University, Diss., 2003--Tübingen.

Energieeffizienz hat in Forschung und Alltag eine zentrale Bedeutung. Arbeiten verschiedene elektronische Geräte zusammen, um gemeinsam eine Regelungsaufgabe zu lösen, müssen sie miteinander kommunizieren. Ein Beispiel aus dem Alltag sind Funk-Raumtemperaturregler, bei denen ein batteriebetriebener Temperatursensor und ein Heizungsaktor (Stellantrieb am Heizungsventil) über drahtlose Kommunikation zusammenarbeiten. Diese Kommunikation benötigt oft mehr Energie als der Betrieb der eigentlichen (elektronischen) Funktionalität der Teilsysteme. Energieeffizienter als die in Regelkreisen übliche periodische (äquidistante) Abtastung ist – durch eine Verringerung der Nachrichtenrate – eine ereignisbasierte Abtastung. Send-on-Delta-Abtastung ist die am weitesten verbreitete Art der ereignisbasierten Abtastung. Dabei wird der Wert der Regelgröße (im Beispiel die Raumtemperatur) nicht in konstanten Zeitintervallen übertragen, sondern nur dann, wenn er sich um einen bestimmten Betrag geändert hat. Der mit einem Anteil von über 90 % im Praxiseinsatz am weitesten verbreitete Reglertyp ist der PID-Regler, wobei die meisten als „PID-Regler“ bezeichneten Regler aus verschiedenen Gründen keinen D-Anteil (Differential-Anteil) verwenden und daher als „PI-Regler“ bezeichnet werden können. Die vorliegende Arbeit verfolgt das Ziel, systematisch zu untersuchen, wie man PI-Regler einstellen sollte, um neben dem Erreichen einer hohen Regelgüte auch die Vorteile der Send-on-Delta-Abtastung bezüglich der Netzlastreduktion und Energieeffizienz bestmöglich auszunutzen. Die „Gewichtung“ dieser sich teilweise widersprechenden Kriterien ist anwendungsspezifisch einstellbar. / Energy efficiency is very important both in science and everyday life. If different electronic devices work together, for example for solving a control task together, they have to communicate with each other. An everyday life example are room temperature controllers using radio communication between a battery-powered temperature sensor and a heating actuator. This communication often needs more energy than the operation of the actual (electronic) functionality of the components. More energy-efficient than the commonly used periodic sampling is event-based sampling, due to the reduction of the message rate. Send-on-delta sampling is the most widely-known kind of event-based sampling. In that case, the value of the controlled variable (e.g. the room temperature) is not transmitted equidistantly but only when it has changed by a specific amount. The most successful controller in practice is the PID controller. The most so-called “PID controllers” do not use the D part (differential action) for several reasons and can therefore be called “PI controllers”. This work analyses systematically how the parameters of a PI controller should be tuned to reach besides a high control quality also a good exploitation of the advantages of send-on-delta sampling regarding network load reduction and energy efficiency. The “weighting” of these partially contradicting criteria is application specifically adjustable.

