example based learning particle swarm optimization for continuous optimization particle swarm optimization pso is a heuristic optimization technique based on swarm intelligence that is inspired by the behavior of bird flocking the canonical pso has the disadvantage of premature convergence several improved pso versions do well in keeping the diversity of the particles during the searching process but at the expense of rapid convergence this paper proposes an example based learning pso elpso to overcome these shortcomings by keeping a balance between swarm diversity and convergence speed inspired by a social phenomenon that multiple good examples can guide a crowd towards making progress elpso uses an example set of multiple global best particles to update the positions of the particles in this study the particles of the example set were selected from the best particles and updated by the better particles in the first in first out order in each iteration the particles in the example set are different and are usually of high quality in terms of the target optimization function elpso has better diversity and convergence speed than single gbest and non gbest pso algorithms which is proved by mathematical and numerical results finally computational experiments on benchmark problems show that elpso outperforms all of the tested pso algorithms in terms of both solution quality and convergence time

dct algorithms for composite sequence lengths this paper presents fast algorithms for the type ii and iii discrete cosine transforms of composite sequence length in particular a radix q algorithm where q is an odd integer is derived for uniform or mixed radix decomposition of the discrete cosine transform by combining the radix q and radix 2 algorithms a general decomposition method for any composite length is developed reduction of computational complexity can be achieved for many sequence lengths compared with that needed by the well known radix 2 algorithm furthermore both the proposed and chan and siu s 1993 mixed radix algorithms achieve the same computational complexity for n 3 2 sup p and 9 2 sup p however our algorithm uses a simpler decomposition approach and provides a wider range of choices of sequence lengths

feature subset selection by gravitational search algorithm optimization a new method for feature subset selection in machine learning fss mgsa feature subset selection by modified gravitational search algorithm is presented fss mgsa is an evolutionary stochastic search algorithm based on the law of gravity and mass interactions and it can be executed when domain knowledge is not available a wrapper approach over naive bayes id3 k nearest neighbor and support vector machine learning algorithms is used to evaluate the goodness of each visited solution the key to the success of the mgsa is to utilize the piecewise linear chaotic map for increasing its diversity of species and to use sequential quadratic programming for accelerating local exploitation promising results are achieved in a variety of tasks where domain knowledge is not available the experimental results show that the proposed method has the ability of selecting the discriminating input features correctly and can achieve high accuracy of classification which is comparable to or better than well known similar classifier systems furthermore the mgsa is tested on ten functions provided by cec 2005 special session and compared with various modified gravitational search algorithm particle swarm optimization and genetic algorithm the obtained results confirm the high performance of the mgsa in solving various problems in optimization

an efficient hybrid multi objective particle swarm optimization with a multi objective dichotomy line search recently more research works are focused on multi objective particle swarm optimization algorithm mopso due to its ability of global and local search for solving multi objective optimization problems moops however most of existing mopsos cannot achieve satisfactory results in solution quality this paper proposes an efficient hybrid multi objective particle swarm optimization with a multi objective dichotomy line search mols named mols mopso to deal with such problem mols mopso combines an effective particle updating strategy with the local search of mols the effective particle updating strategy is used for global search to deal with premature convergence and diversity maintenance within the swarm the mols is periodically activated for fast local search to converge toward the pareto front the exploratory capabilities are enhanced more efficiently by keeping a desirable balance between global search and local search so as to ensure sufficient diversity and well distribution amongst the solutions of the non dominated fronts while retaining at the same time the convergence to the pareto optimal front comparing mols mopso with various state of the art multi objective optimization algorithms developed recently the comparative study shows the effectiveness of mols mopso which not only assures a better convergence to the pareto frontier but also illustrates a good diversity and distribution of solutions

carrier phase and frequency estimation bounds for transmissions with embedded reference symbols the true crame spl acute r rao lower bound crlb for the joint estimation of carrier phase and frequency is derived for transmission bursts with interleaved reference and phase shift keying quadrature amplitude modulated data symbols results are presented for the special cases of midamble and preamble and postamble pilot symbol insertion the derivation reveals that the crlb is a function of the location of the reference symbols in the burst the number of reference symbols the number of data symbols the signal to noise ratio and the data modulation scheme by distributing the reference symbols symmetrically about the center of the burst and analyzing relative to the middle of the signal vector the joint frequency and phase estimation can be decoupled and the optimal phase estimation is achieved in the decoupled case the phase crlb is independent of the location of reference symbols in the burst in a symmetrical burst the use of a preamble and postamble is found to provide a lower frequency estimation crlb than that with a midamble it appears that the frequency crlb is reduced as the reference symbols are symmetrically distributed closer to the ends of the burst

multiple criteria decision making with interval stochastic variables a method based on interval stochastic dominance abstract a novel method based on interval stochastic dominance to solve the stochastic multiple criteria decision making smcdm problem with interval stochastic variables is proposed by analyzing the uncertainty of interval stochastic variables we propose the method of determining cumulative distribution function of interval stochastic variables then three types of interval stochastic dominance isd rules are investigated the definition of interval stochastic dominance degree isdd is presented and some interesting properties of isd and isdd are proved with respect to the smcdm problem with interval stochastic variables to begin with interval cumulative distribution functions of alternative criterion values are determined then the isd relations matrix associated with pairwise comparison of alternatives on each criterion is obtained based on isd rules and the isdd matrix on each criterion is calculated further based on promethee method the net flows of each alternative are derived so as to obtain the ranking result consequently examples are presented to illustrate the effectiveness of the proposed method

low complexity frequency estimation using multiple disjoint pilot blocks in burst mode communications two low complexity data aided frequency estimators suitable for use in burst mode communications are described and analyzed the estimators are based on pilot symbols organized into disjoint blocks embedded in the burst the first estimator is a generalization of the phase increment frequency estimation technique and the second estimator is a generalization of the autocorrelation frequency estimation technique the generalizations are needed to account for the spacings between the pilot blocks it is shown that the frequency estimators exhibit good accuracy while maintaining useful operating ranges

pilot symbol assisted and code aided phase error estimation for high order qam transmission in this paper we present a novel phase error estimation scheme for low density parity check ldpc coded high order quadrature amplitude modulation qam transmission systems and we provide a theoretical analysis of its performance the scheme uses known pilot symbols which are assumed to be placed at equally spaced positions and phase errors occurred at these pilot symbol positions are first estimated they are then interpolated over the other positions by using a wiener filter wf and estimation accuracy is next refined by using an ldpc code aided phase locked loop pll we present an efficient implementation of this two stage pilot symbol assisted and code aided psa ca scheme and evaluate its performance by means of power spectrum analysis we determine the mean square error at each step of the psa ca scheme and we find an optimal value of the noise equivalent loop bandwidth of the pll under the assumption that local oscillator phase noise can be modeled as a single pole single zero model we show through simulations that the psa ca scheme has a very high phase noise tolerance comparable to that of state of the art phase estimation schemes and provides a computationally efficient alternative

