Refine
Document Type
- Master's Thesis (14) (remove)
Language
- English (14) (remove)
Has Fulltext
- yes (14)
Keywords
- 2 D environment Laser data (1)
- Analog-Digital-Umsetzer (1)
- Analog-to-digital-Converter ; Spice Simulation ; Analog integrated circuit design ; (1)
- Arbeitsablauf (1)
- Auftragsabwicklung (1)
- Autonomous Mobile Indoor Robot (1)
- Client Management System ; Haaland Internet Productions ; Client Relationship Management ; workflow (1)
- Codierung (1)
- Corporate Development (1)
- Deep Transformation Model (1)
- Deep learning (1)
- Digitally re-programmable space (1)
- Erkennung (1)
- Extended object tracking (1)
- FACS (1)
- Facial Action Coding System (1)
- Gaussian inverse Wishart PHD Filter (1)
- Gaussian mixture (1)
- Internet of Things (1)
- Interpretability (1)
- Knowledge Management (1)
- Kundenmanagement (1)
- Laser sensor (1)
- Laser-Sensor (1)
- Laserimpuls (1)
- Lasermesstechnik (1)
- Life cycle assessment (1)
- Likelihood based partitioning (1)
- Machine Learning (1)
- Markov chain monte carlo (1)
- Maun Science Park (1)
- Mimik (1)
- Multiple Extended Object Tracking (1)
- Mund-Kiefer-Gesichtsbereich (1)
- Navigation (1)
- Navigieren (1)
- Normalizing Flow (1)
- Objekterkennung (1)
- PHD filter (1)
- Physiognomik (1)
- Platform (1)
- Probabilistic modeling (1)
- Radar (1)
- Regression (1)
- Roboter (1)
- SPICE <Programm> (1)
- Sampling based partitioning (1)
- Smart Building (1)
- Smart City (1)
- Spline extension model (1)
- Statistics (1)
- Sustainable construction (1)
- Tall building structures (1)
- Transistortechnologie (1)
- Un-certainty (1)
- Wissensmanagement (1)
- distance measurement (1)
- facial expression recognition ; action unit recognition (1)
- low-level feature extraction (1)
- support vector machines (1)
- Überwachungsradar (1)
Cities around the world are facing an increasing number of global and local challenges, such as climate change and scarcity of raw materials. At the same time trends like digitalization, globalization and networking gain in importance. For this reason, cities have started imple-menting smart solutions within the urban structure in order to evolve towards a Smart City. In Botswana, the Maun Science Park is intended to provide a best practice approach for a Bot-swanan Smart City. Since Smart City concepts have to be specifically tailored to local condi-tions, the first main goal of this thesis is to develop a synthesis concept for the Maun Science Park. A key problem in cities is the utilization of space, which is further intensified by increasing urbanization and population growth. Therefore, the second main goal is to develop approaches of (digitally) re-programmable space to use available areas intelligently and optimized.
Within the thesis, human-centered design has been applied as structure-giving methodology. By clarifying relevant Smart City contents, considering reference examples as well as identify-ing local challenges and requirements, an appropriate concept has been developed with hu-man-focus. Furthermore, the methodologies of literature research and expert interviews have been used as input in the individual human-centered design phases. In combination with an innovation funnel, the methodology human-centered design forms the structure of the thesis.
In total, ten main solution areas and 37 sub-segments have been identified for the synthesis concept of Maun Science Park. Additionally, a concept for Smart Buildings has been devel-oped as a part of the synthesis concept and as an essential infrastructure component of the Maun Science Park (three main segments, 16 sub-segments). Based on expert input, a priori-tization has been determined by evaluating the impact and economic affordability of the indi-vidual sub-areas. Moreover, individual key areas have been highlighted by identifying direct interactions between sub-segments and on the basis of expert input – these are particularly related to the segments Smart Data and Smart People. Besides the synthesis concept, ap-proaches of (digitally) re-programmable space have been created. Thereby, ten approaches refer to the conversion, reuse or expansion abilities of space within daily, weekly or life cycle. In addition, the conventional (digitally) re-programmable space idea has been extended by two new considerations – “multi-purpose use of built-up space” and “concept programming in the planning phase”. Finally, within an overall consideration – synthesis concept combined with approaches of (digitally) re-programmable space – the added value of the developed contents has been outlined, positive and negative aspects have been identified within a SWOT analysis and the business model of the Maun Science Park approach has been verified in a Business Model Canvas.
Through explicit elaboration, classification and prioritization of solution areas, the developed concept can serve as a basis for further project steps. Based on the defined requirements of the sub-segments, solutions can be developed with regard to the entire Smart City context.
In Maun, Botswana, a self-sufficient, sustainable and future-oriented district will be created, the Maun Science Park. Within this project, several 5-8 storey smart homes shall be built in sustainable construction. The aim of this thesis is to develop a sustainable structural concept for those homes of the Maun Science Park. In a first step, the general basics for tall building structures and sustainable construction were established. Based on those fundamentals, criteria for the structural requirements, the ecological as well as the social sustainability of a structural design could be defined. Subsequently, four structural systems were drafted: a concrete core structure, a steel shear frame structure, a rammed earth shear wall structure and a wooden diagrid structure. In addition to the pre-dimensioning of the systems, a life cycle assessment was set up to evaluate the ecological sustainability of the designs. With the help of a utility value analysis, the wooden diagrid structure was determined as the preferred variant. The comparison of the designs also allows to draw general conclusions for the development of sustainable tall building structures. The results of the life cycle assessment show the advantage of wood as an ecological building material over industrially manufactured building materials, such as steel and concrete. Whereas rammed earth, a likewise ecological building material, is not convincing due to its low strength. In general, a balance is created in the life cycle assessment between ecological and industrially manufactured products in regard of strength and environmental impact. In terms of social sustainability, the design of the structure system can significantly influence the flexibility and use of local resources. However, due to the diversity of sustainable construction, the development of a structural system should be linked to an overarching sustainability concept that takes architecture and stakeholders into account.
