asymptotically optimal blind fractionally spaced channel estimation and performance analysis when the received data are fractionally sampled the magnitude and phase of most linear time invariant fir communications channels can be estimated from second order output only statistics we present a general cyclic correlation matching algorithm for known order fir blind channel identification that has closed form expressions for calculating the asymptotic variance of the channel estimates we show that for a particular choice of weights the weighted matching estimator yields at least for large samples the minimum variance channel estimator among all unbiased estimators based on second order statistics furthermore the matching approach unlike existing methods provides a useful estimate even when the channel is not uniquely identifiable from second order statistics

realization of stable models with subspace methods abstract subspace methods for system identification estimate the dynamics of state space models either by using the shiftinvariance property of an estimated observability or controllability matrix or by estimating a state sequence and then solving a least squares problem to obtain the system matrices in either case it is possible for the estimated system to be unstable we present algorithms to find stable approximants to a least squares problem which can then be applied to subspace methods to ensure stability either asymptotic or marginal stability can be ensured in the latter case a pole or a pair of poles being forced to lie on the unit circle in addition some results on a sufficient condition for stability for least squares solutions obtained by the shift invariance approach are derived

polyhedral cone ratio dea models with an illustrative application to large commercial banks abstract polyhedral cone ratio data envelopment analysis models generalizing the ccr ratio model are developed for situations with a finite number of dmus and employing polyhedral cones of virtual multipliers they provide improved definitions of efficiency over ccr models whose input output data and or numbers of dmus are inadequate to capture aspects or restrictions which should be involved the focus here is on the sum form for cones which easily provides for capturing exogenous expert opinion as well as mathematical reduction to the old form with its very powerful software transformation from the usual intersection form to it and vice versa is explicitly given thereby the advantages of either or both are available the theory is illustrated with two dimensional examples and by real banking examples for motivation

fuzzy multiattribute grey related analysis using dea qualitative input is encountered in many real decision making domains often such domains include tradeoffs among multiple attributes and estimates of parameters are often expressed with some degree of uncertainty grey related analysis has been proposed as a means to use interval fuzzy representation of data when dealing with multiple criteria data model parameters that can involve uncertainty include both performances of alternatives on attributes as well as attribute weights dea is proposed as an objective way to derive weights this paper presents a grey related fuzzy set methodology incorporating data envelopment analysis as a way to more objectively rank alternatives the purpose is to demonstrate the method the focus is on identifying alternatives performing the most efficiently with respect to the decision maker s preference the method is demonstrated on a multiattribute siting problem simulation is applied to validate model efficiency

super efficiency in stochastic data envelopment analysis an input relaxation approach this paper addresses super efficiency issue based on input relaxation model in stochastic data envelopment analysis the proposed model is not limited to using the input amounts of evaluating dmu and one can obtain a total ordering of units by using this method the input relaxation super efficiency model is developed in stochastic data envelopment analysis and its deterministic equivalent also is derived which is a nonlinear program moreover it is shown that the deterministic equivalent of the stochastic super efficiency model can be converted to a quadratic program as an empirical example the proposed method is applied to the data of textile industry of china to rank efficient units finally when allowable limits of data variations for evaluating dmu are permitted the sensitivity analysis of the proposed model is discussed

increasing the efficiency in integer simulation optimization reducing the search space through data envelopment analysis and orthogonal arrays abstract the development of various heuristics has enabled optimization in simulation environments nevertheless this research area remains underexplored primarily with respect to the time required for convergence of these heuristics in this sense simulation optimization is influenced by the complexity of the simulation model the number of variables and by their ranges of variation within this context this paper proposes a method capable of identifying the best ranges for each integer decision variable within the simulation optimization problem thereby providing a reduction in computational cost without loss of the quality in the response the proposed method combines experimental design techniques discrete event simulation and data envelopment analysis the experimental designs called orthogonal arrays are used to generate the input scenarios to be simulated and super efficiency analysis is applied in a data envelopment analysis model with variable returns to scale to rank the input scenarios the use of the super efficiency concept enables to distinguish the most efficient input scenarios which allows for the ranking of all the orthogonal array scenarios used the values of the variables of the two input scenarios that present the highest values of super efficiency are adopted as the new range of the optimization problem to illustrate this method s use and advantages it was applied to real cases associated with integer simulation optimization problems based on the results the effectiveness of this approach is verified because it delivered considerable reductions in the search space and in the computational time required to obtain a solution without affecting the quality

guaranteed stability with subspace methods abstract we show how stability of models can be guaranteed when using the class of identification algorithms which have become known as subspace methods in many of these methods the a matrix is obtained or can be obtained as the product of a shifted matrix with a pseudo inverse we show that whenever the shifted matrix is formed by introducing one block of zeros in the appropriate position then a stable model results the cost of this is some possibly large distortion of the results but in some applications that is outweighed by the advantage of guaranteed stability

a new dea ranking system based on changing the reference set this research proposes a new ranking system for extreme efficient dmus decision making units based upon the omission of these efficient dmus from reference set of the inefficient dmus we state and prove some facts related to our model a numerical example where the proposed method is compared with traditional ranking approaches is shown

blind channel estimation using the second order statistics asymptotic performance and limitations we consider the asymptotic performance and fundamental limitations of the class of blind estimators that use second order statistics an achievable lower bound of the asymptotic normalized mean square error anmse is derived it is shown that the achievable anmse is lower bounded by the condition number of the jacobian matrix of the correlation function with respect to the channel parameters it is shown next that the jacobian matrix is singular if and only if the subchannels share common conjugate reciprocal zeros this condition is different from the existing channel identification conditions asymptotic performance of some existing eigenstructure based algorithms is analyzed closed form expressions of anmse and their lower bounds are derived for the least squares ls and the subspace ss blind channel estimators when there are two subchannels asymptotic efficiency of ls ss algorithms is also evaluated showing that significant performance improvement is possible when the information of the source correlation is exploited

