Fuzzy logic-based approach to detecting a passive RFID tag in an outpatient clinic.

PubMed

Min, Daiki; Yih, Yuehwern

2011-06-01

This study is motivated by the observations on the data collected by radio frequency identification (RFID) readers in a pilot study, which was used to investigate the feasibility of implementing an RFID-based monitoring system in an outpatient eye clinic. The raw RFID data collected from RFID readers contain noise and missing reads, which prevent us from determining the tag location. In this paper, fuzzy logic-based algorithms are proposed to interpret the raw RFID data to extract accurate information. The proposed algorithms determine the location of an RFID tag by evaluating its possibility of presence and absence. To evaluate the performance of the proposed algorithms, numerical experiments are conducted using the data observed in the outpatient eye clinic. Experiments results showed that the proposed algorithms outperform existing static smoothing method in terms of minimizing both false positives and false negatives. Furthermore, the proposed algorithms are applied to a set of simulated data to show the robustness of the proposed algorithms at various levels of RFID reader reliability.

Dynamic routing and spectrum assignment based on multilayer virtual topology and ant colony optimization in elastic software-defined optical networks

NASA Astrophysics Data System (ADS)

Wang, Fu; Liu, Bo; Zhang, Lijia; Zhang, Qi; Tian, Qinghua; Tian, Feng; Rao, Lan; Xin, Xiangjun

2017-07-01

Elastic software-defined optical networks greatly improve the flexibility of the optical switching network while it has brought challenges to the routing and spectrum assignment (RSA). A multilayer virtual topology model is proposed to solve RSA problems. Two RSA algorithms based on the virtual topology are proposed, which are the ant colony optimization (ACO) algorithm of minimum consecutiveness loss and the ACO algorithm of maximum spectrum consecutiveness. Due to the computing power of the control layer in the software-defined network, the routing algorithm avoids the frequent link-state information between routers. Based on the effect of the spectrum consecutiveness loss on the pheromone in the ACO, the path and spectrum of the minimal impact on the network are selected for the service request. The proposed algorithms have been compared with other algorithms. The results show that the proposed algorithms can reduce the blocking rate by at least 5% and perform better in spectrum efficiency. Moreover, the proposed algorithms can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness.

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.

Multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement

NASA Astrophysics Data System (ADS)

Yan, Dan; Bai, Lianfa; Zhang, Yi; Han, Jing

2018-02-01

For the problems of missing details and performance of the colorization based on sparse representation, we propose a conceptual model framework for colorizing gray-scale images, and then a multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement (CEMDC) is proposed based on this framework. The algorithm can achieve a natural colorized effect for a gray-scale image, and it is consistent with the human vision. First, the algorithm establishes a multi-sparse dictionary classification colorization model. Then, to improve the accuracy rate of the classification, the corresponding local constraint algorithm is proposed. Finally, we propose a detail enhancement based on Laplacian Pyramid, which is effective in solving the problem of missing details and improving the speed of image colorization. In addition, the algorithm not only realizes the colorization of the visual gray-scale image, but also can be applied to the other areas, such as color transfer between color images, colorizing gray fusion images, and infrared images.

Peak-to-average power ratio reduction in orthogonal frequency division multiplexing-based visible light communication systems using a modified partial transmit sequence technique

NASA Astrophysics Data System (ADS)

Liu, Yan; Deng, Honggui; Ren, Shuang; Tang, Chengying; Qian, Xuewen

2018-01-01

We propose an efficient partial transmit sequence technique based on genetic algorithm and peak-value optimization algorithm (GAPOA) to reduce high peak-to-average power ratio (PAPR) in visible light communication systems based on orthogonal frequency division multiplexing (VLC-OFDM). By analysis of hill-climbing algorithm's pros and cons, we propose the POA with excellent local search ability to further process the signals whose PAPR is still over the threshold after processed by genetic algorithm (GA). To verify the effectiveness of the proposed technique and algorithm, we evaluate the PAPR performance and the bit error rate (BER) performance and compare them with partial transmit sequence (PTS) technique based on GA (GA-PTS), PTS technique based on genetic and hill-climbing algorithm (GH-PTS), and PTS based on shuffled frog leaping algorithm and hill-climbing algorithm (SFLAHC-PTS). The results show that our technique and algorithm have not only better PAPR performance but also lower computational complexity and BER than GA-PTS, GH-PTS, and SFLAHC-PTS technique.

Optimizing Controlling-Value-Based Power Gating with Gate Count and Switching Activity

NASA Astrophysics Data System (ADS)

Chen, Lei; Kimura, Shinji

In this paper, a new heuristic algorithm is proposed to optimize the power domain clustering in controlling-value-based (CV-based) power gating technology. In this algorithm, both the switching activity of sleep signals (p) and the overall numbers of sleep gates (gate count, N) are considered, and the sum of the product of p and N is optimized. The algorithm effectively exerts the total power reduction obtained from the CV-based power gating. Even when the maximum depth is kept to be the same, the proposed algorithm can still achieve power reduction approximately 10% more than that of the prior algorithms. Furthermore, detailed comparison between the proposed heuristic algorithm and other possible heuristic algorithms are also presented. HSPICE simulation results show that over 26% of total power reduction can be obtained by using the new heuristic algorithm. In addition, the effect of dynamic power reduction through the CV-based power gating method and the delay overhead caused by the switching of sleep transistors are also shown in this paper.

Online selective kernel-based temporal difference learning.

PubMed

Chen, Xingguo; Gao, Yang; Wang, Ruili

2013-12-01

In this paper, an online selective kernel-based temporal difference (OSKTD) learning algorithm is proposed to deal with large scale and/or continuous reinforcement learning problems. OSKTD includes two online procedures: online sparsification and parameter updating for the selective kernel-based value function. A new sparsification method (i.e., a kernel distance-based online sparsification method) is proposed based on selective ensemble learning, which is computationally less complex compared with other sparsification methods. With the proposed sparsification method, the sparsified dictionary of samples is constructed online by checking if a sample needs to be added to the sparsified dictionary. In addition, based on local validity, a selective kernel-based value function is proposed to select the best samples from the sample dictionary for the selective kernel-based value function approximator. The parameters of the selective kernel-based value function are iteratively updated by using the temporal difference (TD) learning algorithm combined with the gradient descent technique. The complexity of the online sparsification procedure in the OSKTD algorithm is O(n). In addition, two typical experiments (Maze and Mountain Car) are used to compare with both traditional and up-to-date O(n) algorithms (GTD, GTD2, and TDC using the kernel-based value function), and the results demonstrate the effectiveness of our proposed algorithm. In the Maze problem, OSKTD converges to an optimal policy and converges faster than both traditional and up-to-date algorithms. In the Mountain Car problem, OSKTD converges, requires less computation time compared with other sparsification methods, gets a better local optima than the traditional algorithms, and converges much faster than the up-to-date algorithms. In addition, OSKTD can reach a competitive ultimate optima compared with the up-to-date algorithms.

An improved conscan algorithm based on a Kalman filter

NASA Technical Reports Server (NTRS)

Eldred, D. B.

1994-01-01

Conscan is commonly used by DSN antennas to allow adaptive tracking of a target whose position is not precisely known. This article describes an algorithm that is based on a Kalman filter and is proposed to replace the existing fast Fourier transform based (FFT-based) algorithm for conscan. Advantages of this algorithm include better pointing accuracy, continuous update information, and accommodation of missing data. Additionally, a strategy for adaptive selection of the conscan radius is proposed. The performance of the algorithm is illustrated through computer simulations and compared to the FFT algorithm. The results show that the Kalman filter algorithm is consistently superior.

Multiple-algorithm parallel fusion of infrared polarization and intensity images based on algorithmic complementarity and synergy

NASA Astrophysics Data System (ADS)

Zhang, Lei; Yang, Fengbao; Ji, Linna; Lv, Sheng

2018-01-01

Diverse image fusion methods perform differently. Each method has advantages and disadvantages compared with others. One notion is that the advantages of different image methods can be effectively combined. A multiple-algorithm parallel fusion method based on algorithmic complementarity and synergy is proposed. First, in view of the characteristics of the different algorithms and difference-features among images, an index vector-based feature-similarity is proposed to define the degree of complementarity and synergy. This proposed index vector is a reliable evidence indicator for algorithm selection. Second, the algorithms with a high degree of complementarity and synergy are selected. Then, the different degrees of various features and infrared intensity images are used as the initial weights for the nonnegative matrix factorization (NMF). This avoids randomness of the NMF initialization parameter. Finally, the fused images of different algorithms are integrated using the NMF because of its excellent data fusing performance on independent features. Experimental results demonstrate that the visual effect and objective evaluation index of the fused images obtained using the proposed method are better than those obtained using traditional methods. The proposed method retains all the advantages that individual fusion algorithms have.

Research on target tracking algorithm based on spatio-temporal context

NASA Astrophysics Data System (ADS)

Li, Baiping; Xu, Sanmei; Kang, Hongjuan

2017-07-01

In this paper, a novel target tracking algorithm based on spatio-temporal context is proposed. During the tracking process, the camera shaking or occlusion may lead to the failure of tracking. The proposed algorithm can solve this problem effectively. The method use the spatio-temporal context algorithm as the main research object. We get the first frame's target region via mouse. Then the spatio-temporal context algorithm is used to get the tracking targets of the sequence of frames. During this process a similarity measure function based on perceptual hash algorithm is used to judge the tracking results. If tracking failed, reset the initial value of Mean Shift algorithm for the subsequent target tracking. Experiment results show that the proposed algorithm can achieve real-time and stable tracking when camera shaking or target occlusion.

Network Community Detection based on the Physarum-inspired Computational Framework.

PubMed

Gao, Chao; Liang, Mingxin; Li, Xianghua; Zhang, Zili; Wang, Zhen; Zhou, Zhili

2016-12-13

Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, which is a large amoeba-like cell consisting of a dendritic network of tube-like pseudopodia, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost. Moreover, a computational complexity analysis verifies the scalability of our framework.

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

PubMed

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

2011-03-01

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

Detection of dominant flow and abnormal events in surveillance video

NASA Astrophysics Data System (ADS)

Kwak, Sooyeong; Byun, Hyeran

2011-02-01

We propose an algorithm for abnormal event detection in surveillance video. The proposed algorithm is based on a semi-unsupervised learning method, a kind of feature-based approach so that it does not detect the moving object individually. The proposed algorithm identifies dominant flow without individual object tracking using a latent Dirichlet allocation model in crowded environments. It can also automatically detect and localize an abnormally moving object in real-life video. The performance tests are taken with several real-life databases, and their results show that the proposed algorithm can efficiently detect abnormally moving objects in real time. The proposed algorithm can be applied to any situation in which abnormal directions or abnormal speeds are detected regardless of direction.

Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization.

PubMed

Sun, Yanfeng; Gao, Junbin; Hong, Xia; Mishra, Bamdev; Yin, Baocai

2016-03-01

Tensor clustering is an important tool that exploits intrinsically rich structures in real-world multiarray or Tensor datasets. Often in dealing with those datasets, standard practice is to use subspace clustering that is based on vectorizing multiarray data. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model taking into account cluster membership information. We propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the multinomial manifold for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.

An evaluation and implementation of rule-based Home Energy Management System using the Rete algorithm.

PubMed

Kawakami, Tomoya; Fujita, Naotaka; Yoshihisa, Tomoki; Tsukamoto, Masahiko

2014-01-01

In recent years, sensors become popular and Home Energy Management System (HEMS) takes an important role in saving energy without decrease in QoL (Quality of Life). Currently, many rule-based HEMSs have been proposed and almost all of them assume "IF-THEN" rules. The Rete algorithm is a typical pattern matching algorithm for IF-THEN rules. Currently, we have proposed a rule-based Home Energy Management System (HEMS) using the Rete algorithm. In the proposed system, rules for managing energy are processed by smart taps in network, and the loads for processing rules and collecting data are distributed to smart taps. In addition, the number of processes and collecting data are reduced by processing rules based on the Rete algorithm. In this paper, we evaluated the proposed system by simulation. In the simulation environment, rules are processed by a smart tap that relates to the action part of each rule. In addition, we implemented the proposed system as HEMS using smart taps.

A Novel Color Image Encryption Algorithm Based on Quantum Chaos Sequence

NASA Astrophysics Data System (ADS)

Liu, Hui; Jin, Cong

2017-03-01

In this paper, a novel algorithm of image encryption based on quantum chaotic is proposed. The keystreams are generated by the two-dimensional logistic map as initial conditions and parameters. And then general Arnold scrambling algorithm with keys is exploited to permute the pixels of color components. In diffusion process, a novel encryption algorithm, folding algorithm, is proposed to modify the value of diffused pixels. In order to get the high randomness and complexity, the two-dimensional logistic map and quantum chaotic map are coupled with nearest-neighboring coupled-map lattices. Theoretical analyses and computer simulations confirm that the proposed algorithm has high level of security.

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

PubMed

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

2018-02-21

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

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

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.

An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

NASA Astrophysics Data System (ADS)

Wang, Juan

2018-03-01

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.

Pilot-based parametric channel estimation algorithm for DCO-OFDM-based visual light communications

NASA Astrophysics Data System (ADS)

Qian, Xuewen; Deng, Honggui; He, Hailang

2017-10-01

Due to wide modulation bandwidth in optical communication, multipath channels may be non-sparse and deteriorate communication performance heavily. Traditional compressive sensing-based channel estimation algorithm cannot be employed in this kind of situation. In this paper, we propose a practical parametric channel estimation algorithm for orthogonal frequency division multiplexing (OFDM)-based visual light communication (VLC) systems based on modified zero correlation code (ZCC) pair that has the impulse-like correlation property. Simulation results show that the proposed algorithm achieves better performances than existing least squares (LS)-based algorithm in both bit error ratio (BER) and frequency response estimation.

Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters

NASA Astrophysics Data System (ADS)

Chitsobhuk, Orachat; Kasemsiri, Watjanapong; Glomglome, Sorayut; Lapamonpinyo, Pipatphon

2018-04-01

In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user's defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.

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.

Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction.

PubMed

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2018-06-01

To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively. Graphical abstract ᅟ.

Adaptive image coding based on cubic-spline interpolation

NASA Astrophysics Data System (ADS)

Jiang, Jian-Xing; Hong, Shao-Hua; Lin, Tsung-Ching; Wang, Lin; Truong, Trieu-Kien

2014-09-01

It has been investigated that at low bit rates, downsampling prior to coding and upsampling after decoding can achieve better compression performance than standard coding algorithms, e.g., JPEG and H. 264/AVC. However, at high bit rates, the sampling-based schemes generate more distortion. Additionally, the maximum bit rate for the sampling-based scheme to outperform the standard algorithm is image-dependent. In this paper, a practical adaptive image coding algorithm based on the cubic-spline interpolation (CSI) is proposed. This proposed algorithm adaptively selects the image coding method from CSI-based modified JPEG and standard JPEG under a given target bit rate utilizing the so called ρ-domain analysis. The experimental results indicate that compared with the standard JPEG, the proposed algorithm can show better performance at low bit rates and maintain the same performance at high bit rates.

An automatic fuzzy-based multi-temporal brain digital subtraction angiography image fusion algorithm using curvelet transform and content selection strategy.

PubMed

Momeni, Saba; Pourghassem, Hossein

2014-08-01

Recently image fusion has prominent role in medical image processing and is useful to diagnose and treat many diseases. Digital subtraction angiography is one of the most applicable imaging to diagnose brain vascular diseases and radiosurgery of brain. This paper proposes an automatic fuzzy-based multi-temporal fusion algorithm for 2-D digital subtraction angiography images. In this algorithm, for blood vessel map extraction, the valuable frames of brain angiography video are automatically determined to form the digital subtraction angiography images based on a novel definition of vessel dispersion generated by injected contrast material. Our proposed fusion scheme contains different fusion methods for high and low frequency contents based on the coefficient characteristic of wrapping second generation of curvelet transform and a novel content selection strategy. Our proposed content selection strategy is defined based on sample correlation of the curvelet transform coefficients. In our proposed fuzzy-based fusion scheme, the selection of curvelet coefficients are optimized by applying weighted averaging and maximum selection rules for the high frequency coefficients. For low frequency coefficients, the maximum selection rule based on local energy criterion is applied to better visual perception. Our proposed fusion algorithm is evaluated on a perfect brain angiography image dataset consisting of one hundred 2-D internal carotid rotational angiography videos. The obtained results demonstrate the effectiveness and efficiency of our proposed fusion algorithm in comparison with common and basic fusion algorithms.

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors

PubMed Central

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-01-01

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms. PMID:26198233

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors.

PubMed

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-07-07

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms.

Minimum-Time Consensus-Based Approach for Power System Applications

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

Yang, Tao; Wu, Di; Sun, Yannan

2016-02-01

This paper presents minimum-time consensus based distributed algorithms for power system applications, such as load shedding and economic dispatch. The proposed algorithms are capable of solving these problems in a minimum number of time steps instead of asymptotically as in most of existing studies. Moreover, these algorithms are applicable to both undirected and directed communication networks. Simulation results are used to validate the proposed algorithms.

Spectrum sensing algorithm based on autocorrelation energy in cognitive radio networks

NASA Astrophysics Data System (ADS)

Ren, Shengwei; Zhang, Li; Zhang, Shibing

2016-10-01

Cognitive radio networks have wide applications in the smart home, personal communications and other wireless communication. Spectrum sensing is the main challenge in cognitive radios. This paper proposes a new spectrum sensing algorithm which is based on the autocorrelation energy of signal received. By taking the autocorrelation energy of the received signal as the statistics of spectrum sensing, the effect of the channel noise on the detection performance is reduced. Simulation results show that the algorithm is effective and performs well in low signal-to-noise ratio. Compared with the maximum generalized eigenvalue detection (MGED) algorithm, function of covariance matrix based detection (FMD) algorithm and autocorrelation-based detection (AD) algorithm, the proposed algorithm has 2 11 dB advantage.

QPSO-Based Adaptive DNA Computing Algorithm

PubMed Central

Karakose, Mehmet; Cigdem, Ugur

2013-01-01

DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm. PMID:23935409

Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

PubMed Central

Hu, Zhongyi; Xiong, Tao

2013-01-01

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature. PMID:24459425

Electricity load forecasting using support vector regression with memetic algorithms.

PubMed

Hu, Zhongyi; Bao, Yukun; Xiong, Tao

2013-01-01

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.

Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2014-04-01

A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the lp pseudo-norm of the second-order difference, called as the lp(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art lp(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm.

Nonuniformity correction for an infrared focal plane array based on diamond search block matching.

PubMed

Sheng-Hui, Rong; Hui-Xin, Zhou; Han-Lin, Qin; Rui, Lai; Kun, Qian

2016-05-01

In scene-based nonuniformity correction algorithms, artificial ghosting and image blurring degrade the correction quality severely. In this paper, an improved algorithm based on the diamond search block matching algorithm and the adaptive learning rate is proposed. First, accurate transform pairs between two adjacent frames are estimated by the diamond search block matching algorithm. Then, based on the error between the corresponding transform pairs, the gradient descent algorithm is applied to update correction parameters. During the process of gradient descent, the local standard deviation and a threshold are utilized to control the learning rate to avoid the accumulation of matching error. Finally, the nonuniformity correction would be realized by a linear model with updated correction parameters. The performance of the proposed algorithm is thoroughly studied with four real infrared image sequences. Experimental results indicate that the proposed algorithm can reduce the nonuniformity with less ghosting artifacts in moving areas and can also overcome the problem of image blurring in static areas.

Multi-robot task allocation based on two dimensional artificial fish swarm algorithm

NASA Astrophysics Data System (ADS)

Zheng, Taixiong; Li, Xueqin; Yang, Liangyi

2007-12-01

The problem of task allocation for multiple robots is to allocate more relative-tasks to less relative-robots so as to minimize the processing time of these tasks. In order to get optimal multi-robot task allocation scheme, a twodimensional artificial swarm algorithm based approach is proposed in this paper. In this approach, the normal artificial fish is extended to be two dimension artificial fish. In the two dimension artificial fish, each vector of primary artificial fish is extended to be an m-dimensional vector. Thus, each vector can express a group of tasks. By redefining the distance between artificial fish and the center of artificial fish, the behavior of two dimension fish is designed and the task allocation algorithm based on two dimension artificial swarm algorithm is put forward. At last, the proposed algorithm is applied to the problem of multi-robot task allocation and comparer with GA and SA based algorithm is done. Simulation and compare result shows the proposed algorithm is effective.

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

PubMed

Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

2017-09-01

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

Algorithm of reducing the false positives in IDS based on correlation Analysis

NASA Astrophysics Data System (ADS)

Liu, Jianyi; Li, Sida; Zhang, Ru

2018-03-01

This paper proposes an algorithm of reducing the false positives in IDS based on correlation Analysis. Firstly, the algorithm analyzes the distinguishing characteristics of false positives and real alarms, and preliminary screen the false positives; then use the method of attribute similarity clustering to the alarms and further reduces the amount of alarms; finally, according to the characteristics of multi-step attack, associated it by the causal relationship. The paper also proposed a reverse causation algorithm based on the attack association method proposed by the predecessors, turning alarm information into a complete attack path. Experiments show that the algorithm simplifies the number of alarms, improve the efficiency of alarm processing, and contribute to attack purposes identification and alarm accuracy improvement.

Time-frequency analysis-based time-windowing algorithm for the inverse synthetic aperture radar imaging of ships

NASA Astrophysics Data System (ADS)

Zhou, Peng; Zhang, Xi; Sun, Weifeng; Dai, Yongshou; Wan, Yong

2018-01-01

An algorithm based on time-frequency analysis is proposed to select an imaging time window for the inverse synthetic aperture radar imaging of ships. An appropriate range bin is selected to perform the time-frequency analysis after radial motion compensation. The selected range bin is that with the maximum mean amplitude among the range bins whose echoes are confirmed to be contributed by a dominant scatter. The criterion for judging whether the echoes of a range bin are contributed by a dominant scatter is key to the proposed algorithm and is therefore described in detail. When the first range bin that satisfies the judgment criterion is found, a sequence composed of the frequencies that have the largest amplitudes in every moment's time-frequency spectrum corresponding to this range bin is employed to calculate the length and the center moment of the optimal imaging time window. Experiments performed with simulation data and real data show the effectiveness of the proposed algorithm, and comparisons between the proposed algorithm and the image contrast-based algorithm (ICBA) are provided. Similar image contrast and lower entropy are acquired using the proposed algorithm as compared with those values when using the ICBA.

Compressive Sensing of Foot Gait Signals and Its Application for the Estimation of Clinically Relevant Time Series.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2016-07-01

A new signal reconstruction algorithm for compressive sensing based on the minimization of a pseudonorm which promotes block-sparse structure on the first-order difference of the signal is proposed. Involved optimization is carried out by using a sequential version of Fletcher-Reeves' conjugate-gradient algorithm, and the line search is based on Banach's fixed-point theorem. The algorithm is suitable for the reconstruction of foot gait signals which admit block-sparse structure on the first-order difference. An additional algorithm for the estimation of stride-interval, swing-interval, and stance-interval time series from the reconstructed foot gait signals is also proposed. This algorithm is based on finding zero crossing indices of the foot gait signal and using the resulting indices for the computation of time series. Extensive simulation results demonstrate that the proposed signal reconstruction algorithm yields improved signal-to-noise ratio and requires significantly reduced computational effort relative to several competing algorithms over a wide range of compression ratio. For a compression ratio in the range from 88% to 94%, the proposed algorithm is found to offer improved accuracy for the estimation of clinically relevant time-series parameters, namely, the mean value, variance, and spectral index of stride-interval, stance-interval, and swing-interval time series, relative to its nearest competitor algorithm. The improvement in performance for compression ratio as high as 94% indicates that the proposed algorithms would be useful for designing compressive sensing-based systems for long-term telemonitoring of human gait signals.

A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

PubMed Central

Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O.

2016-01-01

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. PMID:28025566

Near-lossless multichannel EEG compression based on matrix and tensor decompositions.

PubMed

Dauwels, Justin; Srinivasan, K; Reddy, M Ramasubba; Cichocki, Andrzej

2013-05-01

A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

Medical image reconstruction algorithm based on the geometric information between sensor detector and ROI

NASA Astrophysics Data System (ADS)

Ham, Woonchul; Song, Chulgyu; Lee, Kangsan; Roh, Seungkuk

2016-05-01

In this paper, we propose a new image reconstruction algorithm considering the geometric information of acoustic sources and senor detector and review the two-step reconstruction algorithm which was previously proposed based on the geometrical information of ROI(region of interest) considering the finite size of acoustic sensor element. In a new image reconstruction algorithm, not only mathematical analysis is very simple but also its software implementation is very easy because we don't need to use the FFT. We verify the effectiveness of the proposed reconstruction algorithm by showing the simulation results by using Matlab k-wave toolkit.

Super-resolution algorithm based on sparse representation and wavelet preprocessing for remote sensing imagery

NASA Astrophysics Data System (ADS)

Ren, Ruizhi; Gu, Lingjia; Fu, Haoyang; Sun, Chenglin

2017-04-01

An effective super-resolution (SR) algorithm is proposed for actual spectral remote sensing images based on sparse representation and wavelet preprocessing. The proposed SR algorithm mainly consists of dictionary training and image reconstruction. Wavelet preprocessing is used to establish four subbands, i.e., low frequency, horizontal, vertical, and diagonal high frequency, for an input image. As compared to the traditional approaches involving the direct training of image patches, the proposed approach focuses on the training of features derived from these four subbands. The proposed algorithm is verified using different spectral remote sensing images, e.g., moderate-resolution imaging spectroradiometer (MODIS) images with different bands, and the latest Chinese Jilin-1 satellite images with high spatial resolution. According to the visual experimental results obtained from the MODIS remote sensing data, the SR images using the proposed SR algorithm are superior to those using a conventional bicubic interpolation algorithm or traditional SR algorithms without preprocessing. Fusion algorithms, e.g., standard intensity-hue-saturation, principal component analysis, wavelet transform, and the proposed SR algorithms are utilized to merge the multispectral and panchromatic images acquired by the Jilin-1 satellite. The effectiveness of the proposed SR algorithm is assessed by parameters such as peak signal-to-noise ratio, structural similarity index, correlation coefficient, root-mean-square error, relative dimensionless global error in synthesis, relative average spectral error, spectral angle mapper, and the quality index Q4, and its performance is better than that of the standard image fusion algorithms.

Kalman Filters for Time Delay of Arrival-Based Source Localization

NASA Astrophysics Data System (ADS)

Klee, Ulrich; Gehrig, Tobias; McDonough, John

2006-12-01

In this work, we propose an algorithm for acoustic source localization based on time delay of arrival (TDOA) estimation. In earlier work by other authors, an initial closed-form approximation was first used to estimate the true position of the speaker followed by a Kalman filtering stage to smooth the time series of estimates. In the proposed algorithm, this closed-form approximation is eliminated by employing a Kalman filter to directly update the speaker's position estimate based on the observed TDOAs. In particular, the TDOAs comprise the observation associated with an extended Kalman filter whose state corresponds to the speaker's position. We tested our algorithm on a data set consisting of seminars held by actual speakers. Our experiments revealed that the proposed algorithm provides source localization accuracy superior to the standard spherical and linear intersection techniques. Moreover, the proposed algorithm, although relying on an iterative optimization scheme, proved efficient enough for real-time operation.

Security Analysis of a Block Encryption Algorithm Based on Dynamic Sequences of Multiple Chaotic Systems

NASA Astrophysics Data System (ADS)

Du, Mao-Kang; He, Bo; Wang, Yong

2011-01-01

Recently, the cryptosystem based on chaos has attracted much attention. Wang and Yu (Commun. Nonlin. Sci. Numer. Simulat. 14 (2009) 574) proposed a block encryption algorithm based on dynamic sequences of multiple chaotic systems. We analyze the potential flaws in the algorithm. Then, a chosen-plaintext attack is presented. Some remedial measures are suggested to avoid the flaws effectively. Furthermore, an improved encryption algorithm is proposed to resist the attacks and to keep all the merits of the original cryptosystem.

Gas leak detection in infrared video with background modeling

NASA Astrophysics Data System (ADS)

Zeng, Xiaoxia; Huang, Likun

2018-03-01

Background modeling plays an important role in the task of gas detection based on infrared video. VIBE algorithm is a widely used background modeling algorithm in recent years. However, the processing speed of the VIBE algorithm sometimes cannot meet the requirements of some real time detection applications. Therefore, based on the traditional VIBE algorithm, we propose a fast prospect model and optimize the results by combining the connected domain algorithm and the nine-spaces algorithm in the following processing steps. Experiments show the effectiveness of the proposed method.

Minimalist ensemble algorithms for genome-wide protein localization prediction.

PubMed

Lin, Jhih-Rong; Mondal, Ananda Mohan; Liu, Rong; Hu, Jianjun

2012-07-03

Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. We proposed a method for rational design of minimalist ensemble algorithms using feature selection and classifiers. The proposed minimalist ensemble algorithm based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms. The results also suggested that meta-predictors that take advantage of a variety of features by combining individual predictors tend to achieve the best performance. The LR ensemble server and related benchmark datasets are available at http://mleg.cse.sc.edu/LRensemble/cgi-bin/predict.cgi.

Minimalist ensemble algorithms for genome-wide protein localization prediction

PubMed Central

2012-01-01

Background Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. Results This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. Conclusions We proposed a method for rational design of minimalist ensemble algorithms using feature selection and classifiers. The proposed minimalist ensemble algorithm based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms. The results also suggested that meta-predictors that take advantage of a variety of features by combining individual predictors tend to achieve the best performance. The LR ensemble server and related benchmark datasets are available at http://mleg.cse.sc.edu/LRensemble/cgi-bin/predict.cgi. PMID:22759391

FIVQ algorithm for interference hyper-spectral image compression

NASA Astrophysics Data System (ADS)

Wen, Jia; Ma, Caiwen; Zhao, Junsuo

2014-07-01

Based on the improved vector quantization (IVQ) algorithm [1] which was proposed in 2012, this paper proposes a further improved vector quantization (FIVQ) algorithm for LASIS (Large Aperture Static Imaging Spectrometer) interference hyper-spectral image compression. To get better image quality, IVQ algorithm takes both the mean values and the VQ indices as the encoding rules. Although IVQ algorithm can improve both the bit rate and the image quality, it still can be further improved in order to get much lower bit rate for the LASIS interference pattern with the special optical characteristics based on the pushing and sweeping in LASIS imaging principle. In the proposed algorithm FIVQ, the neighborhood of the encoding blocks of the interference pattern image, which are using the mean value rules, will be checked whether they have the same mean value as the current processing block. Experiments show the proposed algorithm FIVQ can get lower bit rate compared to that of the IVQ algorithm for the LASIS interference hyper-spectral sequences.

Seismic noise attenuation using an online subspace tracking algorithm

NASA Astrophysics Data System (ADS)

Zhou, Yatong; Li, Shuhua; Zhang, Dong; Chen, Yangkang

2018-02-01

We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVD-based singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.

A multipopulation PSO based memetic algorithm for permutation flow shop scheduling.

PubMed

Liu, Ruochen; Ma, Chenlin; Ma, Wenping; Li, Yangyang

2013-01-01

The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA) and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA) and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA), on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP.

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.

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

PubMed Central

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

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

NASA Astrophysics Data System (ADS)

Laban, Shaban; El-Desouky, Aly

2013-04-01

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

Joint Smoothed l₀-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar.

PubMed

Liu, Jing; Zhou, Weidong; Juwono, Filbert H

2017-05-08

Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l 0 -norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed l 0 -norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the l 1 -norm minimization based methods, such as l 1 -SVD (singular value decomposition), RV (real-valued) l 1 -SVD and RV l 1 -SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance.

Corner detection and sorting method based on improved Harris algorithm in camera calibration

NASA Astrophysics Data System (ADS)

Xiao, Ying; Wang, Yonghong; Dan, Xizuo; Huang, Anqi; Hu, Yue; Yang, Lianxiang

2016-11-01

In traditional Harris corner detection algorithm, the appropriate threshold which is used to eliminate false corners is selected manually. In order to detect corners automatically, an improved algorithm which combines Harris and circular boundary theory of corners is proposed in this paper. After detecting accurate corner coordinates by using Harris algorithm and Forstner algorithm, false corners within chessboard pattern of the calibration plate can be eliminated automatically by using circular boundary theory. Moreover, a corner sorting method based on an improved calibration plate is proposed to eliminate false background corners and sort remaining corners in order. Experiment results show that the proposed algorithms can eliminate all false corners and sort remaining corners correctly and automatically.

[Formula: see text]-regularized recursive total least squares based sparse system identification for the error-in-variables.

PubMed

Lim, Jun-Seok; Pang, Hee-Suk

2016-01-01

In this paper an [Formula: see text]-regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse, when both input and output are contaminated by noise (the error-in-variables problem). We proposed an algorithm to handle the error-in-variables problem. The proposed [Formula: see text]-RTLS algorithm is an RLS like iteration using the [Formula: see text] regularization. The proposed algorithm not only gives excellent performance but also reduces the required complexity through the effective inversion matrix handling. Simulations demonstrate the superiority of the proposed [Formula: see text]-regularized RTLS for the sparse system identification setting.

A Novel Fast and Secure Approach for Voice Encryption Based on DNA Computing

NASA Astrophysics Data System (ADS)

Kakaei Kate, Hamidreza; Razmara, Jafar; Isazadeh, Ayaz

2018-06-01

Today, in the world of information communication, voice information has a particular importance. One way to preserve voice data from attacks is voice encryption. The encryption algorithms use various techniques such as hashing, chaotic, mixing, and many others. In this paper, an algorithm is proposed for voice encryption based on three different schemes to increase flexibility and strength of the algorithm. The proposed algorithm uses an innovative encoding scheme, the DNA encryption technique and a permutation function to provide a secure and fast solution for voice encryption. The algorithm is evaluated based on various measures including signal to noise ratio, peak signal to noise ratio, correlation coefficient, signal similarity and signal frequency content. The results demonstrate applicability of the proposed method in secure and fast encryption of voice files

Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zheng, Yang; Chen, Xihao; Zhu, Rui

2017-07-01

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

A community detection algorithm based on structural similarity

NASA Astrophysics Data System (ADS)

Guo, Xuchao; Hao, Xia; Liu, Yaqiong; Zhang, Li; Wang, Lu

2017-09-01

In order to further improve the efficiency and accuracy of community detection algorithm, a new algorithm named SSTCA (the community detection algorithm based on structural similarity with threshold) is proposed. In this algorithm, the structural similarities are taken as the weights of edges, and the threshold k is considered to remove multiple edges whose weights are less than the threshold, and improve the computational efficiency. Tests were done on the Zachary’s network, Dolphins’ social network and Football dataset by the proposed algorithm, and compared with GN and SSNCA algorithm. The results show that the new algorithm is superior to other algorithms in accuracy for the dense networks and the operating efficiency is improved obviously.

Array signal recovery algorithm for a single-RF-channel DBF array

NASA Astrophysics Data System (ADS)

Zhang, Duo; Wu, Wen; Fang, Da Gang

2016-12-01

An array signal recovery algorithm based on sparse signal reconstruction theory is proposed for a single-RF-channel digital beamforming (DBF) array. A single-RF-channel antenna array is a low-cost antenna array in which signals are obtained from all antenna elements by only one microwave digital receiver. The spatially parallel array signals are converted into time-sequence signals, which are then sampled by the system. The proposed algorithm uses these time-sequence samples to recover the original parallel array signals by exploiting the second-order sparse structure of the array signals. Additionally, an optimization method based on the artificial bee colony (ABC) algorithm is proposed to improve the reconstruction performance. Using the proposed algorithm, the motion compensation problem for the single-RF-channel DBF array can be solved effectively, and the angle and Doppler information for the target can be simultaneously estimated. The effectiveness of the proposed algorithms is demonstrated by the results of numerical simulations.

Evolutionary Fuzzy Block-Matching-Based Camera Raw Image Denoising.

PubMed

Yang, Chin-Chang; Guo, Shu-Mei; Tsai, Jason Sheng-Hong

2017-09-01

An evolutionary fuzzy block-matching-based image denoising algorithm is proposed to remove noise from a camera raw image. Recently, a variance stabilization transform is widely used to stabilize the noise variance, so that a Gaussian denoising algorithm can be used to remove the signal-dependent noise in camera sensors. However, in the stabilized domain, the existed denoising algorithm may blur too much detail. To provide a better estimate of the noise-free signal, a new block-matching approach is proposed to find similar blocks by the use of a type-2 fuzzy logic system (FLS). Then, these similar blocks are averaged with the weightings which are determined by the FLS. Finally, an efficient differential evolution is used to further improve the performance of the proposed denoising algorithm. The experimental results show that the proposed denoising algorithm effectively improves the performance of image denoising. Furthermore, the average performance of the proposed method is better than those of two state-of-the-art image denoising algorithms in subjective and objective measures.

A robust firearm identification algorithm of forensic ballistics specimens

NASA Astrophysics Data System (ADS)

Chuan, Z. L.; Jemain, A. A.; Liong, C.-Y.; Ghani, N. A. M.; Tan, L. K.

2017-09-01

There are several inherent difficulties in the existing firearm identification algorithms, include requiring the physical interpretation and time consuming. Therefore, the aim of this study is to propose a robust algorithm for a firearm identification based on extracting a set of informative features from the segmented region of interest (ROI) using the simulated noisy center-firing pin impression images. The proposed algorithm comprises Laplacian sharpening filter, clustering-based threshold selection, unweighted least square estimator, and segment a square ROI from the noisy images. A total of 250 simulated noisy images collected from five different pistols of the same make, model and caliber are used to evaluate the robustness of the proposed algorithm. This study found that the proposed algorithm is able to perform the identical task on the noisy images with noise levels as high as 70%, while maintaining a firearm identification accuracy rate of over 90%.

Active impulsive noise control using maximum correntropy with adaptive kernel size

NASA Astrophysics Data System (ADS)

Lu, Lu; Zhao, Haiquan

2017-03-01

The active noise control (ANC) based on the principle of superposition is an attractive method to attenuate the noise signals. However, the impulsive noise in the ANC systems will degrade the performance of the controller. In this paper, a filtered-x recursive maximum correntropy (FxRMC) algorithm is proposed based on the maximum correntropy criterion (MCC) to reduce the effect of outliers. The proposed FxRMC algorithm does not requires any priori information of the noise characteristics and outperforms the filtered-x least mean square (FxLMS) algorithm for impulsive noise. Meanwhile, in order to adjust the kernel size of FxRMC algorithm online, a recursive approach is proposed through taking into account the past estimates of error signals over a sliding window. Simulation and experimental results in the context of active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.

