Efficient and Robust Signal Detection Algorithms for the Communication Applications

Signal detection and estimation has been prevalent in signal processing and communications for many years. The relevant studies deal with the processing of information-bearing signals for the purpose of information extraction. Nevertheless, new robust and efficient signal detection and estimation techniques are still in demand since there emerge more and more practical applications which rely on them. In this dissertation work, we proposed several novel signal detection schemes for wireless communications applications, such as source localization algorithm, spectrum sensing method, and normality test. The associated theories and practice in robustness, computational complexity, and overall system performance evaluation are also provided

Advanced robust non-invasive foetal heart detection techniques during active labour using one pair of transabdominal electrodes

The thesis proposes and evaluates three state-of-the-art signal processing techniques to detect fetal heartbeats within each maternal cardiac cycle, during labour contractions, using only a pair of transabdominal electrodes. The first and second techniques are, namely, the structured third- order cumulant-slice-template matching and the bispectral-contours-template matching for fetal QRS identification, respectively. The third technique is based on the modified and appropriately weighted spectral multiple signal classification (MUSIC) with incorporated covariance matrix for uterine contraction noise-like interfering signals also contaminated with noise. Essentially, two modifications to the standard MUSIC have been developed in order to enhance the performance of the spectral estimator in our applied work. The first modification involves the introduction of an optimised weighting function to the segmented ECG covariance matrix, and is chiefly aimed at enhancing the fetal QRS major spectral peak which occurs at around 30 Hz against the mother QRS major spectral peak usually occurring around 17 Hz and all other noise contributions. Additional optional pseudo-bispectral enhancement to sharpen the maternal and fetal spectral peaks, in particular when the mother and fetal R-waves are temporally coincident, have been achieved. The second modification to the spectral MUSIC is the removal of the unjustified assumption that only white Gaussian noise is present and the incorporation of the actual measured labour uterine contraction covariance matrix in reconfigured subspace analysis. This inevitably leads to the generalised eigenvectors - eigenvalues decomposition modern signal processing. This is now coined the modified, interference incorporated pseudo-spectral MUSIC. The above mentioned first and second techniques are higher-order statistics-based (HOS) and hybrid involving both signal processing and NN classifiers. The third technique is second-order statistics-based (SOS). In all techniques, the removal of signal non-linearity with the aid of non-linear Volterra synthesisers plays a crucial part in the fetal detection integrity. Accurately assessed fetal heart classification rates as high as 95% have been achieved during labour, thus helping to provide non-invasive transparency to fetal intrapartum welfare. Performance analysis and evaluation processes involved more than 30 critical cases classified as “fetal under stress in labour” recorded in a London hospital database and used both transbadominal ECG electrodes and fetal scalp electrodes. The latter facilitates detection of the instantaneous fetal heart rate which is then used as the Reference Fetal Heart Rate in the assessment of the classification rate of each of the above mentioned techniques. It will be shown that the fetal heartbeats are completely masked by uterine activity and noise artefacts in all the recorded transabdominal maternal ECG signals. The fetal scalp electrode was, therefore, deemed necessary to provide the highest accurate measure of fetal heart functionality (from the hospital viewpoint), and in the assessment of the three non-invasive techniques presented in this thesis. The techniques may also be used during gestation and as early as 10 weeks

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

Target detection and classification using seismic signal processing in unattended ground sensor systems

This thesis studies the problem of target detection and classification in Unat-tended Ground Sensor (UGS) systems. One of the most challenging problems faced by target identification process is the design of a robust feature vector which is sta-ble and specific to a certain type of vehicle. UGS systems have been used to detect and classify a variety of vehicles. In these systems, acoustic and seismic signals are the most popularly used resources. This thesis studies recent development of target detection and classification techniques using seismic signals. Based on these studies, a new feature extraction algorithm. Spectral Statistics and Wavelet Coef-ficients Characterization (SSWCC), is proposed. This algorithm obtains a robust feature vector extracted from the spectrum, the power spectral density (BSD) and the wavelet coefficients of the signals. Shape statistics is used in both spectral and PSD analysis. These features not only describe the frequency distribution in the spectrum and PSD, but also shows the closeness of the magnitude of spectrum to the normal distribution. Furthermore, the wavelet coefficients are calculated to present the signal in the time-frequency domain. The energy and the distribution of the wavelet coefficients are used in feature extraction as well. After the features are obtained, principal component analysis (PGA) is used to reduce the dimension of the features and optimize the feature vector. Minimum-distance classifier and k-nearest neighbor (kNN) classifier are used to carry out the classification. Experimental results show that SSWCC provides a robust feature set for target identification. The overall performance level can reach as high as 90%

