comment on discrete time optimal fuzzy controller design global concept approach in this correspondence we show that the relationship between the finite optimal trajectory and the infinite optimal trajectory in lemma 3 of the aforementioned paper cannot act as a theory basis for the further implementation of other theorems and the dynamic decomposition algorithm

locally s arc transitive graphs with two different quasiprimitive actions abstract previous work of the authors has shown that an important class of locally g 2 arc transitive graphs are those for which g acts faithfully and quasiprimitively on each of its two orbits on vertices in this paper we give a complete classification in the case where the two quasiprimitive actions of g are of different types the graphs obtained have amalgams previously unknown to the authors and involve both an almost simple 2 transitive action and an affine 2 transitive action on the neighbourhoods of vertices

active contours without edges we propose a new model for active contours to detect objects in a given image based on techniques of curve evolution mumford shah 1989 functional for segmentation and level sets our model can detect objects whose boundaries are not necessarily defined by the gradient we minimize an energy which can be seen as a particular case of the minimal partition problem in the level set formulation the problem becomes a mean curvature flow like evolving the active contour which will stop on the desired boundary however the stopping term does not depend on the gradient of the image as in the classical active contour models but is instead related to a particular segmentation of the image we give a numerical algorithm using finite differences finally we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable also the initial curve can be anywhere in the image and interior contours are automatically detected

a modified fuzzy c means algorithm for bias field estimation and segmentation of mri data we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging mri data and estimation of intensity inhomogeneities using fuzzy logic mri intensity inhomogeneities can be attributed to imperfections in the radio frequency coils or to problems associated with the acquisition sequences the result is a slowly varying shading artifact over the image that can produce errors with conventional intensity based classification our algorithm is formulated by modifying the objective function of the standard fuzzy c means fcm algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel voxel to be influenced by the labels in its immediate neighborhood the neighborhood effect acts as a regularizer and biases the solution toward piecewise homogeneous labelings such a regularization is useful in segmenting scans corrupted by salt and pepper noise experimental results on both synthetic images and mr data are given to demonstrate the effectiveness and efficiency of the proposed algorithm

generalized receding horizon control of fuzzy systems based on numerical optimization algorithm the optimal control of fuzzy systems with constraints is still an open problem our focus concerns the optimal control problem of fuzzy systems derived from receding horizon control rhc schemes we consider methods to numerically compute the value function for general fuzzy systems the numerical method that is developed using the finite difference with sigmoidal transformation is a stable and convergent algorithm for the hamilton jacobi bellman hjb equation an optimization procedure is developed to increase the calculation accuracy with less computation time a parallel processing method is employed in the optimization procedure the optimization results are applied to the controller design of general fuzzy dynamic systems employing the principle of conventional rhc schemes rhc form controllers are designed for some classes of fuzzy dynamic systems the basic ideas are as follows first the value function is calculated by numerical methods then the value function is used as controller design parameters to redesign rhc controllers for fuzzy systems which is motivated by the inverse lyapunov function design method it is proven that the closed loop system is asymptotically stable an engineering implementation of the controller redesign scheme is discussed meanwhile the parallel processing framework that can improve the closed loop performance is also introduced

hypergraph partitioning for the parallel computing of fuzzy differential equations in our previous work fuzzy differential equations have been used to describe service composition and the solutions have been used for the performance analysis this paper presents a method to compute these fuzzy differential equations in parallel we first map the underlying petri net that generates fuzzy differential equations to a hypergraph and then partition the hypergraph to minimize inter processor communication while maintaining a good load balance based on the partition result we divide the fuzzy differential equations into several blocks finally we design parallel computing algorithm to compute these equations software hmetis is used to partition the hypergraph and software sundials is used to support the parallel computing of fuzzy differential equations online shop problem from ibm web site has been used to demonstrate the benefit of our method

