robustly identify clusters even when complex configurations are present. Furthermore, quantitative MRI data from 43 multiple sclerosis patients were analyzed, showing a reasonable splitting into subgroups according to the individual patients' disease grade. The good performance of the algorithm on such highly correlated non-spherical datasets, which are typical for MRI derived image features, shows that Nuclear Potential Clustering is a valuable tool for automated data analysis, not only in the MRI domain.

A tight upper bound for quadratic knapsack problems in grid-based wind farm layout optimization

NASA Astrophysics Data System (ADS)

Quan, Ning; Kim, Harrison M.

2018-03-01

The 0-1 quadratic knapsack problem (QKP) in wind farm layout optimization models possible turbine locations as nodes, and power loss due to wake effects between pairs of turbines as edges in a complete graph. The goal is to select up to a certain number of turbine locations such that the sum of selected node and edge coefficients is maximized. Finding the optimal solution to the QKP is difficult in general, but it is possible to obtain a tight upper bound on the QKP's optimal value which facilitates the use of heuristics to solve QKPs by giving a good estimate of the optimality gap of any feasible solution. This article applies an upper bound method that is especially well-suited to QKPs in wind farm layout optimization due to certain features of the formulation that reduce the computational complexity of calculating the upper bound. The usefulness of the upper bound was demonstrated by assessing the performance of the greedy algorithm for solving QKPs in wind farm layout optimization. The results show that the greedy algorithm produces good solutions within 4% of the optimal value for small to medium sized problems considered in this article.

Innovative hyperchaotic encryption algorithm for compressed video

NASA Astrophysics Data System (ADS)

Yuan, Chun; Zhong, Yuzhuo; Yang, Shiqiang

2002-12-01

It is accepted that stream cryptosystem can achieve good real-time performance and flexibility which implements encryption by selecting few parts of the block data and header information of the compressed video stream. Chaotic random number generator, for example Logistics Map, is a comparatively promising substitute, but it is easily attacked by nonlinear dynamic forecasting and geometric information extracting. In this paper, we present a hyperchaotic cryptography scheme to encrypt the compressed video, which integrates Logistics Map with Z(232 - 1) field linear congruential algorithm to strengthen the security of the mono-chaotic cryptography, meanwhile, the real-time performance and flexibility of the chaotic sequence cryptography are maintained. It also integrates with the dissymmetrical public-key cryptography and implements encryption and identity authentification on control parameters at initialization phase. In accord with the importance of data in compressed video stream, encryption is performed in layered scheme. In the innovative hyperchaotic cryptography, the value and the updating frequency of control parameters can be changed online to satisfy the requirement of the network quality, processor capability and security requirement. The innovative hyperchaotic cryprography proves robust security by cryptoanalysis, shows good real-time performance and flexible implement capability through the arithmetic evaluating and test.

Contrast improvement of continuous wave diffuse optical tomography reconstruction by hybrid approach using least square and genetic algorithm

NASA Astrophysics Data System (ADS)

Patra, Rusha; Dutta, Pranab K.

2015-07-01

Reconstruction of the absorption coefficient of tissue with good contrast is of key importance in functional diffuse optical imaging. A hybrid approach using model-based iterative image reconstruction and a genetic algorithm is proposed to enhance the contrast of the reconstructed image. The proposed method yields an observed contrast of 98.4%, mean square error of 0.638×10-3, and object centroid error of (0.001 to 0.22) mm. Experimental validation of the proposed method has also been provided with tissue-like phantoms which shows a significant improvement in image quality and thus establishes the potential of the method for functional diffuse optical tomography reconstruction with continuous wave setup. A case study of finger joint imaging is illustrated as well to show the prospect of the proposed method in clinical diagnosis. The method can also be applied to the concentration measurement of a region of interest in a turbid medium.

Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain.

PubMed

Ali, Anjum A; Dale, Anders M; Badea, Alexandra; Johnson, G Allan

2005-08-15

We present the automated segmentation of magnetic resonance microscopy (MRM) images of the C57BL/6J mouse brain into 21 neuroanatomical structures, including the ventricular system, corpus callosum, hippocampus, caudate putamen, inferior colliculus, internal capsule, globus pallidus, and substantia nigra. The segmentation algorithm operates on multispectral, three-dimensional (3D) MR data acquired at 90-microm isotropic resolution. Probabilistic information used in the segmentation is extracted from training datasets of T2-weighted, proton density-weighted, and diffusion-weighted acquisitions. Spatial information is employed in the form of prior probabilities of occurrence of a structure at a location (location priors) and the pairwise probabilities between structures (contextual priors). Validation using standard morphometry indices shows good consistency between automatically segmented and manually traced data. Results achieved in the mouse brain are comparable with those achieved in human brain studies using similar techniques. The segmentation algorithm shows excellent potential for routine morphological phenotyping of mouse models.

A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography

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

Yang, Xiaogang; De Carlo, Francesco; Phatak, Charudatta

This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential forreducing or removingother artifacts caused by instrument instability, detector non-linearity,etc. An open-source toolbox, which integratesmore » the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.« less

Ant-cuckoo colony optimization for feature selection in digital mammogram.

PubMed

Jona, J B; Nagaveni, N

2014-01-15

Digital mammogram is the only effective screening method to detect the breast cancer. Gray Level Co-occurrence Matrix (GLCM) textural features are extracted from the mammogram. All the features are not essential to detect the mammogram. Therefore identifying the relevant feature is the aim of this work. Feature selection improves the classification rate and accuracy of any classifier. In this study, a new hybrid metaheuristic named Ant-Cuckoo Colony Optimization a hybrid of Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for feature selection in Digital Mammogram. ACO is a good metaheuristic optimization technique but the drawback of this algorithm is that the ant will walk through the path where the pheromone density is high which makes the whole process slow hence CS is employed to carry out the local search of ACO. Support Vector Machine (SVM) classifier with Radial Basis Kernal Function (RBF) is done along with the ACO to classify the normal mammogram from the abnormal mammogram. Experiments are conducted in miniMIAS database. The performance of the new hybrid algorithm is compared with the ACO and PSO algorithm. The results show that the hybrid Ant-Cuckoo Colony Optimization algorithm is more accurate than the other techniques.

Parallel multi-join query optimization algorithm for distributed sensor network in the internet of things

NASA Astrophysics Data System (ADS)

Zheng, Yan

2015-03-01

Internet of things (IoT), focusing on providing users with information exchange and intelligent control, attracts a lot of attention of researchers from all over the world since the beginning of this century. IoT is consisted of large scale of sensor nodes and data processing units, and the most important features of IoT can be illustrated as energy confinement, efficient communication and high redundancy. With the sensor nodes increment, the communication efficiency and the available communication band width become bottle necks. Many research work is based on the instance which the number of joins is less. However, it is not proper to the increasing multi-join query in whole internet of things. To improve the communication efficiency between parallel units in the distributed sensor network, this paper proposed parallel query optimization algorithm based on distribution attributes cost graph. The storage information relations and the network communication cost are considered in this algorithm, and an optimized information changing rule is established. The experimental result shows that the algorithm has good performance, and it would effectively use the resource of each node in the distributed sensor network. Therefore, executive efficiency of multi-join query between different nodes could be improved.

Crystal Identification in Dual-Layer-Offset DOI-PET Detectors Using Stratified Peak Tracking Based on SVD and Mean-Shift Algorithm

NASA Astrophysics Data System (ADS)

Wei, Qingyang; Dai, Tiantian; Ma, Tianyu; Liu, Yaqiang; Gu, Yu

2016-10-01

An Anger-logic based pixelated PET detector block requires a crystal position map (CPM) to assign the position of each detected event to a most probable crystal index. Accurate assignments are crucial to PET imaging performance. In this paper, we present a novel automatic approach to generate the CPMs for dual-layer offset (DLO) PET detectors using a stratified peak tracking method. In which, the top and bottom layers are distinguished by their intensity difference and the peaks of the top and bottom layers are tracked based on a singular value decomposition (SVD) and mean-shift algorithm in succession. The CPM is created by classifying each pixel to its nearest peak and assigning the pixel with the crystal index of that peak. A Matlab-based graphical user interface program was developed including the automatic algorithm and a manual interaction procedure. The algorithm was tested for three DLO PET detector blocks. Results show that the proposed method exhibits good performance as well as robustness for all the three blocks. Compared to the existing methods, our approach can directly distinguish the layer and crystal indices using the information of intensity and offset grid pattern.

