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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.
Im Projekt "Heat4SmartGrid" soll untersucht werden, ob und wie mit Hilfe von Wärmepumpen der Anteil erneuerbarer Energien an der Wärmeversorgung in Baden-Württemberg (BW) gesteigert werden und gleichzeitig das Verteilnetz durch eine intelligente Steuerung der Wärmepumpensysteme entlastet werden kann. Hierzu ist im AP 1 für das Jahr 2050 ein Wärmebedarf in BW von 35 TWh errechnet worden, bei 40 TWh im Jahr 2030. Im Vergleich zum Jahr 2015 ergibt sich so ein Rückgang um 30 % zum Jahr 2030 und bis zum Jahr 2050 um 40 %. Weiterhin steigt auf Grund von energetischer Sanierung im Gebäudebestand das technische Potenzial für Wärmepumpen, ausgehend von 8 TWh im Jahr 2015, auf 20 TWh bis 2030 und auf 23 TWh bis 2040. Insgesamt könnten so 63 % aller Wohnanteile in BW durch Wärmepumpen mit thermischer Energie versorgt werden. Der Einsatz von Wärmepumpensystemen ist somit ein wichtiger Baustein für das Gelingen der Wärmewende. Zur Steuerung der Wärmepumpen sind in AP 2 Betriebsmodi in Abhängigkeit von Anwendung und Gebäudetyp entwickelt worden. Diese werden mittels Korrelationsfunktionen für die Heizleistung für Luft-Wasser- und Sole-Wasser-Wärmepumpen bestimmt. Hierauf aufbauend sind für die in AP 1 ermittelten Gebäudetypen die erreichbare Jahresarbeitszahl der beiden Wärmepumpentechnologien ermittelt worden. Zur intelligenten system- und netzdienlichen Steuerung dieser Wärmepumpensysteme werden Prognosen über die lokale Erzeugung und den lokalen Verbrauch benötigt, die in AP 5 erarbeitet werden. In Abhängigkeit der Prognose-anwendung sind sowohl univariate (elektrische Last und thermische Brauchwarmwasserlast) als auch multivariate Prognosemodelle (PV-Erzeugung und thermische Heizwarmwasserlast) implementiert worden.
In dem vorliegenden Zwischenbericht sind die Arbeiten und Ergebnisse zusammengefasst, die im Rahmen des Projektes GalvanoFlex_BW während der ersten acht Projektmonate der insgesamt 2 1/2 jährigen Laufzeit durchgeführt und erzielt wurden. Ziel des Projektes ist die Untersuchung und Verbesserung der Energieeffizienz in Industrieunternehmen mit dem speziellen Fokus auf der Einführung stromoptimiert betriebener KWK-Anlagen. Entsprechend des Arbeits- und Zeitplans sind die Literaturrecherche, die Festlegung von Prozesstypen, die Datenerfassung, die Strategieentwickung zur stromoptimierten KWK, die Optimierung der Schnittstelle zwischen KWK und Gleichrichtern, der Aufbau der Branchenplattform und die sozial-wissenschaftliche Begleitforschung beschrieben. Der aktuelle Projektstand deckt sich dabei im Wesentlichen mit den Vorgaben aus dem vorgelegten Zeitplan.
In the last 20 years there have been major advances in autonomous robotics. In IoT (Industry 4.0), mobile robots require more intuitive interaction possibilities with humans in order to expand its field of applications. This paper describes a user-friendly setup, which enables a person to lead the robot in an unknown environment. The environment has to be perceived by means of sensory input. For realizing a cost and resource efficient Follow Me application we use a single monocular camera as low-cost sensor. For efficient scaling of our Simultaneous Localization and Mapping (SLAM) algorithm, we integrate an inertial measurement unit (IMU) sensor. With the camera input we detect and track a person. We propose combining state of the art deep learning with Convolutional Neural Network (CNN) and SLAM algorithms functionality on the same input camera image. Based on the output robot navigation is possible. This work presents the specification, workflow for an efficient development of the Follow Me application. Our application’s delivered point clouds are also used for surface construction. For demonstration, we use our platform SCITOS G5 equipped with the afore mentioned sensors. Preliminary tests show the system works robustly in the wild.
SLAM systems are mainly applied for robot navigation while research on feasibility for motion planning with SLAM for tasks like bin-picking, is scarce. Accurate 3D reconstruction of objects and environments is important for planning motion and computing optimal gripper pose to grasp objects. In this work, we propose the methods to analyze the accuracy of a 3D environment reconstructed using a LSD-SLAM system with a monocular camera mounted onto the gripper of a collaborative robot. We discuss and propose a solution to the pose space conversion problem. Finally, we present several criteria to analyze the 3D reconstruction accuracy. These could be used as guidelines to improve the accuracy of 3D reconstructions with monocular LSD-SLAM and other SLAM based solutions.
We present a new method for detecting gait disorders according to their stadium using cluster methods for sensor data. 21 healthy and 18 Parkinson subjects performed the time up and go test. The time series were segmented into separate steps. For the analysis the horizontal acceleration measured by a mobile sensor system was considered. We used dynamic time warping and hierarchical custering to distinguish the stadiums. A specificity of 92% was achieved.
Fitting 3D Morphable Face Models (3DMM) to a 2D face image allows the separation of face shape from skin texture, as well as correction for face expression. However, the recovered 3D face representation is not readily amenable to processing by convolutional neural networks (CNN). We propose a conformal mapping from a 3D mesh to a 2D image, which makes these machine learning tools accessible by 3D face data. Experiments with a CNN based face recognition system designed using the proposed representation have been carried out to validate the advocated approach. The results obtained on standard benchmarking data sets show its promise.
This paper investigates the evaluation of dense 3D face reconstruction from a single 2D image in the wild. To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans. In contrast to previous competitions or challenges, the aim of this new benchmark dataset is to evaluate the accuracy of a 3D dense face reconstruction algorithm using real, accurate and high-resolution 3D ground truth face scans. In addition to the dataset, we provide a standard protocol as well as a Python script for the evaluation. Last, we report the results obtained by three state-of-the-art 3D face reconstruction systems on the new benchmark dataset. The competition is organised along with the 2018 13th IEEE Conference on Automatic Face & Gesture Recognition.
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.
An interactive clothing design and a personalized virtual display with user’s own face are presented in this paper to meet the requirement of personalized clothing customization. A customer interactive clothing design approach based on genetic engineering ideas is analyzed by taking suit as an example. Thus, customers could rearrange the clothing style elements, chose available color, fabric and come up with their own personalized suit style. A web 3D customization prototype system of personalized clothing is developed based on the Unity3D and VR technology. The layout of the structure and functions combined with the flow of the system are given. Practical issues such as 3D face scanning, suit style design, fabric selection, and accessory choices are addressed also. Tests to the prototype system indicate that it could show realistic clothing and fabric effect and offer effective visual and customization experience to users.