connections between the least squares and the subspace approaches to blind channel estimation in this correspondence we study the connections between the least squares and the subspace approaches to blind channel estimation by examining the properties and connections of the so called multichannel filtering and data selection transforms we establish a relationship between the identification equations used in the two approaches next it is shown that the least squares and subspace estimators are identical for the case when there are two subchannels in general the two algorithms are different in their utilization of the noise subspace

on line blind multichannel equalization based on mutually referenced filters this paper presents a novel approach to the blind linear equalization of possibly nonminimum phase and time varying communication channels in the context of channel diversity we introduce the concept of mutually referenced equalizers mres in which several filters are considered the outputs of which act as training signals for each other a corresponding constrained multidimensional mean square error mse cost function is derived the minimization of which is shown to be a necessary and sufficient condition for equalization the links with a standard linear prediction problem are demonstrated the proposed technique exhibits properties of important practical concern 1 the proposed algorithm is globally convergent 2 simple closed form solutions exist for the mres but the mres also lend themselves readily to adaptive implementation in particular the recursive least squares rls algorithm can be used to offer optimal convergence rate 3 the mre method provides a solution for all equalization delays which results in robustness properties with respect to snr and ill defined channel lengths

subspace identification for non linear systems with measured input non linearities this paper uses subspace methods to identify a class of multi input multi output discrete time non linear time varying systems specifically we identify systems that are non linear in measured data and linear in unmeasured states numerical examples are presented to demonstrate the efficacy of the method

using monte carlo method for ranking efficient dmus for ranking efficient dmus some methods have been developed these methods are not able to rank non extreme efficient dmus in this paper using monte carlo method a method has been developed which is able to rank all extreme and non extreme efficient dmus

a soft double regularization approach to parametric blind image deconvolution this paper proposes a blind image deconvolution scheme based on soft integration of parametric blur structures conventional blind image deconvolution methods encounter a difficult dilemma of either imposing stringent and inflexible preconditions on the problem formulation or experiencing poor restoration results due to lack of information this paper attempts to address this issue by assessing the relevance of parametric blur information and incorporating the knowledge into the parametric double regularization pdr scheme the pdr method assumes that the actual blur satisfies up to a certain degree of parametric structure as there are many well known parametric blurs in practical applications further it can be tailored flexibly to include other blur types if some prior parametric knowledge of the blur is available a manifold soft parametric modeling technique is proposed to generate the blur manifolds and estimate the fuzzy blur structure the pdr scheme involves the development of the meaningful cost function the estimation of blur support and structure and the optimization of the cost function experimental results show that it is effective in restoring degraded images under different environments

ranking of decision making units in data envelopment analysis a distance based approach in non parametric methods many units are evaluated as efficient several methods for prioritizing efficient units are reported in the literature andersen et al and thereafter mehrabian et al proposed two methods for ranking efficient units but both methods break down in some cases this paper describes a new dea ranking approach that uses l 2 norm in proposed dea ranking approach the difficulties of the existing methods have been removed

a new ranking method to fuzzy data envelopment analysis due to its wide practical use data envelopment analysis dea has been adapted to many fields to deal with problems that have occurred in practice one adaptation has been in the field of ranking decision making units dmus most methods of ranking dmus assume that all input and output data are exactly known but in real life the data cannot be precisely measured thus this paper will carry out some researches to dea under fuzzy environment a fuzzy comparison of fuzzy variables is defined and the ccr model is extended to be a fuzzy dea model based on credibility measure in order to rank all the dmus a full ranking method will be given since the ranking method involves a fuzzy function a fuzzy simulation is designed and embedded into the genetic algorithm to establish a hybrid intelligent algorithm however it is shown to be possible to avoid some of the need for dealing with these nonlinear problems by identifying conditions under which they can be replaced by linear problems finally we will provide a numerical example to illustrate the fuzzy dea model and the ranking method

a framework for the solution of the generalized realization problem in this paper we present a novel way of constructing generalized state space representations e a b c d of interpolants matching tangential interpolation data the loewner and shifted loewner matrices are the key tools in this approach

preference aggregation and dea an analysis of the methods proposed to discriminate efficient candidates abstract there are different ways to allow the voters to express their preferences on a set of candidates in ranked voting systems each voter selects a subset of the candidates and ranks them in order of preference a well known class of these voting systems are scoring rules where fixed scores are assigned to the different ranks and the candidates with the highest score are the winners one of the most important issues in this context is the choice of the scoring vector since the winning candidate can vary according to the scores used to avoid this problem cook and kress w d cook m kress a data envelopment model for aggregating preference rankings management science 36 11 1990 1302 1310 using a dea ar model proposed to assess each candidate with the most favorable scoring vector for him her however the use of this procedure often causes several candidates to be efficient i e they achieve the maximum score for this reason several methods to discriminate among efficient candidates have been proposed the aim of this paper is to analyze and show some drawbacks of these methods

using monte carlo method for ranking interval data abstract some methods have been presented for ranking efficient decision making units dmus in data envelopment analysis dea this paper addresses the ranking of interval data by using monte carlo method this method is based on a paper g r jahanshahloo f hosseinzadeh lotfi h zhiani rezai f rezai balf using monte carlo method for ranking efficient dmus applied mathematics and computation 162 2005 371 379 the worthwhile this method is its ability in ranking of extreme and non extreme efficient dmus

maximally productive input output units abstract technical or pareto koopmans efficiency models can be based on ratios of weighted sums of outputs to weighted sums of inputs differing meanings have been considered for such factor weights in this paper we use value or cost rate meanings depending on model orientation these meanings permit considering the simultaneous assignment of input and output factor weights along with optimal intensity values for a virtual composite unit constructed from the observed units an optimization principle we call the winners take all criterion is proposed for determining the maximally productive unit s no assumptions are required on the internal transformation processes of the units the model simultaneously determines the intensities and factor weights and results in indefinite quadratic programming problems that simplify to linear programming in certain cases for the general case genetic search is applied numerical illustrations are provided for faculty merit scoring and for the 15 hospital dataset of sherman

