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Steganography is used to hide the occurrence of communication. Discovering and rendering useless such covert message is an art of steganalysis. The importance of techniques that can reliably detect the presence of secret messages in images is increasing as images can hide a large amount of malicious code that could be activated by a small Trojan horse type of virus and also for tracking criminal activities over Internet. This paper presents an improved blind steganalysis technique. The proposed algorithm reduces the initial-bias, and estimates the LSB embedding message ratios by constructing equations with the statistics of difference image histogram. Experimental results show that this algorithm is more accurate and reliable than the conventional difference image histogram method. It outperforms other powerful steganalysis approaches for embedded ratio greater than 40% and comparable with RS steganalysis technique for shorter hidden message length. [ABSTRACT FROM AUTHOR]

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]

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 discusses the formation of math grammar rules for LATEX math equations. These rules are used to generate Abstract Syntax Tree (AST) which extracts structural information from mathematical expressions given in LATEX format. Later AST is used to generate XML structure of mathematical expressions that make mathematical expressions machine-readable in heterogeneous environments. A rule-based algorithm is also proposed that converts LATEX math expressions into Content MathML (CMML), which produces semantic enrichment in web documents. The rules for writing LATEX math equations are formulated and implemented as LATEX Math Grammar (LMG), which are used for generating AST. Further, AST is converted into XML structure which is used to generate CMML encoding. Initially, the conversion algorithm is tested on 20 equations used in an NTCIR-12 math competition, then the algorithm is tested on NTCIR-12 Wikipedia-MathIR and ArXiv data sets. The results show that our algorithm is capable of converting LATEX complex equations into CMML extensively as compared to the existing ones as well as its time efficiency is better than contemporary systems. [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]

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 this paper, we present a new encryption algorithm, Hive Stream Encryption (HSE), based on stream encryption techniques. The proposed algorithm offers the strength and complexity to overcome the weaknesses and shortfall in current stream encryption methods. We present a new symmetric encryption model that encrypts/decrypts information along a path through multi-dimensional numeric space composed of cells arranged to form one encryption map. The main idea is to eliminate any mathematical relationship between the path and the encryption values in the cells in order to eliminate most of the issues inherited in the current classic stream encryption methods. We demonstrate that in our proposed algorithm, the time it takes for a brute-force approach to identify the encryption path increase exponentially with the number of cells in the HSE map, and the number of possible key combination also increases with the map size and key size, unlike the traditional stream encryption such as AES. It illustrated that HSE is effective and efficient for data encryption. [ABSTRACT FROM AUTHOR]

Network physical topology discovery is the key issue for network management and application, the physical topology discovery based on Address Forwarding Table (AFT) is a hot topic on current study. This paper defines three constraints of AFTs, and proposes a tree chopping algorithm based on AFTs satisfying down constraint, which can discover the physical topology of a subnet accurately. The proposed algorithm decreases the demand for AFT integrity dramatically, and is the loosest constraint for discovering physical topology which just relies on AFTs of down ports. The proposed algorithm can also be used in the switch domain of multiple subnets. [ABSTRACT FROM AUTHOR]

The paper describes a method of segmented wavelet transform (SegWT) that makes it possible to compute the discrete-time wavelet transform of a signal segment-by-segment, with exactly the same result as if the whole signal were transformed at once. Due to its generality, the method can be utilized in many situations: for wavelet-type processing of a signal in real time or in case we want to process the signal in parallel or In case we need to process a long signal, but the available memory capacity is insufficient (e.g. in the DSPs). In the paper, the background theory and the emerging principles of both the forward and the inverse SegWT are explained. [ABSTRACT FROM AUTHOR]

This paper presents the Discrete Wavelet based fusion techniques for combining perceptually important image features. SPIHT (Set Partitioning in Hierarchical Trees) algorithm is an efficient method for lossy and lossless coding of fused image. This paper presents some modifications on the SPIHT algorithm. It is based on the idea of insignificant correlation of wavelet coefficient among the medium and high frequency sub bands. In RE-MSPIHT algorithm, wavelet coefficients are scaled prior to SPIHT coding based on the sub band importance, with the goal of minimizing the MSE. [ABSTRACT FROM AUTHOR]

