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Abstract This paper proposes a novel decomposition method based on user-preference and developed a variation of the decomposition based multi-objective optimization algorithm (MOEA/D) targeting only solutions in a small region of the Pareto-front defined by the preference information supplied by the decision maker (DM). This is particularly advantageous for solving multi-objective optimization problems (MOPs) with more than 3 objectives, i.e., many-objective optimization problems (MaOPs). As the number of objectives increases, the ability of an EMO algorithm to approximate the entire Pareto front (PF) is rapidly diminishing. In this paper, we first propose a novel scalarizing function making use of a series of new reference points derived from a reference point specified by the DM in the preference model. Based on this scalarizing function, we then develop a user-preference-based EMO algorithm, namely R-MOEA/D. One key merit of R-MOEA/D is that it does not rely on an estimation of the ideal point, which may impact significantly the performances of state-of-the-art decomposition based EMO algorithms. Our experimental results on multi-objective and many-objective benchmark problems have shown that R-MOEA/D provides a more direct and efficient search towards the preferred PF region, resulting in competitive performances. In an interactive setting when the DM changes the reference point during optimization, R-MOEA/D has a faster response speed and performance than the compared algorithms, showing its robustness and adaptability to changes of the preference model. Furthermore, the effectiveness of R-MOEA/D is verified on a real-world problem of reservoir flood control operations. [ABSTRACT FROM AUTHOR]

Abstract Traditional dynamic multiobjective evolutionary algorithms usually imitate the evolution of nature, maintaining diversity of population through different strategies and making the population track the Pareto optimal solution set efficiently after the environmental change. However, these algorithms neglect the role of the dynamic environment in evolution, leading to the lacking of active guieded search. In this paper, a dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model is proposed (DEE-DMOEA). When the environment has not changed, this algorithm makes use of the evolutionary environment to record the knowledge and information generated in evolution, and in turn, the knowledge and information guide the search. When a change is detected, the algorithm helps the population adapt to the new environment through building a dynamic evolutionary environment model, which enhances the diversity of the population by the guided method, and makes the environment and population evolve simultaneously. In addition, an implementation of the algorithm about the dynamic evolutionary environment model is introduced in this paper. The environment area and the unit area are employed to express the evolutionary environment. Furthermore, the strategies of constraint, facilitation and guidance for the evolution are proposed. Compared with three other state-of-the-art strategies on a series of test problems with linear or nonlinear correlation between design variables, the algorithm has shown its effectiveness for dealing with the dynamic multiobjective problems. [ABSTRACT FROM AUTHOR]

Abstract The paper discusses a niche area problem — the design of reduced-order multiple observers which can achieve the finite-time state reconstruction for Takagi–Sugeno multiple models with unknown inputs. The new reduced-order multiple observer obtained in this paper is only the second ever designed, the author of this paper continuing his work started one year ago with the introduction of the reduced-order multiple observer concept. The obtaining of the new reduced-order multiple observer is achieved by combining two classical reduced-order observers for linear time-invariant multivariable systems with unknown inputs and a full-order multiple observer for Takagi–Sugeno systems. The main innovative idea behind the design of the reduced-order multiple observer for Takagi–Sugeno systems described by the multiple models is the split of the multiple model into two subsystems: an unknown-input-free subsystem and an unknown-input-depending subsystem. The unknown-input-free subsystem is brought to the form of a standard multiple model and, in this situation, any algorithm to design full-order multiple observers can be used in the design of reduced-order multiple observers. The steps of the design procedure have been summarized and software implemented; then, the validation of the suggested algorithm has been done for two concrete cases associated to the motion of an aircraft during its landing Highlights • Paper discusses a niche area problem-design of reduced-order multiple observers. • The reduced-order observer designed is only the second one ever obtained. • The steps of the design procedure have been summarized and software implemented. • The observer's performances are compared with the ones of other observers. [ABSTRACT FROM AUTHOR]

In this paper, we consider the homogeneous m -machine scheduling problem where unit-time jobs have to be scheduled within their time windows so that, for any subset of machines, the set of the time units at which at least one machine is busy, is an interval. For this problem, a time assignment of the jobs satisfying the time windows constraints is a schedule if it has a pyramidal profile and is a k -schedule if this profile property is satisfied up to level k only. We develop a generic algorithm to solve the problem and show it is correct using a proof that mainly relies on a necessary and sufficient condition for a schedule to exist proved in a previous paper. We finally show that the generic algorithm is polynomial. We conclude by giving the directions of ongoing works and by bringing open questions related to different variants of the basic non-idling m -machine scheduling problem. [ABSTRACT FROM AUTHOR]