distance regularized level set evolution and its application to image segmentation level set methods have been widely used in image processing and computer vision in conventional level set formulations the level set function typically develops irregularities during its evolution which may cause numerical errors and eventually destroy the stability of the evolution therefore a numerical remedy called reinitialization is typically applied to periodically replace the degraded level set function with a signed distance function however the practice of reinitialization not only raises serious problems as when and how it should be performed but also affects numerical accuracy in an undesirable way this paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution the level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations the distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward and backward fab diffusion effect which is able to maintain a desired shape of the level set function particularly a signed distance profile near the zero level set this yields a new type of level set evolution called distance regularized level set evolution drlse the distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors in contrast to complicated implementations of conventional level set formulations a simpler and more efficient finite difference scheme can be used to implement the drlse formulation drlse also allows the use of more general and efficient initialization of the level set function in its numerical implementation relatively large time steps can be used in the finite difference scheme to reduce the number of iterations while ensuring sufficient numerical accuracy to demonstrate the effectiveness of the drlse formulation we apply it to an edge based active contour model for image segmentation and provide a simple narrowband implementation to greatly reduce computational cost

linear groups with orders having certain large prime divisors in this paper we obtain a classification of those subgroups of the finite general linear group gld q with orders divisible by a primitive prime divisor of qe 1 for some in the course of the analysis we obtain new results on modular representations of finite almost simple groups in particular in the last section we obtain substantial extensions of the results of landazuri and seitz on small cross characteristic representations of some of the finite classical groups 1991 mathematics subject classification primary 20g40 secondary 20c20 20c33 20c34 20e99

moving horizon control of linear systems with input saturation and plant uncertainty part 1 robustness we present a moving horizon feedback system based on constrained optimal control algorithms for linear plants with input saturation the system is a non conventional sampled data system its sampling periods vary from sampling instant to sampling instant and the control during the sampling time is not constant but determined by the solution of an open loop optimal control problem this is a two part paper in this part we show that the proposed moving horizon control system is robustly stable whether the state of the plant is measurable or not in the second part we show that the proposed moving horizon control system is capable of following a class of reference inputs and suppressing a class of disturbances experimental results show that the behaviour of the moving horizon control system is superior to that resulting from some alternative control laws

snakes active contour models a snake is an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges snakes are active contour models they lock onto nearby edges localizing them accurately scale space continuation can be used to enlarge the capture region surrounding a feature snakes provide a unified account of a number of visual problems including detection of edges lines and subjective contours motion tracking and stereo matching we have used snakes successfully for interactive interpretation in which user imposed constraint forces guide the snake near features of interest

receding horizon control of nonlinear systems the receding horizon control strategy provides a relatively simple method for determining feedback control for linear or nonlinear systems the method is especially useful for the control of slow nonlinear systems such as chemical batch processes where it is possible to solve sequentially open loop fixed horizon optimal control problems online the method has been shown to yield a stable closed loop system when applied to time invariant or time varying linear systems it is shown that the method also yields a stable closed loop system when applied to nonlinear systems

analysing finite locally s arc transitive graphs we present a new approach to analysing finite graphs which admit a vertex intransitive group of automorphisms g and are either locally g s arc transitive for s 2 or g locally primitive such graphs are bipartite with the two parts of the bipartition being the orbits of g given a normal subgroup n which is intransitive on both parts of the bipartition we show that taking quotients with respect to the orbits of n preserves both local primitivity and local s arc transitivity and leads us to study graphs where g acts faithfully on both orbits and quasiprimitively on at least one we determine the possible quasiprimitive types for g in these two cases and give new constructions of examples for each possible type the analysis raises several open problems which are discussed in the final section

robust receding horizon control of constrained nonlinear systems we present a method for the construction of a robust dual mode receding horizon controller which can be employed for a wide class of nonlinear systems with state and control constraints and model error the controller is dual mode in a neighborhood of the origin the control action is generated by a linear feedback controller designed for the linearized system outside this neighborhood receding horizon control is employed existing receding horizon controllers for nonlinear continuous time systems which are guaranteed to stabilize the nonlinear system to which they are applied require the exact solution at every instant of an optimal control problem with terminal equality constraints these requirements are considerably relaxed in the dual mode receding horizon controller presented in this paper stability is achieved by imposing a terminal inequality rather than an equality constraint only approximate minimization is required a variable time horizon is permitted robustness is achieved by employing conservative state and stability constraint sets thereby permitting a margin of error the resultant dual mode controller requires considerably less online computation than existing receding horizon controllers for nonlinear constrained systems

