The longitudinal controller at DaimlerChrysler AG consists of two cascade controllers. The outer control loop contains the driver assistance functions such as speed limiter, cruise control, etc. The inner control loop consists of a PID-controller and an observer. The task of the observer is to estimate the part of the vehicle's acceleration caused by large disturbances, for example by a changed vehicle mass or the slope of the road. As observer the Kalman filter is selected. It is the optimal filter when the process model is linear and the process noise and measurement noise can be modeled as Gaussian noise. In this Master's thesis the theory for the Kalman filter is presented and it is shown how to choose the filter parameters. Simulated annealing is a global optimization technique which can be used when autotuning, i.e., automatically find the optimal parameter settings. To be able to perform autotuning for the longitudinal controller one has to model the environment and driving situations. In this Master's thesis it is verified that the parameter choice is a compromise between a fast but jerky, or a slow but smooth estimate. As the output from the Kalman filter is directly added to the control value for the engine and brakes, it is important that the output is smooth. It is shown that the Kalman filter implemented in the test vehicles today can be exchanged with a first-order lag function, without loss in performance. This makes the filter tuning easier, as there is only one parameter to choose. Change detection is a method that can be used to detect large changes in the signal, and react accordingly - for example by making the filter faster. A filter using change detection is implemented and simulations show that it is possible to improve the estimate using this method. It is suggested to implement the change detection algorithm in a test vehicle and evaluate it further.

Post-fabrication tuning of filters is usually realized by adding number of elements for tuning the frequency and/or controlling the couplings between the resonators. The task of these tuning elements is to control resonators center frequency, inter-resonators coupling and input/output couplings. While the most common tool for the post-fabrication tuning is to use tuning screws and rods, it is not usually practical to tune a planar filter with these tools. This thesis introduces a novel method for global post-fabrication tuning of microwave filters by designing and adding a passive distributed-element circuit in parallel to the detuned filter. The idea, which is demonstrated by experimental results, has several advantages over traditional techniques for filter tuning that use screws. The quality factor of resonator reduces significantly after adding the tuning screws while the proposed method does not affect the Q of resonators. The most important advantage of the proposed compensator circuit is that it can be employed without knowing details of the detuned filters. Since the compensator circuit will be added in parallel to the detuned filter, it will not affect the elements of filter individually. So whether the filter is planar or cavity, the proposed circuit can be used for the tuning. The experimental results obtained demonstrate the validity of this method. The dissertation also presents a novel concept for designing a center frequency and bandwidth tunable microstrip filter by using GaAs varactors. The proposed isolated coupling structure which is used in this filter makes the bandwidth tuning possible by reducing the loading effect of coupling elements on the resonators. The center frequency of this filter can be also tuned by using a different set of varactors connected to resonators. A 3-pole filter based on this concept has been designed and simulated. The concept can be expanded to higher order filters.

<p>Modern industrial, government, and academic organizations are collecting massive amounts of data ("Big Data") at an unprecedented scale and pace. The ability to perform timely and cost-effective analytical processing of such large datasets in order to extract deep insights is now a key ingredient for success. These insights can drive automated processes for advertisement placement, improve customer relationship management, and lead to major scientific breakthroughs.</p><p>Existing database systems are adapting to the new status quo while large-scale dataflow systems (like Dryad and MapReduce) are becoming popular for executing analytical workloads on Big Data. Ensuring good and robust performance automatically on such systems poses several challenges. First, workloads often analyze a hybrid mix of structured and unstructured datasets stored in nontraditional data layouts. The structure and properties of the data may not be known upfront, and will evolve over time. Complex analysis techniques and rapid development needs necessitate the use of both declarative and procedural programming languages for workload specification. Finally, the space of workload tuning choices is very large and high-dimensional, spanning configuration parameter settings, cluster resource provisioning (spurred by recent innovations in cloud computing), and data layouts.</p><p>We have developed a novel dynamic optimization approach that can form the basis for tuning workload performance automatically across different tuning scenarios and systems. Our solution is based on (i) collecting monitoring information in order to learn the run-time behavior of workloads, (ii) deploying appropriate models to predict the impact of hypothetical tuning choices on workload behavior, and (iii) using efficient search strategies to find tuning choices that give good workload performance. The dynamic nature enables our solution to overcome the new challenges posed by Big Data, and also makes our solution applicable to both MapReduce and Database systems. We have developed the first cost-based optimization framework for MapReduce systems for determining the cluster resources and configuration parameter settings to meet desired requirements on execution time and cost for a given analytic workload. We have also developed a novel tuning-based optimizer in Database systems to collect targeted run-time information, perform optimization, and repeat as needed to perform fine-grained tuning of SQL queries.</p> / Dissertation