an adaptive parameter tuning of particle swarm optimization algorithm an adaptive parameter tuning of particle swarm optimization based on velocity information apso vi algorithm is proposed in this paper the velocity convergence of particles is first analyzed and the relationship between the velocity of particle and the search failures is pointed out which reveals the reasons why pso has relative poor global searching ability then this algorithm introduces the velocity information which is defined as the average absolute value of velocity of all the particles a new strategy is presented that the inertia weight is dynamically adjusted according to average absolute value of velocity which follows a given nonlinear ideal velocity by feedback control which can avoid the velocity closed to zero at the early stage under the guide of the nonlinear ideal velocity apso vi can maintain appropriate swarm diversity and alleviate the premature convergence validly numerical experiments are conducted to compare the proposed algorithm with different variants of pso on some benchmark functions experimental results show that the proposed algorithm remarkably improves the ability of pso to jump out of the local optima and significantly enhance the convergence speed and precision

a piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimization this paper presents a novel robust hybrid particle swarm optimization rhpso based on piecewise linear chaotic map pwlcm and sequential quadratic programming sqp the aim of the present research is to develop a new single objective optimization approach which requires no adjustment of its parameters for both unconstrained and constrained optimization problems this novel algorithm makes the best of ergodicity of pwlcm to help pso with the global search while employing the sqp to accelerate the local search five unconstrained benchmarks eighteen constrained benchmarks and three engineering optimization problems from the literature are solved by using the proposed hybrid approach the simulation results compared with other state of art methods demonstrate the effectiveness and robustness of the proposed rhpso for both unconstrained and constrained problems of different dimensions

a new evolutionary search strategy for global optimization of high dimensional problems global optimization of high dimensional problems in practical applications remains a major challenge to the research community of evolutionary computation the weakness of randomization based evolutionary algorithms in searching high dimensional spaces is demonstrated in this paper a new strategy sp uci is developed to treat complexity caused by high dimensionalities this strategy features a slope based searching kernel and a scheme of maintaining the particle population s capability of searching over the full search space examinations of this strategy on a suite of sophisticated composition benchmark functions demonstrate that sp uci surpasses two popular algorithms particle swarm optimizer pso and differential evolution de on high dimensional problems experimental results also corroborate the argument that in high dimensional optimization only problems with well formative fitness landscapes are solvable and slope based schemes are preferable to randomization based ones

approximating pareto frontier using a hybrid line search approach the aggregation of objectives in multiple criteria programming is one of the simplest and widely used approach but it is well known that this technique sometimes fail in different aspects for determining the pareto frontier this paper proposes a new approach for multicriteria optimization which aggregates the objective functions and uses a line search method in order to locate an approximate efficient point once the first pareto solution is obtained a simplified version of the former one is used in the context of pareto dominance to obtain a set of efficient points which will assure a thorough distribution of solutions on the pareto frontier in the current form the proposed technique is well suitable for problems having multiple objectives it is not limited to bi objective problems and require the functions to be continuous twice differentiable in order to assess the effectiveness of this approach some experiments were performed and compared with two recent well known population based metaheuristics namely parego and nsga ii when compared to parego and nsga ii the proposed approach not only assures a better convergence to the pareto frontier but also illustrates a good distribution of solutions from a computational point of view both stages of the line search converge within a short time average about 150ms for the first stage and about 20ms for the second stage apart from this the proposed technique is very simple easy to implement and use to solve multiobjective problems

an experimental comparison of different approaches to determining weights in additive utility models several studies this past decade have examined differences between holistic and decomposed approaches to determining weights in additive utility models some have argued that it matters little which procedure is used whereas others strongly favored particular methods in this paper we address this controversy experimentally by comparing five conceptually different approaches in terms of their weights and predictive ability the five methods are 1 multiple linear and non linear regression analyses of ten and fifteen holistic assessments 2 direct decomposed tradeoffs as proposed by keeney and raiffa keeney r l h raiffa 1977 decisions with multiple objectives wiley new york 3 a recent eigen vector technique of saaty saaty t l 1977 a scaling method for priorities in hierarchical structures j math psych 15 3 234 281 involving redundant pairwise comparisons of attributes 4 a straightforward allocation of hundred importance points and 5 unit weighting i e equal weighting after standardizing the attributes the decision task involved college admissions subjects were asked to evaluate hypothetical college applicants on the basis of verbal sat quantitative sat high school grade point average and a measure of extra curricular activity linear as well as nonlinear attribute utility functions were used in constructing the additive models the nonlinear functions were specified graphically by the subjects through selection from five different shapes i e one per attribute to test the predictive ability of the various models each subject made twenty separate pairwise comparisons of alternatives including direction and strength of preference the prediction criteria were percentage correct predictions as well as correlations using these twenty pairs seventy subjects were tested using an order controlled within subject design in comparing the different methods of weight determination monetary incentives were used to enhance motivation in terms of findings the methods generally differed systematically concerning the weights given to the various attributes as well as the variances of the resulting predictions on average however the methods predicted about equally well except for unit weighting which was clearly inferior the findings differ in this regard from the general literature furthermore nonlinear models were found to be inferior to linear ones finally subjects judged the methods to differ significantly in difficulty and trustworthiness which were found to correlate inversely the overall results raise various applied and theoretical issues which are discussed

ordinal criteria in stochastic multicriteria acceptability analysis smaa abstract we suggest a method for providing descriptive information about the acceptability of decision alternatives in discrete co operative group decision making problems the new smaa o method is a variant of the stochastic multicriteria acceptability analysis smaa smaa o is designed for problems where criteria information for some or all criteria is ordinal that is experts or decision makers have ranked the alternatives according to each ordinal criterion considerable savings can be obtained if rank information for some or all the criteria is sufficient for making decisions without significant loss of quality the approach is particularly useful for group decision making when the group can agree on the use of an additive decision model but only partial preference information or none at all is available

phase noise in mimo systems bayesian cramer rao bounds and soft input estimation this paper addresses the problem of estimating time varying phase noise caused by imperfect oscillators in multiple input multiple output mimo systems the estimation problem is parameterized in detail and based on an equivalent signal model its dimensionality is reduced to minimize the overhead associated with phase noise estimation new exact and closed form expressions for the bayesian cramer rao lower bounds bcrlbs and soft input maximum a posteriori map estimators for online i e filtering and offline i e smoothing estimation of phase noise over the length of a frame are derived simulations demonstrate that the proposed map estimators mean square error mse performances are very close to the derived bcrlbs at moderate to high signal to noise ratios to reduce the overhead and complexity associated with tracking the phase noise processes over the length of a frame a novel soft input extended kalman filter ekf and extended kalman smoother eks that use soft statistics of the transmitted symbols given the current observations are proposed numerical results indicate that by employing the proposed phase tracking approach the bit error rate performance of a mimo system affected by phase noise can be significantly improved in addition simulation results indicate that the proposed phase noise estimation scheme allows for application of higher order modulations and larger numbers of antennas in mimo systems that employ imperfect oscillators