controller synthesis of fuzzy dynamic systems based on piecewise lyapunov functions this paper presents a kind of controller synthesis method for fuzzy dynamic systems based on a piecewise smooth lyapunov function the basic idea of the proposed approach is to construct controllers for the fuzzy dynamic systems in such a way that a piecewise continuous lyapunov function can be used to establish the global stability with h sub spl infin performance of the resulting closed loop fuzzy control systems it is shown that the control laws can be obtained by solving a set of linear matrix inequalities that is numerically feasible with commercially available software an example is given to illustrate the application of the proposed methods

active contours driven by local image fitting energy a new region based active contour model that embeds the image local information is proposed in this paper by introducing the local image fitting lif energy to extract the local image information our model is able to segment images with intensity inhomogeneities moreover a novel method based on gaussian filtering for variational level set is proposed to regularize the level set function it can not only ensure the smoothness of the level set function but also eliminate the requirement of re initialization which is very computationally expensive experiments show that the proposed method achieves similar results to the lbf local binary fitting energy model but it is much more computationally efficient in addition our approach maintains the sub pixel accuracy and boundary regularization properties

an adaptive level set evolution equation for contour extraction level set evolution without re initialization is a novel variational level set method for edge contour extraction which has many advantages over the traditional level set formulations the resulting evolution equation is derived from the minimization of an energy functional which is a linear combination of a weighted length weighted area and deviation penalization energy however the zero level set always keeps evolving in only one direction during evolution depending on the sign of the scale parameter associated with the weighted area this inconvenient makes the evolution highly sensitive to the contour initializations in this paper we propose an adaptive level set evolution equation following this method wherein the scale parameter associated with the weighted area is modified as an adaptive variable sign function and one of two terms associated with the weighted length is removed to reduce computational cost the proposed equation avoids completely the intrinsic limitation mentioned above and offers many advantages over the original equation as illustrated by several examples of contour extraction such as robustness to noise and detection of objects with discontinuous boundaries

the maximal subgroups of the baby monster i abstract in this paper we describe the completion of the determination of the maximal subgroups of the baby monster simple group our results are proved using computer calculations with matrix generators for the group the full details of the calculations will appear elsewhere

a universal construction of artstein s theorem on nonlinear stabilization abstract this note presents an explicit proof of the theorem due to artstein which states that the existence of a smooth control lyapunov function implies smooth stabilizability moreover the result is extended to the real analytic and rational cases as well the proof uses a universal formula given by an algebraic function of lie derivatives this formula originates in the solution of a simple riccati equation

sufficient conditions on general fuzzy systems as function approximators abstract in a constructive way we have found sufficient conditions under which general fuzzy systems can uniformly approximate any real continuous function on a compact domain to any degree of accuracy more importantly we have revealed the underlying mechanism of such function approximation the fuzzy systems can uniformly approximate any polynomials which according to the weierstrass approximation theorem can uniformly approximate any continuous function on a compact domain these findings lead to the following practical results 1 explicit fuzzy rules of fuzzy systems can now be easily obtained according to functions to be approximated and 2 formulas are derived which can calculate the number of input fuzzy sets output fuzzy sets and fuzzy rules needed in order to satisfy any given approximation accuracy the number increases as required approximation error decreases and as approximation error approaches zero the number approaches these formulas also reveal that the number is minimal when the maximum number of intersection between adjacent input fuzzy sets is one practical implications of these results will be discussed especially in the context of fuzzy control and fuzzy modeling two examples are provided to demonstrate how to design fuzzy systems to approximate given functions with required approximation accuracy

