Existence of compatible contact structures on G₂ -manifolds

In this paper, we show the existence of (co-oriented) contact structures on certain classes of G(2)-manifolds, and that these two structures are compatible in certain ways. Moreover, we prove that any seven-manifold with a spin structure (and so any manifold with G(2)-structure) admits an almost contact structure. We also construct explicit almost contact metric structures on manifolds with G(2)-structures

A new algorithm for high-quality ionogram generation and analysis

Standard digital ionograms that are generated by fast Fourier transform or autoregressive modeling suffer from high interference levels due to other users of the HF channel which produce artifacts and distortion, hence complicating automatic processing and information extraction. In this paper, a new method is proposed to obtain high-quality ionograms of the desired layer reflections and automatically extract important information such as critical frequencies. Following the standard procedures, two sets of periodograms are obtained by using rectangular and Blackman windows. These two periodograms are filtered and fused utilizing an automatic edge-detection-based time-frequency detector. The fused ionogram provides sharp description of the layer reflections with very low sidelobe structure (ringing). The performance of this new ionogram algorithm is tested using chirp sounder data collected from an oblique midlatitude path. It is observed that the presented algorithm is highly successful in obtaining robust and sharp ionograms free of artifacts. Furthermore, a new algorithm is proposed for automated computation of dispersion and critical frequencies of the magnetoionic components detected on the ionogram. Since efficient signal-processing algorithms are utilized, the proposed method can be implemented in real time

SAR image reconstruction and autofocus by compressed sensing

Cataloged from PDF version of article.A new SAR signal processing technique based on compressed sensing is proposed for autofocused image reconstruction on subsampled raw SAR data. It is shown that, if the residual phase error after INS/GPS corrected platform motion is captured in the signal model, then the optimal autofocused image formation can be formulated as a sparse reconstruction problem. To further improve image quality, the total variation of the reconstruction is used as a penalty term. In order to demonstrate the performance of the proposed technique in wide-band SAR systems, the measurements used in the reconstruction are formed by a new under-sampling pattern that can be easily implemented in practice by using slower rate A/D converters. Under a variety of metrics for the reconstruction quality, it is demonstrated that, even at high under-sampling ratios, the proposed technique provides reconstruction quality comparable to that obtained by the classical techniques which require full-band data without any under-sampling. (C) 2012 Elsevier Inc. All rights reserved

Multipath Separation-Direction of Arrival (MS-DOA) with Genetic Search Algorithm for HF channels

Cataloged from PDF version of article.Direction-of-Arrival (DOA) defines the estimation of arrival angles of an electromagnetic wave impinging on a set of sensors. For dispersive and time-varying HF channels, where the propagating wave also suffers from the multipath phenomena, estimation of DOA is a very challenging problem. Multipath Separation-Direction of Arrival (MS-DOA), that is developed to estimate both the arrival angles in elevation and azimuth and the incoming signals at the output of the reference antenna with very high accuracy, proves itself as a strong alternative in DOA estimation for HF channels. In MS-DOA, a linear system of equations is formed using the coefficients of the basis vector for the array output vector, the incoming signal vector and the array manifold. The angles of arrival in elevation and azimuth are obtained as the maximizers of the sum of the magnitude squares of the projection of the signal coefficients on the column space of the array manifold. In this study, alternative Genetic Search Algorithms (GA) for the maximizers of the projection sum are investigated using simulated and experimental ionospheric channel data. It is observed that GA combined with MS-DOA is a powerful alternative in online DOA estimation and can be further developed according to the channel characteristics of a specific HF link. (C) 2009 COSPAR. Published by Elsevier Ltd. All rights reserve

Optimal stochastic signaling for power-constrained binary communications systems

Cataloged from PDF version of article.Optimal stochastic signaling is studied under second and fourth moment constraints for the detection of scalar-valued binary signals in additive noise channels. Sufficient conditions are obtained to specify when the use of stochastic signals instead of deterministic ones can or cannot improve the error performance of a given binary communications system. Also, statistical characterization of optimal signals is presented, and it is shown that an optimal stochastic signal can be represented by a randomization of at most three different signal levels. In addition, the power constraints achieved by optimal stochastic signals are specified under various conditions. Furthermore, two approaches for solving the optimal stochastic signaling problem are proposed; one based on particle swarm optimization (PSO) and the other based on convex relaxation of the original optimization problem. Finally, simulations are performed to investigate the theoretical results, and extensions of the results to -ary communications systems and to other criteria than the average probability of error are discussed