the modified cramer rao bound in vector parameter estimation in this paper we extend the scalar modified cramer rao bound mcrb to the estimation of a vector of nonrandom parameters in the presence of nuisance parameters the resulting bound is denoted with the acronym mcrvb where v stands for vector as with the scalar bound the mcrvb is generally looser than the conventional crvb but the two bounds are shown to coincide in some situations of practical interest the mcrvb is applied to the joint estimation of carrier frequency phase and symbol epoch of a linearly modulated waveform corrupted by correlated impulsive noise encompassing white gaussian noise as a particular case wherein data symbols and noise power are regarded as nuisance parameters in this situation calculation of the conventional crvb is infeasible while application of the mcrvb leads to simple useful expressions with moderate analytical effort when specialized to the case of white gaussian noise the mcrvb yields results already available in the literature in fragmentary form and simplified contexts

enhancing particle swarm optimization using generalized opposition based learning particle swarm optimization pso has been shown to yield good performance for solving various optimization problems however it tends to suffer from premature convergence when solving complex problems this paper presents an enhanced pso algorithm called gopso which employs generalized opposition based learning gobl and cauchy mutation to overcome this problem gobl can provide a faster convergence and the cauchy mutation with a long tail helps trapped particles escape from local optima the proposed approach uses a similar scheme as opposition based differential evolution ode with opposition based population initialization and generation jumping using gobl experiments are conducted on a comprehensive set of benchmark functions including rotated multimodal problems and shifted large scale problems the results show that gopso obtains promising performance on a majority of the test problems

adaptive iterative detectors for phase uncertain channels via variational bounding the problem of iterative detection decoding of data symbols transmitted over an additive white gaussian noise awgn channel in the presence of phase uncertainty is addressed in this paper by modelling the phase uncertainty either as an unknown deterministic variable process or random variable process with a known a priori probability density function a number of non bayesian and bayesian detection algorithms with various amount of suboptimality have been proposed in the literature to solve the problem in this paper a new set of suboptimal iterative detection algorithms is obtained by utilizing the variational bounding technique especially applying the generic variational bayesian vb framework efficient iterative joint estimation and detection decoding schemes are derived for the constant phase model as well as for the dynamic phase model in addition the relation of the vb based approach to the optimal noncoherent receiver as well as to the classical approach via the expectation maximization em algorithm is provided performance of the proposed detectors in the presence of a strong dynamic phase noise is compared to the performance of the existing detectors furthermore an incremental scheduling of the vb or em algorithm is shown to reduce the overall complexity of the receiver

block processing soft input soft output demodulator for coded psk using dct based phase noise estimation this paper considers the detection of coded phaseshift keying signals subjected to additive white gaussian noise and oscillator phase noise we propose a detector that partitions the received frame into smaller blocks and models the unknown phasor variations over each block as a truncated discrete cosine transform dct expansion detection and decoding are iteratively performed between a soft input soft output siso demodulator a siso demapper and a siso decoder based on the sum product algorithm and the factor graph framework whereas the expectation maximization algorithm is used in the demodulator for the dct coefficients estimation the resulting demodulator is shown to have an excellent performance complexity tradeoff and to be well suited for parallel processing on multiple cores

reference point approach for multiple decision makers we consider multiple criteria decision making problems where a group of decision makers wants to find the most preferred solution from a discrete set of alternatives we develop a method that uses achievement functions for charting subsets of reference points that would support a certain alternative to be the most preferred one the resulting descriptive information is provided to the decision makers in the form of reference acceptability indices and central reference points for each decision alternative then the decision makers can compare this information with their own preferences we demonstrate the use of the method using a strategic multiple criteria decision model for an electricity retailer

the modified cramer rao bound and its application to synchronization problems we introduce the modified cramer rao bound crb which like the true crb is a lower bound to the error variance of any parameter estimator the modified crb proves useful when in addition to the parameter to be estimated the observed data also depend on other unwanted parameters the relationship between the modified and true crb is established and applications are discussed regarding the estimation of carrier frequency offset carrier phase and timing epoch in linearly modulated signals modified crbs for phase and timing estimation have been already discussed in previous works where it is shown that several practical carrier phase and clock recovery circuits do attain such bounds frequency discrimination instead is not so well represented in the literature and a significant contribution of this paper is the calculation of the modified crb for frequency estimation this bound is compared with the performance of some frequency detectors and it is concluded that further work is needed in search of more efficient frequency discrimination methods

classifying efficient alternatives in smaa using cross confidence factors abstract stochastic multicriteria acceptability analysis smaa is a family of methods for aiding multicriteria group decision making these methods are based on exploring the weight space in order to describe the preferences that make each alternative the most preferred one the main results of the analysis are rank acceptability indices central weight vectors and confidence factors for different alternatives the rank acceptability indices describe the variety of different preferences resulting in a certain rank for an alternative the central weight vectors represent the typical preferences favouring each alternative and the confidence factors measure whether the criteria data are sufficiently accurate for making an informed decision in some cases when the problem involves a large number of efficient alternatives the analysis may fail to discriminate between them this situation is revealed by low confidence factors in this paper we develop cross confidence factors which are based on computing confidence factors for alternatives using each other s central weight vectors the cross confidence factors can be used for classifying efficient alternatives into sets of similar and competing alternatives these sets are related to the concept of reference sets in data envelopment analysis dea but generalized for stochastic models forming these sets is useful when trying to identify one or more most preferred alternatives or suitable compromise alternatives the reference sets can also be used for evaluating whether criteria need to be measured more accurately and at which alternatives the measurements should be focused this may cause considerable savings in measurement costs we demonstrate the use of the cross confidence factors and reference sets using a real life example

on the true and the modified cramer rao bounds for the estimation of a scalar parameter in the presence of nuisance parameters we consider the cramer rao bound crb for the estimation of a scalar parameter in the presence of nuisance parameters whereas this true crb is hard to evaluate in general we present a simple analytical expression for its high snr asymptote i e the asymptotic crb acrb we show that this acrb is related to the crb for the joint estimation of the scalar parameter and the nuisance parameters previously a modified crb mcrb for the estimation of a scalar parameter in the presence of nuisance parameters has been derived this mcrb is also simple to evaluate and is related to the crb for the estimation of the scalar parameter assuming that the value of the nuisance parameters is a priori known we show that the mcrb can be quite loose at high snr when the scalar parameter is coupled with the nuisance parameters in the case of synchronization parameter estimation we find that the acrb equals the mcrb whether or not some of the nuisance parameters are a priori known

