Application of Wilcoxon Norm for increased Outlier Insensitivity in Function Approximation Problems

In system theory, characterization and identification are fundamental problems. When the plant behavior is completely unknown, it may be characterized using certain model and then, its identification may be carried out with some artificial neural networks(ANN) (like multilayer perceptron(MLP) or functional link artificial neural network(FLANN) ) or Radial Basis Functions(RBF) using some learning rules such as the back propagation (BP) algorithm. They offer flexibility, adaptability and versatility, for the use of a variety of approaches to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. The first aim of the present thesis is to provide a framework for the systematic design of adaptation laws for nonlinear system identification and channel equalization. While constructing an artificial neural network or a radial basis function neural network, the designer is often faced with the problem of choosing a network of the right size for the task. Using a smaller neural network decreases the cost of computation and increases generalization ability. However, a network which is too small may never solve the problem, while a larger network might be able to. Transmission bandwidth being one of the most precious resources in digital communication, Communication channels are usually modeled as band-limited linear finite impulse response (FIR) filters with low pass frequency response

Nonlinear receivers for DS-CDMA

The growing demand for capacity in wireless communications is the driving force behind improving established networks and the deployment of a new worldwide mobile standard. Capacity calculations show that the direct sequence code division multiple access (DS-CDMA) technique has more capacity than the time division multiple access technique. Therefore, most 3rd generation mobile systems will incorporate some sort of DS-CDMA. In this thesis DS-CDMA receiver structures are investigated from the view point of pattern recognition which leads to new DS-CDMA receiver structures. It is known that the optimum DS-CDMA receiver has a nonlinear structure with prohibitive complexity for practical implementation. It is also known that the currently implemented receiver in 2nd generation DSCDMA mobile handsets has poor performance, because it suffers from multiuser interference. Consequently, this work focuses on sub-optimum nonlinear receivers for DS-CDMA in the downlink scenario. First, the thesis reviews DS-CDMA, established equalisers, DS-CDMA receivers and pattern recognition techniques. Then the new receivers are proposed. It is shown that DS-CDMA can be considered as a pattern recognition problem and hence, pattern recognition techniques can be exploited in order to develop DS-CDMA receivers. Another approach is to apply known equaliser structures for DS-CDMA. One proposed receiver is based on the Volterra series expansion and processes the received signal at the chip rate. Another receiver is a symbol rate radial basis function network (RBFN) receiver with reduced complexity. Subsequently, a receiver is proposed based on linear programming (LP) which is especially tailored for nonlinearly separable scenarios. The LP based receiver performance is equivalent to the known decorrelating detector in linearly separable scenarios. Finally, a hybrid receiver is proposed which combines LP and RBFN and which exploits knowledge gained from pattern recognition. This structure has lower complexity than the full RBF and good performance, and has a large potential for further improvements. Monte-Carlo simulations compare the proposed DS-CDMA receivers against established linear and nonlinear receivers. It is shown that all proposed receivers outperform the known linear receivers. The Volterra receiver’s complexity is relatively high for the performance gain achieved and might not suit practical implementation. The other receiver’s complexity was greatly reduced but it performs nearly as well as an optimum symbol by symbol detector. This thesis shows that DS-CDMA is a pattern recognition problem and that pattern recognition techniques can simplify DS-CDMA receiver structures. Knowledge is gained from the DSCDMA signal patterns which help to understand the problem of a DS-CDMA receiver. It should be noted that from the large number of known techniques, only a few pattern recognition techniques are considered in this work, and any further work should look at other techniques. Pattern recognition techniques can reduce the complexity of existing DS-CDMA receivers while maintaining performance, leading to novel receiver structures

