on the detection and classification of quadrature digital modulations in broad band noise optimal and suboptimal decision rules for the detection of constant envelope quadrature digital modulations in broadband noise are derived and analyzed the effect of various stochastic models for the carrier phase is examined in detail while no epoch or frequency uncertainty is assumed the delay and multiply type of detector is considered a new binary quadrature phase shift keying bpsk qpsk classifier is compared to the more traditional ad hoc techniques of a square law classifier and a phase based classifier weighting on the phase histogram the new classifier is derived by approximating the likelihood ratio functionals of phase modulated digital signals in white gaussian noise hence is named the quasi log likelihood ratio qllr rule it is shown analytically that its performance is significantly better than that of intuitively designed phase based rules or the conventional square law classifier

likelihood methods for mpsk modulation classification new algorithms based on the likelihood functional lf and approximations thereof are proposed for the problem of classifying mpsk modulations in additive white gaussian noise previously introduced classifiers for this problem are theoretically interpreted as simplified versions of the ones in here the performance of a single term approximation to the optimal lf classifier is evaluated analytically and is shown to be very close to that of the optimal furthermore recursive algorithms for the implementation of this new quasi log likelihood ratio qllr classifier are derived which imply no significant increase in classifier complexity the present method of generating classification algorithms can be generalized to arbitrary two dimensional signal constellations

signal activity detection of phase shift keying signals we propose computationally inexpensive and efficient solutions for signal activity detection of phase shift keying psk signals in additive white gaussian noise we consider the complex amplitude of the signal as well as the information sequence as the unknown parameters in addition the noise variance is assumed unknown we derive the generalized likelihood ratio test glrt and suggest a computationally efficient implementation thereof furthermore we develop a new inexpensive detector for binary psk signals which we will refer to as the generalized energy detector to evaluate the performance of these detectors we attempt to derive a uniformly most powerful invariant test umpi as an optimal detector it turns out that the umpi test exists only if the signal to noise ratio is known we use this umpi test in order to obtain an upper bound performance for the evaluation of invariant detectors such as the above mentioned glrt simulation results illustrate and compare the performance and the efficiency of the proposed signal activity detectors

maximum likelihood classification for digital amplitude phase modulations we apply the maximum likelihood ml method to the classification of digital quadrature modulations we show that under an ideal situation the i q domain data are sufficient statistics for modulation classification and obtain a generic formula for the error probability of a ml classifier our study of asymptotic performance shows that the ml classifier is capable of classifying any finite set of distinctive constellations with zero error rate when the number of available data symbols goes to infinity

a volume comparison theorem for asymptotically hyperbolic manifolds we define a notion of renormalized volume of an asymptotically hyperbolic manifold moreover we prove a sharp volume comparison theorem for metrics with scalar curvature at least 6 finally we show that the inequality is strict unless the metric is isometric to one of the anti desitter schwarzschild metrics

lower bounds of lipschitz constants on foliations in this paper we consider llarull s theorem in the foliation case and get a lower bound of the lipschitz constant of the map m rightarrow s n in the foliation case under the spin condition

a hybrid multi objective immune algorithm for a flow shop scheduling problem with bi objectives weighted mean completion time and weighted mean tardiness this paper investigates a novel multi objective model for a no wait flow shop scheduling problem that minimizes both the weighted mean completion time c w and weighted mean tardiness t w obtaining an optimal solution for this type of complex large sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult this paper presents a new hybrid multi objective algorithm based on the features of a biological immune system is and bacterial optimization bo to find pareto optimal solutions for the given problem to validate the performance of the proposed hybrid multi objective immune algorithm hmoia in terms of solution quality and diversity level various test problems are examined further the efficiency of the proposed algorithm based on various metrics is compared against five prominent multi objective evolutionary algorithms ps nc ga nsga ii spea ii moia and misa our computational results suggest that our proposed hmoia outperforms the five foregoing algorithms especially for large sized problems

rigidity theorems on conformal class of compact manifolds with boundary abstract let m be a compact manifold with boundary in this paper we discuss some rigidity theorems on riemannian metrics in a same conformal class that fix the boundary and satisfy certain integral condition on the scalar curvature and on the mean curvature along the boundary as an application we will state some rigidity theorems on the conformal class of static metrics