Stochastic signaling in the presence of channel state information uncertainty

Cataloged from PDF version of article.In this paper, stochastic signaling is studied for power-constrained scalar valued binary communications systems in the presence of uncertainties in channel state information (CSI). First, stochastic signaling based on the available imperfect channel coefficient at the transmitter is analyzed, and it is shown that optimal signals can be represented by a randomization between at most two distinct signal levels for each symbol. Then, performance of stochastic signaling and conventional deterministic signaling is compared for this scenario, and sufficient conditions are derived for improvability and nonimprovability of deterministic signaling via stochastic signaling in the presence of CSI uncertainty. Furthermore, under CSI uncertainty, two different stochastic signaling strategies, namely, robust stochastic signaling and stochastic signaling with averaging, are proposed. For the robust stochastic signaling problem, sufficient conditions are derived for reducing the problem to a simpler form. It is shown that the optimal signal for each symbol can be expressed as a randomization between at most two distinct signal values for stochastic signaling with averaging, as well as for robust stochastic signaling under certain conditions. Finally, two numerical examples are presented to explore the theoretical results. (C) 2012 Elsevier Inc. All rights reserve

Convexity Properties of Detection Probability Under Additive Gaussian Noise: Optimal Signaling and Jamming Strategies

Cataloged from PDF version of article.In this correspondence, we study the convexity properties for the problem of detecting the presence of a signal emitted from a power constrained transmitter in the presence of additive Gaussian noise under the Neyman-Pearson (NP) framework. It is proved that the detection probability corresponding to the α-level likelihood ratio test (LRT) is either strictly concave or has two inflection points such that the function is strictly concave, strictly convex, and finally strictly concave with respect to increasing values of the signal power. In addition, the analysis is extended from scalar observations to multidimensional colored Gaussian noise corrupted signals. Based on the convexity results, optimal and near-optimal time sharing strategies are proposed for average/peak power constrained transmitters and jammers. Numerical methods with global convergence are also provided to obtain the parameters for the proposed strategies. © 1991-2012 IEE

Maximum likelihood estimation of Gaussian mixture models using stochastic search

Cataloged from PDF version of article.Gaussian mixture models (GMM), commonly used in pattern recognition and machine learning, provide a flexible probabilistic model for the data. The conventional expectation-maximization (EM) algorithm for the maximum likelihood estimation of the parameters of GMMs is very sensitive to initialization and easily gets trapped in local maxima. Stochastic search algorithms have been popular alternatives for global optimization but their uses for GMM estimation have been limited to constrained models using identity or diagonal covariance matrices. Our major contributions in this paper are twofold. First, we present a novel parametrization for arbitrary covariance matrices that allow independent updating of individual parameters while retaining validity of the resultant matrices. Second, we propose an effective parameter matching technique to mitigate the issues related with the existence of multiple candidate solutions that are equivalent under permutations of the GMM components. Experiments on synthetic and real data sets show that the proposed framework has a robust performance and achieves significantly higher likelihood values than the EM algorithm. (C) 2012 Elsevier Ltd. All rights reserved

Optimal Stochastic Parameter Design for Estimation Problems

Cataloged from PDF version of article.In this study, the aim is to perform optimal stochastic parameter design in order to minimize the cost of a given estimator. Optimal probability distributions of signals corresponding to different parameters are obtained in the presence and absence of an average power constraint. It is shown that the optimal parameter design results in either a deterministic signal or a randomization between two different signal levels. In addition, sufficient conditions are obtained to specify the cases in which improvements over the deterministic parameter design can or cannot be achieved via the stochastic parameter design. Numerical examples are presented in order to provide illustrations of theoretical results