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In this paper, we investigate the direction of arrival (DOA) estimation problem with unfolded coprime linear array (UCLA) and propose a low computational complexity signal-subspace fitting (SF) algorithm. SF algorithm is able to achieve excellent DOA estimation performance while it requires global angular search (GAS). Especially in the several source signals situation, expensive complexity cost causes. To decrease computational complexity, we propose an initialized based SF (ISF) algorithm, which involves the several one dimensional (1D) partial angular search (PAS) instead of the multidimensional GAS. Consequently, the complexity is significantly decreased. Due to the full utilization of the array aperture, the proposed method in UCLA can attain better performance than general CLA (GCLA). In addition, as the SF is attractive in practical application, the proposed ISF algorithm lowers the computational cost, while achieving almost approximate estimation performance as traditional SF and noise subspace fitting (NF). Moreover, numerical simulations are provided and verify the effectiveness and the superiority of the proposed algorithm for the UCLA. [ABSTRACT FROM AUTHOR]

Stochastic simulation for in silico studies of large biochemical networks requires a great amount of computational time. We recently proposed a new exact simulation algorithm, called the rejectionbased stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)], to improve simulation performance by postponing and collapsing as much as possible the propensity updates. In this paper, we analyze the performance of this algorithm in detail, and improve it for simulating large-scale biochemical reaction networks. We also present a new algorithm, called simultaneous RSSA (SRSSA), which generates many independent trajectories simultaneously for the analysis of the biochemical behavior. SRSSA improves simulation performance by utilizing a single data structure across simulations to select reaction firings and forming trajectories. The memory requirement for building and storing the data structure is thus independent of the number of trajectories. The updating of the data structure when needed is performed collectively in a single operation across the simulations. The trajectories generated by SRSSA are exact and independent of each other by exploiting the rejection-based mechanism. We test our new improvement on real biological systems with a wide range of reaction networks to demonstrate its applicability and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the direction of arrival (DOA) estimation problem of noncircular signals for coprime linear array (CLA). From the perspective of the CLA as extracted from a filled uniform linear array (ULA), a noncircular root-MUSIC algorithm is proposed to estimate the DOA which can avoid the spectral peak search and lower the computational complexity. Due to the noncircular characteristic, the proposed algorithm enables to resolve more sources than sensors. Meanwhile, the proposed algorithm has better angle estimation performance than some conventional DOA estimation algorithms. Numerical simulation results illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters are popular solutions to the multitarget tracking problem due to their low complexity and ability to estimate the number and states of targets in cluttered environments. The PHD filter propagates the first-order moment (i.e. mean) of the number of targets while the CPHD propagates the cardinality distribution in the number of targets, albeit for a greater computational cost. Introducing the Panjer point process, this paper proposes a Second-Order PHD (SO-PHD) filter, propagating the second-order moment (i.e., variance) of the number of targets alongside its mean. The resulting algorithm is more versatile in the modeling choices than the PHD filter, and its computational cost is significantly lower compared to the CPHD filter. This paper compares the three filters in statistical simulations which demonstrate that the proposed filter reacts more quickly to changes in the number of targets, i.e., target births and target deaths, than the CPHD filter. In addition, a new statistic for multiobject filters is introduced in order to study the correlation between the estimated number of targets in different regions of the state space, and propose a quantitative analysis of the spooky effect for the three filters. [ABSTRACT FROM PUBLISHER]

In this paper, direction of arrival (DOA) estimation of multiple signals with coprime array is investigated and signal subspace fitting (SSF) method is linked to the coprime array, which achieves a better DOA estimation performance than the traditional uniform array. While the SSF method requires expensive computational cost in the case of multiple signals due to the multidimensional global angular searching, we propose a successive SSF (S-SSF) algorithm from a computationally efficient perspective. In the proposed algorithm, we employ rotational invariance and coprime property to obtain the initial estimates. Then, via a successive scheme, we transform the traditional multidimensional global angular searching problem into one-dimensional partial angular searching one. Consequently, the computational complexity has been significantly reduced. Specifically, the proposed S-SSF algorithm can obtain almost the same DOA estimation performance as SSF but with remarkably lower complexity. Finally, Cramer-Rao Bound (CRB) is provided and numerical simulations demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper is devoted to the terrain aided navigation (TAN) systems based on the state estimation algorithms. In particular, the emphasis is laid on the design of the Bayesian Rao–Blackwellized point-mass filter for nonlinear state-space models of a specific structure typically used in the terrain aided navigation. The proposed filter preserves advantages of the point-mass filter, including the high estimation accuracy, robust initialization, deterministic nature of the algorithm, and predictable computational complexity, while the computational complexity is significantly reduced. The performance of the Rao–Blackwellized point-mass filter is illustrated using a simulated numerical example and a TAN system based on the real data. [ABSTRACT FROM AUTHOR]