strict vs weak ordinal relations for multipliers in data envelopment analysis in a recent paper golany 1988 proposes an interesting extension of data envelopment analysis to the situation where ordinal relations among weights corresponding to certain dimensions exist however the development of the extended model contains mathematical errors and the proposed equivalence is incorrect we establish the correct model equivalence and prove that weak ordinal relations require a nonstandard dea model we also show that the case of strict ordinal relations can be handled with the standard dea model these results hold for the ccr bcc additive and multiplicative models and for relations involving both input and output multipliers

dea efficiency assessment using ideal and anti ideal decision making units this paper introduces two virtual decision making units dmus called ideal dmu idmu and anti ideal dmu admu into the data envelopment analysis dea the resultant dea models are respectively referred to as the data envelopment analysis with ideal and anti ideal decision making units one evaluates dmus from the viewpoint of the best possible relative efficiency while the other evaluates them from the perspective of the worst possible relative efficiency the two distinctive efficiencies are combined to form a comprehensive index called the relative closeness rc to the idmu just like the well known topsis approach in multiple attribute decision making madm the rc index is then used as the evidence of overall assessment of each dmu based on which an overall ranking for all the dmus can be obtained two numerical examples are provided to illustrate the applications of the proposed dea models and the rc index

estimation and inference in two stage semi parametric models of production processes many papers have regressed non parametric estimates of productive efficiency on environmental variables in two stage procedures to account for exogenous factors that might affect firms performance none of these have described a coherent data generating process dgp moreover conventional approaches to inference employed in these papers are invalid due to complicated unknown serial correlation among the estimated efficiencies we first describe a sensible dgp for such models we propose single and double bootstrap procedures both permit valid inference and the double bootstrap procedure improves statistical efficiency in the second stage regression we examine the statistical performance of our estimators using monte carlo experiments

non parametric unconditional quantile estimation for efficiency analysis with an application to federal reserve check processing operations abstract this paper examines the technical efficiency of us federal reserve check processing offices over 1980 2003 we extend results from park et al park b simar l weiner c 2000 fdh efficiency scores from a stochastic point of view econometric theory 16 855 877 and daouia and simar daouia a simar l 2007 nonparametric efficiency analysis a multivariate conditional quantile approach journal of econometrics 140 375 400 to develop an unconditional hyperbolic quantile estimator of efficiency our new estimator is fully non parametric and robust with respect to outliers when used to estimate distance to quantiles lying close to the full frontier it is strongly consistent and converges at rate root n thus avoiding the curse of dimensionality that plagues data envelopment analysis dea estimators our methods could be used by policymakers to compare inefficiency levels across offices or by managers of individual offices to identify peer offices

a procedure for ranking efficient units in data envelopment analysis data envelopment analysis dea evaluates the relative efficiency of decision making units dmus but does not allow for a ranking of the efficient units themselves a modified version of dea based upon comparison of efficient dmus relative to a reference technology spanned by all other units is developed the procedure provides a framework for ranking efficient units and facilitates comparison with rankings based on parametric methods

using the gradient line for ranking dmus in dea in this paper a method based on using gradient line for ranking dmus is proposed the advantage of this method is its stability and robustness where data have special structure some numerical examples are presented to show the results

stochastic multicriteria acceptability analysis using the data envelopment model abstract data envelopment analysis dea and stochastic multicriteria acceptability analysis smaa 2 are methods for evaluating alternatives based on multiple criteria while dea is mainly an ex post tool used for classifying alternatives into efficient and inefficient ones smaa 2 is an ex ante tool for supporting multiple criteria decision making both methods use a kind of value function where the importance of criteria is modeled using weights unlike many other methods neither dea nor smaa 2 requires decision makers weights as input instead these so called non parametric methods explore the weight space in order to identify weights favorable for each alternative this paper introduces the smaa d method which is a combination of dea and smaa 2 smaa d can be characterized as an extension of dea to handle uncertain or imprecise data to provide stochastic efficiency measures alternatively the combined method can be seen as a variant of smaa 2 with a dea type value function

a statistical test for detecting influential observations in dea this paper deals with the problem of detecting influential observations in deterministic nonparametric dea models the technique we present is intended to classify for a further analysis those sample observations considerably affecting the measured efficiency for the remaining units then the analyst will have to check whether these observations are contaminated by data errors or not this approach also allows to determine when efficiency changes due to the presence of a given unit in the sample are statistically significant thus ours is a statistical alternative to approach the problem of detecting influential observations in deterministic nonparametric dea models

sigma mu efficiency analysis a methodology for evaluating units through composite indicators abstract we propose a methodology to employ composite indicators for performance analysis of units of interest using and extending the family of stochastic multiattribute acceptability analysis we start evaluating each unit by means of weighted sums of their elementary indicators in the whole set of admissible weights for each unit we compute the mean and the standard deviation of its evaluations clearly the former has to be maximized while the latter has to be minimized as it denotes instability in the evaluations with respect to the variability of weights we consider a unit to be pareto koopmans efficient with respect to and if there is no convex combination of and of the rest of the units with a value of that is not smaller and a value of that is not greater with at least one strict inequality the set of all pareto koopmans efficient units constitutes the first pareto koopmans frontier in the spirit of context dependent data envelopment analysis we assign each unit to one of the sequence of pareto koopmans frontiers we measure the local efficiency of each unit with respect to each frontier but also its global efficiency taking into account all frontiers in the plane thus enhancing the explicative power of the proposed approach to illustrate its potential we present a case study of world happiness based on the data of the homonymous report that is annually produced by the united nations sustainable development solutions network

aggregating preferences rankings with variable weights abstract one of the most important issues for aggregating preferences rankings is the determination of the weights associated with the different ranking places to avoid the subjectivity in determining the weights cook and kress 1990 5 suggested evaluating each candidate with the most favorable scoring vector for him her with this purpose various models based on data envelopment analysis have appeared in the literature although these methods do not require predetermine the weights subjectively some of them have a serious drawback the relative order between two candidates may be altered when the number of first second k th ranks obtained by other candidates changes although there is not any variation in the number of first second k th ranks obtained by both candidates in this paper we propose a model that allows each candidate to be evaluated with the most favorable weighting vector for him her and avoids the previous drawback moreover in some cases we give a closed expression for the score assigned with our model to each candidate

