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With the increasing market demand for wideband multifunctional electronic systems, real-time broadband measurement systems with few picoseconds switching rates are essential. Furthermore, stable millimeter wave sources are required to drive these wideband electronic systems. Nonlinear transmission lines (NLTLs) are high impedance transmission lines periodically loaded with reverse biased diode serving as varactors. Extremely high bandwidths are achievable because of the possibility to fabricate these structures monolithically, which is why pulses with ultra short transitions can be generated using NLTLs. Also, efficient wideband frequency conversion is made possible by NLTL technology. / In this thesis, a comprehensive study of NLTLs and their applications is presented. Sharpening of the edges of electrical pulses, voltage dependent true time delay, and harmonic generation in NLTLs are investigated through analytical studies as well as circuit simulations and experimental measurements. Designing the best possible mixers, frequency doublers, and edge sharpeners and optimizing them are not the objects of this thesis. The main objective is to study an alternative design approach by using NLTLs. To this end, analytical solution for the magnitude of the third harmonic along a nonlinear transmission line is derived for the first time. Also, for the first time the lowpass nature of the NLTL is combined with the solutions for the magnitudes of harmonics in order to improve the validity range of the predicted harmonics. An NLTL harmonic generator is fabricated and measurement results are reported. / Inspired by the distributed nature of nonlinear transmission lines, a novel filtering method is introduced for the suppression of the unwanted signals in different NLTL applications. The filtering method is applied to a nonlinear transmission line frequency multiplier in order to filter the third harmonic. The distributed filtering is also used to suppress the image signal in an NLTL mixer. The proposed filtering method is general and can be applied to other periodic structure as well (such as distributed amplifiers and distributed mixers). For implementing the filtering, compact complementary split ring resonators are proposed and designed for an NLTL frequency doubler.

Micro-Electro-Mechanical Systems (MEMS) have played an important role in modern microelectronics, thermal, mechanical and hybrid systems. MEMS technology is a very promising means that might have a great impact on almost every corner of the society. Although many design methodology of MEMS already exists, not as much attention was given to the synthesis and optimization of MEMS devices. This thesis focuses on the optimization of MEMS actuators and MEMS tunable comb drive capacitors. The optimization is based on changing device geometry to achieve desired output parameter profile. For example in the design of MEMS tunable comb drive capacitors, the output parameter is the capacitance tuning range. Numerical experiments were performed to show the successful implementation of the optimization method.

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Simultaneous Localization and Mapping (SLAM) is a recursive probabilistic inferencing process used for robot navigation when Global Positioning Systems (GPS) are unavailable. SLAM operates by building a map of the robot environment, while concurrently localizing the robot within this map. The ultimate goal of SLAM is to operate anywhere using the environment's natural features as landmarks. Such a goal is difficult to achieve for several reasons. Firstly, different environments contain different types of natural features, each exhibiting large variance in its shape and appearance. Secondly, objects look differently from different viewpoints and it is therefore difficult to always recognize them. Thirdly, in most outdoor environments it is not possible to predict the motion of a vehicle using wheel encoders because of errors caused by slippage. Finally, the design of a SLAM system to operate in a large-scale outdoor setting is in itself a challenge. <br /><br /> The above issues are addressed as follows. Firstly, a camera is used to recognize the environmental context (e. g. , indoor office, outdoor park) by analyzing the holistic spectral content of images of the robot's surroundings. A type of feature (e. g. , trees for a park) is then chosen for SLAM that is likely observable in the recognized setting. A novel tree detection system is introduced, which is based on perceptually organizing the content of images into quasi-vertical structures and marking those structures that intersect ground level as tree trunks. Secondly, a new tree recognition system is proposed, which is based on extracting Scale Invariant Feature Transform (SIFT) features on each tree trunk region and matching trees in feature space. Thirdly, dead-reckoning is performed via an Inertial Navigation System (INS), bounded by non-holonomic constraints. INS are insensitive to slippage and varying ground conditions. Finally, the developed Computer Vision and Inertial systems are integrated within the framework of an Extended Kalman Filter into a working Vision-INS SLAM system, named VisSLAM. <br /><br /> VisSLAM is tested on data collected during a real test run in an outdoor unstructured environment. Three test scenarios are proposed, ranging from semi-automatic detection, recognition, and initialization to a fully automated SLAM system. The first two scenarios are used to verify the presented inertial and Computer Vision algorithms in the context of localization, where results indicate accurate vehicle pose estimation for the majority of its journey. The final scenario evaluates the application of the proposed systems for SLAM, where results indicate successful operation for a long portion of the vehicle journey. Although the scope of this thesis is to operate in an outdoor park setting using tree trunks as landmarks, the developed techniques lend themselves to other environments using different natural objects as landmarks.

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