This paper considers the reconstruction of a discrete-valued random vector from possibly underdetermined linear measurements using sum-of-absolute-value (SOAV) optimization. The proposed algorithm, referred to as discreteness-aware approximate message passing (DAMP), is based on the idea of approximate message passing (AMP), which has been originally proposed for compressed sensing. The DAMP algorithm has low computational complexity and its performance in the large system limit can be predicted analytically via state evolution framework, where we provide a condition for the exact reconstruction with DAMP in the noise-free case. From the analysis, we also propose a method to determine the parameters of the SOAV optimization. Moreover, based on the state evolution, we provide Bayes optimal DAMP, which has the minimum mean-square-error at each iteration of the algorithm. Simulation results show that the DAMP algorithms can reconstruct the discrete-valued vector from underdetermined linear measurements and the empirical performance agrees with our theoretical results in large-scale systems. When the problem size is not large enough, the SOAV optimization with the proposed parameters can achieve better performance than the DAMP algorithms for high signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a rapid local backlight dimming method for interlaced video to improve the efficiency of displaying interlaced video. In the proposed method, we combine the line average deinterlacing algorithm with the local backlight dimming algorithm. In order to reduce the computational complexity and the backlight power consumption, three novel methods are proposed. The first is to add the sparse method to process the deinterlaced image. Second, the improved dynamic threshold method is used to calculate the dimming factor. And third, the odd field and even field of the same frame use the same backlight brightness to do backlight dimming. The simulation results show that the proposed method can reduce the computational complexity and power consumption, and the experiment results verify that the method can provide high display effect. [ABSTRACT FROM AUTHOR]

Tile self-assembly in which tiles may bind in a non-cooperative fashion is often referred to as “temperature 1 self-assembly” or simply “non-cooperative self-assembly”. In this type of self-assembly, a tile may non-cooperatively bind to an assembly via (at least) one of its sides, unlike in cooperative self-assembly, in which some tiles may be required to bind on two or more sides. Cooperative self-assembly leads to highly non-trivial theoretical behavior but two-dimensional non-cooperative self-assembly is conjectured to be only capable of producing highly-regular shapes and patterns, which, in general, cannot be interpreted as complex computation. Remarkably, Cook et al. (Temperature 1 self-assembly: deterministic assembly in 3D and probabilistic assembly in 2D. In: Proceedings of the 22nd Annual ACM-SIAM Symposium on Discrete Algorithms 2011) showed that three-dimensional non-cooperative self-assembly is essentially as powerful as cooperative self-assembly, with respect to Turing universality and self-assembly of N×N squares. In this paper, we consider the non-cooperative self-assembly of just-barely 3D shapes. Working in a three-dimensional variant of Winfree’s abstract Tile Assembly Model, we show that, for an arbitrary finite, connected shape X⊂Z2, there is a tile set that uniquely self-assembles at temperature 1 into a 3D representation of a scaled-up version of X with optimal program-size complexity, where the “program-size complexity”, also known as “tile complexity”, of a shape is the minimum number of tile types required to uniquely self-assemble it. Moreover, our construction is “just barely” 3D in the sense that it only places tiles in the z=0 and z=1 planes. Our result is essentially a just-barely 3D temperature 1 simulation of a similar 2D temperature 2 result by Soloveichik and Winfree (SIAM J Comput 36(6):1544-1569, 2007). [ABSTRACT FROM AUTHOR]

Countering mutual coupling in the inherent structures and the non-availability of proper reference or error signals for the operation of adaptive filters has been a challenge in the design of a Hybrid active noise control (HANC) system. Existing HANC systems have not simultaneously addressed these two issues. In this paper, we propose an improved HANC system which generates most appropriate signals for operating the adaptive filters along with decoupling the inherent feedforward and feedback ANC systems in the HANC system. Here, we use an extra adaptive filter which post-processes the error signal to obtain true signals for operating the feedforward control filter which improves the convergence speed and the overall noise reduction. A Joint-Optimized Normalized Least Mean Squares algorithm is employed for generating the true signal by optimizing the system misalignment, which further improves the convergence of the adaptive filter in the feedforward part. The adaptive filter in the feedback structure also gets appropriate signals by internally generating a reference signal which is independent of the feedforward structure. A complete mathematical analysis of the proposed algorithm is done. Simulation results validate the advantages of the proposed system over its counterparts at the cost of increased computational complexity. [ABSTRACT FROM AUTHOR]

In the past decades, simulation frameworks have greatly increased in complexity, due to coupling of models from various disciplines into so-called integrated models. Recently, the combination with tools for uncertainty quantification, inverse modelling, optimization and control started a development towards what we call extended simulation frameworks. While there is an ongoing discussion on quality assurance and reproducibility for simulation frameworks, we have not observed a similar discussion for the extended case. Particularly for extended frameworks, the need for quality assurance is high: The overwhelming range of options and algorithms is unmanageable by a domain expert and opaque to decision makers or the public. The resulting demand for ‘intelligent software’ with automated configuration can lead to a blind trust in simulation results even if they are incorrect. This is a threatening scenario due to potential consequences in simulation-based engineering or political decisions. In this paper, we analyze the increasing complexity of scientific computing workflows, and discuss the corresponding problems of extended scientific simulation frameworks. We propose a paradigm that regulates the allowable properties of framework components, supports the framework configuration for complex simulations, enforces automatic self-tests of configured frameworks, and communicates automated algorithm choices, potentially critical user settings or convergence issues with adaptive detail level and urgency to the end-user. Our goal is to start transferring the quality assurance discussion in the field of integrated modeling and conventional software frameworks to the area of extended simulation frameworks. With this, we hope to increase the reliability and transparency of (extended) frameworks, framework use and of the corresponding simulation results. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers cross-validation based approaches to automatically determine the appropriate number of dimensions to retain in a Principal Components Analysis (PCA). Three approaches based on a mixture of leaving groups of observations and variables out are described. They are compared through simulation across a range of datasets of differing sizes and differing levels of missingness using the NIPALS algorithm to carry out the PCA. Also included in the paper is an explicit description of how the NIPALS algorithm is implemented to deal with missing data. Finally we provide suggestions as to which approach offers a better compromise between reliability in choosing the optimal number of components, and the computational burden. [Copyright &y& Elsevier]

