Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Multiple input multiple output radar three dimensional imaging technique

Ph.DDOCTOR OF PHILOSOPH

Developing new techniques to analyse and classify EEG signals

A massive amount of biomedical time series data such as Electroencephalograph (EEG), electrocardiography (ECG), Electromyography (EMG) signals are recorded daily to monitor human performance and diagnose different brain diseases. Effectively and accurately analysing these biomedical records is considered a challenge for researchers. Developing new techniques to analyse and classify these signals can help manage, inspect and diagnose these signals. In this thesis novel methods are proposed for EEG signals classification and analysis based on complex networks, a statistical model and spectral graph wavelet transform. Different complex networks attributes were employed and studied in this thesis to investigate the main relationship between behaviours of EEG signals and changes in networks attributes. Three types of EEG signals were investigated and analysed; sleep stages, epileptic and anaesthesia. The obtained results demonstrated the effectiveness of the proposed methods for analysing these three EEG signals types. The methods developed were applied to score sleep stages EEG signals, and to analyse epileptic, as well as anaesthesia EEG signals. The outcomes of the project will help support experts in the relevant medical fields and decrease the cost of diagnosing brain diseases

Guided Matching Pursuit and its Application to Sound Source Separation

In the last couple of decades there has been an increasing interest in the application of source separation technologies to musical signal processing. Given a signal that consists of a mixture of musical sources, source separation aims at extracting and/or isolating the signals that correspond to the original sources. A system capable of high quality source separation could be an invaluable tool for the sound engineer as well as the end user. Applications of source separation include, but are not limited to, remixing, up-mixing, spatial re-configuration, individual source modification such as filtering, pitch detection/correction and time stretching, music transcription, voice recognition and source-specific audio coding to name a few. Of particular interest is the problem of separating sources from a mixture comprising two channels (2.0 format) since this is still the most commonly used format in the music industry and most domestic listening environments. When the number of sources is greater than the number of mixtures (which is usually the case with stereophonic recordings) then the problem of source separation becomes under-determined and traditional source separation techniques, such as “Independent Component Analysis” (ICA) cannot be successfully applied. In such cases a family of techniques known as “Sparse Component Analysis” (SCA) are better suited. In short a mixture signal is decomposed into a new domain were the individual sources are sparsely represented which implies that their corresponding coefficients will have disjoint (or almost) disjoint supports. Taking advantage of this property along with the spatial information within the mixture and other prior information that could be available, it is possible to identify the sources in the new domain and separate them by going back to the time domain. It is a fact that sparse representations lead to higher quality separation. Regardless, the most commonly used front-end for a SCA system is the ubiquitous short-time Fourier transform (STFT) which although is a sparsifying transform it is not the best choice for this job. A better alternative is the matching pursuit (MP) decomposition. MP is an iterative algorithm that decomposes a signal into a set of elementary waveforms called atoms chosen from an over-complete dictionary in such a way so that they represent the inherent signal structures. A crucial part of MP is the creation of the dictionary which directly affects the results of the decomposition and subsequently the quality of source separation. Selecting an appropriate dictionary could prove a difficult task and an adaptive approach would be appropriate. This work proposes a new MP variant termed guided matching pursuit (GMP) which adds a new pre-processing step into the main sequence of the MP algorithm. The purpose of this step is to perform an analysis of the signal and extract important features, termed guide maps, that are used to create dynamic mini-dictionaries comprising atoms which are expected to correlate well with the underlying signal structures thus leading to focused and more efficient searches around particular supports of the signal. This algorithm is accompanied by a modular and highly flexible MATLAB implementation which is suited to the processing of long duration audio signals. Finally the new algorithm is applied to the source separation of two-channel linear instantaneous mixtures and preliminary testing demonstrates that the performance of GMP is on par with the performance of state of the art systems

Low-THz Automotive 3D Imaging Radar

This thesis lays out initial investigations into the 3D imaging capabilities of low-THz radar for automotive applications. This includes a discussion of the state of the art of automotive sensors, and the need for a robust, high-resolution imaging system to compliment and address the short-comings of these sensors. The unique capabilities of low-THz radar may prove to be well-suited to meet these needs, but they require 3D imaging algorithms which can exploit these capabilities effectively. One such unique feature is the extremely wide signal bandwidth, which yields a fine range resolution. This is a feature of low-THz radar which has not been discussed or properly investigated before, particularly in the context of generating the 3D position of an object from range information. The progress and experimental verification of these algorithms with a prototype multi-receiver 300GHz radar throughout this project are described; progressing from simple position estimation to highly detailed 3D radar imaging. The system is tested in a variety of different scenarios which a vehicle must be able to navigate, and the 3D imaging radar is compared with current automotive demonstrators experimentally

Probabilistic segmentation of remotely sensed images

For information extraction from image data to create or update geographic information systems, objects are identified and labeled using an integration of segmentation and classification. This yields geometric and thematic information, respectively.Bayesian image classifiers calculate class posterior probabilities on the basis of estimated class probability densities and prior probabilities. This thesis presents refined probability estimates, which are local, i.e pertain to image regions, rather than to the entire image. Local class probability densities are estimated in a non-parametric way with an extended k-Nearest Neighbor method. Iterative estimation of class mixing proportions in arbitrary image regions yields local prior probabilities.The improved estimates of prior probabilities and probability densities increase the reliability of posterior probabilities and enhance subsequent decision making, such as maximum posterior probability class selection. Moreover, class areas are estimated more accurately, compared to standard Maximum Likelihood classification.Two sources of image regionalization are distinguished. Ancillary data in geographic information systems often divide the image area into regions with different class mixing proportions, in which probabilities are estimated. Otherwise, a regionalization can be obtained by image segmentation. A region based method is presented, being a generalization of connected component labeling in the quadtree domain. It recursively merges leaves in a quadtree representation of a multi-spectral image into segments with arbitrary shapes and sizes. Order dependency is avoided by applying the procedure iteratively with slowly relaxing homogeneity criteria.Region fragmentation and region merging, caused by spectral variation within objects and spectral similarity between adjacent objects, are avoided by regarding class homogeneity in addition to spectral homogeneity. As expected, most terrain objects correspond to image segments. These, however, reside at different levels in a segmentation pyramid. Therefore, class mixing proportions are estimated in all segments of such a pyramid to distinguish between pure and mixed ones. Pure segments are selected at the highest possible level, which may vary over the image. They form a non-overlapping set of labeled objects without fragmentation or merging. In image areas where classes cannot be separated, because of spatial or spectral resolution limitations, mixed segments are selected from the pyramid. They form uncertain objects, to which a mixture of classes with known proportion is assigned.Subsequently, remotely sensed data are used for taking decisions in geographical information systems. These decisions are usually based on crisp classifications and, therefore, influenced by classification errors and uncertainties. Moreover, when processing spatial data for decision making, the objectives and preferences of the decision maker are crucial to deal with. This thesis proposes to exploit mathematical decision analysis for integrating uncertainties and preferences, on the basis of carefully estimated probabilistic class information. It aims to solve complex decision problems on the basis of remotely sensed data.</p