classification based adaptive filtering for multiframe blind image restoration in this paper the blind restoration of a scene is investigated when multiple degraded blurred and noisy acquisitions are available an adaptive filtering technique is proposed where the distorted images are filtered classified and then fused based upon the classification decisions finite normal density mixture fnm models are used to model the filtered outputs at each iteration for simplicity fixed number of gaussian components classes is initially considered for each degraded frame and the selection of the optimal number of classes is performed according to the global relative entropy criterion however there exist cases where dynamically varying fnm models should be considered where the optimal number of classes is selected according to the akaike information criterion the iterative application of classification and fusion followed by optimal adaptive filtering converges to a global enhanced representation of the original scene in only a few iterations the proposed restoration method does not require knowledge of the point spread function support size or exact alignment of the acquired frames simulation results on synthetic and real data using both fixed and dynamically varying fnm models demonstrate its efficiency under both noisy and noise free conditions

robust pole placement in lmi regions discusses analysis and synthesis techniques for robust pole placement in linear matrix inequality lmi regions a class of convex regions of the complex plane that embraces most practically useful stability regions the focus is on linear systems with static uncertainty on the state matrix for this class of uncertain systems the notion of quadratic stability and the related robustness analysis tests are generalized to arbitrary lmi regions the resulting tests for robust pole clustering are all numerically tractable because they involve solving linear matrix inequalities lmis and cover both unstructured and parameter uncertainty these analysis results are then applied to the synthesis of dynamic output feedback controllers that robustly assign the closed loop poles in a prescribed lmi region with some conservatism this problem is again tractable via lmi optimization in addition robust pole placement can be combined with other control objectives such as h sub 2 or h sub spl infin performance to capture realistic sets of design specifications physically motivated examples demonstrate the effectiveness of this robust pole clustering technique

modified maj model for ranking decision making units in data envelopment analysis abstract efficiency data envelopment analysis dea models assessing decision making units dmus are unable to discriminate between efficient dmus from experience we know that usually plural dmus are efficient to discriminate between these efficient dmus is an interesting subject to this aim super efficiency models are proposed in this paper we study a super efficiency model namely maj each time an efficient dmu is excluded from the set of the observed dmus a new production possibility set pps is obtained in this model ranking is done based on the position of each excluded efficient dmu in relation to its corresponding new pps if the efficient dmus and the new ppss remain unchanged it is expected that the ranking also remain unchanged but in this paper we show that the technique used for rendering this model unit invariant causes the ranking to change when some inputs of some inefficient dmus change without causing any change in the new pps in this paper we modify this model so that this problem will not occur

subspace based multivariable system identification from frequency response data two noniterative subspace based algorithms which identify linear time invariant mimo multi input multioutput systems from frequency response data are presented the algorithms are related to the recent time domain subspace identification techniques the first algorithm uses equidistantly in frequency spaced data and is strongly consistent under weak noise assumptions the second algorithm uses arbitrary frequency spacing and is strongly consistent under more restrictive noise assumptions promising results are obtained when the algorithms are applied to real frequency data originating from a large flexible structure

a selection method for a preferential election this paper deals with a recently proposed method for a ranked voting system it is based on a data envelopment analysis model it is shown that the number of problems to be solved and the number of constraints in the model can be decreased in addition the winner can be selected with a fewer efforts and in some cases without solving any problems the obtained results also illustrate the importance of selecting a proper norm in the model

a unified approach to superresolution and multichannel blind deconvolution this paper presents a new approach to the blind deconvolution and superresolution problem of multiple degraded low resolution frames of the original scene we do not assume any prior information about the shape of degradation blurs the proposed approach consists of building a regularized energy function and minimizing it with respect to the original image and blurs where regularization is carried out in both the image and blur domains the image regularization based on variational principles maintains stable performance under severe noise corruption the blur regularization guarantees consistency of the solution by exploiting differences among the acquired low resolution images several experiments on synthetic and real data illustrate the robustness and utilization of the proposed technique in real applications

exact image deconvolution from multiple fir blurs we address the problem of restoring an image from its noisy convolutions with two or more blur functions channels deconvolution from multiple blurs is in general better conditioned than from a single blur and can be performed without regularization for moderate noise levels we characterize the problem of missing data at the image boundaries and show that perfect reconstruction is impossible even in the no noise case almost surely unless there are at least three channels conversely when there are at least three channels we show that perfect reconstruction is not only possible almost surely in the absence of noise but also that it can be accomplished by finite impulse response fir filtering such fir reconstruction is vastly more efficient computationally than the least squares solution and is suitable for low noise levels even in the high noise case the estimates obtained by fir filtering provide useful starting points for iterative least squares algorithms we present results on the minimum possible sizes of such deconvolver filters we derive expressions for the mean square errors in the fir reconstructions and show that performance comparable to that of the least squares reconstruction may be obtained with relatively small deconvolver filters finally we demonstrate the fir reconstruction on synthetic and real data

subspace identification with guaranteed stability using constrained optimization in system identification the true system is often known to be stable however due to finite sample constraints modeling errors plant disturbances and measurement noise the identified model may be unstable we present a constrained optimization method to ensure asymptotic stability of the identified model in the context of subspace identification methods in subspace identification we first obtain an estimate of the state sequence or extended observability matrix and then solve a least squares optimization problem to estimate the system parameters to ensure asymptotic stability of the identified model we write the least squares optimization problem as a convex linear programming problem with mixed equality quadratic and positive semidefinite constraints suitable for existing convex optimization codes such as sedumi we present examples to illustrate the method and compare to existing approaches

a method for discriminating efficient candidates with ranked voting data abstract ranked voting data arise when voters select and rank more than one candidate with order of preference cook and kress management science 36 11 1990 1302 introduced data envelopment analysis dea to analyze ranked voting data by using dea each candidate may use the most favorable weights for him her to estimate his her own preference score however dea often suggests that more than one unit are equally efficient so a method to discriminate these efficient candidates is needed some methods of such purpose are proposed however these methods have a weak point that the order of efficient candidates may be changed by existence of an inefficient candidate in this paper we propose a new method our method does not use information about inefficient candidates to discriminate efficient candidates it is based on a very simple idea and is realizable visually so we hope that it is acceptable to election officials

