Nonlinear parameter estimation in classification problems

A nonlinear generalisation of the perceptron learning algorithm is presented and analysed. The new algorithm is designed for learning nonlinearly parametrised decision regions. It is shown that this algorithm can be viewed as a stepwise gradient descent of a certain cost function. Averaging theory is used to describe the behaviour of the algorithm, and in the process conditions guaranteeing convergence of the algorithm are established. These conditions are hard to test, so some simpler sufficient are derived using the directional derivative of the instantaneous cost. A number of simulation examples and applications are given, showing the variety of situations in which the algorithm can be used. In the initial analysis, a great deal of a priori knowledge about the decision region to be learnt has been assumed-in particular, it is assumed that the decision region is parametrised by some known (nonlinear) function. Often in applications, a general class of decision regions must be assumed, in which case the best approximate from the class is sought. It is shown that function approximation results can be used to derive degree of approximation results for decision regions. The approximating classes of decision regions considered are described by polynomial and neural network parametrisations. One shortcoming of all gradient descent type algorithms, such as the online learning algorithm discussed in the first part of this thesis, is that estimates may be attracted to local minima of the cost function. This is a problem because local minima occur in many interesting cases. Therefore a modified version of the algorithm, which avoids local minima traps, is presented. In the new algorithm, a number of parameter estimates ( called a congregation) are kept at any one time, and periodically all but the best estimate are restarted. Convergence of the new algorithm is established using the averaging theory that was used for the first algorithm. A probabilistic result concerning the expected time to convergence of the algorithm is given, and the effect of different population sizes is investigated. Again, a number of simulation examples are presented, including the application to the CMA algorithm for blind equalisation

Adaptive non linear system identification and channel equalization usinf functional link artificial neural network

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) using some learning rules such as back propagation (BP) algorithm. They offer flexibility, adaptability and versatility, so that a variety of approaches may be used to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. The primary 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 the designer is often faced with the problem of choosing a network of the right size for the task. The advantages of using a smaller neural network are cheaper cost of computation and better generalization ability. However, a network which is too small may never solve the problem, while a larger network may even have the advantage of a faster learning rate. Thus it makes sense to start with a large network and then reduce its size. For this reason a Genetic Algorithm (GA) based pruning strategy is reported. GA is based upon the process of natural selection and does not require error gradient statistics. As a consequence, a GA is able to find a global error minimum. Transmission bandwidth is one of the most precious resources in digital communication systems. Communication channels are usually modeled as band-limited linear finite impulse response (FIR) filters with low pass frequency response. When the amplitude and the envelope delay response are not constant within the bandwidth of the filter, the channel distorts the transmitted signal causing intersymbol interference (ISI). The addition of noise during propagation also degrades the quality of the received signal. All the signal processing methods used at the receiver's end to compensate the introduced channel distortion and recover the transmitted symbols are referred as channel equalization techniques.When the nonlinearity associated with the system or the channel is more the number of branches in FLANN increases even some cases give poor performance. To decrease the number of branches and increase the performance a two stage FLANN called cascaded FLANN (CFLANN) is proposed.This thesis presents a comprehensive study covering artificial neural network (ANN) implementation for nonlinear system identification and channel equalization. Three ANN structures, MLP, FLANN, CFLANN and their conventional gradient-descent training methods are extensively studied. Simulation results demonstrate that FLANN and CFLANN methods are directly applicable for a large class of nonlinear control systems and communication problems

Adaptive Channel Equalization using Radial Basis Function Networks and MLP

One of the major practical problems in digital communication systems is channel distortion which causes errors due to intersymbol interference. Since the source signal is in general broadband, the various frequency components experience different steady state amplitude and phase changes as they pass through the channel, causing distortion in the received message. This distortion translates into errors in the received sequence. Our problem as communication engineers is to restore the transmitted sequence or, equivalently, to identify the inverse of the channel, given the observed sequence at the channel output. This task is accomplished by adaptive equalizers. Typically, adaptive equalizers used in digital communications require an initial training period, during which a known data sequence is transmitted. A replica of this sequence is made available at the receiver in proper synchronism with the transmitter, thereby making it possible for adjustments to be made to the equalizer coefficients in accordance with the adaptive filtering algorithm employed in the equalizer design. When the training is completed, the equalizer is switched to its decision directed mode. Decision feedback equalizers are used extensively in practical communication systems. They are more powerful than linear equalizers especially for severe inter-symbol interference (ISI) channels without as much noise enhancement as the linear equalizers. This thesis addresses the problem of adaptive channel equalization in environments where the interfering noise exhibits Gaussian behavior. In this thesis, radial basis function (RBF) network is used to implement DFE. Advantages and problems of this system are discussed and its results are then compared with DFE using multi layer perceptron net (MLP).Results indicate that the implemented system outperforms both the least-mean square(LMS) algorithm and MLP, given the same signal-to-noise ratio as it offers minimum mean square error. The learning rate of the implemented system is also faster than both LMS and the multilayered case

Romans 12 living : older adults and the call to serve

https://place.asburyseminary.edu/ecommonsatsdissertations/2120/thumbnail.jp

Channel Equalization using GA Family

High speed data transmissions over communication channels distort the trans- mitted signals in both amplitude and phase due to presence of Inter Symbol Inter- ference (ISI). Other impairments like thermal noise, impulse noise and cross talk also cause further distortions to the received symbols. Adaptive equalization of the digital channels at the receiver removes/reduces the e®ects of such ISIs and attempts to recover the transmitted symbols. Basically an equalizer is an inverse ¯lter which is placed at the front end of the receiver. Its transfer function is inverse to the transfer function of the associated channel. The Least-Mean-Square (LMS), Recursive-Least-Square (RLS) and Multilayer perceptron (MLP) based adaptive equalizers aim to minimize the ISI present in the digital communication channel. These are gradient based learning algorithms and therefore there is possibility that during training of the equalizers, its weights do not reach to their optimum values due to ..