Side-by-side ANFIS as a useful tool for estimating correlated thermophysical properties

NASA Astrophysics Data System (ADS)

Grieu, Stéphane; Faugeroux, Olivier; Traoré, Adama; Claudet, Bernard; Bodnar, Jean-Luc

2015-12-01

In the present paper, an artificial intelligence-based approach dealing with the estimation of correlated thermophysical properties is designed and evaluated. This new and "intelligent" approach makes use of photothermal responses obtained when homogeneous materials are subjected to a light flux. Commonly, gradient-based algorithms are used as parameter estimation techniques. Unfortunately, such algorithms show instabilities leading to non-convergence in case of correlated properties to be estimated from a rebuilt impulse response. So, the main objective of the present work was to simultaneously estimate both the thermal diffusivity and conductivity of homogeneous materials, from front-face or rear-face photothermal responses to pseudo random binary signals. To this end, we used side-by-side neuro-fuzzy systems (adaptive network-based fuzzy inference systems) trained with a hybrid algorithm. We focused on the impact on generalization of both the examples used during training and the fuzzification process. In addition, computation time was a key point to consider. That is why the developed algorithm is computationally tractable and allows both the thermal diffusivity and conductivity of homogeneous materials to be simultaneously estimated with very good accuracy (the generalization error ranges between 4.6% and 6.2%).

Retrieve Optically Thick Ice Cloud Microphysical Properties by Using Airborne Dual-Wavelength Radar Measurements

NASA Technical Reports Server (NTRS)

Wang, Zhien; Heymsfield, Gerald M.; Li, Lihua; Heymsfield, Andrew J.

2005-01-01

An algorithm to retrieve optically thick ice cloud microphysical property profiles is developed by using the GSFC 9.6 GHz ER-2 Doppler Radar (EDOP) and the 94 GHz Cloud Radar System (CRS) measurements aboard the high-altitude ER-2 aircraft. In situ size distribution and total water content data from the CRYSTAL-FACE field campaign are used for the algorithm development. To reduce uncertainty in calculated radar reflectivity factors (Ze) at these wavelengths, coincident radar measurements and size distribution data are used to guide the selection of mass-length relationships and to deal with the density and non-spherical effects of ice crystals on the Ze calculations. The algorithm is able to retrieve microphysical property profiles of optically thick ice clouds, such as, deep convective and anvil clouds, which are very challenging for single frequency radar and lidar. Examples of retrieved microphysical properties for a deep convective clouds are presented, which show that EDOP and CRS measurements provide rich information to study cloud structure and evolution. Good agreement between IWPs derived from an independent submillimeter-wave radiometer, CoSSIR, and dual-wavelength radar measurements indicates accuracy of the IWC retrieved from the two-frequency radar algorithm.

Ringed Seal Search for Global Optimization via a Sensitive Search Model.

PubMed

Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar

2016-01-01

The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global optimization problems.

Amplitude inversion of the 2D analytic signal of magnetic anomalies through the differential evolution algorithm

NASA Astrophysics Data System (ADS)

Ekinci, Yunus Levent; Özyalın, Şenol; Sındırgı, Petek; Balkaya, Çağlayan; Göktürkler, Gökhan

2017-12-01

In this work, analytic signal amplitude (ASA) inversion of total field magnetic anomalies has been achieved by differential evolution (DE) which is a population-based evolutionary metaheuristic algorithm. Using an elitist strategy, the applicability and effectiveness of the proposed inversion algorithm have been evaluated through the anomalies due to both hypothetical model bodies and real isolated geological structures. Some parameter tuning studies relying mainly on choosing the optimum control parameters of the algorithm have also been performed to enhance the performance of the proposed metaheuristic. Since ASAs of magnetic anomalies are independent of both ambient field direction and the direction of magnetization of the causative sources in a two-dimensional (2D) case, inversions of synthetic noise-free and noisy single model anomalies have produced satisfactory solutions showing the practical applicability of the algorithm. Moreover, hypothetical studies using multiple model bodies have clearly showed that the DE algorithm is able to cope with complicated anomalies and some interferences from neighbouring sources. The proposed algorithm has then been used to invert small- (120 m) and large-scale (40 km) magnetic profile anomalies of an iron deposit (Kesikköprü-Bala, Turkey) and a deep-seated magnetized structure (Sea of Marmara, Turkey), respectively to determine depths, geometries and exact origins of the source bodies. Inversion studies have yielded geologically reasonable solutions which are also in good accordance with the results of normalized full gradient and Euler deconvolution techniques. Thus, we propose the use of DE not only for the amplitude inversion of 2D analytical signals of magnetic profile anomalies having induced or remanent magnetization effects but also the low-dimensional data inversions in geophysics. A part of this paper was presented as an abstract at the 2nd International Conference on Civil and Environmental Engineering, 8-10 May 2017, Cappadocia-Nevşehir (Turkey).

Minimal spanning tree algorithm for γ-ray source detection in sparse photon images: cluster parameters and selection strategies

DOE PAGES

Campana, R.; Bernieri, E.; Massaro, E.; ...

2013-05-22

We present that the minimal spanning tree (MST) algorithm is a graph-theoretical cluster-finding method. We previously applied it to γ-ray bidimensional images, showing that it is quite sensitive in finding faint sources. Possible sources are associated with the regions where the photon arrival directions clusterize. MST selects clusters starting from a particular “tree” connecting all the point of the image and performing a cut based on the angular distance between photons, with a number of events higher than a given threshold. In this paper, we show how a further filtering, based on some parameters linked to the cluster properties, canmore » be applied to reduce spurious detections. We find that the most efficient parameter for this secondary selection is the magnitudeM of a cluster, defined as the product of its number of events by its clustering degree. We test the sensitivity of the method by means of simulated and real Fermi-Large Area Telescope (LAT) fields. Our results show that √M is strongly correlated with other statistical significance parameters, derived from a wavelet based algorithm and maximum likelihood (ML) analysis, and that it can be used as a good estimator of statistical significance of MST detections. Finally, we apply the method to a 2-year LAT image at energies higher than 3 GeV, and we show the presence of new clusters, likely associated with BL Lac objects.« less

Computational efficient segmentation of cell nuclei in 2D and 3D fluorescent micrographs

NASA Astrophysics Data System (ADS)

De Vylder, Jonas; Philips, Wilfried

2011-02-01

This paper proposes a new segmentation technique developed for the segmentation of cell nuclei in both 2D and 3D fluorescent micrographs. The proposed method can deal with both blurred edges as with touching nuclei. Using a dual scan line algorithm its both memory as computational efficient, making it interesting for the analysis of images coming from high throughput systems or the analysis of 3D microscopic images. Experiments show good results, i.e. recall of over 0.98.

A light intensity monitoring method based on fiber Bragg grating sensing technology and BP neural network

NASA Astrophysics Data System (ADS)

Li, Lu-Ming; Zhu, Qian; Zhang, Zhi-Guo; Cai, Zhi-Min; Liao, Zhi-Jun; Hu, Zhen-Yan

2017-04-01

In this paper, a light intensity monitoring method based on FBG is proposed. The method establishes a light intensity monitoring model with cantilever beam structure and BP neural network algorithm, which is based on fiber grating sensing technology. The accuracy of the model can meet the requirements of engineering project and it can monitor light intensity in real time. The experimental results show that the method has good stability and high sensitivity.

Mapreduce is Good Enough? If All You Have is a Hammer, Throw Away Everything That's Not a Nail!

PubMed

Lin, Jimmy

2013-03-01

Hadoop is currently the large-scale data analysis "hammer" of choice, but there exist classes of algorithms that aren't "nails" in the sense that they are not particularly amenable to the MapReduce programming model. To address this, researchers have proposed MapReduce extensions or alternative programming models in which these algorithms can be elegantly expressed. This article espouses a very different position: that MapReduce is "good enough," and that instead of trying to invent screwdrivers, we should simply get rid of everything that's not a nail. To be more specific, much discussion in the literature surrounds the fact that iterative algorithms are a poor fit for MapReduce. The simple solution is to find alternative, noniterative algorithms that solve the same problem. This article captures my personal experiences as an academic researcher as well as a software engineer in a "real-world" production analytics environment. From this combined perspective, I reflect on the current state and future of "big data" research.