Given a real number α ∈ (0 , 1) , we define the Webster sequence of density α to be W α = (⌈ (n − 1 / 2) / α ⌉) n ∈ N , where ⌈ x ⌉ is the ceiling function. It is known that if α and β are irrational with α + β = 1 , then W α and W β partition N. On the other hand, an analogous result for three-part partitions does not hold: There does not exist a partition of N into sequences W α , W β , W γ with α , β , γ irrational. In this paper, we consider the question of how close one can come to a three-part partition of N into Webster sequences with prescribed densities α , β , γ. We show that if α , β , γ are irrational with α + β + γ = 1 , there exists a partition of N into sequences of densities α , β , γ , in which one of the sequences is a Webster sequence and the other two are "almost" Webster sequences that are obtained from Webster sequences by perturbing some elements by at most 1. We also provide two efficient algorithms to construct such partitions. The first algorithm outputs the n th element of each sequence in O (1) time and the second algorithm gives the assignment of the m th positive integer to a sequence in O (1) time. We show that the results are best-possible in several respects. Moreover, we describe applications of these results to apportionment and optimal scheduling problems. [ABSTRACT FROM AUTHOR]

Time-series modeling is a well-studied topic of classical analysis and machine learning. However, large datasets are required to obtain the model with a better prediction quality with the increasing model complexity. Therefore, some applications demand synthetic datasets that are preserving modeling-sensitive properties. Another application of synthetic data is data anonymization. The synthetic data generation algorithm may be split into two parts: the time-series modeling and the synthetic data generation parts. The model must be interpretable to obtain the synthetic data with good quality. The model parameter interpretation allows controlling generation by adding noise to different groups of parameters. In the paper, the evolutionary multi-objective closed-form algebraic expressions discovery approach that allows obtaining the model in the form that may be analyzed using the mathematics is proposed. The analysis allows the interpretation of the model parameters for the controllable generation of the synthetic data. The notion of synthetic data quality is discussed. The examples of the synthetic time-series generation based on two datasets with different properties are shown. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive fuzzy logic controller (FLC) design technique for controlling an induction motor speed drive using backtracking search algorithm (BSA). This technique avoids the exhaustive traditional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive MFs are implemented in speed controller design for input and output based on the evaluation results of the fitness function formulated by the BSA. In this paper, the mean absolute error (MAE) of the rotor speed response for three phase induction motor (TIM) is used as a fitness function. An optimal BSA-based FLC (BSAF) fitness function is also employed to tune and minimize the MAE to improve the performance of the TIM in terms of changes in speed and torque. Moreover, the measurement of the real TIM parameters via three practical tests is used for simulation the TIM. Results obtained from the BSAF are compared with those obtained through gravitational search algorithm (GSA) and particle swarm optimization (PSO) to validate the developed controller. Design procedure and accuracy of the develop FLC are illustrated and investigated via simulation tests for TIM in a MATLAB/Simulink environment. Results show that the BSAF controller is better than the GSA and PSO controllers in all tested cases in terms of damping capability, and transient response under different mechanical loads and speeds. [ABSTRACT FROM AUTHOR]

Abstract The sensitivity conjecture proposed by Nisan and Szegedy in 1994, which asserts that for any Boolean function, its sensitivity complexity is polynomially related to the block sensitivity complexity, is one of the most important and challenging problems in the study of decision tree complexity. Despite a lot of efforts, the best known upper bound of block sensitivity, as well as the certificate complexity, is still exponential in terms of sensitivity. In this paper, we give a better upper bound for certificate complexity and block sensitivity, b s (f) ≤ C (f) ≤ (8 9 + o (1)) s (f) 2 s (f) − 1 , where b s (f) , C (f) and s (f) are the block sensitivity, certificate complexity and sensitivity, respectively. The proof is based on a deep investigation on the structure of the sensitivity graph. We also provide a tighter relationship between the 0-certificate complexity C 0 (f) and 0-sensitivity s 0 (f) for functions with small 1-sensitivity s 1 (f). [ABSTRACT FROM AUTHOR]