Short-term Power Load Forecasting Based on Balanced KNN

NASA Astrophysics Data System (ADS)

Lv, Xianlong; Cheng, Xingong; YanShuang; Tang, Yan-mei

2018-03-01

To improve the accuracy of load forecasting, a short-term load forecasting model based on balanced KNN algorithm is proposed; According to the load characteristics, the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes, the balanced KNN algorithm is proposed to classify the scene accurately; The local weighted linear regression algorithm is used to fitting and predict the load; Adopting the Apache Hadoop programming framework of cloud computing, the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data. The analysis of the household electricity consumption data for a residential district is done by 23-nodes cloud computing cluster, and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm.

An Efficient Conflict Detection Algorithm for Packet Filters

NASA Astrophysics Data System (ADS)

Lee, Chun-Liang; Lin, Guan-Yu; Chen, Yaw-Chung

Packet classification is essential for supporting advanced network services such as firewalls, quality-of-service (QoS), virtual private networks (VPN), and policy-based routing. The rules that routers use to classify packets are called packet filters. If two or more filters overlap, a conflict occurs and leads to ambiguity in packet classification. This study proposes an algorithm that can efficiently detect and resolve filter conflicts using tuple based search. The time complexity of the proposed algorithm is O(nW+s), and the space complexity is O(nW), where n is the number of filters, W is the number of bits in a header field, and s is the number of conflicts. This study uses the synthetic filter databases generated by ClassBench to evaluate the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than existing conflict detection algorithms both in time and space, particularly for databases with large numbers of conflicts.

A triangle voting algorithm based on double feature constraints for star sensors

NASA Astrophysics Data System (ADS)

Fan, Qiaoyun; Zhong, Xuyang

2018-02-01

A novel autonomous star identification algorithm is presented in this study. In the proposed algorithm, each sensor star constructs multi-triangle with its bright neighbor stars and obtains its candidates by triangle voting process, in which the triangle is considered as the basic voting element. In order to accelerate the speed of this algorithm and reduce the required memory for star database, feature extraction is carried out to reduce the dimension of triangles and each triangle is described by its base and height. During the identification period, the voting scheme based on double feature constraints is proposed to implement triangle voting. This scheme guarantees that only the catalog star satisfying two features can vote for the sensor star, which improves the robustness towards false stars. The simulation and real star image test demonstrate that compared with the other two algorithms, the proposed algorithm is more robust towards position noise, magnitude noise and false stars.

Multi-modulus algorithm based on global artificial fish swarm intelligent optimization of DNA encoding sequences.

PubMed

Guo, Y C; Wang, H; Wu, H P; Zhang, M Q

2015-12-21

Aimed to address the defects of the large mean square error (MSE), and the slow convergence speed in equalizing the multi-modulus signals of the constant modulus algorithm (CMA), a multi-modulus algorithm (MMA) based on global artificial fish swarm (GAFS) intelligent optimization of DNA encoding sequences (GAFS-DNA-MMA) was proposed. To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. The simulation results show that the proposed algorithm has a faster convergence speed and smaller MSE in comparison with the CMA, the MMA, and the AFS-DNA-MMA.

Parallel Directionally Split Solver Based on Reformulation of Pipelined Thomas Algorithm

NASA Technical Reports Server (NTRS)

Povitsky, A.

1998-01-01

In this research an efficient parallel algorithm for 3-D directionally split problems is developed. The proposed algorithm is based on a reformulated version of the pipelined Thomas algorithm that starts the backward step computations immediately after the completion of the forward step computations for the first portion of lines This algorithm has data available for other computational tasks while processors are idle from the Thomas algorithm. The proposed 3-D directionally split solver is based on the static scheduling of processors where local and non-local, data-dependent and data-independent computations are scheduled while processors are idle. A theoretical model of parallelization efficiency is used to define optimal parameters of the algorithm, to show an asymptotic parallelization penalty and to obtain an optimal cover of a global domain with subdomains. It is shown by computational experiments and by the theoretical model that the proposed algorithm reduces the parallelization penalty about two times over the basic algorithm for the range of the number of processors (subdomains) considered and the number of grid nodes per subdomain.

The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model

NASA Astrophysics Data System (ADS)

Di, Nur Faraidah Muhammad; Satari, Siti Zanariah

2017-05-01

Outlier detection in linear data sets has been done vigorously but only a small amount of work has been done for outlier detection in circular data. In this study, we proposed multiple outliers detection in circular regression models based on the clustering algorithm. Clustering technique basically utilizes distance measure to define distance between various data points. Here, we introduce the similarity distance based on Euclidean distance for circular model and obtain a cluster tree using the single linkage clustering algorithm. Then, a stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height is proposed. We classify the cluster group that exceeds the stopping rule as potential outlier. Our aim is to demonstrate the effectiveness of proposed algorithms with the similarity distances in detecting the outliers. It is found that the proposed methods are performed well and applicable for circular regression model.

Human resource recommendation algorithm based on ensemble learning and Spark

NASA Astrophysics Data System (ADS)

Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie

2017-08-01

Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.

Vision-based measurement for rotational speed by improving Lucas-Kanade template tracking algorithm.

PubMed

Guo, Jie; Zhu, Chang'an; Lu, Siliang; Zhang, Dashan; Zhang, Chunyu

2016-09-01

Rotational angle and speed are important parameters for condition monitoring and fault diagnosis of rotating machineries, and their measurement is useful in precision machining and early warning of faults. In this study, a novel vision-based measurement algorithm is proposed to complete this task. A high-speed camera is first used to capture the video of the rotational object. To extract the rotational angle, the template-based Lucas-Kanade algorithm is introduced to complete motion tracking by aligning the template image in the video sequence. Given the special case of nonplanar surface of the cylinder object, a nonlinear transformation is designed for modeling the rotation tracking. In spite of the unconventional and complex form, the transformation can realize angle extraction concisely with only one parameter. A simulation is then conducted to verify the tracking effect, and a practical tracking strategy is further proposed to track consecutively the video sequence. Based on the proposed algorithm, instantaneous rotational speed (IRS) can be measured accurately and efficiently. Finally, the effectiveness of the proposed algorithm is verified on a brushless direct current motor test rig through the comparison with results obtained by the microphone. Experimental results demonstrate that the proposed algorithm can extract accurately rotational angles and can measure IRS with the advantage of noncontact and effectiveness.

a Threshold-Free Filtering Algorithm for Airborne LIDAR Point Clouds Based on Expectation-Maximization

NASA Astrophysics Data System (ADS)

Hui, Z.; Cheng, P.; Ziggah, Y. Y.; Nie, Y.

2018-04-01

Filtering is a key step for most applications of airborne LiDAR point clouds. Although lots of filtering algorithms have been put forward in recent years, most of them suffer from parameters setting or thresholds adjusting, which will be time-consuming and reduce the degree of automation of the algorithm. To overcome this problem, this paper proposed a threshold-free filtering algorithm based on expectation-maximization. The proposed algorithm is developed based on an assumption that point clouds are seen as a mixture of Gaussian models. The separation of ground points and non-ground points from point clouds can be replaced as a separation of a mixed Gaussian model. Expectation-maximization (EM) is applied for realizing the separation. EM is used to calculate maximum likelihood estimates of the mixture parameters. Using the estimated parameters, the likelihoods of each point belonging to ground or object can be computed. After several iterations, point clouds can be labelled as the component with a larger likelihood. Furthermore, intensity information was also utilized to optimize the filtering results acquired using the EM method. The proposed algorithm was tested using two different datasets used in practice. Experimental results showed that the proposed method can filter non-ground points effectively. To quantitatively evaluate the proposed method, this paper adopted the dataset provided by the ISPRS for the test. The proposed algorithm can obtain a 4.48 % total error which is much lower than most of the eight classical filtering algorithms reported by the ISPRS.

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme.

PubMed

Li, Dong; Pan, Zhisong; Hu, Guyu; Zhu, Zexuan; He, Shan

2017-03-14

Active modules are connected regions in biological network which show significant changes in expression over particular conditions. The identification of such modules is important since it may reveal the regulatory and signaling mechanisms that associate with a given cellular response. In this paper, we propose a novel active module identification algorithm based on a memetic algorithm. We propose a novel encoding/decoding scheme to ensure the connectedness of the identified active modules. Based on the scheme, we also design and incorporate a local search operator into the memetic algorithm to improve its performance. The effectiveness of proposed algorithm is validated on both small and large protein interaction networks.

A Novel Range Compression Algorithm for Resolution Enhancement in GNSS-SARs.

PubMed

Zheng, Yu; Yang, Yang; Chen, Wu

2017-06-25

In this paper, a novel range compression algorithm for enhancing range resolutions of a passive Global Navigation Satellite System-based Synthetic Aperture Radar (GNSS-SAR) is proposed. In the proposed algorithm, within each azimuth bin, firstly range compression is carried out by correlating a reflected GNSS intermediate frequency (IF) signal with a synchronized direct GNSS base-band signal in the range domain. Thereafter, spectrum equalization is applied to the compressed results for suppressing side lobes to obtain a final range-compressed signal. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR images can be achieved by the proposed range compression algorithm, compared to the conventional range compression algorithm.

A Modified Artificial Bee Colony Algorithm for p-Center Problems

PubMed Central

Yurtkuran, Alkın

2014-01-01

The objective of the p-center problem is to locate p-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to balance exploration and exploitation. Moreover, random key-based coding schemes are used to solve the p-center problem effectively. The proposed algorithm is compared to state-of-the-art techniques using different benchmark problems, and computational results reveal that the proposed approach is very efficient. PMID:24616648

Automatic arrival time detection for earthquakes based on Modified Laplacian of Gaussian filter

NASA Astrophysics Data System (ADS)

Saad, Omar M.; Shalaby, Ahmed; Samy, Lotfy; Sayed, Mohammed S.

2018-04-01

Precise identification of onset time for an earthquake is imperative in the right figuring of earthquake's location and different parameters that are utilized for building seismic catalogues. P-wave arrival detection of weak events or micro-earthquakes cannot be precisely determined due to background noise. In this paper, we propose a novel approach based on Modified Laplacian of Gaussian (MLoG) filter to detect the onset time even in the presence of very weak signal-to-noise ratios (SNRs). The proposed algorithm utilizes a denoising-filter algorithm to smooth the background noise. In the proposed algorithm, we employ the MLoG mask to filter the seismic data. Afterward, we apply a Dual-threshold comparator to detect the onset time of the event. The results show that the proposed algorithm can detect the onset time for micro-earthquakes accurately, with SNR of -12 dB. The proposed algorithm achieves an onset time picking accuracy of 93% with a standard deviation error of 0.10 s for 407 field seismic waveforms. Also, we compare the results with short and long time average algorithm (STA/LTA) and the Akaike Information Criterion (AIC), and the proposed algorithm outperforms them.

Multi-frame knowledge based text enhancement for mobile phone captured videos

NASA Astrophysics Data System (ADS)

Ozarslan, Suleyman; Eren, P. Erhan

2014-02-01

In this study, we explore automated text recognition and enhancement using mobile phone captured videos of store receipts. We propose a method which includes Optical Character Resolution (OCR) enhanced by our proposed Row Based Multiple Frame Integration (RB-MFI), and Knowledge Based Correction (KBC) algorithms. In this method, first, the trained OCR engine is used for recognition; then, the RB-MFI algorithm is applied to the output of the OCR. The RB-MFI algorithm determines and combines the most accurate rows of the text outputs extracted by using OCR from multiple frames of the video. After RB-MFI, KBC algorithm is applied to these rows to correct erroneous characters. Results of the experiments show that the proposed video-based approach which includes the RB-MFI and the KBC algorithm increases the word character recognition rate to 95%, and the character recognition rate to 98%.

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.

Infrared traffic image enhancement algorithm based on dark channel prior and gamma correction

NASA Astrophysics Data System (ADS)

Zheng, Lintao; Shi, Hengliang; Gu, Ming

2017-07-01

The infrared traffic image acquired by the intelligent traffic surveillance equipment has low contrast, little hierarchical differences in perceptions of image and the blurred vision effect. Therefore, infrared traffic image enhancement, being an indispensable key step, is applied to nearly all infrared imaging based traffic engineering applications. In this paper, we propose an infrared traffic image enhancement algorithm that is based on dark channel prior and gamma correction. In existing research dark channel prior, known as a famous image dehazing method, here is used to do infrared image enhancement for the first time. Initially, in the proposed algorithm, the original degraded infrared traffic image is transformed with dark channel prior as the initial enhanced result. A further adjustment based on the gamma curve is needed because initial enhanced result has lower brightness. Comprehensive validation experiments reveal that the proposed algorithm outperforms the current state-of-the-art algorithms.

A street rubbish detection algorithm based on Sift and RCNN

NASA Astrophysics Data System (ADS)

Yu, XiPeng; Chen, Zhong; Zhang, Shuo; Zhang, Ting

2018-02-01

This paper presents a street rubbish detection algorithm based on image registration with Sift feature and RCNN. Firstly, obtain the rubbish region proposal on the real-time street image and set up the CNN convolution neural network trained by the rubbish samples set consists of rubbish and non-rubbish images; Secondly, for every clean street image, obtain the Sift feature and do image registration with the real-time street image to obtain the differential image, the differential image filters a lot of background information, obtain the rubbish region proposal rect where the rubbish may appear on the differential image by the selective search algorithm. Then, the CNN model is used to detect the image pixel data in each of the region proposal on the real-time street image. According to the output vector of the CNN, it is judged whether the rubbish is in the region proposal or not. If it is rubbish, the region proposal on the real-time street image is marked. This algorithm avoids the large number of false detection caused by the detection on the whole image because the CNN is used to identify the image only in the region proposal on the real-time street image that may appear rubbish. Different from the traditional object detection algorithm based on the region proposal, the region proposal is obtained on the differential image not whole real-time street image, and the number of the invalid region proposal is greatly reduced. The algorithm has the high mean average precision (mAP).

MADM-based smart parking guidance algorithm

PubMed Central

Li, Bo; Pei, Yijian; Wu, Hao; Huang, Dijiang

2017-01-01

In smart parking environments, how to choose suitable parking facilities with various attributes to satisfy certain criteria is an important decision issue. Based on the multiple attributes decision making (MADM) theory, this study proposed a smart parking guidance algorithm by considering three representative decision factors (i.e., walk duration, parking fee, and the number of vacant parking spaces) and various preferences of drivers. In this paper, the expected number of vacant parking spaces is regarded as an important attribute to reflect the difficulty degree of finding available parking spaces, and a queueing theory-based theoretical method was proposed to estimate this expected number for candidate parking facilities with different capacities, arrival rates, and service rates. The effectiveness of the MADM-based parking guidance algorithm was investigated and compared with a blind search-based approach in comprehensive scenarios with various distributions of parking facilities, traffic intensities, and user preferences. Experimental results show that the proposed MADM-based algorithm is effective to choose suitable parking resources to satisfy users’ preferences. Furthermore, it has also been observed that this newly proposed Markov Chain-based availability attribute is more effective to represent the availability of parking spaces than the arrival rate-based availability attribute proposed in existing research. PMID:29236698

A polarized low-coherence interferometry demodulation algorithm by recovering the absolute phase of a selected monochromatic frequency.

PubMed

Jiang, Junfeng; Wang, Shaohua; Liu, Tiegen; Liu, Kun; Yin, Jinde; Meng, Xiange; Zhang, Yimo; Wang, Shuang; Qin, Zunqi; Wu, Fan; Li, Dingjie

2012-07-30

A demodulation algorithm based on absolute phase recovery of a selected monochromatic frequency is proposed for optical fiber Fabry-Perot pressure sensing system. The algorithm uses Fourier transform to get the relative phase and intercept of the unwrapped phase-frequency linear fit curve to identify its interference-order, which are then used to recover the absolute phase. A simplified mathematical model of the polarized low-coherence interference fringes was established to illustrate the principle of the proposed algorithm. Phase unwrapping and the selection of monochromatic frequency were discussed in detail. Pressure measurement experiment was carried out to verify the effectiveness of the proposed algorithm. Results showed that the demodulation precision by our algorithm could reach up to 0.15kPa, which has been improved by 13 times comparing with phase slope based algorithm.

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

PubMed

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

2017-12-20

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

A Smartphone Indoor Localization Algorithm Based on WLAN Location Fingerprinting with Feature Extraction and Clustering.

PubMed

Luo, Junhai; Fu, Liang

2017-06-09

With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS), which is collected from Access Points (APs). The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA) is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC) algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML) estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity.

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

NASA Astrophysics Data System (ADS)

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

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

New knowledge-based genetic algorithm for excavator boom structural optimization

NASA Astrophysics Data System (ADS)

Hua, Haiyan; Lin, Shuwen

2014-03-01

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Recursive approach to the moment-based phase unwrapping method.

PubMed

Langley, Jason A; Brice, Robert G; Zhao, Qun

2010-06-01

The moment-based phase unwrapping algorithm approximates the phase map as a product of Gegenbauer polynomials, but the weight function for the Gegenbauer polynomials generates artificial singularities along the edge of the phase map. A method is presented to remove the singularities inherent to the moment-based phase unwrapping algorithm by approximating the phase map as a product of two one-dimensional Legendre polynomials and applying a recursive property of derivatives of Legendre polynomials. The proposed phase unwrapping algorithm is tested on simulated and experimental data sets. The results are then compared to those of PRELUDE 2D, a widely used phase unwrapping algorithm, and a Chebyshev-polynomial-based phase unwrapping algorithm. It was found that the proposed phase unwrapping algorithm provides results that are comparable to those obtained by using PRELUDE 2D and the Chebyshev phase unwrapping algorithm.

Efficient FFT Algorithm for Psychoacoustic Model of the MPEG-4 AAC

NASA Astrophysics Data System (ADS)

Lee, Jae-Seong; Lee, Chang-Joon; Park, Young-Cheol; Youn, Dae-Hee

This paper proposes an efficient FFT algorithm for the Psycho-Acoustic Model (PAM) of MPEG-4 AAC. The proposed algorithm synthesizes FFT coefficients using MDCT and MDST coefficients through circular convolution. The complexity of the MDCT and MDST coefficients is approximately half of the original FFT. We also design a new PAM based on the proposed FFT algorithm, which has 15% lower computational complexity than the original PAM without degradation of sound quality. Subjective as well as objective test results are presented to confirm the efficiency of the proposed FFT computation algorithm and the PAM.

Estimation of Comfort/Disconfort Based on EEG in Massage by Use of Clustering according to Correration and Incremental Learning type NN

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

Authors proposed the estimation method combining k-means algorithm and NN for evaluating massage. However, this estimation method has a problem that discrimination ratio is decreased to new user. There are two causes of this problem. One is that generalization of NN is bad. Another one is that clustering result by k-means algorithm has not high correlation coefficient in a class. Then, this research proposes k-means algorithm according to correlation coefficient and incremental learning for NN. The proposed k-means algorithm is method included evaluation function based on correlation coefficient. Incremental learning is method that NN is learned by new data and initialized weight based on the existing data. The effect of proposed methods are verified by estimation result using EEG data when testee is given massage.

A deblocking algorithm based on color psychology for display quality enhancement

NASA Astrophysics Data System (ADS)

Yeh, Chia-Hung; Tseng, Wen-Yu; Huang, Kai-Lin

2012-12-01

This article proposes a post-processing deblocking filter to reduce blocking effects. The proposed algorithm detects blocking effects by fusing the results of Sobel edge detector and wavelet-based edge detector. The filtering stage provides four filter modes to eliminate blocking effects at different color regions according to human color vision and color psychology analysis. Experimental results show that the proposed algorithm has better subjective and objective qualities for H.264/AVC reconstructed videos when compared to several existing methods.

Two-wavelength Lidar inversion algorithm for determining planetary boundary layer height

NASA Astrophysics Data System (ADS)

Liu, Boming; Ma, Yingying; Gong, Wei; Jian, Yang; Ming, Zhang

2018-02-01

This study proposes a two-wavelength Lidar inversion algorithm to determine the boundary layer height (BLH) based on the particles clustering. Color ratio and depolarization ratio are used to analyze the particle distribution, based on which the proposed algorithm can overcome the effects of complex aerosol layers to calculate the BLH. The algorithm is used to determine the top of the boundary layer under different mixing state. Experimental results demonstrate that the proposed algorithm can determine the top of the boundary layer even in a complex case. Moreover, it can better deal with the weak convection conditions. Finally, experimental data from June 2015 to December 2015 were used to verify the reliability of the proposed algorithm. The correlation between the results of the proposed algorithm and the manual method is R2 = 0.89 with a RMSE of 131 m and mean bias of 49 m; the correlation between the results of the ideal profile fitting method and the manual method is R2 = 0.64 with a RMSE of 270 m and a mean bias of 165 m; and the correlation between the results of the wavelet covariance transform method and manual method is R2 = 0.76, with a RMSE of 196 m and mean bias of 23 m. These findings indicate that the proposed algorithm has better reliability and stability than traditional algorithms.

Red Blood Cell Count Automation Using Microscopic Hyperspectral Imaging Technology.

PubMed

Li, Qingli; Zhou, Mei; Liu, Hongying; Wang, Yiting; Guo, Fangmin

2015-12-01

Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells.

Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images

NASA Astrophysics Data System (ADS)

Su, Yuanchao; Sun, Xu; Gao, Lianru; Li, Jun; Zhang, Bing

2016-10-01

Endmember extraction is a key step in hyperspectral unmixing. A new endmember extraction framework is proposed for hyperspectral endmember extraction. The proposed approach is based on the swarm intelligence (SI) algorithm, where discretization is used to solve the SI algorithm because pixels in a hyperspectral image are naturally defined within a discrete space. Moreover, a "distance" factor is introduced into the objective function to limit the endmember numbers which is generally limited in real scenarios, while traditional SI algorithms likely produce superabundant spectral signatures, which generally belong to the same classes. Three endmember extraction methods are proposed based on the artificial bee colony, ant colony optimization, and particle swarm optimization algorithms. Experiments with both simulated and real hyperspectral images indicate that the proposed framework can improve the accuracy of endmember extraction.

A similarity based agglomerative clustering algorithm in networks

NASA Astrophysics Data System (ADS)

Liu, Zhiyuan; Wang, Xiujuan; Ma, Yinghong

2018-04-01

The detection of clusters is benefit for understanding the organizations and functions of networks. Clusters, or communities, are usually groups of nodes densely interconnected but sparsely linked with any other clusters. To identify communities, an efficient and effective community agglomerative algorithm based on node similarity is proposed. The proposed method initially calculates similarities between each pair of nodes, and form pre-partitions according to the principle that each node is in the same community as its most similar neighbor. After that, check each partition whether it satisfies community criterion. For the pre-partitions who do not satisfy, incorporate them with others that having the biggest attraction until there are no changes. To measure the attraction ability of a partition, we propose an attraction index that based on the linked node's importance in networks. Therefore, our proposed method can better exploit the nodes' properties and network's structure. To test the performance of our algorithm, both synthetic and empirical networks ranging in different scales are tested. Simulation results show that the proposed algorithm can obtain superior clustering results compared with six other widely used community detection algorithms.

PGA/MOEAD: a preference-guided evolutionary algorithm for multi-objective decision-making problems with interval-valued fuzzy preferences

NASA Astrophysics Data System (ADS)

Luo, Bin; Lin, Lin; Zhong, ShiSheng

2018-02-01

In this research, we propose a preference-guided optimisation algorithm for multi-criteria decision-making (MCDM) problems with interval-valued fuzzy preferences. The interval-valued fuzzy preferences are decomposed into a series of precise and evenly distributed preference-vectors (reference directions) regarding the objectives to be optimised on the basis of uniform design strategy firstly. Then the preference information is further incorporated into the preference-vectors based on the boundary intersection approach, meanwhile, the MCDM problem with interval-valued fuzzy preferences is reformulated into a series of single-objective optimisation sub-problems (each sub-problem corresponds to a decomposed preference-vector). Finally, a preference-guided optimisation algorithm based on MOEA/D (multi-objective evolutionary algorithm based on decomposition) is proposed to solve the sub-problems in a single run. The proposed algorithm incorporates the preference-vectors within the optimisation process for guiding the search procedure towards a more promising subset of the efficient solutions matching the interval-valued fuzzy preferences. In particular, lots of test instances and an engineering application are employed to validate the performance of the proposed algorithm, and the results demonstrate the effectiveness and feasibility of the algorithm.

A Multipopulation PSO Based Memetic Algorithm for Permutation Flow Shop Scheduling

PubMed Central

Liu, Ruochen; Ma, Chenlin; Ma, Wenping; Li, Yangyang

2013-01-01

The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA) and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA) and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA), on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP. PMID:24453841

Image segmentation algorithm based on improved PCNN

NASA Astrophysics Data System (ADS)

Chen, Hong; Wu, Chengdong; Yu, Xiaosheng; Wu, Jiahui

2017-11-01

A modified simplified Pulse Coupled Neural Network (PCNN) model is proposed in this article based on simplified PCNN. Some work have done to enrich this model, such as imposing restrictions items of the inputs, improving linking inputs and internal activity of PCNN. A self-adaptive parameter setting method of linking coefficient and threshold value decay time constant is proposed here, too. At last, we realized image segmentation algorithm for five pictures based on this proposed simplified PCNN model and PSO. Experimental results demonstrate that this image segmentation algorithm is much better than method of SPCNN and OTSU.

Salient object detection: manifold-based similarity adaptation approach

NASA Astrophysics Data System (ADS)

Zhou, Jingbo; Ren, Yongfeng; Yan, Yunyang; Gao, Shangbing

2014-11-01

A saliency detection algorithm based on manifold-based similarity adaptation is proposed. The proposed algorithm is divided into three steps. First, we segment an input image into superpixels, which are represented as the nodes in a graph. Second, a new similarity measurement is used in the proposed algorithm. The weight matrix of the graph, which indicates the similarities between the nodes, uses a similarity-based method. It also captures the manifold structure of the image patches, in which the graph edges are determined in a data adaptive manner in terms of both similarity and manifold structure. Then, we use local reconstruction method as a diffusion method to obtain the saliency maps. The objective function in the proposed method is based on local reconstruction, with which estimated weights capture the manifold structure. Experiments on four bench-mark databases demonstrate the accuracy and robustness of the proposed method.

A novel symbiotic organisms search algorithm for congestion management in deregulated environment

NASA Astrophysics Data System (ADS)

Verma, Sumit; Saha, Subhodip; Mukherjee, V.

2017-01-01

In today's competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

A novel symbiotic organisms search algorithm for congestion management in deregulated environment

NASA Astrophysics Data System (ADS)

Verma, Sumit; Saha, Subhodip; Mukherjee, V.

2017-01-01

In today's competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool-based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population-based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

A Novel Hybrid Firefly Algorithm for Global Optimization.

PubMed

Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.

A Novel Hybrid Firefly Algorithm for Global Optimization

PubMed Central

Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao

2016-01-01

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate. PMID:27685869

PCA-LBG-based algorithms for VQ codebook generation

NASA Astrophysics Data System (ADS)

Tsai, Jinn-Tsong; Yang, Po-Yuan

2015-04-01

Vector quantisation (VQ) codebooks are generated by combining principal component analysis (PCA) algorithms with Linde-Buzo-Gray (LBG) algorithms. All training vectors are grouped according to the projected values of the principal components. The PCA-LBG-based algorithms include (1) PCA-LBG-Median, which selects the median vector of each group, (2) PCA-LBG-Centroid, which adopts the centroid vector of each group, and (3) PCA-LBG-Random, which randomly selects a vector of each group. The LBG algorithm finds a codebook based on the better vectors sent to an initial codebook by the PCA. The PCA performs an orthogonal transformation to convert a set of potentially correlated variables into a set of variables that are not linearly correlated. Because the orthogonal transformation efficiently distinguishes test image vectors, the proposed PCA-LBG-based algorithm is expected to outperform conventional algorithms in designing VQ codebooks. The experimental results confirm that the proposed PCA-LBG-based algorithms indeed obtain better results compared to existing methods reported in the literature.

A consensus algorithm for approximate string matching and its application to QRS complex detection

NASA Astrophysics Data System (ADS)

Alba, Alfonso; Mendez, Martin O.; Rubio-Rincon, Miguel E.; Arce-Santana, Edgar R.

2016-08-01

In this paper, a novel algorithm for approximate string matching (ASM) is proposed. The novelty resides in the fact that, unlike most other methods, the proposed algorithm is not based on the Hamming or Levenshtein distances, but instead computes a score for each symbol in the search text based on a consensus measure. Those symbols with sufficiently high scores will likely correspond to approximate instances of the pattern string. To demonstrate the usefulness of the proposed method, it has been applied to the detection of QRS complexes in electrocardiographic signals with competitive results when compared against the classic Pan-Tompkins (PT) algorithm. The proposed method outperformed PT in 72% of the test cases, with no extra computational cost.

Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

PubMed Central

Zhuang, Yuan; Yang, Jun; Li, You; Qi, Longning; El-Sheimy, Naser

2016-01-01

Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target’s location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment. PMID:27128917

Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons.

PubMed

Zhuang, Yuan; Yang, Jun; Li, You; Qi, Longning; El-Sheimy, Naser

2016-04-26

Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target's location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment.

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

A hybrid Jaya algorithm for reliability-redundancy allocation problems

NASA Astrophysics Data System (ADS)

Ghavidel, Sahand; Azizivahed, Ali; Li, Li

2018-04-01

This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching-learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability-redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series-parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30-100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results.

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

Katti, Amogh; Di Fatta, Giuseppe; Naughton III, Thomas J

Future extreme-scale high-performance computing systems will be required to work under frequent component failures. The MPI Forum's User Level Failure Mitigation proposal has introduced an operation, MPI_Comm_shrink, to synchronize the alive processes on the list of failed processes, so that applications can continue to execute even in the presence of failures by adopting algorithm-based fault tolerance techniques. This MPI_Comm_shrink operation requires a fault tolerant failure detection and consensus algorithm. This paper presents and compares two novel failure detection and consensus algorithms. The proposed algorithms are based on Gossip protocols and are inherently fault-tolerant and scalable. The proposed algorithms were implementedmore » and tested using the Extreme-scale Simulator. The results show that in both algorithms the number of Gossip cycles to achieve global consensus scales logarithmically with system size. The second algorithm also shows better scalability in terms of memory and network bandwidth usage and a perfect synchronization in achieving global consensus.« less

VDLLA: A virtual daddy-long legs optimization

NASA Astrophysics Data System (ADS)

Yaakub, Abdul Razak; Ghathwan, Khalil I.

2016-08-01

Swarm intelligence is a strong optimization algorithm based on a biological behavior of insects or animals. The success of any optimization algorithm is depending on the balance between exploration and exploitation. In this paper, we present a new swarm intelligence algorithm, which is based on daddy long legs spider (VDLLA) as a new optimization algorithm with virtual behavior. In VDLLA, each agent (spider) has nine positions which represent the legs of spider and each position represent one solution. The proposed VDLLA is tested on four standard functions using average fitness, Medium fitness and standard deviation. The results of proposed VDLLA have been compared against Particle Swarm Optimization (PSO), Differential Evolution (DE) and Bat Inspired Algorithm (BA). Additionally, the T-Test has been conducted to show the significant deference between our proposed and other algorithms. VDLLA showed very promising results on benchmark test functions for unconstrained optimization problems and also significantly improved the original swarm algorithms.

A Novel Range Compression Algorithm for Resolution Enhancement in GNSS-SARs

PubMed Central

Zheng, Yu; Yang, Yang; Chen, Wu

2017-01-01

In this paper, a novel range compression algorithm for enhancing range resolutions of a passive Global Navigation Satellite System-based Synthetic Aperture Radar (GNSS-SAR) is proposed. In the proposed algorithm, within each azimuth bin, firstly range compression is carried out by correlating a reflected GNSS intermediate frequency (IF) signal with a synchronized direct GNSS base-band signal in the range domain. Thereafter, spectrum equalization is applied to the compressed results for suppressing side lobes to obtain a final range-compressed signal. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR images can be achieved by the proposed range compression algorithm, compared to the conventional range compression algorithm. PMID:28672830

Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

PubMed

Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

2015-01-01

A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

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

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

The cascaded moving k-means and fuzzy c-means clustering algorithms for unsupervised segmentation of malaria images

NASA Astrophysics Data System (ADS)

Abdul-Nasir, Aimi Salihah; Mashor, Mohd Yusoff; Halim, Nurul Hazwani Abd; Mohamed, Zeehaida

2015-05-01

Malaria is a life-threatening parasitic infectious disease that corresponds for nearly one million deaths each year. Due to the requirement of prompt and accurate diagnosis of malaria, the current study has proposed an unsupervised pixel segmentation based on clustering algorithm in order to obtain the fully segmented red blood cells (RBCs) infected with malaria parasites based on the thin blood smear images of P. vivax species. In order to obtain the segmented infected cell, the malaria images are first enhanced by using modified global contrast stretching technique. Then, an unsupervised segmentation technique based on clustering algorithm has been applied on the intensity component of malaria image in order to segment the infected cell from its blood cells background. In this study, cascaded moving k-means (MKM) and fuzzy c-means (FCM) clustering algorithms has been proposed for malaria slide image segmentation. After that, median filter algorithm has been applied to smooth the image as well as to remove any unwanted regions such as small background pixels from the image. Finally, seeded region growing area extraction algorithm has been applied in order to remove large unwanted regions that are still appeared on the image due to their size in which cannot be cleaned by using median filter. The effectiveness of the proposed cascaded MKM and FCM clustering algorithms has been analyzed qualitatively and quantitatively by comparing the proposed cascaded clustering algorithm with MKM and FCM clustering algorithms. Overall, the results indicate that segmentation using the proposed cascaded clustering algorithm has produced the best segmentation performances by achieving acceptable sensitivity as well as high specificity and accuracy values compared to the segmentation results provided by MKM and FCM algorithms.

Accuracy and robustness evaluation in stereo matching

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

K-means-clustering-based fiber nonlinearity equalization techniques for 64-QAM coherent optical communication system.

PubMed

Zhang, Junfeng; Chen, Wei; Gao, Mingyi; Shen, Gangxiang

2017-10-30

In this work, we proposed two k-means-clustering-based algorithms to mitigate the fiber nonlinearity for 64-quadrature amplitude modulation (64-QAM) signal, the training-sequence assisted k-means algorithm and the blind k-means algorithm. We experimentally demonstrated the proposed k-means-clustering-based fiber nonlinearity mitigation techniques in 75-Gb/s 64-QAM coherent optical communication system. The proposed algorithms have reduced clustering complexity and low data redundancy and they are able to quickly find appropriate initial centroids and select correctly the centroids of the clusters to obtain the global optimal solutions for large k value. We measured the bit-error-ratio (BER) performance of 64-QAM signal with different launched powers into the 50-km single mode fiber and the proposed techniques can greatly mitigate the signal impairments caused by the amplified spontaneous emission noise and the fiber Kerr nonlinearity and improve the BER performance.

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.

Scheduling in Sensor Grid Middleware for Telemedicine Using ABC Algorithm

PubMed Central

Vigneswari, T.; Mohamed, M. A. Maluk

2014-01-01

Advances in microelectromechanical systems (MEMS) and nanotechnology have enabled design of low power wireless sensor nodes capable of sensing different vital signs in our body. These nodes can communicate with each other to aggregate data and transmit vital parameters to a base station (BS). The data collected in the base station can be used to monitor health in real time. The patient wearing sensors may be mobile leading to aggregation of data from different BS for processing. Processing real time data is compute-intensive and telemedicine facilities may not have appropriate hardware to process the real time data effectively. To overcome this, sensor grid has been proposed in literature wherein sensor data is integrated to the grid for processing. This work proposes a scheduling algorithm to efficiently process telemedicine data in the grid. The proposed algorithm uses the popular swarm intelligence algorithm for scheduling to overcome the NP complete problem of grid scheduling. Results compared with other heuristic scheduling algorithms show the effectiveness of the proposed algorithm. PMID:25548557

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.

Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

PubMed Central

Sun, Baoliang; Jiang, Chunlan; Li, Ming

2016-01-01

An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. PMID:27809271

Particle swarm optimization based space debris surveillance network scheduling

NASA Astrophysics Data System (ADS)

Jiang, Hai; Liu, Jing; Cheng, Hao-Wen; Zhang, Yao

2017-02-01

The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of in-orbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.

Visual Perception Based Rate Control Algorithm for HEVC

NASA Astrophysics Data System (ADS)

Feng, Zeqi; Liu, PengYu; Jia, Kebin

2018-01-01

For HEVC, rate control is an indispensably important video coding technology to alleviate the contradiction between video quality and the limited encoding resources during video communication. However, the rate control benchmark algorithm of HEVC ignores subjective visual perception. For key focus regions, bit allocation of LCU is not ideal and subjective quality is unsatisfied. In this paper, a visual perception based rate control algorithm for HEVC is proposed. First bit allocation weight of LCU level is optimized based on the visual perception of luminance and motion to ameliorate video subjective quality. Then λ and QP are adjusted in combination with the bit allocation weight to improve rate distortion performance. Experimental results show that the proposed algorithm reduces average 0.5% BD-BR and maximum 1.09% BD-BR at no cost in bitrate accuracy compared with HEVC (HM15.0). The proposed algorithm devotes to improving video subjective quality under various video applications.

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.

Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning.

PubMed

Liu, Weirong; Zhuang, Peng; Liang, Hao; Peng, Jun; Huang, Zhiwu; Weirong Liu; Peng Zhuang; Hao Liang; Jun Peng; Zhiwu Huang; Liu, Weirong; Liang, Hao; Peng, Jun; Zhuang, Peng; Huang, Zhiwu

2018-06-01

Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

PubMed

Zafar, Raheel; Dass, Sarat C; Malik, Aamir Saeed

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.

Path connectivity based spectral defragmentation in flexible bandwidth networks.

PubMed

Wang, Ying; Zhang, Jie; Zhao, Yongli; Zhang, Jiawei; Zhao, Jie; Wang, Xinbo; Gu, Wanyi

2013-01-28

Optical networks with flexible bandwidth provisioning have become a very promising networking architecture. It enables efficient resource utilization and supports heterogeneous bandwidth demands. In this paper, two novel spectrum defragmentation approaches, i.e. Maximum Path Connectivity (MPC) algorithm and Path Connectivity Triggering (PCT) algorithm, are proposed based on the notion of Path Connectivity, which is defined to represent the maximum variation of node switching ability along the path in flexible bandwidth networks. A cost-performance-ratio based profitability model is given to denote the prons and cons of spectrum defragmentation. We compare these two proposed algorithms with non-defragmentation algorithm in terms of blocking probability. Then we analyze the differences of defragmentation profitability between MPC and PCT algorithms.