Ultra-Wideband ¡}UWB¡~Communication Technology is one of the potential technologies in indoor wireless communication applications. Because of the property of fine resolution of transmitted signal by UWB, it is powerful on indoor location applications. In the first place, we need to estimate the time of arrival based on the wireless location applications. Whether synchronization between the template signals and received signals affects directly the SNR of the estimator output and decreases the ranging accuracy. Because of the transmitted signal is the type of impulse radio for UWB system, it is more important on the time accuracy of the internal oscillator. In the thesis, we utilize the Delay-Locked Loop ¡}DLL¡~ structure with Self-tuning function to reduce the timing jitter in the internal oscillator. We can improve the stability in the tracking loop and utilize multipath canceller to cancel the multipath interference in the indoor environment. When reaching synchronization between the template signal and received signal by using the tracking loop, we can improve ranging accuracy and increase location precision according to the received signal.

Automatic segmentation of speech is compulsory for building large speech databases to be used in speech processing applications. This study proposes a bimodal automatic speech segmentation system that uses either articulator motion information (AMI) or visual information obtained by a camera in collaboration with auditory information. The presence of visual modality is shown to be very beneficial in speech recognition applications, improving the performance and noise robustness of those systems. In this dissertation a significant increase in the performance of the automatic speech segmentation system is achieved by using a bimodal approach. Automatic speech segmentation systems have a tradeoff between precision and resulting number of gross errors. Boundary refinement techniques are used in order to increase precision of these systems without decreasing the system performance. Two novel boundary refinement techniques are proposed in this thesis / a hidden Markov model (HMM) based fine tuning system and an inverse filtering based fine tuning system. The segment boundaries obtained by the bimodal speech segmentation system are improved further by using these techniques. To fulfill these goals, a complete two-stage automatic speech segmentation system is produced and tested in two different databases. A phonetically rich Turkish audiovisual speech database, that contains acoustic data and camera recordings of 1600 Turkish sentences uttered by a male speaker, is build from scratch in order to be used in the experiments. The visual features of the recordings are extracted and manual phonetic alignment of the database is done to be used as a ground truth for the performance tests of the automatic speech segmentation systems.

The main purpose of this thesis is to design a positioning system that matches the demand of the high-accuracy and the high-speed positioning. Hereon, the linear DC motor will be chosen as the main body of the whole system. Individually, we design the controller for macro model and micro model. Among them, using the genetic algorithms¡]GA¡^to find the near-optimum controller parameters for PID controller to complete the macro target. And adopting the relay-feedback auto-tuning PID controller to carry out the micro region position control. Through the dynamic transition condition, the two-step position control system is integrated. We hope that the positioning results can achieve the position sensor resolution, , in 0.2 second¡]positioning distance <1.0cm¡^. By adopting the principle and operation procedure of the genetic algorithms to make a search for the near-optimum controller parameters, and through the process of selection, reproduction, crossover, and mutation of genes, and then the performance of the closed-loop system with PID controller is improved. According to the computer simulations and the experimental results, it is obvious that the GA-based near-optimal controller can satisfactorily control the linear DC motor system.

Integrated high-Q continuous-time filters require adaptive tuning circuits that will correct the filter parameters such as center frequency and quality factor (Q). Three different automatic tuning techniques are introduced. In all of the proposed methods, frequencyand quality factor tuning loops are controlled digitally, providing stable tuning by activating only one loop at a given time. In addition, a direct relationship between passband gain and quality factor is not required, so the techniques can be applied to active LC filters as well as Gm-C filters. The digital-tuning method based on phase comparison was verified with 1% tuning accuracy at 5.5 MHz for Q of 20. It uses phase information for both Q and center-frequency tuning. The filter output phase is tuned to the known references, which are generated by a frequency synthesizer. The core tuning circuit consists of D flip-flops (DFF) and simple logic gates. DFFs are utilized to perform binary phase comparisons. The second method, high-order digital tuning based on phase comparison, is an extension of the previous technique to high-order analog filters without depending on the master-slave approach. Direct tuning of the overall filter response is achieved without separating individual biquad sections, eliminating switches and their parasitics. The tuning system was verified with a prototype 6th order bandpass filter at 19 MHz with 0.6 MHz bandwidth, which was fabricated in a conventional 0.5 [mu]m CMOS technology. Analysis of different practical limitations is also provided. Finally, the digital-tuning method based on magnitude comparison is proposed for second-order filters for higher frequency operations. It incorporates a frequency synthesizer to generate reference signals, an envelope detector and a switched comparator to compare output magnitudes at three reference frequencies. The theoretical analysis of the technique and the simulation results are provided.