active contour model driven by global and local intensity information for ultrasound image segmentation abstract due to the abundant noise blurry boundaries and intensity inhomogeneities present in ultrasound us images it is a difficult task to segment us images accurately in this paper we propose a novel active contour method that combines global and local region information to achieve this task the global information can segment us images with noise and blurry boundaries while the local information can settle the intensity homogeneities of images the proposed method can be directly applied to synthetic real and us images segmentation results demonstrate the superiority of the proposed method over other representative algorithms moreover we also extend the proposed method to vector valued images experiments are performed to testify the feasibility of the method and the proposed vector valued idea can be applied to the medical co segmentation in the future

on primitive permutation groups with small suborbits and their orbital graphs abstract in this paper we study finite primitive permutation groups with a small suborbit based on the classification result of quirin math z 122 1971 267 and wang comm algebra 20 1992 889 we first produce a precise list of primitive permutation groups with a suborbit of length 4 in particular we show that there exist no examples of such groups with the point stabiliser of order 2 4 3 6 clarifying an uncertain question since 1970s then we analyse the orbital graphs of primitive permutation groups with a suborbit of length 3 or of length 4 we obtain a complete classification of vertex primitive arc transitive graphs of valency 3 and valency 4 and we prove that there exist no vertex primitive half arc transitive graphs of valency less than 10 finally we construct vertex primitive half arc transitive graphs of valency 2 k for infinitely many integers k with 14 as the smallest valency

optimal regulator for the inverted pendulum via euler lagrange backward integration we present a time invariant nonlinear controller for the inverted pendulum based on optimal control which is defined for all pendulum angles the paper provides a case study in the brute force method of calculating the extremal field by solving the euler lagrange equations backward in time thus exploring the stable manifold of the hamiltonian flow the controller is only given numerically on an interpolation grid we use this controller for closed loop simulations corroborating considerable gain robustness

a characterization and another construction of janko s group graphs r with the following properties are classified i f is g s transitive for some s 4 and some group g g 1 2 where g is the girth of f and ii f is connected a new construction of j3 is given

large subgroups of simple groups abstract let g be a finite group a proper subgroup h of g is said to be large if the order of h satisfies the bound h 3 g in this note we determine all the large maximal subgroups of finite simple groups and we establish an analogous result for simple algebraic groups in this context largeness is defined in terms of dimension an application to triple factorisations of simple groups both finite and algebraic is discussed

arc transitive elementary abelian covers of the octahedron graph abstract in this paper we construct the pairwise non congruent elementary abelian covers of the octahedron graph o 6 which admit a lift of an arc transitive group of automorphisms of o 6 it shows that if the covering transformation group is a 2 group then its rank is less than or equal to 7 and there exist exactly 14 non congruent covering projections in total which admit lifts of arc transitive subgroups of the full automorphism group of o 6 if the covering transformation group is an odd prime p group then its rank is 1 3 4 6 or 7 and there exist p 4 such non congruent covering projections in total

locally s arc transitive graphs related to sporadic simple groups we show that up to isomorphism there are ten connected locally m11 2 arc transitive graphs we compute up to conjugacy the connected locally g s arc transitive graphs for g m11 m12 m22 m23 m24 j1 j2 j3 hs mcl he ru suz co3 and we obtain a partial classification for o n the classification provides locally g 7 and g 9 arc transitive graphs for he and ru and locally g 7 arc transitive graphs for j3 and o n we also obtain new cubic 5 arc transitive graphs for he and ru

optimal fuzzy controller design local concept approach in this paper we present a global optimal and stable fuzzy controller design method for both continuous and discrete time fuzzy systems under both finite and infinite horizons first a sufficient condition is proposed which indicates that the global optimal effect can be achieved by the fuzzily combined local optimal controllers based on this sufficient condition we derive a local concept approach to designing the optimal fuzzy controller by applying traditional linear optimal control theory the stability of the entire closed loop continuous fuzzy system can be ensured by the designed optimal fuzzy controller the optimal feedback continuous fuzzy system can not only be guaranteed to be exponentially stable but also be stabilized to any desired degree also the total energy of system output is absolutely finite moreover the resultant feedback continuous fuzzy system possesses an infinite gain margin that is its stability is guaranteed no matter how large the feedback gain becomes two examples are given to illustrate the proposed optimal fuzzy controller design approach and to demonstrate the proved stability properties

