Mixed-signal designs are becoming more and more complex every day. In order to adapt to the new market requirements, a formal process for design and verification of mixed signal systems i. e. top-down design and bottom-up verification methodology is required. This methodology has already been established for digital design. The goal of this research is to propose a new design methodology for mixed signal systems. In the first two chapters of this thesis, the need for a mixed signal design flow based on top-down design methodology will be discussed. The proposed design flow is based on behavioral modeling of the mixed signal system using one of the mixed signal behavioral modeling languages. These models can be used for design and verification through different steps of the design from system level modeling to final physical design. The other advantage of the proposed flow is analog and digital co-design. In the remaining chapters of this thesis, the proposed design flow was verified by designing an 800 MHz mixed signal PLL. The PLL uses a charge pump phase frequency detector, a single capacitor loop filter, and a feed forward error correction architecture using an active damping control circuit instead of passive resistor in loop filter. The design was done in 0. 18- <i>µ</i> m CMOS process technology.

Radio frequency (RF) communications are limited to a number of frequency bands scattered over the radio spectrum. Applications over such bands increasingly require more versatile, data extensive wireless communications that leads to the necessity of high bandwidth efficient interfaces, operating over wideband frequency ranges. Whether for a base station or mobile device, the regulations and adequate transmission of such schemes place stringent requirements on the design of transmitter front-ends. Increasingly strenuous and challenging hardware design criteria are to be met, especially so in the design of power amplifiers (PA), the bottle neck of the transmitter’s design tradeoff between linearity and power efficiency. The power amplifier exhibits a nonideal behavior, characterized by both nonlinearity and memory effects, heavily affecting that tradeoff, and therefore requiring an effective linearization technique, namely Digital Predistortion (DPD). The effectiveness of the DPD is highly dependent on the modeling scheme used to compensate for the PA’s nonideal behavior. In fact, its viability is determined by the scheme’s accuracy and implementation complexity. Generic behavioral models for nonlinear systems with memory have been used, considering the PA as a black box, and requiring RF designers to perform extensive testing to determine the minimal complexity structure that achieves satisfactory results. This thesis first proposes a direct systematic approach based on the parallel Hammerstein structure to determine the exact number of coefficients needed in a DPD. Then a physical explanation of memory effects is detailed, which leads to a close-form expression for the characteristic behavior of the PA entirely based on circuit properties. The physical expression is implemented and tested as a modeling scheme. Moreover, a link between this formulation and the proven behavioral models is explored, namely the Volterra series and Memory Polynomial. The formulation shows the correlation between parameters of generic behavioral modeling schemes when applied to RF PAs and demonstrates redundancy based on the physical existence or absence of modeling terms, detailed for the proven Memory polynomial modeling and linearization scheme.

Complex engineering systems involve large numbers of functional elements. Each functional element can exhibit complex behavior itself. Ensuring the ability of such systems to meet the customer's needs and requirements requires modeling the behavior of these systems. Behavioral modeling allows a quantitative assessment of the ability of a system to meet specific requirements. However, modeling the behavior of complex systems is difficult due to the complexity of the elements involved and more importantly the complexity of these elements' interactions. In prior work, formal functional modeling techniques have been applied as a means of performing a qualitative decomposition of systems to ensure that needs and requirements are addressed by the functional elements of the system. Extending this functional decomposition to a quantitative representation of the behavior of a system represents a significant opportunity to improve the design process of complex systems. To this end, a functionality-based behavioral modeling framework is proposed along with a sensitivity analysis method to support the design process of complex systems. These design tools have been implemented in a computational framework and have been used to model the behavior of various engineering systems to demonstrate their maturity, application and effectiveness. The most significant result is a multi-fidelity model of a hybrid internal combustion-electric racecar powertrain that enabled a comprehensive quantitative study of longitudinal vehicle performance during various stages in the design process. This model was developed using the functionality-based framework and allowed a thorough exploration of the design space at various levels of fidelity. The functionality-based sensitivity analysis implemented along with the behavioral modeling approach provides measures similar to a variance-based approach with a computation burden of a local approach. The use of a functional decomposition in both the behavioral modeling and sensitivity analysis significantly contributes to the flexibility of the models and their application in current and future design efforts. This contribution was demonstrated in the application of the model to the 2009 Texas A&M Formula Hybrid powertrain design.