Low-Altitude Operation of Unmanned Rotorcraft

NASA Astrophysics Data System (ADS)

Scherer, Sebastian

Currently deployed unmanned rotorcraft rely on preplanned missions or teleoperation and do not actively incorporate information about obstacles, landing sites, wind, position uncertainty, and other aerial vehicles during online motion planning. Prior work has successfully addressed some tasks such as obstacle avoidance at slow speeds, or landing at known to be good locations. However, to enable autonomous missions in cluttered environments, the vehicle has to react quickly to previously unknown obstacles, respond to changing environmental conditions, and find unknown landing sites. We consider the problem of enabling autonomous operation at low-altitude with contributions to four problems. First we address the problem of fast obstacle avoidance for a small aerial vehicle and present results from over a 1000 rims at speeds up to 10 m/s. Fast response is achieved through a reactive algorithm whose response is learned based on observing a pilot. Second, we show an algorithm to update the obstacle cost expansion for path planning quickly and demonstrate it on a micro aerial vehicle, and an autonomous helicopter avoiding obstacles. Next, we examine the mission of finding a place to land near a ground goal. Good landing sites need to be detected and found and the final touch down goal is unknown. To detect the landing sites we convey a model based algorithm for landing sites that incorporates many helicopter relevant constraints such as landing sites, approach, abort, and ground paths in 3D range data. The landing site evaluation algorithm uses a patch-based coarse evaluation for slope and roughness, and a fine evaluation that fits a 3D model of the helicopter and landing gear to calculate a goodness measure. The data are evaluated in real-time to enable the helicopter to decide on a place to land. We show results from urban, vegetated, and desert environments, and demonstrate the first autonomous helicopter that selects its own landing sites. We present a generalized planning framework that enables reaching a goal point, searching for unknown landing sites, and approaching a landing zone. In the framework, sub-objective functions, constraints, and a state machine define the mission and behavior of an UAV. As the vehicle gathers information by moving through the environment, the objective functions account for this new information. The operator in this framework can directly specify his intent as an objective function that defines the mission rather than giving a sequence of pre-specified goal points. This allows the robot to react to new information received and adjust its path accordingly. The objective is used in a combined coarse planning and trajectory optimization algorithm to determine the best patch the robot should take. We show simulated results for several different missions and in particular focus on active landing zone search. We presented several effective approaches for perception and action for low-altitude flight and demonstrated their effectiveness in field experiments on three autonomous aerial vehicles: a 1m quadrocopter, a 36m helicopter, and a hill-size helicopter. These techniques permit rotorcraft to operate where they have their greatest advantage: In unstructured, unknown environments at low-altitude.

Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge

PubMed Central

Litjens, Geert; Toth, Robert; van de Ven, Wendy; Hoeks, Caroline; Kerkstra, Sjoerd; van Ginneken, Bram; Vincent, Graham; Guillard, Gwenael; Birbeck, Neil; Zhang, Jindang; Strand, Robin; Malmberg, Filip; Ou, Yangming; Davatzikos, Christos; Kirschner, Matthias; Jung, Florian; Yuan, Jing; Qiu, Wu; Gao, Qinquan; Edwards, Philip “Eddie”; Maan, Bianca; van der Heijden, Ferdinand; Ghose, Soumya; Mitra, Jhimli; Dowling, Jason; Barratt, Dean; Huisman, Henkjan; Madabhushi, Anant

2014-01-01

Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p < 0.05) and had an efficient implementation with a run time of 8 minutes and 3 second per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/. PMID:24418598

Comparison of aerosol optical depth from satellite (MODIS), sun photometer and broadband pyrheliometer ground-based observations in Cuba

NASA Astrophysics Data System (ADS)

Antuña-Marrero, Juan Carlos; Cachorro Revilla, Victoria; García Parrado, Frank; de Frutos Baraja, Ángel; Rodríguez Vega, Albeth; Mateos, David; Estevan Arredondo, René; Toledano, Carlos

2018-04-01

In the present study, we report the first comparison between the aerosol optical depth (AOD) and Ångström exponent (AE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra (AODt) and Aqua (AODa) satellites and those measured using a sun photometer (AODSP) at Camagüey, Cuba, for the period 2008 to 2014. The comparison of Terra and Aqua data includes AOD derived with both deep blue (DB) and dark target (DT) algorithms from MODIS Collection 6. Combined Terra and Aqua (AODta) data were also considered. Assuming an interval of ±30 min around the overpass time and an area of 25 km around the sun photometer site, two coincidence criteria were considered: individual pairs of observations and both spatial and temporal mean values, which we call collocated daily means. The usual statistics (root mean square error, RMSE; mean absolute error, MAE; median bias, BIAS), together with linear regression analysis, are used for this comparison. Results show very similar values for both coincidence criteria: the DT algorithm generally displays better statistics and higher homogeneity than the DB algorithm in the behaviour of AODt, AODa, AODta compared to AODSP. For collocated daily means, (a) RMSEs of 0.060 and 0.062 were obtained for Terra and Aqua with the DT algorithm and 0.084 and 0.065 for the DB algorithm, (b) MAE follows the same patterns, (c) BIAS for both Terra and Aqua presents positive and negative values but its absolute values are lower for the DT algorithm; (d) combined AODta data also give lower values of these three statistical indicators for the DT algorithm; (e) both algorithms present good correlations for comparing AODt, AODa, AODta vs. AODSP, with a slight overestimation of satellite data compared to AODSP, (f). The DT algorithm yields better figures with slopes of 0.96 (Terra), 0.96 (Aqua) and 0.96 (Terra + Aqua) compared to the DB algorithm (1.07, 0.90, 0.99), which displays greater variability. Multi-annual monthly means of AODta establish a first climatology that is more comparable to that given by the sun photometer and their statistical evaluation reveals better agreement with AODSP for the DT algorithm. Results of the AE comparison showed similar results to those reported in the literature concerning the two algorithms' capacity for retrieval. A comparison between broadband aerosol optical depth (BAOD), derived from broadband pyrheliometer observations at the Camagüey site and three other meteorological stations in Cuba, and AOD observations from MODIS on board Terra and Aqua show a poor correlation with slopes below 0.4 for both algorithms. Aqua (Terra) showed RMSE values of 0.073 (0.080) and 0.088 (0.087) for the DB and DT algorithms. As expected, RMSE values are higher than those from the MODIS-sun photometer comparison, but within the same order of magnitude. Results from the BAOD derived from solar radiation measurements demonstrate its reliability in describing climatological AOD series estimates.

Calculations of Electron Inelastic Mean Free Paths. XI. Data for Liquid Water for Energies from 50 eV to 30 keV

PubMed Central

Shinotsuka, H.; Da, B.; Tanuma, S.; Yoshikawa, H.; Powell, C. J.; Penn, D. R.

2017-01-01

We calculated electron inelastic mean free paths (IMFPs) for liquid water from its optical energy-loss function (ELF) for electron energies from 50 eV to 30 keV. These calculations were made with the relativistic full Penn algorithm (FPA) that has been used for previous IMFP and electron stopping-power calculations for many elemental solids. We also calculated IMFPs of water with three additional algorithms: the relativistic single-pole approximation (SPA), the relativistic simplified SPA, and the relativistic extended Mermin method. These calculations were made using the same optical ELF in order to assess any differences of the IMFPs arising from choice of the algorithm. We found good agreement among the IMFPs from the four algorithms for energies over 300 eV. For energies less than 100 eV, however, large differences became apparent. IMFPs from the relativistic TPP-2M equation for predicting IMFPs were in good agreement with IMFPs from the four algorithms for energies between 300 eV and 30 keV but there was poorer agreement for lower energies. We calculated values of the static structure factor as a function of momentum transfer from the FPA. The resulting values were in good agreement with results from first-principles calculations and with inelastic X-ray scattering spectroscopy experiments. We made comparisons of our IMFPs with earlier calculations from authors who had used different algorithms and different ELF data sets. IMFP differences could then be analyzed in terms of the algorithms and the data sets. Finally, we compared our IMFPs with measurements of IMFPs and of a related quantity, the effective attenuation length (EAL). There were large variations in the measured IMFPs and EALs (as well as their dependence on electron energy). Further measurements are therefore required to establish consistent data sets and for more detailed comparisons with calculated IMFPs. PMID:28751796

Calculations of Electron Inelastic Mean Free Paths. XI. Data for Liquid Water for Energies from 50 eV to 30 keV.