optimal fuzzy controller design in continuous fuzzy system global concept approach we propose a design method for a global optimal fuzzy controller to control and stabilize a continuous fuzzy system with free or fixed end point under finite or infinite horizon time a linear like global system representation of continuous fuzzy system is first proposed by viewing a continuous fuzzy system in global concept and unifying the individual matrices into synthetical matrices based on this the optimal control law which can achieve global minimum effect is developed theoretically the nonlinear segmental two point boundary value problem is derived for the finite horizon problem and a forward riccati like differential equation for the infinite horizon problem the stability of the closed loop fuzzy system can be ensured by the designed optimal fuzzy controller the optimal closed loop fuzzy system cannot only be guaranteed to be exponentially stable but also be stabilized to any desired degree also the total energy of system output is absolutely finite moreover the resultant closed loop fuzzy system possesses an infinite gain margin

the infinitude of 7 arc transitive graphs abstract by theorems of tutte weiss and others it is known that there are no finite symmetric graphs of degree greater than 2 with automorphism group transitive on 8 arcs and that 7 arc transitivity can occur only in the case of graphs of degree 3 m 1 in this article it is shown that there are infinitely many 7 arc transitive finite quartic graphs indeed for all but finitely many positive integers n there is a finite connected 7 arc transitive quartic graph with the alternating group a n acting transitively on its 7 arcs and another with the symmetric group s n acting transitively on its 7 arcs the proof uses a construction involving permutation representations of a generic infinite group to produce an infinite family of finite graphs with the required properties

active contour model based on improved fuzzy c means algorithm and adaptive functions abstract distance regularized level set evolution drlse model which solves the re initialization problem in early active contours is a ground breaking edge based model for image segmentation however it has the disadvantages of unsatisfactory robustness to initialization and noise unidirectional movement slow convergence and poor stability in this paper we propose an active contour model driven by improved fuzzy c means algorithm fcm and adaptive functions an adaptive sign function based on image clustering information not only increases stability but also solves the problem of unidirectional movement furthermore it gives our model the ability to selectively segment targets in image an adaptive edge indicator function accelerates convergence with better function performance to further increase stability a novel double well potential function and the corresponding evolution speed function are proposed due to the improved fcm the proposed model is robust to initialization and noise in addition our model exhibits an edge based and region based characteristic experimental results have proved that the proposed model can not only effectively segment images with intensity inhomogeneity but also show a good robustness to initialization moreover it has shorter time spent and higher segmentation accuracy compared with other models

optimal approximations by piecewise smooth functions and associated variational problems abstract this reprint will introduce and study the most basic properties of three new variational problems which are suggested by applications to computer vision in computer vision a fundamental problem is to appropriately decompose the domain r of a function g x y of two variables this problem starts by describing the physical situation which produces images assume that a three dimensional world is observed by an eye or camera from some point p and that g1 rho represents the intensity of the light in this world approaching the point sub 1 from a direction rho if one has a lens at p focusing this light on a retina or a film in both cases a plane domain r in which we may introduce coordinates x y then let g x y be the strength of the light signal striking r at a point with coordinates x y g x y is essentially the same as sub 1 rho possibly after a simple transformation given by the geometry of the imaging syste the function g x y defined on the plane domain r will be called an image what sort of function is g the light reflected off the surfaces si of various solid objects o sub i visible from p will strike the domain r in various open subsets r sub i when one object o1 is partially in front of another object o2 as seen from p but some of object o2 appears as the background to the sides of o1 then the open sets r1 and r2 will have a common boundary the edge of object o1 in the image defined on r and one usually expects the image g x y to be discontinuous along this boundary jhd

s arc transitive graphs and normal subgroups abstract we study s arc transitive graphs with s 2 and give a characterisation of the actions of vertex transitive normal subgroups an interesting consequence of this characterisation states that each non bipartite 3 arc transitive graph is a normal cover of a 2 arc transitive graph admitting a simple group or a locally primitive graph admitting a simple group with a soluble vertex stabiliser

