In phylogenetic analysis, an important issue is to construct the confidence region for gene trees from DNA sequences. Usually estimation of the trees is the initial step. Maximum likelihood methods are widely applied but few tests are based on distance methods. In this thesis, we propose a new test based on balanced minimum evolution. We first examine the normality assumption of pairwise distance estimates under various model misspeci cations and also examine their variances, MSEs and squared biases. Then we compare the BME method with the WLS method in true tree reconstruction under different variance structures and model pairs. Finally, we develop a new test for finding a confidence region for the tree based on the BME method and demonstrate its effectiveness through simulation.

State estimation is widely used as a tool to evaluate the real time power system prevailing conditions. State estimation algorithms could suffer divergence under stressed system conditions. This dissertation first investigates impacts of variations of load levels and topology errors on the convergence property of the commonly used weighted least square (WLS) state estimator. The influence of topology errors on the condition number of the gain matrix in the state estimator is also analyzed. The minimum singular value of gain matrix is proposed to measure the distance between the operating point and state estimation divergence. To study the impact of the load increment on the convergence property of WLS state estimator, two types of load increment are utilized: one is the load increment of all load buses, and the other is a single load increment. In addition, phasor measurement unit (PMU) measurements are applied in state estimation to verify if they could solve the divergence problem and improve state estimation accuracy. The dissertation investigates the impacts of variations of line power flow increment and topology errors on convergence property of the WLS state estimator. A simple 3-bus system and the IEEE 118-bus system are used as the test cases to verify the common rule. Furthermore, the simulation results show that adding PMU measurements could generally improve the robustness of state estimation. Two new approaches for improving the robustness of the state estimation with PMU measurements are proposed. One is the equality-constrained state estimation with PMU measurements, and the other is Hachtel's matrix state estimation with PMU measurements approach. The dissertation also proposed a new heuristic approach for optimal placement of phasor measurement units (PMUs) in power system for improving state estimation accuracy. In the problem of adding PMU measurements into the estimator, two methods are investigated. Method I is to mix PMU measurements with conventional measurements in the estimator, and method II is to add PMU measurements through a post-processing step. These two methods can achieve very similar state estimation results, but method II is a more time-efficient approach which does not modify the existing state estimation software.

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An adaptive autopilot design for an uninhabited surface vehicle Andy SK Annamalai The work described herein concerns the development of an innovative approach to the design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of autonomous missions, uninhabited surface vehicles must be able to operate with a minimum of external intervention. Existing strategies are limited by their dependence on a fixed model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect on performance. This thesis presents an approach based on an adaptive model predictive control that is capable of retaining full functionality even in the face of sudden changes in dynamics. In the first part of this work recent developments in the field of uninhabited surface vehicles and trends in marine control are discussed. Historical developments and different strategies for model predictive control as applicable to surface vehicles are also explored. This thesis also presents innovative work done to improve the hardware on existing Springer uninhabited surface vehicle to serve as an effective test and research platform. Advanced controllers such as a model predictive controller are reliant on the accuracy of the model to accomplish the missions successfully. Hence, different techniques to obtain the model of Springer are investigated. Data obtained from experiments at Roadford Reservoir, United Kingdom are utilised to derive a generalised model of Springer by employing an innovative hybrid modelling technique that incorporates the different forward speeds and variable payload on-board the vehicle. Waypoint line of sight guidance provides the reference trajectory essential to complete missions successfully. The performances of traditional autopilots such as proportional integral and derivative controllers when applied to Springer are analysed. Autopilots based on modern controllers such as linear quadratic Gaussian and its innovative variants are integrated with the navigation and guidance systems on-board Springer. The modified linear quadratic Gaussian is obtained by combining various state estimators based on the Interval Kalman filter and the weighted Interval Kalman filter. Change in system dynamics is a challenge faced by uninhabited surface vehicles that result in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms are analysed and an innovative, adaptive autopilot based on model predictive control is designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that is obtained by combining the advances made to weighted least squares during this research and is used in conjunction with model predictive control. Successful experimentation is undertaken to validate the performance and autonomous mission capabilities of the adaptive autopilot despite change in system dynamics.