The current technological evolutions enter 3D geo-informatics into their digital age, enabling new potential applications in the field of virtual tourism, pleasure, entertainment and cultural heritage. It is argued that 3D information provides the natural way of navigation. However, personalization is a key aspect in a navigation system, since a route that incorporates user preferences is ultimately more suitable than the route with the shortest distance or travel time. Usually, user's preferences are expressed as a set of weights that regulate the degree of importance of the scene metadata on the route selection process. These weights, however, are defined by the users, setting the complexity to the user's side, which makes personalization an arduous task. In this paper, we propose an alternative approach in which metadata weights are estimated implicitly and transparently to the users, transferring the complexity to the system side. This is achieved by introducing a relevance feedback on-line learning strategy which automatically adjusts metadata weights by exploiting information fed back to the system about the relevance of user's preferences judgments given in a form of pair-wise comparisons. Practically implementing a relevance feedback algorithm presents the limitation that several pair-wise comparisons (samples) are required to converge to a set of reliable metadata weights. For this reason, we propose in this paper a weight rectification strategy that improves weight estimation by exploiting metadata interrelations defined through an ontology. In the sequel, a genetic optimization algorithm is incorporated to select the most user preferred routes based on a multi-criteria minimization approach. To increase the degree of personalization in 3D navigation, we have also introduced an efficient algorithm for estimating 3D trajectories around objects of interest by merging best selected 2D projected views that contain faces which are mostly preferred by the users. We have conducted simulations and comparisons with other approaches either in the field of on-line learning or route selection using objective metrics in terms of precision and recall values. The results indicate that our system yields on average a 13.76 % improvement of precision as regards the learning strategy and an improvement of 8.75 % regarding route selection. In addition, we conclude that the ontology driven weight rectification strategy can reduce the number of samples (pair-wise comparisons) required of 76 % to achieve the same precision. Qualitative comparisons have been also performed using a use case route scenario in the city of Athens. [ABSTRACT FROM AUTHOR]

Abstract: Motion is a common phenomenon in the real world so the consistence of virtual and real motion is one of hot focuses in augmented reality system. This paper mainly analyzes virtual and real motion blur consistence in augmented reality system, and mainly do improvement in two aspects, one is how to get the motion blur parameters, the other is how to render motion blur. From the available literature it can be seen that, most achieved methods to get motion blur parameters are based on machine vision. But this paper first acquires the motion trajectory by magnetic tracking, and then gets motion blur parameters. Considering the available literature on motion blur rendering methods, they mainly contain convolution and accumulator caching and so on. Due to rotation translation compound motion blur, this paper does rendering in advance based on improved mathematical model, and then render virtual object through looking up table in augmented reality system. Experimental results show that the new virtual and real motion blur consistence render algorithm has low time complexity and achieve the desired effect in simulation environment and real scenes. [Copyright &y& Elsevier]

In this paper, we consider a feasible primal–dual interior point method for linear semidefinite programming problem ( S D P ) based on Alizadeh–Haeberly–Overton ( AHO ) direction (Monteiro, 1997). Firstly, and by a new and simple technique, we establish the existence and uniqueness of optimal solution of the perturbed problem ( S D P ) μ and its convergence to optimal solution of ( S D P ) . Next, we present new different alternatives to calculate the displacement step. After, we establish the convergence of the obtained algorithm and we show that its complexity is O ( n ln [ ε − 1 ( 〈 X 0 , S 0 〉 ) ] ) . Finally, we present some numerical simulations which show the effectiveness of the algorithm developed in this work. [ABSTRACT FROM AUTHOR]

This paper investigates a subarray based algorithm for direction of arrival (DOA) estimation of wideband uniform linear array (ULA), under the presence of frequency-dependent mutual coupling effects. Based on the Toeplitz structure of mutual coupling matrices, the whole array is divided into the middle subarray and the auxiliary subarray. Then two-sided correlation transformation is applied to the correlation matrix of the middle subarray instead of the whole array. In this way, the mutual coupling effects can be eliminated. Finally, the multiple signal classification (MUSIC) method is utilized to derive the DOAs. For the condition when the blind angles exist, we refine DOA estimation by using a simple approach based on the frequency-dependent mutual coupling matrixes (MCMs). The proposed method can achieve high estimation accuracy without any calibration sources. It has a low computational complexity because iterative processing is not required. Simulation results validate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

As higher performance is demanded in 5G networks, energy consumption in wireless networks increases along with the advances of various technologies, so enhancing energy efficiency also becomes an important goal to implement 5G wireless networks. In this paper, we study the energy efficiency maximization problem focused on finding a suitable set of turned-on small cell access points (APs). Finding the suitable on/off states of APs is challenging since the APs can be deployed by users while centralized network planning is not always possible. Therefore, when APs in small cells are randomly deployed and thus redundant in many cases, a mechanism of dynamic AP turning-on/off is required. We propose a device-assisted framework that exploits feedback messages from the user equipment (UE). To solve the problem, we apply an optimization method using belief propagation (BP) on a factor graph. Then, we propose a family of online algorithms inspired by BP, called DANCE, that requires low computational complexity. We perform numerical simulations, and the extensive simulations confirm that BP enhances energy efficiency significantly. Furthermore, simple, but practical DANCE exhibits close performance to BP and also better performance than other popular existing methods. Specifically, in a small-sized network, BP enhances energy efficiency 129%. Furthermore, in ultra-dense networks, DANCE algorithms successfully achieve orders of magnitude higher energy efficiency than that of the baseline. [ABSTRACT FROM AUTHOR]