Let G(V, E, W) be a graph with |V| = n and |E| = m. Each vertex v is associated with a cost W(ν). For any Q ⊆ V, define δ(Q) = ΣνεQ W(ν) (sum cost) and β(Q) = maxνεQ{W(ν)} (bottleneck cost). Finding an independent dominating set D of G such that δ(D) or β(D) is minimized are two important fundamental graph problems, called the Weighted Minimum Independent Dominating Set problem (the WMIDS problem) and the Bottleneck Independent Dominating Set problem (the BIDS problem), respectively. This paper studies a further issue that all vertices in F cannot be included in any feasible solution D, for a given F ⊆ V. The vertices in F are called the forbidden vertices. The resulting problems are called the RWMIDS problem and RBIDS problem, respectively. When W(ν) = 1, for all ν ε V, the corresponding problems are called the MIDS problem and the RMIDS problem, respectively. The WMIDS problem is NP-Hard, but linear-time solvable on chordal graphs with W(ν) ε {1,0, -1, -2 ...), for all ν ε V. This paper first proves that the RMIDS problem is NP-Hard on chordal graphs, but the RWMIDS problem is O(m)-time solvable on split graphs. Second, an O(n)-time algorithm for the RWMIDS problem on trees is proposed. Then, we state a quite different result on the RBIDS problem--it is linear-time equivalent to the BIDS problem on all classes of graphs. Finally, we prove that both the RBIDS problem and the RMIDS problem are NP-Hard on planar-bipartite graphs of maximum degree 3. [ABSTRACT FROM AUTHOR]

The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K , to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and a new measure to assist the selection is proposed. The paper concludes with an analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different data sets. [ABSTRACT FROM AUTHOR]

A new linear-time algorithm is presented in this paper that simultaneously labels connected components (to be referred to merely as components in this paper) and their contours in binary images. The main step of this algorithm is to use a contour tracing technique to detect the external contour and possible internal contours of each component, and also to identify and label the interior area of each component. Labeling is done in a single pass over the image, while contour points are revisited more than once, but no more than a constant number of times. Moreover, no re-labeling is required throughout the entire process, as it is required by other algorithms. Experimentation on various types of images (characters, half-tone pictures, photographs, newspaper, etc.) shows that our method outperforms methods that use the equivalence technique. Our algorithm not only labels components but also extracts component contours and sequential orders of contour points, which can be useful for many applications. [Copyright &y& Elsevier]

This paper presents RULES-5, a new induction algorithm for effectively handling problems involving continuous attributes. RULES-5 is a 'covering' algorithm that extracts IF–THEN rules from examples presented to it. The paper first reviews existing methods of rule extraction and dealing with continuous attributes. It then describes the techniques adopted for RULES-5 and gives a step-by-step example to illustrate their operation. The paper finally gives the results of applying RULES-5 and other algorithms to benchmark problems. These clearly show that RULES-5 generates rule sets that are more accurate than those produced by its immediate predecessor RULES-3 Plus and by a well-known commercially available divide-and-conquer machine learning algorithm. [ABSTRACT FROM AUTHOR]

A stable set in a graph G is a set of pairwise nonadjacent vertices. The problem of finding a maximum weight stable set is one of the most basic NP-hard problems. An important approach to this problem is to formulate it as the problem of optimizing a linear function over the convex hull STAB(G) of incidence vectors of stable sets. Since it is impossible (unless NP = coNP) to obtain a “concise” characterization of STAB(G) as the solution set of a system of linear inequalities, a more realistic goal is to find large classes of valid inequalities with the property that the corresponding separation problem (given a point x*, find, if possible, an inequality in the class that x* violates) is efficiently solvable.Some known large classes of separable inequalities are the trivial, edge, cycle and wheel inequalities. In this paper, we give a polynomial time separation algorithm for the (t)-antiweb inequalities of Trotter. We then introduce an even larger class (in fact, a sequence of classes) of valid inequalities, called (t)-antiweb-wheel inequalities. This class can be seen as a common generalization of the (t)-antiweb inequalities and the wheel inequalities. We also give efficient separation algorithms for them.This is an abridged version of a paper just submitted to a refereed journal. Complete details can be found in the unabridged version. (See also [14, 5].) [Copyright &y& Elsevier]