force imitated particle swarm optimization using the near neighbor effect for locating multiple optima multimodal optimization problems pose a great challenge of locating multiple optima simultaneously in the search space to the particle swarm optimization pso community in this paper the motion principle of particles in pso is extended by using the near neighbor effect in mechanical theory which is a universal phenomenon in nature and society in the proposed near neighbor effect based force imitated pso nn fpso algorithm each particle explores the promising regions where it resides under the composite forces produced by the near neighbor attractor and near neighbor repeller which are selected from the set of memorized personal best positions and the current swarm based on the principles of superior and nearer and inferior and nearer respectively these two forces pull and push a particle to search for the nearby optimum hence particles can simultaneously locate multiple optima quickly and precisely experiments are carried out to investigate the performance of nn fpso in comparison with a number of state of the art pso algorithms for locating multiple optima over a series of multimodal benchmark test functions the experimental results indicate that the proposed nn fpso algorithm can efficiently locate multiple optima in multimodal fitness landscapes

rank based modelling of preferences in multi criteria decision making abstract in multi criteria decision making one approach to utilise mixed data consisting of both ordinal and ratio scale information is to consider ratio scale random samples fulfilling the existing ordinal data however some of the distributional assumptions proposed in the literature for the phase of transforming the ordinal data into ratio scale may lead to over interpretation of the available information in this paper we propose a statistical model for overcoming these difficulties in the cardinal part of the model the ratio scale measurements are analysed by a regression model developed for pairwise comparisons data the model for ordinal measurements is proposed by taking the regression model as a background model and by assuming that instead of the ratio scale only the ranks of the items are observed

pilot aided carrier recovery in the presence of phase noise the paper deals with carrier recovery based on pilot symbols in single carrier systems the system model considered in the paper includes the channel additive white noise and the phase noise that affects the local oscillators used for up down conversion wiener s method is used to determine the optimal filter in estimation of phase noise assuming that a sequence of equally spaced pilot symbols is available our analysis allows to capture the cyclostationary performance of the estimate a phenomenon that is not considered in the previous literature in the paper closed form formulas for the transfer function of the optimal filter and for the mean square phase error are derived for the case where the phase noise is modelled as random phase walk for this case a suboptimal filter is proposed numerical results are presented to substantiate the analysis

multi objective particle swarm optimization with time variant inertia and acceleration coefficients in this article we describe a novel particle swarm optimization pso approach to multi objective optimization moo called time variant multi objective particle swarm optimization tv mopso tv mopso is made adaptive in nature by allowing its vital parameters viz inertia weight and acceleration coefficients to change with iterations this adaptiveness helps the algorithm to explore the search space more efficiently a new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non dominated fronts while retaining at the same time the convergence to the pareto optimal front tv mopso has been compared with some recently developed multi objective pso techniques and evolutionary algorithms for 11 function optimization problems using different performance measures

joint iterative detection and decoding in the presence of phase noise and frequency offset we present a new algorithm for joint detection and decoding of iteratively decodable codes transmitted over channels affected by a time varying phase noise pn and a constant frequency offset the proposed algorithm is obtained as an application of the sum product algorithm to the factor graph representing the joint a posteriori distribution of the information symbols and the channel parameters given the channel output the resulting algorithm employs the soft output information on the coded symbols provided by the decoder and performs forward backward recursions taking into account the joint probability distribution of phase and frequency offset we present simulation results for high order coded modulation schemes based on low density parity check codes and serially concatenated convolutional codes showing that despite its low complexity the algorithm is able to cope with a strong pn and a significant uncompensated frequency offset thus avoiding the use of complicated data aided frequency estimation schemes operating on a known preamble the robustness of the algorithm in the presence of a time varying frequency offset is also discussed

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

fast algorithms for type iii dct of composite sequence lengths this correspondence presents an odd factor algorithm for the type iii discrete cosine transform dct for uniform or mixed radix decomposition by jointly using the old factor and the existing radix 2 algorithms a general decomposition method for arbitrarily composite sequence length is developed a reduction of computational complexity can be achieved compared with that needed by other reported algorithms for m 2 sup m the decomposition approach has a simple computational structure and supports a wider range of choices for different sequence lengths

optimum pilot sequences for data aided synchronization we compute the pilot sequences that minimize the cramer rao bound crb for carrier phase offset carrier frequency offset and symbol timing offset estimation if we require the pilot sequence to be drawn from a discrete set of constellation points then the alternating sequence is the crb minimizing sequence if we relax the constellation constraint and apply a total energy constraint we obtain three different sequences that minimize the crb for each of the synchronization parameters we show that the maximum likelihood ml estimator achieves the crb for any of the sequences presented we also explore the impact of using the pilot symbols in a preamble with unknown data symbols immediately following the interference caused by the unknown data symbols reduces the performance of the ml estimator beyond an snr threshold

multivariate gaussian criteria in smaa abstract we consider stochastic multicriteria decision making problems with multiple decision makers in such problems the uncertainty or inaccuracy of the criteria measurements and the partial or missing preference information can be represented through probability distributions in many real life problems the uncertainties of criteria measurements may be dependent however it is often difficult to quantify these dependencies also most of the existing methods are unable to handle such dependency information in this paper we develop a method for handling dependent uncertainties in stochastic multicriteria group decision making problems we measure the criteria their uncertainties and dependencies using a stochastic simulation model the model is based on decision variables and stochastic parameters with given distributions based on the simulation results we determine for the criteria measurements a joint probability distribution that quantifies the uncertainties and their dependencies we then use the smaa 2 stochastic multicriteria acceptability analysis method for comparing the alternatives based on the criteria distributions we demonstrate the use of the method in the context of a strategic decision support model for a retailer operating in the liberated european electricity market

multi objective robust pid controller tuning using two lbests multi objective particle swarm optimization in this paper two lbests multi objective particle swarm optimization 2lb mopso is applied to design multi objective robust proportional integral derivative pid controllers for two mimo systems namely distillation column plant and longitudinal control system of the super maneuverable f18 harv fighter aircraft multi objective robust pid controller design problem is formulated by minimizing integral squared error ise and balanced robust performance criteria during the search 2lb mopso can focus on small regions in the parameter space in the vicinity of the best existing fronts as the lbests are chosen from the top fronts in a non domination sorted external archive of reasonably large size the offspring obtained can be more diverse with good fitness the performance of various optimal pid controllers is compared in terms of the sum of ise and balanced robust performance criteria for the purpose of comparison 2lb mopso nsga ii as well as earlier reported riccati iga and osa methods are considered the performance of pid controllers obtained using 2lb mopso is better than that of others in addition hypervolume based comparisons are carried out to show the superior performance of 2lb mopso over nsga ii the results reveal that 2lb mopso yields better robustness and consistency in terms of the sum of ise and balanced robust performance criteria than various optimal pid controllers