As one of data mining techniques, outlier detection aims to discover outlying observations that deviate substantially from the reminder of the data. Recently, the Local Outlier Factor (LOF) algorithm has been successfully applied to outlier detection. However, due to the computational complexity of the LOF algorithm, its application to large data with high dimension has been limited. The aim of this paper is to propose grid-based algorithm that reduces the computation time required by the LOF algorithm to determine the k-nearest neighbors. The algorithm divides the data spaces in to a smaller number of regions, called as a “grid”, and calculates the LOF value of each grid. To examine the effectiveness of the proposed method, several experiments incorporating different parameters were conducted. The proposed method demonstrated a significant computation time reduction with predictable and acceptable trade-off errors. Then, the proposed methodology was successfully applied to real database transaction logs of Korea Atomic Energy Research Institute. As a result, we show that for a very large dataset, the grid-LOF can be considered as an acceptable approximation for the original LOF. Moreover, it can also be effectively used for real-time outlier detection. [ABSTRACT FROM AUTHOR]

Abstract: Blind source extraction (BSE) is a special class of blind source separation (BSS) method. Due to its low computation load and fast processing speed, BSE has become one of the promising methods in signal processing and analysis. This paper addresses BSE problem when a desired source signal has temporal structures. Based on the generalized autocorrelations of the desired signal and the non-Gaussianity of its innovations, we develop an objective function. Maximizing this objective function, we present a BSE algorithm and further give its stability analysis in this paper. Simulations on image data and electrocardiogram (ECG) data indicate its better performance and the better property of tolerance to the estimation error of the time delay. [Copyright &y& Elsevier]

In this paper, we investigate the joint design of a transmit beampattern and angular waveform (AW) for colocated multiple-input multiple-output (MIMO) radars. The importance of the AW in the proposed signal processing strategy is first clarified, and then, two optimization models are established, which are aimed at either the power spectral density (PSD) design or the spectral compatibility and similarity design of the AW. There are two main differences between the proposed models and existing models. First, instead of matching a desired template or maximizing the transmit power on specific regions, the transmit beampattern in this paper is optimized to approach several key points, which guarantees the high transmit gain and the flexible adjustment of each beam gain. Second, instead of optimizing the performance of the transmit waveform, only the characteristics of the AW are examined, and they can be constrained quantitatively according to their relationship with the transmit gain. The two models can be unified into the same framework, and an efficient algorithm is proposed to solve the problem under a constant modulus constraint. The convergence of the proposed algorithm is demonstrated, and some improvements to reduce the computational complexity are proposed. Numerical simulations showed that compared to the existing methods, the proposed approach can be used to obtain a higher transmit gain, flexibly adjust each beam gain, and more accurately control the PSD, spectral compatibility, and similarity of the AW. Moreover, numerical simulations showed that, compared to the use of existing methods, the proposed algorithm has higher computational efficiency. [ABSTRACT FROM AUTHOR]

Propagation source location is an important problem which can help authorities developing control strategies on complex networks. In this paper, we study this problem by assuming that there is a single propagation source and the spreading process on networks follows the Susceptible-Infected (SI) model. We define a Rationality Observation Value ( ROV ) on infected tree that has a fixed root and propose the corresponding measuring method. Using ROV , we construct a source estimator for tree graph and generalize it to arbitrary graph. Based on the generalized estimator, a novel Propagation Centrality (PC) algorithm is proposed, which could locate the propagation source on arbitrary graph with complexity O ( N 3 ). With PC algorithm, source location is converted into finding out a Breadth-First-Search (BFS) spanning tree with the maximal ROV from infected graph. We perform extensive simulations on a series of synthetic and real networks, the results show that PC performs better than other 5 existing methods. More importantly, the results also show that PC performs stable on the infected graph with sparse observation phenomenon. [ABSTRACT FROM AUTHOR]

Existing principal curve algorithms have some drawbacks such as time consuming and narrow application scope in practice, since these algorithms are mainly based on global optimization. In this paper, we present the concept of Constraint Local Principal Curve (CLPC), which uses local optimization methods and restricts the principal curve with two fixed endpoints to reduce the computational complexity. In addition, we propose three CLPC algorithms by Local Optimization and Adaptive Radius to expand the range of applications and increase the solution quality. The first algorithm, i.e., CLPCg is based on greedy thinking. The second algorithm, i.e., CLPCs uses one dimensional search and the last algorithm CLPCc combines the greedy thinking and one dimensional search. Then, we define six performance indices to evaluate the performance of the CLPC algorithms. Finally, we present some numerical experiments with three simulation data sets and two GPS measured data sets in both highway and railway. The results indicate that all of the three CLPC algorithms can obtain high-accuracy data from multiple low-accuracy data efficiently. The CLPC algorithms can improve the accuracy and computational speed compared with the existing K-segment principal curve (KPC) algorithm. In addition, CLPCc outperforms CLPCg and CLPCs according to the comprehensive experiments while CLPCg runs much faster than other ones. [ABSTRACT FROM AUTHOR]

This paper presents a new collision avoidance strategy and its integration with the collision detection for five-axis NC machining that improves upon the earlier collision detection algorithm developed by the present authors [T.D. Tang, Erik L.J. Bohez, Pisut Koomsap (2007) The sweep plane algorithm for global collision detection with workpiece geometry update for five-axis NC machining. Computer-Aided Design 39(11):1012-1024]. The proposed algorithm automatically detects and corrects the collision based on the biggest collision boxes. The collision detection algorithm is based on the bounding volume and the sweep plane approach. The collision is firstly detected by using the bounding sphere algorithm, and the colliding spheres are then further checked with the sweep plane algorithm. The change of the workpiece geometry is included in the detection process. After the collision detection, the collision data are stored. Only the biggest collision boxes (the boxes with the biggest edge in the X, Y, or Z direction) for each type of collisions are stored. The collision avoidance algorithm corrects the biggest collision based on heuristic strategy. With this strategy, when the biggest collision is corrected, most of other collisions will disappear automatically. Therefore, the time complexity of the collision avoidance strategy is considerably reduced. The algorithm has been implemented in Visual C++ and OpenGL, demonstrated for five-axis machine with two rotary axes on the table (Deckel MAHO 600e) and can be customized to apply for any five-axis CNC machines. [ABSTRACT FROM AUTHOR]