Counting the number of paths in a graph is easily accomplished using powers of the adjacency matrix for the graph, while enumerating these paths is difficult. There appears to be only one technique, Floyd's algorithm [1], whose end result can be used, with a considerable amount of paper and pencil manipulation, to actually list in order the vertices that make up each path. Even so, Floyd's algorithm is used only to find shortest paths. This paper describes a technique in Maple that automatically prints out all paths of any desired length, along with the name of each vertex, proceeding in order from start (beginning vertex) to finish (ending vertex). This is accomplished by defining a new matrix multiplication that produces all paths, with the option of listing only the simple ones; can handle alphanumeric entries; and eliminates the clustering of parentheses and messy output obtained when using ordinary matrix multiplication with alphabetic entries. [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]

This paper describes a mathematical model of dust particles having complex shapes, and an algorithm to identify them, aiming mainly at categorizing types of dust on an LSI wafer surface. Fractal dimension is used to represent the dust features. A conventional fractal dimension applied to specimens as a whole, however, does not sufficiently represent the shape of dust in each category of the model. This paper introduces a local fractal dimension into a global fractal dimension so that feature of a mathematical model can be represented. Also proposed is method based on mahalanobis' distance using a fractal characteristic valuable to categorize the data. The proposed method has been tested successively by using real data. [ABSTRACT FROM AUTHOR]

Many real-world optimization problems are dynamic in nature. The applied algorithms in this environment can pose serious challenges, especially when the search space is multimodal with multiple, time-varying optima. To address these challenges, this paper proposed a speciation-based firefly algorithm to maintain the population diversity in different areas of the landscape. To improve the performance of the algorithm, multiple adaptation techniques have been used such as adapting the number of species, number of fireflies in each specie and number of active fireflies in each specie. A set of experiments are conducted to study the performance of the proposed algorithm on Moving Peaks Benchmark (MPB) which is currently the most well-known benchmark for evaluating algorithm in dynamic environments. The experimental results indicate that the proposed algorithm statistically performs better than several state-of-the-art algorithms in terms of offline-error. [ABSTRACT FROM AUTHOR]

Set-valued information systems are the generalized models of single-valued information systems. A feature set in such systems may vary dynamically over time when new information arrives, and the feature subset selected by feature selection algorithms need updating for knowledge discovery under a dynamic environment. Knowledge granulation as a feature measure is an effective way to evaluate the discemibility power of the features. But less effort has been made to investigate the feature selection issue from the perspective of knowledge granulation in dynamic conjunctive set-valued infonnation systems. In this paper, we firstly apply the knowledge granulation for measuring features in the conjunctive set-valued information system. With the variation of a feature set in the system, an incremental approach for updating the knowledge granulation is discussed. Correspondingly, an incremental feature selection algorithm is developed when a feature set adds into and deletes from the system simultaneously. The experimental results show that the feasibility and effectiveness of the proposed algorithm in comparison with existing feature selection algorithms. [ABSTRACT FROM AUTHOR]

Single image defogging has received increased attention recently. However, most existing methods are generic restoration methods which may cause partial over-enhancing to the image at a short distance. This certainly makes these defogging methods may be not suitable for road images. In this paper, we propose a new image defogging algorithm that divides the original foggy image into three regions and different enhancement processes are performed for the different region. As the results, the far-away region image that is most concerned by a car driver can be moderately enhanced, and over-enhancement of the bottom region image can be also limited effectively. To effectively measure the defogging effects of various algorithms, the assessment methods based on image feature and visibility distance are proposed for road scene image. The comparative study and quantitative evaluation show that the proposed method can obtain very good defogging image with relatively fast speed. [ABSTRACT FROM AUTHOR]

In TCC 2005, Groth proposed the cryptographic usefulness of a small subgroup G of Z*N of hidden order. Sofar, the best attack of previous method for a subgroup of Z*N had a complexity about O(√p'/2). In this paper, we propose the interval and the double walks method to speed up the computation of the semi-smooth RSA subgroup problem. Our new algorithm reduces the complexity to O(√p'/2) rather than O(√p')- Besides the theoretical analysis, we also compare the performances of our new algorithm with the previous algorithm in experiments, and the efficiency of our new algorithm is approach to 50% faster than the previous. [ABSTRACT FROM AUTHOR]