Proximal methods are iterative optimization techniques for functionals, J = J1 + J2, consisting of a differentiable part J2 and a possibly nondifferentiable part J1. In this thesis proximal methods for finite- and infinite-dimensional optimization problems are discussed. In finite dimensions, they solve l1- and TV-minimization problems that are effectively applied to image reconstruction in magnetic resonance imaging (MRI). Convergence of these methods in this setting is proved. The proposed proximal scheme is compared to a split proximal scheme and it achieves a better signal-to-noise ratio. In addition, an application that uses parallel imaging is presented. In infinite dimensions, these methods are discussed to solve nonsmooth linear and bilinear elliptic and parabolic optimal control problems. In particular, fast convergence of these methods is proved. Furthermore, for benchmarking purposes, truncated proximal schemes are compared to an inexact semismooth Newton method. Results of numerical experiments are presented to demonstrate the computational effectiveness of our proximal schemes that need less computation time than the semismooth Newton method in most cases. Results of numerical experiments are presented that successfully validate the theoretical estimates. / Proximale Methoden sind iterative Optimierungsverfahren für Funktionale J = J1 +J2, die aus einem differenzierbaren Teil J2 und einem möglicherweise nichtdifferenzierbaren Teil bestehen. In dieser Arbeit werden proximale Methoden für endlich- und unendlichdimensionale Optimierungsprobleme diskutiert. In endlichen Dimensionen lösen diese `1- und TV-Minimierungsprobleme welche erfolgreich in der Bildrekonstruktion der Magnetresonanztomographie (MRT) angewendet wurden. Die Konvergenz dieser Methoden wurde in diesem Zusammenhang bewiesen. Die vorgestellten proximalen Methoden wurden mit einer geteilten proximalen Methode verglichen und konnten ein besseres Signal-Rausch-Verhältnis erzielen. Zusätzlich wurde eine Anwendung präsentiert, die parallele Bildgebung verwendet. Diese Methoden werden auch für unendlichdimensionale Probleme zur Lösung von nichtglatten linearen und bilinearen elliptischen und parabolischen optimalen Steuerungsproblemen diskutiert. Insbesondere wird die schnelle Konvergenz dieser Methoden bewiesen. Außerdem werden abgeschnittene proximale Methoden mit einem inexakten halbglatten Newtonverfahren verglichen. Die numerischen Ergebnisse demonstrieren die Effektivität der proximalen Methoden, welche im Vergleich zu den halbglatten Newtonverfahren in den meisten Fällen weniger Rechenzeit benötigen. Zusätzlich werden die theoretischen Abschätzungen bestätigt.

Originally presented as the author's thesis--Universität Hannover. / Includes bibliographical references.

Stuttgart, Univ., Diss., 2004.

Zugl.: Rostock, Univ., Diss., 2007

Energieeffizienz hat in Forschung und Alltag eine zentrale Bedeutung. Arbeiten verschiedene elektronische Geräte zusammen, um gemeinsam eine Regelungsaufgabe zu lösen, müssen sie miteinander kommunizieren. Ein Beispiel aus dem Alltag sind Funk-Raumtemperaturregler, bei denen ein batteriebetriebener Temperatursensor und ein Heizungsaktor (Stellantrieb am Heizungsventil) über drahtlose Kommunikation zusammenarbeiten. Diese Kommunikation benötigt oft mehr Energie als der Betrieb der eigentlichen (elektronischen) Funktionalität der Teilsysteme. Energieeffizienter als die in Regelkreisen übliche periodische (äquidistante) Abtastung ist – durch eine Verringerung der Nachrichtenrate – eine ereignisbasierte Abtastung. Send-on-Delta-Abtastung ist die am weitesten verbreitete Art der ereignisbasierten Abtastung. Dabei wird der Wert der Regelgröße (im Beispiel die Raumtemperatur) nicht in konstanten Zeitintervallen übertragen, sondern nur dann, wenn er sich um einen bestimmten Betrag geändert hat. Der mit einem Anteil von über 90 % im Praxiseinsatz am weitesten verbreitete Reglertyp ist der PID-Regler, wobei die meisten als „PID-Regler“ bezeichneten Regler aus verschiedenen Gründen keinen D-Anteil (Differential-Anteil) verwenden und daher als „PI-Regler“ bezeichnet werden können. Die vorliegende Arbeit verfolgt das Ziel, systematisch zu untersuchen, wie man PI-Regler einstellen sollte, um neben dem Erreichen einer hohen Regelgüte auch die Vorteile der Send-on-Delta-Abtastung bezüglich der Netzlastreduktion und Energieeffizienz bestmöglich auszunutzen. Die „Gewichtung“ dieser sich teilweise widersprechenden Kriterien ist anwendungsspezifisch einstellbar. / Energy efficiency is very important both in science and everyday life. If different electronic devices work together, for example for solving a control task together, they have to communicate with each other. An everyday life example are room temperature controllers using radio communication between a battery-powered temperature sensor and a heating actuator. This communication often needs more energy than the operation of the actual (electronic) functionality of the components. More energy-efficient than the commonly used periodic sampling is event-based sampling, due to the reduction of the message rate. Send-on-delta sampling is the most widely-known kind of event-based sampling. In that case, the value of the controlled variable (e.g. the room temperature) is not transmitted equidistantly but only when it has changed by a specific amount. The most successful controller in practice is the PID controller. The most so-called “PID controllers” do not use the D part (differential action) for several reasons and can therefore be called “PI controllers”. This work analyses systematically how the parameters of a PI controller should be tuned to reach besides a high control quality also a good exploitation of the advantages of send-on-delta sampling regarding network load reduction and energy efficiency. The “weighting” of these partially contradicting criteria is application specifically adjustable.

