Requirements of a Volvo car are stored in a requirements management system at Volvo Car Group (VCG). VCG recently implemented a new requirements management system, a system called SystemWeaver. Many different types of requirements are stored in the SystemWeaver software, where the requirements can only be described in text and pictures. However, some requirements are geometry-based, describing some type of shape or measurement in space that the car should fulfil. Geometry-based requirements are stored in Teamcenter and have two components, the requirement text and requirement geometry in the form of CAD-models. The models are used to illustrate the requirement in space. This master thesis examines the possibilities of connecting text-based requirements in SystemWeaver to requirement geometries. The technical aspects are studied as well as the organizational mechanisms of creating and changing a geometry-based requirement. To find a working solution, research relating to the issue gave input to the project. Furthermore, interviews were conducted at different departments at VCG to get insight in the working tasks of requirement management at the company. The project resulted in a concept of a new process, describing the actions of geometry-based requirement management and how requirement geometries should be connected to SystemWeaver. The new concept outlined the logical steps that are required to work with SystemWeaver and geometry-based requirements. The work has laid a foundation on which future studies can be conducted to further streamline management of geometry-based requirements at VCG.

Biological nanomaterials are defined as materials with sizes within the nanoscale range of 1 - 100 nm. The fundamental functionalities and biocompatibility of these materials can be tailored for biotechnology applications. In 1983, Ned Seeman successfully developed the first customized DNA nanostructures, Holliday junctions. Since then, the field has continued to expand rapidly and various 2D and 3D nanostructures has been designed. Although the high predictability of DNA base-pairing is essential for the design of complex DNA nanostructures, it greatly limits its functional versatility; therefore, proteins are conjugated with DNA nanostructures to compensate for that. DNA origami-protein hybrid nanostructures were introduced in 2012. However, the structural units based on DNA origami-protein hybrid nanostructures are still limited, and the majority are constructed by covalent or sequence-specific non-covalent interactions. Here we utilize the inherent, non-sequence-specific interaction between DNA and histones to present sequence-independent self-assembled DNA origami-protein hybrid nanostructures. We demonstrated using various molecular biology and imaging techniques that ssDNAs and histone proteins self-assemble into structurally well-defined complexes. We successfully assembled DNA origami–histone hybrid nanostructures using two different shapes of DNA origami: rectangular (PF-3), and rectangular with central aperture (PF-2) nanostructures. We observed precise localization of nucleosome-like histone-ssDNA nanostructures at the edge (PF-3) or the center (PF-2) of the DNA origami. In addition, we demonstrated that this DNA origami-histone interaction results in the assembly of larger DNA origami complexes, including a head-to-head type dimer and a cross-shape complex. Our results suggest the successful self-assembly of the DNA origami–histone hybrid nanostructures provide a principal structural unit for constructing higher-order nanostructures. Given the reversible nature of the geometry-based noncovalent interaction between the DNA origami and the nucleosome-like histone-ssDNA nanostructures, the self-assembly/disassembly of DNA-histones hybrid nanostructures may open new opportunities to construct stimuli-responsive DNA-protein hybrid nanostructures.

Color has been widely used in content-based image retrieval (CBIR) applications. In such applications the color properties of an image are usually characterized by the probability distribution of the colors in the image. A distance measure is then used to measure the (dis-)similarity between images based on the descriptions of their color distributions in order to quickly find relevant images. The development and investigation of statistical methods for robust representations of such distributions, the construction of distance measures between them and their applications in efficient retrieval, browsing, and structuring of very large image databases are the main contributions of the thesis. In particular we have addressed the following problems in CBIR. Firstly, different non-parametric density estimators are used to describe color information for CBIR applications. Kernel-based methods using nonorthogonal bases together with a Gram-Schmidt procedure and the application of the Fourier transform are introduced and compared to previously used histogram-based methods. Our experiments show that efficient use of kernel density estimators improves the retrieval performance of CBIR. The practical problem of how to choose an optimal smoothing parameter for such density estimators as well as the selection of the histogram bin-width for CBIR applications are also discussed. Distance measures between color distributions are then described in a differential geometry-based framework. This allows the incorporation of geometrical features of the underlying color space into the distance measure between the probability distributions. The general framework is illustrated with two examples: Normal distributions and linear representations of distributions. The linear representation of color distributions is then used to derive new compact descriptors for color-based image retrieval. These descriptors are based on the combination of two ideas: Incorporating information from the structure of the color space with information from images and application of projection methods in the space of color distribution and the space of differences between neighboring color distributions. In our experiments we used several image databases containing more than 1,300,000 images. The experiments show that the method developed in this thesis is very fast and that the retrieval performance chievedcompares favorably with existing methods. A CBIR system has been developed and is currently available at http://www.media.itn.liu.se/cse. We also describe color invariant descriptors that can be used to retrieve images of objects independent of geometrical factors and the illumination conditions under which these images were taken. Both statistics- and physics-based methods are proposed and examined. We investigated the interaction between light and material using different physical models and applied the theory of transformation groups to derive geometry color invariants. Using the proposed framework, we are able to construct all independent invariants for a given physical model. The dichromatic reflection model and the Kubelka-Munk model are used as examples for the framework. The proposed color invariant descriptors are then applied to both CBIR, color image segmentation, and color correction applications. In the last chapter of the thesis we describe an industrial application where different color correction methods are used to optimize the layout of a newspaper page. / <p>A search engine based, on the methodes discribed in this thesis, can be found at http://pub.ep.liu.se/cse/db/?. Note that the question mark must be included in the address.</p>

Machine-to-machine (M2M) communication is becoming an important aspect of warehouse management, remote control, robotics, traffic control, supply chain management, fleet management and telemedicine. M2M is expected to become a significant portion of the Industrial Internet and, more broadly, the Internet of Things (IoT). The environments in which M2M systems are expected to operate may be challenging in terms of radio wave propagation due to their cluttered, multipath nature, which can cause deep signal fades and signal depolarization. Polarization diversity in two dimensions is a well-known technique to mitigate such fades. But in the presence of reflectors and retarders where multipath components arrive from any direction, we find the detrimental effects to be three-dimensional and thus consider herein mitigation approaches that are also 3D. The objectives of this dissertation are three. First, to provide a theoretical framework for depolarization in three dimensions. Second, to prepare a tripolar antenna design that meets cost, power consumption, and simplicity requirements of M2M applications and that can mitigate the expected channel effects. Finally, to develop new channel models in three dimensional space for wireless systems. Accordingly, this dissertation presents a complete description of 3D electromagnetic fields, in terms of their polarization characteristics and confirms the advantage of employing tripolar antennas in multipath conditions. Furthermore, the experimental results illustrate that highly variable depolarization occurs across all three spatial dimensions and is dependent on small changes in frequency and space. Motivated by these empirical results, we worked with a collaborating institution to develop a three-dimensional tripolar antenna that can be integrated with a commercially available wireless sensor. This dissertation presents the testing results that show that this design significantly improves channels over traditional 2D approaches. The implications of tripolar antenna integration on M2M systems include reduction in energy use, longer wireless communication link distances, and/or greater link reliability. Similar results are shown for a planar antenna design that enables four different polarization configurations. Finally, the work presents a novel three-dimensional geometry-based stochastic channel model that builds the channel as a sum of shell-like sub-regions, where each sub-region consists of groups of multipath components. The model is validated with empirical data to show the approach may be used for system analyses in indoor environments.