stochastic multiobjective acceptability analysis for the choquet integral preference model and the scale construction problem abstract the choquet integral preference model is adopted in multiple criteria decision aiding mcda to deal with interactions between criteria while the stochastic multiobjective acceptability analysis smaa is an mcda methodology considered to take into account uncertainty or imprecision on the considered data and preference parameters in this paper we propose to combine the choquet integral preference model with the smaa methodology in order to get robust recommendations taking into account all parameters compatible with the preference information provided by the decision maker dm in case the criteria are on a common scale one has to elicit only a set of non additive weights technically a capacity compatible with the dm s preference information instead if the criteria are on different scales besides the capacity one has to elicit also a common scale compatible with the preferences given by the dm our approach permits to explore the whole space of capacities and common scales compatible with the dm s preference information

handling boundary constraints for particle swarm optimization in high dimensional search space despite the fact that the popular particle swarm optimizer pso is currently being extensively applied to many real world problems that often have high dimensional and complex fitness landscapes the effects of boundary constraints on pso have not attracted adequate attention in the literature however in accordance with the theoretical analysis in 11 our numerical experiments show that particles tend to fly outside of the boundary in the first few iterations at a very high probability in high dimensional search spaces consequently the method used to handle boundary violations is critical to the performance of pso in this study we reveal that the widely used random and absorbing bound handling schemes may paralyze pso for high dimensional and complex problems we also explore in detail the distinct mechanisms responsible for the failures of these two bound handling schemes finally we suggest that using high dimensional and complex benchmark functions such as the composition functions in 19 is a prerequisite to identifying the potential problems in applying pso to many real world applications because certain properties of standard benchmark functions make problems inexplicit

implementing stochastic multicriteria acceptability analysis abstract stochastic multicriteria acceptability analysis smaa is a family of methods for aiding multicriteria group decision making in problems with inaccurate uncertain or missing information these methods are based on exploring the weight space in order to describe the preferences that make each alternative the most preferred one or that would give a certain rank for a specific alternative the main results of the analysis are rank acceptability indices central weight vectors and confidence factors for different alternatives the rank acceptability indices describe the variety of different preferences resulting in a certain rank for an alternative the central weight vectors represent the typical preferences favouring each alternative and the confidence factors measure whether the criteria measurements are sufficiently accurate for making an informed decision the computations in smaa require the evaluation of multidimensional integrals that must in practice be computed numerically in this paper we present efficient methods for performing the computations through monte carlo simulation analyze the complexity and assess the accuracy of the presented algorithms we also test the efficiency of these methods empirically based on the tests the implementation is fast enough to analyze typical sized discrete problems interactively within seconds due to almost linear time complexity the method is also suitable for analysing very large decision problems for example discrete approximations of continuous decision problems

robust multi criteria ranking with additive value models and holistic pair wise preference statements we consider a problem of ranking alternatives based on their deterministic performance evaluations on multiple criteria we apply additive value theory and assume the decision maker s dm preferences to be representable with general additive monotone value functions the dm provides indirect preference information in form of pair wise comparisons of reference alternatives and we use this to derive the set of compatible value functions then this set is analyzed to describe 1 the possible and necessary preference relations 2 probabilities of the possible relations 3 ranges of ranks the alternatives may obtain and 4 the distributions of these ranks our work combines previous results from robust ordinal regression extreme ranking analysis and stochastic multicriteria acceptability analysis under a unified decision support framework we show how the four different results complement each other discuss extensions of the main proposal and demonstrate practical use of the approach by considering a problem of ranking 20 european countries in terms of 4 criteria reflecting the quality of their universities

algorithms for joint phase estimation and decoding for mimo systems in the presence of phase noise and quasi static fading channels in this paper we derive the maximum a posteriori map symbol detector for a multiple input multiple output system in the presence of wiener phase noise due to noisy local oscillators and quasi static fading channels as in single antenna systems the computation of the optimum receiver is an analytically intractable problem and is unimplementable in practice in this purview we propose three suboptimal low complexity algorithms for approximately implementing the map symbol detector which involve joint phase noise estimation and data detection our first algorithm is obtained by means of the sum product algorithm where we use the multivariate tikhonov canonical distribution approach in our next algorithm we derive an approximate map symbol detector based on the smoother detector framework wherein the detector is properly designed by incorporating the phase noise statistics from the smoother the third algorithm is derived based on the variational bayesian framework by simulations we evaluate the performance of the proposed algorithms for both uncoded and coded data transmissions and observe that the proposed techniques significantly outperform the other important algorithms from prior works which are considered herein

joint estimation of channel and oscillator phase noise in mimo systems oscillator phase noise limits the performance of high speed communication systems since it results in time varying channels and rotation of the signal constellation from symbol to symbol in this paper joint estimation of channel gains and wiener phase noise in multi input multi output mimo systems is analyzed the signal model for the estimation problem is outlined in detail and new expressions for the cramer rao lower bounds crlbs for the multi parameter estimation problem are derived a data aided least squares ls estimator for jointly obtaining the channel gains and phase noise parameters is derived next a decision directed weighted least squares wls estimator is proposed where pilots and estimated data symbols are employed to track the time varying phase noise parameters over a frame in order to reduce the overhead and delay associated with the estimation process a new decision directed extended kalman filter ekf is proposed for tracking the mimo phase noise throughout a frame numerical results show that the proposed ls wls and ekf estimators performances are close to the crlb finally simulation results demonstrate that by employing the proposed channel and time varying phase noise estimators the bit error rate performance of a mimo system can be significantly improved

the use of domains in multicriteria decision making abstract this paper describes a domain criterion for a multicriteria problem given decision maker it then outlines possibilities for aggregating individual preference structures in the case of multiple decision makers

multicriteria estimation of probabilities on basis of expert non numeric non exact and non complete knowledge abstract a new method of alternatives probabilities estimation under deficiency of expert numeric information obtained from different sources is proposed the method is based on the bayesian model of uncertainty randomization additional non numeric non exact and non complete expert knowledge nnn knowledge nnn information is used for final estimation of the alternatives probabilities an illustrative example demonstrates the proposed method application to forecasting of oil shares price with the use of nnn information obtained from different experts investment firms

a method for stochastic multiple criteria decision making based on dominance degrees this paper proposes a method for solving the stochastic multiple criteria decision making smcdm problem where consequences of alternatives with respect to criteria are represented by random variables with probability distributions firstly definitions and related analysis of dominance degree of one probability distribution over another are given then by calculating the dominance degrees the dominance degree matrix of alternative pairwise comparisons with respect to each criterion is built further using promethee ii method an overall dominance degree matrix of alternative pairwise comparisons is constructed and a net flow of each alternative is calculated based on the obtained net flows a ranking of alternatives is determined finally numerical examples for the three cases are given to illustrate the use of the proposed method