Abstract In the last few years, location-awareness has emerged as a key technology for the future development of mobile, ad hoc and sensor networks. Thanks to location information, several network optimization strategies as well as services can be developed. However, the problem of determining accurate location, i.e. positioning, is still a challenge and robust algorithms are yet to be developed. In this thesis, we focus on the development of distance-based non-cooperative and cooperative algorithms, which is derived based on a non-parametric non- Bayesian framework, specifically with a Weighted Least Square (WLS) optimization. From a theoretic perspective, we study the WLS problem and establish the optimality through the relationship with a Maximum Likelihood (ML) estimator. We investigate the fundamental limits and derive the consistency conditions by creating a connection between Euclidean geometry and inference theory. Furthermore, we derive the closed-form expression of a distance-model based Cramér-Rao Lower Bound (CRLB), as well as the formulas, that characterize information coupling in the Fisher information matrix. Non-cooperative positioning is addressed as follows. We propose a novel framework, namely the Distance Contraction, to develop robust non-cooperative positioning techniques. We prove that distance contraction can mitigate the global minimum problem and structured distance contraction yields nearly optimal performance in severe channel conditions. Based on these results, we show how classic algorithms such as the Weighted Centroid (WC) and the Non-Linear Least Square (NLS) can be modified to cope with biased ranging. For cooperative positioning, we derive a novel, low complexity and nearly optimal global optimization algorithm, namely the Range-Global Distance Continuation method, to use in centralized and distributed positioning schemes. We propose an effective weighting strategy to cope with biased measurements, which consists of a dispersion weight that captures the effect of noise while maximizing the diversity of the information, and a geometric-based penalty weight, that penalizes the assumption of bias-free measurements. Finally, we show the results of a positioning test where we employ the proposed algorithms and utilize commercial Ultra-Wideband (UWB) devices. / Tiivistelmä Viime vuosina paikkatietoisuudesta on tullut eräs merkittävä avainteknologia mobiili- ja sensoriverkkojen tulevaisuuden kehitykselle. Paikkatieto mahdollistaa useiden verkko-optimointistrategioiden sekä palveluiden kehittämisen. Kuitenkin tarkan paikkatiedon määrittäminen, esimerkiksi kohteen koordinaattien, on edelleen vaativa tehtävä ja robustit algoritmit vaativat kehittämistä. Tässä väitöskirjassa keskitytään etäisyyspohjaisten, yhteistoiminnallisten sekä ei-yhteistoiminnallisten, algoritmien kehittämiseen. Algoritmit pohjautuvat parametrittömään ei-bayesilaiseen viitekehykseen, erityisesti painotetun pienimmän neliösumman (WLS) optimointimenetelmään. Väitöskirjassa tutkitaan WLS ongelmaa teoreettisesti ja osoitetaan sen optimaalisuus todeksi tarkastelemalla sen suhdetta suurimman todennäköisyyden (ML) estimaattoriin. Lisäksi tässä työssä tutkitaan perustavanlaatuisia raja-arvoja sekä johdetaan yhtäpitävyysehdot luomalla yhteys euklidisen geometrian ja inferenssiteorian välille. Väitöskirjassa myös johdetaan suljettu ilmaisu etäisyyspohjaiselle Cramér-Rao -alarajalle (CRLB) sekä esitetään yhtälöt, jotka karakterisoivat informaation liittämisen Fisherin informaatiomatriisiin. Väitöskirjassa ehdotetaan uutta viitekehystä, nimeltään etäisyyden supistaminen, robustin ei-yhteistoiminnallisen paikannustekniikan perustaksi. Tässä työssä todistetaan, että etäisyyden supistaminen pienentää globaali minimi -ongelmaa ja jäsennetty etäisyyden supistaminen johtaa lähes optimaaliseen suorituskykyyn vaikeissa radiokanavan olosuhteissa. Näiden tulosten pohjalta väitöskirjassa esitetään, kuinka klassiset algoritmit, kuten painotetun keskipisteen (WC) sekä epälineaarinen pienimmän neliösumman (NLS) menetelmät, voidaan muokata ottamaan huomioon etäisyysmittauksen harha. Yhteistoiminnalliseksi paikannusmenetelmäksi johdetaan uusi, lähes optimaalinen algoritmi, joka on kompleksisuudeltaan matala. Algoritmi on etäisyyspohjainen globaalin optimoinnin menetelmä ja sitä käytetään keskitetyissä ja hajautetuissa paikannusjärjestelmissä. Lisäksi tässä työssä ehdotetaan tehokasta painotusstrategiaa ottamaan huomioon mittausharha. Strategia pitää sisällään dispersiopainon, joka tallentaa häiriön aiheuttaman vaikutuksen maksimoiden samalla informaation hajonnan, sekä geometrisen sakkokertoimen, joka rankaisee harhattomuuden ennakko-oletuksesta. Lopuksi väitöskirjassa esitetään tulokset kokeellisista mittauksista, joissa ehdotettuja algoritmeja käytettiin kaupallisissa erittäin laajakaistaisissa (UWB) laitteissa.