Abstract In the current paper, an error estimate has been proposed to find a second-order finite difference scheme for solving the Riesz space distributed-order diffusion equation. The convergence order of the proposed method is O (τ 2 + h 2). The numerical results show the efficiency of the new technique. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we defined the scattering energy intensity based on the Poynting vector to quantitatively study the cloak effect of electromagnetic waves in the time domain. The influences of the effective working frequency bands of four kinds of electromagnetic cloak materials, incidence angle of electromagnetic waves and the number of approximately cloak layers on the cloak effect are studied. To the best of our knowledge, this is the first time to use the time domain method to quantitatively study the effective working frequency band and the scattering energy intensity of cloak materials. [ABSTRACT FROM AUTHOR]

Abstract In this work, we are interested in the steganography domain where the problem of security represents the basic key in the dissimulation of relevant information. The paper highlights two facets of research. The first facet represents a novel proposition of a naturally-inspired metaheuristic based on the smell sense of a honeybee insect. Herein, the collective foraging behavior is not explicitly intended but the search driven by smell sense is critical. The second facet highlights the proposition of two steganographic embedding schemes. The main idea behind the 2-Bit embedding scheme focus on the embedding rate of two secret bits by pixel. Compared to other steganographic algorithms, the intended bit that will support the elementary secret information can be anyone in the pixel byte provided that its content matches with the secret bit to embed. The proposed steganographic algorithm named HIBE takes advantage from transform domain since it presents more robustness against image processing attacks. And eventually, takes advantage from spatial domain since the lower distortion results achieved in comparison with other well-knew algorithm like HUGO, and WOW gives it more efficiency. Moreover, to evaluate its performance against steganalyzers methods based machine learning and classifiers some experiments were carried out on the BOSSbase dataset images. The reached results have shown that the used detectors, also well-knew, failed to classify the learned images as stego since their achieved detection accuracies approaches the random detection. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we present an integral imaging microscopy, which can achieve continuous depth extraction. The multiple stereo matching algorithm is optimized with data point interpolation and calculation range correction, which can improve the computing efficiency and accuracy. A water suspension of silica-embedded magnetic particles in light-trap environment is used as a specimen. The elementary image array is obtained through sequentially imaging the specimen by the objective lens and microlens array, which contains continuous depth information that can give the optical tweezers a feedback for precise 3-D manipulation. Experimental and calculated results show the feasibility of our method. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose a low-overhead equalization algorithm used in the intensity modulation direct detection (IMDD) system with offset-quadrature amplitude modulation orthogonal frequency division multiplexing (OQAM-OFDM) modulation. By utilizing the complex interference approximation (IA) structure and the comb pilot-based frequency domain intra-symbol averaging and intra-subcarrier linear interpolation (CFAL) method, the OQAM-OFDM signal's intrinsic imaginary non-orthogonal distortions can be mitigated and the low overhead of channel estimation with high estimation accuracy can be achieved. The experiment and numerical simulation are constructed to evaluate the proposed IA-CFAL algorithm from the aspects of bit error rate (BER) performance, mean square error (MSE), dispersion tolerance, time jitter tolerance and complexity. Comparison with the previous algorithms are made to prove its robustness to transmission impairments with low training overhead. Highlights • Low pilot overhead. • Simultaneously Estimating Channel and Mitigating Intrinsic imaginary interference. • High estimation accuracy. • Complex interference approximation (IA) structure. • The comb pilot-based frequency domain intra-symbol intra-subcarrier linear interpolation. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we present a newfound in the two-dimensional Visible Light Communication (VLC) system, i.e., additional modulation orders are generated in the receiver. Meanwhile, an algorithm named High/Low order Bit Interleaving (HLBI) is proposed in a formulated VLC Multiple-Input-Single-Output (MISO) model, which aims to decrease part of these orders. A data rate of 1.24Gbit/s is achieved in 1.3 m indoor Line-Of-Sight (LOS) scenario with the Bit Error Rate (BER) is under 3.8 × 10−3. Since the algorithm also aids STBC decoding, the Q factor can be improved to 1.92dB. [ABSTRACT FROM AUTHOR]

By introducing sign constraints on the weights, this paper proposes sign constrained rectifier networks (SCRNs), whose training can be solved efficiently by the well known majorization–minimization (MM) algorithms. We prove that the proposed two-hidden-layer SCRNs, which exhibit negative weights in the second hidden layer and negative weights in the output layer, are capable of separating any number of disjoint pattern sets. Furthermore, the proposed two-hidden-layer SCRNs can decompose the patterns of each class into several clusters so that each cluster is convexly separable from all the patterns from the other classes. This provides a means to learn the pattern structures and analyse the discriminant factors between different classes of patterns. Experimental results are provided to show the benefits of sign constraints in improving classification performance and the efficiency of the proposed MM algorithm. [ABSTRACT FROM AUTHOR]