ordinal data in multicriteria decision making a stochastic dominance approach to siting nuclear power plants abstract this paper addresses decision problems with ordinal data weights criterion scores a random sampling approach is proposed to generate quantitative values which are consistent with the underlying ordinal information an attractive feature of the approach is that it is applicable with mixed quantitative ordinal data another feature is that the approach can be extended to rankings with degrees of difference the outcome of the approach is a distribution of performance scores stochastic dominance concepts are proposed to arrive at a final ranking of alternatives an application of these procedures is given for a location study of nuclear power plants

a multi objective portfolio selection model with fuzzy value at risk ratio considering nonstatistical uncertainties and or insufficient historical data in security return forecasts fuzzy set theory has been applied in the past decades to build portfolio selection models meanwhile various risk measurements such as variance entropy and value at risk have been proposed in fuzzy environments to evaluate investment risks from different perspectives sharpe ratio also known as the reward to variability ratio which measures the risk premium per unit of the nonsystematic risk asset deviation has received great attention in modern portfolio theory in this study the sharpe ratio in fuzzy environments is introduced whereafter a fuzzy value at risk ratio is proposed compared with sharpe ratio value at risk ratio is an index with dimensional knowledge that reflects the risk premium per unit of the systematic risk the greatest loss under a given confidence level on the basis of the two ratios a multi objective model is built to evaluate their joint impact on portfolio selection then the proposed model is solved by a fuzzy simulation based multi objective particle swarm optimization algorithm where the global best of each iteration is determined by an improved dominance times based method finally the algorithm superiority is justified via comparing with existing solvers on benchmark problems and the model effectiveness is exemplified by using three case studies on portfolio selection

pseudo criteria versus linear utility function in stochastic multi criteria acceptability analysis abstract stochastic multi criteria acceptability analysis smaa is a multi criteria decision support method for multiple decision makers dms in discrete problems smaa does not require explicit or implicit preference information from the dms instead the method is based on exploring the weight space in order to describe the valuations that would make each alternative the preferred one partial preference information can be represented in the weight space analysis through weight distributions in this paper we compare two variants of the smaa method using randomly generated test problems with 2 12 criteria and 4 12 alternatives in the original smaa a utility or value function models the dms preference structure and the inaccuracy or uncertainty of the criteria is represented by probability distributions in smaa 3 electre iii type pseudo criteria are used instead both methods compute for each alternative an acceptability index measuring the variety of different valuations that supports this alternative and a central weight vector representing the typical valuations resulting in this decision we seek answers to three questions 1 how similar are the results provided by the decision models 2 what kind of systematic differences exists between the models and 3 how could one select indifference and preference thresholds of the pseudo criteria model to match a utility model with given probability distributions

on phase locked loops and kalman filters driessen 1994 and christiansen 1994 independently showed that for a specific dynamic model the proportional integral phase locked loop pll has the same structure as the kalman filter in this paper closed form expressions of the corresponding kalman gain values are derived both in acquisition and tracking modes of the pll

message passing algorithms for phase noise tracking using tikhonov mixtures phase noise poses a serious challenge for high speed digital communications systems mainly when going to higher and higher carrier frequencies such as in satellite communications traditionally phase noise estimation was performed separately from the decoding task and it was shown recently that there is much to be gained from joint estimation and decoding particularly when using ldpc low density parity check turbo codes however jointly estimating phase noise and decoding is a very complex and computationally demanding task in this paper we propose several algorithms based on the sum and product algorithm spa for low complexity joint decoding and estimation of coded information in strong phase noise channels these algorithms are based on a novel approximation of spa messages as tikhonov mixtures of a given order since mixture based bayesian inference such as spa creates an exponential increase in mixture order for consecutive messages a mixture reduction scheme is a must therefore in this paper we propose a low complexity mixture reduction algorithm which provably satisfies an upper bound on the kullback leibler kl divergence between the mixture and the reduced mixture we then reduce the complexity even further including limiting the model order and reducing the clustering effort to simple component selection as an extreme case it is even possible to reduce the number of modes to one we show the relation between the simplified algorithm to the phase locked loop pll finally we show simulation results and complexity analysis for the proposed algorithms which show superior performance over other state of the art low complexity algorithms

on convergence of the multi objective particle swarm optimizers several variants of the particle swarm optimization pso algorithm have been proposed in recent past to tackle the multi objective optimization mo problems based on the concept of pareto optimality although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of pso as a single objective optimizer till date to the best of our knowledge no such analysis exists for the multi objective pso mopso algorithms this paper presents a first simple analysis of the general pareto based mopso and finds conditions on its most important control parameters the inertia factor and acceleration coefficients that govern the convergence behavior of the algorithm to the optimal pareto front in the objective function space computer simulations over benchmark mo problems have also been provided to substantiate the theoretical derivations

smaa 2 stochastic multicriteria acceptability analysis for group decision making stochastic multicriteria acceptability analysis smaa is a multicriteria decision support method for multiple decision makers in discrete problems in smaa the decision makers need not express their preferences explicitly or implicitly instead the method is based on exploring the weight space in order to describe the valuations that would make each alternative the preferred one inaccurate or uncertain criteria values are represented by probability distributions from which the method computes confidence factors describing the reliability of the analysis in this paper we introduce the smaa 2 method which extends the original smaa by considering all ranks in the analysis in situations where the elitistic smaa may assess large acceptability only for extreme alternatives without sufficient majority support the more holistic smaa 2 analysis can be used to identify good compromise candidates the results are presented graphically we consider also situations where partial preference information is available we demonstrate the new method using a real life decision problem

a method for stochastic multiple criteria decision making based on pairwise comparisons of alternatives with random evaluations this paper proposes a method for solving stochastic multiple criteria decision making mcdm problems where evaluations of alternatives on considered criteria are random variables with known probability density functions or probability mass functions probabilities on all possible results of pairwise comparisons of alternatives are first calculated using probability theory then all possible results of pairwise comparisons are classified into superior indifferent and inferior ones using a predefined identification rule consequently the probabilities on all possible results of pairwise comparisons are partitioned into superior indifferent and inferior probabilities furthermore based on the derived probabilities an algorithm is developed to rank the alternatives finally a numerical example is used to illustrate the feasibility and validity of the proposed method

pilot symbol assisted phase noise compensation with forward backward wiener smoothing filters we present a novel wiener smoothing filter procedure for pilot symbol assisted estimation of oscillator phase noise the procedure consists of forward and backward first order infinite impulse response filtering the pilot symbols and combining the filter outputs to generate an estimate of the phase noise we analyze the mean square error performance of this pilot symbol assisted forward backward wiener smoothing filter fbwsf procedure and show its optimality under the assumption that the phase noise is modeled as a first order autoregressive moving average process we then apply the fbwsf as a code aided phase noise compensation technique in an iterative receiver framework the iterative receiver presented here first estimates phase noise roughly by using the pilot symbol assisted fbwsf then it refines the estimates with the code aided version of fbwsf the resulting receiver achieves excellent error performance at a feasible level of computational complexity

