Diplomová práce pojednává o návrhu nového hardwaru využívající komparátor ve zpětné vazbě systému pro digitální předzkreslování signálu. Vybrané vlastnosti navrhované architektury jsou ověřeny pomocí simulací a následně jsou zvoleny komponenty vhodné pro vysokofrekvenční použití za účelem implementace. Na bázi předložené architektury je navržen akviziční modul včetně obvodové realizace a vytvoření plošného spoje. Zhotovený plošný spoj je osazen a připraven pro další testování. Dále je navržen příslušný firmware pro příjem a vysílání signálu a získávání naměřených dat. Obdržené výsledky jsou určeny pro zhodnocení vlastností hardwaru a budoucího využití architektury v systémech digitálních předzkreslovačů.

The theory of deterministic chaos has generated a lot of interest and continues to be one of the much-focused research areas in the field of dynamics today. This is due to its prevalence in essential parts of human lives such as electrical circuits, chemical reactions, the flow of blood through the human system, the weather, etc. This thesis presents a study of the Lorenz equations, a famous example of chaotic systems. In particular, it presents the analysis of the Lorenz equations from stability to chaos and various bifurcation scenarios with numerical and graphical interpretations. It studies concepts of non-linear dynamical systems such as equilibrium points, stability, linearization, bifurcation, Lyapunov function, etc. Finally, it discusses how the Lorenz equations serve as a model for the waterwheel (in detail), and the convection roll for fluid.

Linearization display unit will receive pulses from a gear flow meter KRACHT whose frequency corresponds to a flow. The measured value is flow that nonlinearly depends on the temperature and viscosity of the medium. The dependency is described by calibration curves for each flow meter. The task of the diploma thesis is to design, implement, and verify the operation of the display unit, which calculates the actual flow rate from the input data (pulse rate, temperature, viscosity, and flow meter calibration curves). The device will contain a suitable user interface to display all measured values, inputs for connecting measuring sensors, and outputs for transmission of measured data. A part of the device will be a USB interface for configuration and uploading calibration curves from a PC application, which is designed by diploma student.

Thesis describes the design and realization of linearization display unit that will receive pulses from a gear flow meter KRACHT whose frequency corresponds to a flow. The measured value is flow that nonlinearly depends on the temperature and viscosity of the medium. The dependency is described by calibration curves for each flow meter. The task of the diploma thesis is to design, implement, and verify the operation of the display unit, which calculates the actual flow rate from the input data (pulse rate, temperature, viscosity, and flow meter calibration curves). The device will contain a suitable user interface to display all measured values, inputs for connecting measuring sensors, and outputs for transmission of measured data. A part of the device will be a USB interface for configuration and uploading calibration curves from a PC application, which is designed by diploma student.

Steelmaking industry, one of the most electricity-intensive industrial processes, is seeking for new approaches to improve its competitiveness in terms of energy savings by taking advantage of the volatile electricity prices. This fluctuation in the price is mainly caused by the increasing share of renewable energy sources, the liberalization of energy markets and the growing demand of the energy. Therefore, making the production scheduling of steelmaking processes with knowledge about the cost of the energy may lead to significant cost savings in the electricity bills. With this aim in mind, different models are developed in this project in order to improve the existing monolithic models (continuous-time based scheduling) to find an efficient formulation of accounting for electricity consumption and also to expand them with more detailed scheduling of Electric Arc Furnace stage in the production process. The optimization of the energy cost with multiple electricity sources and contracts and the production planning are usually done as stand-alone optimizers due to their complexity, therefore as a new approach in addition to the monolithic model an iterative framework is developed in this work. The idea to integrate the two models in an iterative manner has potential to be useful in the industry due to low effort for reformulation of existing models. The implemented framework uses multiparametric programming together with bilevel programming in order to direct the schedule to find a compromise between the production constraints and goals, and the energy cost. To ensure applicability heuristic approaches are also examined whenever full sized models are not meeting computational performance requirements. The results show that the monolithic model implemented has a considerable advantage in terms of computational time compared to the models in the literature and in some cases, the solution can be obtained in a few minutes instead of hours. In the contrary, the iterative framework shows a bad performance in terms of computational time when dealing with real world instances. For that matter a heuristic approach, which is easy to implement, is investigated based on coordination theory and the results show that it has a potential since it provides solutions close to the optimal solutions in a reasonable amount of time. Multiparametric programming is the main core of the iterative framework developed in this internship and it is not able to give the solutions for real world instances due to computational time limitations. This computational problem is related to the nature of the algorithm behind mixed integer multiparametric programming and its ability to handle the binary variables. Therefore, further work to this project is to develop new approaches to approximate multiparametric technique or develop some heuristics to approximate the mp-MILP solutions.