The pastor as leader : assessment, change, and growth in pastoral leadership style and ability

https://place.asburyseminary.edu/ecommonsatsdissertations/1807/thumbnail.jp

Neighbourhood and household socio-economic influences on diet and anthropometric status in urban South African adolescents

Background and Aims Many low- and middle-income countries are undergoing epidemiological and health transitions. South Africa has one of the highest prevalences of overweight and obesity in Sub-Saharan Africa. This research examined neighbourhood and household socio-economic influences on the risk of overweight and obesity in terms of anthropometric status and dietary intake among urban South African adolescents. A further aim was to conduct a qualitative study on the potential for religious groups such as Churches to be used as community-based organisations for obesity intervention. Methods A secondary analysis of neighbourhood and household socio-economic status (SES), anthropometric and dietary data was carried out on adolescents aged 17-19 years from the Birth to Twenty Plus cohort study in Johannesburg-Soweto. Qualitative data were collected through focus groups discussions and a community readiness survey with church leaders. Results No significant associations were observed between SES (household and neighbourhood) and energy, protein, fat, or carbohydrate intakes in males. Some significant associations were found between SES and dietary intake in females. Females had a higher prevalence of overweight and obesity than males (26.2% vs. 8.2%, p<0.0001). In males, poor household SES was associated with lower odds of overweight, fatness and high waist-to-height ratio (WHTR). For females, household SES was not significantly associated with overweight, fatness and high WHTR. The qualitative research showed that there was a very low level of community readiness among church leaders for obesity prevention programmes. Conclusions The dietary results suggest that the diet of these adolescents is transitioning to that seen in high income countries. It also highlights that even within the same relatively small urban area, nutrition transition does not affect different groups in uniform ways. The qualitative results indicate that programmes should focus around raising awareness of the problem of overweight/obesity in this community

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

Racialized Health, COVID-19, and Religious Responses

This volume draws attention to multiple ways black health prospects and outcomes are configured by the actions, inactions, and cultural capital of social institutions and leaders, including within the governmental sector, the healthcare sector, and the religious sector. Facilitating and ensuring conditions conducive to public health, and capacities for provision of public healthcare, are macro tasks, requiring substantial institutional, financial, and technological resources. Government sectors and healthcare sectors around the globe are where this scale of resources are concentrated, though in varying degrees reflective of global wealth disparities. As these disparities and inequities have become increasingly evident, including as a result of the COVID-19 crisis, it has become more urgent to hold sectors charged with public health accountable in fulfilling their public charge. Racialized Health, COVID-19, and Religious Responses: Black Atlantic Contexts and Perspectives explores black religious responses to black health concerns amidst persistent race-based health disparities and healthcare inequities. This cutting-edge edited volume provides theoretically and descriptively rich analysis of cases and contexts where race factors strongly in black health outcomes and dynamics, viewing these matters from various disciplinary and national vantage points. The volume is divided into the following four parts: Systemic and Socio-Cultural Dimensions of Black Health Ecclesial Responses to Black Health Vulnerabilities Public Education and Policy Considerations Spirituality and the Wellness of Black Minds, Bodies and Souls Part I explores ways social and cultural factors such as racial bias, religious conviction, and resource capacity have influenced and delimited black health prospects. Part II looks historically and contemporarily at denominational and ecumenical responses to collective black health emergencies in places such as Nigeria, the UK, the US, and the Caribbean. Part III focuses on public advocacy, particularly collective black health, both in terms of policy and education. The final section deals with spiritual, psychological, and theological dimensions, understandings, and pursuits of black health and wholeness. Collectively, the essays in the volume delineate analysis and action that wrestle with the multidimensional nature of black wellness and with ways broad public resources and black religious resources should be mobilized and leveraged to ensure collective black wellness

Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit

The primary focus of this thesis is to make Sanskrit manuscripts more accessible to the end-users through natural language technologies. The morphological richness, compounding, free word orderliness, and low-resource nature of Sanskrit pose significant challenges for developing deep learning solutions. We identify four fundamental tasks, which are crucial for developing a robust NLP technology for Sanskrit: word segmentation, dependency parsing, compound type identification, and poetry analysis. The first task, Sanskrit Word Segmentation (SWS), is a fundamental text processing task for any other downstream applications. However, it is challenging due to the sandhi phenomenon that modifies characters at word boundaries. Similarly, the existing dependency parsing approaches struggle with morphologically rich and low-resource languages like Sanskrit. Compound type identification is also challenging for Sanskrit due to the context-sensitive semantic relation between components. All these challenges result in sub-optimal performance in NLP applications like question answering and machine translation. Finally, Sanskrit poetry has not been extensively studied in computational linguistics. While addressing these challenges, this thesis makes various contributions: (1) The thesis proposes linguistically-informed neural architectures for these tasks. (2) We showcase the interpretability and multilingual extension of the proposed systems. (3) Our proposed systems report state-of-the-art performance. (4) Finally, we present a neural toolkit named SanskritShala, a web-based application that provides real-time analysis of input for various NLP tasks. Overall, this thesis contributes to making Sanskrit manuscripts more accessible by developing robust NLP technology and releasing various resources, datasets, and web-based toolkit.Comment: Ph.D. dissertatio