fuzzy basis functions universal approximation and orthogonal least squares learning fuzzy systems are represented as series expansions of fuzzy basis functions which are algebraic superpositions of fuzzy membership functions using the stone weierstrass theorem it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy based on the fuzzy basis function representations an orthogonal least squares ols learning algorithm is developed for designing fuzzy systems based on given input output pairs then the ols algorithm is used to select significant fuzzy basis functions which are used to construct the final fuzzy system the fuzzy basis function expansion is used to approximate a controller for the nonlinear ball and beam system and the simulation results show that the control performance is improved by incorporating some common sense fuzzy control rules

a global and local active contour model based on dual algorithm for image segmentation most of local region based active contour models in terms of the level set approach are able to segment images with intensity inhomogeneity however these models do not utilize global statistical information and are quite sensitive to the initial placement of the contour this paper presents a new global and local region based active contour model to segment images with intensity inhomogeneity first in order to reduce number of iterations the global energy functional of the chanvese cv model is used as the global term then the local term is proposed to incorporate both local spatial information and local intensity information to handle intensity inhomogeneity moreover to increase robustness of the initialization of the contour and reduce number of iterations a convex energy functional and the dual algorithm are designed in the numerical implementation experimental results for synthetic and medical images have shown the efficiency and robustness of the proposed method

a quasi infinite horizon nonlinear model predictive control scheme with guaranteed stability we present in this paper a novel nonlinear model predictive control scheme that guarantees asymptotic closed loop stability the scheme can be applied to both stable and unstable systems with input constraints the objective functional to be minimized consists of an integral square error ise part over a finite time horizon plus a quadratic terminal cost the terminal state penalty matrix of the terminal cost term has to be chosen as the solution of an appropriate lyapunov equation furthermore the setup includes a terminal inequality constraint that forces the states at the end of the finite prediction horizon to lie within a prescribed terminal region if the jacobian linearization of the nonlinear system to be controlled is stabilizable we prove that feasibility of the open loop optimal control problem at time t 0 implies asymptotic stability of the closed loop system the size of the region of attraction is only restricted by the requirement for feasibility of the optimization problem due to the input and terminal inequality constraints and is thus maximal in some sense

the maximal subgroups of the chevalley groups g2 q with q odd the ree groups 2g2 q and their automorphism groups

analysis and design for a class of complex control systems part i fuzzy modelling and identification this paper is the first of two dealing with the analysis and design of a class of complex control systems this part deals with fuzzy modelling and identification the second paper is concerned with fuzzy controller design we believe that these two papers show a way to analyze and design complex control systems using a combination of fuzzy logic and modern control theory the complex systems studied in these two papers can be represented by a fuzzy aggregation of a set of local linear models the identification can be divided into two procedures the first procedure is the identification of the membership functions including the determination of the number of fuzzy rules and the estimation of the parameters in the membership functions the second is the identification of the local linear models including the determination of the structure and the parameters of the local rule maps and the global rule interpolation two examples are used to demonstrate the application of the modelling method

approximation theory of fuzzy systems spl minus siso case in this paper the approximation properties of mimo fuzzy systems generated by the product inference are discussed we first give an analysis of fuzzy basic functions fbf s and present several properties of fbf s based on these properties of fbf s we obtain several basic approximation properties of fuzzy systems 1 basic approximation property which reveals the basic approximation mechanism of fuzzy systems 2 uniform approximation bounds which give the uniform approximation bounds between the desired control or decision functions and fuzzy systems 3 uniform convergent property which shows that fuzzy systems with defined approximation accuracy can always be obtained by dividing the input space into finer fuzzy regions and 4 universal approximation property which shows that fuzzy systems are universal approximators and extends some previous results on this aspect the similarity between fuzzy systems and mathematical approximation is discussed and an idea to improve approximation accuracy is suggested based on uniform approximation bounds