Linear feedback shift registers (LFSRs) play a significant role in communications security and we investigate design of a selected class of word-based LFSRs known as σ -LFSRs. Both the search algorithm and the construction algorithm generate efficient primitive σ -LFSRs. The search algorithm first constructs the σ -polynomial and then checks the primitiveness of the σ -polynomial, whereas the construction algorithm for the σ -LFSR, first finds a primitive polynomial f ( x ) and then constructs the primitive σ -LFSR from f ( x ) . In this paper, we present some novel results pertaining to the search algorithm for primitive σ -LFSR along with the exhaustive search space complexity of the search algorithm for σ -LFSRs. Then we investigate and compare the performance of the construction algorithm with the search algorithm for the primitive σ -LFSR. Finally, the number of σ -LFSRs similar to the σ -LFSRs generated by the construction algorithm is provided. [ABSTRACT FROM AUTHOR]

The penalty-based boundary intersection (PBI) approach is widely used in the decomposition-based multi-objective evolutionary algorithm (MOEA/D). Generally, a uniform distribution of weight vectors in PBI approach will lead to a set of evenly distributed solutions on the Pareto-optimal front (POF), but this approach cannot work well in practice when the target multi-objective optimization problem (MOP) has a complex POF. For example, the POF may have disconnected regions and a long tail and a sharp peak and a degenerate geometry, which significantly degrades the performance of the original MOEA/D. This paper proposes a modified PBI (MPBI) approach and a strategy of adjusting reference points (ARP) to handle these MOPs with complex fronts. A two-stage strategy is adopted in the proposed algorithm. The first stage is to determine a hyperplane based on the modified PBI approach, so that the projection points derived from the solutions obtained in second stage to this hyperplane are all in the first quadrant. Exploring those regions where the solution exists is also a key task in this stage. The second stage is to adjust the reference points periodically so that the reference points can be redistributed adaptively to improve the distribution of solutions. The framework of the proposed algorithm is based on θ -DEA and named NSGA-MPBI. Some widely used test instances and three many-objective MOPs with complex POFs are employed in the experiments. The experimental results indicate that NSGA-MPBI outperforms the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In Gordon (1998), Gordon presented alternate proofs of several well known results in elementary real analysis using tagged partitions. In this paper, we provide a set of alternative proofs based on the dyadic partitions. An important difference between tagged and dyadic partitions is that the results based on the dyadic partition can be obtained constructively, i.e. an algorithm is supplied to reach the conclusion of each proof. In addition, the construction involves only concepts and results presented in a first course real analysis, which is the reason why, comparing tagged partition and its theory, the proofs based on dyadic partition are more straightforward and accessible to a wide range of audience. [ABSTRACT FROM AUTHOR]

Successful classification of questioned footwear has tremendous evidentiary value; the result can minimize the potential suspect pool and link a suspect to a victim, a crime scene, or even multiple crime scenes to each other. With this in mind, several different automated and semi-automated classification models have been applied to the forensic footwear recognition problem, with superior performance commonly associated with two different approaches: correlation of image power (magnitude) or phase, and the use of local interest points transformed using the Scale Invariant Feature Transform (SIFT) and compared using Random Sample Consensus (RANSAC). Despite the distinction associated with each of these methods, all three have not been cross-compared using a single dataset, of limited quality (i.e., characteristic of crime scene-like imagery), and created using a wide combination of image inputs. To address this question, the research presented here examines the classification performance of the Fourier-Mellin transform (FMT), phase-only correlation (POC), and local interest points (transformed using SIFT and compared using RANSAC), as a function of inputs that include mixed media (blood and dust), transfer mechanisms (gel lifters), enhancement techniques (digital and chemical) and variations in print substrate (ceramic tiles, vinyl tiles and paper). Results indicate that POC outperforms both FMT and SIFT+RANSAC, regardless of image input (type, quality and totality), and that the difference in stochastic dominance detected for POC is significant across all image comparison scenarios evaluated in this study. [ABSTRACT FROM AUTHOR]