In this paper we propose a new algorithm called MEPda (Motif Extraction algorithm based on Push-down automata) to solve the problem of finding patterns containing loops. These loop-patterns or loop-motifs are very known and used in many domains, especially in mathematics and bioinformatics. MEPda meant tofind these kinds of patterns by using pushdown automata as a mechanism of matching process alongside with a counter to verify the acceptance length of loop in an optimistic way of looking. The results obtained from MEPda have shown high accuracy and much reduced runtime for finding patterns containing loops compared to using a push-down automata based algorithm without implementing a counter, a regular expression based algorithm, an Aho-Corasick algorithm, a KMP algorithm, and MoTeX algorithm. [ABSTRACT FROM AUTHOR]

In the last decades, heuristic algorithms are widely used in solving problems in different fields of science and engineering. Most of these methods are inspired by natural phenomena, such as biological behaviours or physical principles. In this paper, a new optimization method based on the dynamic of sliding motion along a frictionless inclined plane is introduced. In the proposed algorithm, a collection of agents cooperate with each other and move toward better positions in the search space by employing Newton's second law and equations of motion. Our method is compared with other popular optimization algorithms and the results on 23 standard benchmark functions show its effectiveness in most cases. [ABSTRACT FROM AUTHOR]

Not all NP-complete problems share the same practical hardness with respect to exact computation. Whereas some NP-complete problems are amenable to efficient computational methods, others are yet to show any such sign. It becomes a major challenge to develop a theoretical framework that is more fine-grained than the theory of NP-completeness, and that can explain the distinction between the exact complexities of various NP-complete problems. This distinction is highly relevant for constraint satisfaction problems under natural restrictions, where various shades of hardness can be observed in practice. Acknowledging the NP-hardness of such problems, one has to look beyond polynomial time computation. The theory of subexponential-time complexity provides such a framework, and has been enjoying increasing popularity in complexity theory. An instance of the constraint satisfaction problem with n variables over a domain of d values can be solved by brute-force in dn steps (omitting a polynomial factor). In this paper we study the existence of subexponential-time algorithms, that is, algorithms running in do(n) steps, for various natural restrictions of the constraint satisfaction problem. We consider both the constraint satisfaction problem in which all the constraints are given extensionally as tables, and that in which all the constraints are given intensionally in the form of global constraints. We provide tight characterizations of the subexponential-time complexity of the aforementioned problems with respect to several natural structural parameters, which allows us to draw a detailed landscape of the subexponential-time complexity of the constraint satisfaction problem. Our analysis provides fundamental results indicating whether and when one can significantly improve on the brute-force search approach for solving the constraint satisfaction problem. [ABSTRACT FROM AUTHOR]

The feasible differential problem is solved using a tension rectification algorithm. In this paper, we present a scaling implementation of a tension rectification algorithm. Let n, m,U denote the number of nodes, number of arcs, and maximum arc capacity value of an arc, respectively. Our implementation runs in O(mn log U), which is O(mn log n) under the similarity assumption. The tension rectification algorithm runs in O(m2) time, so, our implementation is an improvement if n log n < m. Another merit of our algorithm is that, in cases where the feasible differential problem does not have a solution, it presents some information that is useful to the modeler in estimating the maximum cost of adjusting the network. [ABSTRACT FROM AUTHOR]

S-measures are Loeb measures restricted to the sigma algebra generated by standard sets. This paper gives new extensions of the S-measure machinery, with applications to standard measure theory. [ABSTRACT FROM AUTHOR]

In this paper, a Hierarchical Trust Region Algorithm for solving PDE-constrained optimization problems is developed. A hierarchy of finite element meshes is used to define a hierarchy of quadratic models for the approximation of the discrete reduced cost functional on the finest mesh. The proposed algorithm simultaneously controls the choice of the model and the size of the trust region radius. Application of the trust region convergence theory allows for proving that every accumulation point of the sequence produced by the algorithm is a stationary point of the discretized problem. Numerical examples illustrate the behavior of the method and show a considerable reduction of computation time compared to the standard Newton trust region scheme. [ABSTRACT FROM AUTHOR]