PubMed

Shinotsuka, H; Da, B; Tanuma, S; Yoshikawa, H; Powell, C J; Penn, D R

2017-04-01

We calculated electron inelastic mean free paths (IMFPs) for liquid water from its optical energy-loss function (ELF) for electron energies from 50 eV to 30 keV. These calculations were made with the relativistic full Penn algorithm (FPA) that has been used for previous IMFP and electron stopping-power calculations for many elemental solids. We also calculated IMFPs of water with three additional algorithms: the relativistic single-pole approximation (SPA), the relativistic simplified SPA, and the relativistic extended Mermin method. These calculations were made using the same optical ELF in order to assess any differences of the IMFPs arising from choice of the algorithm. We found good agreement among the IMFPs from the four algorithms for energies over 300 eV. For energies less than 100 eV, however, large differences became apparent. IMFPs from the relativistic TPP-2M equation for predicting IMFPs were in good agreement with IMFPs from the four algorithms for energies between 300 eV and 30 keV but there was poorer agreement for lower energies. We calculated values of the static structure factor as a function of momentum transfer from the FPA. The resulting values were in good agreement with results from first-principles calculations and with inelastic X-ray scattering spectroscopy experiments. We made comparisons of our IMFPs with earlier calculations from authors who had used different algorithms and different ELF data sets. IMFP differences could then be analyzed in terms of the algorithms and the data sets. Finally, we compared our IMFPs with measurements of IMFPs and of a related quantity, the effective attenuation length (EAL). There were large variations in the measured IMFPs and EALs (as well as their dependence on electron energy). Further measurements are therefore required to establish consistent data sets and for more detailed comparisons with calculated IMFPs.

New Algorithms and Lower Bounds for Sequential-Access Data Compression

NASA Astrophysics Data System (ADS)

Gagie, Travis

2009-02-01

This thesis concerns sequential-access data compression, i.e., by algorithms that read the input one or more times from beginning to end. In one chapter we consider adaptive prefix coding, for which we must read the input character by character, outputting each character's self-delimiting codeword before reading the next one. We show how to encode and decode each character in constant worst-case time while producing an encoding whose length is worst-case optimal. In another chapter we consider one-pass compression with memory bounded in terms of the alphabet size and context length, and prove a nearly tight tradeoff between the amount of memory we can use and the quality of the compression we can achieve. In a third chapter we consider compression in the read/write streams model, which allows us passes and memory both polylogarithmic in the size of the input. We first show how to achieve universal compression using only one pass over one stream. We then show that one stream is not sufficient for achieving good grammar-based compression. Finally, we show that two streams are necessary and sufficient for achieving entropy-only bounds.

Chiari malformation Type I surgery in pediatric patients. Part 1: validation of an ICD-9-CM code search algorithm.

PubMed

Ladner, Travis R; Greenberg, Jacob K; Guerrero, Nicole; Olsen, Margaret A; Shannon, Chevis N; Yarbrough, Chester K; Piccirillo, Jay F; Anderson, Richard C E; Feldstein, Neil A; Wellons, John C; Smyth, Matthew D; Park, Tae Sung; Limbrick, David D

2016-05-01

OBJECTIVE Administrative billing data may facilitate large-scale assessments of treatment outcomes for pediatric Chiari malformation Type I (CM-I). Validated International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code algorithms for identifying CM-I surgery are critical prerequisites for such studies but are currently only available for adults. The objective of this study was to validate two ICD-9-CM code algorithms using hospital billing data to identify pediatric patients undergoing CM-I decompression surgery. METHODS The authors retrospectively analyzed the validity of two ICD-9-CM code algorithms for identifying pediatric CM-I decompression surgery performed at 3 academic medical centers between 2001 and 2013. Algorithm 1 included any discharge diagnosis code of 348.4 (CM-I), as well as a procedure code of 01.24 (cranial decompression) or 03.09 (spinal decompression or laminectomy). Algorithm 2 restricted this group to the subset of patients with a primary discharge diagnosis of 348.4. The positive predictive value (PPV) and sensitivity of each algorithm were calculated. RESULTS Among 625 first-time admissions identified by Algorithm 1, the overall PPV for CM-I decompression was 92%. Among the 581 admissions identified by Algorithm 2, the PPV was 97%. The PPV for Algorithm 1 was lower in one center (84%) compared with the other centers (93%-94%), whereas the PPV of Algorithm 2 remained high (96%-98%) across all subgroups. The sensitivity of Algorithms 1 (91%) and 2 (89%) was very good and remained so across subgroups (82%-97%). CONCLUSIONS An ICD-9-CM algorithm requiring a primary diagnosis of CM-I has excellent PPV and very good sensitivity for identifying CM-I decompression surgery in pediatric patients. These results establish a basis for utilizing administrative billing data to assess pediatric CM-I treatment outcomes.

Hyperspectral Image Classification for Land Cover Based on an Improved Interval Type-II Fuzzy C-Means Approach

PubMed Central

Li, Zhao-Liang

2018-01-01

Few studies have examined hyperspectral remote-sensing image classification with type-II fuzzy sets. This paper addresses image classification based on a hyperspectral remote-sensing technique using an improved interval type-II fuzzy c-means (IT2FCM*) approach. In this study, in contrast to other traditional fuzzy c-means-based approaches, the IT2FCM* algorithm considers the ranking of interval numbers and the spectral uncertainty. The classification results based on a hyperspectral dataset using the FCM, IT2FCM, and the proposed improved IT2FCM* algorithms show that the IT2FCM* method plays the best performance according to the clustering accuracy. In this paper, in order to validate and demonstrate the separability of the IT2FCM*, four type-I fuzzy validity indexes are employed, and a comparative analysis of these fuzzy validity indexes also applied in FCM and IT2FCM methods are made. These four indexes are also applied into different spatial and spectral resolution datasets to analyze the effects of spectral and spatial scaling factors on the separability of FCM, IT2FCM, and IT2FCM* methods. The results of these validity indexes from the hyperspectral datasets show that the improved IT2FCM* algorithm have the best values among these three algorithms in general. The results demonstrate that the IT2FCM* exhibits good performance in hyperspectral remote-sensing image classification because of its ability to handle hyperspectral uncertainty. PMID:29373548

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.

An implementation of the look-ahead Lanczos algorithm for non-Hermitian matrices, part 2

NASA Technical Reports Server (NTRS)

Freund, Roland W.; Nachtigal, Noel M.

1990-01-01

It is shown how the look-ahead Lanczos process (combined with a quasi-minimal residual QMR) approach) can be used to develop a robust black box solver for large sparse non-Hermitian linear systems. Details of an implementation of the resulting QMR algorithm are presented. It is demonstrated that the QMR method is closely related to the biconjugate gradient (BCG) algorithm; however, unlike BCG, the QMR algorithm has smooth convergence curves and good numerical properties. We report numerical experiments with our implementation of the look-ahead Lanczos algorithm, both for eigenvalue problem and linear systems. Also, program listings of FORTRAN implementations of the look-ahead algorithm and the QMR method are included.

Sorting on STAR. [CDC computer algorithm timing comparison

NASA Technical Reports Server (NTRS)

Stone, H. S.

1978-01-01

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

Pixelated Checkerboard Metasurface for Ultra-Wideband Radar Cross Section Reduction.

PubMed

Haji-Ahmadi, Mohammad-Javad; Nayyeri, Vahid; Soleimani, Mohammad; Ramahi, Omar M

2017-09-12

In this paper we designed and fabricated a metasurface working as a radar cross section (RCS) reducer over an ultra wide band of frequency from 3.8 to 10.7 GHz. The designed metasurface is a chessboard-like surface made of alternating tiles, with each tile composed of identical unit cells. We develop a novel, simple, highly robust and fully automated approach for designing the unit cells. First, a topology optimization algorithm is used to engineer the shape of the two unit cells. The area of each unit cell is pixelated. A particle swarm optimization algorithm is applied wherein each pixel corresponds to a bit having a binary value of 1 or 0 indicating metallization or no metallization. With the objective of reducing the RCS over a specified frequency range, the optimization algorithm is then linked to a full wave three-dimensional electromagnetic simulator. To validate the design procedure, a surface was designed, fabricated and experimentally tested showing significantly enhanced performance than previous works. Additionally, angular analysis is also presented showing good stability and wide-angle behavior of the designed RCS reducer. The automated design procedure has a wide range of applications and can be easily extended to design surfaces for antennas, energy harvesters, noise mitigation in electronic circuit boards amongst others.

MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy.

PubMed

Svoboda, David; Ulman, Vladimir

2017-01-01

The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations. MitoGen shows biologically justified cell motility, shape and texture changes as well as cell divisions. Standard fluorescence microscopy phenomena such as photobleaching, blur with real point spread function (PSF), and several types of noise, are simulated to obtain realistic images. The MitoGen framework is scalable in both space and time. MitoGen generates visually plausible data that shows good agreement with real data in terms of image descriptors and mean square displacement (MSD) trajectory analysis. Additionally, it is also shown in this paper that four publicly available segmentation and tracking algorithms exhibit similar performance on both real and MitoGen-generated data. The implementation of MitoGen is freely available.

Color transfer algorithm in medical images

NASA Astrophysics Data System (ADS)

Wang, Weihong; Xu, Yangfa

2007-12-01

In digital virtual human project, image data acquires from the freezing slice of human body specimen. The color and brightness between a group of images of a certain organ could be quite different. The quality of these images could bring great difficulty in edge extraction, segmentation, as well as 3D reconstruction process. Thus it is necessary to unify the color of the images. The color transfer algorithm is a good algorithm to deal with this kind of problem. This paper introduces the principle of this algorithm and uses it in the medical image processing.

Structural damage identification using an enhanced thermal exchange optimization algorithm

NASA Astrophysics Data System (ADS)

Kaveh, A.; Dadras, A.

2018-03-01

The recently developed optimization algorithm-the so-called thermal exchange optimization (TEO) algorithm-is enhanced and applied to a damage detection problem. An offline parameter tuning approach is utilized to set the internal parameters of the TEO, resulting in the enhanced heat transfer optimization (ETEO) algorithm. The damage detection problem is defined as an inverse problem, and ETEO is applied to a wide range of structures. Several scenarios with noise and noise-free modal data are tested and the locations and extents of damages are identified with good accuracy.

Segmentation of ribs in digital chest radiographs

NASA Astrophysics Data System (ADS)

Cong, Lin; Guo, Wei; Li, Qiang

2016-03-01

Ribs and clavicles in posterior-anterior (PA) digital chest radiographs often overlap with lung abnormalities such as nodules, and cause missing of these abnormalities, it is therefore necessary to remove or reduce the ribs in chest radiographs. The purpose of this study was to develop a fully automated algorithm to segment ribs within lung area in digital radiography (DR) for removal of the ribs. The rib segmentation algorithm consists of three steps. Firstly, a radiograph was pre-processed for contrast adjustment and noise removal; second, generalized Hough transform was employed to localize the lower boundary of the ribs. In the third step, a novel bilateral dynamic programming algorithm was used to accurately segment the upper and lower boundaries of ribs simultaneously. The width of the ribs and the smoothness of the rib boundaries were incorporated in the cost function of the bilateral dynamic programming for obtaining consistent results for the upper and lower boundaries. Our database consisted of 93 DR images, including, respectively, 23 and 70 images acquired with a DR system from Shanghai United-Imaging Healthcare Co. and from GE Healthcare Co. The rib localization algorithm achieved a sensitivity of 98.2% with 0.1 false positives per image. The accuracy of the detected ribs was further evaluated subjectively in 3 levels: "1", good; "2", acceptable; "3", poor. The percentages of good, acceptable, and poor segmentation results were 91.1%, 7.2%, and 1.7%, respectively. Our algorithm can obtain good segmentation results for ribs in chest radiography and would be useful for rib reduction in our future study.

An adaptive bias - hybrid MD/kMC algorithm for protein folding and aggregation.

PubMed

Peter, Emanuel K; Shea, Joan-Emma

2017-07-05

In this paper, we present a novel hybrid Molecular Dynamics/kinetic Monte Carlo (MD/kMC) algorithm and apply it to protein folding and aggregation in explicit solvent. The new algorithm uses a dynamical definition of biases throughout the MD component of the simulation, normalized in relation to the unbiased forces. The algorithm guarantees sampling of the underlying ensemble in dependency of one average linear coupling factor 〈α〉 τ . We test the validity of the kinetics in simulations of dialanine and compare dihedral transition kinetics with long-time MD-simulations. We find that for low 〈α〉 τ values, kinetics are in good quantitative agreement. In folding simulations of TrpCage and TrpZip4 in explicit solvent, we also find good quantitative agreement with experimental results and prior MD/kMC simulations. Finally, we apply our algorithm to study growth of the Alzheimer Amyloid Aβ 16-22 fibril by monomer addition. We observe two possible binding modes, one at the extremity of the fibril (elongation) and one on the surface of the fibril (lateral growth), on timescales ranging from ns to 8 μs.

Long-term spatial distributions and trends of the latent heat fluxes over the global cropland ecosystem using multiple satellite-based models

PubMed Central

Feng, Fei; Yao, Yunjun; Liu, Meng

2017-01-01

Estimating cropland latent heat flux (LE) from continental to global scales is vital to modeling crop production and managing water resources. Over the past several decades, numerous LE models were developed, such as the moderate resolution imaging spectroradiometer LE (MOD16) algorithm, revised remote sensing-based Penman–Monteith LE algorithm (RRS), the Priestley–Taylor LE algorithm of the Jet Propulsion Laboratory (PT-JPL) and the modified satellite-based Priestley-Taylor LE algorithm (MS-PT). However, these LE models have not been directly compared over the global cropland ecosystem using various algorithms. In this study, we evaluated the performances of these four LE models using 34 eddy covariance (EC) sites. The results showed that mean annual LE for cropland varied from 33.49 to 58.97 W/m2 among the four models. The interannual LE slightly increased during 1982–2009 across the global cropland ecosystem. All models had acceptable performances with the coefficient of determination (R2) ranging from 0.4 to 0.7 and a root mean squared error (RMSE) of approximately 35 W/m2. MS-PT had good overall performance across the cropland ecosystem with the highest R2, lowest RMSE and a relatively low bias. The reduced performances of MOD16 and RRS, with R2 ranging from 0.4 to 0.6 and RMSEs from 30 to 39 W/m2, might be attributed to empirical parameters in the structure algorithms and calibrated coefficients. PMID:28837704

Research based on the SoPC platform of feature-based image registration

NASA Astrophysics Data System (ADS)

Shi, Yue-dong; Wang, Zhi-hui

2015-12-01

This paper focuses on the study of implementing feature-based image registration by System on a Programmable Chip (SoPC) hardware platform. We solidify the image registration algorithm on the FPGA chip, in which embedded soft core processor Nios II can speed up the image processing system. In this way, we can make image registration technology get rid of the PC. And, consequently, this kind of technology will be got an extensive use. The experiment result indicates that our system shows stable performance, particularly in terms of matching processing which noise immunity is good. And feature points of images show a reasonable distribution.

Revealing how network structure affects accuracy of link prediction

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

Link prediction plays an important role in network reconstruction and network evolution. The network structure affects the accuracy of link prediction, which is an interesting problem. In this paper we use common neighbors and the Gini coefficient to reveal the relation between them, which can provide a good reference for the choice of a suitable link prediction algorithm according to the network structure. Moreover, the statistical analysis reveals correlation between the common neighbors index, Gini coefficient index and other indices to describe the network structure, such as Laplacian eigenvalues, clustering coefficient, degree heterogeneity, and assortativity of network. Furthermore, a new method to predict missing links is proposed. The experimental results show that the proposed algorithm yields better prediction accuracy and robustness to the network structure than existing currently used methods for a variety of real-world networks.

Automated boundary detection of the optic disc and layer segmentation of the peripapillary retina in volumetric structural and angiographic optical coherence tomography.