Let P be a set of n points in general position in the plane which is partitioned into color classes. The set P is said to be color-balanced if the number of points of each color is at most [n/2] . Given a color-balanced point set P, a balanced cut is a line which partitions P into two color-balanced point sets, each of size at most 2n/3+1 . A colored matching of P is a perfect matching in which every edge connects two points of distinct colors by a straight line segment. A plane colored matching is a colored matching which is non-crossing. In this paper, we present an algorithm which computes a balanced cut for P in linear time. Consequently, we present an algorithm which computes a plane colored matching of P optimally in Θ(nlog n) time. [ABSTRACT FROM AUTHOR]

The Small Exponent Test (SET) for exponentiation is an essential batch-verification technique that is widely applied. In this paper, we propose a simplified SET that can securely batch-verify n instances with only n − 1 randomizing exponents. We show that the structure of the proposed batch test is compact in the sense that it works with a minimal number of randomizing exponents for the SET. Thus, our test offers various advantages. Overall, compared to the original SET, the proposed simplified SET is more efficient for any sized batch instance. In particular, unlike the SET, our proposal performs well even when the size of a batch instance is small, e.g., n = 1 , 2 , 3 , and 4. This feature can be also used to significantly reduce pairing computations in a signature scheme where several pairing equations are verified. In addition, our test can be combined easily and generically with existing batch techniques such as the use of sparse exponents, the bucket test for large batch sizes, or an automated tool to generate a batch algorithm. Finally, with our simplified test, an efficient identification algorithm can be constructed to discover incorrect instances in a batch. [ABSTRACT FROM AUTHOR]

In this paper, we propose a blind Volterra series based nonlinear equalization (VNLE) with low complexity for the nonlinear distortion mitigation in short reach optical carrierless amplitude and phase (CAP) modulation system. The principle of the blind VNLE is presented and the performance of its blind adaptive algorithms including the modified cascaded multi-mode algorithm (MCMMA) and direct detection LMS (DD-LMS) are investigated experimentally. Compared to the conventional VNLE using training symbols before demodulation, it is performed after matched filtering and downsampling, so shorter memory length is required but similar performance improvement is observed. About 1 dB improvement is observed at BER of 3.8×10 −3 for 40 Gb/s CAP32 signal over 40 km standard single mode fiber. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing of rocks has various engineering applications. However, there has been limited research into crack propagation prediction by three dimensional analytical techniques. This paper discusses such a technique for predicting the propagation surface of a pressurized circular crack subjected to various loading conditions. The propagation surfaces predicted from the proposed crack front propagation algorithm align well with published results. The suggested method consumes only a fraction of the time needed for a numerical simulation, and therefore it could be useful in assisting the design of hydraulic fracturing operations. [ABSTRACT FROM AUTHOR]

In conventional Kalman-based state estimation algorithms, there is an assumption that the uncertainties in the system state and measurements are Gaussian distributed. However, this Gaussian assumption ignores the periodic nature of angular or orientation quantities. In this paper, the Gauss von Mises (GVM) distribution model defined on a cylindrical manifold is employed, the Dirac mixture approximation method is extended to deal with sampling with GVM, in order to perform recursive filtering, the GVM approximation to joint distribution is proposed, the formula to compute posterior distribution is derived. Finally, the measurement update algorithm is developed. Simulation results show that when the system state contains a circular variable, the proposed GVM filter can achieve more accurate estimates than the traditional extended Kalmanfilter(EKF), thereby providing a novel method to estimate system state specialized to GVM distribution. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Suppose a measurand can be computed by two different but consistent measurement models. Then, the output of one of the models would serve as prior knowledge to the other. In this paper, two alternative methods to produce a PDF for the measurand that take into account both models are presented. The first method proceeds by propagating the PDFs for the input quantities through the corresponding models in the usual way and then merging the resulting PDFs using the logarithmic or linear pooling techniques. The result is a kind of ‘compromise distribution’ of the pooled PDFs. The second method starts by propagating the PDFs for all input quantities except one, say X 1 , through the model that relates the former quantities to the latter. In this way the PDF for X 1 is obtained, which is then updated using its likelihood. The resulting PDF, which encodes all information available, is finally propagated through the model that relates X 1 to the measurand. This second method is the preferred way of analysis, because it results in a PDF that is narrower than the one obtained with the first method. [ABSTRACT FROM AUTHOR]

We can define the adjacency algebra of an association scheme over an arbitrary field. It is not always semisimple over a field of positive characteristic. The structures of adjacency algebras over a field of positive characteristic have not been sufficiently studied. In this paper, we consider the structures of adjacency algebras of dual polar schemes over a field of positive characteristic and determine them when their algebras are local algebras. [ABSTRACT FROM AUTHOR]