Applying the Hertz theory to some non-Hertzian contact problems can produce acceptable results. Nevertheless, including the influence of free surfaces requires numerical methods, many of. which are based on the Boussinesq-Cerruti solution. This paper presents a new approach, which is better capable of releasing quarter-space free surfaces from shear and normal internal stresses without engendering any increase in calculation times. The mirrored pressure for shear correction is multiplied by a correction factor (ψ), which accounts for the normal load. The expression ψ is derived from the Hetényi correction process, and the resulting displacements show an enhanced correspondence with validation finite element method models; with an imposed fluctuating pressure, the maximum edge displacement error was -21.90% for a shear load correction (Poisson coefficient  = 0.3), and introducing the ψ factor reduced the deviation to -9.55%, while for u of 0.15, the maximum error was -11.30%, which was reduced to +0.60% with the ψ factor. This study introduces the factor ψ, in a 3D elastic contact algorithm. The resulting calculation scheme is then able to simulate any point or line Contact problems. Compared with coincident ends and sharp edge contact validation values, the model shows high conformity levels. [ABSTRACT FROM AUTHOR]

A new optimum approach is presented for strapdown coning algorithm design based on Explicit matching of desired response to expected coning input magnitude as a function of coning frequency. Unlike previous time or frequency Taylor series expansion techniques, the new method achieves optimization through minimum least-squares estimation over a user selected design frequency range. The error being minimized is the square of the weighted algorithm error, the weighting factor being user specified coning amplitude over frequency. This methodology allows the coning algorithm coefficients to be independently designed for each caning axis to achieve optimal balanced performance over the frequency range of expected coning inputs. Performance of the new algorithm design technique is evaluated by comparison with previous time and frequency-series approaches, in discrete and stochastic coning environments, and over a time based extreme dynamic maneuvering profile. A generalized method is presented for translating system performance requirements into expected coning amplitude versus frequency profiles for calculating Explicit algorithm coefficients, and for evaluating coning algorithm accuracy. The paper includes derivations of generalized formulas for calculating algorithm coefficients for previous time and frequency-series design approaches. [ABSTRACT FROM AUTHOR]

Ohya and Volovich discussed a quantum algorithm for the SAT problem with a chaos amplification process (OMV SAT algorithm) and showed that the number of steps it performed was polynomial in input size. In this paper, we define a generalized quantum Turing machine (GQTM) and related computational complexity. Then we show that there exists a GQTM which recognizes the SAT problem in polynomial time. Moreover, we discuss the problem of finding the quantum algorithm for a partial recursive function. [ABSTRACT FROM AUTHOR]

This paper presents a method called fusion of over-segmentations (FOOS) that gives the optimal or near-optimal clustering solution in just three runs of k-means. The algorithm over-segments the dataset twice. The two over-segmentations fuse together and determine the initial cluster-centres for the third and last run of k-means. FOOS gives a plausible solution even if an approximate number of clusters is known. Theoretical, analytical and experimental results compare FOOS with single-run k-means and k-means with multiple restarts. This technique addresses the issue of data clustering in all the possible areas, including data mining, pattern recognition, machine learning and statistics. The results obtained while segmenting a variety of texture images and non-image datasets are encouraging. The 40% over-segmentation gives the best results. [ABSTRACT FROM AUTHOR]

This paper focuses on the three dimensional flight path planning for a UAV on a low altitude terrain following/terrain avoidance mission. Using an approximate grid-based discretisation scheme, we transform the continuous optimisation problem into a search problem over a finite network, and apply a variant of the shortest-path algorithm to this problem. In other words using the three dimensional terrain information, three dimensional flight path from a starting point to an end point, minimising a cost function and regarding the kinematics constraints of the UAV is calculated. A network flow model is constructed based on the digital terrain elevation data (DTED) and a layered network is obtained. The cost function for each arc is defined as the length of the arc, then a constrained shortest path algorithm which considers the kinematics and the altitude constraints of the UAV is used to obtain the best route. Moreover the important performance parameters of the UAV are discussed. Finally a new algorithm is proposed to smooth the path din order to reduce the workload of the autopilot and control system of the UAV. The numeric results are presented to verify the capability of the procedure to generate admissible route in minimum possible time in comparison to the previous procedures. So this algorithm is potentially suited for using in online systems. [ABSTRACT FROM AUTHOR]

In this paper, we describe the conditions, providing the existence of splicing of the measures λ , μ and v - finite, finitely additive and integral functions of sets, defined on the algebras of subsets B and C of some set Ω and the algebra U, based on the algebras B and C . Moreover, we show that the measure λ should be a common extension of the measures μ and v , and λ(B∩C)= λ(B)λ(C) for any B ε B, C ε C. [ABSTRACT FROM AUTHOR]

