Radars have been used for detection purposes in safety applications (i.e., blind spot detection radar in cars) extensively. The existing detection methods, however, are not flawless. So far, the main focus of these methods is on detecting an object based on its reflectiveness. In this thesis, the limitation of conventional methods are addressed, and alternative approaches are proposed. The main objective is to model/identify the noise with statistical and machine learning approaches as an alternative to conventional methods that focus on the object. The second objective is to improve the time efficiency of these methods. The data for this thesis contains measurements collected from radars at ABB AB, Sweden. These measurements reflect the received signal strength. These radars are meant to be used in safety applications, such as in industrial environments. Thus, the trade-off between accuracy and complexity of the algorithms is crucial. One way to ensure there is nothing but noise in the surveillance field of the radar is to model the noise only. A new input can then be compared to this model and be classified as noise or not noise (object). One-class classifiers can be employed to approach this problem as they only need noise for training; hence they have been one of the initial proposals in this thesis. Alternatively, binary classifiers are investigated to classify noise and object given a new input data. Moreover, a mathematical model for noise is computed using the Fourier series expansion. While the derived model holds useful information in itself, it can be used, e.g., for hypothesis testing purposes. Furthermore, to make the classification more time-efficient, dimension reduction methods are considered. Feature extraction has been performed for this purpose with the help of the derived noise model. In order to evaluate the performance of the considered methods, three different datasets have been formed. In the first dataset,

This thesis presents a long-term asset liability management for Tanzania pension funds. As an application, the largest pension fund in Tanzania is considered. This is a pay-as-you-go pension fund where the contributions are used to pay current benefits. The Pension plan analyzed is a final salary defined benefit. Two kinds of pension benefit are considered, a commuted (at retirement) and a monthly (old age) pension. A decision factor in the analysis is the increased life expectancy of the members of the pension fund. The presentation is divided into two parts. First is a long-term projection of the fund using a fixed and relatively low return on asset value. Basing on the number of members in 2015, a 50 years projection of members and retirees is done. The corresponding amount of contributions, asset values, benefit payouts, and liabilities are also projected. The evaluation of some possible reforms of the fund is done. Then, the growth of asset values using different asset returns is studied. The projection shows that the fund will not be fully sustainable in a long future due to the increase in life expectancy of its members. The contributions will not cover the benefit payouts and the asset value will not fully cover liabilities. Evaluation of some reforms of the fund shows that they cannot guarantee a long-term sustainability. Higher returns on asset value will improve the asset to liability ratio, but contributions are still insufficient to cover benefit payouts. Second is a management based on stochastic programming. This approach allocates investment in assets with the best return to raise the asset value closer to the level of liabilities. The model is based on work by Kouwenberg in 2001 includes some features from Tanzania pension system. In contrast with most asset liability management models for pension funds by stochastic programming, liabilities are modeled by number of years of life expectancy. Scenario trees are generated by using Monte Carlo simulation. Two models according to different investment guidelines are built. First is using the existing investment guidelines and second is using modified guidelines which are practical and suitable for modeling. Numerical results suggest that, in order to improve a long-term sustainability of the Tanzania pension fund system, it is necessary to make reforms concerning the contribution rate, investment guidelines and formulate target levels (funding ratios) to characterize the pension funds’ solvency situation. These reforms will improve the sustainability of the system.

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