An improved image magnification algorithm for gray and color images is presented in this paper to meet the challenge of preserving high-frequency components of an image, including both image edges and texture structures. In the proposed algorithm, a new edge detection method that uses the well-known Otsu automatic optimum thresholding is proposed to distinguish strong edge pixels. The parameters of the original directional cubic convolution interpolation algorithm, which were selected based on training, were eliminated. As a result, our algorithm achieves more accurate edge detection, better interpolation results, and less computational complexity. Simulation results demonstrate that the improved algorithm can reconstruct the magnified image, preserve edges and textures simultaneously, and reduce common interpolation artifacts. Furthermore, it generates higher visual quality of the magnified images and achieves higher peak signal-to-noise ratio, structural similarity, and feature similarity compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

We describe a successive-cancellation list decoder for polar codes, which is a generalization of the classic successive-cancellation decoder of Arıkan. In the proposed list decoder, $L$ decoding paths are considered concurrently at each decoding stage, where L$ is an integer parameter. At the end of the decoding process, the most likely among the L$ paths is selected as the single codeword at the decoder output. Simulations show that the resulting performance is very close to that of maximum-likelihood decoding, even for moderate values of L$ . Alternatively, if a genie is allowed to pick the transmitted codeword from the list, the results are comparable with the performance of current state-of-the-art LDPC codes. We show that such a genie can be easily implemented using simple CRC precoding. The specific list-decoding algorithm that achieves this performance doubles the number of decoding paths for each information bit, and then uses a pruning procedure to discard all but the L time, which is in stark contrast with the $O(n \log n)$ complexity of the original successive-cancellation decoder. In this paper, we utilize the structure of polar codes along with certain algorithmic transformations in order to overcome this problem: we devise an efficient, numerically stable, implementation of the proposed list decoder that takes only $O(L n \log n)$ time and $O(L n)$ space. [ABSTRACT FROM AUTHOR]

If equipped with several radar emitters, a target will produce more than one measurement per time step and is denoted as an extended target. However, due to the requirement of all possible measurement set partitions, the exact probability hypothesis density filter for extended target tracking is computationally intractable. To reduce the computational burden, a fast partitioning algorithm based on hierarchy clustering is proposed in this paper. It combines the two most similar cells to obtain new partitions step by step. The pseudo-likelihoods in the Gaussian-mixture probability hypothesis density filter can then be computed iteratively. Furthermore, considering the additional measurement information from the emitter target, the signal feature is also used in partitioning the measurement set to improve the tracking performance. The simulation results show that the proposed method can perform better with lower computational complexity in scenarios with different clutter densities. [ABSTRACT FROM AUTHOR]

SUMMARY Recently, multiple cell types with overlapping coverage have been deployed simultaneously to increase cellular network capacity. Cross-tier interference is one of the key technical challenges in the use of this method. A simple and practical beamforming scheme assisted by locally positioned communication devices for the downlink of a multi-cell wireless hierarchical cell structure system is proposed in this paper to maximize the capacity of the embedded small cell and simultaneously ensure minimal impact on the performance of existing macrocells. The locally positioned communication devices can be implemented with low complexity and at low cost and can continuously provide helpful and accurate information to base stations for the proper configuration of geographical cell coverage, allowing neighboring cells to cooperate effectively with each other. Simulation results verify that the proposed scheme can provide a significant gain over conventional systems not using the proposed algorithm. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Binary sparse measurement matrices are widely used in compressed sensing (CS) due to their low computational complexity. However, binary sparse measurement matrices perform well in CS-based binary signal recovery only when the source signals are very sparse (e.g., k/ n=0.1, where k is the sparsity of the source signal, n is the length of the source signal). In this paper, we propose to construct a non-binary sparse measurement matrix to recover binary source signals which are not so sparse (e.g., k/ n=0.2) accurately with few measurements. The novel measurement matrix enables us to design a suboptimal and effective recovery algorithm by fully exploiting the structural features. Moreover, we analyze and estimate the un-recovery probability based on the tree structure to evaluate the recovery performance. The simulation results validate that non-binary sparse measurement matrices can be used to recover binary source signals which are not so sparse, the recovery performance of non-binary sparse measurement matrices is better than that of binary sparse measurement matrices in terms of the un-recovery probability. [ABSTRACT FROM AUTHOR]

Abstract: Histogram equalization (HE), is a simple and less complex technique for image contrast enhancement, but it fails to preserve their brightness and natural appearance. It usually introduces visual artifacts due to saturation and un-even expansion of intensities. This paper proposes a novel modification of HE method, which decomposes the histogram of the input image into multiple segments utilizing median values as thresholds. Prior to equalization process, segments having high probability regions are identified and allocated with new dynamic range, while other segments remain unaltered. In order to reduce the algorithmic complexity, the HE is applied over selected segments instead of complete histogram. Finally combination of equalized and un-equalized segments are normalized. Simulation results show that for wide-range of test images, the proposed method enhances contrast while preserving the brightness and natural appearance of the images. [Copyright &y& Elsevier]