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

PubMed

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

2013-09-01

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

Algorithm-Based Fault Tolerance Integrated with Replication

NASA Technical Reports Server (NTRS)

Some, Raphael; Rennels, David

2008-01-01

In a proposed approach to programming and utilization of commercial off-the-shelf computing equipment, a combination of algorithm-based fault tolerance (ABFT) and replication would be utilized to obtain high degrees of fault tolerance without incurring excessive costs. The basic idea of the proposed approach is to integrate ABFT with replication such that the algorithmic portions of computations would be protected by ABFT, and the logical portions by replication. ABFT is an extremely efficient, inexpensive, high-coverage technique for detecting and mitigating faults in computer systems used for algorithmic computations, but does not protect against errors in logical operations surrounding algorithms.

Real-time estimation of ionospheric delay using GPS measurements

NASA Astrophysics Data System (ADS)

Lin, Lao-Sheng

1997-12-01

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

Extended reactance domain algorithms for DoA estimation onto an ESPAR antennas

NASA Astrophysics Data System (ADS)

Harabi, F.; Akkar, S.; Gharsallah, A.

2016-07-01

Based on an extended reactance domain (RD) covariance matrix, this article proposes new alternatives for directions of arrival (DoAs) estimation of narrowband sources through an electronically steerable parasitic array radiator (ESPAR) antennas. Because of the centro symmetry of the classic ESPAR antennas, an unitary transformation is applied to the collected data that allow an important reduction in both computational cost and processing time and, also, an enhancement of the resolution capabilities of the proposed algorithms. Moreover, this article proposes a new approach for eigenvalues estimation through only some linear operations. The developed DoAs estimation algorithms based on this new approach has illustrated a good behaviour with less calculation cost and processing time as compared to other schemes based on the classic eigenvalues approach. The conducted simulations demonstrate that high-precision and high-resolution DoAs estimation can be reached especially in very closely sources situation and low sources power as compared to the RD-MUSIC algorithm and the RD-PM algorithm. The asymptotic behaviours of the proposed DoAs estimators are analysed in various scenarios and compared with the Cramer-Rao bound (CRB). The conducted simulations testify the high-resolution of the developed algorithms and prove the efficiently of the proposed approach.

Super-resolution reconstruction of MR image with a novel residual learning network algorithm

NASA Astrophysics Data System (ADS)

Shi, Jun; Liu, Qingping; Wang, Chaofeng; Zhang, Qi; Ying, Shihui; Xu, Haoyu

2018-04-01

Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.

3-Dimensional stereo implementation of photoacoustic imaging based on a new image reconstruction algorithm without using discrete Fourier transform

NASA Astrophysics Data System (ADS)

Ham, Woonchul; Song, Chulgyu

2017-05-01

In this paper, we propose a new three-dimensional stereo image reconstruction algorithm for a photoacoustic medical imaging system. We also introduce and discuss a new theoretical algorithm by using the physical concept of Radon transform. The main key concept of proposed theoretical algorithm is to evaluate the existence possibility of the acoustic source within a searching region by using the geometric distance between each sensor element of acoustic detector and the corresponding searching region denoted by grid. We derive the mathematical equation for the magnitude of the existence possibility which can be used for implementing a new proposed algorithm. We handle and derive mathematical equations of proposed algorithm for the one-dimensional sensing array case as well as two dimensional sensing array case too. A mathematical k-wave simulation data are used for comparing the image quality of the proposed algorithm with that of general conventional algorithm in which the FFT should be necessarily used. From the k-wave Matlab simulation results, we can prove the effectiveness of the proposed reconstruction algorithm.

Computation-aware algorithm selection approach for interlaced-to-progressive conversion

NASA Astrophysics Data System (ADS)

Park, Sang-Jun; Jeon, Gwanggil; Jeong, Jechang

2010-05-01

We discuss deinterlacing results in a computationally constrained and varied environment. The proposed computation-aware algorithm selection approach (CASA) for fast interlaced to progressive conversion algorithm consists of three methods: the line-averaging (LA) method for plain regions, the modified edge-based line-averaging (MELA) method for medium regions, and the proposed covariance-based adaptive deinterlacing (CAD) method for complex regions. The proposed CASA uses two criteria, mean-squared error (MSE) and CPU time, for assigning the method. We proposed a CAD method. The principle idea of CAD is based on the correspondence between the high and low-resolution covariances. We estimated the local covariance coefficients from an interlaced image using Wiener filtering theory and then used these optimal minimum MSE interpolation coefficients to obtain a deinterlaced image. The CAD method, though more robust than most known methods, was not found to be very fast compared to the others. To alleviate this issue, we proposed an adaptive selection approach using a fast deinterlacing algorithm rather than using only one CAD algorithm. The proposed hybrid approach of switching between the conventional schemes (LA and MELA) and our CAD was proposed to reduce the overall computational load. A reliable condition to be used for switching the schemes was presented after a wide set of initial training processes. The results of computer simulations showed that the proposed methods outperformed a number of methods presented in the literature.

Solving Fractional Programming Problems based on Swarm Intelligence

NASA Astrophysics Data System (ADS)

Raouf, Osama Abdel; Hezam, Ibrahim M.

2014-04-01

This paper presents a new approach to solve Fractional Programming Problems (FPPs) based on two different Swarm Intelligence (SI) algorithms. The two algorithms are: Particle Swarm Optimization, and Firefly Algorithm. The two algorithms are tested using several FPP benchmark examples and two selected industrial applications. The test aims to prove the capability of the SI algorithms to solve any type of FPPs. The solution results employing the SI algorithms are compared with a number of exact and metaheuristic solution methods used for handling FPPs. Swarm Intelligence can be denoted as an effective technique for solving linear or nonlinear, non-differentiable fractional objective functions. Problems with an optimal solution at a finite point and an unbounded constraint set, can be solved using the proposed approach. Numerical examples are given to show the feasibility, effectiveness, and robustness of the proposed algorithm. The results obtained using the two SI algorithms revealed the superiority of the proposed technique among others in computational time. A better accuracy was remarkably observed in the solution results of the industrial application problems.

Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data.

PubMed

Luna, Jose Maria; Padillo, Francisco; Pechenizkiy, Mykola; Ventura, Sebastian

2017-09-27

Pattern mining is one of the most important tasks to extract meaningful and useful information from raw data. This task aims to extract item-sets that represent any type of homogeneity and regularity in data. Although many efficient algorithms have been developed in this regard, the growing interest in data has caused the performance of existing pattern mining techniques to be dropped. The goal of this paper is to propose new efficient pattern mining algorithms to work in big data. To this aim, a series of algorithms based on the MapReduce framework and the Hadoop open-source implementation have been proposed. The proposed algorithms can be divided into three main groups. First, two algorithms [Apriori MapReduce (AprioriMR) and iterative AprioriMR] with no pruning strategy are proposed, which extract any existing item-set in data. Second, two algorithms (space pruning AprioriMR and top AprioriMR) that prune the search space by means of the well-known anti-monotone property are proposed. Finally, a last algorithm (maximal AprioriMR) is also proposed for mining condensed representations of frequent patterns. To test the performance of the proposed algorithms, a varied collection of big data datasets have been considered, comprising up to 3 · 10#x00B9;⁸ transactions and more than 5 million of distinct single-items. The experimental stage includes comparisons against highly efficient and well-known pattern mining algorithms. Results reveal the interest of applying MapReduce versions when complex problems are considered, and also the unsuitability of this paradigm when dealing with small data.

Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine

PubMed Central

Zhou, Jingyu; Tian, Shulin; Yang, Chenglin; Ren, Xuelong

2014-01-01

This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. PMID:25610458

Cloud computing-based TagSNP selection algorithm for human genome data.

PubMed

Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2015-01-05

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used.

Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data

PubMed Central

Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2015-01-01

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used. PMID:25569088

Scheduling Algorithms for Maximizing Throughput with Zero-Forcing Beamforming in a MIMO Wireless System

NASA Astrophysics Data System (ADS)

Foronda, Augusto; Ohta, Chikara; Tamaki, Hisashi

Dirty paper coding (DPC) is a strategy to achieve the region capacity of multiple input multiple output (MIMO) downlink channels and a DPC scheduler is throughput optimal if users are selected according to their queue states and current rates. However, DPC is difficult to implement in practical systems. One solution, zero-forcing beamforming (ZFBF) strategy has been proposed to achieve the same asymptotic sum rate capacity as that of DPC with an exhaustive search over the entire user set. Some suboptimal user group selection schedulers with reduced complexity based on ZFBF strategy (ZFBF-SUS) and proportional fair (PF) scheduling algorithm (PF-ZFBF) have also been proposed to enhance the throughput and fairness among the users, respectively. However, they are not throughput optimal, fairness and throughput decrease if each user queue length is different due to different users channel quality. Therefore, we propose two different scheduling algorithms: a throughput optimal scheduling algorithm (ZFBF-TO) and a reduced complexity scheduling algorithm (ZFBF-RC). Both are based on ZFBF strategy and, at every time slot, the scheduling algorithms have to select some users based on user channel quality, user queue length and orthogonality among users. Moreover, the proposed algorithms have to produce the rate allocation and power allocation for the selected users based on a modified water filling method. We analyze the schedulers complexity and numerical results show that ZFBF-RC provides throughput and fairness improvements compared to the ZFBF-SUS and PF-ZFBF scheduling algorithms.

A novel power efficient location-based cooperative routing with transmission power-upper-limit for wireless sensor networks.

PubMed

Shi, Juanfei; Calveras, Anna; Cheng, Ye; Liu, Kai

2013-05-15

The extensive usage of wireless sensor networks (WSNs) has led to the development of many power- and energy-efficient routing protocols. Cooperative routing in WSNs can improve performance in these types of networks. In this paper we discuss the existing proposals and we propose a routing algorithm for wireless sensor networks called Power Efficient Location-based Cooperative Routing with Transmission Power-upper-limit (PELCR-TP). The algorithm is based on the principle of minimum link power and aims to take advantage of nodes cooperation to make the link work well in WSNs with a low transmission power. In the proposed scheme, with a determined transmission power upper limit, nodes find the most appropriate next nodes and single-relay nodes with the proposed algorithm. Moreover, this proposal subtly avoids non-working nodes, because we add a Bad nodes Avoidance Strategy (BAS). Simulation results show that the proposed algorithm with BAS can significantly improve the performance in reducing the overall link power, enhancing the transmission success rate and decreasing the retransmission rate.

A Novel Power Efficient Location-Based Cooperative Routing with Transmission Power-Upper-Limit for Wireless Sensor Networks

PubMed Central

Shi, Juanfei; Calveras, Anna; Cheng, Ye; Liu, Kai

2013-01-01

The extensive usage of wireless sensor networks (WSNs) has led to the development of many power- and energy-efficient routing protocols. Cooperative routing in WSNs can improve performance in these types of networks. In this paper we discuss the existing proposals and we propose a routing algorithm for wireless sensor networks called Power Efficient Location-based Cooperative Routing with Transmission Power-upper-limit (PELCR-TP). The algorithm is based on the principle of minimum link power and aims to take advantage of nodes cooperation to make the link work well in WSNs with a low transmission power. In the proposed scheme, with a determined transmission power upper limit, nodes find the most appropriate next nodes and single-relay nodes with the proposed algorithm. Moreover, this proposal subtly avoids non-working nodes, because we add a Bad nodes Avoidance Strategy (BAS). Simulation results show that the proposed algorithm with BAS can significantly improve the performance in reducing the overall link power, enhancing the transmission success rate and decreasing the retransmission rate. PMID:23676625

Acceleration of image-based resolution modelling reconstruction using an expectation maximization nested algorithm.

PubMed

Angelis, G I; Reader, A J; Markiewicz, P J; Kotasidis, F A; Lionheart, W R; Matthews, J C

2013-08-07

Recent studies have demonstrated the benefits of a resolution model within iterative reconstruction algorithms in an attempt to account for effects that degrade the spatial resolution of the reconstructed images. However, these algorithms suffer from slower convergence rates, compared to algorithms where no resolution model is used, due to the additional need to solve an image deconvolution problem. In this paper, a recently proposed algorithm, which decouples the tomographic and image deconvolution problems within an image-based expectation maximization (EM) framework, was evaluated. This separation is convenient, because more computational effort can be placed on the image deconvolution problem and therefore accelerate convergence. Since the computational cost of solving the image deconvolution problem is relatively small, multiple image-based EM iterations do not significantly increase the overall reconstruction time. The proposed algorithm was evaluated using 2D simulations, as well as measured 3D data acquired on the high-resolution research tomograph. Results showed that bias reduction can be accelerated by interleaving multiple iterations of the image-based EM algorithm solving the resolution model problem, with a single EM iteration solving the tomographic problem. Significant improvements were observed particularly for voxels that were located on the boundaries between regions of high contrast within the object being imaged and for small regions of interest, where resolution recovery is usually more challenging. Minor differences were observed using the proposed nested algorithm, compared to the single iteration normally performed, when an optimal number of iterations are performed for each algorithm. However, using the proposed nested approach convergence is significantly accelerated enabling reconstruction using far fewer tomographic iterations (up to 70% fewer iterations for small regions). Nevertheless, the optimal number of nested image-based EM iterations is hard to be defined and it should be selected according to the given application.

A new distributed systems scheduling algorithm: a swarm intelligence approach

NASA Astrophysics Data System (ADS)

Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi

2011-12-01

The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.

Study on Privacy Protection Algorithm Based on K-Anonymity

NASA Astrophysics Data System (ADS)

FeiFei, Zhao; LiFeng, Dong; Kun, Wang; Yang, Li

Basing on the study of K-Anonymity algorithm in privacy protection issue, this paper proposed a "Degree Priority" method of visiting Lattice nodes on the generalization tree to improve the performance of K-Anonymity algorithm. This paper also proposed a "Two Times K-anonymity" methods to reduce the information loss in the process of K-Anonymity. Finally, we used experimental results to demonstrate the effectiveness of these methods.

Coarse Alignment Technology on Moving base for SINS Based on the Improved Quaternion Filter Algorithm.

PubMed

Zhang, Tao; Zhu, Yongyun; Zhou, Feng; Yan, Yaxiong; Tong, Jinwu

2017-06-17

Initial alignment of the strapdown inertial navigation system (SINS) is intended to determine the initial attitude matrix in a short time with certain accuracy. The alignment accuracy of the quaternion filter algorithm is remarkable, but the convergence rate is slow. To solve this problem, this paper proposes an improved quaternion filter algorithm for faster initial alignment based on the error model of the quaternion filter algorithm. The improved quaternion filter algorithm constructs the K matrix based on the principle of optimal quaternion algorithm, and rebuilds the measurement model by containing acceleration and velocity errors to make the convergence rate faster. A doppler velocity log (DVL) provides the reference velocity for the improved quaternion filter alignment algorithm. In order to demonstrate the performance of the improved quaternion filter algorithm in the field, a turntable experiment and a vehicle test are carried out. The results of the experiments show that the convergence rate of the proposed improved quaternion filter is faster than that of the tradition quaternion filter algorithm. In addition, the improved quaternion filter algorithm also demonstrates advantages in terms of correctness, effectiveness, and practicability.

DNABIT Compress - Genome compression algorithm.

PubMed

Rajarajeswari, Pothuraju; Apparao, Allam

2011-01-22

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

Data depth based clustering analysis

DOE PAGES

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

2016-01-01

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

Computational path planner for product assembly in complex environments

NASA Astrophysics Data System (ADS)

Shang, Wei; Liu, Jianhua; Ning, Ruxin; Liu, Mi

2013-03-01

Assembly path planning is a crucial problem in assembly related design and manufacturing processes. Sampling based motion planning algorithms are used for computational assembly path planning. However, the performance of such algorithms may degrade much in environments with complex product structure, narrow passages or other challenging scenarios. A computational path planner for automatic assembly path planning in complex 3D environments is presented. The global planning process is divided into three phases based on the environment and specific algorithms are proposed and utilized in each phase to solve the challenging issues. A novel ray test based stochastic collision detection method is proposed to evaluate the intersection between two polyhedral objects. This method avoids fake collisions in conventional methods and degrades the geometric constraint when a part has to be removed with surface contact with other parts. A refined history based rapidly-exploring random tree (RRT) algorithm which bias the growth of the tree based on its planning history is proposed and employed in the planning phase where the path is simple but the space is highly constrained. A novel adaptive RRT algorithm is developed for the path planning problem with challenging scenarios and uncertain environment. With extending values assigned on each tree node and extending schemes applied, the tree can adapts its growth to explore complex environments more efficiently. Experiments on the key algorithms are carried out and comparisons are made between the conventional path planning algorithms and the presented ones. The comparing results show that based on the proposed algorithms, the path planner can compute assembly path in challenging complex environments more efficiently and with higher success. This research provides the references to the study of computational assembly path planning under complex environments.

Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

PubMed

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

2015-04-01

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

Quality Scalability Aware Watermarking for Visual Content.

PubMed

Bhowmik, Deepayan; Abhayaratne, Charith

2016-11-01

Scalable coding-based content adaptation poses serious challenges to traditional watermarking algorithms, which do not consider the scalable coding structure and hence cannot guarantee correct watermark extraction in media consumption chain. In this paper, we propose a novel concept of scalable blind watermarking that ensures more robust watermark extraction at various compression ratios while not effecting the visual quality of host media. The proposed algorithm generates scalable and robust watermarked image code-stream that allows the user to constrain embedding distortion for target content adaptations. The watermarked image code-stream consists of hierarchically nested joint distortion-robustness coding atoms. The code-stream is generated by proposing a new wavelet domain blind watermarking algorithm guided by a quantization based binary tree. The code-stream can be truncated at any distortion-robustness atom to generate the watermarked image with the desired distortion-robustness requirements. A blind extractor is capable of extracting watermark data from the watermarked images. The algorithm is further extended to incorporate a bit-plane discarding-based quantization model used in scalable coding-based content adaptation, e.g., JPEG2000. This improves the robustness against quality scalability of JPEG2000 compression. The simulation results verify the feasibility of the proposed concept, its applications, and its improved robustness against quality scalable content adaptation. Our proposed algorithm also outperforms existing methods showing 35% improvement. In terms of robustness to quality scalable video content adaptation using Motion JPEG2000 and wavelet-based scalable video coding, the proposed method shows major improvement for video watermarking.

Trajectory data privacy protection based on differential privacy mechanism

NASA Astrophysics Data System (ADS)

Gu, Ke; Yang, Lihao; Liu, Yongzhi; Liao, Niandong

2018-05-01

In this paper, we propose a trajectory data privacy protection scheme based on differential privacy mechanism. In the proposed scheme, the algorithm first selects the protected points from the user’s trajectory data; secondly, the algorithm forms the polygon according to the protected points and the adjacent and high frequent accessed points that are selected from the accessing point database, then the algorithm calculates the polygon centroids; finally, the noises are added to the polygon centroids by the differential privacy method, and the polygon centroids replace the protected points, and then the algorithm constructs and issues the new trajectory data. The experiments show that the running time of the proposed algorithms is fast, the privacy protection of the scheme is effective and the data usability of the scheme is higher.

Structural model constructing for optical handwritten character recognition

NASA Astrophysics Data System (ADS)

Khaustov, P. A.; Spitsyn, V. G.; Maksimova, E. I.

2017-02-01

The article is devoted to the development of the algorithms for optical handwritten character recognition based on the structural models constructing. The main advantage of these algorithms is the low requirement regarding the number of reference images. The one-pass approach to a thinning of the binary character representation has been proposed. This approach is based on the joint use of Zhang-Suen and Wu-Tsai algorithms. The effectiveness of the proposed approach is confirmed by the results of the experiments. The article includes the detailed description of the structural model constructing algorithm’s steps. The proposed algorithm has been implemented in character processing application and has been approved on MNIST handwriting characters database. Algorithms that could be used in case of limited reference images number were used for the comparison.

Chaotic particle swarm optimization with mutation for classification.

PubMed

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.

Optimal Golomb Ruler Sequences Generation for Optical WDM Systems: A Novel Parallel Hybrid Multi-objective Bat Algorithm

NASA Astrophysics Data System (ADS)

Bansal, Shonak; Singh, Arun Kumar; Gupta, Neena

2017-02-01

In real-life, multi-objective engineering design problems are very tough and time consuming optimization problems due to their high degree of nonlinearities, complexities and inhomogeneity. Nature-inspired based multi-objective optimization algorithms are now becoming popular for solving multi-objective engineering design problems. This paper proposes original multi-objective Bat algorithm (MOBA) and its extended form, namely, novel parallel hybrid multi-objective Bat algorithm (PHMOBA) to generate shortest length Golomb ruler called optimal Golomb ruler (OGR) sequences at a reasonable computation time. The OGRs found their application in optical wavelength division multiplexing (WDM) systems as channel-allocation algorithm to reduce the four-wave mixing (FWM) crosstalk. The performances of both the proposed algorithms to generate OGRs as optical WDM channel-allocation is compared with other existing classical computing and nature-inspired algorithms, including extended quadratic congruence (EQC), search algorithm (SA), genetic algorithms (GAs), biogeography based optimization (BBO) and big bang-big crunch (BB-BC) optimization algorithms. Simulations conclude that the proposed parallel hybrid multi-objective Bat algorithm works efficiently as compared to original multi-objective Bat algorithm and other existing algorithms to generate OGRs for optical WDM systems. The algorithm PHMOBA to generate OGRs, has higher convergence and success rate than original MOBA. The efficiency improvement of proposed PHMOBA to generate OGRs up to 20-marks, in terms of ruler length and total optical channel bandwidth (TBW) is 100 %, whereas for original MOBA is 85 %. Finally the implications for further research are also discussed.

A Particle Swarm Optimization-Based Approach with Local Search for Predicting Protein Folding.

PubMed

Yang, Cheng-Hong; Lin, Yu-Shiun; Chuang, Li-Yeh; Chang, Hsueh-Wei

2017-10-01

The hydrophobic-polar (HP) model is commonly used for predicting protein folding structures and hydrophobic interactions. This study developed a particle swarm optimization (PSO)-based algorithm combined with local search algorithms; specifically, the high exploration PSO (HEPSO) algorithm (which can execute global search processes) was combined with three local search algorithms (hill-climbing algorithm, greedy algorithm, and Tabu table), yielding the proposed HE-L-PSO algorithm. By using 20 known protein structures, we evaluated the performance of the HE-L-PSO algorithm in predicting protein folding in the HP model. The proposed HE-L-PSO algorithm exhibited favorable performance in predicting both short and long amino acid sequences with high reproducibility and stability, compared with seven reported algorithms. The HE-L-PSO algorithm yielded optimal solutions for all predicted protein folding structures. All HE-L-PSO-predicted protein folding structures possessed a hydrophobic core that is similar to normal protein folding.

Adaptive control of nonlinear system using online error minimum neural networks.

PubMed

Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

2016-11-01

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Random Walk Quantum Clustering Algorithm Based on Space

NASA Astrophysics Data System (ADS)

Xiao, Shufen; Dong, Yumin; Ma, Hongyang

2018-01-01

In the random quantum walk, which is a quantum simulation of the classical walk, data points interacted when selecting the appropriate walk strategy by taking advantage of quantum-entanglement features; thus, the results obtained when the quantum walk is used are different from those when the classical walk is adopted. A new quantum walk clustering algorithm based on space is proposed by applying the quantum walk to clustering analysis. In this algorithm, data points are viewed as walking participants, and similar data points are clustered using the walk function in the pay-off matrix according to a certain rule. The walk process is simplified by implementing a space-combining rule. The proposed algorithm is validated by a simulation test and is proved superior to existing clustering algorithms, namely, Kmeans, PCA + Kmeans, and LDA-Km. The effects of some of the parameters in the proposed algorithm on its performance are also analyzed and discussed. Specific suggestions are provided.

3-D Image Encryption Based on Rubik's Cube and RC6 Algorithm

NASA Astrophysics Data System (ADS)

Helmy, Mai; El-Rabaie, El-Sayed M.; Eldokany, Ibrahim M.; El-Samie, Fathi E. Abd

2017-12-01

A novel encryption algorithm based on the 3-D Rubik's cube is proposed in this paper to achieve 3D encryption of a group of images. This proposed encryption algorithm begins with RC6 as a first step for encrypting multiple images, separately. After that, the obtained encrypted images are further encrypted with the 3-D Rubik's cube. The RC6 encrypted images are used as the faces of the Rubik's cube. From the concepts of image encryption, the RC6 algorithm adds a degree of diffusion, while the Rubik's cube algorithm adds a degree of permutation. The simulation results demonstrate that the proposed encryption algorithm is efficient, and it exhibits strong robustness and security. The encrypted images are further transmitted over wireless Orthogonal Frequency Division Multiplexing (OFDM) system and decrypted at the receiver side. Evaluation of the quality of the decrypted images at the receiver side reveals good results.

Mobile robot dynamic path planning based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.

An improved non-uniformity correction algorithm and its GPU parallel implementation

NASA Astrophysics Data System (ADS)

Cheng, Kuanhong; Zhou, Huixin; Qin, Hanlin; Zhao, Dong; Qian, Kun; Rong, Shenghui

2018-05-01

The performance of SLP-THP based non-uniformity correction algorithm is seriously affected by the result of SLP filter, which always leads to image blurring and ghosting artifacts. To address this problem, an improved SLP-THP based non-uniformity correction method with curvature constraint was proposed. Here we put forward a new way to estimate spatial low frequency component. First, the details and contours of input image were obtained respectively by minimizing local Gaussian curvature and mean curvature of image surface. Then, the guided filter was utilized to combine these two parts together to get the estimate of spatial low frequency component. Finally, we brought this SLP component into SLP-THP method to achieve non-uniformity correction. The performance of proposed algorithm was verified by several real and simulated infrared image sequences. The experimental results indicated that the proposed algorithm can reduce the non-uniformity without detail losing. After that, a GPU based parallel implementation that runs 150 times faster than CPU was presented, which showed the proposed algorithm has great potential for real time application.

An extended continuous estimation of distribution algorithm for solving the permutation flow-shop scheduling problem

NASA Astrophysics Data System (ADS)

Shao, Zhongshi; Pi, Dechang; Shao, Weishi

2017-11-01

This article proposes an extended continuous estimation of distribution algorithm (ECEDA) to solve the permutation flow-shop scheduling problem (PFSP). In ECEDA, to make a continuous estimation of distribution algorithm (EDA) suitable for the PFSP, the largest order value rule is applied to convert continuous vectors to discrete job permutations. A probabilistic model based on a mixed Gaussian and Cauchy distribution is built to maintain the exploration ability of the EDA. Two effective local search methods, i.e. revolver-based variable neighbourhood search and Hénon chaotic-based local search, are designed and incorporated into the EDA to enhance the local exploitation. The parameters of the proposed ECEDA are calibrated by means of a design of experiments approach. Simulation results and comparisons based on some benchmark instances show the efficiency of the proposed algorithm for solving the PFSP.

A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream

PubMed Central

Ying Wah, Teh

2014-01-01

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets. PMID:25110753

A fast density-based clustering algorithm for real-time Internet of Things stream.

PubMed

Amini, Amineh; Saboohi, Hadi; Wah, Teh Ying; Herawan, Tutut

2014-01-01

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.

Distributed convex optimisation with event-triggered communication in networked systems

NASA Astrophysics Data System (ADS)

Liu, Jiayun; Chen, Weisheng

2016-12-01

This paper studies the distributed convex optimisation problem over directed networks. Motivated by practical considerations, we propose a novel distributed zero-gradient-sum optimisation algorithm with event-triggered communication. Therefore, communication and control updates just occur at discrete instants when some predefined condition satisfies. Thus, compared with the time-driven distributed optimisation algorithms, the proposed algorithm has the advantages of less energy consumption and less communication cost. Based on Lyapunov approaches, we show that the proposed algorithm makes the system states asymptotically converge to the solution of the problem exponentially fast and the Zeno behaviour is excluded. Finally, simulation example is given to illustrate the effectiveness of the proposed algorithm.

A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm

PubMed Central

Chen, Jui-Le; Yang, Chu-Sing

2013-01-01

The potential of predicting druggability for a particular disease by integrating biological and computer science technologies has witnessed success in recent years. Although the computer science technologies can be used to reduce the costs of the pharmaceutical research, the computation time of the structure-based protein-ligand docking prediction is still unsatisfied until now. Hence, in this paper, a novel docking prediction algorithm, named fast cloud-based protein-ligand docking prediction algorithm (FCPLDPA), is presented to accelerate the docking prediction algorithm. The proposed algorithm works by leveraging two high-performance operators: (1) the novel migration (information exchange) operator is designed specially for cloud-based environments to reduce the computation time; (2) the efficient operator is aimed at filtering out the worst search directions. Our simulation results illustrate that the proposed method outperforms the other docking algorithms compared in this paper in terms of both the computation time and the quality of the end result. PMID:23762864

A generalized Condat's algorithm of 1D total variation regularization

NASA Astrophysics Data System (ADS)

Makovetskii, Artyom; Voronin, Sergei; Kober, Vitaly

2017-09-01

A common way for solving the denosing problem is to utilize the total variation (TV) regularization. Many efficient numerical algorithms have been developed for solving the TV regularization problem. Condat described a fast direct algorithm to compute the processed 1D signal. Also there exists a direct algorithm with a linear time for 1D TV denoising referred to as the taut string algorithm. The Condat's algorithm is based on a dual problem to the 1D TV regularization. In this paper, we propose a variant of the Condat's algorithm based on the direct 1D TV regularization problem. The usage of the Condat's algorithm with the taut string approach leads to a clear geometric description of the extremal function. Computer simulation results are provided to illustrate the performance of the proposed algorithm for restoration of degraded signals.

Optimization of view weighting in tilted-plane-based reconstruction algorithms to minimize helical artifacts in multi-slice helical CT

NASA Astrophysics Data System (ADS)

Tang, Xiangyang

2003-05-01

In multi-slice helical CT, the single-tilted-plane-based reconstruction algorithm has been proposed to combat helical and cone beam artifacts by tilting a reconstruction plane to fit a helical source trajectory optimally. Furthermore, to improve the noise characteristics or dose efficiency of the single-tilted-plane-based reconstruction algorithm, the multi-tilted-plane-based reconstruction algorithm has been proposed, in which the reconstruction plane deviates from the pose globally optimized due to an extra rotation along the 3rd axis. As a result, the capability of suppressing helical and cone beam artifacts in the multi-tilted-plane-based reconstruction algorithm is compromised. An optomized tilted-plane-based reconstruction algorithm is proposed in this paper, in which a matched view weighting strategy is proposed to optimize the capability of suppressing helical and cone beam artifacts and noise characteristics. A helical body phantom is employed to quantitatively evaluate the imaging performance of the matched view weighting approach by tabulating artifact index and noise characteristics, showing that the matched view weighting improves both the helical artifact suppression and noise characteristics or dose efficiency significantly in comparison to the case in which non-matched view weighting is applied. Finally, it is believed that the matched view weighting approach is of practical importance in the development of multi-slive helical CT, because it maintains the computational structure of fan beam filtered backprojection and demands no extra computational services.

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.

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

PubMed Central

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

Estimation of Attitude and External Acceleration Using Inertial Sensor Measurement During Various Dynamic Conditions

PubMed Central

Lee, Jung Keun; Park, Edward J.; Robinovitch, Stephen N.

2012-01-01

This paper proposes a Kalman filter-based attitude (i.e., roll and pitch) estimation algorithm using an inertial sensor composed of a triaxial accelerometer and a triaxial gyroscope. In particular, the proposed algorithm has been developed for accurate attitude estimation during dynamic conditions, in which external acceleration is present. Although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, this problem that can be critical for the attitude estimation has not been addressed explicitly in the literature. Accordingly, this paper addresses the combined estimation problem of the attitude and external acceleration. Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy. Furthermore, two different approaches for dealing with the estimation problem during dynamic conditions were compared, i.e., threshold-based switching approach versus acceleration model-based approach. Based on an external acceleration model, the proposed algorithm was capable of estimating accurate attitudes and external accelerations for short accelerated periods, showing its high effectiveness during short-term fast dynamic conditions. Contrariwise, when the testing condition involved prolonged high external accelerations, the proposed algorithm exhibited gradually increasing errors. However, as soon as the condition returned to static or quasi-static conditions, the algorithm was able to stabilize the estimation error, regaining its high estimation accuracy. PMID:22977288

A Modified Subpulse SAR Processing Procedure Based on the Range-Doppler Algorithm for Synthetic Wideband Waveforms

PubMed Central

Lim, Byoung-Gyun; Woo, Jea-Choon; Lee, Hee-Young; Kim, Young-Soo

2008-01-01

Synthetic wideband waveforms (SWW) combine a stepped frequency CW waveform and a chirp signal waveform to achieve high range resolution without requiring a large bandwidth or the consequent very high sampling rate. If an efficient algorithm like the range-Doppler algorithm (RDA) is used to acquire the SAR images for synthetic wideband signals, errors occur due to approximations, so the images may not show the best possible result. This paper proposes a modified subpulse SAR processing algorithm for synthetic wideband signals which is based on RDA. An experiment with an automobile-based SAR system showed that the proposed algorithm is quite accurate with a considerable improvement in resolution and quality of the obtained SAR image. PMID:27873984

Model-based clustering for RNA-seq data.

PubMed

Si, Yaqing; Liu, Peng; Li, Pinghua; Brutnell, Thomas P

2014-01-15

RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis. In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectation-maximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models. An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.r-project.org

Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application.

PubMed

Chen, Pengyun; Zhang, Yichen; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

2017-06-06

Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information's relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM) algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM), the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM), the pointwise approach and graph theory (PA-GT), and the Principal Component Analysis-Nonlocal Means (PCA-NLM) denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection.

Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application

PubMed Central

Chen, Pengyun; Zhang, Yichen; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

2017-01-01

Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information’s relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM) algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM), the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM), the pointwise approach and graph theory (PA-GT), and the Principal Component Analysis-Nonlocal Means (PCA-NLM) denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection. PMID:28587299

Image restoration by minimizing zero norm of wavelet frame coefficients

NASA Astrophysics Data System (ADS)

Bao, Chenglong; Dong, Bin; Hou, Likun; Shen, Zuowei; Zhang, Xiaoqun; Zhang, Xue

2016-11-01

In this paper, we propose two algorithms, namely the extrapolated proximal iterative hard thresholding (EPIHT) algorithm and the EPIHT algorithm with line-search, for solving the {{\\ell }}0-norm regularized wavelet frame balanced approach for image restoration. Under the theoretical framework of Kurdyka-Łojasiewicz property, we show that the sequences generated by the two algorithms converge to a local minimizer with linear convergence rate. Moreover, extensive numerical experiments on sparse signal reconstruction and wavelet frame based image restoration problems including CT reconstruction, image deblur, demonstrate the improvement of {{\\ell }}0-norm based regularization models over some prevailing ones, as well as the computational efficiency of the proposed algorithms.

An Algorithm for Timely Transmission of Solicitation Messages in RPL for Energy-Efficient Node Mobility.

PubMed

Park, Jihong; Kim, Ki-Hyung; Kim, Kangseok

2017-04-19

The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) was proposed for various applications of IPv6 low power wireless networks. While RPL supports various routing metrics and is designed to be suitable for wireless sensor network environments, it does not consider the mobility of nodes. Therefore, there is a need for a method that is energy efficient and that provides stable and reliable data transmission by considering the mobility of nodes in RPL networks. This paper proposes an algorithm to support node mobility in RPL in an energy-efficient manner and describes its operating principle based on different scenarios. The proposed algorithm supports the mobility of nodes by dynamically adjusting the transmission interval of the messages that request the route based on the speed and direction of the motion of mobile nodes, as well as the costs between neighboring nodes. The performance of the proposed algorithm and previous algorithms for supporting node mobility were examined experimentally. From the experiment, it was observed that the proposed algorithm requires fewer messages per unit time for selecting a new parent node following the movement of a mobile node. Since fewer messages are used to select a parent node, the energy consumption is also less than that of previous algorithms.

An Algorithm for Timely Transmission of Solicitation Messages in RPL for Energy-Efficient Node Mobility

PubMed Central

Park, Jihong; Kim, Ki-Hyung; Kim, Kangseok

2017-01-01

The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) was proposed for various applications of IPv6 low power wireless networks. While RPL supports various routing metrics and is designed to be suitable for wireless sensor network environments, it does not consider the mobility of nodes. Therefore, there is a need for a method that is energy efficient and that provides stable and reliable data transmission by considering the mobility of nodes in RPL networks. This paper proposes an algorithm to support node mobility in RPL in an energy-efficient manner and describes its operating principle based on different scenarios. The proposed algorithm supports the mobility of nodes by dynamically adjusting the transmission interval of the messages that request the route based on the speed and direction of the motion of mobile nodes, as well as the costs between neighboring nodes. The performance of the proposed algorithm and previous algorithms for supporting node mobility were examined experimentally. From the experiment, it was observed that the proposed algorithm requires fewer messages per unit time for selecting a new parent node following the movement of a mobile node. Since fewer messages are used to select a parent node, the energy consumption is also less than that of previous algorithms. PMID:28422084

Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

PubMed

Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo

2017-10-12

Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.

Feature selection method based on multi-fractal dimension and harmony search algorithm and its application

NASA Astrophysics Data System (ADS)

Zhang, Chen; Ni, Zhiwei; Ni, Liping; Tang, Na

2016-10-01

Feature selection is an important method of data preprocessing in data mining. In this paper, a novel feature selection method based on multi-fractal dimension and harmony search algorithm is proposed. Multi-fractal dimension is adopted as the evaluation criterion of feature subset, which can determine the number of selected features. An improved harmony search algorithm is used as the search strategy to improve the efficiency of feature selection. The performance of the proposed method is compared with that of other feature selection algorithms on UCI data-sets. Besides, the proposed method is also used to predict the daily average concentration of PM2.5 in China. Experimental results show that the proposed method can obtain competitive results in terms of both prediction accuracy and the number of selected features.

Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network.

PubMed

Kang, Eunhee; Chang, Won; Yoo, Jaejun; Ye, Jong Chul

2018-06-01

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images.

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.

Hierarchical trie packet classification algorithm based on expectation-maximization clustering.

