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Compared to diesel or gasoline, using compressed natural gas as a fuel allows for significantly decreased carbon dioxide emissions. With the benefits of this technology fully exploited, substantial increases of engine efficiency can be expected in the near future. However, this will lead to exhaust gas temperatures well below the range required for the catalytic removal of residual methane, which is a strong greenhouse gas. By combination with a countercurrent heat exchanger, the temperature level of the catalyst can be raised significantly in order to achieve sufficient levels of methane conversion with minimal additional fuel penalty. This thesis provides fundamental theoretical background of these so-called heat-integrated exhaust purification systems. On this basis, prototype heat exchangers and appropriate operating strategies for highly dynamic operation in passenger cars are developed and evaluated.
Service robots need to be aware of persons in their vicinity in order to interact with them. People tracking enables the robot to perceive persons by fusing the information of several sensors. Most robots rely on laser range scanners and RGB cameras for this task. The thesis focuses on the detection and tracking of heads. This allows the robot to establish eye contact, which makes interactions feel more natural.
Developing a fast and reliable pose invariant head detector is challenging. The head detector that is proposed in this thesis works well on frontal heads, but is not fully pose-invariant. This thesis further explores adaptive tracking to keep track of heads that do not face the robot. Finally, head detector and adaptive tracker are combined within a new people tracking framework and experiments show its effectiveness compared to a state-of the-art system.
After more than three decades of electronic design automation, most layouts for analog integrated circuits are still handcrafted in a laborious manual fashion today. Obverse to the highly automated synthesis tools in the digital domain (coping with the quantitative difficulty of packing more and more components onto a single chip – a desire well known as More Moore), analog layout automation struggles with the many diverse and heavily correlated functional requirements that turn the analog design problem into a More than Moore challenge. Facing this qualitative complexity, seasoned layout engineers rely on their comprehensive expert knowledge to consider all design constraints that uncompromisingly need to be satisfied. This usually involves both formally specified and nonformally communicated pieces of expert knowledge, which entails an explicit and implicit consideration of design constraints, respectively.
Existing automation approaches can be basically divided into optimization algorithms (where constraint consideration occurs explicitly) and procedural generators (where constraints can only be taken into account implicitly). As investigated in this thesis, these two automation strategies follow two fundamentally different paradigms denoted as top-down automation and bottom-up automation. The major trait of top-down automation is that it requires a thorough formalization of the problem to enable a self-intelligent solution finding, whereas a bottom-up automatism –controlled by parameters– merely reproduces solutions that have been preconceived by a layout expert in advance. Since the strengths of one paradigm may compensate the weaknesses of the other, it is assumed that a combination of both paradigms –called bottom-up meets top-down– has much more potential to tackle the analog design problem in its entirety than either optimization-based or generator-based approaches alone.
Against this background, the thesis at hand presents Self-organized Wiring and Arrangement of Responsive Modules (SWARM), an interdisciplinary methodology addressing the design problem with a decentralized multi-agent system. Its basic principle, similar to the roundup of a sheep herd, is to let responsive mobile layout modules (implemented as context-aware procedural generators) interact with each other inside a user-defined layout zone. Each module is allowed to autonomously move, rotate and deform itself, while a supervising control organ successively tightens the layout zone to steer the interaction towards increasingly compact (and constraint compliant) layout arrangements. Considering various principles of self-organization and incorporating ideas from existing decentralized systems, SWARM is able to evoke the phenomenon of emergence: although each module only has a limited viewpoint and selfishly pursues its personal objectives, remarkable overall solutions can emerge on the global scale.
Several examples exhibit this emergent behavior in SWARM, and it is particularly interesting that even optimal solutions can arise from the module interaction. Further examples demonstrate SWARM’s suitability for floorplanning purposes and its application to practical place-and-route problems. The latter illustrates how the interacting modules take care of their respective design requirements implicitly (i.e., bottom-up) while simultaneously paying respect to high level constraints (such as the layout outline imposed top-down by the supervising control organ). Experimental results show that SWARM can outperform optimization algorithms and procedural generators both in terms of layout quality and design productivity. From an academic point of view, SWARM’s grand achievement is to tap fertile virgin soil for future works on novel bottom-up meets top-down automatisms. These may one day be the key to close the automation gap in analog layout design.
After more than three decades of electronic design automation, most layouts for analog integrated circuits are still handcrafted in a laborious manual fashion today. This book presents Self-organized Wiring and Arrangement of Responsive Modules (SWARM), a novel interdisciplinary methodology addressing the design problem with a decentralized multi-agent system. Its basic approach, similar to the roundup of a sheep herd, is to let autonomous layout modules interact with each other inside a successively tightened layout zone. Considering various principles of self-organization, remarkable overall solutions can result from the individual, local, selfish actions of the modules. Displaying this fascinating phenomenon of emergence, examples demonstrate SWARM’s suitability for floorplanning purposes and its application to practical place-and-route problems. From an academic point of view, SWARM combines the strengths of procedural generators with the assets of optimization algorithms, thus paving the way for a new automation paradigm called bottom-up meets top-down.