continuity of dea efficiency measures data envelopment analysis dea is a methodology that allows in one way or other the assignment of efficiency scores to members of a group of decision making units we call an efficiency measure continuous if small perturbations of the input output data cause only small changes in the score continuity is a desirable property of an efficiency measure in particular in the presence of measurement tolerances continuity is also desirable from a numerical point of view because the scores are computed by linear programming software focusing on convex production possibility sets we give examples where radial dea measures fail to be continuous i e they jump under small data perturbations we present necessary and sufficient conditions for continuity in terms of the data and show that these conditions are satisfied for almost all data we also discuss continuity of nonradial measures and identify possible problems of multistage approaches to compute mix efficiencies

blind channel estimation using the second order statistics algorithms most second order moment based blind channel estimators belong to two categories i optimal correlation spectral fitting techniques and ii eigenstructure based techniques these two classes of algorithms have complementary advantages and disadvantages a new optimization criterion referred to as the joint optimization with subspace constraints josc is proposed to unify the two types of approaches based on this criterion a new algorithm is developed to combine the strength of the two classes of blind channel estimators among a number of attractive features the josc algorithm does not require the accurate detection of the channel order when compared with existing eigenstructure based techniques the josc performs better especially when the channel is close to being unidentifiable when compared with correlation spectral fitting schemes the josc is less affected by the presence of local minima

a note on some of dea models and finding efficiency and complete ranking using common set of weights efficiency evaluation in data envelopment analysis dea depends on different factors the most important factors arc the values of input and output in this paper we present an alternative proof that if one component of output or input vectors of a dmu dominates the corresponding component of other dmus whatever the value of other components of this dmu may be then that dmu is efficient in some of the dea models an important outcome of such an analysis is a set of virtual multipliers or weights accorded to each factor taken into account these sets of weights are typically different for each of the participating dmus in this paper by means of solving only one problem we can determine common set of weights csw for all dmus and their efficiencies finally a method for ranking dmus is presented in this method by solving only two problems efficient dmus are ranked

robust multichannel blind deconvolution via fast alternating minimization blind deconvolution which comprises simultaneous blur and image estimations is a strongly ill posed problem it is by now well known that if multiple images of the same scene are acquired this multichannel mc blind deconvolution problem is better posed and allows blur estimation directly from the degraded images we improve the mc idea by adding robustness to noise and stability in the case of large blurs or if the blur size is vastly overestimated we formulate blind deconvolution as an l1 regularized optimization problem and seek a solution by alternately optimizing with respect to the image and with respect to blurs each optimization step is converted to a constrained problem by variable splitting and then is addressed with an augmented lagrangian method which permits simple and fast implementation in the fourier domain the rapid convergence of the proposed method is illustrated on synthetically blurred data applicability is also demonstrated on the deconvolution of real photos taken by a digital camera

h sub spl infin design with pole placement constraints an lmi approach this paper addresses the design of state or output feedback h sub spl infin controllers that satisfy additional constraints on the closed loop pole location sufficient conditions for feasibility are derived for a general class of convex regions of the complex plane these conditions are expressed in terms of linear matrix inequalities lmis and the authors formulation is therefore numerically tractable via lmi optimization in the state feedback case mixed h sub 2 h sub spl infin synthesis with regional pole placement is also discussed finally the validity and applicability of this approach are illustrated by a benchmark example

a least squares approach to blind channel identification conventional blind channel identification algorithms are based on channel outputs and knowledge of the probabilistic model of channel input in some practical applications however the input statistical model may not be known or there may not be sufficient data to obtain accurate enough estimates of certain statistics in this paper we consider the system input to be an unknown deterministic signal and study the problem of blind identification of multichannel fir systems without requiring the knowledge of the input statistical model a new blind identification algorithm based solely on the system outputs is proposed necessary and sufficient identifiability conditions in terms of the multichannel systems and the deterministic input signal are also presented

restoration of differently blurred versions of an image with measurement errors in the psf s restoration of an object from t observations is considered each image is distorted by a different deterministic blur and additive noise the point spread function psf for each observation is unknown however a noisy measurement of it is available taking the errors in measurements of the psfs into consideration the maximum likelihood and wiener filters are derived it is shown that these filters give better results when the regression filter and the conventional wiener filter i e the one which ignores the presence of the noise in the psfs the consistency and the ill conditioning characteristics of the filters are discussed regularized forms for these filers are obtained

frequency domain subspace system identification using non parametric noise models in the general case of non uniformly spaced frequency domain data and or arbitrarily coloured disturbing noise the frequency domain subspace identification algorithms described in mckelvey akcay and ljung ieee trans automatic control 41 7 1996 960 and van overschee and de moor signal processing 52 2 1996 179 are consistent only if the covariance matrix of the disturbing noise is known this paper studies the asymptotic properties strong convergence convergence rate asymptotic normality strong consistency and loss in efficiency of these algorithms when the true noise covariance matrix is replaced by the sample noise covariance matrix obtained from a small number of independent repeated experiments as an additional result the strong convergence in case of model errors the convergence rate and the asymptotic normality of the subspace algorithms with known noise covariance matrix follows

dea and the discriminant analysis of ratios for ranking units the purpose of this study is to develop a new method which provides for given inputs and outputs the best common weights for all the units that discriminate optimally between the efficient and inefficient units as pregiven by the data envelopment analysis dea in order to rank all the units on the same scale this new method discriminant data envelopment analysis of ratios dr dea presents a further post optimality analysis of dea for organizational units when their multiple inputs and outputs are given we construct the ratio between the composite output and the composite input where their common weights are computed by a new non linear optimization of goodness of separation between the two pregiven groups a practical use of dr dea is that the common weights may be utilized for ranking the units on a unified scale dr dea is a new use of a two group discriminant criterion that has been presented here for ratios rather than the traditional discriminant analysis which applies to a linear function moreover non parametric statistical tests are employed to verify the consistency between the classification from dea efficient and inefficient units and the post classification as generated by dr dea