Abstract: A rough set theory is a new mathematical tool to deal with uncertainty and vagueness of decision system and it has been applied successfully in all the fields. It is used to identify the reduct set of the set of all attributes of the decision system. The reduct set is used as preprocessing technique for classification of the decision system in order to bring out the potential patterns or association rules or knowledge through data mining techniques. Several researchers have contributed variety of algorithms for computing the reduct sets by considering different cases like inconsistency, missing attribute values and multiple decision attributes of the decision system. This paper focuses on the review of the techniques for dimensionality reduction under rough set theory environment. Further, the rough sets hybridization with fuzzy sets, neural network and metaheuristic algorithms have also been reviewed. The performance analysis of the algorithms has been discussed in connection with the classification. [Copyright &y& Elsevier]

As there is a conflict between the requirements of computational complexity and information-richness within the point-feature based SLAM algorithm in outdoor environment, a point-to-line feature-based SLAM map building algorithm is presented in this paper. The tree information is sampled as point feature which is matched as line feature using math methods and associated to the map using the probability scheme. It is a new method to express the outdoor environment. A robust H∞ filter has been used to simulate the feasibility of the algorithm with MATLAB,the result shows that the algorithm can reduce the complexity of the information expression,which establishes the foundation for vehicle experimentation. [ABSTRACT FROM AUTHOR]

The security of most elliptic curve cryptosystems is based on the intractability of the Elliptic Curve Discrete Logarithm Problem (ECDLP). Such a problem turns out to be computationally unfeasible when elliptic curves are suitably chosen. This paper provides an algorithm to obtain cryptographically good elliptic curves from a given one. The core of such a procedure lies on the usage of successive chains of isogenies, visiting different volcanoes of isogenies which are located in different l-cordilleras. [ABSTRACT FROM AUTHOR]

In this paper we present two new algorithms that factorize skew-Hamiltonian matrices M = RJR. This factorization is a key ingredient of algorithms for solving eigenvalue problem with Hamiltonian structure. [ABSTRACT FROM AUTHOR]

Abstract: An efficient exterior point simplex type algorithm for the assignment problem has been developed by Paparrizos [K. Paparrizos, An infeasible (exterior point) simplex algorithm for assignment problems, Math. Program. 51 (1991) 45–54]. This algorithm belongs to the category of forest algorithms and solves an assignment problem in at most iterations and in at most time. In this paper worst case examples are presented. Specifically, a systematic procedure to construct worst case assignment problems is presented for the exterior point algorithm. The algorithm applied to these examples executes exactly iterations. This result verifies that the bound is the best possible for the above-mentioned algorithm. [Copyright &y& Elsevier]

In this paper, we present the application of adaptive wavelet in lossy image compression. The construction of this adaptive wavelet is realised by the use of lifting scheme. In our application the lifting scheme is composed by an adaptive update lifting step and a fixed prediction lifting step. Finally, experiments with The EBCOT (Embedded Block Coding with Optimized Truncations) algorithm applied on synthetic and real images are reported. [ABSTRACT FROM AUTHOR]

The essential problem in signal analysis is to find a numerically stable algorithm for reconstruction of a signal from its atomic decomposition [2]. This leads to the notion of frames [3, 7] which is a main ingredient in the analysis and synthesis of signals. In this paper, we have obtained the frame bounds for wavelet packet frames which are more general than that of wavelet frames. [ABSTRACT FROM AUTHOR]

Abstract: Consider a problem of scheduling activities of a research and development project, where precedence relations of the activities constituting the project are represented by edges of an in-forest. Each activity is characterized by two parameters: a cost for attempting that activity and a probability that attempting the activity will lead to successful completion. The problem is to find a policy for attempting activities that minimizes the expected cost incurred until termination (successful completion of the project or the first activity failure). The main result of the paper is the design of an efficient algorithm to determine an optimal sequence in which to attempt the activities; a result which is established for linear and exponential utility functions. It is also shown that, unlike the related problem with out-forest precedence relations, there need not exist an optimal policy that is based on an index-rule for determining priority of edges by evaluating their successors. [Copyright &y& Elsevier]

Abstract: This paper considers how to optimally set the basestock level for a single buffer when demand is uncertain, in a robust framework. We present a family of algorithms based on decomposition that scale well to problems with hundreds of time periods, and theoretical results on more general models. [Copyright &y& Elsevier]