PubMed

Bi, Xia-An; Zhao, Junxia

2017-01-01

With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is beneficial to save large storage space, it has several shortcomings such as the existence of backtracking and empty nodes. This paper proposes a new packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC). Firstly, this paper uses the formalization method to deal with the packet classification problem by means of mapping the rules and data packets into a two-dimensional space. Secondly, this paper uses expectation-maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed. Thirdly, this paper proposes a hierarchical trie based on the results of expectation-maximization clustering. Finally, this paper respectively conducts simulation experiments and real-environment experiments to compare the performances of our algorithm with other typical algorithms, and analyzes the results of the experiments. The hierarchical trie structure in our algorithm not only adopts trie path compression to eliminate backtracking, but also solves the problem of low efficiency of trie updates, which greatly improves the performance of the algorithm.

An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data.

PubMed

Nidheesh, N; Abdul Nazeer, K A; Ameer, P M

2017-12-01

Clustering algorithms with steps involving randomness usually give different results on different executions for the same dataset. This non-deterministic nature of algorithms such as the K-Means clustering algorithm limits their applicability in areas such as cancer subtype prediction using gene expression data. It is hard to sensibly compare the results of such algorithms with those of other algorithms. The non-deterministic nature of K-Means is due to its random selection of data points as initial centroids. We propose an improved, density based version of K-Means, which involves a novel and systematic method for selecting initial centroids. The key idea of the algorithm is to select data points which belong to dense regions and which are adequately separated in feature space as the initial centroids. We compared the proposed algorithm to a set of eleven widely used single clustering algorithms and a prominent ensemble clustering algorithm which is being used for cancer data classification, based on the performances on a set of datasets comprising ten cancer gene expression datasets. The proposed algorithm has shown better overall performance than the others. There is a pressing need in the Biomedical domain for simple, easy-to-use and more accurate Machine Learning tools for cancer subtype prediction. The proposed algorithm is simple, easy-to-use and gives stable results. Moreover, it provides comparatively better predictions of cancer subtypes from gene expression data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Enhanced image fusion using directional contrast rules in fuzzy transform domain.

PubMed

Nandal, Amita; Rosales, Hamurabi Gamboa

2016-01-01

In this paper a novel image fusion algorithm based on directional contrast in fuzzy transform (FTR) domain is proposed. Input images to be fused are first divided into several non-overlapping blocks. The components of these sub-blocks are fused using directional contrast based fuzzy fusion rule in FTR domain. The fused sub-blocks are then transformed into original size blocks using inverse-FTR. Further, these inverse transformed blocks are fused according to select maximum based fusion rule for reconstructing the final fused image. The proposed fusion algorithm is both visually and quantitatively compared with other standard and recent fusion algorithms. Experimental results demonstrate that the proposed method generates better results than the other methods.

Temporal high-pass non-uniformity correction algorithm based on grayscale mapping and hardware implementation

NASA Astrophysics Data System (ADS)

Jin, Minglei; Jin, Weiqi; Li, Yiyang; Li, Shuo

2015-08-01

In this paper, we propose a novel scene-based non-uniformity correction algorithm for infrared image processing-temporal high-pass non-uniformity correction algorithm based on grayscale mapping (THP and GM). The main sources of non-uniformity are: (1) detector fabrication inaccuracies; (2) non-linearity and variations in the read-out electronics and (3) optical path effects. The non-uniformity will be reduced by non-uniformity correction (NUC) algorithms. The NUC algorithms are often divided into calibration-based non-uniformity correction (CBNUC) algorithms and scene-based non-uniformity correction (SBNUC) algorithms. As non-uniformity drifts temporally, CBNUC algorithms must be repeated by inserting a uniform radiation source which SBNUC algorithms do not need into the view, so the SBNUC algorithm becomes an essential part of infrared imaging system. The SBNUC algorithms' poor robustness often leads two defects: artifacts and over-correction, meanwhile due to complicated calculation process and large storage consumption, hardware implementation of the SBNUC algorithms is difficult, especially in Field Programmable Gate Array (FPGA) platform. The THP and GM algorithm proposed in this paper can eliminate the non-uniformity without causing defects. The hardware implementation of the algorithm only based on FPGA has two advantages: (1) low resources consumption, and (2) small hardware delay: less than 20 lines, it can be transplanted to a variety of infrared detectors equipped with FPGA image processing module, it can reduce the stripe non-uniformity and the ripple non-uniformity.

A new algorithm for distorted fingerprints matching based on normalized fuzzy similarity measure.

PubMed

Chen, Xinjian; Tian, Jie; Yang, Xin

2006-03-01

Coping with nonlinear distortions in fingerprint matching is a challenging task. This paper proposes a novel algorithm, normalized fuzzy similarity measure (NFSM), to deal with the nonlinear distortions. The proposed algorithm has two main steps. First, the template and input fingerprints were aligned. In this process, the local topological structure matching was introduced to improve the robustness of global alignment. Second, the method NFSM was introduced to compute the similarity between the template and input fingerprints. The proposed algorithm was evaluated on fingerprints databases of FVC2004. Experimental results confirm that NFSM is a reliable and effective algorithm for fingerprint matching with nonliner distortions. The algorithm gives considerably higher matching scores compared to conventional matching algorithms for the deformed fingerprints.

Research and implementation of finger-vein recognition algorithm

NASA Astrophysics Data System (ADS)

Pang, Zengyao; Yang, Jie; Chen, Yilei; Liu, Yin

2017-06-01

In finger vein image preprocessing, finger angle correction and ROI extraction are important parts of the system. In this paper, we propose an angle correction algorithm based on the centroid of the vein image, and extract the ROI region according to the bidirectional gray projection method. Inspired by the fact that features in those vein areas have similar appearance as valleys, a novel method was proposed to extract center and width of palm vein based on multi-directional gradients, which is easy-computing, quick and stable. On this basis, an encoding method was designed to determine the gray value distribution of texture image. This algorithm could effectively overcome the edge of the texture extraction error. Finally, the system was equipped with higher robustness and recognition accuracy by utilizing fuzzy threshold determination and global gray value matching algorithm. Experimental results on pairs of matched palm images show that, the proposed method has a EER with 3.21% extracts features at the speed of 27ms per image. It can be concluded that the proposed algorithm has obvious advantages in grain extraction efficiency, matching accuracy and algorithm efficiency.

Development of a new metal artifact reduction algorithm by using an edge preserving method for CBCT imaging

NASA Astrophysics Data System (ADS)

Kim, Juhye; Nam, Haewon; Lee, Rena

2015-07-01

CT (computed tomography) images, metal materials such as tooth supplements or surgical clips can cause metal artifact and degrade image quality. In severe cases, this may lead to misdiagnosis. In this research, we developed a new MAR (metal artifact reduction) algorithm by using an edge preserving filter and the MATLAB program (Mathworks, version R2012a). The proposed algorithm consists of 6 steps: image reconstruction from projection data, metal segmentation, forward projection, interpolation, applied edge preserving smoothing filter, and new image reconstruction. For an evaluation of the proposed algorithm, we obtained both numerical simulation data and data for a Rando phantom. In the numerical simulation data, four metal regions were added into the Shepp Logan phantom for metal artifacts. The projection data of the metal-inserted Rando phantom were obtained by using a prototype CBCT scanner manufactured by medical engineering and medical physics (MEMP) laboratory research group in medical science at Ewha Womans University. After these had been adopted the proposed algorithm was performed, and the result were compared with the original image (with metal artifact without correction) and with a corrected image based on linear interpolation. Both visual and quantitative evaluations were done. Compared with the original image with metal artifacts and with the image corrected by using linear interpolation, both the numerical and the experimental phantom data demonstrated that the proposed algorithm reduced the metal artifact. In conclusion, the evaluation in this research showed that the proposed algorithm outperformed the interpolation based MAR algorithm. If an optimization and a stability evaluation of the proposed algorithm can be performed, the developed algorithm is expected to be an effective tool for eliminating metal artifacts even in commercial CT systems.

Computationally Efficient Power Allocation Algorithm in Multicarrier-Based Cognitive Radio Networks: OFDM and FBMC Systems

NASA Astrophysics Data System (ADS)

Shaat, Musbah; Bader, Faouzi

2010-12-01

Cognitive Radio (CR) systems have been proposed to increase the spectrum utilization by opportunistically access the unused spectrum. Multicarrier communication systems are promising candidates for CR systems. Due to its high spectral efficiency, filter bank multicarrier (FBMC) can be considered as an alternative to conventional orthogonal frequency division multiplexing (OFDM) for transmission over the CR networks. This paper addresses the problem of resource allocation in multicarrier-based CR networks. The objective is to maximize the downlink capacity of the network under both total power and interference introduced to the primary users (PUs) constraints. The optimal solution has high computational complexity which makes it unsuitable for practical applications and hence a low complexity suboptimal solution is proposed. The proposed algorithm utilizes the spectrum holes in PUs bands as well as active PU bands. The performance of the proposed algorithm is investigated for OFDM and FBMC based CR systems. Simulation results illustrate that the proposed resource allocation algorithm with low computational complexity achieves near optimal performance and proves the efficiency of using FBMC in CR context.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

PubMed Central

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method. PMID:28558002

Simulation and experimental design of a new advanced variable step size Incremental Conductance MPPT algorithm for PV systems.

PubMed

Loukriz, Abdelhamid; Haddadi, Mourad; Messalti, Sabir

2016-05-01

Improvement of the efficiency of photovoltaic system based on new maximum power point tracking (MPPT) algorithms is the most promising solution due to its low cost and its easy implementation without equipment updating. Many MPPT methods with fixed step size have been developed. However, when atmospheric conditions change rapidly , the performance of conventional algorithms is reduced. In this paper, a new variable step size Incremental Conductance IC MPPT algorithm has been proposed. Modeling and simulation of different operational conditions of conventional Incremental Conductance IC and proposed methods are presented. The proposed method was developed and tested successfully on a photovoltaic system based on Flyback converter and control circuit using dsPIC30F4011. Both, simulation and experimental design are provided in several aspects. A comparative study between the proposed variable step size and fixed step size IC MPPT method under similar operating conditions is presented. The obtained results demonstrate the efficiency of the proposed MPPT algorithm in terms of speed in MPP tracking and accuracy. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Grey Wolf based control for speed ripple reduction at low speed operation of PMSM drives.

PubMed

Djerioui, Ali; Houari, Azeddine; Ait-Ahmed, Mourad; Benkhoris, Mohamed-Fouad; Chouder, Aissa; Machmoum, Mohamed

2018-03-01

Speed ripple at low speed-high torque operation of Permanent Magnet Synchronous Machine (PMSM) drives is considered as one of the major issues to be treated. The presented work proposes an efficient PMSM speed controller based on Grey Wolf (GW) algorithm to ensure a high-performance control for speed ripple reduction at low speed operation. The main idea of the proposed control algorithm is to propose a specific objective function in order to incorporate the advantage of fast optimization process of the GW optimizer. The role of GW optimizer is to find the optimal input controls that satisfy the speed tracking requirements. The synthesis methodology of the proposed control algorithm is detailed and the feasibility and performances of the proposed speed controller is confirmed by simulation and experimental results. The GW algorithm is a model-free controller and the parameters of its objective function are easy to be tuned. The GW controller is compared to PI one on real test bench. Then, the superiority of the first algorithm is highlighted. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Congestion Pricing for Aircraft Pushback Slot Allocation.

PubMed

Liu, Lihua; Zhang, Yaping; Liu, Lan; Xing, Zhiwei

2017-01-01

In order to optimize aircraft pushback management during rush hour, aircraft pushback slot allocation based on congestion pricing is explored while considering monetary compensation based on the quality of the surface operations. First, the concept of the "external cost of surface congestion" is proposed, and a quantitative study on the external cost is performed. Then, an aircraft pushback slot allocation model for minimizing the total surface cost is established. An improved discrete differential evolution algorithm is also designed. Finally, a simulation is performed on Xinzheng International Airport using the proposed model. By comparing the pushback slot control strategy based on congestion pricing with other strategies, the advantages of the proposed model and algorithm are highlighted. In addition to reducing delays and optimizing the delay distribution, the model and algorithm are better suited for use for actual aircraft pushback management during rush hour. Further, it is also observed they do not result in significant increases in the surface cost. These results confirm the effectiveness and suitability of the proposed model and algorithm.

Congestion Pricing for Aircraft Pushback Slot Allocation

PubMed Central

Zhang, Yaping

2017-01-01

In order to optimize aircraft pushback management during rush hour, aircraft pushback slot allocation based on congestion pricing is explored while considering monetary compensation based on the quality of the surface operations. First, the concept of the “external cost of surface congestion” is proposed, and a quantitative study on the external cost is performed. Then, an aircraft pushback slot allocation model for minimizing the total surface cost is established. An improved discrete differential evolution algorithm is also designed. Finally, a simulation is performed on Xinzheng International Airport using the proposed model. By comparing the pushback slot control strategy based on congestion pricing with other strategies, the advantages of the proposed model and algorithm are highlighted. In addition to reducing delays and optimizing the delay distribution, the model and algorithm are better suited for use for actual aircraft pushback management during rush hour. Further, it is also observed they do not result in significant increases in the surface cost. These results confirm the effectiveness and suitability of the proposed model and algorithm. PMID:28114429

Automatic building detection based on Purposive FastICA (PFICA) algorithm using monocular high resolution Google Earth images

NASA Astrophysics Data System (ADS)

Ghaffarian, Saman; Ghaffarian, Salar

2014-11-01

This paper proposes an improved FastICA model named as Purposive FastICA (PFICA) with initializing by a simple color space transformation and a novel masking approach to automatically detect buildings from high resolution Google Earth imagery. ICA and FastICA algorithms are defined as Blind Source Separation (BSS) techniques for unmixing source signals using the reference data sets. In order to overcome the limitations of the ICA and FastICA algorithms and make them purposeful, we developed a novel method involving three main steps: 1-Improving the FastICA algorithm using Moore-Penrose pseudo inverse matrix model, 2-Automated seeding of the PFICA algorithm based on LUV color space and proposed simple rules to split image into three regions; shadow + vegetation, baresoil + roads and buildings, respectively, 3-Masking out the final building detection results from PFICA outputs utilizing the K-means clustering algorithm with two number of clusters and conducting simple morphological operations to remove noises. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.6% and 85.5% overall pixel-based and object-based precision performances, respectively.

T-L Plane Abstraction-Based Energy-Efficient Real-Time Scheduling for Multi-Core Wireless Sensors.

PubMed

Kim, Youngmin; Lee, Ki-Seong; Pham, Ngoc-Son; Lee, Sun-Ro; Lee, Chan-Gun

2016-07-08

Energy efficiency is considered as a critical requirement for wireless sensor networks. As more wireless sensor nodes are equipped with multi-cores, there are emerging needs for energy-efficient real-time scheduling algorithms. The T-L plane-based scheme is known to be an optimal global scheduling technique for periodic real-time tasks on multi-cores. Unfortunately, there has been a scarcity of studies on extending T-L plane-based scheduling algorithms to exploit energy-saving techniques. In this paper, we propose a new T-L plane-based algorithm enabling energy-efficient real-time scheduling on multi-core sensor nodes with dynamic power management (DPM). Our approach addresses the overhead of processor mode transitions and reduces fragmentations of the idle time, which are inherent in T-L plane-based algorithms. Our experimental results show the effectiveness of the proposed algorithm compared to other energy-aware scheduling methods on T-L plane abstraction.

The model of encryption algorithm based on non-positional polynomial notations and constructed on an SP-network

NASA Astrophysics Data System (ADS)

Kapalova, N.; Haumen, A.

2018-05-01

This paper addresses to structures and properties of the cryptographic information protection algorithm model based on NPNs and constructed on an SP-network. The main task of the research is to increase the cryptostrength of the algorithm. In the paper, the transformation resulting in the improvement of the cryptographic strength of the algorithm is described in detail. The proposed model is based on an SP-network. The reasons for using the SP-network in this model are the conversion properties used in these networks. In the encryption process, transformations based on S-boxes and P-boxes are used. It is known that these transformations can withstand cryptanalysis. In addition, in the proposed model, transformations that satisfy the requirements of the "avalanche effect" are used. As a result of this work, a computer program that implements an encryption algorithm model based on the SP-network has been developed.

An adaptive clustering algorithm for image matching based on corner feature

NASA Astrophysics Data System (ADS)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-04-01

The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same 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.

Research on adaptive optics image restoration algorithm based on improved joint maximum a posteriori method

NASA Astrophysics Data System (ADS)

Zhang, Lijuan; Li, Yang; Wang, Junnan; Liu, Ying

2018-03-01

In this paper, we propose a point spread function (PSF) reconstruction method and joint maximum a posteriori (JMAP) estimation method for the adaptive optics image restoration. Using the JMAP method as the basic principle, we establish the joint log likelihood function of multi-frame adaptive optics (AO) images based on the image Gaussian noise models. To begin with, combining the observed conditions and AO system characteristics, a predicted PSF model for the wavefront phase effect is developed; then, we build up iterative solution formulas of the AO image based on our proposed algorithm, addressing the implementation process of multi-frame AO images joint deconvolution method. We conduct a series of experiments on simulated and real degraded AO images to evaluate our proposed algorithm. Compared with the Wiener iterative blind deconvolution (Wiener-IBD) algorithm and Richardson-Lucy IBD algorithm, our algorithm has better restoration effects including higher peak signal-to-noise ratio ( PSNR) and Laplacian sum ( LS) value than the others. The research results have a certain application values for actual AO image restoration.

Mean curvature and texture constrained composite weighted random walk algorithm for optic disc segmentation towards glaucoma screening.

PubMed

Panda, Rashmi; Puhan, N B; Panda, Ganapati

2018-02-01

Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.

Evaluation of an Agricultural Meteorological Disaster Based on Multiple Criterion Decision Making and Evolutionary Algorithm

PubMed Central

Yu, Xiaobing; Yu, Xianrui; Lu, Yiqun

2018-01-01

The evaluation of a meteorological disaster can be regarded as a multiple-criteria decision making problem because it involves many indexes. Firstly, a comprehensive indexing system for an agricultural meteorological disaster is proposed, which includes the disaster rate, the inundated rate, and the complete loss rate. Following this, the relative weights of the three criteria are acquired using a novel proposed evolutionary algorithm. The proposed algorithm consists of a differential evolution algorithm and an evolution strategy. Finally, a novel evaluation model, based on the proposed algorithm and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), is presented to estimate the agricultural meteorological disaster of 2008 in China. The geographic information system (GIS) technique is employed to depict the disaster. The experimental results demonstrated that the agricultural meteorological disaster of 2008 was very serious, especially in Hunan and Hubei provinces. Some useful suggestions are provided to relieve agriculture meteorological disasters. PMID:29597243

Joint Power Charging and Routing in Wireless Rechargeable Sensor Networks.

PubMed

Jia, Jie; Chen, Jian; Deng, Yansha; Wang, Xingwei; Aghvami, Abdol-Hamid

2017-10-09

The development of wireless power transfer (WPT) technology has inspired the transition from traditional battery-based wireless sensor networks (WSNs) towards wireless rechargeable sensor networks (WRSNs). While extensive efforts have been made to improve charging efficiency, little has been done for routing optimization. In this work, we present a joint optimization model to maximize both charging efficiency and routing structure. By analyzing the structure of the optimization model, we first decompose the problem and propose a heuristic algorithm to find the optimal charging efficiency for the predefined routing tree. Furthermore, by coding the many-to-one communication topology as an individual, we further propose to apply a genetic algorithm (GA) for the joint optimization of both routing and charging. The genetic operations, including tree-based recombination and mutation, are proposed to obtain a fast convergence. Our simulation results show that the heuristic algorithm reduces the number of resident locations and the total moving distance. We also show that our proposed algorithm achieves a higher charging efficiency compared with existing algorithms.

Joint Power Charging and Routing in Wireless Rechargeable Sensor Networks

PubMed Central

Jia, Jie; Chen, Jian; Deng, Yansha; Wang, Xingwei; Aghvami, Abdol-Hamid

2017-01-01

The development of wireless power transfer (WPT) technology has inspired the transition from traditional battery-based wireless sensor networks (WSNs) towards wireless rechargeable sensor networks (WRSNs). While extensive efforts have been made to improve charging efficiency, little has been done for routing optimization. In this work, we present a joint optimization model to maximize both charging efficiency and routing structure. By analyzing the structure of the optimization model, we first decompose the problem and propose a heuristic algorithm to find the optimal charging efficiency for the predefined routing tree. Furthermore, by coding the many-to-one communication topology as an individual, we further propose to apply a genetic algorithm (GA) for the joint optimization of both routing and charging. The genetic operations, including tree-based recombination and mutation, are proposed to obtain a fast convergence. Our simulation results show that the heuristic algorithm reduces the number of resident locations and the total moving distance. We also show that our proposed algorithm achieves a higher charging efficiency compared with existing algorithms. PMID:28991200

Blind Channel Equalization with Colored Source Based on Constrained Optimization Methods

NASA Astrophysics Data System (ADS)

Wang, Yunhua; DeBrunner, Linda; DeBrunner, Victor; Zhou, Dayong

2008-12-01

Tsatsanis and Xu have applied the constrained minimum output variance (CMOV) principle to directly blind equalize a linear channel—a technique that has proven effective with white inputs. It is generally assumed in the literature that their CMOV method can also effectively equalize a linear channel with a colored source. In this paper, we prove that colored inputs will cause the equalizer to incorrectly converge due to inadequate constraints. We also introduce a new blind channel equalizer algorithm that is based on the CMOV principle, but with a different constraint that will correctly handle colored sources. Our proposed algorithm works for channels with either white or colored inputs and performs equivalently to the trained minimum mean-square error (MMSE) equalizer under high SNR. Thus, our proposed algorithm may be regarded as an extension of the CMOV algorithm proposed by Tsatsanis and Xu. We also introduce several methods to improve the performance of our introduced algorithm in the low SNR condition. Simulation results show the superior performance of our proposed methods.

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

NASA Astrophysics Data System (ADS)

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

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

A Self Adaptive Differential Evolution Algorithm for Global Optimization

NASA Astrophysics Data System (ADS)

Kumar, Pravesh; Pant, Millie

This paper presents a new Differential Evolution algorithm based on hybridization of adaptive control parameters and trigonometric mutation. First we propose a self adaptive DE named ADE where choice of control parameter F and Cr is not fixed at some constant value but is taken iteratively. The proposed algorithm is further modified by applying trigonometric mutation in it and the corresponding algorithm is named as ATDE. The performance of ATDE is evaluated on the set of 8 benchmark functions and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical result shows the competence of the proposed algorithm.

Fast ℓ1-regularized space-time adaptive processing using alternating direction method of multipliers

NASA Astrophysics Data System (ADS)

Qin, Lilong; Wu, Manqing; Wang, Xuan; Dong, Zhen

2017-04-01

Motivated by the sparsity of filter coefficients in full-dimension space-time adaptive processing (STAP) algorithms, this paper proposes a fast ℓ1-regularized STAP algorithm based on the alternating direction method of multipliers to accelerate the convergence and reduce the calculations. The proposed algorithm uses a splitting variable to obtain an equivalent optimization formulation, which is addressed with an augmented Lagrangian method. Using the alternating recursive algorithm, the method can rapidly result in a low minimum mean-square error without a large number of calculations. Through theoretical analysis and experimental verification, we demonstrate that the proposed algorithm provides a better output signal-to-clutter-noise ratio performance than other algorithms.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

PubMed

Hong, Xia

2006-07-01

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

ECG Sensor Card with Evolving RBP Algorithms for Human Verification.

PubMed

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

2015-08-21

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

Finding all solutions of nonlinear equations using the dual simplex method

NASA Astrophysics Data System (ADS)

Yamamura, Kiyotaka; Fujioka, Tsuyoshi

2003-03-01

Recently, an efficient algorithm has been proposed for finding all solutions of systems of nonlinear equations using linear programming. This algorithm is based on a simple test (termed the LP test) for nonexistence of a solution to a system of nonlinear equations using the dual simplex method. In this letter, an improved version of the LP test algorithm is proposed. By numerical examples, it is shown that the proposed algorithm could find all solutions of a system of 300 nonlinear equations in practical computation time.

New calibration algorithms for dielectric-based microwave moisture sensors

USDA-ARS?s Scientific Manuscript database

New calibration algorithms for determining moisture content in granular and particulate materials from measurement of the dielectric properties at a single microwave frequency are proposed. The algorithms are based on identifying empirically correlations between the dielectric properties and the par...

DNABIT Compress – Genome compression algorithm

PubMed Central

Rajarajeswari, Pothuraju; Apparao, Allam

2011-01-01

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

An Innovative Thinking-Based Intelligent Information Fusion Algorithm

PubMed Central

Hu, Liang; Liu, Gang; Zhou, Jin

2013-01-01

This study proposes an intelligent algorithm that can realize information fusion in reference to the relative research achievements in brain cognitive theory and innovative computation. This algorithm treats knowledge as core and information fusion as a knowledge-based innovative thinking process. Furthermore, the five key parts of this algorithm including information sense and perception, memory storage, divergent thinking, convergent thinking, and evaluation system are simulated and modeled. This algorithm fully develops innovative thinking skills of knowledge in information fusion and is a try to converse the abstract conception of brain cognitive science to specific and operable research routes and strategies. Furthermore, the influences of each parameter of this algorithm on algorithm performance are analyzed and compared with those of classical intelligent algorithms trough test. Test results suggest that the algorithm proposed in this study can obtain the optimum problem solution by less target evaluation times, improve optimization effectiveness, and achieve the effective fusion of information. PMID:23956699

An innovative thinking-based intelligent information fusion algorithm.

PubMed

Lu, Huimin; Hu, Liang; Liu, Gang; Zhou, Jin

2013-01-01

This study proposes an intelligent algorithm that can realize information fusion in reference to the relative research achievements in brain cognitive theory and innovative computation. This algorithm treats knowledge as core and information fusion as a knowledge-based innovative thinking process. Furthermore, the five key parts of this algorithm including information sense and perception, memory storage, divergent thinking, convergent thinking, and evaluation system are simulated and modeled. This algorithm fully develops innovative thinking skills of knowledge in information fusion and is a try to converse the abstract conception of brain cognitive science to specific and operable research routes and strategies. Furthermore, the influences of each parameter of this algorithm on algorithm performance are analyzed and compared with those of classical intelligent algorithms trough test. Test results suggest that the algorithm proposed in this study can obtain the optimum problem solution by less target evaluation times, improve optimization effectiveness, and achieve the effective fusion of information.

Design of Restoration Method Based on Compressed Sensing and TwIST Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Fei; Piao, Yan

2018-04-01

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

Research and implementation of the algorithm for unwrapped and distortion correction basing on CORDIC for panoramic image

NASA Astrophysics Data System (ADS)

Zhang, Zhenhai; Li, Kejie; Wu, Xiaobing; Zhang, Shujiang

2008-03-01

The unwrapped and correcting algorithm based on Coordinate Rotation Digital Computer (CORDIC) and bilinear interpolation algorithm was presented in this paper, with the purpose of processing dynamic panoramic annular image. An original annular panoramic image captured by panoramic annular lens (PAL) can be unwrapped and corrected to conventional rectangular image without distortion, which is much more coincident with people's vision. The algorithm for panoramic image processing is modeled by VHDL and implemented in FPGA. The experimental results show that the proposed panoramic image algorithm for unwrapped and distortion correction has the lower computation complexity and the architecture for dynamic panoramic image processing has lower hardware cost and power consumption. And the proposed algorithm is valid.

Community detection in complex networks by using membrane algorithm

NASA Astrophysics Data System (ADS)

Liu, Chuang; Fan, Linan; Liu, Zhou; Dai, Xiang; Xu, Jiamei; Chang, Baoren

Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

Auction-based Security Game for Multiuser Cooperative Networks

NASA Astrophysics Data System (ADS)

Wang, An; Cai, Yueming; Yang, Wendong; Cheng, Yunpeng

2013-04-01

In this paper, we develop an auction-based algorithm to allocate the relay power efficiently to improve the system secrecy rate in a cooperative network, where several source-destination pairs and one cooperative relay are involved. On the one hand, the cooperative relay assists these pairs to transmit under a peak power constraint. On the other hand, the relay is untrusty and is also a passive eavesdropper. The whole auction process is completely distributed and no instantaneous channel state information exchange is needed. We also prove the existence and uniqueness of the Nash Equilibrium (NE) for the proposed power auction game. Moreover, the Pareto optimality is also validated. Simulation results show that our proposed auction-based algorithm can effectively improve the system secrecy rate. Besides, the proposed auction-based algorithm can converge to the unique NE point within a finite number of iterations. More interestingly, we also find that the proposed power auction mechanism is cheat-proof.

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

PubMed

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

2009-08-17

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

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

PubMed

Ye, Fei; Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

Incoherent beam combining based on the momentum SPGD algorithm

NASA Astrophysics Data System (ADS)

Yang, Guoqing; Liu, Lisheng; Jiang, Zhenhua; Guo, Jin; Wang, Tingfeng

2018-05-01

Incoherent beam combining (ICBC) technology is one of the most promising ways to achieve high-energy, near-diffraction laser output. In this paper, the momentum method is proposed as a modification of the stochastic parallel gradient descent (SPGD) algorithm. The momentum method can improve the speed of convergence of the combining system efficiently. The analytical method is employed to interpret the principle of the momentum method. Furthermore, the proposed algorithm is testified through simulations as well as experiments. The results of the simulations and the experiments show that the proposed algorithm not only accelerates the speed of the iteration, but also keeps the stability of the combining process. Therefore the feasibility of the proposed algorithm in the beam combining system is testified.

Approximated affine projection algorithm for feedback cancellation in hearing aids.

PubMed

Lee, Sangmin; Kim, In-Young; Park, Young-Cheol

2007-09-01

We propose an approximated affine projection (AP) algorithm for feedback cancellation in hearing aids. It is based on the conventional approach using the Gauss-Seidel (GS) iteration, but provides more stable convergence behaviour even with small step sizes. In the proposed algorithm, a residue of the weighted error vector, instead of the current error sample, is used to provide stable convergence. A new learning rate control scheme is also applied to the proposed algorithm to prevent signal cancellation and system instability. The new scheme determines step size in proportion to the prediction factor of the input, so that adaptation is inhibited whenever tone-like signals are present in the input. Simulation results verified the efficiency of the proposed algorithm.

Exact and Heuristic Algorithms for Runway Scheduling

NASA Technical Reports Server (NTRS)

Malik, Waqar A.; Jung, Yoon C.

2016-01-01

This paper explores the Single Runway Scheduling (SRS) problem with arrivals, departures, and crossing aircraft on the airport surface. Constraints for wake vortex separations, departure area navigation separations and departure time window restrictions are explicitly considered. The main objective of this research is to develop exact and heuristic based algorithms that can be used in real-time decision support tools for Air Traffic Control Tower (ATCT) controllers. The paper provides a multi-objective dynamic programming (DP) based algorithm that finds the exact solution to the SRS problem, but may prove unusable for application in real-time environment due to large computation times for moderate sized problems. We next propose a second algorithm that uses heuristics to restrict the search space for the DP based algorithm. A third algorithm based on a combination of insertion and local search (ILS) heuristics is then presented. Simulation conducted for the east side of Dallas/Fort Worth International Airport allows comparison of the three proposed algorithms and indicates that the ILS algorithm performs favorably in its ability to find efficient solutions and its computation times.

Log-linear model based behavior selection method for artificial fish swarm algorithm.

PubMed

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

Design optimization of single mixed refrigerant LNG process using a hybrid modified coordinate descent algorithm

NASA Astrophysics Data System (ADS)

Qyyum, Muhammad Abdul; Long, Nguyen Van Duc; Minh, Le Quang; Lee, Moonyong

2018-01-01

Design optimization of the single mixed refrigerant (SMR) natural gas liquefaction (LNG) process involves highly non-linear interactions between decision variables, constraints, and the objective function. These non-linear interactions lead to an irreversibility, which deteriorates the energy efficiency of the LNG process. In this study, a simple and highly efficient hybrid modified coordinate descent (HMCD) algorithm was proposed to cope with the optimization of the natural gas liquefaction process. The single mixed refrigerant process was modeled in Aspen Hysys® and then connected to a Microsoft Visual Studio environment. The proposed optimization algorithm provided an improved result compared to the other existing methodologies to find the optimal condition of the complex mixed refrigerant natural gas liquefaction process. By applying the proposed optimization algorithm, the SMR process can be designed with the 0.2555 kW specific compression power which is equivalent to 44.3% energy saving as compared to the base case. Furthermore, in terms of coefficient of performance (COP), it can be enhanced up to 34.7% as compared to the base case. The proposed optimization algorithm provides a deep understanding of the optimization of the liquefaction process in both technical and numerical perspectives. In addition, the HMCD algorithm can be employed to any mixed refrigerant based liquefaction process in the natural gas industry.

Chaotic Particle Swarm Optimization with Mutation for Classification

PubMed Central

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937

Experimental Investigation of the Performance of Vertical Handover Algorithms between WiFi and UMTS Networks

NASA Astrophysics Data System (ADS)

Busanelli, Stefano; Martalò, Marco; Ferrari, Gianluigi; Spigoni, Giovanni; Iotti, Nicola

In this paper, we analyze the performance of vertical handover (VHO) algorithms for seamless mobility between WiFi and UMTS networks. We focus on a no-coupling scenario, characterized by the lack of any form of cooperation between the involved players (users and network operators). In this context, we first propose a low-complexity Received Signal Strength Indicator (RSSI)-based algorithm, and then an improved hybrid RSSI/goodput version. We present experimental results based on the implementation of a real testbed with commercial WiFi (Guglielmo) and UMTS (Telecom Italia) deployed networks. Despite the relatively long handover times experienced in our testbed, the proposed RSSI-based VHO algorithm guarantees an effective goodput increase at the MTs. Moreover, this algorithm mitigates the ping-pong phenomenon.

Adaptive optics compensation of orbital angular momentum beams with a modified Gerchberg-Saxton-based phase retrieval algorithm

NASA Astrophysics Data System (ADS)

Chang, Huan; Yin, Xiao-li; Cui, Xiao-zhou; Zhang, Zhi-chao; Ma, Jian-xin; Wu, Guo-hua; Zhang, Li-jia; Xin, Xiang-jun

2017-12-01

Practical orbital angular momentum (OAM)-based free-space optical (FSO) communications commonly experience serious performance degradation and crosstalk due to atmospheric turbulence. In this paper, we propose a wave-front sensorless adaptive optics (WSAO) system with a modified Gerchberg-Saxton (GS)-based phase retrieval algorithm to correct distorted OAM beams. We use the spatial phase perturbation (SPP) GS algorithm with a distorted probe Gaussian beam as the only input. The principle and parameter selections of the algorithm are analyzed, and the performance of the algorithm is discussed. The simulation results show that the proposed adaptive optics (AO) system can significantly compensate for distorted OAM beams in single-channel or multiplexed OAM systems, which provides new insights into adaptive correction systems using OAM beams.

New segmentation-based tone mapping algorithm for high dynamic range image

NASA Astrophysics Data System (ADS)

Duan, Weiwei; Guo, Huinan; Zhou, Zuofeng; Huang, Huimin; Cao, Jianzhong

2017-07-01

The traditional tone mapping algorithm for the display of high dynamic range (HDR) image has the drawback of losing the impression of brightness, contrast and color information. To overcome this phenomenon, we propose a new tone mapping algorithm based on dividing the image into different exposure regions in this paper. Firstly, the over-exposure region is determined using the Local Binary Pattern information of HDR image. Then, based on the peak and average gray of the histogram, the under-exposure and normal-exposure region of HDR image are selected separately. Finally, the different exposure regions are mapped by differentiated tone mapping methods to get the final result. The experiment results show that the proposed algorithm achieve the better performance both in visual quality and objective contrast criterion than other algorithms.

A novel line segment detection algorithm based on graph search

NASA Astrophysics Data System (ADS)

Zhao, Hong-dan; Liu, Guo-ying; Song, Xu

2018-02-01

To overcome the problem of extracting line segment from an image, a method of line segment detection was proposed based on the graph search algorithm. After obtaining the edge detection result of the image, the candidate straight line segments are obtained in four directions. For the candidate straight line segments, their adjacency relationships are depicted by a graph model, based on which the depth-first search algorithm is employed to determine how many adjacent line segments need to be merged. Finally we use the least squares method to fit the detected straight lines. The comparative experimental results verify that the proposed algorithm has achieved better results than the line segment detector (LSD).

Predicting missing links in complex networks based on common neighbors and distance

PubMed Central

Yang, Jinxuan; Zhang, Xiao-Dong

2016-01-01

The algorithms based on common neighbors metric to predict missing links in complex networks are very popular, but most of these algorithms do not account for missing links between nodes with no common neighbors. It is not accurate enough to reconstruct networks by using these methods in some cases especially when between nodes have less common neighbors. We proposed in this paper a new algorithm based on common neighbors and distance to improve accuracy of link prediction. Our proposed algorithm makes remarkable effect in predicting the missing links between nodes with no common neighbors and performs better than most existing currently used methods for a variety of real-world networks without increasing complexity. PMID:27905526

One cutting plane algorithm using auxiliary functions

NASA Astrophysics Data System (ADS)

Zabotin, I. Ya; Kazaeva, K. E.

2016-11-01

We propose an algorithm for solving a convex programming problem from the class of cutting methods. The algorithm is characterized by the construction of approximations using some auxiliary functions, instead of the objective function. Each auxiliary function bases on the exterior penalty function. In proposed algorithm the admissible set and the epigraph of each auxiliary function are embedded into polyhedral sets. In connection with the above, the iteration points are found by solving linear programming problems. We discuss the implementation of the algorithm and prove its convergence.

A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path.

PubMed

Xie, Zhiqiang; Shao, Xia; Xin, Yu

2016-01-01

To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective.

A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path

PubMed Central

Xie, Zhiqiang; Shao, Xia; Xin, Yu

2016-01-01

To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective. PMID:27490901

Design a software real-time operation platform for wave piercing catamarans motion control using linear quadratic regulator based genetic algorithm.