a novel active contour model for image segmentation using distance regularization term abstract in this paper a novel active contour model r drlse model based on level set method is proposed for image segmentation the r drlse model is a variational level set approach that utilizes the region information to find image contours by minimizing the presented energy functional to avoid the time consuming re initialization step the distance regularization term is used to penalize the deviation of the level set function from a signed distance function the numerical implementation scheme of the model can significantly reduce the iteration number and computation time the results of experiments performed on some synthetic and real images show that the r drlse model is effective and efficient in particular our method has been applied to mr kidney image segmentation with desirable results

computing minimal surfaces via level set curvature flow given a fixed closed curve or curves in r sp3 there is an associated problem of finding a surface with mean curvature zero which has that curve or curves as its boundary in this paper a new approach to numerically solving the minimal surface problem is introduced the surface is represented as a level set of a global function phi r sp3 to r the family of surfaces evolve in time according to mean curvature flow a solution is found when a steady state condition is achieved a new system of interpolatory boundary conditions are used to maintain the connection between the surface and the fixed boundary contour several computer surfaces are shown including examples of topological changes

minimization of region scalable fitting energy for image segmentation intensity inhomogeneities often occur in real world images and may cause considerable difficulties in image segmentation in order to overcome the difficulties caused by intensity inhomogeneities we propose a region based active contour model that draws upon intensity information in local regions at a controllable scale a data fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour this energy is then incorporated into a variational level set formulation with a level set regularization term from which a curve evolution equation is derived for energy minimization due to a kernel function in the data fitting term intensity information in local regions is extracted to guide the motion of the contour which thereby enables our model to cope with intensity inhomogeneity in addition the regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and avoids expensive reinitialization of the evolving level set function experimental results for synthetic and real images show desirable performances of our method

on primitive overgroups of quasiprimitive permutation groups a permutation group is said to be quasiprimitive if all its non trivial normal subgroups are transitive we investigate pairs g h of permutation groups of degree n such that g h sn with g quasiprimitive and h primitive an explicit classification of such pairs is obtained except in the cases where the primitive group h is either almost simple or the blow up of an almost simple group the theory in these remaining cases is investigated in separate papers the results depend on the finite simple group classification

a novel active contour model for medical images via the hessian matrix and eigenvalues abstract this paper presents a new level set formulation for active contour models acm we propose the idea of integrating the eigenvalue information of hessian matrix into the level set function by this new level set function the principal curvature information of images is used to enhance the ability of segmenting boundary regions the advantages of our model are as follows firstly the interior and exterior object boundaries can be segmented with the initial contour being anywhere in the input image secondly this method can work with heterogeneous images thirdly the proposed model can produce smooth and right boundaries of objects having vital importance in medical operations extensive experiments demonstrate that the proposed model can obtain better segmentation results

normalizers of primitive permutation groups abstract let g be a transitive normal subgroup of a permutation group a of finite degree n the factor group a g can be considered as a certain galois group and one would like to bound its size one of the results of the paper is that a g n if g is primitive unless n 3 4 54 38 58 or 316 this bound is sharp when n is prime in fact when g is primitive out g n unless g is a member of a given infinite sequence of primitive groups and n is different from the previously listed integers many other results of this flavor are established not only for permutation groups but also for linear groups and galois groups

predictive control of switched nonlinear systems with scheduled mode transitions in this work a predictive control framework is proposed for the constrained stabilization of switched nonlinear systems that transit between their constituent modes at prescribed switching times the main idea is to design a lyapunov based predictive controller for each constituent mode in which the switched system operates and incorporate constraints in the predictive controller design which upon satisfaction ensure that the prescribed transitions between the modes occur in a way that guarantees stability of the switched closed loop system this is achieved as follows for each constituent mode a lyapunov based model predictive controller mpc is designed and an analytic bounded controller using the same lyapunov function is used to explicitly characterize a set of initial conditions for which the mpc irrespective of the controller parameters is guaranteed to be feasible and hence stabilizing then constraints are incorporated in the mpc design which upon satisfaction ensure that 1 the state of the closed loop system at the time of the transition resides in the stability region of the mode that the system is switched into and 2 the lyapunov function for each mode is nonincreasing wherever the mode is reactivated thereby guaranteeing stability the proposed control method is demonstrated through application to a chemical process example