Abstract: Adaptation of mobile robot controllers commonly requires the computation of optimal points of operation. Specifically, for miniature mobile robots with serious computational limitations, that are typical of embedded systems, one of the main challenges is the adaptation of efficient computational methods in order to find solutions of complex optimization problems, which demand large execution times. This drawback compels the design of high-performance parallel optimization algorithms which must run over embedded system platforms. This paper describes how adequate hardware implementations of the Particle Swarm Optimization (PSO) algorithm can be useful for real time adaptation of mobile robot controllers. For achieving this, a new architecture is proposed, which is based on an FPGA implementation of the opposition-based learning (OBL) approach applied to the PSO (for short HPOPSO), and which explores the intrinsic parallelism of this algorithm in order to adjust the weights of a neural robot controller in real time according to desired behaviors. The proposed HPOPSO was applied to the learning-from-demonstration problem in which a teacher performs executions of the desired behavior. Effectiveness of the proposed architecture was demonstrated by numerical simulations and the feasibility of the adaptive behavior of the neural robot controller was confirmed for two obstacle avoidance case studies that were preserved when one or more failures on the distance sensors occur. The HPOPSO, which uses the OBL technique, improves the quality of the solutions in comparison with the standard PSO. Comparisons of the adaptation time between hardware and software approaches have demonstrated the suitability of the FPGA implementation of the proposed HPOPSO for attending specific requirements of embedded system applications. [Copyright &y& Elsevier]

Quality of service (QoS) model of composite services and web service selection based on QoS are currently the hot issues in the web service composition area. Service selection based on QoS, which is a globally optimal selection issue, is a NP-hard problem. Taking engine into consideration, this paper develops a QoS model for service selection in the web composite services. We use the algorithm on the estimation of distribution to solve the NP-hard problem of service selection and present a web service selection method based on the univariate marginal distribution algorithm (UMDA). Simulation analysis and experimental study based on the UMDA method are carried out. It is proved that the method is effective in solving the NP-hard problem. [ABSTRACT FROM AUTHOR]

Finite element based formulations for flexible multibody systems are becoming increasingly popular and as the complexity of the configurations to be treated increases, so does the computational cost. It seems natural to investigate the applicability of parallel processing to this type of problems; domain decomposition techniques have been used extensively for this purpose. In this approach, the computational domain is divided into non-overlapping sub-domains, and the continuity of the displacement field across sub-domain boundaries is enforced via the Lagrange multiplier technique. In the finite element literature, this approach is presented as a mathematical algorithm that enables parallel processing. In this paper, the divided system is viewed as a flexible multibody system, and the sub-domains are connected by kinematic constraints. Consequently, all the techniques applicable to the enforcement of constraints in multibody systems become applicable to the present problem. In particular, it is shown that a combination of the localized Lagrange multiplier technique with the augmented Lagrange formulation leads to interesting solution strategies. The proposed algorithm is compared with the well-known FETI approach with regards to convergence and efficiency characteristics. The present algorithm is relatively simple and leads to improved convergence and efficiency characteristics. Finally, implementation on a parallel computer was conducted for the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new regularized kernel-based approach for the estimation of the second order moments of stationary stochastic processes. The proposed estimator is defined by a Tikhonov-type variational problem. It contains few unknown parameters which can be estimated by cross validation solving a sequence of problems whose computational complexity scales linearly with the number of noisy moments (derived from the samples of the process). The correlation functions are assumed to be summable and the hypothesis space is a reproducing kernel Hilbert space induced by the recently introduced stable spline kernel. In this way, information on the decay to zero of the functions to be reconstructed is incorporated in the estimation process. An application to the identification of transfer functions in the case of white noise as input is also presented. Numerical simulations show that the proposed method compares favorably with respect to standard nonparametric estimation algorithms that exploit an oracle-type tuning of the parameters. [Copyright &y& Elsevier]

NTRU is one of the most widely used public-key cryptosystems and its security has been an active research topic. This paper proposes a new way to find NTRU-2005 private key. The algorithm is based on meet-in-the-middle attack and a quantum algorithm for searching the fixed weight target. Compared with the current classical and quantum meet-in-the-middle attacks, our algorithm has lower time and space complexity. Moreover, this attack can also be applied against different versions of NTRU. The result can help to understand the security of NTRU better. [ABSTRACT FROM AUTHOR]

Abstract: Realistic rain simulation is a challenging problem due to the variety of different phenomena to consider. In this paper we propose a new rain rendering algorithm that extends present state of the art in the field, achieving real-time rendering of rain streaks and splashes with complex illumination effects, along with fog, halos and light glows as hints of the participating media. Our algorithm creates particles in the scene using an artist-defined storm distribution (e.g., provided as a 2D cloud distribution). Unlike previous algorithms, no restrictions are imposed on the rain area dimension or shape. Our technique adaptively samples the storm area to simulate rain particles only in the relevant regions and only around the observer. Particle simulation is executed entirely in the graphics hardware, by placing the particles at their updated coordinates at each time-step, also checking for collisions with the scene. To render the rain streaks, we use precomputed images and combine them to achieve complex illumination effects. Several optimizations are introduced to render realistic rain with virtually millions of falling rain droplets. [Copyright &y& Elsevier]