PubMed

Zang, Pengxiao; Gao, Simon S; Hwang, Thomas S; Flaxel, Christina J; Wilson, David J; Morrison, John C; Huang, David; Li, Dengwang; Jia, Yali

2017-03-01

To improve optic disc boundary detection and peripapillary retinal layer segmentation, we propose an automated approach for structural and angiographic optical coherence tomography. The algorithm was performed on radial cross-sectional B-scans. The disc boundary was detected by searching for the position of Bruch's membrane opening, and retinal layer boundaries were detected using a dynamic programming-based graph search algorithm on each B-scan without the disc region. A comparison of the disc boundary using our method with that determined by manual delineation showed good accuracy, with an average Dice similarity coefficient ≥0.90 in healthy eyes and eyes with diabetic retinopathy and glaucoma. The layer segmentation accuracy in the same cases was on average less than one pixel (3.13 μm).

Automated boundary detection of the optic disc and layer segmentation of the peripapillary retina in volumetric structural and angiographic optical coherence tomography

PubMed Central

Zang, Pengxiao; Gao, Simon S.; Hwang, Thomas S.; Flaxel, Christina J.; Wilson, David J.; Morrison, John C.; Huang, David; Li, Dengwang; Jia, Yali

2017-01-01

To improve optic disc boundary detection and peripapillary retinal layer segmentation, we propose an automated approach for structural and angiographic optical coherence tomography. The algorithm was performed on radial cross-sectional B-scans. The disc boundary was detected by searching for the position of Bruch’s membrane opening, and retinal layer boundaries were detected using a dynamic programming-based graph search algorithm on each B-scan without the disc region. A comparison of the disc boundary using our method with that determined by manual delineation showed good accuracy, with an average Dice similarity coefficient ≥0.90 in healthy eyes and eyes with diabetic retinopathy and glaucoma. The layer segmentation accuracy in the same cases was on average less than one pixel (3.13 μm). PMID:28663830

Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

2011-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

Enhance Video Film using Retnix method

NASA Astrophysics Data System (ADS)

Awad, Rasha; Al-Zuky, Ali A.; Al-Saleh, Anwar H.; Mohamad, Haidar J.

2018-05-01

An enhancement technique used to improve the studied video quality. Algorithms like mean and standard deviation are used as a criterion within this paper, and it applied for each video clip that divided into 80 images. The studied filming environment has different light intensity (315, 566, and 644Lux). This different environment gives similar reality to the outdoor filming. The outputs of the suggested algorithm are compared with the results before applying it. This method is applied into two ways: first, it is applied for the full video clip to get the enhanced film; second, it is applied for every individual image to get the enhanced image then compiler them to get the enhanced film. This paper shows that the enhancement technique gives good quality video film depending on a statistical method, and it is recommended to use it in different application.

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

NASA Astrophysics Data System (ADS)

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

2015-07-01

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

New Galerkin operational matrices for solving Lane-Emden type equations

NASA Astrophysics Data System (ADS)

Abd-Elhameed, W. M.; Doha, E. H.; Saad, A. S.; Bassuony, M. A.

2016-04-01

Lane-Emden type equations model many phenomena in mathematical physics and astrophysics, such as thermal explosions. This paper is concerned with introducing third and fourth kind Chebyshev-Galerkin operational matrices in order to solve such problems. The principal idea behind the suggested algorithms is based on converting the linear or nonlinear Lane-Emden problem, through the application of suitable spectral methods, into a system of linear or nonlinear equations in the expansion coefficients, which can be efficiently solved. The main advantage of the proposed algorithm in the linear case is that the resulting linear systems are specially structured, and this of course reduces the computational effort required to solve such systems. As an application, we consider the solar model polytrope with n=3 to show that the suggested solutions in this paper are in good agreement with the numerical results.

Comparison of Automated Atlas-Based Segmentation Software for Postoperative Prostate Cancer Radiotherapy

PubMed Central

Delpon, Grégory; Escande, Alexandre; Ruef, Timothée; Darréon, Julien; Fontaine, Jimmy; Noblet, Caroline; Supiot, Stéphane; Lacornerie, Thomas; Pasquier, David

2016-01-01

Automated atlas-based segmentation (ABS) algorithms present the potential to reduce the variability in volume delineation. Several vendors offer software that are mainly used for cranial, head and neck, and prostate cases. The present study will compare the contours produced by a radiation oncologist to the contours computed by different automated ABS algorithms for prostate bed cases, including femoral heads, bladder, and rectum. Contour comparison was evaluated by different metrics such as volume ratio, Dice coefficient, and Hausdorff distance. Results depended on the volume of interest showed some discrepancies between the different software. Automatic contours could be a good starting point for the delineation of organs since efficient editing tools are provided by different vendors. It should become an important help in the next few years for organ at risk delineation. PMID:27536556

Predictive Caching Using the TDAG Algorithm

NASA Technical Reports Server (NTRS)

Laird, Philip; Saul, Ronald

1992-01-01

We describe how the TDAG algorithm for learning to predict symbol sequences can be used to design a predictive cache store. A model of a two-level mass storage system is developed and used to calculate the performance of the cache under various conditions. Experimental simulations provide good confirmation of the model.

Use of Probability Distribution Functions for Discriminating Between Cloud and Aerosol in Lidar Backscatter Data

NASA Technical Reports Server (NTRS)

Liu, Zhaoyan; Vaughan, Mark A.; Winker, Davd M.; Hostetler, Chris A.; Poole, Lamont R.; Hlavka, Dennis; Hart, William; McGill, Mathew

2004-01-01

In this paper we describe the algorithm hat will be used during the upcoming Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission for discriminating between clouds and aerosols detected in two wavelength backscatter lidar profiles. We first analyze single-test and multiple-test classification approaches based on one-dimensional and multiple-dimensional probability density functions (PDFs) in the context of a two-class feature identification scheme. From these studies we derive an operational algorithm based on a set of 3-dimensional probability distribution functions characteristic of clouds and aerosols. A dataset acquired by the Cloud Physics Lidar (CPL) is used to test the algorithm. Comparisons are conducted between the CALIPSO algorithm results and the CPL data product. The results obtained show generally good agreement between the two methods. However, of a total of 228,264 layers analyzed, approximately 5.7% are classified as different types by the CALIPSO and CPL algorithm. This disparity is shown to be due largely to the misclassification of clouds as aerosols by the CPL algorithm. The use of 3-dimensional PDFs in the CALIPSO algorithm is found to significantly reduce this type of error. Dust presents a special case. Because the intrinsic scattering properties of dust layers can be very similar to those of clouds, additional algorithm testing was performed using an optically dense layer of Saharan dust measured during the Lidar In-space Technology Experiment (LITE). In general, the method is shown to distinguish reliably between dust layers and clouds. The relatively few erroneous classifications occurred most often in the LITE data, in those regions of the Saharan dust layer where the optical thickness was the highest.

Medical Image Encryption: An Application for Improved Padding Based GGH Encryption Algorithm

PubMed Central

Sokouti, Massoud; Zakerolhosseini, Ali; Sokouti, Babak

2016-01-01

Medical images are regarded as important and sensitive data in the medical informatics systems. For transferring medical images over an insecure network, developing a secure encryption algorithm is necessary. Among the three main properties of security services (i.e., confidentiality, integrity, and availability), the confidentiality is the most essential feature for exchanging medical images among physicians. The Goldreich Goldwasser Halevi (GGH) algorithm can be a good choice for encrypting medical images as both the algorithm and sensitive data are represented by numeric matrices. Additionally, the GGH algorithm does not increase the size of the image and hence, its complexity will remain as simple as O(n2). However, one of the disadvantages of using the GGH algorithm is the Chosen Cipher Text attack. In our strategy, this shortcoming of GGH algorithm has been taken in to consideration and has been improved by applying the padding (i.e., snail tour XORing), before the GGH encryption process. For evaluating their performances, three measurement criteria are considered including (i) Number of Pixels Change Rate (NPCR), (ii) Unified Average Changing Intensity (UACI), and (iii) Avalanche effect. The results on three different sizes of images showed that padding GGH approach has improved UACI, NPCR, and Avalanche by almost 100%, 35%, and 45%, respectively, in comparison to the standard GGH algorithm. Also, the outcomes will make the padding GGH resist against the cipher text, the chosen cipher text, and the statistical attacks. Furthermore, increasing the avalanche effect of more than 50% is a promising achievement in comparison to the increased complexities of the proposed method in terms of encryption and decryption processes. PMID:27857824

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

PubMed

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

2013-11-01

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

A novel discrete PSO algorithm for solving job shop scheduling problem to minimize makespan

NASA Astrophysics Data System (ADS)

Rameshkumar, K.; Rajendran, C.