Motion estimation is one of the most demanding and complex tools in block based video encoders. Variable block size motion estimation (ME) and multiple reference frames in H.264/AVC make motion estimation even more complex and time consuming. In HEVC, the complexity is even higher since there are more block sizes. This paper presents an analysis of various tools involved in some fast ME algorithms and proposes some improvements to them in order to achieve a novel fast hybrid algorithm. The proposed algorithm has been tested with HEVC reference software. Simulation results show that the algorithm achieves up to 44.7% decrease in ME complexity when compared to the fast ME algorithm (Test Zone Search or TZSearch) and up to 99% reduction in ME complexity compared to full search algorithm with negligible loss in PSNR and bitrate. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of block-based disparity map estimation for stereoscopic image coding where the estimated map is transmitted to the decoder in order to predict one view from the other. The estimation problem of the disparity map is thus a trade-off between the quality prediction and the binary cost of the disparities to be stored or transmitted. This trade-off is modeled as a joint entropy-distortion metric assuming that the disparity map is encoded with an entropy coder; and one of the two views is fully predicted using this map when applied to the other view. However minimizing this joint metric is a complex combinatorial optimization problem where choices of disparities are all interrelated. A sub-optimal optimization solution is then proposed. It is based on a tree structure which is constructed sequentially whenever a block is matched. The developed algorithm, called Modified M -Algorithm (MMA), processes the reference view in a raster scanning order and assumes that the disparities to be selected in the unprocessed area are likely to follow a chosen disparity distribution. This algorithm has the ability at each step of the process not only to retain the M -best paths of the tree in terms of entropy-distortion cost but also to explore all possible extensions of each of these M paths until reading the last block of the view. Simulations, conducted on stereoscopic images extracted from Middlebury and Deimos datasets, show the advantage of our MMA compared to the conventional Block Matching Algorithm (BMA) with and without regularization both in terms of reducing bitrate and distortion. [ABSTRACT FROM AUTHOR]

In this paper, we propose a single-frame super-resolution algorithm using a finite impulse response (FIR) Wiener-filter, where the correlation matrices are estimated using the nonlocal means filter. The major contribution of this work is to make use of the nonlocal means-based FIR Wiener filter to form a new iterative framework which alternately improves the estimated correlation and the estimated high-resolution (HR) image. To minimize the mean squared error of the estimated HR image, we have tried to optimize several parameters empirically, including the hyper-parameters of the nonlocal means filter by using an efficient offline training process. Experimental results show that the trained iterative framework performs better than the state-of-the-art algorithms using sparse representations and Gaussian process regression in terms of PSNR and SSIM values on a set of commonly used standard testing images. The proposed framework can be directly applied to other image processing tasks, such as denoising and restoration, and content-specific tasks such as face super-resolution. [ABSTRACT FROM AUTHOR]

• A fast contact detection algorithm (FCDA) for ice-ship interaction (ISI) is proposed • The MPI parallelization for 2-D Peridynamic (PD) theory is first introduced • The newly developed ice-ship interaction (ISI) model is effective in predicting ice damage and crack propagation in the icebreaking process • The developed ice-ship interaction (ISI) model is applied to investigate icebreaking effectiveness for two different bow shapes The bow shape is the most critical factor to determine the icebreaking performance of an icebreaker. Mechanism study on the icebreaking process for different bow types is necessary for the initial design of the icebreaker hull form. This paper proposed an ice-ship interaction model based on the meshfree method, Peridynamics, in which the geometric mathematics concept is embedded to detect the contact between material points and ship hull. Furthermore, a fast contact detection algorithm based on Massage Passing Interface (MPI) solver is built to improve the computational efficiency of the developed numerical method. Two typical icebreaker bows, the conventional bow and the unconventional bow, breaking the level ice with constant speed is numerically studied by the above model. The results of the conventional icebreaker bow are compared with the experimental results, which verifies the simulation accuracy of the model developed in the present work. Afterwards, the icebreaking modes and icebreaking loads of two different shapes of icebreaker bows are compared and analysed. The results show that the developed ice-ship interaction model effectively predicts differences of icebreaking processes between different icebreakers, such as ice damage pattern, ice loads, and channel, despite their common point in domain bending failure mode. Moreover, this research significantly improves computational efficiency and provides theoretical guidance for designing the icebreaker bow. [ABSTRACT FROM AUTHOR]