A microwave module supplying up-to 1 Watt output power at 2.4-2.5 GHz frequency band was investigated. First the module was operated at low power levels and the output power was predicted using the small signal S-parameters of the module. A method was developed to obtain its large signal model using Advanced Design System (ADS) simulator&rsquo / s nonlinear analyses facilities. Later using the large signal model of the module simulations carried out to obtain larger powers up-to 1 Watt. The implementation of the module was performed using the SMD components on a microstrip substrate and the characteristics of the module were compared to the ones obtained using simulation results.

In this thesis we contribute to the Progress IDE, an integrated development enviroment for real-time embedded systems and more precisely to the REMES toolchain, a set of tools to enabling construction and analysis of embedded system behavior models. The contribution aims to facilitate the formal analysis of behavioral models, so that certain extra-functional properties might be verified during early stages of development. Previous work in the field proposes use of the Priced Timed Automata framework for verification of such properties. The thesis outlines the main points where the current toolchain should be extended in order to allow formal analysis of modeled components. Result of the work is a prototype, which minimizes the manual efforts of system designer by model to model transformations and provides seamless integration with existing tools for formal analysis.

In this thesis we contribute to the Progress IDE, an integrated development enviroment for real-time embedded systems and more precisely to the REMES toolchain, a set of tools to enabling construction and analysis of embedded system behavior models. The contribution aims to facilitate the formal analysis of behavioral models, so that certain extra-functional properties might be verified during early stages of development. Previous work in the field proposes use of the Priced Timed Automata framework for verification of such properties. The thesis outlines the main points where the current toolchain should be extended in order to allow formal analysis of modeled components. Result of the work is a prototype, which minimizes the manual efforts of system designer by model to model transformations and provides seamless integration with existing tools for formal analysis.

abstract: The pervasive use of the Web has connected billions of people all around the globe and enabled them to obtain information at their fingertips. This results in tremendous amounts of user-generated data which makes users traceable and vulnerable to privacy leakage attacks. In general, there are two types of privacy leakage attacks for user-generated data, i.e., identity disclosure and private-attribute disclosure attacks. These attacks put users at potential risks ranging from persecution by governments to targeted frauds. Therefore, it is necessary for users to be able to safeguard their privacy without leaving their unnecessary traces of online activities. However, privacy protection comes at the cost of utility loss defined as the loss in quality of personalized services users receive. The reason is that this information of traces is crucial for online vendors to provide personalized services and a lack of it would result in deteriorating utility. This leads to a dilemma of privacy and utility. Protecting users' privacy while preserving utility for user-generated data is a challenging task. The reason is that users generate different types of data such as Web browsing histories, user-item interactions, and textual information. This data is heterogeneous, unstructured, noisy, and inherently different from relational and tabular data and thus requires quantifying users' privacy and utility in each context separately. In this dissertation, I investigate four aspects of protecting user privacy for user-generated data. First, a novel adversarial technique is introduced to assay privacy risks in heterogeneous user-generated data. Second, a novel framework is proposed to boost users' privacy while retaining high utility for Web browsing histories. Third, a privacy-aware recommendation system is developed to protect privacy w.r.t. the rich user-item interaction data by recommending relevant and privacy-preserving items. Fourth, a privacy-preserving framework for text representation learning is presented to safeguard user-generated textual data as it can reveal private information. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2020

Einsatz von Optimierungen innerhalb von Pro/Engineer zur Konstruktion von Kanälen

Der Vortrag zeigt die Einsatzmöglichkeiten und den Nutzen von BMX auf. Zudem wird die Vorgehensweise bei BMX-Analysen erläutert. Die Neuerungen in der Pro/ENGINEER-Version Wildfire 5 werden vorgestellt. Anhand von 3 Live-Demonstrationen wird die Anwendung von BMX gezeigt: - Flaschen-Volumen untersuchen - Messerkopf dynamisch auswuchten - Sichtfelduntersuchung

No description available.