This thesis deals with background, theory, design, layout and experimental test results of an analogue CMOS VLSI current-mode analog-to-digital converter. This system supports a project, whose goal it is to build a biologically relevant model of synaptic plasticity, named the Artificial Synapse. A critical part of the design, which is based on analogue CMOS VLSI circuits, is the ability to activate a discrete number of channels by sampling an analogue signal. Since currents are the signal of interest and transistors are biased in weak inversion (subthreshold regime), the system requires a current mode A/D circuit that it can operate at ultra-low power and current levels. To meet this need, two new innovative A/D converter approaches are proposed to replace the system’s previous A/D converter design which suffered from a non-linear resolution, uncoded output code and heavy bit oscillations. The initial technical requirements and key criteria for the new converter comprise a resolution of one nano ampere, an input current range between 0 – 100nA, conversion frequencies of up to 5kHz, and a power supply voltage of less than 1.5V. Temperature range, space occupation and power dissipation aspects were not specified due to the early stage of the related Artificial Synapse project. The novel converters both produce seven bit thermometer codes, their functional principle can be best described as current mode flash analog-to-digital converters (ADCs). Due to the fact that the input signal is in the area of a subthreshold current, it is selfevident that the A/D converter design should operate at a subthreshold realm. To support low power operation, clocks or high currents could not be used and were excluded from the design from the very start. To encode the thermometer code into standard binary code, a seven-to-three encoder was designed and integrated on the chip. In October 2003, the design was submitted for production to the MOSIS circuit fabrication service. The AMI Semiconductor 1.5 micron ABN CMOS process was chosen to manufacture the chip. When it was returned in January 2004, simulation results showed that both new A/D converter approaches accomplished excellent results which were expected from SPICE simulation results. With the new chip installed, it became possible to resolve input currents as small as one nano ampere and achieve conversion frequencies of up to 5kHz. The circuits also both meet the requirements which were set at the beginning of the project to operate at a power supply voltage of less than 1.5V, processing input currents in the range between 0 – 100nA. A prototype printed circuit board (PCB) was developed, produced and employed for experiments with the chip. The major application of this test-bed is the ability to generate and measure extremely low currents with high precision. This enables the monitoring of the very small currents that are processed by the chip.
This thesis deals with the object tracking problem of multiple extended objects. For instance, this tracking problem occurs when a car with sensors drives on the road and detects multiple other cars in front of it. When the setup between the senor and the other cars is in a such way that multiple measurements are created by each single car, the cars are called extended objects. This can occur in real world scenarios, mainly with the use of high resolution sensors in near field applications. Such a near field scenario leads a single object to occupy several resolution cells of the sensor so that multiple measurements are generated per scan. The measurements are additionally superimposed by the sensor’s noise. Beside the object generated measurements, there occur false alarms, which are not caused by any object and sometimes in a sensor scan, single objects could be missed so that they not generate any measurements.
To handle these scenarios, object tracking filters are needed to process the sensor measurements in order to obtain a stable and accurate estimate of the objects in each sensor scan. In this thesis, the scope is to implement such a tracking filter that handles the extended objects, i.e. the filter estimates their positions and extents. In context of this, the topic of measurement partitioning occurs, which is a pre-processing of the measurement data. With the use of partitioning, the measurements that are likely generated by one object are put into one cluster, also called cell. Then, the obtained cells are processed by the tracking filter for the estimation process. The partitioning of measurement data is a crucial part for the performance of tracking filter because insufficient partitioning leads to bad tracking performance, i.e. inaccurate object estimates.
In this thesis, a Gaussian inverse Wishart Probability Hypothesis Density (GIW-PHD) filter was implemented to handle the multiple extended object tracking problem. Within this filter framework, the number of objects are modelled as Random Finite Sets (RFSs) and the objects’ extent as random matrices (RM). The partitioning methods that are used to cluster the measurement data are existing ones as well as a new approach that is based on likelihood sampling methods. The applied classical heuristic methods are Distance Partitioning (DP) and Sub-Partitioning (SP), whereas the proposed likelihood-based approach is called Stochastic Partitioning (StP). The latter was developed in this thesis based on the Stochastic Optimisation approach by Granström et al. An implementation, including the StP method and its integration into the filter framework, is provided within this thesis.
The implementations, using the different partitioning methods, were tested on simulated random multi-object scenarios and in a fixed parallel tracking scenario using Monte Carlo methods. Further, a runtime analysis was done to provide an insight into the computational effort using the different partitioning methods. It emphasized, that the StP method outperforms the classical partitioning methods in scenarios, where the objects move spatially close. The filter using StP performs more stable and with more accurate estimates. However, this advantage is associated with a higher computational effort compared to the classical heuristic partitioning methods.