Kernel-based nonlinear beamforming construction using orthogonal forward selection with Fisher ratio class separability measure

This letter shows that the wireless communication system capacity is greatly enhanced by employing nonlinear beamforming and the optimal Bayesian beamformer outperforms the standard linear beamformer significantly in terms of a reduced bit error rate, at a cost of increased complexity. Block-data adaptive implementation of the Bayesian beamformer is realized based on an orthogonal forward selection procedure with Fisher ratio for class separability measure

Symmetric RBF classifier for nonlinear detection in multiple-antenna aided systems

In this paper, we propose a powerful symmetric radial basis function (RBF) classifier for nonlinear detection in the so-called “overloaded” multiple-antenna-aided communication systems. By exploiting the inherent symmetry property of the optimal Bayesian detector, the proposed symmetric RBF classifier is capable of approaching the optimal classification performance using noisy training data. The classifier construction process is robust to the choice of the RBF width and is computationally efficient. The proposed solution is capable of providing a signal-to-noise ratio (SNR) gain in excess of 8 dB against the powerful linear minimum bit error rate (BER) benchmark, when supporting four users with the aid of two receive antennas or seven users with four receive antenna elements. Index Terms—Classification, multiple-antenna system, orthogonal forward selection, radial basis function (RBF), symmetry

Matrix pixel and Kernel density analysis from the topographic maps

Complex networks with building density play a significant role in many fields of science, especially in urban sciences. That includes road networks, hydrological networks, computer networks and building changes into geo-space through some period. Using these networks we can solve the problems like the shortest path, the total capacity of networks, density population or traffic density in an urban or suburban area. In this paper for quantifying the complexity of road networks and a novel method for determining building density by using a matrix pixel analysis and Kernel distribution with a concrete example of the city of Belgrade. Both of them represent geo-spatial data. In this case we have analyzed road networks, building density, with the help of specially created software for analyzing pixels on the maps from 1971, including the properties of geo-spatial data we have analyzed from old topographic maps in ratio 1:25.000

Brain signal analysis in space-time-frequency domain : an application to brain computer interfacing

In this dissertation, advanced methods for electroencephalogram (EEG) signal analysis in the space-time-frequency (STF) domain with applications to eye-blink (EB) artifact removal and brain computer interfacing (BCI) are developed. The two methods for EB artifact removal from EEGs are presented which respectively include the estimated spatial signatures of the EB artifacts into the signal extraction and the robust beamforming frameworks. In the developed signal extraction algorithm, the EB artifacts are extracted as uncorrelated signals from EEGs. The algorithm utilizes the spatial signatures of the EB artifacts as priori knowledge in the signal extraction stage. The spatial distributions are identified using the STF model of EEGs. In the robust beamforming approach, first a novel space-time-frequency/time-segment (STF-TS) model for EEGs is introduced. The estimated spatial signatures of the EBs are then taken into account in order to restore the artifact contaminated EEG measurements. Both algorithms are evaluated by using the simulated and real EEGs and shown to produce comparable results to that of conventional approaches. Finally, an effective paradigm for BCI is introduced. In this approach prior physiological knowledge of spectrally band limited steady-state movement related potentials is exploited. The results consolidate the method.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Brain signal analysis in space-time-frequency domain: an application to brain computer interfacing

In this dissertation, advanced methods for electroencephalogram (EEG) signal analysis in the space-time-frequency (STF) domain with applications to eye-blink (EB) artifact removal and brain computer interfacing (BCI) are developed. The two methods for EB artifact removal from EEGs are presented which respectively include the estimated spatial signatures of the EB artifacts into the signal extraction and the robust beamforming frameworks. In the developed signal extraction algorithm, the EB artifacts are extracted as uncorrelated signals from EEGs. The algorithm utilizes the spatial signatures of the EB artifacts as priori knowledge in the signal extraction stage. The spatial distributions are identified using the STF model of EEGs. In the robust beamforming approach, first a novel space-time-frequency/time-segment (STF-TS) model for EEGs is introduced. The estimated spatial signatures of the EBs are then taken into account in order to restore the artifact contaminated EEG measurements. Both algorithms are evaluated by using the simulated and real EEGs and shown to produce comparable results to that of conventional approaches. Finally, an effective paradigm for BCI is introduced. In this approach prior physiological knowledge of spectrally band limited steady-state movement related potentials is exploited. The results consolidate the method