blind recognition of linear space time block codes a likelihood based approach blind recognition of communication parameters is a research topic of high importance for both military and civilian communication systems numerous studies about carrier frequency estimation modulation recognition as well as channel identification are available in literature this paper deals with the blind recognition of the space time block coding stbc scheme used in multiple input multiple output mimo communication systems assuming there is perfect synchronization at the receiver side this paper proposes three maximum likelihood ml based approaches for stbc classification the optimal classifier the second order statistic sos classifier and the code parameter cp classifier while the optimal and the sos approaches require ideal conditions the cp classifier is well suited for the blind context where the communication parameters are unknown at the receiver side our simulations show that this blind classifier is more easily implemented and yields better performance than those available in literature

rigidity of min max minimal spheres in three manifolds in this paper we consider min max minimal surfaces in three manifolds and prove some rigidity results for instance we prove that any metric on a three sphere which has scalar curvature greater than or equal to 6 and is not round must have an embedded minimal sphere of area strictly smaller than 4 and index at most one if the ricci curvature is positive we also prove sharp estimates for the width

sms emoa multiobjective selection based on dominated hypervolume abstract the hypervolume measure or s metric is a frequently applied quality measure for comparing the results of evolutionary multiobjective optimisation algorithms emoa the new idea is to aim explicitly for the maximisation of the dominated hypervolume within the optimisation process a steady state emoa is proposed that features a selection operator based on the hypervolume measure combined with the concept of non dominated sorting the algorithm s population evolves to a well distributed set of solutions thereby focussing on interesting regions of the pareto front the performance of the devised s metric selection emoa sms emoa is compared to state of the art methods on two and three objective benchmark suites as well as on aeronautical real world applications

hierarchical digital modulation classification using cumulants a simple method based on elementary fourth order cumulants is proposed for the classification of digital modulation schemes these statistics are natural in this setting as they characterize the shape of the distribution of the noisy baseband i and q samples it is shown that cumulant based classification is particularly effective when used in a hierarchical scheme enabling separation into subclasses at low signal to noise ratio with small sample size thus the method can be used as a preliminary classifier if desired computational complexity is order n where n is the number of complex baseband data samples this method is robust in the presence of carrier phase and frequency offsets and can be implemented recursively theoretical arguments are verified via extensive simulations and comparisons with existing approaches

deformations of the hemisphere that increase scalar curvature consider a compact riemannian manifold m of dimension n whose boundary m is totally geodesic and is isometric to the standard sphere s n 1 a natural conjecture of min oo asserts that if the scalar curvature of m is at least n n 1 then m is isometric to the hemisphere s n equipped with its standard metric this conjecture is inspired by the positive mass theorem in general relativity and has been verified in many special cases in this paper we construct counterexamples to min oo s conjecture in dimension n 3

a grid based adaptive multi objective differential evolution algorithm differential evolution is an excellent optimizer for single objective optimization problems to extend its use for multi objective optimization problems with promising performance this paper proposes a grid based adaptive multi objective differential evolution algorithm the main feature of the proposed algorithm is its dynamical adjustment of convergence and diversity by exploiting the feedback information during the evolutionary process in the algorithm the objective space is divided into grids according to the nondominated solutions in the population based on the grid three indexes including grid fitness grid density and grid objective wise standard deviation are defined to measure individual rank individual density and population search status quo respectively afterwards three main components of the algorithm i e parents selection parameter control and population update are implemented based on grid index values to validate algorithm performance comprehensive experiments are carried out on thirty one benchmark problems the results show that the proposed algorithm outperforms nine state of the art competitors in terms of three performance metrics also the effectiveness of three components and the sensitivity of two design parameters in the algorithm are empirically quantified

kernel induced fuzzy clustering of image pixels with an improved differential evolution algorithm a modified differential evolution de algorithm is presented for clustering the pixels of an image in the gray scale intensity space the algorithm requires no prior information about the number of naturally occurring clusters in the image it uses a kernel induced similarity measure instead of the conventional sum of squares distance use of the kernel function makes it possible to partition data that is linearly non separable and non hyper spherical in the original input space into homogeneous groups in a transformed high dimensional feature space a novel search variable representation scheme is adopted for selecting the optimal number of clusters from several possible choices extensive performance comparison over a test suite of 10 gray scale images and objective comparison with manually segmented ground truth indicates that the proposed algorithm has an edge over a few state of the art algorithms for automatic multi class image segmentation