PubMed

Liang, Lihua; Yuan, Jia; Zhang, Songtao; Zhao, Peng

2018-01-01

This work presents optimal linear quadratic regulator (LQR) based on genetic algorithm (GA) to solve the two degrees of freedom (2 DoF) motion control problem in head seas for wave piercing catamarans (WPC). The proposed LQR based GA control strategy is to select optimal weighting matrices (Q and R). The seakeeping performance of WPC based on proposed algorithm is challenged because of multi-input multi-output (MIMO) system of uncertain coefficient problems. Besides the kinematical constraint problems of WPC, the external conditions must be considered, like the sea disturbance and the actuators (a T-foil and two flaps) control. Moreover, this paper describes the MATLAB and LabVIEW software plats to simulate the reduction effects of WPC. Finally, the real-time (RT) NI CompactRIO embedded controller is selected to test the effectiveness of the actuators based on proposed techniques. In conclusion, simulation and experimental results prove the correctness of the proposed algorithm. The percentage of heave and pitch reductions are more than 18% in different high speeds and bad sea conditions. And the results also verify the feasibility of NI CompactRIO embedded controller.

Design a software real-time operation platform for wave piercing catamarans motion control using linear quadratic regulator based genetic algorithm

PubMed Central

Liang, Lihua; Zhang, Songtao; Zhao, Peng

2018-01-01

This work presents optimal linear quadratic regulator (LQR) based on genetic algorithm (GA) to solve the two degrees of freedom (2 DoF) motion control problem in head seas for wave piercing catamarans (WPC). The proposed LQR based GA control strategy is to select optimal weighting matrices (Q and R). The seakeeping performance of WPC based on proposed algorithm is challenged because of multi-input multi-output (MIMO) system of uncertain coefficient problems. Besides the kinematical constraint problems of WPC, the external conditions must be considered, like the sea disturbance and the actuators (a T-foil and two flaps) control. Moreover, this paper describes the MATLAB and LabVIEW software plats to simulate the reduction effects of WPC. Finally, the real-time (RT) NI CompactRIO embedded controller is selected to test the effectiveness of the actuators based on proposed techniques. In conclusion, simulation and experimental results prove the correctness of the proposed algorithm. The percentage of heave and pitch reductions are more than 18% in different high speeds and bad sea conditions. And the results also verify the feasibility of NI CompactRIO embedded controller. PMID:29709008

An efficient hole-filling method based on depth map in 3D view generation

NASA Astrophysics Data System (ADS)

Liang, Haitao; Su, Xiu; Liu, Yilin; Xu, Huaiyuan; Wang, Yi; Chen, Xiaodong

2018-01-01

New virtual view is synthesized through depth image based rendering(DIBR) using a single color image and its associated depth map in 3D view generation. Holes are unavoidably generated in the 2D to 3D conversion process. We propose a hole-filling method based on depth map to address the problem. Firstly, we improve the process of DIBR by proposing a one-to-four (OTF) algorithm. The "z-buffer" algorithm is used to solve overlap problem. Then, based on the classical patch-based algorithm of Criminisi et al., we propose a hole-filling algorithm using the information of depth map to handle the image after DIBR. In order to improve the accuracy of the virtual image, inpainting starts from the background side. In the calculation of the priority, in addition to the confidence term and the data term, we add the depth term. In the search for the most similar patch in the source region, we define the depth similarity to improve the accuracy of searching. Experimental results show that the proposed method can effectively improve the quality of the 3D virtual view subjectively and objectively.

Inter-slice bidirectional registration-based segmentation of the prostate gland in MR and CT image sequences

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

Khalvati, Farzad, E-mail: farzad.khalvati@uwaterloo.ca; Tizhoosh, Hamid R.; Salmanpour, Aryan

Purpose: Accurate segmentation and volume estimation of the prostate gland in magnetic resonance (MR) and computed tomography (CT) images are necessary steps in diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semiautomated segmentation of individual slices in T2-weighted MR and CT image sequences. Methods: The proposedInter-Slice Bidirectional Registration-based Segmentation (iBRS) algorithm relies on interslice image registration of volume data to segment the prostate gland without the use of an anatomical atlas. It requires the user to mark only three slices in a given volume dataset, i.e., themore » first, middle, and last slices. Next, the proposed algorithm uses a registration algorithm to autosegment the remaining slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid techniques). Results: The results with the proposed technique were compared with manual marking using prostate MR and CT images from 117 patients. Manual marking was performed by an expert user for all 117 patients. The median accuracies for individual slices measured using the Dice similarity coefficient (DSC) were 92% and 91% for MR and CT images, respectively. The iBRS algorithm was also evaluated regarding user variability, which confirmed that the algorithm was robust to interuser variability when marking the prostate gland. Conclusions: The proposed algorithm exploits the interslice data redundancy of the images in a volume dataset of MR and CT images and eliminates the need for an atlas, minimizing the computational cost while producing highly accurate results which are robust to interuser variability.« less

Inter-slice bidirectional registration-based segmentation of the prostate gland in MR and CT image sequences

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

Khalvati, Farzad, E-mail: farzad.khalvati@uwaterloo.ca; Tizhoosh, Hamid R.; Salmanpour, Aryan

2013-12-15

Purpose: Accurate segmentation and volume estimation of the prostate gland in magnetic resonance (MR) and computed tomography (CT) images are necessary steps in diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semiautomated segmentation of individual slices in T2-weighted MR and CT image sequences. Methods: The proposedInter-Slice Bidirectional Registration-based Segmentation (iBRS) algorithm relies on interslice image registration of volume data to segment the prostate gland without the use of an anatomical atlas. It requires the user to mark only three slices in a given volume dataset, i.e., themore » first, middle, and last slices. Next, the proposed algorithm uses a registration algorithm to autosegment the remaining slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid techniques). Results: The results with the proposed technique were compared with manual marking using prostate MR and CT images from 117 patients. Manual marking was performed by an expert user for all 117 patients. The median accuracies for individual slices measured using the Dice similarity coefficient (DSC) were 92% and 91% for MR and CT images, respectively. The iBRS algorithm was also evaluated regarding user variability, which confirmed that the algorithm was robust to interuser variability when marking the prostate gland. Conclusions: The proposed algorithm exploits the interslice data redundancy of the images in a volume dataset of MR and CT images and eliminates the need for an atlas, minimizing the computational cost while producing highly accurate results which are robust to interuser variability.« less

Low-dose CT reconstruction via L1 dictionary learning regularization using iteratively reweighted least-squares.

PubMed

Zhang, Cheng; Zhang, Tao; Li, Ming; Peng, Chengtao; Liu, Zhaobang; Zheng, Jian

2016-06-18

In order to reduce the radiation dose of CT (computed tomography), compressed sensing theory has been a hot topic since it provides the possibility of a high quality recovery from the sparse sampling data. Recently, the algorithm based on DL (dictionary learning) was developed to deal with the sparse CT reconstruction problem. However, the existing DL algorithm focuses on the minimization problem with the L2-norm regularization term, which leads to reconstruction quality deteriorating while the sampling rate declines further. Therefore, it is essential to improve the DL method to meet the demand of more dose reduction. In this paper, we replaced the L2-norm regularization term with the L1-norm one. It is expected that the proposed L1-DL method could alleviate the over-smoothing effect of the L2-minimization and reserve more image details. The proposed algorithm solves the L1-minimization problem by a weighting strategy, solving the new weighted L2-minimization problem based on IRLS (iteratively reweighted least squares). Through the numerical simulation, the proposed algorithm is compared with the existing DL method (adaptive dictionary based statistical iterative reconstruction, ADSIR) and other two typical compressed sensing algorithms. It is revealed that the proposed algorithm is more accurate than the other algorithms especially when further reducing the sampling rate or increasing the noise. The proposed L1-DL algorithm can utilize more prior information of image sparsity than ADSIR. By transforming the L2-norm regularization term of ADSIR with the L1-norm one and solving the L1-minimization problem by IRLS strategy, L1-DL could reconstruct the image more exactly.

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

NASA Astrophysics Data System (ADS)

Li, Xin; Fung, Richard Y. K.

2018-02-01

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

Distributed Multi-Cell Resource Allocation with Price Based ICI Coordination in Downlink OFDMA Networks

NASA Astrophysics Data System (ADS)

Lv, Gangming; Zhu, Shihua; Hui, Hui

Multi-cell resource allocation under minimum rate request for each user in OFDMA networks is addressed in this paper. Based on Lagrange dual decomposition theory, the joint multi-cell resource allocation problem is decomposed and modeled as a limited-cooperative game, and a distributed multi-cell resource allocation algorithm is thus proposed. Analysis and simulation results show that, compared with non-cooperative iterative water-filling algorithm, the proposed algorithm can remarkably reduce the ICI level and improve overall system performances.

A noise resistant symmetric key cryptosystem based on S8 S-boxes and chaotic maps

NASA Astrophysics Data System (ADS)

Hussain, Iqtadar; Anees, Amir; Aslam, Muhammad; Ahmed, Rehan; Siddiqui, Nasir

2018-04-01

In this manuscript, we have proposed an encryption algorithm to encrypt any digital data. The proposed algorithm is primarily based on the substitution-permutation in which the substitution process is performed by the S 8 Substitution boxes. The proposed algorithm incorporates three different chaotic maps. We have analysed the behaviour of chaos by secure communication in great length, and accordingly, we have applied those chaotic sequences in the proposed encryption algorithm. The simulation and statistical results revealed that the proposed encryption scheme is secure against different attacks. Moreover, the encryption scheme can tolerate the channel noise as well; if the encrypted data is corrupted by the unauthenticated user or by the channel noise, the decryption can still be successfully done with some distortion. The overall results confirmed that the presented work has good cryptographic features, low computational complexity and resistant to the channel noise which makes it suitable for low profile mobile applications.

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

PubMed Central

Billeci, Lucia; Varanini, Maurizio

2017-01-01

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

An Approach to a Comprehensive Test Framework for Analysis and Evaluation of Text Line Segmentation Algorithms

PubMed Central

Brodic, Darko; Milivojevic, Dragan R.; Milivojevic, Zoran N.

2011-01-01

The paper introduces a testing framework for the evaluation and validation of text line segmentation algorithms. Text line segmentation represents the key action for correct optical character recognition. Many of the tests for the evaluation of text line segmentation algorithms deal with text databases as reference templates. Because of the mismatch, the reliable testing framework is required. Hence, a new approach to a comprehensive experimental framework for the evaluation of text line segmentation algorithms is proposed. It consists of synthetic multi-like text samples and real handwritten text as well. Although the tests are mutually independent, the results are cross-linked. The proposed method can be used for different types of scripts and languages. Furthermore, two different procedures for the evaluation of algorithm efficiency based on the obtained error type classification are proposed. The first is based on the segmentation line error description, while the second one incorporates well-known signal detection theory. Each of them has different capabilities and convenience, but they can be used as supplements to make the evaluation process efficient. Overall the proposed procedure based on the segmentation line error description has some advantages, characterized by five measures that describe measurement procedures. PMID:22164106

An approach to a comprehensive test framework for analysis and evaluation of text line segmentation algorithms.

PubMed

Brodic, Darko; Milivojevic, Dragan R; Milivojevic, Zoran N

2011-01-01

The paper introduces a testing framework for the evaluation and validation of text line segmentation algorithms. Text line segmentation represents the key action for correct optical character recognition. Many of the tests for the evaluation of text line segmentation algorithms deal with text databases as reference templates. Because of the mismatch, the reliable testing framework is required. Hence, a new approach to a comprehensive experimental framework for the evaluation of text line segmentation algorithms is proposed. It consists of synthetic multi-like text samples and real handwritten text as well. Although the tests are mutually independent, the results are cross-linked. The proposed method can be used for different types of scripts and languages. Furthermore, two different procedures for the evaluation of algorithm efficiency based on the obtained error type classification are proposed. The first is based on the segmentation line error description, while the second one incorporates well-known signal detection theory. Each of them has different capabilities and convenience, but they can be used as supplements to make the evaluation process efficient. Overall the proposed procedure based on the segmentation line error description has some advantages, characterized by five measures that describe measurement procedures.

Graph Based Models for Unsupervised High Dimensional Data Clustering and Network Analysis

DTIC Science & Technology

2015-01-01

ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for...algorithms we proposed improve the time e ciency signi cantly for large scale datasets. In the last chapter, we also propose an incremental reseeding...plume detection in hyper-spectral video data. These graph based clustering algorithms we proposed improve the time efficiency significantly for large

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

[Combining speech sample and feature bilateral selection algorithm for classification of Parkinson's disease].

PubMed

Zhang, Xiaoheng; Wang, Lirui; Cao, Yao; Wang, Pin; Zhang, Cheng; Yang, Liuyang; Li, Yongming; Zhang, Yanling; Cheng, Oumei

2018-02-01

Diagnosis of Parkinson's disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.

Fast clustering algorithm for large ECG data sets based on CS theory in combination with PCA and K-NN methods.

PubMed

Balouchestani, Mohammadreza; Krishnan, Sridhar

2014-01-01

Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) they suffer from huge energy consumption and load of sampling. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for establishing low power long-term ECG recording. In this paper, we present an advanced K-means clustering algorithm based on Compressed Sensing (CS) theory as a random sampling procedure. Then, two dimensionality reduction methods: Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) followed by sorting the data using the K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers are applied to the proposed algorithm. We show our algorithm based on PCA features in combination with K-NN classifier shows better performance than other methods. The proposed algorithm outperforms existing algorithms by increasing 11% classification accuracy. In addition, the proposed algorithm illustrates classification accuracy for K-NN and PNN classifiers, and a Receiver Operating Characteristics (ROC) area of 99.98%, 99.83%, and 99.75% respectively.

A Novel Grid SINS/DVL Integrated Navigation Algorithm for Marine Application

PubMed Central

Kang, Yingyao; Zhao, Lin; Cheng, Jianhua; Fan, Xiaoliang

2018-01-01

Integrated navigation algorithms under the grid frame have been proposed based on the Kalman filter (KF) to solve the problem of navigation in some special regions. However, in the existing study of grid strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated navigation algorithms, the Earth models of the filter dynamic model and the SINS mechanization are not unified. Besides, traditional integrated systems with the KF based correction scheme are susceptible to measurement errors, which would decrease the accuracy and robustness of the system. In this paper, an adaptive robust Kalman filter (ARKF) based hybrid-correction grid SINS/DVL integrated navigation algorithm is designed with the unified reference ellipsoid Earth model to improve the navigation accuracy in middle-high latitude regions for marine application. Firstly, to unify the Earth models, the mechanization of grid SINS is introduced and the error equations are derived based on the same reference ellipsoid Earth model. Then, a more accurate grid SINS/DVL filter model is designed according to the new error equations. Finally, a hybrid-correction scheme based on the ARKF is proposed to resist the effect of measurement errors. Simulation and experiment results show that, compared with the traditional algorithms, the proposed navigation algorithm can effectively improve the navigation performance in middle-high latitude regions by the unified Earth models and the ARKF based hybrid-correction scheme. PMID:29373549

Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction.

PubMed

Huang, Hsuan-Ming; Hsiao, Ing-Tsung

2017-01-01

Over the past decade, image quality in low-dose computed tomography has been greatly improved by various compressive sensing- (CS-) based reconstruction methods. However, these methods have some disadvantages including high computational cost and slow convergence rate. Many different speed-up techniques for CS-based reconstruction algorithms have been developed. The purpose of this paper is to propose a fast reconstruction framework that combines a CS-based reconstruction algorithm with several speed-up techniques. First, total difference minimization (TDM) was implemented using the soft-threshold filtering (STF). Second, we combined TDM-STF with the ordered subsets transmission (OSTR) algorithm for accelerating the convergence. To further speed up the convergence of the proposed method, we applied the power factor and the fast iterative shrinkage thresholding algorithm to OSTR and TDM-STF, respectively. Results obtained from simulation and phantom studies showed that many speed-up techniques could be combined to greatly improve the convergence speed of a CS-based reconstruction algorithm. More importantly, the increased computation time (≤10%) was minor as compared to the acceleration provided by the proposed method. In this paper, we have presented a CS-based reconstruction framework that combines several acceleration techniques. Both simulation and phantom studies provide evidence that the proposed method has the potential to satisfy the requirement of fast image reconstruction in practical CT.

Low Power S-Box Architecture for AES Algorithm using Programmable Second Order Reversible Cellular Automata: An Application to WBAN.

PubMed

Gangadari, Bhoopal Rao; Ahamed, Shaik Rafi

2016-12-01

In this paper, we presented a novel approach of low energy consumption architecture of S-Box used in Advanced Encryption Standard (AES) algorithm using programmable second order reversible cellular automata (RCA 2 ). The architecture entails a low power implementation with minimal delay overhead and the performance of proposed RCA 2 based S-Box in terms of security is evaluated using the cryptographic properties such as nonlinearity, correlation immunity bias, strict avalanche criteria, entropy and also found that the proposed architecture is secure enough for cryptographic applications. Moreover, the proposed AES algorithm architecture simulation studies show that energy consumption of 68.726 nJ, power dissipation of 3.856 mW for 0.18- μm at 13.69 MHz and energy consumption of 29.408 nJ, power dissipation of 1.65 mW for 0.13- μm at 13.69 MHz. The proposed AES algorithm with RCA 2 based S-Box shows a reduction power consumption by 50 % and energy consumption by 5 % compared to best classical S-Box and composite field arithmetic based AES algorithm. Apart from that, it is also shown that RCA 2 based S-Boxes are dynamic in nature, invertible, low power dissipation compared to that of LUT based S-Box and hence suitable for Wireless Body Area Network (WBAN) applications.

Despeckling Polsar Images Based on Relative Total Variation Model

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

A new improved artificial bee colony algorithm for ship hull form optimization

NASA Astrophysics Data System (ADS)

Huang, Fuxin; Wang, Lijue; Yang, Chi

2016-04-01

The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.

Multiparty Quantum Key Agreement Based on Quantum Search Algorithm

PubMed Central

Cao, Hao; Ma, Wenping

2017-01-01

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

Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.

PubMed

Tian, Yuling; Zhang, Hongxian

2016-01-01

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.

Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy

PubMed Central

Tian, Yuling; Zhang, Hongxian

2016-01-01

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic–there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions. PMID:27487242

Correlation-coefficient-based fast template matching through partial elimination.

PubMed

Mahmood, Arif; Khan, Sohaib

2012-04-01

Partial computation elimination techniques are often used for fast template matching. At a particular search location, computations are prematurely terminated as soon as it is found that this location cannot compete with an already known best match location. Due to the nonmonotonic growth pattern of the correlation-based similarity measures, partial computation elimination techniques have been traditionally considered inapplicable to speed up these measures. In this paper, we show that partial elimination techniques may be applied to a correlation coefficient by using a monotonic formulation, and we propose basic-mode and extended-mode partial correlation elimination algorithms for fast template matching. The basic-mode algorithm is more efficient on small template sizes, whereas the extended mode is faster on medium and larger templates. We also propose a strategy to decide which algorithm to use for a given data set. To achieve a high speedup, elimination algorithms require an initial guess of the peak correlation value. We propose two initialization schemes including a coarse-to-fine scheme for larger templates and a two-stage technique for small- and medium-sized templates. Our proposed algorithms are exact, i.e., having exhaustive equivalent accuracy, and are compared with the existing fast techniques using real image data sets on a wide variety of template sizes. While the actual speedups are data dependent, in most cases, our proposed algorithms have been found to be significantly faster than the other algorithms.

Hybrid glowworm swarm optimization for task scheduling in the cloud environment

NASA Astrophysics Data System (ADS)

Zhou, Jing; Dong, Shoubin

2018-06-01

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

A parallel algorithm for generation and assembly of finite element stiffness and mass matrices

NASA Technical Reports Server (NTRS)

Storaasli, O. O.; Carmona, E. A.; Nguyen, D. T.; Baddourah, M. A.

1991-01-01

A new algorithm is proposed for parallel generation and assembly of the finite element stiffness and mass matrices. The proposed assembly algorithm is based on a node-by-node approach rather than the more conventional element-by-element approach. The new algorithm's generality and computation speed-up when using multiple processors are demonstrated for several practical applications on multi-processor Cray Y-MP and Cray 2 supercomputers.

Multipoint to multipoint routing and wavelength assignment in multi-domain optical networks

NASA Astrophysics Data System (ADS)

Qin, Panke; Wu, Jingru; Li, Xudong; Tang, Yongli

2018-01-01

In multi-point to multi-point (MP2MP) routing and wavelength assignment (RWA) problems, researchers usually assume the optical networks to be a single domain. However, the optical networks develop toward to multi-domain and larger scale in practice. In this context, multi-core shared tree (MST)-based MP2MP RWA are introduced problems including optimal multicast domain sequence selection, core nodes belonging in which domains and so on. In this letter, we focus on MST-based MP2MP RWA problems in multi-domain optical networks, mixed integer linear programming (MILP) formulations to optimally construct MP2MP multicast trees is presented. A heuristic algorithm base on network virtualization and weighted clustering algorithm (NV-WCA) is proposed. Simulation results show that, under different traffic patterns, the proposed algorithm achieves significant improvement on network resources occupation and multicast trees setup latency in contrast with the conventional algorithms which were proposed base on a single domain network environment.

An annealed chaotic maximum neural network for bipartite subgraph problem.

PubMed

Wang, Jiahai; Tang, Zheng; Wang, Ronglong

2004-04-01

In this paper, based on maximum neural network, we propose a new parallel algorithm that can help the maximum neural network escape from local minima by including a transient chaotic neurodynamics for bipartite subgraph problem. The goal of the bipartite subgraph problem, which is an NP- complete problem, is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. Lee et al. presented a parallel algorithm using the maximum neural model (winner-take-all neuron model) for this NP- complete problem. The maximum neural model always guarantees a valid solution and greatly reduces the search space without a burden on the parameter-tuning. However, the model has a tendency to converge to a local minimum easily because it is based on the steepest descent method. By adding a negative self-feedback to the maximum neural network, we proposed a new parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm is then fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the maximum neural network and the chaotic neurodynamics. A large number of instances have been simulated to verify the proposed algorithm. The simulation results show that our algorithm finds the optimum or near-optimum solution for the bipartite subgraph problem superior to that of the best existing parallel algorithms.

Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

PubMed

Rani, R Ranjani; Ramyachitra, D

2016-12-01

Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map.

PubMed

Tan, Yihua; Li, Qingyun; Li, Yansheng; Tian, Jinwen

2015-09-11

This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradient distribution detector is used to obtain a gradient textural saliency map in the favor of the candidate regions. In addition, the final targets will be detected by segmenting the saliency map using CFAR-type algorithm. The real high-resolution airborne SAR image data is used to verify the proposed algorithm. The results demonstrate that this algorithm can detect aircraft targets quickly and accurately, and decrease the false alarm rate.

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

PubMed

Li, Yang; Li, Guoqing; Wang, Zhenhao

2015-01-01

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

75 FR 66404 - Self-Regulatory Organizations; C2 Options Exchange, Incorporated; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-28

... Change The Exchange proposes to modify the wording of Rule 6.12 relating to the C2 matching algorithm... matching algorithm and subsequently overlay certain priorities over the selected base algorithm. There are currently two base algorithms: price-time (often referred to as first in, first out or FIFO) in which...

Distributed k-Means Algorithm and Fuzzy c-Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory.

PubMed

Qin, Jiahu; Fu, Weiming; Gao, Huijun; Zheng, Wei Xing

2016-03-03

This paper is concerned with developing a distributed k-means algorithm and a distributed fuzzy c-means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optimum, a distributed k-means++ algorithm is first proposed to find the initial centroids before executing the distributed k-means algorithm and the distributed fuzzy c-means algorithm. The proposed distributed k-means algorithm is capable of partitioning the data observed by the nodes into measure-dependent groups which have small in-group and large out-group distances, while the proposed distributed fuzzy c-means algorithm is capable of partitioning the data observed by the nodes into different measure-dependent groups with degrees of membership values ranging from 0 to 1. Simulation results show that the proposed distributed algorithms can achieve almost the same results as that given by the centralized clustering algorithms.

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

PubMed

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

2013-06-01

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

Hierarchical trie packet classification algorithm based on expectation-maximization clustering

PubMed Central

Bi, Xia-an; Zhao, Junxia

2017-01-01

With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is beneficial to save large storage space, it has several shortcomings such as the existence of backtracking and empty nodes. This paper proposes a new packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC). Firstly, this paper uses the formalization method to deal with the packet classification problem by means of mapping the rules and data packets into a two-dimensional space. Secondly, this paper uses expectation-maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed. Thirdly, this paper proposes a hierarchical trie based on the results of expectation-maximization clustering. Finally, this paper respectively conducts simulation experiments and real-environment experiments to compare the performances of our algorithm with other typical algorithms, and analyzes the results of the experiments. The hierarchical trie structure in our algorithm not only adopts trie path compression to eliminate backtracking, but also solves the problem of low efficiency of trie updates, which greatly improves the performance of the algorithm. PMID:28704476

A novel material detection algorithm based on 2D GMM-based power density function and image detail addition scheme in dual energy X-ray images.

PubMed

Pourghassem, Hossein

2012-01-01

Material detection is a vital need in dual energy X-ray luggage inspection systems at security of airport and strategic places. In this paper, a novel material detection algorithm based on statistical trainable models using 2-Dimensional power density function (PDF) of three material categories in dual energy X-ray images is proposed. In this algorithm, the PDF of each material category as a statistical model is estimated from transmission measurement values of low and high energy X-ray images by Gaussian Mixture Models (GMM). Material label of each pixel of object is determined based on dependency probability of its transmission measurement values in the low and high energy to PDF of three material categories (metallic, organic and mixed materials). The performance of material detection algorithm is improved by a maximum voting scheme in a neighborhood of image as a post-processing stage. Using two background removing and denoising stages, high and low energy X-ray images are enhanced as a pre-processing procedure. For improving the discrimination capability of the proposed material detection algorithm, the details of the low and high energy X-ray images are added to constructed color image which includes three colors (orange, blue and green) for representing the organic, metallic and mixed materials. The proposed algorithm is evaluated on real images that had been captured from a commercial dual energy X-ray luggage inspection system. The obtained results show that the proposed algorithm is effective and operative in detection of the metallic, organic and mixed materials with acceptable accuracy.

Method for Optimal Sensor Deployment on 3D Terrains Utilizing a Steady State Genetic Algorithm with a Guided Walk Mutation Operator Based on the Wavelet Transform

PubMed Central

Unaldi, Numan; Temel, Samil; Asari, Vijayan K.

2012-01-01

One of the most critical issues of Wireless Sensor Networks (WSNs) is the deployment of a limited number of sensors in order to achieve maximum coverage on a terrain. The optimal sensor deployment which enables one to minimize the consumed energy, communication time and manpower for the maintenance of the network has attracted interest with the increased number of studies conducted on the subject in the last decade. Most of the studies in the literature today are proposed for two dimensional (2D) surfaces; however, real world sensor deployments often arise on three dimensional (3D) environments. In this paper, a guided wavelet transform (WT) based deployment strategy (WTDS) for 3D terrains, in which the sensor movements are carried out within the mutation phase of the genetic algorithms (GAs) is proposed. The proposed algorithm aims to maximize the Quality of Coverage (QoC) of a WSN via deploying a limited number of sensors on a 3D surface by utilizing a probabilistic sensing model and the Bresenham's line of sight (LOS) algorithm. In addition, the method followed in this paper is novel to the literature and the performance of the proposed algorithm is compared with the Delaunay Triangulation (DT) method as well as a standard genetic algorithm based method and the results reveal that the proposed method is a more powerful and more successful method for sensor deployment on 3D terrains. PMID:22666078

Efficient RNA structure comparison algorithms.

PubMed

Arslan, Abdullah N; Anandan, Jithendar; Fry, Eric; Monschke, Keith; Ganneboina, Nitin; Bowerman, Jason

2017-12-01

Recently proposed relative addressing-based ([Formula: see text]) RNA secondary structure representation has important features by which an RNA structure database can be stored into a suffix array. A fast substructure search algorithm has been proposed based on binary search on this suffix array. Using this substructure search algorithm, we present a fast algorithm that finds the largest common substructure of given multiple RNA structures in [Formula: see text] format. The multiple RNA structure comparison problem is NP-hard in its general formulation. We introduced a new problem for comparing multiple RNA structures. This problem has more strict similarity definition and objective, and we propose an algorithm that solves this problem efficiently. We also develop another comparison algorithm that iteratively calls this algorithm to locate nonoverlapping large common substructures in compared RNAs. With the new resulting tools, we improved the RNASSAC website (linked from http://faculty.tamuc.edu/aarslan ). This website now also includes two drawing tools: one specialized for preparing RNA substructures that can be used as input by the search tool, and another one for automatically drawing the entire RNA structure from a given structure sequence.

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.

Power Control and Optimization of Photovoltaic and Wind Energy Conversion Systems

NASA Astrophysics Data System (ADS)

Ghaffari, Azad

Power map and Maximum Power Point (MPP) of Photovoltaic (PV) and Wind Energy Conversion Systems (WECS) highly depend on system dynamics and environmental parameters, e.g., solar irradiance, temperature, and wind speed. Power optimization algorithms for PV systems and WECS are collectively known as Maximum Power Point Tracking (MPPT) algorithm. Gradient-based Extremum Seeking (ES), as a non-model-based MPPT algorithm, governs the system to its peak point on the steepest descent curve regardless of changes of the system dynamics and variations of the environmental parameters. Since the power map shape defines the gradient vector, then a close estimate of the power map shape is needed to create user assignable transients in the MPPT algorithm. The Hessian gives a precise estimate of the power map in a neighborhood around the MPP. The estimate of the inverse of the Hessian in combination with the estimate of the gradient vector are the key parts to implement the Newton-based ES algorithm. Hence, we generate an estimate of the Hessian using our proposed perturbation matrix. Also, we introduce a dynamic estimator to calculate the inverse of the Hessian which is an essential part of our algorithm. We present various simulations and experiments on the micro-converter PV systems to verify the validity of our proposed algorithm. The ES scheme can also be used in combination with other control algorithms to achieve desired closed-loop performance. The WECS dynamics is slow which causes even slower response time for the MPPT based on the ES. Hence, we present a control scheme, extended from Field-Oriented Control (FOC), in combination with feedback linearization to reduce the convergence time of the closed-loop system. Furthermore, the nonlinear control prevents magnetic saturation of the stator of the Induction Generator (IG). The proposed control algorithm in combination with the ES guarantees the closed-loop system robustness with respect to high level parameter uncertainty in the IG dynamics. The simulation results verify the effectiveness of the proposed algorithm.

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

NASA Astrophysics Data System (ADS)

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

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

A learning approach to the bandwidth multicolouring problem

NASA Astrophysics Data System (ADS)

Akbari Torkestani, Javad

2016-05-01

In this article, a generalisation of the vertex colouring problem known as bandwidth multicolouring problem (BMCP), in which a set of colours is assigned to each vertex such that the difference between the colours, assigned to each vertex and its neighbours, is by no means less than a predefined threshold, is considered. It is shown that the proposed method can be applied to solve the bandwidth colouring problem (BCP) as well. BMCP is known to be NP-hard in graph theory, and so a large number of approximation solutions, as well as exact algorithms, have been proposed to solve it. In this article, two learning automata-based approximation algorithms are proposed for estimating a near-optimal solution to the BMCP. We show, for the first proposed algorithm, that by choosing a proper learning rate, the algorithm finds the optimal solution with a probability close enough to unity. Moreover, we compute the worst-case time complexity of the first algorithm for finding a 1/(1-ɛ) optimal solution to the given problem. The main advantage of this method is that a trade-off between the running time of algorithm and the colour set size (colouring optimality) can be made, by a proper choice of the learning rate also. Finally, it is shown that the running time of the proposed algorithm is independent of the graph size, and so it is a scalable algorithm for large graphs. The second proposed algorithm is compared with some well-known colouring algorithms and the results show the efficiency of the proposed algorithm in terms of the colour set size and running time of algorithm.

On an adaptive preconditioned Crank-Nicolson MCMC algorithm for infinite dimensional Bayesian inference

NASA Astrophysics Data System (ADS)

Hu, Zixi; Yao, Zhewei; Li, Jinglai

2017-03-01

Many scientific and engineering problems require to perform Bayesian inference for unknowns of infinite dimension. In such problems, many standard Markov Chain Monte Carlo (MCMC) algorithms become arbitrary slow under the mesh refinement, which is referred to as being dimension dependent. To this end, a family of dimensional independent MCMC algorithms, known as the preconditioned Crank-Nicolson (pCN) methods, were proposed to sample the infinite dimensional parameters. In this work we develop an adaptive version of the pCN algorithm, where the covariance operator of the proposal distribution is adjusted based on sampling history to improve the simulation efficiency. We show that the proposed algorithm satisfies an important ergodicity condition under some mild assumptions. Finally we provide numerical examples to demonstrate the performance of the proposed method.

Integrating image quality in 2nu-SVM biometric match score fusion.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2007-10-01

This paper proposes an intelligent 2nu-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image. The match score and the corresponding quality score of an image are fused using 2nu-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms.

A shifted hyperbolic augmented Lagrangian-based artificial fish two-swarm algorithm with guaranteed convergence for constrained global optimization

NASA Astrophysics Data System (ADS)

Rocha, Ana Maria A. C.; Costa, M. Fernanda P.; Fernandes, Edite M. G. P.

2016-12-01

This article presents a shifted hyperbolic penalty function and proposes an augmented Lagrangian-based algorithm for non-convex constrained global optimization problems. Convergence to an ?-global minimizer is proved. At each iteration k, the algorithm requires the ?-global minimization of a bound constrained optimization subproblem, where ?. The subproblems are solved by a stochastic population-based metaheuristic that relies on the artificial fish swarm paradigm and a two-swarm strategy. To enhance the speed of convergence, the algorithm invokes the Nelder-Mead local search with a dynamically defined probability. Numerical experiments with benchmark functions and engineering design problems are presented. The results show that the proposed shifted hyperbolic augmented Lagrangian compares favorably with other deterministic and stochastic penalty-based methods.

A grammar-based semantic similarity algorithm for natural language sentences.

PubMed

Lee, Ming Che; Chang, Jia Wei; Hsieh, Tung Cheng

2014-01-01

This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to "artificial language", such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontology-based approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure.

Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, S.; Bez, H. E.

2007-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithm's robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach.

A new image encryption algorithm based on the fractional-order hyperchaotic Lorenz system

NASA Astrophysics Data System (ADS)

Wang, Zhen; Huang, Xia; Li, Yu-Xia; Song, Xiao-Na

2013-01-01

We propose a new image encryption algorithm on the basis of the fractional-order hyperchaotic Lorenz system. While in the process of generating a key stream, the system parameters and the derivative order are embedded in the proposed algorithm to enhance the security. Such an algorithm is detailed in terms of security analyses, including correlation analysis, information entropy analysis, run statistic analysis, mean-variance gray value analysis, and key sensitivity analysis. The experimental results demonstrate that the proposed image encryption scheme has the advantages of large key space and high security for practical image encryption.

Uncovering the overlapping community structure of complex networks by maximal cliques

NASA Astrophysics Data System (ADS)

Li, Junqiu; Wang, Xingyuan; Cui, Yaozu

2014-12-01

In this paper, a unique algorithm is proposed to detect overlapping communities in the un-weighted and weighted networks with considerable accuracy. The maximal cliques, overlapping vertex, bridge vertex and isolated vertex are introduced. First, all the maximal cliques are extracted by the algorithm based on the deep and bread searching. Then two maximal cliques can be merged into a larger sub-graph by some given rules. In addition, the proposed algorithm successfully finds overlapping vertices and bridge vertices between communities. Experimental results using some real-world networks data show that the performance of the proposed algorithm is satisfactory.

Impulsive noise removal from color video with morphological filtering

NASA Astrophysics Data System (ADS)

Ruchay, Alexey; Kober, Vitaly

2017-09-01

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

3 x 3 free-space optical router based on crossbar network and its control algorithm

NASA Astrophysics Data System (ADS)

Hou, Peipei; Sun, Jianfeng; Yu, Zhou; Lu, Wei; Wang, Lijuan; Liu, Liren

2015-08-01

A 3 × 3 free-space optical router, which comprises optical switches and polarizing beam splitter (PBS) and based on crossbar network, is proposed in this paper. A control algorithm for the 3 × 3 free-space optical router is also developed to achieve rapid control without rearrangement. In order to test the performance of the network based on 3 × 3 free-space optical router and that of the algorithm developed for the optical router, experiments are designed. The experiment results show that the interconnection network based on the 3 × 3 free-space optical router has low cross talk, fast connection speed. Under the control of the algorithm developed, a non-block and real free interconnection network is obtained based on the 3 × 3 free-space optical router we proposed.

Parameterization of typhoon-induced ocean cooling using temperature equation and machine learning algorithms: an example of typhoon Soulik (2013)

NASA Astrophysics Data System (ADS)

Wei, Jun; Jiang, Guo-Qing; Liu, Xin

2017-09-01

This study proposed three algorithms that can potentially be used to provide sea surface temperature (SST) conditions for typhoon prediction models. Different from traditional data assimilation approaches, which provide prescribed initial/boundary conditions, our proposed algorithms aim to resolve a flow-dependent SST feedback between growing typhoons and oceans in the future time. Two of these algorithms are based on linear temperature equations (TE-based), and the other is based on an innovative technique involving machine learning (ML-based). The algorithms are then implemented into a Weather Research and Forecasting model for the simulation of typhoon to assess their effectiveness, and the results show significant improvement in simulated storm intensities by including ocean cooling feedback. The TE-based algorithm I considers wind-induced ocean vertical mixing and upwelling processes only, and thus obtained a synoptic and relatively smooth sea surface temperature cooling. The TE-based algorithm II incorporates not only typhoon winds but also ocean information, and thus resolves more cooling features. The ML-based algorithm is based on a neural network, consisting of multiple layers of input variables and neurons, and produces the best estimate of the cooling structure, in terms of its amplitude and position. Sensitivity analysis indicated that the typhoon-induced ocean cooling is a nonlinear process involving interactions of multiple atmospheric and oceanic variables. Therefore, with an appropriate selection of input variables and neuron sizes, the ML-based algorithm appears to be more efficient in prognosing the typhoon-induced ocean cooling and in predicting typhoon intensity than those algorithms based on linear regression methods.

Escalated convergent artificial bee colony

NASA Astrophysics Data System (ADS)

Jadon, Shimpi Singh; Bansal, Jagdish Chand; Tiwari, Ritu

2016-03-01

Artificial bee colony (ABC) optimisation algorithm is a recent, fast and easy-to-implement population-based meta heuristic for optimisation. ABC has been proved a rival algorithm with some popular swarm intelligence-based algorithms such as particle swarm optimisation, firefly algorithm and ant colony optimisation. The solution search equation of ABC is influenced by a random quantity which helps its search process in exploration at the cost of exploitation. In order to find a fast convergent behaviour of ABC while exploitation capability is maintained, in this paper basic ABC is modified in two ways. First, to improve exploitation capability, two local search strategies, namely classical unidimensional local search and levy flight random walk-based local search are incorporated with ABC. Furthermore, a new solution search strategy, namely stochastic diffusion scout search is proposed and incorporated into the scout bee phase to provide more chance to abandon solution to improve itself. Efficiency of the proposed algorithm is tested on 20 benchmark test functions of different complexities and characteristics. Results are very promising and they prove it to be a competitive algorithm in the field of swarm intelligence-based algorithms.