Artificial Neural Network Based Channel Equalization

The field of digital data communications has experienced an explosive growth in the last three decade with the growth of internet technologies, high speed and efficient data transmission over communication channel has gained significant importance. The rate of data transmissions over a communication system is limited due to the effects of linear and nonlinear distortion. Linear distortions occure in from of inter-symbol interference (ISI), co-channel interference (CCI) and adjacent channel interference (ACI) in the presence of additive white Gaussian noise. Nonlinear distortions are caused due to the subsystems like amplifiers, modulator and demodulator along with nature of the medium. Some times burst noise occurs in communication system. Different equalization techniques are used to mitigate these effects. Adaptive channel equalizers are used in digital communication systems. The equalizer located at the receiver removes the effects of ISI, CCI, burst noise interference and attempts to recover the transmitted symbols. It has been seen that linear equalizers show poor performance, where as nonlinear equalizer provide superior performance. Artificial neural network based multi layer perceptron (MLP) based equalizers have been used for equalization in the last two decade. The equalizer is a feed-forward network consists of one or more hidden nodes between its input and output layers and is trained by popular error based back propagation (BP) algorithm. However this algorithm suffers from slow convergence rate, depending on the size of network. It has been seen that an optimal equalizer based on maximum a-posterior probability (MAP) criterion can be implemented using Radial basis function (RBF) network. In a RBF equalizer, centres are fixed using K-mean clustering and weights are trained using LMS algorithm. RBF equalizer can mitigate ISI interference effectively providing minimum BER plot. But when the input order is increased the number of centre of the network increases and makes the network more complicated. A RBF network, to mitigate the effects of CCI is very complex with large number of centres. To overcome computational complexity issues, a single neuron based chebyshev neural network (ChNN) and functional link ANN (FLANN) have been proposed. These neural networks are single layer network in which the original input pattern is expanded to a higher dimensional space using nonlinear functions and have capability to provide arbitrarily complex decision regions. More recently, a rank based statistics approach known as Wilcoxon learning method has been proposed for signal processing application. The Wilcoxon learning algorithm has been applied to neural networks like Wilcoxon Multilayer Perceptron Neural Network (WMLPNN), Wilcoxon Generalized Radial Basis Function Network (WGRBF). The Wilcoxon approach provides promising methodology for many machine learning problems. This motivated us to introduce these networks in the field of channel equalization application. In this thesis we have used WMLPNN and WGRBF network to mitigate ISI, CCI and burst noise interference. It is observed that the equalizers trained with Wilcoxon learning algorithm offers improved performance in terms of convergence characteristic and bit error rate performance in comparison to gradient based training for MLP and RBF. Extensive simulation studies have been carried out to validate the proposed technique. The performance of Wilcoxon networks is better then linear equalizers trained with LMS and RLS algorithm and RBF equalizer in the case of burst noise and CCI mitigations

Mid-Price Movement Prediction in Limit Order Books Using Feature Engineering and Machine Learning

The increasing complexity of financial trading in recent years revealed the need for methods that can capture its underlying dynamics. An efficient way to organize this chaotic system is by contracting limit order book ordering mechanisms that operate under price and time filters. Limit order book can be analyzed using linear and nonlinear models. The thesis develops novelmethods for the identification of limit order book characteristics which provide traders and market makers an information edge in their trading. A good proxy for traders and market makers is the prediction of mid-price movement, which is the main target of this thesis. The contributions of this thesis are categorized chronologically into three parts. The first part refers to the introduction in the literature of the first publicly available limit order book dataset for high-frequency trading for the task of mid-price movement prediction. This dataset comes together with the development of an experimental protocol that utilizes methods inspired by ridge regression and a single layer feed-forward neural network as classifiers. These classifiers use state-of-the-art limit order book features as inputs for the target task. The next contribution of this thesis is the use and development of a wide range of technical and quantitative indicators for the task of mid-price movement prediction via an extensive feature selection process. This feature selection process identifies which features improve predictability performance. The results suggest that the newly introduced quantitative feature based on an adaptive logistic regression model for online learning was selected first according to several criteria. These criteria operate according to entropy, linear discriminant analysis, and least mean square error. The third contribution is the introduction of econometric features as inputs to deep learning models for the task of mid-price movement prediction. An extensive comparison against other state-of-the-art hand-crafted features and fully automated feature extraction processes is provided. Furthermore, a new experimental protocol is developed for the task of mid-price prediction, to overcome the problem of time irregularities, which characterizes high-frequency data. Results suggest that advanced hand-crafted features such as econometric indicators can predict movements of proxies, such as mid-price

Performance Evaluation of Phase Optimized Spreading Codes in Non Linear DS-CDMA Receiver

Spread spectrum (SS) is a modulation technique in which the signal occupies a bandwidth much larger than the minimum necessary to send the information. A synchronized reception with the code at the receiver is used for despreading the information before data recovery. Bandspread is accomplished by means of a code which is independent of the data. Bandspreading code is pseudo-random, thus the spread signal resembles noise. The coded modulation characteristic of SS system uniquely qualifies it for navigation applications. Any signal used in ranging is subject to time/distance relations. A SS signal has advantage that its phase is easily resolvable. Direct-sequence (DS) form of modulation is mostly preferred over Frequency Hopping system (FH) as FH systems do not normally possess high resolution properties. Higher the chip rate, the better the measurement capability. The basic resolution is one code chip. Initially, some existing code families e.g. Gold, Kasami (large and smal..