2018-02-01

In this work, a discrete version of PSO algorithm is proposed to minimize the makespan of a job-shop. A novel schedule builder has been utilized to generate active schedules. The discrete PSO is tested using well known benchmark problems available in the literature. The solution produced by the proposed algorithms is compared with best known solution published in the literature and also compared with hybrid particle swarm algorithm and variable neighborhood search PSO algorithm. The solution construction methodology adopted in this study is found to be effective in producing good quality solutions for the various benchmark job-shop scheduling problems.

Autism Diagnostic Interview-Revised (ADI-R) Algorithms for Toddlers and Young Preschoolers: Application in a Non-US Sample of 1,104 Children

ERIC Educational Resources Information Center

de Bildt, Annelies; Sytema, Sjoerd; Zander, Eric; Bölte, Sven; Sturm, Harald; Yirmiya, Nurit; Yaari, Maya; Charman, Tony; Salomone, Erica; LeCouteur, Ann; Green, Jonathan; Bedia, Ricardo Canal; Primo, Patricia García; van Daalen, Emma; de Jonge, Maretha V.; Guðmundsdóttir, Emilía; Jóhannsdóttir, Sigurrós; Raleva, Marija; Boskovska, Meri; Rogé, Bernadette; Baduel, Sophie; Moilanen, Irma; Yliherva, Anneli; Buitelaar, Jan; Oosterling, Iris J.

2015-01-01

The current study aimed to investigate the Autism Diagnostic Interview-Revised (ADI-R) algorithms for toddlers and young preschoolers (Kim and Lord, "J Autism Dev Disord" 42(1):82-93, 2012) in a non-US sample from ten sites in nine countries (n = 1,104). The construct validity indicated a good fit of the algorithms. The diagnostic…

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

PubMed

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

2000-01-01

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

Minimizing the average distance to a closest leaf in a phylogenetic tree.

PubMed

Matsen, Frederick A; Gallagher, Aaron; McCoy, Connor O

2013-11-01

When performing an analysis on a collection of molecular sequences, it can be convenient to reduce the number of sequences under consideration while maintaining some characteristic of a larger collection of sequences. For example, one may wish to select a subset of high-quality sequences that represent the diversity of a larger collection of sequences. One may also wish to specialize a large database of characterized "reference sequences" to a smaller subset that is as close as possible on average to a collection of "query sequences" of interest. Such a representative subset can be useful whenever one wishes to find a set of reference sequences that is appropriate to use for comparative analysis of environmentally derived sequences, such as for selecting "reference tree" sequences for phylogenetic placement of metagenomic reads. In this article, we formalize these problems in terms of the minimization of the Average Distance to the Closest Leaf (ADCL) and investigate algorithms to perform the relevant minimization. We show that the greedy algorithm is not effective, show that a variant of the Partitioning Around Medoids (PAM) heuristic gets stuck in local minima, and develop an exact dynamic programming approach. Using this exact program we note that the performance of PAM appears to be good for simulated trees, and is faster than the exact algorithm for small trees. On the other hand, the exact program gives solutions for all numbers of leaves less than or equal to the given desired number of leaves, whereas PAM only gives a solution for the prespecified number of leaves. Via application to real data, we show that the ADCL criterion chooses chimeric sequences less often than random subsets, whereas the maximization of phylogenetic diversity chooses them more often than random. These algorithms have been implemented in publicly available software.

Evaluation of protein docking predictions using Hex 3.1 in CAPRI rounds 1 and 2.

PubMed

Ritchie, David W

2003-07-01

This article describes and reviews our efforts using Hex 3.1 to predict the docking modes of the seven target protein-protein complexes presented in the CAPRI (Critical Assessment of Predicted Interactions) blind docking trial. For each target, the structure of at least one of the docking partners was given in its unbound form, and several of the targets involved large multimeric structures (e.g., Lactobacillus HPr kinase, hemagglutinin, bovine rotavirus VP6). Here we describe several enhancements to our original spherical polar Fourier docking correlation algorithm. For example, a novel surface sphere smothering algorithm is introduced to generate multiple local coordinate systems around the surface of a large receptor molecule, which may be used to define a small number of initial ligand-docking orientations distributed over the receptor surface. High-resolution spherical polar docking correlations are performed over the resulting receptor surface patches, and candidate docking solutions are refined by using a novel soft molecular mechanics energy minimization procedure. Overall, this approach identified two good solutions at rank 5 or less for two of the seven CAPRI complexes. Subsequent analysis of our results shows that Hex 3.1 is able to place good solutions within a list of

Design of a handheld infrared imaging device based on uncooled infrared detector

NASA Astrophysics Data System (ADS)

Sun, Xianzhong; Li, Junwei; Zhang, Yazhou

2017-02-01

This paper, we introduced the system structure and operation principle of the device, and discussed our solutions for image data acquisition and storage, operating states and modes control and power management in detail. Besides, we proposed a algorithm of pseudo color for thermal image and applied it to the image processing module of the device. The thermal images can be real time displayed in a 1.8 inches TFT-LCD. The device has a compacted structure and can be held easily by one hand. It also has a good imaging performance with low power consumption, thermal sensitivity is less than 150mK. At last, we introduced one of its applications for fault diagnosis in electronic circuits, the test shows that: it's a good solution for fast fault detection.

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.

Real-time Collision Avoidance and Path Optimizer for Semi-autonomous UAVs.

NASA Astrophysics Data System (ADS)

Hawary, A. F.; Razak, N. A.

2018-05-01

Whilst UAV offers a potentially cheaper and more localized observation platform than current satellite or land-based approaches, it requires an advance path planner to reveal its true potential, particularly in real-time missions. Manual control by human will have limited line-of-sights and prone to errors due to careless and fatigue. A good alternative solution is to equip the UAV with semi-autonomous capabilities that able to navigate via a pre-planned route in real-time fashion. In this paper, we propose an easy-and-practical path optimizer based on the classical Travelling Salesman Problem and adopts a brute force search method to re-optimize the route in the event of collisions using range finder sensor. The former utilizes a Simple Genetic Algorithm and the latter uses Nearest Neighbour algorithm. Both algorithms are combined to optimize the route and avoid collision at once. Although many researchers proposed various path planning algorithms, we find that it is difficult to integrate on a basic UAV model and often lacks of real-time collision detection optimizer. Therefore, we explore a practical benefit from this approach using on-board Arduino and Ardupilot controllers by manually emulating the motion of an actual UAV model prior to test on the flying site. The result showed that the range finder sensor provides a real-time data to the algorithm to find a collision-free path and eventually optimized the route successfully.

Budget Online Learning Algorithm for Least Squares SVM.

PubMed

Jian, Ling; Shen, Shuqian; Li, Jundong; Liang, Xijun; Li, Lei

2017-09-01

Batch-mode least squares support vector machine (LSSVM) is often associated with unbounded number of support vectors (SVs'), making it unsuitable for applications involving large-scale streaming data. Limited-scale LSSVM, which allows efficient updating, seems to be a good solution to tackle this issue. In this paper, to train the limited-scale LSSVM dynamically, we present a budget online LSSVM (BOLSSVM) algorithm. Methodologically, by setting a fixed budget for SVs', we are able to update the LSSVM model according to the updated SVs' set dynamically without retraining from scratch. In particular, when a new small chunk of SVs' substitute for the old ones, the proposed algorithm employs a low rank correction technology and the Sherman-Morrison-Woodbury formula to compute the inverse of saddle point matrix derived from the LSSVM's Karush-Kuhn-Tucker (KKT) system, which, in turn, updates the LSSVM model efficiently. In this way, the proposed BOLSSVM algorithm is especially useful for online prediction tasks. Another merit of the proposed BOLSSVM is that it can be used for k -fold cross validation. Specifically, compared with batch-mode learning methods, the computational complexity of the proposed BOLSSVM method is significantly reduced from O(n 4 ) to O(n 3 ) for leave-one-out cross validation with n training samples. The experimental results of classification and regression on benchmark data sets and real-world applications show the validity and effectiveness of the proposed BOLSSVM algorithm.

Pattern Recognition and Size Prediction of Microcalcification Based on Physical Characteristics by Using Digital Mammogram Images.