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

PubMed Central

Hammad, Karim; El Bakly, Ahmed M.

2018-01-01

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

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

PubMed

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

2018-01-01

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

Wiener filter preprocessing for OFDM systems in the presence of both nonstationary and stationary phase noises

NASA Astrophysics Data System (ADS)

Zhong, Ke; Lei, Xia; Li, Shaoqian

2013-12-01

Statistics-based intercarrier interference (ICI) mitigation algorithm is proposed for orthogonal frequency division multiplexing systems in presence of both nonstationary and stationary phase noises. By utilizing the statistics of phase noise, which can be obtained from measurements or data sheets, a Wiener filter preprocessing algorithm for ICI mitigation is proposed. The proposed algorithm can be regarded as a performance-improving technique for the previous researches on phase noise cancelation. Simulation results show that the proposed algorithm can effectively mitigate ICI and lower the error floor, and therefore significantly improve the performances of previous researches on phase noise cancelation, especially in the presence of severe phase noise.

Multitarget mixture reduction algorithm with incorporated target existence recursions

NASA Astrophysics Data System (ADS)

Ristic, Branko; Arulampalam, Sanjeev

2000-07-01

The paper derives a deferred logic data association algorithm based on the mixture reduction approach originally due to Salmond [SPIE vol.1305, 1990]. The novelty of the proposed algorithm provides the recursive formulae for both data association and target existence (confidence) estimation, thus allowing automatic track initiation and termination. T he track initiation performance of the proposed filter is investigated by computer simulations. It is observed that at moderately high levels of clutter density the proposed filter initiates tracks more reliably than its corresponding PDA filter. An extension of the proposed filter to the multi-target case is also presented. In addition, the paper compares the track maintenance performance of the MR algorithm with an MHT implementation.

Analyzing gene expression time-courses based on multi-resolution shape mixture model.

PubMed

Li, Ying; He, Ye; Zhang, Yu

2016-11-01

Biological processes actually are a dynamic molecular process over time. Time course gene expression experiments provide opportunities to explore patterns of gene expression change over a time and understand the dynamic behavior of gene expression, which is crucial for study on development and progression of biology and disease. Analysis of the gene expression time-course profiles has not been fully exploited so far. It is still a challenge problem. We propose a novel shape-based mixture model clustering method for gene expression time-course profiles to explore the significant gene groups. Based on multi-resolution fractal features and mixture clustering model, we proposed a multi-resolution shape mixture model algorithm. Multi-resolution fractal features is computed by wavelet decomposition, which explore patterns of change over time of gene expression at different resolution. Our proposed multi-resolution shape mixture model algorithm is a probabilistic framework which offers a more natural and robust way of clustering time-course gene expression. We assessed the performance of our proposed algorithm using yeast time-course gene expression profiles compared with several popular clustering methods for gene expression profiles. The grouped genes identified by different methods are evaluated by enrichment analysis of biological pathways and known protein-protein interactions from experiment evidence. The grouped genes identified by our proposed algorithm have more strong biological significance. A novel multi-resolution shape mixture model algorithm based on multi-resolution fractal features is proposed. Our proposed model provides a novel horizons and an alternative tool for visualization and analysis of time-course gene expression profiles. The R and Matlab program is available upon the request. Copyright © 2016 Elsevier Inc. All rights reserved.

A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts.

PubMed

Ye, Yalan; He, Wenwen; Cheng, Yunfei; Huang, Wenxia; Zhang, Zhilin

2017-02-16

The estimation of heart rate (HR) based on wearable devices is of interest in fitness. Photoplethysmography (PPG) is a promising approach to estimate HR due to low cost; however, it is easily corrupted by motion artifacts (MA). In this work, a robust approach based on random forest is proposed for accurately estimating HR from the photoplethysmography signal contaminated by intense motion artifacts, consisting of two stages. Stage 1 proposes a hybrid method to effectively remove MA with a low computation complexity, where two MA removal algorithms are combined by an accurate binary decision algorithm whose aim is to decide whether or not to adopt the second MA removal algorithm. Stage 2 proposes a random forest-based spectral peak-tracking algorithm, whose aim is to locate the spectral peak corresponding to HR, formulating the problem of spectral peak tracking into a pattern classification problem. Experiments on the PPG datasets including 22 subjects used in the 2015 IEEE Signal Processing Cup showed that the proposed approach achieved the average absolute error of 1.65 beats per minute (BPM) on the 22 PPG datasets. Compared to state-of-the-art approaches, the proposed approach has better accuracy and robustness to intense motion artifacts, indicating its potential use in wearable sensors for health monitoring and fitness tracking.

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems.

PubMed

Kazemi, Mahdi; Arefi, Mohammad Mehdi

2017-03-01

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Hue-preserving and saturation-improved color histogram equalization algorithm.

PubMed

Song, Ki Sun; Kang, Hee; Kang, Moon Gi

2016-06-01

In this paper, an algorithm is proposed to improve contrast and saturation without color degradation. The local histogram equalization (HE) method offers better performance than the global HE method, whereas the local HE method sometimes produces undesirable results due to the block-based processing. The proposed contrast-enhancement (CE) algorithm reflects the characteristics of the global HE method in the local HE method to avoid the artifacts, while global and local contrasts are enhanced. There are two ways to apply the proposed CE algorithm to color images. One is luminance processing methods, and the other one is each channel processing methods. However, these ways incur excessive or reduced saturation and color degradation problems. The proposed algorithm solves these problems by using channel adaptive equalization and similarity of ratios between the channels. Experimental results show that the proposed algorithm enhances contrast and saturation while preserving the hue and producing better performance than existing methods in terms of objective evaluation metrics.

Fuzzy Algorithm for the Detection of Incidents in the Transport System

ERIC Educational Resources Information Center

Nikolaev, Andrey B.; Sapego, Yuliya S.; Jakubovich, Anatolij N.; Berner, Leonid I.; Stroganov, Victor Yu.

2016-01-01

In the paper it's proposed an algorithm for the management of traffic incidents, aimed at minimizing the impact of incidents on the road traffic in general. The proposed algorithm is based on the theory of fuzzy sets and provides identification of accidents, as well as the adoption of appropriate measures to address them as soon as possible. A…

New spatial diversity equalizer based on PLL

NASA Astrophysics Data System (ADS)

Rao, Wei

2011-10-01

A new Spatial Diversity Equalizer (SDE) based on phase-locked loop (PLL) is proposed to overcome the inter-symbol interference (ISI) and phase rotations simultaneously in the digital communication system. The proposed SDE consists of equal gain combining technique based on a famous blind equalization algorithm constant modulus algorithm (CMA) and a PLL. Compared with conventional SDE, the proposed SDE has not only faster convergence rate and lower residual error but also the ability to recover carrier phase rotation. The efficiency of the method is proved by computer simulation.

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

PubMed

Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

2012-04-01

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

Secured Hash Based Burst Header Authentication Design for Optical Burst Switched Networks

NASA Astrophysics Data System (ADS)

Balamurugan, A. M.; Sivasubramanian, A.; Parvathavarthini, B.

2017-12-01

The optical burst switching (OBS) is a promising technology that could meet the fast growing network demand. They are featured with the ability to meet the bandwidth requirement of applications that demand intensive bandwidth. OBS proves to be a satisfactory technology to tackle the huge bandwidth constraints, but suffers from security vulnerabilities. The objective of this proposed work is to design a faster and efficient burst header authentication algorithm for core nodes. There are two important key features in this work, viz., header encryption and authentication. Since the burst header is an important in optical burst switched network, it has to be encrypted; otherwise it is be prone to attack. The proposed MD5&RC4-4S based burst header authentication algorithm runs 20.75 ns faster than the conventional algorithms. The modification suggested in the proposed RC4-4S algorithm gives a better security and solves the correlation problems between the publicly known outputs during key generation phase. The modified MD5 recommended in this work provides 7.81 % better avalanche effect than the conventional algorithm. The device utilization result also shows the suitability of the proposed algorithm for header authentication in real time applications.

A novel image encryption algorithm based on synchronized random bit generated in cascade-coupled chaotic semiconductor ring lasers

NASA Astrophysics Data System (ADS)

Li, Jiafu; Xiang, Shuiying; Wang, Haoning; Gong, Junkai; Wen, Aijun

2018-03-01

In this paper, a novel image encryption algorithm based on synchronization of physical random bit generated in a cascade-coupled semiconductor ring lasers (CCSRL) system is proposed, and the security analysis is performed. In both transmitter and receiver parts, the CCSRL system is a master-slave configuration consisting of a master semiconductor ring laser (M-SRL) with cross-feedback and a solitary SRL (S-SRL). The proposed image encryption algorithm includes image preprocessing based on conventional chaotic maps, pixel confusion based on control matrix extracted from physical random bit, and pixel diffusion based on random bit stream extracted from physical random bit. Firstly, the preprocessing method is used to eliminate the correlation between adjacent pixels. Secondly, physical random bit with verified randomness is generated based on chaos in the CCSRL system, and is used to simultaneously generate the control matrix and random bit stream. Finally, the control matrix and random bit stream are used for the encryption algorithm in order to change the position and the values of pixels, respectively. Simulation results and security analysis demonstrate that the proposed algorithm is effective and able to resist various typical attacks, and thus is an excellent candidate for secure image communication application.

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection.

PubMed

Hu, Weiming; Gao, Jun; Wang, Yanguo; Wu, Ou; Maybank, Stephen

2014-01-01

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

A proposed adaptive step size perturbation and observation maximum power point tracking algorithm based on photovoltaic system modeling

NASA Astrophysics Data System (ADS)

Huang, Yu

Solar energy becomes one of the major alternative renewable energy options for its huge abundance and accessibility. Due to the intermittent nature, the high demand of Maximum Power Point Tracking (MPPT) techniques exists when a Photovoltaic (PV) system is used to extract energy from the sunlight. This thesis proposed an advanced Perturbation and Observation (P&O) algorithm aiming for relatively practical circumstances. Firstly, a practical PV system model is studied with determining the series and shunt resistances which are neglected in some research. Moreover, in this proposed algorithm, the duty ratio of a boost DC-DC converter is the object of the perturbation deploying input impedance conversion to achieve working voltage adjustment. Based on the control strategy, the adaptive duty ratio step size P&O algorithm is proposed with major modifications made for sharp insolation change as well as low insolation scenarios. Matlab/Simulink simulation for PV model, boost converter control strategy and various MPPT process is conducted step by step. The proposed adaptive P&O algorithm is validated by the simulation results and detail analysis of sharp insolation changes, low insolation condition and continuous insolation variation.

Improving KPCA Online Extraction by Orthonormalization in the Feature Space.

PubMed

Souza Filho, Joao B O; Diniz, Paulo S R

2018-04-01

Recently, some online kernel principal component analysis (KPCA) techniques based on the generalized Hebbian algorithm (GHA) were proposed for use in large data sets, defining kernel components using concise dictionaries automatically extracted from data. This brief proposes two new online KPCA extraction algorithms, exploiting orthogonalized versions of the GHA rule. In both the cases, the orthogonalization of kernel components is achieved by the inclusion of some low complexity additional steps to the kernel Hebbian algorithm, thus not substantially affecting the computational cost of the algorithm. Results show improved convergence speed and accuracy of components extracted by the proposed methods, as compared with the state-of-the-art online KPCA extraction algorithms.

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

PubMed

Kumar, Manjeet; Rawat, Tarun Kumar; Aggarwal, Apoorva

2017-03-01

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Enhanced object-based tracking algorithm for convective rain storms and cells

NASA Astrophysics Data System (ADS)

Muñoz, Carlos; Wang, Li-Pen; Willems, Patrick

2018-03-01

This paper proposes a new object-based storm tracking algorithm, based upon TITAN (Thunderstorm Identification, Tracking, Analysis and Nowcasting). TITAN is a widely-used convective storm tracking algorithm but has limitations in handling small-scale yet high-intensity storm entities due to its single-threshold identification approach. It also has difficulties to effectively track fast-moving storms because of the employed matching approach that largely relies on the overlapping areas between successive storm entities. To address these deficiencies, a number of modifications are proposed and tested in this paper. These include a two-stage multi-threshold storm identification, a new formulation for characterizing storm's physical features, and an enhanced matching technique in synergy with an optical-flow storm field tracker, as well as, according to these modifications, a more complex merging and splitting scheme. High-resolution (5-min and 529-m) radar reflectivity data for 18 storm events over Belgium are used to calibrate and evaluate the algorithm. The performance of the proposed algorithm is compared with that of the original TITAN. The results suggest that the proposed algorithm can better isolate and match convective rainfall entities, as well as to provide more reliable and detailed motion estimates. Furthermore, the improvement is found to be more significant for higher rainfall intensities. The new algorithm has the potential to serve as a basis for further applications, such as storm nowcasting and long-term stochastic spatial and temporal rainfall generation.

A maximally stable extremal region based scene text localization method

NASA Astrophysics Data System (ADS)

Xiao, Chengqiu; Ji, Lixin; Gao, Chao; Li, Shaomei

2015-07-01

Text localization in natural scene images is an important prerequisite for many content-based image analysis tasks. This paper proposes a novel text localization algorithm. Firstly, a fast pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSER) as basic character candidates. Secondly, these candidates are filtered by using the properties of fitting ellipse and the distribution properties of characters to exclude most non-characters. Finally, a new extremal regions projection merging algorithm is designed to group character candidates into words. Experimental results show that the proposed method has an advantage in speed and achieve relatively high precision and recall rates than the latest published algorithms.

Fast parallel approach for 2-D DHT-based real-valued discrete Gabor transform.

PubMed

Tao, Liang; Kwan, Hon Keung

2009-12-01

Two-dimensional fast Gabor transform algorithms are useful for real-time applications due to the high computational complexity of the traditional 2-D complex-valued discrete Gabor transform (CDGT). This paper presents two block time-recursive algorithms for 2-D DHT-based real-valued discrete Gabor transform (RDGT) and its inverse transform and develops a fast parallel approach for the implementation of the two algorithms. The computational complexity of the proposed parallel approach is analyzed and compared with that of the existing 2-D CDGT algorithms. The results indicate that the proposed parallel approach is attractive for real time image processing.

Distributed Function Mining for Gene Expression Programming Based on Fast Reduction.

PubMed

Deng, Song; Yue, Dong; Yang, Le-chan; Fu, Xiong; Feng, Ya-zhou

2016-01-01

For high-dimensional and massive data sets, traditional centralized gene expression programming (GEP) or improved algorithms lead to increased run-time and decreased prediction accuracy. To solve this problem, this paper proposes a new improved algorithm called distributed function mining for gene expression programming based on fast reduction (DFMGEP-FR). In DFMGEP-FR, fast attribution reduction in binary search algorithms (FAR-BSA) is proposed to quickly find the optimal attribution set, and the function consistency replacement algorithm is given to solve integration of the local function model. Thorough comparative experiments for DFMGEP-FR, centralized GEP and the parallel gene expression programming algorithm based on simulated annealing (parallel GEPSA) are included in this paper. For the waveform, mushroom, connect-4 and musk datasets, the comparative results show that the average time-consumption of DFMGEP-FR drops by 89.09%%, 88.85%, 85.79% and 93.06%, respectively, in contrast to centralized GEP and by 12.5%, 8.42%, 9.62% and 13.75%, respectively, compared with parallel GEPSA. Six well-studied UCI test data sets demonstrate the efficiency and capability of our proposed DFMGEP-FR algorithm for distributed function mining.

Local-search based prediction of medical image registration error

NASA Astrophysics Data System (ADS)

Saygili, Görkem

2018-03-01

Medical image registration is a crucial task in many different medical imaging applications. Hence, considerable amount of work has been published recently that aim to predict the error in a registration without any human effort. If provided, these error predictions can be used as a feedback to the registration algorithm to further improve its performance. Recent methods generally start with extracting image-based and deformation-based features, then apply feature pooling and finally train a Random Forest (RF) regressor to predict the real registration error. Image-based features can be calculated after applying a single registration but provide limited accuracy whereas deformation-based features such as variation of deformation vector field may require up to 20 registrations which is a considerably high time-consuming task. This paper proposes to use extracted features from a local search algorithm as image-based features to estimate the error of a registration. The proposed method comprises a local search algorithm to find corresponding voxels between registered image pairs and based on the amount of shifts and stereo confidence measures, it predicts the amount of registration error in millimetres densely using a RF regressor. Compared to other algorithms in the literature, the proposed algorithm does not require multiple registrations, can be efficiently implemented on a Graphical Processing Unit (GPU) and can still provide highly accurate error predictions in existence of large registration error. Experimental results with real registrations on a public dataset indicate a substantially high accuracy achieved by using features from the local search algorithm.

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.

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

PubMed Central

Gao, Wei; Zhang, Ya; Wang, Jianguo

2014-01-01

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

A weighted information criterion for multiple minor components and its adaptive extraction algorithms.

PubMed

Gao, Yingbin; Kong, Xiangyu; Zhang, Huihui; Hou, Li'an

2017-05-01

Minor component (MC) plays an important role in signal processing and data analysis, so it is a valuable work to develop MC extraction algorithms. Based on the concepts of weighted subspace and optimum theory, a weighted information criterion is proposed for searching the optimum solution of a linear neural network. This information criterion exhibits a unique global minimum attained if and only if the state matrix is composed of the desired MCs of an autocorrelation matrix of an input signal. By using gradient ascent method and recursive least square (RLS) method, two algorithms are developed for multiple MCs extraction. The global convergences of the proposed algorithms are also analyzed by the Lyapunov method. The proposed algorithms can extract the multiple MCs in parallel and has advantage in dealing with high dimension matrices. Since the weighted matrix does not require an accurate value, it facilitates the system design of the proposed algorithms for practical applications. The speed and computation advantages of the proposed algorithms are verified through simulations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

PubMed Central

Xue, Ming; Yang, Hua; Zheng, Shibao; Zhou, Yi; Yu, Zhenghua

2014-01-01

To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks. PMID:24549252

A spectral, quasi-cylindrical and dispersion-free Particle-In-Cell algorithm

DOE PAGES

Lehe, Remi; Kirchen, Manuel; Andriyash, Igor A.; ...

2016-02-17

We propose a spectral Particle-In-Cell (PIC) algorithm that is based on the combination of a Hankel transform and a Fourier transform. For physical problems that have close-to-cylindrical symmetry, this algorithm can be much faster than full 3D PIC algorithms. In addition, unlike standard finite-difference PIC codes, the proposed algorithm is free of spurious numerical dispersion, in vacuum. This algorithm is benchmarked in several situations that are of interest for laser-plasma interactions. These benchmarks show that it avoids a number of numerical artifacts, that would otherwise affect the physics in a standard PIC algorithm - including the zero-order numerical Cherenkov effect.

Multirate-based fast parallel algorithms for 2-D DHT-based real-valued discrete Gabor transform.

PubMed

Tao, Liang; Kwan, Hon Keung

2012-07-01

Novel algorithms for the multirate and fast parallel implementation of the 2-D discrete Hartley transform (DHT)-based real-valued discrete Gabor transform (RDGT) and its inverse transform are presented in this paper. A 2-D multirate-based analysis convolver bank is designed for the 2-D RDGT, and a 2-D multirate-based synthesis convolver bank is designed for the 2-D inverse RDGT. The parallel channels in each of the two convolver banks have a unified structure and can apply the 2-D fast DHT algorithm to speed up their computations. The computational complexity of each parallel channel is low and is independent of the Gabor oversampling rate. All the 2-D RDGT coefficients of an image are computed in parallel during the analysis process and can be reconstructed in parallel during the synthesis process. The computational complexity and time of the proposed parallel algorithms are analyzed and compared with those of the existing fastest algorithms for 2-D discrete Gabor transforms. The results indicate that the proposed algorithms are the fastest, which make them attractive for real-time image processing.

Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models.

PubMed

Yuan, Gonglin; Duan, Xiabin; Liu, Wenjie; Wang, Xiaoliang; Cui, Zengru; Sheng, Zhou

2015-01-01

Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1) βk ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations.

Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models

PubMed Central

Yuan, Gonglin; Duan, Xiabin; Liu, Wenjie; Wang, Xiaoliang; Cui, Zengru; Sheng, Zhou

2015-01-01

Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1)β k ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations. PMID:26502409

Wavelet compression of multichannel ECG data by enhanced set partitioning in hierarchical trees algorithm.

PubMed

Sharifahmadian, Ershad

2006-01-01

The set partitioning in hierarchical trees (SPIHT) algorithm is very effective and computationally simple technique for image and signal compression. Here the author modified the algorithm which provides even better performance than the SPIHT algorithm. The enhanced set partitioning in hierarchical trees (ESPIHT) algorithm has performance faster than the SPIHT algorithm. In addition, the proposed algorithm reduces the number of bits in a bit stream which is stored or transmitted. I applied it to compression of multichannel ECG data. Also, I presented a specific procedure based on the modified algorithm for more efficient compression of multichannel ECG data. This method employed on selected records from the MIT-BIH arrhythmia database. According to experiments, the proposed method attained the significant results regarding compression of multichannel ECG data. Furthermore, in order to compress one signal which is stored for a long time, the proposed multichannel compression method can be utilized efficiently.

A proximity algorithm accelerated by Gauss-Seidel iterations for L1/TV denoising models

NASA Astrophysics Data System (ADS)

Li, Qia; Micchelli, Charles A.; Shen, Lixin; Xu, Yuesheng

2012-09-01

Our goal in this paper is to improve the computational performance of the proximity algorithms for the L1/TV denoising model. This leads us to a new characterization of all solutions to the L1/TV model via fixed-point equations expressed in terms of the proximity operators. Based upon this observation we develop an algorithm for solving the model and establish its convergence. Furthermore, we demonstrate that the proposed algorithm can be accelerated through the use of the componentwise Gauss-Seidel iteration so that the CPU time consumed is significantly reduced. Numerical experiments using the proposed algorithm for impulsive noise removal are included, with a comparison to three recently developed algorithms. The numerical results show that while the proposed algorithm enjoys a high quality of the restored images, as the other three known algorithms do, it performs significantly better in terms of computational efficiency measured in the CPU time consumed.

An Improved WiFi Indoor Positioning Algorithm by Weighted Fusion

PubMed Central

Ma, Rui; Guo, Qiang; Hu, Changzhen; Xue, Jingfeng

2015-01-01

The rapid development of mobile Internet has offered the opportunity for WiFi indoor positioning to come under the spotlight due to its low cost. However, nowadays the accuracy of WiFi indoor positioning cannot meet the demands of practical applications. To solve this problem, this paper proposes an improved WiFi indoor positioning algorithm by weighted fusion. The proposed algorithm is based on traditional location fingerprinting algorithms and consists of two stages: the offline acquisition and the online positioning. The offline acquisition process selects optimal parameters to complete the signal acquisition, and it forms a database of fingerprints by error classification and handling. To further improve the accuracy of positioning, the online positioning process first uses a pre-match method to select the candidate fingerprints to shorten the positioning time. After that, it uses the improved Euclidean distance and the improved joint probability to calculate two intermediate results, and further calculates the final result from these two intermediate results by weighted fusion. The improved Euclidean distance introduces the standard deviation of WiFi signal strength to smooth the WiFi signal fluctuation and the improved joint probability introduces the logarithmic calculation to reduce the difference between probability values. Comparing the proposed algorithm, the Euclidean distance based WKNN algorithm and the joint probability algorithm, the experimental results indicate that the proposed algorithm has higher positioning accuracy. PMID:26334278

An Improved WiFi Indoor Positioning Algorithm by Weighted Fusion.

PubMed

Ma, Rui; Guo, Qiang; Hu, Changzhen; Xue, Jingfeng

2015-08-31

The rapid development of mobile Internet has offered the opportunity for WiFi indoor positioning to come under the spotlight due to its low cost. However, nowadays the accuracy of WiFi indoor positioning cannot meet the demands of practical applications. To solve this problem, this paper proposes an improved WiFi indoor positioning algorithm by weighted fusion. The proposed algorithm is based on traditional location fingerprinting algorithms and consists of two stages: the offline acquisition and the online positioning. The offline acquisition process selects optimal parameters to complete the signal acquisition, and it forms a database of fingerprints by error classification and handling. To further improve the accuracy of positioning, the online positioning process first uses a pre-match method to select the candidate fingerprints to shorten the positioning time. After that, it uses the improved Euclidean distance and the improved joint probability to calculate two intermediate results, and further calculates the final result from these two intermediate results by weighted fusion. The improved Euclidean distance introduces the standard deviation of WiFi signal strength to smooth the WiFi signal fluctuation and the improved joint probability introduces the logarithmic calculation to reduce the difference between probability values. Comparing the proposed algorithm, the Euclidean distance based WKNN algorithm and the joint probability algorithm, the experimental results indicate that the proposed algorithm has higher positioning accuracy.

An optimized digital watermarking algorithm in wavelet domain based on differential evolution for color image.

PubMed

Cui, Xinchun; Niu, Yuying; Zheng, Xiangwei; Han, Yingshuai

2018-01-01

In this paper, a new color watermarking algorithm based on differential evolution is proposed. A color host image is first converted from RGB space to YIQ space, which is more suitable for the human visual system. Then, apply three-level discrete wavelet transformation to luminance component Y and generate four different frequency sub-bands. After that, perform singular value decomposition on these sub-bands. In the watermark embedding process, apply discrete wavelet transformation to a watermark image after the scrambling encryption processing. Our new algorithm uses differential evolution algorithm with adaptive optimization to choose the right scaling factors. Experimental results show that the proposed algorithm has a better performance in terms of invisibility and robustness.

Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation

PubMed Central

Shen, Liang; Huang, Xiaotao; Fan, Chongyi

2018-01-01

Particle Swarm Optimization (PSO) is a well-known meta-heuristic. It has been widely used in both research and engineering fields. However, the original PSO generally suffers from premature convergence, especially in multimodal problems. In this paper, we propose a double-group PSO (DG-PSO) algorithm to improve the performance. DG-PSO uses a double-group based evolution framework. The individuals are divided into two groups: an advantaged group and a disadvantaged group. The advantaged group works according to the original PSO, while two new strategies are developed for the disadvantaged group. The proposed algorithm is firstly evaluated by comparing it with the other five popular PSO variants and two state-of-the-art meta-heuristics on various benchmark functions. The results demonstrate that DG-PSO shows a remarkable performance in terms of accuracy and stability. Then, we apply DG-PSO to multilevel thresholding for remote sensing image segmentation. The results show that the proposed algorithm outperforms five other popular algorithms in meta-heuristic-based multilevel thresholding, which verifies the effectiveness of the proposed algorithm. PMID:29724013

Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation

PubMed Central

Liu, Yang; Liu, Junfei

2016-01-01

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

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

PubMed

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

2016-01-01

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

Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation.

PubMed

Shen, Liang; Huang, Xiaotao; Fan, Chongyi

2018-05-01

Particle Swarm Optimization (PSO) is a well-known meta-heuristic. It has been widely used in both research and engineering fields. However, the original PSO generally suffers from premature convergence, especially in multimodal problems. In this paper, we propose a double-group PSO (DG-PSO) algorithm to improve the performance. DG-PSO uses a double-group based evolution framework. The individuals are divided into two groups: an advantaged group and a disadvantaged group. The advantaged group works according to the original PSO, while two new strategies are developed for the disadvantaged group. The proposed algorithm is firstly evaluated by comparing it with the other five popular PSO variants and two state-of-the-art meta-heuristics on various benchmark functions. The results demonstrate that DG-PSO shows a remarkable performance in terms of accuracy and stability. Then, we apply DG-PSO to multilevel thresholding for remote sensing image segmentation. The results show that the proposed algorithm outperforms five other popular algorithms in meta-heuristic-based multilevel thresholding, which verifies the effectiveness of the proposed algorithm.

Optimal fractional order PID design via Tabu Search based algorithm.

PubMed

Ateş, Abdullah; Yeroglu, Celaleddin

2016-01-01

This paper presents an optimization method based on the Tabu Search Algorithm (TSA) to design a Fractional-Order Proportional-Integral-Derivative (FOPID) controller. All parameter computations of the FOPID employ random initial conditions, using the proposed optimization method. Illustrative examples demonstrate the performance of the proposed FOPID controller design method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar

PubMed Central

Shin, Young Hoon; Seo, Jiwon

2016-01-01

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker’s vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing. PMID:27801867

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar.

PubMed

Shin, Young Hoon; Seo, Jiwon

2016-10-29

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker's vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing.

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

PubMed Central

Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem. PMID:28369096

Reliable and Efficient Parallel Processing Algorithms and Architectures for Modern Signal Processing. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Liu, Kuojuey Ray

1990-01-01

Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered.

A new kernel-based fuzzy level set method for automated segmentation of medical images in the presence of intensity inhomogeneity.

PubMed

Rastgarpour, Maryam; Shanbehzadeh, Jamshid

2014-01-01

Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

T-L Plane Abstraction-Based Energy-Efficient Real-Time Scheduling for Multi-Core Wireless Sensors

PubMed Central

Kim, Youngmin; Lee, Ki-Seong; Pham, Ngoc-Son; Lee, Sun-Ro; Lee, Chan-Gun

2016-01-01

Energy efficiency is considered as a critical requirement for wireless sensor networks. As more wireless sensor nodes are equipped with multi-cores, there are emerging needs for energy-efficient real-time scheduling algorithms. The T-L plane-based scheme is known to be an optimal global scheduling technique for periodic real-time tasks on multi-cores. Unfortunately, there has been a scarcity of studies on extending T-L plane-based scheduling algorithms to exploit energy-saving techniques. In this paper, we propose a new T-L plane-based algorithm enabling energy-efficient real-time scheduling on multi-core sensor nodes with dynamic power management (DPM). Our approach addresses the overhead of processor mode transitions and reduces fragmentations of the idle time, which are inherent in T-L plane-based algorithms. Our experimental results show the effectiveness of the proposed algorithm compared to other energy-aware scheduling methods on T-L plane abstraction. PMID:27399722

Cluster-Based Multipolling Sequencing Algorithm for Collecting RFID Data in Wireless LANs

NASA Astrophysics Data System (ADS)

Choi, Woo-Yong; Chatterjee, Mainak

2015-03-01

With the growing use of RFID (Radio Frequency Identification), it is becoming important to devise ways to read RFID tags in real time. Access points (APs) of IEEE 802.11-based wireless Local Area Networks (LANs) are being integrated with RFID networks that can efficiently collect real-time RFID data. Several schemes, such as multipolling methods based on the dynamic search algorithm and random sequencing, have been proposed. However, as the number of RFID readers associated with an AP increases, it becomes difficult for the dynamic search algorithm to derive the multipolling sequence in real time. Though multipolling methods can eliminate the polling overhead, we still need to enhance the performance of the multipolling methods based on random sequencing. To that extent, we propose a real-time cluster-based multipolling sequencing algorithm that drastically eliminates more than 90% of the polling overhead, particularly so when the dynamic search algorithm fails to derive the multipolling sequence in real time.

An Efficient Optimization Method for Solving Unsupervised Data Classification Problems.

PubMed

Shabanzadeh, Parvaneh; Yusof, Rubiyah

2015-01-01

Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.

A rate-constrained fast full-search algorithm based on block sum pyramid.

PubMed

Song, Byung Cheol; Chun, Kang-Wook; Ra, Jong Beom

2005-03-01

This paper presents a fast full-search algorithm (FSA) for rate-constrained motion estimation. The proposed algorithm, which is based on the block sum pyramid frame structure, successively eliminates unnecessary search positions according to rate-constrained criterion. This algorithm provides the identical estimation performance to a conventional FSA having rate constraint, while achieving considerable reduction in computation.

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.

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-01-01

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

Inertial sensor-based smoother for gait analysis.

PubMed

Suh, Young Soo

2014-12-17

An off-line smoother algorithm is proposed to estimate foot motion using an inertial sensor unit (three-axis gyroscopes and accelerometers) attached to a shoe. The smoother gives more accurate foot motion estimation than filter-based algorithms by using all of the sensor data instead of using the current sensor data. The algorithm consists of two parts. In the first part, a Kalman filter is used to obtain initial foot motion estimation. In the second part, the error in the initial estimation is compensated using a smoother, where the problem is formulated in the quadratic optimization problem. An efficient solution of the quadratic optimization problem is given using the sparse structure. Through experiments, it is shown that the proposed algorithm can estimate foot motion more accurately than a filter-based algorithm with reasonable computation time. In particular, there is significant improvement in the foot motion estimation when the foot is moving off the floor: the z-axis position error squared sum (total time: 3.47 s) when the foot is in the air is 0.0807 m2 (Kalman filter) and 0.0020 m2 (the proposed smoother).

Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

PubMed Central

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895

A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks

PubMed Central

Zhang, Shukui; Chen, Hao; Zhu, Qiaoming

2014-01-01

The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic. PMID:25136690

Density-independent algorithm for sensing moisture content of sawdust based on reflection measurements

USDA-ARS?s Scientific Manuscript database

A density-independent algorithm for moisture content determination in sawdust, based on a one-port reflection measurement technique is proposed for the first time. Performance of this algorithm is demonstrated through measurement of the dielectric properties of sawdust with an open-ended haft-mode s...

A hybrid CS-SA intelligent approach to solve uncertain dynamic facility layout problems considering dependency of demands

NASA Astrophysics Data System (ADS)

Moslemipour, Ghorbanali

2018-07-01

This paper aims at proposing a quadratic assignment-based mathematical model to deal with the stochastic dynamic facility layout problem. In this problem, product demands are assumed to be dependent normally distributed random variables with known probability density function and covariance that change from period to period at random. To solve the proposed model, a novel hybrid intelligent algorithm is proposed by combining the simulated annealing and clonal selection algorithms. The proposed model and the hybrid algorithm are verified and validated using design of experiment and benchmark methods. The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time points of view. Besides, the proposed model can be used in both of the stochastic and deterministic situations.

On Deployment of Multiple Base Stations for Energy-Efficient Communication in Wireless Sensor Networks

DOE PAGES

Lin, Yunyue; Wu, Qishi; Cai, Xiaoshan; ...

2010-01-01

Data transmission from sensor nodes to a base station or a sink node often incurs significant energy consumption, which critically affects network lifetime. We generalize and solve the problem of deploying multiple base stations to maximize network lifetime in terms of two different metrics under one-hop and multihop communication models. In the one-hop communication model, the sensors far away from base stations always deplete their energy much faster than others. We propose an optimal solution and a heuristic approach based on the minimal enclosing circle algorithm to deploy a base station at the geometric center of each cluster. In themore » multihop communication model, both base station location and data routing mechanism need to be considered in maximizing network lifetime. We propose an iterative algorithm based on rigorous mathematical derivations and use linear programming to compute the optimal routing paths for data transmission. Simulation results show the distinguished performance of the proposed deployment algorithms in maximizing network lifetime.« less

Discrete size optimization of steel trusses using a refined big bang-big crunch algorithm

NASA Astrophysics Data System (ADS)

Hasançebi, O.; Kazemzadeh Azad, S.

2014-01-01

This article presents a methodology that provides a method for design optimization of steel truss structures based on a refined big bang-big crunch (BB-BC) algorithm. It is shown that a standard formulation of the BB-BC algorithm occasionally falls short of producing acceptable solutions to problems from discrete size optimum design of steel trusses. A reformulation of the algorithm is proposed and implemented for design optimization of various discrete truss structures according to American Institute of Steel Construction Allowable Stress Design (AISC-ASD) specifications. Furthermore, the performance of the proposed BB-BC algorithm is compared to its standard version as well as other well-known metaheuristic techniques. The numerical results confirm the efficiency of the proposed algorithm in practical design optimization of truss structures.

Interior tomography in microscopic CT with image reconstruction constrained by full field of view scan at low spatial resolution

NASA Astrophysics Data System (ADS)

Luo, Shouhua; Shen, Tao; Sun, Yi; Li, Jing; Li, Guang; Tang, Xiangyang

2018-04-01

In high resolution (microscopic) CT applications, the scan field of view should cover the entire specimen or sample to allow complete data acquisition and image reconstruction. However, truncation may occur in projection data and results in artifacts in reconstructed images. In this study, we propose a low resolution image constrained reconstruction algorithm (LRICR) for interior tomography in microscopic CT at high resolution. In general, the multi-resolution acquisition based methods can be employed to solve the data truncation problem if the project data acquired at low resolution are utilized to fill up the truncated projection data acquired at high resolution. However, most existing methods place quite strict restrictions on the data acquisition geometry, which greatly limits their utility in practice. In the proposed LRICR algorithm, full and partial data acquisition (scan) at low and high resolutions, respectively, are carried out. Using the image reconstructed from sparse projection data acquired at low resolution as the prior, a microscopic image at high resolution is reconstructed from the truncated projection data acquired at high resolution. Two synthesized digital phantoms, a raw bamboo culm and a specimen of mouse femur, were utilized to evaluate and verify performance of the proposed LRICR algorithm. Compared with the conventional TV minimization based algorithm and the multi-resolution scout-reconstruction algorithm, the proposed LRICR algorithm shows significant improvement in reduction of the artifacts caused by data truncation, providing a practical solution for high quality and reliable interior tomography in microscopic CT applications. The proposed LRICR algorithm outperforms the multi-resolution scout-reconstruction method and the TV minimization based reconstruction for interior tomography in microscopic CT.

Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm

NASA Astrophysics Data System (ADS)

Elahi, Sana; kaleem, Muhammad; Omer, Hammad

2018-01-01

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k -space. This paper introduces an improved iterative algorithm based on p -thresholding technique for CS-MRI image reconstruction. The use of p -thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p -thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p -thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.

Joint estimation of 2D-DOA and frequency based on space-time matrix and conformal array.