Abstract: The high advanced techniques in parallel computing can be employed for a better understanding of groundwater flow fluids. Generally, the geological media are very heterogeneous and contain complex structures. Decomposing these structures into, approximately, equivalent sub-structures for a load-balancing is a major challenge. This paper proposes and analyses a new algorithm to simulate parallel flow fluid in such complex media. Fully parallel software is developed, and two well-known sparse linear solvers, based respectively on a multifrontal Cholesky factorization and an iterative structured multigrid method, are compared. The mixed finite element (MFE) method is used to discretize Darcy’s equation. Numerical examples are presented to show the efficiency and robustness of the algorithm proposed. [Copyright &y& Elsevier]

Abstract: Failure Detection and Diagnosis (FDD) using Discrete Event System (DES) framework is used for wide range of applications because of simplicity of both the model and associated algorithms. Initial research was focussed on permanent failures. Many systems exhibit temporary failures in the sense that system recovers to normal condition after failure. Contant et al. extended the event based DES framework, developed for permanent failure by Sampath et al. to handle temporary failures. Zad et al. developed a state based DES framework for permanent failures which has several advantages compared to the event based approach. In this paper, we extend the state based DES framework, developed for permanent failures, to handle temporary failures, maintaining the same order of complexity. The proposed DES framework has several advantages compared to that by Contant et al. namely, capability to detect failures that occur before starting execution of the diagnoser, same diagnoser for both temporary and permanent failures, etc. [Copyright &y& Elsevier]

This paper presents a partial element equivalent circuit (PEEC)-based solver that has been accelerated to exploit the massively parallel structure of graphics processing unit (GPU) technology, in order to employ a reluctance-based method in an efficient way. A grouping algorithm is also presented which makes reluctance calculation efficient, suitable for GPUs, and feasible even for very large problems. It has been shown that by using the reluctance method, the coefficient matrix in the system equation can be safely sparsified whilst the required accuracy is maintained. Because the calculation of the reluctance matrix includes matrix inversion, which is a task with high computational complexity, GPUs as cooperative units are utilized to reduce computational costs by taking advantage of parallelism. Two test models have been simulated and analyzed to benchmark the solver, and the results have been compared with the previously developed solver. Furthermore, analyzing the results reveals that the reluctance method makes it possible to use a considerably sparser system and thereby solve large problems by decreasing the memory demands and the solution time. It is also proven that the solution is reliable and accurate, whereas the problem has become noticeably smaller. [ABSTRACT FROM AUTHOR]

Signal detection algorithms providing likelihood information for coded spatial-multiplexing multiple-input–multiple-output (MIMO) wireless communication systems pose a critical design challenge due to their prohibitively high computational complexity. In this paper, we present a low-complexity soft-output detection algorithm by adopting four implementation-friendly algorithm-level improvements to the fixed-complexity sphere decoder. More specifically, we introduce a reliability-dependent tree expansion approach and an on-demand list-size reduction scheme for low-cost candidate list generation. In terms of performance improvement, we apply an early bit-flipping strategy and utilize the \ell^1-norm distance representation. The algorithm is evaluated by computer simulations performed over Rayleigh flat fading channels and computational complexity analysis. Compared with the soft-output K-Best algorithm, the proposed algorithm saves at least 60% of the computations for detecting 4 \times 4 64-quadrature amplitude modulation (QAM) MIMO signal and, at the same time, provides better detection performance, making it a promising detection scheme for real-life hardware implementation. [ABSTRACT FROM PUBLISHER]

This paper presents and puts forward an optimal automatic distributing of physical cell identity (ADPCI) scheme for the selforganizing network (SON). Considering the high number and the layered structure of the evolved node B (eNodeB, eNB) in the initial rollout phase, the assigning of PCI for cells would be quite complex. The PCI self-distributing problem is mapped to the well-known minimum spanning tree (MST) problem in order to optimize the PCI reuse distance and decrease the multiplexing interference. The correlation property of PCI is analyzed and taken into consideration in the assigning phase. Moreover, a suboptimal algorithm (SADPCI) is presented as it performs approximately to ADPCI but the computational complexity is lower. To demonstrate the proposal validity, performances of ADPCI and SADPCI are evaluated. Simulation results illustrate that these schemes can achieve significantly higher performance even under the condition of severe PCI deficiency. [ABSTRACT FROM AUTHOR]

Endmember extraction is very important in hyperspectral image analysis. The accurate identification of endmembers enables target detection and classification and efficient spectral unmixing. Although a number of endmember extraction algorithms have been proposed, such as two state-of-the-art algorithms-vertex component analysis (VCA) and simplex growing algorithm (SGA)-it is still a rather challenging task. In this paper, a new maximum simplex volume method based on Householder transformation (HT), referred to as maximum volume by HT (MVHT), is presented for endmember extraction. The proposed algorithm provides consistent results with low computational complexity, which overcomes the disadvantage of the inconsistent result of VCA and the shortcoming of the high computational cost of SGA resulted from calculating the simplex volume. A comparative study and analysis are conducted among the three endmember extraction algorithms, VCA, SGA, and MVHT, on both simulated and real hyperspectral data. The obtained experimental results demonstrate that the proposed MVHT algorithm generally provides a competitive or even better performance over VCA and SGA. [ABSTRACT FROM AUTHOR]

Bioluminescence tomography (BLT) is a molecular imaging modality which can three-dimensionally resolve the molecular processes in small animals in vivo. The ill-posedness nature of BLT problem makes its reconstruction bears nonunique solution and is sensitive to noise. In this paper, we proposed a sparse BLT reconstruction algorithm based on semigreedy method. To reduce the ill-posedness and computational cost, the optimal permissible source region was automatically chosen by using an iterative search tree. The proposed method obtained fast and stable source reconstruction from the whole body and imposed constraint without using a regularization penalty term. Numerical simulations on a mouse atlas, and in vivo mouse experiments were conducted to validate the effectiveness and potential of the method. [ABSTRACT FROM AUTHOR]