when does data envelopment analysis outperform a naive efficiency measurement model abstract this paper compares the results from data envelopment analysis dea to a naive efficiency measurement model which generates a scalar efficiency score by averaging all output input ratios random data and real life data are used to test the relative performance of the naive model against various dea models the results suggest that the proposed the naive model replicates dea efficiency scores almost perfectly for constant return to scales and low heterogeneity in output input data it is therefore concluded that heterogeneity in output input data is important to take advantage of the capability of dea it is also shown that heterogeneity is more relevant to efficiency measurement than the number of dimensions

the weighted additive distance function abstract distance functions in production theory are mathematical structures that characterize the belonging to the reference technology through a numerical value behave as technical efficiency measures when the focus is analyzing an observed input output vector within its production possibility set and present a dual relationship with some support function profit revenue cost function in this paper we endow the well known weighted additive models in data envelopment analysis with a distance function structure introducing the weighted additive distance function and showing its main properties

perfect blind restoration of images blurred by multiple filters theory and efficient algorithms we address the problem of restoring an image from its noisy convolutions with two or more unknown finite impulse response fir filters we develop theoretical results about the existence and uniqueness of solutions and show that under some generically true assumptions both the filters and the image can be determined exactly in the absence of noise and stably estimated in its presence we present efficient algorithms to estimate the blur functions and their sizes these algorithms are of two types subspace based and likelihood based and are extensions of techniques proposed for the solution of the multichannel blind deconvolution problem in one dimension we present memory and computation efficient techniques to handle the very large matrices arising in the two dimensional 2 d case once the blur functions are determined they are used in a multichannel deconvolution step to reconstruct the unknown image the theoretical and practical implications of edge effects and weakly exciting images are examined finally the algorithms are demonstrated on synthetic and real data

super efficiency evaluation using a common platform on a cooperative game abstract classical dea models such as ccr and bcc models compute efficiencies of decision making units dmus based on a common efficient frontier but with no capability of differentiating efficient units the super efficiency model was developed to rank efficient dmus based a new efficient frontier comprised by all other dmus this may lead to a multi platform problem that different efficient dmus are evaluated based on different efficient frontiers and the resulted super efficiencies of efficient dmus are not comparable this paper addresses the multi platform problem from the perspective of a cooperative game efficient dmus are regarded as players and subsets of these efficient dmus as coalitions the effect of a coalition on a specific efficient dmu is defined as the dmu s efficiency change proportion ecp based on the traditional dea models when the coalition is removed from the reference set basing on the ecp we define a characteristic function as the sum of all efficient dmus ecps and prove that this function is super additive then the shapley value is introduced as a solution of this cooperative game and applied to rank efficient dmus the proposed approach is demonstrated by two numerical examples finally we extend the proposed approach in this paper to the vrs assumption

identifying the anchor points in dea using sensitivity analysis in linear programming abstract in this paper the anchor points in dea as an important subset of the set of extreme efficient points of the production possibility set pps are studied a basic definition utilizing the multiplier dea models is given then two theorems are proved which provide necessary and sufficient conditions for characterization of these points the main results of the paper lead to a new interesting connection between dea and sensitivity analysis in linear programming theory by utilizing the established theoretical results a successful procedure for identification of the anchor points is presented

blind identification of multichannel fir blurs and perfect image restoration despite its practical importance in image processing and computer vision blind blur identification and blind image restoration have so far been addressed under restrictive assumptions such as all pole stationary image models blurred by zero or minimum phase point spread functions relying upon diversity availability of a sufficient number of multiple blurred images we develop blind fir blur identification and order determination schemes apart from a minimal persistence of the excitation condition also present with nonblind setups the inaccessible input image is allowed to be deterministic or random and of unknown color of distribution with the blurs satisfying a certain co primeness condition in addition we establish existence and uniqueness results which guarantee that single input multiple output fir blurred images can be restored blindly though perfectly in the absence of noise using linear fir filters results of simulations employing the blind order determination blind blur identification and blind image restoration algorithms are presented when the snr is high direct image restoration is found to yield better results than indirect image restoration which employs the estimated blurs in low snr indirect image restoration performs well while the direct restoration results vary with the delay but improve with larger equalizer orders

fuzzy data envelopment analysis dea model and ranking method data envelopment analysis dea is a very effective method to evaluate the relative efficiency of decision making units dmus since the data of production processes cannot be precisely measured in some cases the uncertain theory has played an important role in dea this paper attempts to extend the traditional dea models to a fuzzy framework thus producing a fuzzy dea model based on credibility measure following is a method of ranking all the dmus in order to solve the fuzzy model we have designed the hybrid algorithm combined with fuzzy simulation and genetic algorithm when the inputs and outputs are all trapezoidal or triangular fuzzy variables the model can be transformed to linear programming finally a numerical example is presented to illustrate the fuzzy dea model and the method of ranking all the dmus

a multivariate statistical approach to reducing the number of variables in data envelopment analysis abstract the usefulness of data envelopment analysis dea depends on its ability to calculate the relative efficiency of decision making units dmus using multiple inputs and outputs unfortunately the greater the number of input and output variables the less discerning the analysis in practice the input and output variables are usually highly correlated with one another often reflecting no more than the relative size of each dmu to counteract the limited distinction provided by a dea with many variables analysts for many years have taken the approach of retaining only some of the variables originally planned for the analysis omitting on an ad hoc basis variables that are highly correlated with those retained in this paper we describe a systematic statistical method for deciding which of the original correlated variables can be omitted with least loss of information and which should be retained results on a number of published datasets reveal that even omitting variables that are highly correlated and thereby contain little additional information on performance can have a major influence on the computed efficiency measures