decision aid for discrete multiple criteria decision making problems with imprecise data abstract we describe ways of aiding decision making with a discrete set of alternatives in many decision situations it is not possible to obtain explicit preference information from the decision makers instead useful decision aid can be provided to the decision makers by describing what kind of weighting of the criteria result in certain choices of the alternatives the suggested treatment is based on the basic ideas of the electre iii method the modelling of the preferences by pseudo criteria is especially helpful in case the data that is the criterion values are imprecise unlike electre iii no ranking of the alternatives is produced based on a minimum procedure in the exploitation of the outranking relations we provide information about the weights of the criteria that make a certain alternative the best we also present an interactive searching procedure in the weighting space the auxiliary optimization problems to be solved are nondifferentiable cases with both single and multiple decision makers are considered

a study of particle swarm optimization particle trajectories particle swarm optimization pso has shown to be an efficient robust and simple optimization algorithm most of the pso studies are empirical with only a few theoretical analyses that concentrate on understanding particle trajectories these theoretical studies concentrate mainly on simplified pso systems this paper overviews current theoretical studies and extend these studies to investigate particle trajectories for general swarms to include the influence of the inertia term the paper also provides a formal proof that each particle converges to a stable point an empirical analysis of multi dimensional stochastic particles is also presented experimental results are provided to support the conclusions drawn from the theoretical findings

integral analysis method iam this paper presents the theoretical foundations of the new integral analysis method iam and its application to a facility location problem this methodology integrates the cardinal and ordinal criteria of combinatorial stochastic optimization problems in four stages definition of the problem cardinal analysis ordinal analysis and integration analysis the method uses the concepts of stochastic multicriteria acceptability analysis smaa monte carlo simulation optimization techniques and elements of probability the proposed method iam was used to determine optimal locations for the retail stores of a colombian coffee marketing company

nonlinear mappings in problem solving and their pso based development the study is devoted to a concept and algorithmic realization of nonlinear mappings aimed at increasing the effectiveness of the problem solving method given the original input space x and a certain problem solving method m designed is a nonlinear mapping f so that the method operating in the transformed space m f x becomes more efficient the nonlinear mappings realize a transformation of x through contractions and expansions of selected regions of the original space in particular we show how a piecewise linear mapping is optimized by using particle swarm optimization pso and a suitable fitness function quantifying the objective of the problem several families of problems are investigated and illustrated through illustrative experimental results

fundamentals of statistical signal processing estimation theory minimum variance unbiased estimation cramer rao lower bound linear models general minimum variance unbiased estimation best linear unbiased estimators maximum likelihood estimation least squares method of moments the bayesian philosophy general bayesian estimators linear bayesian estimators kalman filters summary of estimators extension for complex data and parameters

a dominance tree and its application in evolutionary multi objective optimization most contemporary multi objective evolutionary algorithms moeas store and handle a population with a linear list and this may impose high computational complexities on the comparisons of solutions and the fitness assignment processes this paper presents a data structure for storing the whole population and their dominating information in moeas this structure called a dominance tree dt is a binary tree that can effectively and efficiently store three valued relations namely dominating dominated or non dominated among vector values this paper further demonstrates dt s potential applications in evolutionary multi objective optimization with two cases the first case utilizes the dt to improve nsga ii as a fitness assignment strategy the second case demonstrates a dt based moea called a dtea which is designed by leveraging the favorable properties of the dt the simulation results show that the dt improved nsga ii is significantly faster than nsga ii meanwhile dtea is much faster than spea2 nsga ii and an improved version of nsga ii on the other hand in regard to converging to the pareto optimal front and maintaining the diversity of solutions dt improved nsga ii and dtea are found to be competitive with nsga ii and spea2

a new pso algorithm with random c d switchings abstract this paper investigates the overall convergence analysis and proposes a novel random c d switchings pso algorithm with random switchings between convergence operator and divergence operator with respect to the standard pso algorithm its convergence analysis provides a fundamental theory of selecting convergence operator and divergence operator during the process of finding the suboptimal or global solution the random switchings between two typical operators namely operator c and operator d which are two different ways to update velocities of all particles are conducted by a so called convergence ratio parameter which can determine the tradeoff between exploration ability and exploitation ability from the quantitative perspective numerical results on several benchmark functions demonstrate the following observations 1 the proper convergence ratio is closely related to the landscape of objective function the dimension of solution space and the number of local optimums 2 small convergence ratio setting to 0 60 or 0 65 may benefit the optimization problem which has many local optimums in the high dimensional space while large convergence ratio setting to 0 85 or 0 9 is probably helpful for the optimization problem with few local optimums or flat landscape

capso centripetal accelerated particle swarm optimization meta heuristic search algorithms are developed to solve optimization problems such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities swarm intelligence si algorithms comprise a branch of meta heuristic algorithms that imitate the behavior of insects birds fishes and other natural phenomena to find solutions for complex optimization problems in this study an improved particle swarm optimization pso scheme combined with newton s laws of motion the centripetal accelerated particle swarm optimization capso scheme is introduced capso accelerates the learning and convergence of optimization problems in addition the binary mode of the proposed algorithm binary centripetal accelerated particle swarm optimization bcapso is introduced for binary search spaces these algorithms are evaluated using nonlinear benchmark functions and the results are compared with the gravitational search algorithm gsa and pso in both the real and the binary search spaces moreover the performance of capso in solving the functions is compared with some well known pso algorithms in the literature the experimental results showed that the proposed methods enhance the performance of pso in terms of convergence speed solution accuracy and global optimality

phase and frequency estimation for psk packets bounds and algorithms new cramer rao lower bounds crbs are derived for the estimation of phase offset and frequency offset from a block of random bpsk or qpsk symbols at low signal to noise snr ratios these bounds are compared to existing bounds which apply to the unmodulated carrier or to known psk symbols and to the performance of some estimation algorithms

bayesian and hybrid cramer rao bounds for the carrier recovery under dynamic phase uncertain channels in this paper we study bayesian and hybrid cramer rao bounds bcrb and hcrb for the code aided ca the data aided da and the non data aided nda dynamical phase estimation of qam modulated signals we address the bounds derivation for both the offline scenario for which the whole observation frame is used and the online which only takes into account the current and the previous observations for the ca scenario we show that the computation of the bayesian information matrix bim and of the hybrid information matrix him is np hard we then resort to the belief propagation bp algorithm or to the bahl cocke jelinek raviv bcjr algorithm to obtain some approximate values moreover in order to avoid the calculus of the inverse of the bim and of the him we present some closed form expressions for the various crbs which greatly reduces the computation complexity finally some simulations allow us to compare the possible improvements enabled by the offline and the ca scenarios