Missing link prediction provides significant instruction for both analysis of network structure and mining of unknown links in incomplete networks. Recently, many algorithms have been proposed based on various node-similarity measures. Among these measures, the common neighbour index, the resource allocation index, and the local path index, stemming from different source, have been proved to have relatively high accuracy and low computational effort. In this paper, we propose a similarity index by combining the resource allocation index and the local path index. Simulation results on six unweighted networks show that the accuracy of the proposed index is higher than that of the local path one. Based on the same idea of the present index, we develop its corresponding weighted version and test it on several weighted networks. It is found that, except for the USAir network, the weighted variant also performs better than both the weighted resource allocation index and the weighted local path index. Due to the improved accuracy and the still low computational complexity, the indices may be useful for link prediction. [ABSTRACT FROM AUTHOR]

In view of the characteristics of the meteor burst channel, the variable rate data transmission should be adopted to improve the system average throughput, which results in channel tracing and equalization problems at the receiver. Although the joint data and channel estimation of maximum likelihood sequence detection performing the principle of per-survivor processing (PSP) is considered as an optimal detection scheme, its great computational complexity is a major problem and can hardly agree with the decreasing of the meteor channel. Based on the estimation of the system parameters, an adaptive state reduction of the PSP (ASRP) algorithm with only a few states in the trellis diagram is employed, while these states are chosen by the time-varying threshold according to the exponential decay of meteor channels. It is shown that, ASRP can make a good tradeoff between the performance and the computational complexity, and provides reliable data transmission for adaptive modulation and coding of the meteor burst communication system. Computer simulation results and performance analysis are also included to support our developments. Copyright © 2010 John Wiley & Sons, Ltd. In this paper, we proposed a variable rate data transmission scheme to improve the system average throughput of the meteor burst channel. Based on the estimation of the system parameters, an adaptive state reduction of the PSP (ASRP) algorithm with only a few states in the trellis diagram is employed to reduce computational complexity. The computer simulation results and performance analysis show that ASRP can make a good tradeoff between the performance and the computational complexity, and provides reliable data transmission for adaptive modulation and coding of the meteor burst communication system. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We present in this paper an integrated solution to rapidly recognizing dynamic objects in surveillance videos by exploring various contextual information. This solution consists of three components. The first one is a multi-view object representation. It contains a set of deformable object templates, each of which comprises an ensemble of active features for an object category in a specific view/pose. The template can be efficiently learned via a small set of roughly aligned positive samples without negative samples. The second component is a unified spatio-temporal context model, which integrates two types of contextual information in a Bayesian way. One is the spatial context, including main surface property (constraints on object type and density) and camera geometric parameters (constraints on object size at a specific location). The other is the temporal context, containing the pixel-level and instance-level consistency models, used to generate the foreground probability map and local object trajectory prediction. We also combine the above spatial and temporal contextual information to estimate the object pose in scene and use it as a strong prior for inference. The third component is a robust sampling-based inference procedure. Taking the spatio-temporal contextual knowledge as the prior model and deformable template matching as the likelihood model, we formulate the problem of object category recognition as a maximum-a-posteriori problem. The probabilistic inference can be achieved by a simple Markov chain Mento Carlo sampler, owing to the informative spatio-temporal context model which is able to greatly reduce the computation complexity and the category ambiguities. The system performance and benefit gain from the spatio-temporal contextual information are quantitatively evaluated on several challenging datasets and the comparison results clearly demonstrate that our proposed algorithm outperforms other state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]

Abstract: The present paper introduces a new model for teaching randomized learners. Our new model, though based on the classical teaching dimension model, allows to study the influence of the learner’s memory size and of the presence or absence of feedback. Moreover, in the new model the order in which examples are presented may influence the teaching process. The resulting models are related to Markov decision processes, and characterizations of optimal teachers for memoryless learners with feedback and for learners with infinite memory and feedback are shown. Furthermore, in the new model it is possible to investigate new aspects of teaching like teaching from positive data only or teaching with inconsistent teachers. Characterization theorems for teachability from positive data for both ordinary teachers and inconsistent teachers with and without feedback are provided. [Copyright &y& Elsevier]

Abstract: This article proposes an unbiased plain gradient algorithm for a second-order adaptive IIR notch filter with constrained poles and zeros. The proposed algorithm employs removing a dominant parameter that produces inherent bias. By using this technique, the performances are improved with slight expense in computational complexity. In this paper, theoretical analysis for deriving the estimations of bias and mean square error (MSE) at steady state are presented in closed form. Moreover, the stability bound of the algorithm is also derived. To confirm the analytical results, the computer simulations are provided to corroborate the effectiveness of the proposed algorithm. Furthermore, the performances of the algorithm are also compared with the plain gradient (PG) and modified plain gradient (MPG) algorithms. [Copyright &y& Elsevier]

Abstract: This paper presents three novel moving-horizon estimation (MHE) methods for discrete-time partitioned linear systems, i.e., systems decomposed into coupled subsystems with non-overlapping states. The MHE approach is used due to its capability of exploiting physical constraints on states and noise in the estimation process. In the proposed algorithms, each subsystem solves reduced-order MHE problems to estimate its own state and different estimators have different computational complexity, accuracy and transmission requirements among subsystems. In all cases, proper tuning of the design parameters, i.e., the penalties on the states at the beginning of the estimation horizon, guarantees convergence of the estimation error to zero. Numerical simulations demonstrate the viability of the approach. [Copyright &y& Elsevier]