Acoustic classification of buried objects with mobile sonar platforms

Thesis (Ph. D. in Ocean Engineering)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.Includes bibliographical references (p. 229-237).In this thesis, the use of highly mobile sonar platforms is investigated for the purpose of acoustically classifying compact objects on or below the seabed. The extension of existing strategies, including synthetic aperture sonar and conventional imaging, are explored within the context of the buried object problem. In particular, the need to employ low frequencies for seabed penetration is shown to have a significant impact both due to the relative length of the characteristic scattering mechanisms and due to the interface effects on the target scattering. New sonar strategies are also shown that exploit incoherent wide apertures that are created by multiple sonar platforms. For example, target shape can be inverted by mapping the scattered field from the target with a team of receiver vehicles. A single sonar-adaptive sonar platform is shown to have the ability to perform hunting and classification tasks more efficiently than its pre-programmed counterpart. While the monostatic sonar platform is often dominated by the source component, the bistatic or passive receiver platform behavior is controlled by the target response. The sonar-adaptive platform trajectory, however, can result in the platform finishing its classification effort out of position to complete further tasks.(cont.) Within the context of a larger mission, the use of predetermined adaptive behaviors is shown to provide improved detection and classification performance while minimizing the risk to the overall mission. Finally, it is shown that multiple sonar-adaptive platforms can be used to create new sonar strategies for hunting and classifying objects by shape and content. The ability to sample the scattered field from the target across a wide variety of positions allows an analysis of the aspect-dependent behavior of the target. The aspect-dependence of the specular returns indicate the shape of the target, while the secondary returns from an elastic target are also strongly aspect-dependent. These features are exploited for improved classification performance in the buried object hunting mission.by Joseph R. Edwards.Ph.D.in Ocean Engineerin

Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

Multivariate multiscale complexity analysis

Established dynamical complexity analysis measures operate at a single scale and thus fail to quantify inherent long-range correlations in real world data, a key feature of complex systems. They are designed for scalar time series, however, multivariate observations are common in modern real world scenarios and their simultaneous analysis is a prerequisite for the understanding of the underlying signal generating model. To that end, this thesis first introduces a notion of multivariate sample entropy and thus extends the current univariate complexity analysis to the multivariate case. The proposed multivariate multiscale entropy (MMSE) algorithm is shown to be capable of addressing the dynamical complexity of such data directly in the domain where they reside, and at multiple temporal scales, thus making full use of all the available information, both within and across the multiple data channels. Next, the intrinsic multivariate scales of the input data are generated adaptively via the multivariate empirical mode decomposition (MEMD) algorithm. This allows for both generating comparable scales from multiple data channels, and for temporal scales of same length as the length of input signal, thus, removing the critical limitation on input data length in current complexity analysis methods. The resulting MEMD-enhanced MMSE method is also shown to be suitable for non-stationary multivariate data analysis owing to the data-driven nature of MEMD algorithm, as non-stationarity is the biggest obstacle for meaningful complexity analysis. This thesis presents a quantum step forward in this area, by introducing robust and physically meaningful complexity estimates of real-world systems, which are typically multivariate, finite in duration, and of noisy and heterogeneous natures. This also allows us to gain better understanding of the complexity of the underlying multivariate model and more degrees of freedom and rigor in the analysis. Simulations on both synthetic and real world multivariate data sets support the analysis

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader

The estimation and compensation of processes with time delays

The estimation and compensation of processes with time delays have been of interest to academics and practitioners for several decades. A full review of the literature for both model parameter and time delay estimation is presented. Gradient methods of parameter estimation, in open loop, in the time and frequency domains are subsequently considered in detail. Firstly, an algorithm is developed, using an appropriate gradient algorithm, for the estimation of all the parameters of an appropriate process model with time delay, in open loop, in the time domain. The convergence of the model parameters to the process parameters is considered analytically and in simulation. The estimation of the process parameters in the frequency domain is also addressed, with analytical procedures being defined to provide initial estimates of the model parameters, and a gradient algorithm being used to refine these estimates to attain the global minimum of the cost function that is optimised. The focus of the thesis is subsequently broadened with the consideration of compensation methods for processes with time delays. These methods are reviewed in a comprehensive manner, and the design of a modified Smith predictor, which facilitates a better regulator response than does the Smith predictor, is considered in detail. Gradient algorithms are subsequently developed for the estimation of process parameters (including time delay) in closed loop, in the Smith predictor and modified Smith predictor structures, in the time domain; the convergence of the model parameters to the process parameters is considered analytically and in simulation. The thesis concludes with an overview of the methods developed, and projections regarding future developments in the topics under consideration