cramer rao lower bounds for qam phase and frequency estimation in this paper we present the true cramer rao lower bounds crlbs for the estimation of phase offset for common quadrature amplitude modulation qam psk and pam signals in awgn channels it is shown that the same analysis also applies to the qam fsk and pam crlbs for frequency offset estimation the ratio of the modulated to the unmodulated crlbs is derived for all qam psk and pam signals and calculated for specific cases of interest this is useful to determine the limiting performance of synchronization circuits for coherent receivers without the need to simulate particular algorithms the hounds are compared to the existing true crlbs for an unmodulated carrier wave cw bpsk and qpsk we investigated new and existing qam phase estimation algorithms in order to verify the new phase crlb this showed that new minimum distance estimator performs close to the qam bound and provides a large improvement over the power law estimator at moderate to high signal to noise ratios

smaa stochastic multiobjective acceptability analysis stochastic multiobjective acceptability analysis smaa is a multicriteria decision support technique for multiple decision makers based on exploring the weight space inaccurate or uncertain input data can be represented as probability distributions in smaa the decision makers need not express their preferences explicitly or implicitly instead the technique analyses what kind of valuations would make each alternative the preferred one the method produces for each alternative an acceptability index measuring the variety of different valuations that support that alternative a central weight vector representing the typical valuations resulting in that decision and a confidence factor measuring whether the input data is accurate enough for making an informed decision

a new bound and algorithm for star 16 qam carrier phase estimation the true cramer rao lower bound crlb is derived and evaluated for the estimation of carrier phase of star 16 quadrature amplitude modulation qam and can be simply applied to carrier frequency estimation different geometries are investigated by varying the ring ratio rr for signal to noise ratios snrs between 6 15 db the crlb with rr 3 is lower than that of square 16 qam a modified phase estimator is presented which closely follows the new crlb investigation of symbol error performance in short packet length reveals star 16 qam to be superior to square 16 qam for snr 13 db which is a reasonable operating range for a coded system although square 16 qam and star rr 1 8 are optimum for a perfect receiver when the effect of phase estimation is considered we find star rr 3 to be better for snr below 10 db

soft metrics and their performance analysis for optimal data detection in the presence of strong oscillator phase noise in this paper we address the classical problem of maximum likelihood ml detection of data in the presence of random phase noise we consider a system where the random phase noise affecting the received signal is first compensated by a tracker estimator then the phase error and its statistics are used for deriving the ml detector specifically we derive an ml detector based on a gaussian assumption for the phase error probability density function pdf further without making any assumptions on the phase error pdf we show that the actual ml detector can be reformulated as a weighted sum of central moments of the phase error pdf we present a simple approximation of this new ml rule assuming that the phase error distribution is unknown the ml detectors derived are also the aposteriori probabilities of the transmitted symbols and are referred to as soft metrics then using the detector developed based on gaussian phase error assumption we derive the symbol error probability sep performance and error floor analytically for arbitrary constellations finally we compare sep performance of the various detectors metrics in this work and those from literature for different signal constellations phase noise scenarios and snr values

complex system fault diagnosis based on a fuzzy robust wavelet support vector classifier and an adaptive gaussian particle swarm optimization this paper proposes a robust loss function that penalizes hybrid noise i e gaussian noise singularity points and larger magnitude noise in a complex fuzzy fault diagnosis system a mapping relationship between fuzzy numbers and crisp real numbers that allows a fuzzy sample set to be transformed into a crisp real sample set is also presented furthermore the paper proposes a novel fuzzy robust wavelet support vector classifier frwsvc based on a wavelet base function and develops an adaptive gaussian particle swarm optimization agpso algorithm to seek the optimal unknown parameter of the frwsvc the results of experiments that apply the hybrid diagnosis model based on the frwsvc and the agpso algorithm to fault diagnosis demonstrate that it is both feasible and effective tests comparing the method proposed in this paper against other fuzzy support vector classifier fsvc machines show that it outperforms them

reduced complexity decoding of ldpc codes various log likelihood ratio based belief propagation llr bp decoding algorithms and their reduced complexity derivatives for low density parity check ldpc codes are presented numerically accurate representations of the check node update computation used in llr bp decoding are described furthermore approximate representations of the decoding computations are shown to achieve a reduction in complexity by simplifying the check node update or symbol node update or both in particular two main approaches for simplified check node updates are presented that are based on the so called min sum approximation coupled with either a normalization term or an additive offset term density evolution is used to analyze the performance of these decoding algorithms to determine the optimum values of the key parameters and to evaluate finite quantization effects simulation results show that these reduced complexity decoding algorithms for ldpc codes achieve a performance very close to that of the bp algorithm the unified treatment of decoding techniques for ldpc codes presented here provides flexibility in selecting the appropriate scheme from performance latency computational complexity and memory requirement perspectives

the extended qualiflex method for multiple criteria decision analysis based on interval type 2 fuzzy sets and applications to medical decision making abstract qualiflex a generalization of jacquet lagreze s permutation method is a useful outranking method in decision analysis because of its flexibility with respect to cardinal and ordinal information this paper develops an extended qualiflex method for handling multiple criteria decision making problems in the context of interval type 2 fuzzy sets interval type 2 fuzzy sets contain membership values that are crisp intervals which are the most widely used of the higher order fuzzy sets because of their relative simplicity using the linguistic rating system converted into interval type 2 trapezoidal fuzzy numbers the extended qualiflex method investigates all possible permutations of the alternatives with respect to the level of concordance of the complete preference order based on a signed distance based approach this paper proposes the concordance discordance index the weighted concordance discordance index and the comprehensive concordance discordance index as evaluative criteria of the chosen hypothesis for ranking the alternatives the feasibility and applicability of the proposed methods are illustrated by a medical decision making problem concerning acute inflammatory demyelinating disease and a comparative analysis with another outranking approach is conducted to validate the effectiveness of the proposed methodology

carrier phase and frequency estimation for pilot symbol assisted transmission bounds and algorithms in this paper we consider the cramer rao lower bound crb for the joint estimation of the carrier phase and the frequency offset from a noisy linearly modulated burst signal containing random data symbols dss as well as known pilot symbols pss we point out that the crb depends on the location of the pss in the burst the number of pss the number of dss the signal to noise ratio snr and the data modulation scheme distributing the pss symmetrically about the center of the burst and estimating the carrier phase in the center of the burst interval decouples the frequency and phase estimation making the crb for phase estimation independent of the specific location of the pss at low and moderate snr the crbs for both phase and frequency estimation decrease as the fraction of the pss in the burst increases in addition the crb for frequency estimation decreases as the pss are separated with more dss numerical evaluation of the crb indicates that the carrier phase and frequency of a hybrid burst i e containing pss and dss can be estimated more accurately when exploiting both the presence of the dss and the a priori knowledge about the pss instead of using only the knowledge about the pss and ignoring the dss or considering all the received symbols pss and dss as unknown and ignoring the knowledge about the pss comparison of the crb with the performance of existing carrier synchronizers shows that the iterative soft decision directed sdd estimator with data aided da initialization performs very closely to the crb and provides a large improvement over the classical non data aided nda estimator at lower snr