PubMed

Wan, Liang-Tian; Liu, Lu-Tao; Si, Wei-Jian; Tian, Zuo-Xi

2013-01-01

Each element in the conformal array has a different pattern, which leads to the performance deterioration of the conventional high resolution direction-of-arrival (DOA) algorithms. In this paper, a joint frequency and two-dimension DOA (2D-DOA) estimation algorithm for conformal array are proposed. The delay correlation function is used to suppress noise. Both spatial and time sampling are utilized to construct the spatial-time matrix. The frequency and 2D-DOA estimation are accomplished based on parallel factor (PARAFAC) analysis without spectral peak searching and parameter pairing. The proposed algorithm needs only four guiding elements with precise positions to estimate frequency and 2D-DOA. Other instrumental elements can be arranged flexibly on the surface of the carrier. Simulation results demonstrate the effectiveness of the proposed algorithm.

Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map

PubMed Central

Tan, Yihua; Li, Qingyun; Li, Yansheng; Tian, Jinwen

2015-01-01

This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradient distribution detector is used to obtain a gradient textural saliency map in the favor of the candidate regions. In addition, the final targets will be detected by segmenting the saliency map using CFAR-type algorithm. The real high-resolution airborne SAR image data is used to verify the proposed algorithm. The results demonstrate that this algorithm can detect aircraft targets quickly and accurately, and decrease the false alarm rate. PMID:26378543

Reliability-based design optimization of reinforced concrete structures including soil-structure interaction using a discrete gravitational search algorithm and a proposed metamodel

NASA Astrophysics Data System (ADS)

Khatibinia, M.; Salajegheh, E.; Salajegheh, J.; Fadaee, M. J.

2013-10-01

A new discrete gravitational search algorithm (DGSA) and a metamodelling framework are introduced for reliability-based design optimization (RBDO) of reinforced concrete structures. The RBDO of structures with soil-structure interaction (SSI) effects is investigated in accordance with performance-based design. The proposed DGSA is based on the standard gravitational search algorithm (GSA) to optimize the structural cost under deterministic and probabilistic constraints. The Monte-Carlo simulation (MCS) method is considered as the most reliable method for estimating the probabilities of reliability. In order to reduce the computational time of MCS, the proposed metamodelling framework is employed to predict the responses of the SSI system in the RBDO procedure. The metamodel consists of a weighted least squares support vector machine (WLS-SVM) and a wavelet kernel function, which is called WWLS-SVM. Numerical results demonstrate the efficiency and computational advantages of DGSA and the proposed metamodel for RBDO of reinforced concrete structures.

[Design and Implementation of Image Interpolation and Color Correction for Ultra-thin Electronic Endoscope on FPGA].

PubMed

Luo, Qiang; Yan, Zhuangzhi; Gu, Dongxing; Cao, Lei

This paper proposed an image interpolation algorithm based on bilinear interpolation and a color correction algorithm based on polynomial regression on FPGA, which focused on the limited number of imaging pixels and color distortion of the ultra-thin electronic endoscope. Simulation experiment results showed that the proposed algorithm realized the real-time display of 1280 x 720@60Hz HD video, and using the X-rite color checker as standard colors, the average color difference was reduced about 30% comparing with that before color correction.

An Optimal Seed Based Compression Algorithm for DNA Sequences

PubMed Central

Gopalakrishnan, Gopakumar; Karunakaran, Muralikrishnan

2016-01-01

This paper proposes a seed based lossless compression algorithm to compress a DNA sequence which uses a substitution method that is similar to the LempelZiv compression scheme. The proposed method exploits the repetition structures that are inherent in DNA sequences by creating an offline dictionary which contains all such repeats along with the details of mismatches. By ensuring that only promising mismatches are allowed, the method achieves a compression ratio that is at par or better than the existing lossless DNA sequence compression algorithms. PMID:27555868

LTI system order reduction approach based on asymptotical equivalence and the Co-operation of biology-related algorithms

NASA Astrophysics Data System (ADS)

Ryzhikov, I. S.; Semenkin, E. S.; Akhmedova, Sh A.

2017-02-01

A novel order reduction method for linear time invariant systems is described. The method is based on reducing the initial problem to an optimization one, using the proposed model representation, and solving the problem with an efficient optimization algorithm. The proposed method of determining the model allows all the parameters of the model with lower order to be identified and by definition, provides the model with the required steady-state. As a powerful optimization tool, the meta-heuristic Co-Operation of Biology-Related Algorithms was used. Experimental results proved that the proposed approach outperforms other approaches and that the reduced order model achieves a high level of accuracy.

Beam-steering efficiency optimization method based on a rapid-search algorithm for liquid crystal optical phased array.

PubMed

Xiao, Feng; Kong, Lingjiang; Chen, Jian

2017-06-01

A rapid-search algorithm to improve the beam-steering efficiency for a liquid crystal optical phased array was proposed and experimentally demonstrated in this paper. This proposed algorithm, in which the value of steering efficiency is taken as the objective function and the controlling voltage codes are considered as the optimization variables, consisted of a detection stage and a construction stage. It optimized the steering efficiency in the detection stage and adjusted its search direction adaptively in the construction stage to avoid getting caught in a wrong search space. Simulations had been conducted to compare the proposed algorithm with the widely used pattern-search algorithm using criteria of convergence rate and optimized efficiency. Beam-steering optimization experiments had been performed to verify the validity of the proposed method.

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems

PubMed Central

de Paula, Lauro C. M.; Soares, Anderson S.; de Lima, Telma W.; Delbem, Alexandre C. B.; Coelho, Clarimar J.; Filho, Arlindo R. G.

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation. PMID:25493625

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems.

PubMed

de Paula, Lauro C M; Soares, Anderson S; de Lima, Telma W; Delbem, Alexandre C B; Coelho, Clarimar J; Filho, Arlindo R G

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.

Micro-Doppler Signal Time-Frequency Algorithm Based on STFRFT.

PubMed

Pang, Cunsuo; Han, Yan; Hou, Huiling; Liu, Shengheng; Zhang, Nan

2016-09-24

This paper proposes a time-frequency algorithm based on short-time fractional order Fourier transformation (STFRFT) for identification of a complicated movement targets. This algorithm, consisting of a STFRFT order-changing and quick selection method, is effective in reducing the computation load. A multi-order STFRFT time-frequency algorithm is also developed that makes use of the time-frequency feature of each micro-Doppler component signal. This algorithm improves the estimation accuracy of time-frequency curve fitting through multi-order matching. Finally, experiment data were used to demonstrate STFRFT's performance in micro-Doppler time-frequency analysis. The results validated the higher estimate accuracy of the proposed algorithm. It may be applied to an LFM (Linear frequency modulated) pulse radar, SAR (Synthetic aperture radar), or ISAR (Inverse synthetic aperture radar), for improving the probability of target recognition.

Hierarchical Dirichlet process model for gene expression clustering

PubMed Central

2013-01-01

Clustering is an important data processing tool for interpreting microarray data and genomic network inference. In this article, we propose a clustering algorithm based on the hierarchical Dirichlet processes (HDP). The HDP clustering introduces a hierarchical structure in the statistical model which captures the hierarchical features prevalent in biological data such as the gene express data. We develop a Gibbs sampling algorithm based on the Chinese restaurant metaphor for the HDP clustering. We apply the proposed HDP algorithm to both regulatory network segmentation and gene expression clustering. The HDP algorithm is shown to outperform several popular clustering algorithms by revealing the underlying hierarchical structure of the data. For the yeast cell cycle data, we compare the HDP result to the standard result and show that the HDP algorithm provides more information and reduces the unnecessary clustering fragments. PMID:23587447

Adaptive optics image restoration algorithm based on wavefront reconstruction and adaptive total variation method

NASA Astrophysics Data System (ADS)

Li, Dongming; Zhang, Lijuan; Wang, Ting; Liu, Huan; Yang, Jinhua; Chen, Guifen

2016-11-01

To improve the adaptive optics (AO) image's quality, we study the AO image restoration algorithm based on wavefront reconstruction technology and adaptive total variation (TV) method in this paper. Firstly, the wavefront reconstruction using Zernike polynomial is used for initial estimated for the point spread function (PSF). Then, we develop our proposed iterative solutions for AO images restoration, addressing the joint deconvolution issue. The image restoration experiments are performed to verify the image restoration effect of our proposed algorithm. The experimental results show that, compared with the RL-IBD algorithm and Wiener-IBD algorithm, we can see that GMG measures (for real AO image) from our algorithm are increased by 36.92%, and 27.44% respectively, and the computation time are decreased by 7.2%, and 3.4% respectively, and its estimation accuracy is significantly improved.

SDIA: A dynamic situation driven information fusion algorithm for cloud environment

NASA Astrophysics Data System (ADS)

Guo, Shuhang; Wang, Tong; Wang, Jian

2017-09-01

Information fusion is an important issue in information integration domain. In order to form an extensive information fusion technology under the complex and diverse situations, a new information fusion algorithm is proposed. Firstly, a fuzzy evaluation model of tag utility was proposed that can be used to count the tag entropy. Secondly, a ubiquitous situation tag tree model is proposed to define multidimensional structure of information situation. Thirdly, the similarity matching between the situation models is classified into three types: the tree inclusion, the tree embedding, and the tree compatibility. Next, in order to reduce the time complexity of the tree compatible matching algorithm, a fast and ordered tree matching algorithm is proposed based on the node entropy, which is used to support the information fusion by ubiquitous situation. Since the algorithm revolve from the graph theory of disordered tree matching algorithm, it can improve the information fusion present recall rate and precision rate in the situation. The information fusion algorithm is compared with the star and the random tree matching algorithm, and the difference between the three algorithms is analyzed in the view of isomorphism, which proves the innovation and applicability of the algorithm.

A new effective operator for the hybrid algorithm for solving global optimisation problems

NASA Astrophysics Data System (ADS)

Duc, Le Anh; Li, Kenli; Nguyen, Tien Trong; Yen, Vu Minh; Truong, Tung Khac

2018-04-01

Hybrid algorithms have been recently used to solve complex single-objective optimisation problems. The ultimate goal is to find an optimised global solution by using these algorithms. Based on the existing algorithms (HP_CRO, PSO, RCCRO), this study proposes a new hybrid algorithm called MPC (Mean-PSO-CRO), which utilises a new Mean-Search Operator. By employing this new operator, the proposed algorithm improves the search ability on areas of the solution space that the other operators of previous algorithms do not explore. Specifically, the Mean-Search Operator helps find the better solutions in comparison with other algorithms. Moreover, the authors have proposed two parameters for balancing local and global search and between various types of local search, as well. In addition, three versions of this operator, which use different constraints, are introduced. The experimental results on 23 benchmark functions, which are used in previous works, show that our framework can find better optimal or close-to-optimal solutions with faster convergence speed for most of the benchmark functions, especially the high-dimensional functions. Thus, the proposed algorithm is more effective in solving single-objective optimisation problems than the other existing algorithms.

Walking Distance Estimation Using Walking Canes with Inertial Sensors

PubMed Central

Suh, Young Soo

2018-01-01

A walking distance estimation algorithm for cane users is proposed using an inertial sensor unit attached to various positions on the cane. A standard inertial navigation algorithm using an indirect Kalman filter was applied to update the velocity and position of the cane during movement. For quadripod canes, a standard zero-velocity measurement-updating method is proposed. For standard canes, a velocity-updating method based on an inverted pendulum model is proposed. The proposed algorithms were verified by three walking experiments with two different types of canes and different positions of the sensor module. PMID:29342971

Hybrid radiosity-SP3 equation based bioluminescence tomography reconstruction for turbid medium with low- and non-scattering regions

NASA Astrophysics Data System (ADS)

Chen, Xueli; Zhang, Qitan; Yang, Defu; Liang, Jimin

2014-01-01

To provide an ideal solution for a specific problem of gastric cancer detection in which low-scattering regions simultaneously existed with both the non- and high-scattering regions, a novel hybrid radiosity-SP3 equation based reconstruction algorithm for bioluminescence tomography was proposed in this paper. In the algorithm, the third-order simplified spherical harmonics approximation (SP3) was combined with the radiosity equation to describe the bioluminescent light propagation in tissues, which provided acceptable accuracy for the turbid medium with both low- and non-scattering regions. The performance of the algorithm was evaluated with digital mouse based simulations and a gastric cancer-bearing mouse based in situ experiment. Primary results demonstrated the feasibility and superiority of the proposed algorithm for the turbid medium with low- and non-scattering regions.

A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences

PubMed Central

Chang, Jia Wei; Hsieh, Tung Cheng

2014-01-01

This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to “artificial language”, such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontology-based approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure. PMID:24982952

Object Detection Based on Template Matching through Use of Best-So-Far ABC

PubMed Central

2014-01-01

Best-so-far ABC is a modified version of the artificial bee colony (ABC) algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI) algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution. PMID:24812556

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

PRESS-based EFOR algorithm for the dynamic parametrical modeling of nonlinear MDOF systems

NASA Astrophysics Data System (ADS)

Liu, Haopeng; Zhu, Yunpeng; Luo, Zhong; Han, Qingkai

2017-09-01

In response to the identification problem concerning multi-degree of freedom (MDOF) nonlinear systems, this study presents the extended forward orthogonal regression (EFOR) based on predicted residual sums of squares (PRESS) to construct a nonlinear dynamic parametrical model. The proposed parametrical model is based on the non-linear autoregressive with exogenous inputs (NARX) model and aims to explicitly reveal the physical design parameters of the system. The PRESS-based EFOR algorithm is proposed to identify such a model for MDOF systems. By using the algorithm, we built a common-structured model based on the fundamental concept of evaluating its generalization capability through cross-validation. The resulting model aims to prevent over-fitting with poor generalization performance caused by the average error reduction ratio (AERR)-based EFOR algorithm. Then, a functional relationship is established between the coefficients of the terms and the design parameters of the unified model. Moreover, a 5-DOF nonlinear system is taken as a case to illustrate the modeling of the proposed algorithm. Finally, a dynamic parametrical model of a cantilever beam is constructed from experimental data. Results indicate that the dynamic parametrical model of nonlinear systems, which depends on the PRESS-based EFOR, can accurately predict the output response, thus providing a theoretical basis for the optimal design of modeling methods for MDOF nonlinear systems.

Efficient L1 regularization-based reconstruction for fluorescent molecular tomography using restarted nonlinear conjugate gradient.

PubMed

Shi, Junwei; Zhang, Bin; Liu, Fei; Luo, Jianwen; Bai, Jing

2013-09-15

For the ill-posed fluorescent molecular tomography (FMT) inverse problem, the L1 regularization can protect the high-frequency information like edges while effectively reduce the image noise. However, the state-of-the-art L1 regularization-based algorithms for FMT reconstruction are expensive in memory, especially for large-scale problems. An efficient L1 regularization-based reconstruction algorithm based on nonlinear conjugate gradient with restarted strategy is proposed to increase the computational speed with low memory consumption. The reconstruction results from phantom experiments demonstrate that the proposed algorithm can obtain high spatial resolution and high signal-to-noise ratio, as well as high localization accuracy for fluorescence targets.

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

PubMed

Le, Duc-Hau

2015-01-01

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

A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection.

PubMed

Guvensan, M Amac; Dusun, Burak; Can, Baris; Turkmen, H Irem

2017-12-30

Transportation planning and solutions have an enormous impact on city life. To minimize the transport duration, urban planners should understand and elaborate the mobility of a city. Thus, researchers look toward monitoring people's daily activities including transportation types and duration by taking advantage of individual's smartphones. This paper introduces a novel segment-based transport mode detection architecture in order to improve the results of traditional classification algorithms in the literature. The proposed post-processing algorithm, namely the Healing algorithm, aims to correct the misclassification results of machine learning-based solutions. Our real-life test results show that the Healing algorithm could achieve up to 40% improvement of the classification results. As a result, the implemented mobile application could predict eight classes including stationary, walking, car, bus, tram, train, metro and ferry with a success rate of 95% thanks to the proposed multi-tier architecture and Healing algorithm.

Handwritten digits recognition based on immune network

NASA Astrophysics Data System (ADS)

Li, Yangyang; Wu, Yunhui; Jiao, Lc; Wu, Jianshe

2011-11-01

With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.

A constraint optimization based virtual network mapping method

NASA Astrophysics Data System (ADS)

Li, Xiaoling; Guo, Changguo; Wang, Huaimin; Li, Zhendong; Yang, Zhiwen

2013-03-01

Virtual network mapping problem, maps different virtual networks onto the substrate network is an extremely challenging work. This paper proposes a constraint optimization based mapping method for solving virtual network mapping problem. This method divides the problem into two phases, node mapping phase and link mapping phase, which are all NP-hard problems. Node mapping algorithm and link mapping algorithm are proposed for solving node mapping phase and link mapping phase, respectively. Node mapping algorithm adopts the thinking of greedy algorithm, mainly considers two factors, available resources which are supplied by the nodes and distance between the nodes. Link mapping algorithm is based on the result of node mapping phase, adopts the thinking of distributed constraint optimization method, which can guarantee to obtain the optimal mapping with the minimum network cost. Finally, simulation experiments are used to validate the method, and results show that the method performs very well.

Geometrical pose and structural estimation from a single image for automatic inspection of filter components

NASA Astrophysics Data System (ADS)

Liu, Yonghuai; Rodrigues, Marcos A.

2000-03-01

This paper describes research on the application of machine vision techniques to a real time automatic inspection task of air filter components in a manufacturing line. A novel calibration algorithm is proposed based on a special camera setup where defective items would show a large calibration error. The algorithm makes full use of rigid constraints derived from the analysis of geometrical properties of reflected correspondence vectors which have been synthesized into a single coordinate frame and provides a closed form solution to the estimation of all parameters. For a comparative study of performance, we also developed another algorithm based on this special camera setup using epipolar geometry. A number of experiments using synthetic data have shown that the proposed algorithm is generally more accurate and robust than the epipolar geometry based algorithm and that the geometric properties of reflected correspondence vectors provide effective constraints to the calibration of rigid body transformations.

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.

A Collaborative Recommend Algorithm Based on Bipartite Community

PubMed Central

Fu, Yuchen; Liu, Quan; Cui, Zhiming

2014-01-01

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

A Distributed Algorithm for Economic Dispatch Over Time-Varying Directed Networks With Delays

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

Yang, Tao; Lu, Jie; Wu, Di

In power system operation, economic dispatch problem (EDP) is designed to minimize the total generation cost while meeting the demand and satisfying generator capacity limits. This paper proposes an algorithm based on the gradient-push method to solve the EDP in a distributed manner over communication networks potentially with time-varying topologies and communication delays. It has been shown that the proposed method is guaranteed to solve the EDP if the time-varying directed communication network is uniformly jointly strongly connected. Moreover, the proposed algorithm is also able to handle arbitrarily large but bounded time delays on communication links. Numerical simulations are usedmore » to illustrate and validate the proposed algorithm.« less

Cooperative Optimal Coordination for Distributed Energy Resources

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

Yang, Tao; Wu, Di; Ren, Wei

In this paper, we consider the optimal coordination problem for distributed energy resources (DERs) including distributed generators and energy storage devices. We propose an algorithm based on the push-sum and gradient method to optimally coordinate storage devices and distributed generators in a distributed manner. In the proposed algorithm, each DER only maintains a set of variables and updates them through information exchange with a few neighbors over a time-varying directed communication network. We show that the proposed distributed algorithm solves the optimal DER coordination problem if the time-varying directed communication network is uniformly jointly strongly connected, which is a mildmore » condition on the connectivity of communication topologies. The proposed distributed algorithm is illustrated and validated by numerical simulations.« less

CMOS analogue amplifier circuits optimisation using hybrid backtracking search algorithm with differential evolution

NASA Astrophysics Data System (ADS)

Mallick, S.; Kar, R.; Mandal, D.; Ghoshal, S. P.

2016-07-01

This paper proposes a novel hybrid optimisation algorithm which combines the recently proposed evolutionary algorithm Backtracking Search Algorithm (BSA) with another widely accepted evolutionary algorithm, namely, Differential Evolution (DE). The proposed algorithm called BSA-DE is employed for the optimal designs of two commonly used analogue circuits, namely Complementary Metal Oxide Semiconductor (CMOS) differential amplifier circuit with current mirror load and CMOS two-stage operational amplifier (op-amp) circuit. BSA has a simple structure that is effective, fast and capable of solving multimodal problems. DE is a stochastic, population-based heuristic approach, having the capability to solve global optimisation problems. In this paper, the transistors' sizes are optimised using the proposed BSA-DE to minimise the areas occupied by the circuits and to improve the performances of the circuits. The simulation results justify the superiority of BSA-DE in global convergence properties and fine tuning ability, and prove it to be a promising candidate for the optimal design of the analogue CMOS amplifier circuits. The simulation results obtained for both the amplifier circuits prove the effectiveness of the proposed BSA-DE-based approach over DE, harmony search (HS), artificial bee colony (ABC) and PSO in terms of convergence speed, design specifications and design parameters of the optimal design of the analogue CMOS amplifier circuits. It is shown that BSA-DE-based design technique for each amplifier circuit yields the least MOS transistor area, and each designed circuit is shown to have the best performance parameters such as gain, power dissipation, etc., as compared with those of other recently reported literature.

Low complexity Reed-Solomon-based low-density parity-check design for software defined optical transmission system based on adaptive puncturing decoding algorithm

NASA Astrophysics Data System (ADS)

Pan, Xiaolong; Liu, Bo; Zheng, Jianglong; Tian, Qinghua

2016-08-01

We propose and demonstrate a low complexity Reed-Solomon-based low-density parity-check (RS-LDPC) code with adaptive puncturing decoding algorithm for elastic optical transmission system. Partial received codes and the relevant column in parity-check matrix can be punctured to reduce the calculation complexity by adaptive parity-check matrix during decoding process. The results show that the complexity of the proposed decoding algorithm is reduced by 30% compared with the regular RS-LDPC system. The optimized code rate of the RS-LDPC code can be obtained after five times iteration.

Channel estimation based on quantized MMP for FDD massive MIMO downlink

NASA Astrophysics Data System (ADS)

Guo, Yao-ting; Wang, Bing-he; Qu, Yi; Cai, Hua-jie

2016-10-01

In this paper, we consider channel estimation for Massive MIMO systems operating in frequency division duplexing mode. By exploiting the sparsity of propagation paths in Massive MIMO channel, we develop a compressed sensing(CS) based channel estimator which can reduce the pilot overhead. As compared with the conventional least squares (LS) and linear minimum mean square error(LMMSE) estimation, the proposed algorithm is based on the quantized multipath matching pursuit - MMP - reduced the pilot overhead and performs better than other CS algorithms. The simulation results demonstrate the advantage of the proposed algorithm over various existing methods including the LS, LMMSE, CoSaMP and conventional MMP estimators.

Web page sorting algorithm based on query keyword distance relation

NASA Astrophysics Data System (ADS)

Yang, Han; Cui, Hong Gang; Tang, Hao

2017-08-01

In order to optimize the problem of page sorting, according to the search keywords in the web page in the relationship between the characteristics of the proposed query keywords clustering ideas. And it is converted into the degree of aggregation of the search keywords in the web page. Based on the PageRank algorithm, the clustering degree factor of the query keyword is added to make it possible to participate in the quantitative calculation. This paper proposes an improved algorithm for PageRank based on the distance relation between search keywords. The experimental results show the feasibility and effectiveness of the method.

Hybrid EEG--Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal.

PubMed

Mannan, Malik M Naeem; Kim, Shinjung; Jeong, Myung Yung; Kamran, M Ahmad

2016-02-19

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data.

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

NASA Astrophysics Data System (ADS)

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

2015-05-01

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

A soft decoding algorithm and hardware implementation for the visual prosthesis based on high order soft demodulation.

PubMed

Yang, Yuan; Quan, Nannan; Bu, Jingjing; Li, Xueping; Yu, Ningmei

2016-09-26

High order modulation and demodulation technology can solve the frequency requirement between the wireless energy transmission and data communication. In order to achieve reliable wireless data communication based on high order modulation technology for visual prosthesis, this work proposed a Reed-Solomon (RS) error correcting code (ECC) circuit on the basis of differential amplitude and phase shift keying (DAPSK) soft demodulation. Firstly, recognizing the weakness of the traditional DAPSK soft demodulation algorithm based on division that is complex for hardware implementation, an improved phase soft demodulation algorithm for visual prosthesis to reduce the hardware complexity is put forward. Based on this new algorithm, an improved RS soft decoding method is hence proposed. In this new decoding method, the combination of Chase algorithm and hard decoding algorithms is used to achieve soft decoding. In order to meet the requirements of implantable visual prosthesis, the method to calculate reliability of symbol-level based on multiplication of bit reliability is derived, which reduces the testing vectors number of Chase algorithm. The proposed algorithms are verified by MATLAB simulation and FPGA experimental results. During MATLAB simulation, the biological channel attenuation property model is added into the ECC circuit. The data rate is 8 Mbps in the MATLAB simulation and FPGA experiments. MATLAB simulation results show that the improved phase soft demodulation algorithm proposed in this paper saves hardware resources without losing bit error rate (BER) performance. Compared with the traditional demodulation circuit, the coding gain of the ECC circuit has been improved by about 3 dB under the same BER of [Formula: see text]. The FPGA experimental results show that under the condition of data demodulation error with wireless coils 3 cm away, the system can correct it. The greater the distance, the higher the BER. Then we use a bit error rate analyzer to measure BER of the demodulation circuit and the RS ECC circuit with different distance of two coils. And the experimental results show that the RS ECC circuit has about an order of magnitude lower BER than the demodulation circuit when under the same coils distance. Therefore, the RS ECC circuit has more higher reliability of the communication in the system. The improved phase soft demodulation algorithm and soft decoding algorithm proposed in this paper enables data communication that is more reliable than other demodulation system, which also provide a significant reference for further study to the visual prosthesis system.

Fault Tolerant Frequent Pattern Mining

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

Shohdy, Sameh; Vishnu, Abhinav; Agrawal, Gagan

FP-Growth algorithm is a Frequent Pattern Mining (FPM) algorithm that has been extensively used to study correlations and patterns in large scale datasets. While several researchers have designed distributed memory FP-Growth algorithms, it is pivotal to consider fault tolerant FP-Growth, which can address the increasing fault rates in large scale systems. In this work, we propose a novel parallel, algorithm-level fault-tolerant FP-Growth algorithm. We leverage algorithmic properties and MPI advanced features to guarantee an O(1) space complexity, achieved by using the dataset memory space itself for checkpointing. We also propose a recovery algorithm that can use in-memory and disk-based checkpointing,more » though in many cases the recovery can be completed without any disk access, and incurring no memory overhead for checkpointing. We evaluate our FT algorithm on a large scale InfiniBand cluster with several large datasets using up to 2K cores. Our evaluation demonstrates excellent efficiency for checkpointing and recovery in comparison to the disk-based approach. We have also observed 20x average speed-up in comparison to Spark, establishing that a well designed algorithm can easily outperform a solution based on a general fault-tolerant programming model.« less

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

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

NASA Astrophysics Data System (ADS)

Janaki Sathya, D.; Geetha, K.

2017-12-01

Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR images, the systems need to improve both the sensitivity and specificity of DCE-MR image interpretation in order to be successful for clinical use. A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper. The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network. An optimal set of features based on the statistical textural features is presented. The investigational outcomes of the proposed suspicious lesion classifier algorithm therefore confirm that the resulting classifier performs better than other such classifiers reported in the literature. Therefore this classifier demonstrates that the improvement in both the sensitivity and specificity are possible through automated image analysis.

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.

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

NASA Astrophysics Data System (ADS)

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

2015-02-01

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

A Differential Evolution-Based Routing Algorithm for Environmental Monitoring Wireless Sensor Networks

PubMed Central

Li, Xiaofang; Xu, Lizhong; Wang, Huibin; Song, Jie; Yang, Simon X.

2010-01-01

The traditional Low Energy Adaptive Cluster Hierarchy (LEACH) routing protocol is a clustering-based protocol. The uneven selection of cluster heads results in premature death of cluster heads and premature blind nodes inside the clusters, thus reducing the overall lifetime of the network. With a full consideration of information on energy and distance distribution of neighboring nodes inside the clusters, this paper proposes a new routing algorithm based on differential evolution (DE) to improve the LEACH routing protocol. To meet the requirements of monitoring applications in outdoor environments such as the meteorological, hydrological and wetland ecological environments, the proposed algorithm uses the simple and fast search features of DE to optimize the multi-objective selection of cluster heads and prevent blind nodes for improved energy efficiency and system stability. Simulation results show that the proposed new LEACH routing algorithm has better performance, effectively extends the working lifetime of the system, and improves the quality of the wireless sensor networks. PMID:22219670

A Collaborative Secure Localization Algorithm Based on Trust Model in Underwater Wireless Sensor Networks

PubMed Central

Han, Guangjie; Liu, Li; Jiang, Jinfang; Shu, Lei; Rodrigues, Joel J.P.C.

2016-01-01

Localization is one of the hottest research topics in Underwater Wireless Sensor Networks (UWSNs), since many important applications of UWSNs, e.g., event sensing, target tracking and monitoring, require location information of sensor nodes. Nowadays, a large number of localization algorithms have been proposed for UWSNs. How to improve location accuracy are well studied. However, few of them take location reliability or security into consideration. In this paper, we propose a Collaborative Secure Localization algorithm based on Trust model (CSLT) for UWSNs to ensure location security. Based on the trust model, the secure localization process can be divided into the following five sub-processes: trust evaluation of anchor nodes, initial localization of unknown nodes, trust evaluation of reference nodes, selection of reference node, and secondary localization of unknown node. Simulation results demonstrate that the proposed CSLT algorithm performs better than the compared related works in terms of location security, average localization accuracy and localization ratio. PMID:26891300

A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm

NASA Astrophysics Data System (ADS)

Zheng, Genrang; Lin, ZhengChun

The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.

A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval.

PubMed

Cai, Jia; Tang, Yi

2018-02-01

Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method. The main contributions of the paper are: (1) A new kernel CCA algorithm is developed, (2) theoretical convergence of the proposed algorithm is addressed by means of scaled condition number, (3) a lower bound which addresses the minimum number of iterations is presented. We test on both synthetic dataset and several real-world datasets in cross-language document retrieval and content-based image retrieval to demonstrate the effectiveness of the proposed algorithm. Numerical results imply the performance and efficiency of the new algorithm, which is competitive with several state-of-the-art kernel CCA methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

A new three-dimensional manufacturing service composition method under various structures using improved Flower Pollination Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Wenyu; Yang, Yushu; Zhang, Shuai; Yu, Dejian; Chen, Yong

2018-05-01

With the growing complexity of customer requirements and the increasing scale of manufacturing services, how to select and combine the single services to meet the complex demand of the customer has become a growing concern. This paper presents a new manufacturing service composition method to solve the multi-objective optimization problem based on quality of service (QoS). The proposed model not only presents different methods for calculating the transportation time and transportation cost under various structures but also solves the three-dimensional composition optimization problem, including service aggregation, service selection, and service scheduling simultaneously. Further, an improved Flower Pollination Algorithm (IFPA) is proposed to solve the three-dimensional composition optimization problem using a matrix-based representation scheme. The mutation operator and crossover operator of the Differential Evolution (DE) algorithm are also used to extend the basic Flower Pollination Algorithm (FPA) to improve its performance. Compared to Genetic Algorithm, DE, and basic FPA, the experimental results confirm that the proposed method demonstrates superior performance than other meta heuristic algorithms and can obtain better manufacturing service composition solutions.

Direct position determination for digital modulation signals based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Yu, Wan-Ting; Yu, Hong-yi; Du, Jian-Ping; Wang, Ding

2018-04-01

The Direct Position Determination (DPD) algorithm has been demonstrated to achieve a better accuracy with known signal waveforms. However, the signal waveform is difficult to be completely known in the actual positioning process. To solve the problem, we proposed a DPD method for digital modulation signals based on improved particle swarm optimization algorithm. First, a DPD model is established for known modulation signals and a cost function is obtained on symbol estimation. Second, as the optimization of the cost function is a nonlinear integer optimization problem, an improved Particle Swarm Optimization (PSO) algorithm is considered for the optimal symbol search. Simulations are carried out to show the higher position accuracy of the proposed DPD method and the convergence of the fitness function under different inertia weight and population size. On the one hand, the proposed algorithm can take full advantage of the signal feature to improve the positioning accuracy. On the other hand, the improved PSO algorithm can improve the efficiency of symbol search by nearly one hundred times to achieve a global optimal solution.

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

PubMed Central

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-01-01

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.

PubMed

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-12-19

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.

Pattern Discovery and Change Detection of Online Music Query Streams

NASA Astrophysics Data System (ADS)

Li, Hua-Fu

In this paper, an efficient stream mining algorithm, called FTP-stream (Frequent Temporal Pattern mining of streams), is proposed to find the frequent temporal patterns over melody sequence streams. In the framework of our proposed algorithm, an effective bit-sequence representation is used to reduce the time and memory needed to slide the windows. The FTP-stream algorithm can calculate the support threshold in only a single pass based on the concept of bit-sequence representation. It takes the advantage of "left" and "and" operations of the representation. Experiments show that the proposed algorithm only scans the music query stream once, and runs significant faster and consumes less memory than existing algorithms, such as SWFI-stream and Moment.

An improved self-adaptive ant colony algorithm based on genetic strategy for the traveling salesman problem

NASA Astrophysics Data System (ADS)

Wang, Pan; Zhang, Yi; Yan, Dong

2018-05-01

Ant Colony Algorithm (ACA) is a powerful and effective algorithm for solving the combination optimization problem. Moreover, it was successfully used in traveling salesman problem (TSP). But it is easy to prematurely converge to the non-global optimal solution and the calculation time is too long. To overcome those shortcomings, a new method is presented-An improved self-adaptive Ant Colony Algorithm based on genetic strategy. The proposed method adopts adaptive strategy to adjust the parameters dynamically. And new crossover operation and inversion operation in genetic strategy was used in this method. We also make an experiment using the well-known data in TSPLIB. The experiment results show that the performance of the proposed method is better than the basic Ant Colony Algorithm and some improved ACA in both the result and the convergence time. The numerical results obtained also show that the proposed optimization method can achieve results close to the theoretical best known solutions at present.

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.

Algorithm based on regional separation for automatic grain boundary extraction using improved mean shift method

NASA Astrophysics Data System (ADS)

Zhenying, Xu; Jiandong, Zhu; Qi, Zhang; Yamba, Philip

2018-06-01

Metallographic microscopy shows that the vast majority of metal materials are composed of many small grains; the grain size of a metal is important for determining the tensile strength, toughness, plasticity, and other mechanical properties. In order to quantitatively evaluate grain size in metals, grain boundaries must be identified in metallographic images. Based on the phenomenon of grain boundary blurring or disconnection in metallographic images, this study develops an algorithm based on regional separation for automatically extracting grain boundaries by an improved mean shift method. Experimental observation shows that the grain boundaries obtained by the proposed algorithm are highly complete and accurate. This research has practical value because the proposed algorithm is suitable for grain boundary extraction from most metallographic images.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case

PubMed Central

Tsai, Chun-Wei; Tseng, Shih-Pang; Yang, Chu-Sing

2014-01-01

This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA. PMID:24892038

A high-performance genetic algorithm: using traveling salesman problem as a case.

PubMed

Tsai, Chun-Wei; Tseng, Shih-Pang; Chiang, Ming-Chao; Yang, Chu-Sing; Hong, Tzung-Pei

2014-01-01

This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA.

A vertical handoff decision algorithm based on ARMA prediction model

NASA Astrophysics Data System (ADS)

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

2012-01-01

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

Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction.

PubMed

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2016-12-01

To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.

Outdoor Illegal Construction Identification Algorithm Based on 3D Point Cloud Segmentation

NASA Astrophysics Data System (ADS)

An, Lu; Guo, Baolong

2018-03-01

Recently, various illegal constructions occur significantly in our surroundings, which seriously restrict the orderly development of urban modernization. The 3D point cloud data technology is used to identify the illegal buildings, which could address the problem above effectively. This paper proposes an outdoor illegal construction identification algorithm based on 3D point cloud segmentation. Initially, in order to save memory space and reduce processing time, a lossless point cloud compression method based on minimum spanning tree is proposed. Then, a ground point removing method based on the multi-scale filtering is introduced to increase accuracy. Finally, building clusters on the ground can be obtained using a region growing method, as a result, the illegal construction can be marked. The effectiveness of the proposed algorithm is verified using a publicly data set collected from the International Society for Photogrammetry and Remote Sensing (ISPRS).

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

PubMed

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

2016-08-01

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

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

Asymptotic analysis of SPTA-based algorithms for no-wait flow shop scheduling problem with release dates.

PubMed

Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

2014-01-01

We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

Asymptotic Analysis of SPTA-Based Algorithms for No-Wait Flow Shop Scheduling Problem with Release Dates

PubMed Central

Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

2014-01-01

We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms. PMID:24764774

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

PubMed Central

Cao, Xiaoguang; Wang, Peng; Meng, Cai; Gong, Guoping; Liu, Miaoming; Qi, Jun

2018-01-01

In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment. PMID:29494524

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement.

PubMed

Cao, Xiaoguang; Wang, Peng; Meng, Cai; Bai, Xiangzhi; Gong, Guoping; Liu, Miaoming; Qi, Jun

2018-03-01

In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment.

An Evolutionary Algorithm for Fast Intensity Based Image Matching Between Optical and SAR Satellite Imagery

NASA Astrophysics Data System (ADS)

Fischer, Peter; Schuegraf, Philipp; Merkle, Nina; Storch, Tobias

2018-04-01

This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

A generalized leaky FxLMS algorithm for tuning the waterbed effect of feedback active noise control systems

NASA Astrophysics Data System (ADS)

Wu, Lifu; Qiu, Xiaojun; Guo, Yecai

2018-06-01

To tune the noise amplification in the feedback system caused by the waterbed effect effectively, an adaptive algorithm is proposed in this paper by replacing the scalar leaky factor of the leaky FxLMS algorithm with a real symmetric Toeplitz matrix. The elements in the matrix are calculated explicitly according to the noise amplification constraints, which are defined based on a simple but efficient method. Simulations in an ANC headphone application demonstrate that the proposed algorithm can adjust the frequency band of noise amplification more effectively than the FxLMS algorithm and the leaky FxLMS algorithm.

A hybrid algorithm for the segmentation of books in libraries

NASA Astrophysics Data System (ADS)

Hu, Zilong; Tang, Jinshan; Lei, Liang

2016-05-01

This paper proposes an algorithm for book segmentation based on bookshelves images. The algorithm can be separated into three parts. The first part is pre-processing, aiming at eliminating or decreasing the effect of image noise and illumination conditions. The second part is near-horizontal line detection based on Canny edge detector, and separating a bookshelves image into multiple sub-images so that each sub-image contains an individual shelf. The last part is book segmentation. In each shelf image, near-vertical line is detected, and obtained lines are used for book segmentation. The proposed algorithm was tested with the bookshelf images taken from OPIE library in MTU, and the experimental results demonstrate good performance.