PubMed

Jothilakshmi, G R; Raaza, Arun; Rajendran, V; Sreenivasa Varma, Y; Guru Nirmal Raj, R

2018-06-05

Breast cancer is one of the life-threatening cancers occurring in women. In recent years, from the surveys provided by various medical organizations, it has become clear that the mortality rate of females is increasing owing to the late detection of breast cancer. Therefore, an automated algorithm is needed to identify the early occurrence of microcalcification, which would assist radiologists and physicians in reducing the false predictions via image processing techniques. In this work, we propose a new algorithm to detect the pattern of a microcalcification by calculating its physical characteristics. The considered physical characteristics are the reflection coefficient and mass density of the binned digital mammogram image. The calculation of physical characteristics doubly confirms the presence of malignant microcalcification. Subsequently, by interpolating the physical characteristics via thresholding and mapping techniques, a three-dimensional (3D) projection of the region of interest (RoI) is obtained in terms of the distance in millimeter. The size of a microcalcification is determined using this 3D-projected view. This algorithm is verified with 100 abnormal mammogram images showing microcalcification and 10 normal mammogram images. In addition to the size calculation, the proposed algorithm acts as a good classifier that is used to classify the considered input image as normal or abnormal with the help of only two physical characteristics. This proposed algorithm exhibits a classification accuracy of 99%.

Quantum Algorithm for K-Nearest Neighbors Classification Based on the Metric of Hamming Distance

NASA Astrophysics Data System (ADS)

Ruan, Yue; Xue, Xiling; Liu, Heng; Tan, Jianing; Li, Xi

2017-11-01

K-nearest neighbors (KNN) algorithm is a common algorithm used for classification, and also a sub-routine in various complicated machine learning tasks. In this paper, we presented a quantum algorithm (QKNN) for implementing this algorithm based on the metric of Hamming distance. We put forward a quantum circuit for computing Hamming distance between testing sample and each feature vector in the training set. Taking advantage of this method, we realized a good analog for classical KNN algorithm by setting a distance threshold value t to select k - n e a r e s t neighbors. As a result, QKNN achieves O( n 3) performance which is only relevant to the dimension of feature vectors and high classification accuracy, outperforms Llyod's algorithm (Lloyd et al. 2013) and Wiebe's algorithm (Wiebe et al. 2014).

The Superior Lambert Algorithm

NASA Astrophysics Data System (ADS)

der, G.

2011-09-01

Lambert algorithms are used extensively for initial orbit determination, mission planning, space debris correlation, and missile targeting, just to name a few applications. Due to the significance of the Lambert problem in Astrodynamics, Gauss, Battin, Godal, Lancaster, Gooding, Sun and many others (References 1 to 15) have provided numerous formulations leading to various analytic solutions and iterative methods. Most Lambert algorithms and their computer programs can only work within one revolution, break down or converge slowly when the transfer angle is near zero or 180 degrees, and their multi-revolution limitations are either ignored or barely addressed. Despite claims of robustness, many Lambert algorithms fail without notice, and the users seldom have a clue why. The DerAstrodynamics lambert2 algorithm, which is based on the analytic solution formulated by Sun, works for any number of revolutions and converges rapidly at any transfer angle. It provides significant capability enhancements over every other Lambert algorithm in use today. These include improved speed, accuracy, robustness, and multirevolution capabilities as well as implementation simplicity. Additionally, the lambert2 algorithm provides a powerful tool for solving the angles-only problem without artificial singularities (pointed out by Gooding in Reference 16), which involves 3 lines of sight captured by optical sensors, or systems such as the Air Force Space Surveillance System (AFSSS). The analytic solution is derived from the extended Godal’s time equation by Sun, while the iterative method of solution is that of Laguerre, modified for robustness. The Keplerian solution of a Lambert algorithm can be extended to include the non-Keplerian terms of the Vinti algorithm via a simple targeting technique (References 17 to 19). Accurate analytic non-Keplerian trajectories can be predicted for satellites and ballistic missiles, while performing at least 100 times faster in speed than most numerical integration methods.

Fast readout algorithm for cylindrical beam position monitors providing good accuracy for particle bunches with large offsets

NASA Astrophysics Data System (ADS)

Thieberger, P.; Gassner, D.; Hulsart, R.; Michnoff, R.; Miller, T.; Minty, M.; Sorrell, Z.; Bartnik, A.

2018-04-01

A simple, analytically correct algorithm is developed for calculating "pencil" relativistic beam coordinates using the signals from an ideal cylindrical particle beam position monitor (BPM) with four pickup electrodes (PUEs) of infinitesimal widths. The algorithm is then applied to simulations of realistic BPMs with finite width PUEs. Surprisingly small deviations are found. Simple empirically determined correction terms reduce the deviations even further. The algorithm is then tested with simulations for non-relativistic beams. As an example of the data acquisition speed advantage, a Field Programmable Gate Array-based BPM readout implementation of the new algorithm has been developed and characterized. Finally, the algorithm is tested with BPM data from the Cornell Preinjector.

Fast readout algorithm for cylindrical beam position monitors providing good accuracy for particle bunches with large offsets

DOE PAGES

Thieberger, Peter; Gassner, D.; Hulsart, R.; ...

2018-04-25

Here, a simple, analytically correct algorithm is developed for calculating “pencil” relativistic beam coordinates using the signals from an ideal cylindrical particle beam position monitor (BPM) with four pickup electrodes (PUEs) of infinitesimal widths. The algorithm is then applied to simulations of realistic BPMs with finite width PUEs. Surprisingly small deviations are found. Simple empirically determined correction terms reduce the deviations even further. The algorithm is then tested with simulations for non-relativistic beams. As an example of the data acquisition speed advantage, a FPGA-based BPM readout implementation of the new algorithm has been developed and characterized. Lastly, the algorithm ismore » tested with BPM data from the Cornell Preinjector.« less

Single-step methods for predicting orbital motion considering its periodic components

NASA Astrophysics Data System (ADS)

Lavrov, K. N.

1989-01-01

Modern numerical methods for integration of ordinary differential equations can provide accurate and universal solutions to celestial mechanics problems. The implicit single sequence algorithms of Everhart and multiple step computational schemes using a priori information on periodic components can be combined to construct implicit single sequence algorithms which combine their advantages. The construction and analysis of the properties of such algorithms are studied, utilizing trigonometric approximation of the solutions of differential equations containing periodic components. The algorithms require 10 percent more machine memory than the Everhart algorithms, but are twice as fast, and yield short term predictions valid for five to ten orbits with good accuracy and five to six times faster than algorithms using other methods.

Fast readout algorithm for cylindrical beam position monitors providing good accuracy for particle bunches with large offsets

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

Thieberger, Peter; Gassner, D.; Hulsart, R.

Here, a simple, analytically correct algorithm is developed for calculating “pencil” relativistic beam coordinates using the signals from an ideal cylindrical particle beam position monitor (BPM) with four pickup electrodes (PUEs) of infinitesimal widths. The algorithm is then applied to simulations of realistic BPMs with finite width PUEs. Surprisingly small deviations are found. Simple empirically determined correction terms reduce the deviations even further. The algorithm is then tested with simulations for non-relativistic beams. As an example of the data acquisition speed advantage, a FPGA-based BPM readout implementation of the new algorithm has been developed and characterized. Lastly, the algorithm ismore » tested with BPM data from the Cornell Preinjector.« less

Fast readout algorithm for cylindrical beam position monitors providing good accuracy for particle bunches with large offsets.

PubMed

Thieberger, P; Gassner, D; Hulsart, R; Michnoff, R; Miller, T; Minty, M; Sorrell, Z; Bartnik, A

2018-04-01

A simple, analytically correct algorithm is developed for calculating "pencil" relativistic beam coordinates using the signals from an ideal cylindrical particle beam position monitor (BPM) with four pickup electrodes (PUEs) of infinitesimal widths. The algorithm is then applied to simulations of realistic BPMs with finite width PUEs. Surprisingly small deviations are found. Simple empirically determined correction terms reduce the deviations even further. The algorithm is then tested with simulations for non-relativistic beams. As an example of the data acquisition speed advantage, a Field Programmable Gate Array-based BPM readout implementation of the new algorithm has been developed and characterized. Finally, the algorithm is tested with BPM data from the Cornell Preinjector.

An Effective Hybrid Evolutionary Algorithm for Solving the Numerical Optimization Problems

NASA Astrophysics Data System (ADS)

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

2018-04-01

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