There is a variety of tools to measure the performance of Linux systems and the applications running on them. However, the resulting performance data is often presented in plain text format or only with a very basic user interface. For large systems with many cores and concurrent threads, it is increasingly difficult to present the data in a clear way for analysis. Moreover, certain performance analysis and debugging tasks require the use of a high-resolution time-line based approach, again entailing data visualization challenges. Tools in the area of High Performance Computing (HPC) have long been able to scale to hundreds or thousands of parallel threads and help finding performance anomalies. We therefore present a solution to gather performance data using Linux performance monitoring interfaces. A combination of sampling and careful instrumentation allows us to obtain detailed performance traces with manageable overhead. We then convert the resulting output to the Open Trace Format (OTF) to bridge the gap between the recording infrastructure and HPC analysis tools. We explore ways to visualize the data by using the graphical tool Vampir. The combination of established Linux and HPC tools allows us to create an interface for easy navigation through time-ordered performance data grouped by thread or CPU and to help users find opportunities for performance optimizations.

There is a variety of tools to measure the performance of Linux systems and the applications running on them. However, the resulting performance data is often presented in plain text format or only with a very basic user interface. For large systems with many cores and concurrent threads, it is increasingly difficult to present the data in a clear way for analysis. Moreover, certain performance analysis and debugging tasks require the use of a high-resolution time-line based approach, again entailing data visualization challenges. Tools in the area of High Performance Computing (HPC) have long been able to scale to hundreds or thousands of parallel threads and help finding performance anomalies. We therefore present a solution to gather performance data using Linux performance monitoring interfaces. A combination of sampling and careful instrumentation allows us to obtain detailed performance traces with manageable overhead. We then convert the resulting output to the Open Trace Format (OTF) to bridge the gap between the recording infrastructure and HPC analysis tools. We explore ways to visualize the data by using the graphical tool Vampir. The combination of established Linux and HPC tools allows us to create an interface for easy navigation through time-ordered performance data grouped by thread or CPU and to help users find opportunities for performance optimizations.

Ubiquitous Internet of Things (IoT) is destined to connect everybody and everything on a never-before-seen scale. Such networks, however, have to tackle the inherent issues created by the presence of very heterogeneous data transmissions over the same shared network. This very diverse communication, in turn, produces network packets of various sizes ranging from very small sensory readings to comparatively humongous video frames. Such a massive amount of data itself, as in the case of sensory networks, is also continuously captured at varying rates and contributes to increasing the load on the network itself, which could hinder transmission efficiency. However, they also open up possibilities to exploit various correlations in the transmitted data due to their sheer number. Reductions based on this also enable the networks to keep up with the new wave of big data-driven communications by simply investing in the promotion of select techniques that efficiently utilize the resources of the communication systems. One of the solutions to tackle the erroneous transmission of data employs linear coding techniques, which are ill-equipped to handle the processing of packets with differing sizes. Random Linear Network Coding (RLNC), for instance, generates unreasonable amounts of padding overhead to compensate for the different message lengths, thereby suppressing the pervasive benefits of the coding itself. We propose a set of approaches that overcome such issues, while also reducing the decoding delays at the same time. Specifically, we introduce and elaborate on the concept of macro-symbols and the design of different coding schemes. Due to the heterogeneity of the packet sizes, our progressive shortening scheme is the first RLNC-based approach that generates and recodes unequal-sized coded packets. Another of our solutions is deterministic shifting that reduces the overall number of transmitted packets. Moreover, the RaSOR scheme employs coding using XORing operations on shifted packets, without the need for coding coefficients, thus favoring linear encoding and decoding complexities. Another facet of IoT applications can be found in sensory data known to be highly correlated, where compressed sensing is a potential approach to reduce the overall transmissions. In such scenarios, network coding can also help. Our proposed joint compressed sensing and real network coding design fully exploit the correlations in cluster-based wireless sensor networks, such as the ones advocated by Industry 4.0. This design focused on performing one-step decoding to reduce the computational complexities and delays of the reconstruction process at the receiver and investigates the effectiveness of combined compressed sensing and network coding.