A Two-Phase Time Synchronization-Free Localization Algorithm for Underwater Sensor Networks.

PubMed

Luo, Junhai; Fan, Liying

2017-03-30

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assistance. Sensor nodes location in UWSNs is an especially relevant topic. Global Positioning System (GPS) information is not suitable for use in UWSNs because of the underwater propagation problems. Hence, some localization algorithms based on the precise time synchronization between sensor nodes that have been proposed for UWSNs are not feasible. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme based on the Particle Swarm Optimization (PSO) algorithm to obtain the coordinates of the unknown sensor nodes. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence, in this algorithm, we use a small number of mobile beacons to help obtain the location information without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA achieved by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

A Two-Phase Time Synchronization-Free Localization Algorithm for Underwater Sensor Networks

PubMed Central

Luo, Junhai; Fan, Liying

2017-01-01

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assistance. Sensor nodes location in UWSNs is an especially relevant topic. Global Positioning System (GPS) information is not suitable for use in UWSNs because of the underwater propagation problems. Hence, some localization algorithms based on the precise time synchronization between sensor nodes that have been proposed for UWSNs are not feasible. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme based on the Particle Swarm Optimization (PSO) algorithm to obtain the coordinates of the unknown sensor nodes. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence, in this algorithm, we use a small number of mobile beacons to help obtain the location information without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA achieved by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization. PMID:28358342

Variable disparity-motion estimation based fast three-view video coding

NASA Astrophysics Data System (ADS)

Bae, Kyung-Hoon; Kim, Seung-Cheol; Hwang, Yong Seok; Kim, Eun-Soo

2009-02-01

In this paper, variable disparity-motion estimation (VDME) based 3-view video coding is proposed. In the encoding, key-frame coding (KFC) based motion estimation and variable disparity estimation (VDE) for effectively fast three-view video encoding are processed. These proposed algorithms enhance the performance of 3-D video encoding/decoding system in terms of accuracy of disparity estimation and computational overhead. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm's PSNRs is 37.66 and 40.55 dB, and the processing time is 0.139 and 0.124 sec/frame, respectively.

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

PubMed

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

2013-11-13

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

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

PubMed Central

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

2013-01-01

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

Adaptive multi-view clustering based on nonnegative matrix factorization and pairwise co-regularization

NASA Astrophysics Data System (ADS)

Zhang, Tianzhen; Wang, Xiumei; Gao, Xinbo

2018-04-01

Nowadays, several datasets are demonstrated by multi-view, which usually include shared and complementary information. Multi-view clustering methods integrate the information of multi-view to obtain better clustering results. Nonnegative matrix factorization has become an essential and popular tool in clustering methods because of its interpretation. However, existing nonnegative matrix factorization based multi-view clustering algorithms do not consider the disagreement between views and neglects the fact that different views will have different contributions to the data distribution. In this paper, we propose a new multi-view clustering method, named adaptive multi-view clustering based on nonnegative matrix factorization and pairwise co-regularization. The proposed algorithm can obtain the parts-based representation of multi-view data by nonnegative matrix factorization. Then, pairwise co-regularization is used to measure the disagreement between views. There is only one parameter to auto learning the weight values according to the contribution of each view to data distribution. Experimental results show that the proposed algorithm outperforms several state-of-the-arts algorithms for multi-view clustering.

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2015-06-26

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

Screening for Human Immunodeficiency Virus, Hepatitis B Virus, Hepatitis C Virus, and Treponema pallidum by Blood Testing Using a Bio-Flash Technology-Based Algorithm before Gastrointestinal Endoscopy

PubMed Central

Zhen, Chen; QuiuLi, Zhang; YuanQi, An; Casado, Verónica Vocero; Fan, Yuan

2016-01-01

Currently, conventional enzyme immunoassays which use manual gold immunoassays and colloidal tests (GICTs) are used as screening tools to detect Treponema pallidum (syphilis), hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus type 1 (HIV-1), and HIV-2 in patients undergoing surgery. The present observational, cross-sectional study compared the sensitivity, specificity, and work flow characteristics of the conventional algorithm with manual GICTs with those of a newly proposed algorithm that uses the automated Bio-Flash technology as a screening tool in patients undergoing gastrointestinal (GI) endoscopy. A total of 956 patients were examined for the presence of serological markers of infection with HIV-1/2, HCV, HBV, and T. pallidum. The proposed algorithm with the Bio-Flash technology was superior for the detection of all markers (100.0% sensitivity and specificity for detection of anti-HIV and anti-HCV antibodies, HBV surface antigen [HBsAg], and T. pallidum) compared with the conventional algorithm based on the manual method (80.0% sensitivity and 98.6% specificity for the detection of anti-HIV, 75.0% sensitivity for the detection of anti-HCV, 94.7% sensitivity for the detection of HBsAg, and 100% specificity for the detection of anti-HCV and HBsAg) in these patients. The automated Bio-Flash technology-based screening algorithm also reduced the operation time by 85.0% (205 min) per day, saving up to 24 h/week. In conclusion, the use of the newly proposed screening algorithm based on the automated Bio-Flash technology can provide an advantage over the use of conventional algorithms based on manual methods for screening for HIV, HBV, HCV, and syphilis before GI endoscopy. PMID:27707942

Screening for Human Immunodeficiency Virus, Hepatitis B Virus, Hepatitis C Virus, and Treponema pallidum by Blood Testing Using a Bio-Flash Technology-Based Algorithm before Gastrointestinal Endoscopy.

PubMed

Jun, Zhou; Zhen, Chen; QuiuLi, Zhang; YuanQi, An; Casado, Verónica Vocero; Fan, Yuan

2016-12-01

Currently, conventional enzyme immunoassays which use manual gold immunoassays and colloidal tests (GICTs) are used as screening tools to detect Treponema pallidum (syphilis), hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus type 1 (HIV-1), and HIV-2 in patients undergoing surgery. The present observational, cross-sectional study compared the sensitivity, specificity, and work flow characteristics of the conventional algorithm with manual GICTs with those of a newly proposed algorithm that uses the automated Bio-Flash technology as a screening tool in patients undergoing gastrointestinal (GI) endoscopy. A total of 956 patients were examined for the presence of serological markers of infection with HIV-1/2, HCV, HBV, and T. pallidum The proposed algorithm with the Bio-Flash technology was superior for the detection of all markers (100.0% sensitivity and specificity for detection of anti-HIV and anti-HCV antibodies, HBV surface antigen [HBsAg], and T. pallidum) compared with the conventional algorithm based on the manual method (80.0% sensitivity and 98.6% specificity for the detection of anti-HIV, 75.0% sensitivity for the detection of anti-HCV, 94.7% sensitivity for the detection of HBsAg, and 100% specificity for the detection of anti-HCV and HBsAg) in these patients. The automated Bio-Flash technology-based screening algorithm also reduced the operation time by 85.0% (205 min) per day, saving up to 24 h/week. In conclusion, the use of the newly proposed screening algorithm based on the automated Bio-Flash technology can provide an advantage over the use of conventional algorithms based on manual methods for screening for HIV, HBV, HCV, and syphilis before GI endoscopy. Copyright © 2016 Jun et al.

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification.

PubMed

Haque, Mohammad Nazmul; Noman, Nasimul; Berretta, Regina; Moscato, Pablo

2016-01-01

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) - k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer's disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

PubMed Central

Haque, Mohammad Nazmul; Noman, Nasimul; Berretta, Regina; Moscato, Pablo

2016-01-01

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases. PMID:26764911

Research on rolling element bearing fault diagnosis based on genetic algorithm matching pursuit

NASA Astrophysics Data System (ADS)

Rong, R. W.; Ming, T. F.

2017-12-01

In order to solve the problem of slow computation speed, matching pursuit algorithm is applied to rolling bearing fault diagnosis, and the improvement are conducted from two aspects that are the construction of dictionary and the way to search for atoms. To be specific, Gabor function which can reflect time-frequency localization characteristic well is used to construct the dictionary, and the genetic algorithm to improve the searching speed. A time-frequency analysis method based on genetic algorithm matching pursuit (GAMP) algorithm is proposed. The way to set property parameters for the improvement of the decomposition results is studied. Simulation and experimental results illustrate that the weak fault feature of rolling bearing can be extracted effectively by this proposed method, at the same time, the computation speed increases obviously.

A Social Potential Fields Approach for Self-Deployment and Self-Healing in Hierarchical Mobile Wireless Sensor Networks

PubMed Central

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

2017-01-01

Autonomous mobile nodes in mobile wireless sensor networks (MWSN) allow self-deployment and self-healing. In both cases, the goals are: (i) to achieve adequate coverage; and (ii) to extend network life. In dynamic environments, nodes may use reactive algorithms so that each node locally decides when and where to move. This paper presents a behavior-based deployment and self-healing algorithm based on the social potential fields algorithm. In the proposed algorithm, nodes are attached to low cost robots to autonomously navigate in the coverage area. The proposed algorithm has been tested in environments with and without obstacles. Our study also analyzes the differences between non-hierarchical and hierarchical routing configurations in terms of network life and coverage. PMID:28075364

A Social Potential Fields Approach for Self-Deployment and Self-Healing in Hierarchical Mobile Wireless Sensor Networks.

PubMed

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

2017-01-09

Autonomous mobile nodes in mobile wireless sensor networks (MWSN) allow self-deployment and self-healing. In both cases, the goals are: (i) to achieve adequate coverage; and (ii) to extend network life. In dynamic environments, nodes may use reactive algorithms so that each node locally decides when and where to move. This paper presents a behavior-based deployment and self-healing algorithm based on the social potential fields algorithm. In the proposed algorithm, nodes are attached to low cost robots to autonomously navigate in the coverage area. The proposed algorithm has been tested in environments with and without obstacles. Our study also analyzes the differences between non-hierarchical and hierarchical routing configurations in terms of network life and coverage.

Bare-Bones Teaching-Learning-Based Optimization

PubMed Central

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao; Jiang, Qiaoyong; Li, Hongye

2014-01-01

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms. PMID:25013844

Bare-bones teaching-learning-based optimization.

PubMed

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao; Jiang, Qiaoyong; Li, Hongye

2014-01-01

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

Diversified models for portfolio selection based on uncertain semivariance

NASA Astrophysics Data System (ADS)

Chen, Lin; Peng, Jin; Zhang, Bo; Rosyida, Isnaini

2017-02-01

Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts' estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts' estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

Single-step reinitialization and extending algorithms for level-set based multi-phase flow simulations

NASA Astrophysics Data System (ADS)

Fu, Lin; Hu, Xiangyu Y.; Adams, Nikolaus A.

2017-12-01

We propose efficient single-step formulations for reinitialization and extending algorithms, which are critical components of level-set based interface-tracking methods. The level-set field is reinitialized with a single-step (non iterative) "forward tracing" algorithm. A minimum set of cells is defined that describes the interface, and reinitialization employs only data from these cells. Fluid states are extrapolated or extended across the interface by a single-step "backward tracing" algorithm. Both algorithms, which are motivated by analogy to ray-tracing, avoid multiple block-boundary data exchanges that are inevitable for iterative reinitialization and extending approaches within a parallel-computing environment. The single-step algorithms are combined with a multi-resolution conservative sharp-interface method and validated by a wide range of benchmark test cases. We demonstrate that the proposed reinitialization method achieves second-order accuracy in conserving the volume of each phase. The interface location is invariant to reapplication of the single-step reinitialization. Generally, we observe smaller absolute errors than for standard iterative reinitialization on the same grid. The computational efficiency is higher than for the standard and typical high-order iterative reinitialization methods. We observe a 2- to 6-times efficiency improvement over the standard method for serial execution. The proposed single-step extending algorithm, which is commonly employed for assigning data to ghost cells with ghost-fluid or conservative interface interaction methods, shows about 10-times efficiency improvement over the standard method while maintaining same accuracy. Despite their simplicity, the proposed algorithms offer an efficient and robust alternative to iterative reinitialization and extending methods for level-set based multi-phase simulations.

Public-key encryption with chaos.

PubMed

Kocarev, Ljupco; Sterjev, Marjan; Fekete, Attila; Vattay, Gabor

2004-12-01

We propose public-key encryption algorithms based on chaotic maps, which are generalization of well-known and commercially used algorithms: Rivest-Shamir-Adleman (RSA), ElGamal, and Rabin. For the case of generalized RSA algorithm we discuss in detail its software implementation and properties. We show that our algorithm is as secure as RSA algorithm.

Public-key encryption with chaos

NASA Astrophysics Data System (ADS)

Kocarev, Ljupco; Sterjev, Marjan; Fekete, Attila; Vattay, Gabor

2004-12-01

We propose public-key encryption algorithms based on chaotic maps, which are generalization of well-known and commercially used algorithms: Rivest-Shamir-Adleman (RSA), ElGamal, and Rabin. For the case of generalized RSA algorithm we discuss in detail its software implementation and properties. We show that our algorithm is as secure as RSA algorithm.

A Genetic-Based Scheduling Algorithm to Minimize the Makespan of the Grid Applications

NASA Astrophysics Data System (ADS)

Entezari-Maleki, Reza; Movaghar, Ali

Task scheduling algorithms in grid environments strive to maximize the overall throughput of the grid. In order to maximize the throughput of the grid environments, the makespan of the grid tasks should be minimized. In this paper, a new task scheduling algorithm is proposed to assign tasks to the grid resources with goal of minimizing the total makespan of the tasks. The algorithm uses the genetic approach to find the suitable assignment within grid resources. The experimental results obtained from applying the proposed algorithm to schedule independent tasks within grid environments demonstrate the applicability of the algorithm in achieving schedules with comparatively lower makespan in comparison with other well-known scheduling algorithms such as, Min-min, Max-min, RASA and Sufferage algorithms.

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

PubMed Central

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems. PMID:25961028

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

PubMed

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

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

PubMed

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

2014-01-01

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

DVD-COOP: Innovative Conjunction Prediction Using Voronoi-filter based on the Dynamic Voronoi Diagram of 3D Spheres

NASA Astrophysics Data System (ADS)

Cha, J.; Ryu, J.; Lee, M.; Song, C.; Cho, Y.; Schumacher, P.; Mah, M.; Kim, D.

Conjunction prediction is one of the critical operations in space situational awareness (SSA). For geospace objects, common algorithms for conjunction prediction are usually based on all-pairwise check, spatial hash, or kd-tree. Computational load is usually reduced through some filters. However, there exists a good chance of missing potential collisions between space objects. We present a novel algorithm which both guarantees no missing conjunction and is efficient to answer to a variety of spatial queries including pairwise conjunction prediction. The algorithm takes only O(k log N) time for N objects in the worst case to answer conjunctions where k is a constant which is linear to prediction time length. The proposed algorithm, named DVD-COOP (Dynamic Voronoi Diagram-based Conjunctive Orbital Object Predictor), is based on the dynamic Voronoi diagram of moving spherical balls in 3D space. The algorithm has a preprocessing which consists of two steps: The construction of an initial Voronoi diagram (taking O(N) time on average) and the construction of a priority queue for the events of topology changes in the Voronoi diagram (taking O(N log N) time in the worst case). The scalability of the proposed algorithm is also discussed. We hope that the proposed Voronoi-approach will change the computational paradigm in spatial reasoning among space objects.

An evaluation of talker localization based on direction of arrival estimation and statistical sound source identification

NASA Astrophysics Data System (ADS)

Nishiura, Takanobu; Nakamura, Satoshi

2002-11-01

It is very important to capture distant-talking speech for a hands-free speech interface with high quality. A microphone array is an ideal candidate for this purpose. However, this approach requires localizing the target talker. Conventional talker localization algorithms in multiple sound source environments not only have difficulty localizing the multiple sound sources accurately, but also have difficulty localizing the target talker among known multiple sound source positions. To cope with these problems, we propose a new talker localization algorithm consisting of two algorithms. One is DOA (direction of arrival) estimation algorithm for multiple sound source localization based on CSP (cross-power spectrum phase) coefficient addition method. The other is statistical sound source identification algorithm based on GMM (Gaussian mixture model) for localizing the target talker position among localized multiple sound sources. In this paper, we particularly focus on the talker localization performance based on the combination of these two algorithms with a microphone array. We conducted evaluation experiments in real noisy reverberant environments. As a result, we confirmed that multiple sound signals can be identified accurately between ''speech'' or ''non-speech'' by the proposed algorithm. [Work supported by ATR, and MEXT of Japan.

Phase-unwrapping algorithm by a rounding-least-squares approach

NASA Astrophysics Data System (ADS)

Juarez-Salazar, Rigoberto; Robledo-Sanchez, Carlos; Guerrero-Sanchez, Fermin

2014-02-01

A simple and efficient phase-unwrapping algorithm based on a rounding procedure and a global least-squares minimization is proposed. Instead of processing the gradient of the wrapped phase, this algorithm operates over the gradient of the phase jumps by a robust and noniterative scheme. Thus, the residue-spreading and over-smoothing effects are reduced. The algorithm's performance is compared with four well-known phase-unwrapping methods: minimum cost network flow (MCNF), fast Fourier transform (FFT), quality-guided, and branch-cut. A computer simulation and experimental results show that the proposed algorithm reaches a high-accuracy level than the MCNF method by a low-computing time similar to the FFT phase-unwrapping method. Moreover, since the proposed algorithm is simple, fast, and user-free, it could be used in metrological interferometric and fringe-projection automatic real-time applications.

Android Malware Classification Using K-Means Clustering Algorithm

NASA Astrophysics Data System (ADS)

Hamid, Isredza Rahmi A.; Syafiqah Khalid, Nur; Azma Abdullah, Nurul; Rahman, Nurul Hidayah Ab; Chai Wen, Chuah

2017-08-01

Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.

EDMC: An enhanced distributed multi-channel anti-collision algorithm for RFID reader system

NASA Astrophysics Data System (ADS)

Zhang, YuJing; Cui, Yinghua

2017-05-01

In this paper, we proposes an enhanced distributed multi-channel reader anti-collision algorithm for RFID environments which is based on the distributed multi-channel reader anti-collision algorithm for RFID environments (called DiMCA). We proposes a monitor method to decide whether reader receive the latest control news after it selected the data channel. The simulation result shows that it improves interrogation delay.

Robust frequency diversity based algorithm for clutter noise reduction of ultrasonic signals using multiple sub-spectrum phase coherence

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

Gongzhang, R.; Xiao, B.; Lardner, T.

2014-02-18

This paper presents a robust frequency diversity based algorithm for clutter reduction in ultrasonic A-scan waveforms. The performance of conventional spectral-temporal techniques like Split Spectrum Processing (SSP) is highly dependent on the parameter selection, especially when the signal to noise ratio (SNR) is low. Although spatial beamforming offers noise reduction with less sensitivity to parameter variation, phased array techniques are not always available. The proposed algorithm first selects an ascending series of frequency bands. A signal is reconstructed for each selected band in which a defect is present when all frequency components are in uniform sign. Combining all reconstructed signalsmore » through averaging gives a probability profile of potential defect position. To facilitate data collection and validate the proposed algorithm, Full Matrix Capture is applied on the austenitic steel and high nickel alloy (HNA) samples with 5MHz transducer arrays. When processing A-scan signals with unrefined parameters, the proposed algorithm enhances SNR by 20dB for both samples and consequently, defects are more visible in B-scan images created from the large amount of A-scan traces. Importantly, the proposed algorithm is considered robust, while SSP is shown to fail on the austenitic steel data and achieves less SNR enhancement on the HNA data.« less

An improved reversible data hiding algorithm based on modification of prediction errors

NASA Astrophysics Data System (ADS)

Jafar, Iyad F.; Hiary, Sawsan A.; Darabkh, Khalid A.

2014-04-01

Reversible data hiding algorithms are concerned with the ability of hiding data and recovering the original digital image upon extraction. This issue is of interest in medical and military imaging applications. One particular class of such algorithms relies on the idea of histogram shifting of prediction errors. In this paper, we propose an improvement over one popular algorithm in this class. The improvement is achieved by employing a different predictor, the use of more bins in the prediction error histogram in addition to multilevel embedding. The proposed extension shows significant improvement over the original algorithm and its variations.

Efficient Fingercode Classification

NASA Astrophysics Data System (ADS)

Sun, Hong-Wei; Law, Kwok-Yan; Gollmann, Dieter; Chung, Siu-Leung; Li, Jian-Bin; Sun, Jia-Guang

In this paper, we present an efficient fingerprint classification algorithm which is an essential component in many critical security application systems e. g. systems in the e-government and e-finance domains. Fingerprint identification is one of the most important security requirements in homeland security systems such as personnel screening and anti-money laundering. The problem of fingerprint identification involves searching (matching) the fingerprint of a person against each of the fingerprints of all registered persons. To enhance performance and reliability, a common approach is to reduce the search space by firstly classifying the fingerprints and then performing the search in the respective class. Jain et al. proposed a fingerprint classification algorithm based on a two-stage classifier, which uses a K-nearest neighbor classifier in its first stage. The fingerprint classification algorithm is based on the fingercode representation which is an encoding of fingerprints that has been demonstrated to be an effective fingerprint biometric scheme because of its ability to capture both local and global details in a fingerprint image. We enhance this approach by improving the efficiency of the K-nearest neighbor classifier for fingercode-based fingerprint classification. Our research firstly investigates the various fast search algorithms in vector quantization (VQ) and the potential application in fingerprint classification, and then proposes two efficient algorithms based on the pyramid-based search algorithms in VQ. Experimental results on DB1 of FVC 2004 demonstrate that our algorithms can outperform the full search algorithm and the original pyramid-based search algorithms in terms of computational efficiency without sacrificing accuracy.

New fast DCT algorithms based on Loeffler's factorization

NASA Astrophysics Data System (ADS)

Hong, Yoon Mi; Kim, Il-Koo; Lee, Tammy; Cheon, Min-Su; Alshina, Elena; Han, Woo-Jin; Park, Jeong-Hoon

2012-10-01

This paper proposes a new 32-point fast discrete cosine transform (DCT) algorithm based on the Loeffler's 16-point transform. Fast integer realizations of 16-point and 32-point transforms are also provided based on the proposed transform. For the recent development of High Efficiency Video Coding (HEVC), simplified quanti-zation and de-quantization process are proposed. Three different forms of implementation with the essentially same performance, namely matrix multiplication, partial butterfly, and full factorization can be chosen accord-ing to the given platform. In terms of the number of multiplications required for the realization, our proposed full-factorization is 3~4 times faster than a partial butterfly, and about 10 times faster than direct matrix multiplication.

A novel minimum cost maximum power algorithm for future smart home energy management.

PubMed

Singaravelan, A; Kowsalya, M

2017-11-01

With the latest development of smart grid technology, the energy management system can be efficiently implemented at consumer premises. In this paper, an energy management system with wireless communication and smart meter are designed for scheduling the electric home appliances efficiently with an aim of reducing the cost and peak demand. For an efficient scheduling scheme, the appliances are classified into two types: uninterruptible and interruptible appliances. The problem formulation was constructed based on the practical constraints that make the proposed algorithm cope up with the real-time situation. The formulated problem was identified as Mixed Integer Linear Programming (MILP) problem, so this problem was solved by a step-wise approach. This paper proposes a novel Minimum Cost Maximum Power (MCMP) algorithm to solve the formulated problem. The proposed algorithm was simulated with input data available in the existing method. For validating the proposed MCMP algorithm, results were compared with the existing method. The compared results prove that the proposed algorithm efficiently reduces the consumer electricity consumption cost and peak demand to optimum level with 100% task completion without sacrificing the consumer comfort.

Efficient operator splitting algorithm for joint sparsity-regularized SPIRiT-based parallel MR imaging reconstruction.

PubMed

Duan, Jizhong; Liu, Yu; Jing, Peiguang

2018-02-01

Self-consistent parallel imaging (SPIRiT) is an auto-calibrating model for the reconstruction of parallel magnetic resonance imaging, which can be formulated as a regularized SPIRiT problem. The Projection Over Convex Sets (POCS) method was used to solve the formulated regularized SPIRiT problem. However, the quality of the reconstructed image still needs to be improved. Though methods such as NonLinear Conjugate Gradients (NLCG) can achieve higher spatial resolution, these methods always demand very complex computation and converge slowly. In this paper, we propose a new algorithm to solve the formulated Cartesian SPIRiT problem with the JTV and JL1 regularization terms. The proposed algorithm uses the operator splitting (OS) technique to decompose the problem into a gradient problem and a denoising problem with two regularization terms, which is solved by our proposed split Bregman based denoising algorithm, and adopts the Barzilai and Borwein method to update step size. Simulation experiments on two in vivo data sets demonstrate that the proposed algorithm is 1.3 times faster than ADMM for datasets with 8 channels. Especially, our proposal is 2 times faster than ADMM for the dataset with 32 channels. Copyright © 2017 Elsevier Inc. All rights reserved.

A Novel Robot Visual Homing Method Based on SIFT Features

PubMed Central

Zhu, Qidan; Liu, Chuanjia; Cai, Chengtao

2015-01-01

Warping is an effective visual homing method for robot local navigation. However, the performance of the warping method can be greatly influenced by the changes of the environment in a real scene, thus resulting in lower accuracy. In order to solve the above problem and to get higher homing precision, a novel robot visual homing algorithm is proposed by combining SIFT (scale-invariant feature transform) features with the warping method. The algorithm is novel in using SIFT features as landmarks instead of the pixels in the horizon region of the panoramic image. In addition, to further improve the matching accuracy of landmarks in the homing algorithm, a novel mismatching elimination algorithm, based on the distribution characteristics of landmarks in the catadioptric panoramic image, is proposed. Experiments on image databases and on a real scene confirm the effectiveness of the proposed method. PMID:26473880

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue

2018-04-01

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

Optimal clustering of MGs based on droop controller for improving reliability using a hybrid of harmony search and genetic algorithms.

PubMed

Abedini, Mohammad; Moradi, Mohammad H; Hosseinian, S M

2016-03-01

This paper proposes a novel method to address reliability and technical problems of microgrids (MGs) based on designing a number of self-adequate autonomous sub-MGs via adopting MGs clustering thinking. In doing so, a multi-objective optimization problem is developed where power losses reduction, voltage profile improvement and reliability enhancement are considered as the objective functions. To solve the optimization problem a hybrid algorithm, named HS-GA, is provided, based on genetic and harmony search algorithms, and a load flow method is given to model different types of DGs as droop controller. The performance of the proposed method is evaluated in two case studies. The results provide support for the performance of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Cache-Oblivious parallel SIMD Viterbi decoding for sequence search in HMMER.

PubMed

Ferreira, Miguel; Roma, Nuno; Russo, Luis M S

2014-05-30

HMMER is a commonly used bioinformatics tool based on Hidden Markov Models (HMMs) to analyze and process biological sequences. One of its main homology engines is based on the Viterbi decoding algorithm, which was already highly parallelized and optimized using Farrar's striped processing pattern with Intel SSE2 instruction set extension. A new SIMD vectorization of the Viterbi decoding algorithm is proposed, based on an SSE2 inter-task parallelization approach similar to the DNA alignment algorithm proposed by Rognes. Besides this alternative vectorization scheme, the proposed implementation also introduces a new partitioning of the Markov model that allows a significantly more efficient exploitation of the cache locality. Such optimization, together with an improved loading of the emission scores, allows the achievement of a constant processing throughput, regardless of the innermost-cache size and of the dimension of the considered model. The proposed optimized vectorization of the Viterbi decoding algorithm was extensively evaluated and compared with the HMMER3 decoder to process DNA and protein datasets, proving to be a rather competitive alternative implementation. Being always faster than the already highly optimized ViterbiFilter implementation of HMMER3, the proposed Cache-Oblivious Parallel SIMD Viterbi (COPS) implementation provides a constant throughput and offers a processing speedup as high as two times faster, depending on the model's size.

Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV.

PubMed

Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

2016-05-09

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.

A graph decomposition-based approach for water distribution network optimization

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.; Deuerlein, Jochen W.

2013-04-01

A novel optimization approach for water distribution network design is proposed in this paper. Using graph theory algorithms, a full water network is first decomposed into different subnetworks based on the connectivity of the network's components. The original whole network is simplified to a directed augmented tree, in which the subnetworks are substituted by augmented nodes and directed links are created to connect them. Differential evolution (DE) is then employed to optimize each subnetwork based on the sequence specified by the assigned directed links in the augmented tree. Rather than optimizing the original network as a whole, the subnetworks are sequentially optimized by the DE algorithm. A solution choice table is established for each subnetwork (except for the subnetwork that includes a supply node) and the optimal solution of the original whole network is finally obtained by use of the solution choice tables. Furthermore, a preconditioning algorithm is applied to the subnetworks to produce an approximately optimal solution for the original whole network. This solution specifies promising regions for the final optimization algorithm to further optimize the subnetworks. Five water network case studies are used to demonstrate the effectiveness of the proposed optimization method. A standard DE algorithm (SDE) and a genetic algorithm (GA) are applied to each case study without network decomposition to enable a comparison with the proposed method. The results show that the proposed method consistently outperforms the SDE and GA (both with tuned parameters) in terms of both the solution quality and efficiency.

Vector Quantization Algorithm Based on Associative Memories

NASA Astrophysics Data System (ADS)

Guzmán, Enrique; Pogrebnyak, Oleksiy; Yáñez, Cornelio; Manrique, Pablo

This paper presents a vector quantization algorithm for image compression based on extended associative memories. The proposed algorithm is divided in two stages. First, an associative network is generated applying the learning phase of the extended associative memories between a codebook generated by the LBG algorithm and a training set. This associative network is named EAM-codebook and represents a new codebook which is used in the next stage. The EAM-codebook establishes a relation between training set and the LBG codebook. Second, the vector quantization process is performed by means of the recalling stage of EAM using as associative memory the EAM-codebook. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantages offered by the proposed algorithm is high processing speed and low demand of resources (system memory); results of image compression and quality are presented.

Online Coregularization for Multiview Semisupervised Learning

PubMed Central

Li, Guohui; Huang, Kuihua

2013-01-01

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

An improved dehazing algorithm of aerial high-definition image

NASA Astrophysics Data System (ADS)

Jiang, Wentao; Ji, Ming; Huang, Xiying; Wang, Chao; Yang, Yizhou; Li, Tao; Wang, Jiaoying; Zhang, Ying

2016-01-01

For unmanned aerial vehicle(UAV) images, the sensor can not get high quality images due to fog and haze weather. To solve this problem, An improved dehazing algorithm of aerial high-definition image is proposed. Based on the model of dark channel prior, the new algorithm firstly extracts the edges from crude estimated transmission map and expands the extracted edges. Then according to the expended edges, the algorithm sets a threshold value to divide the crude estimated transmission map into different areas and makes different guided filter on the different areas compute the optimized transmission map. The experimental results demonstrate that the performance of the proposed algorithm is substantially the same as the one based on dark channel prior and guided filter. The average computation time of the new algorithm is around 40% of the one as well as the detection ability of UAV image is improved effectively in fog and haze weather.

Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm

PubMed Central

Wang, Jie-Sheng; Han, Shuang

2015-01-01

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:26583034

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.

Modified retrieval algorithm for three types of precipitation distribution using x-band synthetic aperture radar

NASA Astrophysics Data System (ADS)

Xie, Yanan; Zhou, Mingliang; Pan, Dengke

2017-10-01

The forward-scattering model is introduced to describe the response of normalized radar cross section (NRCS) of precipitation with synthetic aperture radar (SAR). Since the distribution of near-surface rainfall is related to the rate of near-surface rainfall and horizontal distribution factor, a retrieval algorithm called modified regression empirical and model-oriented statistical (M-M) based on the volterra integration theory is proposed. Compared with the model-oriented statistical and volterra integration (MOSVI) algorithm, the biggest difference is that the M-M algorithm is based on the modified regression empirical algorithm rather than the linear regression formula to retrieve the value of near-surface rainfall rate. Half of the empirical parameters are reduced in the weighted integral work and a smaller average relative error is received while the rainfall rate is less than 100 mm/h. Therefore, the algorithm proposed in this paper can obtain high-precision rainfall information.

Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.

PubMed

Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu

2015-08-01

This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.

Optimal Bi-Objective Redundancy Allocation for Systems Reliability and Risk Management.

PubMed

Govindan, Kannan; Jafarian, Ahmad; Azbari, Mostafa E; Choi, Tsan-Ming

2016-08-01

In the big data era, systems reliability is critical to effective systems risk management. In this paper, a novel multiobjective approach, with hybridization of a known algorithm called NSGA-II and an adaptive population-based simulated annealing (APBSA) method is developed to solve the systems reliability optimization problems. In the first step, to create a good algorithm, we use a coevolutionary strategy. Since the proposed algorithm is very sensitive to parameter values, the response surface method is employed to estimate the appropriate parameters of the algorithm. Moreover, to examine the performance of our proposed approach, several test problems are generated, and the proposed hybrid algorithm and other commonly known approaches (i.e., MOGA, NRGA, and NSGA-II) are compared with respect to four performance measures: 1) mean ideal distance; 2) diversification metric; 3) percentage of domination; and 4) data envelopment analysis. The computational studies have shown that the proposed algorithm is an effective approach for systems reliability and risk management.

A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs.

PubMed

Han, Lianghao; Dong, Hua; McClelland, Jamie R; Han, Liangxiu; Hawkes, David J; Barratt, Dean C

2017-07-01

This paper presents a new hybrid biomechanical model-based non-rigid image registration method for lung motion estimation. In the proposed method, a patient-specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non-rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1.37 mm) than with biomechanical modelling (TRE = 3.81 mm) and intensity-based image registration without specific considerations for sliding motion (TRE = 4.57 mm). The proposed method achieved a comparable accuracy as several recently developed intensity-based registration algorithms with sliding handling on the same datasets. A detailed comparison on the distributions of TREs with three non-rigid intensity-based algorithms showed that the proposed method performed especially well on estimating the displacement field of lung surface regions (mean TRE = 1.33 mm, maximum TRE = 5.3 mm). The effects of biomechanical model parameters (such as Poisson's ratio, friction and tissue heterogeneity) on displacement estimation were investigated. The potential of the algorithm in optimising biomechanical models of lungs through analysing the pattern of displacement compensation from the image registration process has also been demonstrated. Copyright © 2017 Elsevier B.V. All rights reserved.

Real-time inverse kinematics for the upper limb: a model-based algorithm using segment orientations.

PubMed

Borbély, Bence J; Szolgay, Péter

2017-01-17

Model based analysis of human upper limb movements has key importance in understanding the motor control processes of our nervous system. Various simulation software packages have been developed over the years to perform model based analysis. These packages provide computationally intensive-and therefore off-line-solutions to calculate the anatomical joint angles from motion captured raw measurement data (also referred as inverse kinematics). In addition, recent developments in inertial motion sensing technology show that it may replace large, immobile and expensive optical systems with small, mobile and cheaper solutions in cases when a laboratory-free measurement setup is needed. The objective of the presented work is to extend the workflow of measurement and analysis of human arm movements with an algorithm that allows accurate and real-time estimation of anatomical joint angles for a widely used OpenSim upper limb kinematic model when inertial sensors are used for movement recording. The internal structure of the selected upper limb model is analyzed and used as the underlying platform for the development of the proposed algorithm. Based on this structure, a prototype marker set is constructed that facilitates the reconstruction of model-based joint angles using orientation data directly available from inertial measurement systems. The mathematical formulation of the reconstruction algorithm is presented along with the validation of the algorithm on various platforms, including embedded environments. Execution performance tables of the proposed algorithm show significant improvement on all tested platforms. Compared to OpenSim's Inverse Kinematics tool 50-15,000x speedup is achieved while maintaining numerical accuracy. The proposed algorithm is capable of real-time reconstruction of standardized anatomical joint angles even in embedded environments, establishing a new way for complex applications to take advantage of accurate and fast model-based inverse kinematics calculations.

Long-term surface EMG monitoring using K-means clustering and compressive sensing

NASA Astrophysics Data System (ADS)

Balouchestani, Mohammadreza; Krishnan, Sridhar

2015-05-01

In this work, we present an advanced K-means clustering algorithm based on Compressed Sensing theory (CS) in combination with the K-Singular Value Decomposition (K-SVD) method for Clustering of long-term recording of surface Electromyography (sEMG) signals. The long-term monitoring of sEMG signals aims at recording of the electrical activity produced by muscles which are very useful procedure for treatment and diagnostic purposes as well as for detection of various pathologies. The proposed algorithm is examined for three scenarios of sEMG signals including healthy person (sEMG-Healthy), a patient with myopathy (sEMG-Myopathy), and a patient with neuropathy (sEMG-Neuropathr), respectively. The proposed algorithm can easily scan large sEMG datasets of long-term sEMG recording. We test the proposed algorithm with Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) dimensionality reduction methods. Then, the output of the proposed algorithm is fed to K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers in order to calclute the clustering performance. The proposed algorithm achieves a classification accuracy of 99.22%. This ability allows reducing 17% of Average Classification Error (ACE), 9% of Training Error (TE), and 18% of Root Mean Square Error (RMSE). The proposed algorithm also reduces 14% clustering energy consumption compared to the existing K-Means clustering algorithm.

A hybrid algorithm for clustering of time series data based on affinity search technique.

PubMed

Aghabozorgi, Saeed; Ying Wah, Teh; Herawan, Tutut; Jalab, Hamid A; Shaygan, Mohammad Amin; Jalali, Alireza

2014-01-01

Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets.

A Novel Speed Compensation Method for ISAR Imaging with Low SNR

PubMed Central

Liu, Yongxiang; Zhang, Shuanghui; Zhu, Dekang; Li, Xiang

2015-01-01

In this paper, two novel speed compensation algorithms for ISAR imaging under a low signal-to-noise ratio (SNR) condition have been proposed, which are based on the cubic phase function (CPF) and the integrated cubic phase function (ICPF), respectively. These two algorithms can estimate the speed of the target from the wideband radar echo directly, which breaks the limitation of speed measuring in a radar system. With the utilization of non-coherent accumulation, the ICPF-based speed compensation algorithm is robust to noise and can meet the requirement of speed compensation for ISAR imaging under a low SNR condition. Moreover, a fast searching implementation strategy, which consists of coarse search and precise search, has been introduced to decrease the computational burden of speed compensation based on CPF and ICPF. Experimental results based on radar data validate the effectiveness of the proposed algorithms. PMID:26225980

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

PubMed Central

Patwary, Nurmohammed; Preza, Chrysanthe

2015-01-01

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