Wireless data broadcast is an efficient way to disseminate digital information to clients equipped with mobile devices. It allows a huge number of the mobile clients simultaneously access data at anytime and anywhere in the wireless environments. Applications using wireless data broadcast to disseminate information include accessing stock activities and traffic conditions. Using index technologies on the broadcast file, i.e., selective tuning, can reduce a lot of energy consumption of the mobile devices without significantly increasing client waiting time. Most of the research work for selective tuning assumes that each data item broadcast on the wireless channel is fairly evenly accessed by mobile clients. In real-life applications, more popular data may be frequently accessed by clients than less popular ones, i.e., skewed access patterns. In this dissertation, to support efficiently selective tuning with skewed access patterns in the single-channel wireless environments, we first propose a skewed distributed index, SDI, on the uniform data broadcast, on which each data item is broadcast once in a broadcast cycle. Second, we propose a skewed index, SI, on the nonuniform data broadcast, on which a few popular data items are broadcast more frequently in a broadcast cycle than the others. The first proposed algorithm, SDI, considers the access probabilities of data items and the replication of index nodes. The proposed algorithm traverses a balanced tree to determine whether an index node should be replicated by considering the access probability of its child node. In our performance analysis and simulation results, we have shown that our proposed algorithm outperforms the variant-fanout tree index and the distributed index. The second proposed algorithm, SI, applies Acharya et al.'s Broadcast Disks to generate a broadcast program, in which the popular data items are broadcast more times than the others, in order to reduce client waiting time. Moreover, the proposed algorithm builds a skewed tree for these data items and allocates index nodes for the popular data items more times than those for the less popular ones in a broadcast cycle. From our performance analysis and simulation results, we have shown that our proposed SI outperforms the flexible index and the flexible distributed index.

This dissertation maps various kernels and applications to a spectrum of programming models and architectures and also presents architecture-aware algorithms for different systems. The kernels and applications discussed in this dissertation have widely varying computational characteristics. For example, we consider both dense numerical computations and sparse graph algorithms. This dissertation also covers emerging applications from image processing, complex network analysis, and computational biology. We map these problems to diverse multicore processors and manycore accelerators. We also use new programming models (such as Transactional Memory, MapReduce, and Intel TBB) to address the performance and productivity challenges in the problems. Our experiences highlight the importance of mapping applications to appropriate programming models and architectures. We also find several limitations of current system software and architectures and directions to improve those. The discussion focuses on system software and architectural support for nested irregular parallelism, Transactional Memory, and hybrid data transfer mechanisms. We believe that the complexity of parallel programming can be significantly reduced via collaborative efforts among researchers and practitioners from different domains. This dissertation participates in the efforts by providing benchmarks and suggestions to improve system software and architectures.

<p>Auto-tuning has become an important function in distributed control systems (DCS) and is especially appreciated in large industries that can have hundreds of controllers. In the DCS 800xA manufactured by ABB, there is an auto-tuning method implemented based on a relay experiment to determine the ultimate gain and the ultimate period, with which the PID parameters are obtained using the modified Ziegler-Nichols tuning rules. The tuning procedure can then proceed with a step identification experiment to get additional parameters for kappa-tau tuning. In the previous DCS, called Advant, there was another auto-tuning approach implemented. This method was based on dominant pole design, which included an identification of the process. The purpose of this thesis is to compare these auto-tuning methods, to investigate if the dominant pole placement method should be migrated to the 800xA system.</p> / <p>Automatisk trimning har blivit en viktig funktion i distribuerade styrsystem (DCS och är speciellt av intresse för stora industrier som kan ha flera hundra regulatorer. Den automatiska trimningen som idag är implementerad i ABB:s DCS 800xA är baserad på ett reläexperiment för att bestämma den ultimata förstärkningen och den ultimata periodtiden. Modifierade Ziegler-Nichols trimningsregler används sedan för att bestämma PID parametrarna. Vidare kan trimningen fortsätta med ett stegsvars-experiment för att erhålla ytterliggare parametrar och trimma med kappa-tau metoden. Den automatiska trimningsmetoden som var implementerad i tidigare DCS, Advant, var baserad på dominant polplacering med identifiering av processen. Syftet med detta examensarbete är att jämföra dessa automatiska trimningsmetoder för att undersöka om den tidigare trimningsmetoden baserad på dominant polplacering ska implementeras i 800xA systemet.</p>