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This dissertation introduces desalination processes in general and multistage flash (MSF) and reverse osmosis (RO) in particular. It presents the fundamental and practical aspects of neural networks and provides an overview of their structures, topology, strengths, and limitations. This study includes the neural network applications to prediction problems of large-scale commercial MSF and RO desalination plants in conjunction with statistical techniques to identify the major independent variables to optimize the process performance. In contrast to several recent studies, this work utilizes actual operating data (not simulated) from a large-scale commercial MSF desalination plant (48 million gallonsper day capacity, MGPD) and RO plant (15 MGPD) located in Kuwait and the Kingdom of Saudi Arabia, respectively. We apply Neural Works Professional II/Plus (NeuralWare, 1993) and SAS (SAS Institute Inc., 1996) software to accomplish this task. This dissertation demonstrates how to apply modular and equation-solving approaches for steady-state and dynamic simulations of large-scale commercial MSF desalination plants using ASPEN PLUS (Advanced System for Process Engineering PLUS) and SPEEDUP (Simulation Program for Evaluation and Evolutionary Design of Unsteady Processes) marketed by Aspen Technology, Cambridge, MA. This work illustrates the development of an optimal operating envelope for achieving a stable operation of a commercial MSF desalination plant using the SPEEDUP model. We then discuss model linearization around nominal operating conditions and arrive at pairing schemes for manipulated and controlled variables by interaction analysis. Finally, this dissertation describes our experience in applying a commercial software, DynaPLUS, for combined steady-state and dynamic simulations of a commercial MSF desalination plant. This dissertation is unique and significant in that it reports the first comprehensive study of predictive modeling, simulation, and optimization of large-scale commercial desalination plants. It is the first detailed and comparative study of commercial desalination plants using both artificial intelligence and computer-aided design techniques. The resulting models are able to reproduce accurately the actual operating data and to predict the optimal operating conditions of commercial desalination plants. / Ph. D.

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The emphasis on higher data rates, spectral efficiency and cost reduction has driven the field towards linear modulation techniques such as quadrature phase shift keying (QPSK), quadrature amplitude modulation (QAM), wideband code division multiple access (WCDMA), and orthogonal frequency division multiplexing (OFDM). The result is a complex signal with a non-constant envelope and a high peak-to-average power ratio. This characteristic makes these signals particularly sensitive to the intrinsic nonlinearity of the RF power amplifier (PA) in the transmitter. The nonlinearity will generate intermodulation (IMD) components, also referred to as out-of-band emission or spectral re-growth, which interfere with adjacent channels. Such distortion, or so called Adjacent Channel Interference (ACI), is strictly limited by FCC and ETSI regulations. Meanwhile, the nonlinearity also causes in-band distortion which degrades the bit error rate performance. Typically, the required linearity can be achieved either by reducing power efficiency or by using linearization techniques. For a Class-A PA, simply “backing off” the input power level can improve linearity; however, for high peak to average power ration (PAPR) signals, this normally reduces the power efficiency down to 10% while increasing heat dissipation up to 90%.When considering the vast number of base stations that wireless operators need to account for, increasing power consumption, or in other words, power back-off is not a viable tradeoff. Therefore, amplifier linearization has become an important technology and a desirable alternative to backing-off an amplifier in modern communications systems. In this work, a novel adaptive algorithm is presented for predistorter linearization of power amplifiers. This algorithm uses Pade-Chebyshev polynomials and a QR decomposition followed by back substitution to find the pre-distorter coefficients.This algorithm is implemented on a Field Programmable Gate Array (Stratix 1S80).The implementation provides improved linearization and also runs the algorithm fast enough so that the adaptive part can be done quickly. Yet another challenge was the integration of a transmitter, receiver and this adaptive algorithm into a single FPGA chip and its communication with a base station. The work thus presents a novel pre-distortion implementation technique using an FPGA and a soft processor (Nios 2) which provides significant intermodulation distortion suppression.

This thesis work consists of two research projects. The first project presented is on waveform design for car radars. These radars are used to detect other vehicles to avoid collision. In this project, we attempt to find the best waveform that distinguishes large objects from small ones. This helps the radar system reach more reliable decisions. We consider several models of the problem with varying complexity. For each model, we present optimization results calculated under various constraints regarding how the waveform is generated and how the reflected signal is processed. The results show that changing the radar waveform can result in better target classification. The second project is about digital-to-analog converter (DAC) linearization. Ideally, DACs have a linear input-output relation. In practice, however, this relation is nonlinear which may be harmful for many applications. A more linear input-output relation can be achieved by modifying the input to a DAC. This method, called predistortion, requires a good understanding of how DAC errors contribute to the nonlinearity. Assuming a simple DAC model, we investigate how different error functions lead to different types of nonlinearities through theoretical analyses and supporting computer simulations. We present our results in terms of frequency spectrum calculations. We show that the nonlinearity observed at the output strongly depends on how the error is modeled. These results are helpful in designing a predistorter for linearization.