identification of stable models in subspace identification by using regularization in subspace identification methods the system matrices are usually estimated by least squares based on estimated kalman filter state sequences and the observed inputs and outputs for a finite number of data points the estimated system matrix is not guaranteed to be stable even when the true linear system is known to be stable in this paper stability is imposed by using regularization the regularization term used here is the trace of a matrix which involves the dynamical system matrix and a positive semi definite weighting matrix the amount of regularization can be determined from a generalized eigenvalue problem the data augmentation method of chui and maciejowski 1996 is obtained by using specific choices for the weighting matrix in the regularization term

a note on dea efficiency assessment using ideal point an improvement of wang and luo s model abstract evaluating the efficiency of organizational units continues to be a difficult problem to solve especially when the multiplicity of inputs resources costs and outputs services products associated with these units is considered this paper considers a previous article published by wang and luo y wang y luo dea efficiency assessment using ideal and anti ideal decision making units applied mathematics and computation 173 2 2006 902 915 in the journal of applied mathematics and computation where data envelopment analysis dea and the technique for order preference by similarity to ideal solution topsis are integrated in ranking of decision making units dmus wang and luo 2006 contribute to a very interesting topic by showing that the topsis idea can be combined to dea for a comprehensive ranking of dmus however this paper finds that their approach is problematic in employing the negative ideal point nip for dea computation their ideal point based models rely on using conflicting efficiency concepts we slightly revised the models so that a dea analysis using topsis idea can be performed numerical demonstration reveals that our approach produces a ranking result that is consistent with that in previous studies

on eigenstructure based direct multichannel blind image restoration existing eigenstructure based direct multichannel blind image restoration techniques include nullspace based and direct deconvolver estimation techniques the nullspace based approach can be formulated as an optimization problem we show that this formulation implies a new subspace based approach that uses matrix operations this new approach has the same advantages as the nullspace based one but requires less computational complexity under some mild conditions its complexity is equal to that of the fft furthermore the relation among the nullspace based approach the direct deconvolver estimation and the new subspace based approach is studied

multichannel blind deconvolution of spatially misaligned images existing multichannel blind restoration techniques assume perfect spatial alignment of channels correct estimation of blur size and are prone to noise we developed an alternating minimization scheme based on a maximum a posteriori estimation with a priori distribution of blurs derived from the multichannel framework and a priori distribution of original images defined by the variational integral this stochastic approach enables us to recover the blurs and the original image from channels severely corrupted by noise we observe that the exact knowledge of the blur size is not necessary and we prove that translation misregistration up to a certain extent can be automatically removed in the restoration process

technologies ranking in the presence of both cardinal and ordinal data ranking of technologies is an important phase for technology transfer to rank the best technologies in the presence of both cardinal and ordinal data this paper proposes an innovative approach which is based on imprecise data envelopment analysis idea the objective of this paper is to propose a comprehensive reference that discusses the use of idea in technology ranking a numerical example demonstrates the application of the proposed method

a complete ranking of dmus using restrictions in dea models abstract data envelopment analyses dea is a linear programming based method which evaluates relative efficiency of decision making units dmus it can include multiple outputs and inputs without a priori weights and without requiring explicit specification of functional forms between inputs and outputs it computes a scalar measure of efficiency and determines efficient levels of inputs and outputs for each dmu under evaluation which has a range of zero to 1 hence it has the ability to rank dmus unless when some dmus are the same in efficiency score such as efficient dmus or inefficient dmus with the same efficiency score in many cases it is necessary to give a full ranking of the dmus hence this paper introduces a new method for complete ranking of decision making units which it does not need any changes in the models also for presentation of the ability of this method it is employed to rank the bank branch of a commercial bank branches in an empirical example

estimating the hyperbolic distance function a directional distance function approach abstract fare grosskopf and lovell 1985 merged farrell s input and output oriented technical efficiency measures into a new graph type approach known as hyperbolic distance function hdf in spite of its appealing special structure in allowing for the simultaneous and equiproportionate reduction in inputs and increase in outputs hdf is a non linear optimization and it is hard to solve particularly when dealing with technologies operating under variable returns to scale by connecting hdf to the directional distance function we propose a linear programming based procedure for estimating the exact value of hdf within the non parametric framework of data envelopment analysis we illustrate the computational effectiveness of the algorithm on several real world and simulated data sets generating the optimal value of hdf through generally solving at most two linear programs moreover our approach has several desirable properties such as 1 introducing a computational dual formulation for the hdf and providing an economic interpretation in terms of shadow prices 2 being readily adaptable to measure hyperbolic oriented super efficiency and 3 being flexible to deal with hdf based efficiency measures on environmental technologies

a note on including ordinal relations among multipliers in data envelopment analysis data envelopment analysis dea evaluates the efficiency of decision making units without imposing a priori weights on the input and output dimensions involved this note extends dea to situations where ordinal relations among weights corresponding to certain dimensions exist the extended model is reduced to the original dea formulation where some of the input and output factors are accumulated an illustrative example is provided and potential areas of applications are pointed out

ranking decision making units by imposing a minimum weight restriction in the data envelopment analysis it has been widely recognized that data envelopment analysis dea lacks discrimination power to distinguish between dea efficient units this paper proposes a new methodology for ranking decision making units dmus the new methodology ranks dmus by imposing an appropriate minimum weight restriction on all inputs and outputs which is decided by a decision maker dm or an assessor in terms of the solutions to a series of linear programming lp models that are specially constructed to determine a maximin weight for each dea efficient unit the dm can decide how many dmus to be retained as dea efficient in final efficiency ranking according to the requirement of real applications which provides flexibility for dea ranking three numerical examples are investigated using the proposed ranking methodology to illustrate its power in discriminating between dmus particularly dea efficient units

on some methods for performance ranking and correspondence analysis in the dea context two novel methods named performance baseline and performance correspondence matrices are proposed to evaluate the performance of decision making units dmus based on the techniques of singular value decomposition svd the performance baseline matrix can be used to rank all the dmus because it provides a common basis for performance comparison the performance correspondence matrix can be used to conduct performance cluster analysis with which to explore the structure of input output variables that are associated with dmus the analysis can reveal the performance difference of the dmus and the key input output variables determining the efficiency of a certain dmu and provides valuable quantitative information for adjusting variables to improve efficiency of the dmu three case studies are presented to demonstrate that the proposed methods in this work are effective and easy to use and can provide insights into proper selection of input output variables for performance comparison to avoid over manipulating dea models in practice

