Yes / Beamforming in multi-user MIMO (MU-MIMO) systems is a vital part of modern wireless communication systems. Researchers looking for best operational performance normally optimize the problem and then solve for best weight solutions. The weight optimization problem contains variables in numerator and dominator: this leads to so-called variable coupling, making the problem hard to solve. Formulating the optimization in terms of the signal to leakage and noise ratio (SLNR) helps in decoupling the problem variables. In this paper we study the performance of the SLNR with variable numbers of users and handset antennas. The results show that there is an optimum and the capacity curve is a concave over these two parameters. The performances of two further variations of this method are also considered.

In this thesis, a mixture-model cluster analysis technique under different covariance structures of the component densities is developed and presented, to capture the compactness, orientation, shape, and the volume of component clusters in one expert system to handle Gaussian high dimensional heterogeneous data sets to achieve flexibility in currently practiced cluster analysis techniques. Two approaches to parameter estimation are considered and compared; one using the Expectation-Maximization (EM) algorithm and another following a Bayesian framework using the Gibbs sampler. We develop and score several forms of the ICOMP criterion of Bozdogan (1994, 2004) as our fitness function; to choose the number of component clusters, to choose the correct component covariance matrix structure among nine candidate covariance structures, and to select the optimal parameters and the best fitting mixture-model. We demonstrate our approach on simulated datasets and a real large data set, focusing on early detection of breast cancer. We show that our approach improves the probability of classification error over the existing methods.

Today the multisensor array signal processing of noisy measurements has received much attention. The classical problem in array signal processing is determining the location of an energy-radiating source relative to the location of the array, in other words, direction-of-arrival (DOA) estimation. One is considering the signal estimation problem when together with the signal(s) of interest some noise and interfering signals are present. In this report a direction-of-arrival estimation system is described based on an antenna array for detecting arrival angles in azimuth plane of signals pitched by the antenna array. For this, the Multiple Signal Classication (MUSIC) algorithmis first of all considered. Studies show that in spite of its good reputation and popularity among researches, it has a certain limit of its performance. In this subspace-based method for DOA estimation of signal wavefronts, the term corresponding to additive noise is initially assumed spatially white. In our paper, we address the problem of DOA estimation of multiple target signals in a particular noise situation - in correlated spherically isotropic noise, which, in many practical cases, models a more real context than under the white noise assumption. The purpose of this work is to analyze the behaviour of the MUSIC algorithm and compare its performance with some other algorithms (such as the Capon and the Classical algorithms) and, uppermost, to explore the quality of the detected angles in terms of precision depending on different parameters, e.g. number of samples, noise variance, number of incoming signals. Some modifications of the algorithms are also done is order to increase their performance. Program MATLAB is used to conduct the studies. The simulation results on the considered antenna array system indicate that in complex conditions the algorithms in question (and first of all, the MUSIC algorithm) are unable to automatically detect and localize the DOA signals with high accuracy. Other algorithms andways for simplification the problem (for example, procedure of denoising) exist and may provide more precision but require more computation time.

The interest in sustainable investing has increased significantly during recent years. Asset managers and institutional investors are urged to invest more sustainable from their stakeholders, reducing their investment universe. This thesis has found that sustainable investments have a different linear dependence structure compared to the regional markets in Europe and North America, but not in Asia-Pacific. However, the largest drawdowns of an sustainable compliant portfolio has historically been lower compared to the a random market portfolio, especially in Europe and North America. / Intresset för hållbara investeringar har ökat avsevärt de senaste åren. Fondförvaltare och institutionella investerare är, från deras intressenter, manade att investera mer hållbart vilket minskar förvaltarnas investeringsuniversum. Denna uppsats har funnit att hållbara investeringar har en beroendestruktur som är skild från de regionala marknaderna i Europa och Nordamerika, men inte för Asien-Stillahavsregionen. De största värdeminskningarna i en hållbar portfölj har historiskt varit mindre än värdeminskningarna från en slumpmässig marknadsportfölj, framförallt i Europa och Nordamerika.