Non Linear Blind Source Separation Using Different Optimization Techniques

The Independent Component Analysis technique has been used in Blind Source separation of non linear mixtures. The project involves the blind source separation of a non linear mixture of signals based on their mutual independence as the evaluation criteria. The linear mixer is modeled by the Fast ICA algorithm while the Non linear mixer is modeled by an odd polynomial function whose parameters are updated by four separate optimization techniques which are Particle Swarm Optimization, Real coded Genetic Algorithm, Binary Genetic Algorithm and Bacterial Foraging Optimization. The separated mixture outputs of each case was studied and the mean square error in each case was compared giving an idea of the effectiveness of each optimization technique

Bacterial Foraging Based Channel Equalizers

A channel equalizer is one of the most important subsystems in any digital communication receiver. It is also the subsystem that consumes maximum computation time in the receiver. Traditionally maximum-likelihood sequence estimation (MLSE) was the most popular form of equalizer. Owing to non-stationary characteristics of the communication channel MLSE receivers perform poorly. Under these circumstances ‘Maximum A-posteriori Probability (MAP)’ receivers also called Bayesian receivers perform better. Natural selection tends to eliminate animals with poor “foraging strategies” and favor the propagation of genes of those animals that have successful foraging strategies since they are more likely to enjoy reproductive success. After many generations, poor foraging strategies are either eliminated or shaped into good ones (redesigned). Logically, such evolutionary principles have led scientists in the field of “foraging theory” to hypothesize that it is appropriate to model the activity of foraging as an optimization process. This thesis presents an investigation on design of bacterial foraging based channel equalizer for digital communication. Extensive simulation studies shows that the performance of the proposed receiver is close to optimal receiver for variety of channel conditions. The proposed receiver also provides near optimal performance when channel suffers from nonlinearities

Energy Efficient Communication Protocols for Wireless Sensor Networks

The popularity of Wireless Sensor Networks have increased tremendously due to the vast potential of the sensor networks to connect the physical world with the virtual world. Since these devices rely on battery power and may be placed in hostile environments replacing them becomes a tedious task. Thus, improving the energy of these networks becomes important.The thesis provides methods for clustering and cluster head selection to WSN to improve energy efficiency. It presents a comparison between the different methods on the basis of the network lifetime . It proposes a modified approach for cluster head selection with good performance and reduced computational complexity .In addition it also proposes BFO as an algorithm for clustering of WSN which would result improved performance with faster convergence

Analysis and design of multifunctional agricultural landscapes : a graph theoretic approach

This thesis deals with the development of quantitative methodologies for the evaluation of landscape functions and their interactions in multifunctional agricultural landscapes. It focuses on the spatial coherence of hedgerow networks for ecological functions and landscape character for perception of landscape identity, and on their integration in a multifunctional and multiscale trade-off analysis. Graph theory provided the basis for new methodologies that are applied in this research

Exploring the Complexity of Layout Parameters in Tournaments and Semi-Complete Digraphs

A simple digraph is semi-complete if for any two of its vertices u and v, at least one of the arcs (u,v) and (v,u) is present. We study the complexity of computing two layout parameters of semi-complete digraphs: cutwidth and optimal linear arrangement (OLA). We prove that: -Both parameters are NP-hard to compute and the known exact and parameterized algorithms for them have essentially optimal running times, assuming the Exponential Time Hypothesis. - The cutwidth parameter admits a quadratic Turing kernel, whereas it does not admit any polynomial kernel unless coNP/poly contains NP. By contrast, OLA admits a linear kernel. These results essentially complete the complexity analysis of computing cutwidth and OLA on semi-complete digraphs. Our techniques can be also used to analyze the sizes of minimal obstructions for having small cutwidth under the induced subdigraph relation

Seismic imaging in complex media with the common reflection surface stack

Reflection seismic is one of the most commonly used geophysical method for the oil and gas exploration. In this thesis I show the application of the Common Reflection Surface (CRS) stack technique to improve the quality of reflection seismic images. Conventional seismic imaging method based on the CMP stacking does not use the full potential of the dataset due to reflection point dispersal in the presence of dipping reflectors or laterally inhomogeneous media. Application of the CRS stack technique is advantageous in complex areas, since it involves information about the shape of seismic reflectors, i.e., dip and curvature, into processing. Moreover, a multiparameter formula allows to sum up more traces during the stack. All together, this leads to better imaging results, especially to an improvement of the signal-to-noise (S/N) ratio. Reflection events in the CRS stack sections appear clearer and more continuous compared to conventional CMP stack sections...thesi

Iodine monoxide in the Antarctic snowpack

Recent ground-based and space borne observations suggest the presence of significant amounts of iodine monoxide in the boundary layer of Antarctica, which are expected to have an impact on the ozone budget and might contribute to the formation of new airborne particles. So far, the source of these iodine radicals has been unknown. This paper presents long-term measurements of iodine monoxide at the German Antarctic research station Neumayer, which indicate that high IO concentrations in the order of 50 ppb are present in the snow interstitial air. The measurements have been performed using multi-axis differential optical absorption spectroscopy (MAX-DOAS). Using a coupled atmosphere snowpack radiative transfer model, the comparison of the signals observed from scattered skylight and from light reflected by the snowpack yields several ppb of iodine monoxide in the upper layers of the sunlit snowpack throughout the year. Snow pit samples from Neumayer Station contain up to 700 ng/l of total iodine, representing a sufficient reservoir for these extraordinarily high IO concentrations

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