Infrasound as upper atmospheric monitor

Understanding and specification of the higher altitudes of the atmosphere with global coverage over all local times is hampered by the challenges of obtaining direct measurements in the upper atmosphere. Methods to measure the properties of the atmosphere above the stratopause is an active area of scientific research. In this thesis, we revisit the use of infrasound as a passive remote sensing technique for the upper atmosphere. Signals from the Tungurahua volcano in Ecuador are used to investigate the behavior of the upper atmosphere. Depending on the atmospheric conditions, stratospheric, mesospheric and thermospheric arrivals are observed during intervals of explosive volcanic activity. It is found that the travel times and dominant frequencies of the thermospheric arrivals exhibit a coherent variability with periods equal to those of the tidal harmonics. Theoretical predictions using atmospheric specifications show that the stratospheric arrivals are predicted within 1% of the observed value. For thermospheric arrivals, this error can be as high as 10%. The error in thermospheric celerities is found to be in accord with the typical uncertainty in upper atmospheric winds. Given the observed response of the infrasound celerities to upper atmospheric tidal variability, it is suggested that infrasound observations may be used as an additional source of information to constrain the atmospheric specifications in the upper atmosphere. We present corrected wind profiles that have been obtained by minimizing misfits in traveltime and source location using a Bayesian statistics grid search algorithm. Also, a Levenberg-Marquardt search algorithm is developed. Additionally, a new numerical method has been developed to solve the problem of infrasound propagation in a stratified medium with (high Mach number) background flow, based on a modal expansion. The underlying mathematics is by no means new and has been earlier described. This solution goes beyond the effective sound speed approximation, which is typically used in infrasound propagation modeling for computational efficiency reasons. Using the wide-angle high Mach number modal solution, it is shown that traveltimes and shadow zones are under predicted using the effective sound speed approximation, with increasing grazing angle and Mach number

Physics-Based Detection of Subpixel Targets in Hyperspectral Imagery

Hyperspectral imagery provides the ability to detect targets that are smaller than the size of a pixel. They provide this ability by measuring the reflection and absorption of light at different wavelengths creating a spectral signature for each pixel in the image. This spectral signature contains information about the different materials within the pixel; therefore, the challenge in subpixel target detection lies in separating the target's spectral signature from competing background signatures. Most research has approached this problem in a purely statistical manner. Our approach fuses statistical signal processing techniques with the physics of reflectance spectroscopy and radiative transfer theory. Using this approach, we provide novel algorithms for all aspects of subpixel detection from parameter estimation to threshold determination. Characterization of the target and background spectral signatures is a key part of subpixel detection. We develop an algorithm to generate target signatures based on radiative transfer theory using only the image and a reference signature without the need for calibration, weather information, or source-target-receiver geometries. For background signatures, our work identifies that even slight estimation errors in the number of background signatures can severely degrade detection performance. To this end, we present a new method to estimate the number of background signatures specifically for subpixel target detection. At the core of the dissertation is the development of two hybrid detectors which fuse spectroscopy with statistical hypothesis testing. Our results show that the hybrid detectors provide improved performance in three different ways: insensitivity to the number of background signatures, improved detection performance, and consistent performance across multiple images leading to improved receiver operating characteristic curves. Lastly, we present a novel adaptive threshold estimate via extreme value theory. The method can be used on any detector type - not just those that are constant false alarm rate (CFAR) detectors. Even on CFAR detectors our proposed method can estimate thresholds that are better than theoretical predictions due to the inherent mismatch between the CFAR model assumptions and real data. Additionally, our method works in the presence of target detections while still estimating an accurate threshold for a desired false alarm rate