Methodology for utilising prior knowledge in constructing data-based process monitoring systems with an application to a dearomatisation process

Global competition is forcing the process industry to optimise the production processes. One key factor in optimisation is effective process state monitoring and fault detection. Another motivator to improve process monitoring systems are the substantial losses of revenue resulting from abnormal process conditions. It has been estimated that the petrochemical industry in the US alone loses 20 billion dollars per year because of unoptimal handling of abnormal process situations. Traditionally, the monitoring systems have been based on first principle models, constructed by specialists with process specific expertise. In contrast, the use of data-based modelling methods require less expertise and offers the possibilities to build and update the monitoring models in a short period of time, thus allowing more efficient development of monitoring systems. The aims of this thesis are to augment data-driven modelling with existing process knowledge, to combine different data-based modelling methods, and to utilise calculated variables in modelling in order to improve the accuracy of fault detection and identification (FDI) and to provide all necessary diagnostic information for fault tolerant control. The suggested improvements are included in a methodology for setting up FDI systems. The methodology has been tested by building FDI systems for detecting faults in two online quality analysers in a simulated and in a real industrial dearomatisation process at the Naantali oil refinery (Neste Oil Oyj). In developing an FDI system, background information about the user requirements for the monitoring system is first acquired. The information is then analysed and suitable modelling methods are selected according to the guidelines given in the methodology. Second, the process data are prepared for the modelling methods and augmented with appropriate calculated variables. Next, the input variable sets are determined with the introduced method and the models are constructed. After the estimation accuracy of the models is validated, the values of the fault detection parameters are determined. Finally, the fault detection performance of the system is tested. The system was evaluated during a period of one month at the Naantali refinery in 2007. The monitoring system was able to detect all the introduced analyser faults and to provide the information needed for a fault tolerant control system, thus validating the methodology. The effects of a number of suggested improvements in data-based modelling are analysed by means of a comparison study

Hematological image analysis for acute lymphoblastic leukemia detection and classification

Microscopic analysis of peripheral blood smear is a critical step in detection of leukemia.However, this type of light microscopic assessment is time consuming, inherently subjective, and is governed by hematopathologists clinical acumen and experience. To circumvent such problems, an efficient computer aided methodology for quantitative analysis of peripheral blood samples is required to be developed. In this thesis, efforts are therefore made to devise methodologies for automated detection and subclassification of Acute Lymphoblastic Leukemia (ALL) using image processing and machine learning methods.Choice of appropriate segmentation scheme plays a vital role in the automated disease recognition process. Accordingly to segment the normal mature lymphocyte and malignant lymphoblast images into constituent morphological regions novel schemes have been proposed. In order to make the proposed schemes viable from a practical and real–time stand point, the segmentation problem is addressed in both supervised and unsupervised framework. These proposed methods are based on neural network,feature space clustering, and Markov random field modeling, where the segmentation problem is formulated as pixel classification, pixel clustering, and pixel labeling problem respectively. A comprehensive validation analysis is presented to evaluate the performance of four proposed lymphocyte image segmentation schemes against manual segmentation results provided by a panel of hematopathologists. It is observed that morphological components of normal and malignant lymphocytes differ significantly. To automatically recognize lymphoblasts and detect ALL in peripheral blood samples, an efficient methodology is proposed.Morphological, textural and color features are extracted from the segmented nucleus and cytoplasm regions of the lymphocyte images. An ensemble of classifiers represented as EOC3 comprising of three classifiers shows highest classification accuracy of 94.73% in comparison to individual members. The subclassification of ALL based on French–American–British (FAB) and World Health Organization (WHO) criteria is essential for prognosis and treatment planning. Accordingly two independent methodologies are proposed for automated classification of malignant lymphocyte (lymphoblast) images based on morphology and phenotype. These methods include lymphoblast image segmentation, nucleus and cytoplasm feature extraction, and efficient classification

ARTIFACT CHARACTERIZATION, DETECTION AND REMOVAL FROM NEURAL SIGNALS

Ph.DDOCTOR OF PHILOSOPH