scalar curvature estimates by parallel alternating torsion we generalize llarull s scalar curvature comparison to riemann ian manifolds admitting metric connections with parallel and alternating torsion and having a nonnegative curvature operator on 2 tm as a by product we show that the euler number and signature of such manifolds are determined by their global holonomy representation our result holds in particular for all quotients of compact lie groups of equal rank equipped with a normal homogeneous metric we also correct a mistake in the treatment of odd dimensional spaces in our earlier papers

ensemble strategies with adaptive evolutionary programming mutation operators such as gaussian levy and cauchy have been used with evolutionary programming ep according to the no free lunch theorem it is impossible for ep with a single mutation operator to outperform always for example classical ep cep with gaussian mutation is better at searching in a local neighborhood while the fast ep fep with the cauchy mutation performs better over a larger neighborhood motivated by these observations we propose an ensemble approach where each mutation operator has its associated population and every population benefits from every function call this approach enables us to benefit from different mutation operators with different parameter values whenever they are effective during different stages of the search process in addition the recently proposed adaptive ep aep using gaussian acep and cauchy afep mutations is also evaluated in the aep the strategy parameter values are adapted based on the search performance in the previous few generations the performance of ensemble is compared with a mixed mutation strategy which integrates several mutation operators into a single algorithm as well as against the aep with a single mutation operator improved performance of the ensemble over the single mutation based algorithms and mixed mutation algorithm is verified using statistical tests

an efficient differential evolution based algorithm for solving multi objective optimization problems in the present study a modified variant of differential evolution de algorithm for solving multi objective optimization problems is presented the proposed algorithm named multi objective differential evolution algorithm modea utilizes the advantages of opposition based learning for generating an initial population of potential candidates and the concept of random localization in mutation step finally it introduces a new selection mechanism for generating a well distributed pareto optimal front the performance of proposed algorithm is investigated on a set of nine bi objective and five tri objective benchmark test functions and the results are compared with some recently modified versions of de for mops and some other multi objective evolutionary algorithms moeas the empirical analysis of the numerical results shows the efficiency of the proposed algorithm

a fuzzy logic method for modulation classification in nonideal environments in this paper we present a fuzzy logic modulation classifier that works in nonideal environments in which it is difficult or impossible to use precise probabilistic methods we first transform a general pattern classification problem into one of function approximation so that fuzzy logic systems fls can be used to construct a classifier then we introduce the concepts of fuzzy modulation type and fuzzy decision and develop a nonsingleton fuzzy logic classifier nsflc by using an additive fls as a core building block our nsflc uses 2d fuzzy sets whose membership functions are isotropic so that they are well suited for a modulation classifier mc we establish that our nsflc although completely based on heuristics reduces to the maximum likelihood modulation classifier ml mc in ideal conditions in our application of nsflc to mc in a mixture of spl alpha stable and gaussian noises we demonstrate that our nsflc performs consistently better than the ml mc and it gives the same performance as the ml mc when no impulsive noise is present

automatic modulation classification methods for wireless ofdm systems in tdd mode this paper presents a novel automatic modulation classification amc algorithm in wireless time division duplex tdd orthogonal frequency division multiplexing ofdm systems with adaptive modulation am based upon the maximum a posteriori map criterion a classification method for digital quadrature amplitude modulation qam schemes is developed that exploits the channel reciprocity in tdd systems and the receiver knowledge about the overall transmission data rate the amc algorithm enables the application of adaptive modulation in wireless ofdm systems without loss in the effective data rate caused by signaling of the bit allocation table bat numerical results show almost no performance loss in terms of the packet error ratio per compared to the case with perfect knowledge of the modulation schemes if the proposed amc technique is used

signal interception a unifying theoretical framework for feature detection the unifying framework of the spectral correlation theory of cyclostationary signals is used to present a broad treatment of weak random signal detection for interception purposes the relationships among a variety of previously proposed ad hoc detectors optimum detectors and newly proposed detectors are established the spectral correlation plane approach to the interception problem is put forth as especially promising for detection classification and estimation in particularly difficult environments involving unknown and changing noise levels and interference activity a fundamental drawback of the popular radiometric methods in such environments is explained