a new frequency domain subspace algorithm with restricted poles location through lmi regions and its application to a wind tunnel test abstractin this paper an innovative method is presented to identify models with a modified frequency domain subspace method this new approach allows to introduce in the subspace resolution constraints on the identified model poles location here a very general formulation is proposed to take into account regions in continuous discrete map this formulation is based on an lmi linear matrix inequalities description where the stability domain represents a particular case these lmi constraints are combined with the frequency domain subspace resolution to obtain identified models whose poles are situated in the specified lmi regions this approach is benchmarked with the loewner one which belongs to the class of frequency domain input output model identification and approximation methods besides the fact that they both belong to the data driven model approximation class they result to have slightly different objectives and show complementary performances this discussion is illustrated in practice wit

evam an eigenvector based algorithm for multichannel blind deconvolution of input colored signals a new algorithm is proposed for the deconvolution of an unknown possibly colored gaussian or nonstationary signal that is observed through two or more unknown channels described by rational system transfer functions more specifically not only the root pole and zero locations but also the orders of the channel transfer functions are unknown it is assumed that the channel orders may be overestimated the proposed algorithm estimates the orders and root locations of the channel transfer functions therefore it can also be used in multichannel system identification problems the input signal is allowed to be nonstationary and the channel transfer functions may be a nonminimum phase as well as noncausal hence the proposed algorithm is particularly suitable for applications such as dereverberation of speech signals recorded through multiple microphones several experimental results indicate improvement compared to the existing methods in the literature

scaling units via the canonical correlation analysis in the dea context abstract this paper deals with the evaluation of decision making units which have multiple inputs and outputs a new method cca dea is developed where the canonical correlation analysis cca is utilized to provide a full rank scaling for all the units rather than a categorical classification for efficient and inefficient units as done by the data envelopment analysis dea the cca dea approach is an attempt to bridge the gap between the frontier approach of dea and the average tendencies of statistics econometrics nonparametric statistical tests are employed to validate the consistency between the classification from the dea and the postclassification that was generated by the cca dea

nonsmooth h_infty synthesis we develop nonsmooth optimization techniques to solve h infty synthesis problems under additional structural constraints on the controller our approach avoids the use of lyapunov variables and therefore leads to moderate size optimization programs even for very large systems the proposed framework is versatile and can accommodate a number of challenging design problems including static fixed order fixed structure decentralized control design of pid controllers and simultaneous design and stabilization problems our algorithmic strategy uses generalized gradients and bundling techniques suited for the h infty norm and other nonsmooth performance criteria we compute descent directions by solving quadratic programs and generate steps via line search convergence to a critical point from an arbitrary starting point is proved and numerical tests are included to validate our methods the proposed approach proves to be efficient even for systems with several hundreds of states

super efficiencies or super inefficiencies insights from a joint computation model for slacks based measures in dea abstract the slacks based measure sbm can incorporate input and output slacks that would otherwise be neglected in the classical dea model in parallel the super efficiency model for sbm s sbm has been developed for the purpose of ranking sbm efficient decision making units dmus when implementing sbm in conjunction with s sbm however several issues can arise first unlike the standard super efficiency model s sbm can only solve for super efficiency scores but not sbm scores second the s sbm model may result in weakly efficient reference points third the s sbm and sbm scores for certain dmus may be discontinuous with a perturbation to their inputs and outputs making it hard to interpret and justify the scores in applications and the efficiency scores may be sensitive to small changes errors in data due to this discontinuity the s sbm model may overestimate the super efficiency score this paper extends the existing sbm approaches and develops a joint model j sbm that addresses the above issues namely the j sbm model can 1 simultaneously compute sbm scores for inefficient dmus and super efficiency for efficient dmus 2 guarantee the reference points generated by the joint model are pareto efficient and 3 the j sbm scores of a firm are continuous in the input and output space interestingly the radial dea efficiency and super efficiency scores for a dmu are continuous in the input output space the j sbm model combines the merits of the radial and sbm models i e continuity and pareto efficiency

integrating dea oriented performance assessment and target setting using interactive molp methods data envelopment analysis dea and multiple objective linear programming molp are tools that can be used in management control and planning whilst these two types of model are similar in structure dea is directed to assessing past performances as part of management control function and molp to planning future performance targets this paper is devoted to investigating equivalence models and interactive tradeoff analysis procedures in molp such that dea oriented performance assessment and target setting can be integrated in a way that the decision makers preferences can be taken into account in an interactive fashion three equivalence models are investigated between the output oriented dual dea model and the minimax reference point formulations namely the super ideal point model the ideal point model and the shortest distance model these models can be used to support efficiency analysis in the same way as the conventional dea model does and also support tradeoff analysis for setting target values by individuals or groups a case study is conducted to illustrate how dea oriented efficiency analysis can be conducted using the molp methods and how such performance assessment can be integrated into an interactive procedure for setting realistic target values

a recursive soft decision approach to blind image deconvolution this paper presents a new approach to blind image deconvolution based on soft decision blur identification and hierarchical neural networks traditional blind algorithms require a hard decision on whether the blur satisfies a parametric form before their formulations as the blurring function is usually unknown a priori this precondition inhibits the incorporation of parametric blur knowledge domain into the restoration schemes the new technique addresses this difficulty by providing a continual soft decision blur adaptation with respect to the best fit parametric structure throughout deconvolution the approach integrates the knowledge of well known blur models without compromising its flexibility in restoring images degraded by nonstandard blurs an optimization scheme is developed where a new cost function is projected and minimized with respect to the image and blur domains a nested neural network called the hierarchical cluster model is employed to provide an adaptive perception based restoration its sparse synaptic connections are instrumental in reducing the computational cost of restoration conjugate gradient optimization is adopted to identify the blur due to its computational efficiency the approach is shown experimentally to be effective in restoring images degraded by different blurs