classification of multiple m ary frequency shift keying signals over a rayleigh fading channel we develop and analyze classifiers for multiple m ary frequency shift keying mfsk signals traveled over a static frequency nonselective rayleigh fading channel the classifier is based on approximating the likelihood function lf of the frequency modulated digital signals and is derived for both synchronous and asynchronous waveforms two cases are considered the first one is concerned with the signals that have identical modulation type for example binary fsk bfsk or quaternary fsk qfsk the second case deals with the signals with different modulation type we use complex envelope representation of the signals and noise to derive the lf simulation experiments are illustrated to validate the theoretical developments

multi objective evolutionary algorithms based on the summation of normalized objectives and diversified selection most multi objective evolutionary algorithms moeas use the concept of dominance in the search process to select the top solutions as parents in an elitist manner however as moeas are probabilistic search methods some useful information may be wasted if the dominated solutions are completely disregarded in addition the diversity may be lost during the early stages of the search process leading to a locally optimal or partial pareto front beside this the non domination sorting process is complex and time consuming to overcome these problems this paper proposes multi objective evolutionary algorithms based on summation of normalized objective values and diversified selection snov ds the performance of this algorithm is tested on a set of benchmark problems using both multi objective evolutionary programming moep and multi objective differential evolution mode with the proposed method the performance metric has improved significantly and the speed of the parent selection process has also increased when compared with the non domination sorting in addition the proposed algorithm also outperforms ten other algorithms

sharp estimates and the dirac operator this paper establishes and extends a conjecture posed by m gromov which states that every riemannian metric g on s n that strictly dominates the standard metric g 0 must have somewhere scalar curvature strictly less than that of g 0 more generally if m is any compact spin manifold of dimension n which admits a distance decreasing map f m rightarrow s n of non zero degree then either there is a point x in m with normalized scalar curvature tilde kappa x 1 or m is isometric to s n the distance decreasing hypothesis can be replaced by the weaker assumption f is contracting on 2 forms in both cases the results are sharp an explicit counterexample is given to show that the result is no longer valid if one replaces 2 forms by k forms with k geq 3

adaptive differential evolution algorithm for multiobjective optimization problems in this paper a new adaptive differential evolution algorithm adea is proposed for multiobjective optimization problems in adea the variable parameter f based on the number of the current pareto front and the diversity of the current solutions is given for adjusting search size in every generation to find pareto solutions in mutation operator and the select operator combines the advantages of de with the mechanisms of pareto based ranking and crowding distance sorting adea is implemented on five classical multiobjective problems the results illustrate that adea efficiently achieves two goals of multiobjective optimization problems find the solutions converge to the true pareto front and uniform spread along the front

structural characterization of locally optimum detectors in terms of locally optimum estimators and correlators explicit formulas for locally snr rightarrow 0 optimum mmse signal estimators smoother filter and predictor for discrete time observations of a random signal in additive random noise are derived and used to characterize the locally optimum likelihood ratio signal detector for on off signaling the characterizations are canonical distribution free detector structures involving estimator correlators these structural characterizations provide new interpretations of known detectors for various special cases if the one step signal predictor is recursive and the noise is white possibly non gaussian or nonstationary there is a canonical structure that admits recursive computation the primary motivation for these structural characterizations is to render the estimator correlator design philosophy applicable for the purpose of simplifying implementations and enhancing adaptability unlike the known esfimator correlator structural characterizations for continuous time globally optimum detectors the new characterizations apply for non gaussian as well as gaussian noise and the estimators are explicit rather than implicit

c0 coarse geometry and scalar curvature in this paper we introduce an alternative form of coarse geometry on proper metric spaces which is more delicate at infinity than the standard metric coarse structure there is an assembly map from the k homology of a space to the k theory of the c algebra associated to the new coarse structure which factors through the coarse k homology of the space with the new coarse structure a dirac type operator on a complete riemannian manifold m gives rise to a class in k homology and its image under assembly gives a higher index in the k theory group the main result of this paper is a vanishing theorem for the index of the dirac operator on an open spin manifold for which the scalar curvature x tends to infinity as x tends to infinity this is derived from a spectral vanishing theorem for any dirac type operator with discrete spectrum and finite dimensional eigenspaces