Probabilistic position and orientation estimation of rigid objects using an RGB-D sensor

The recent emergence of low-cost RGB-D sensors has brought new opportunities for robotics by providing affordable devices that can provide synchronized images with both color and depth information. In this thesis, recent work on pose estimation utilizing RGBD sensors is reviewed. Also, a pose recognition system for rigid objects using RGB-D data is implemented. The implementation uses half-edge primitives extracted from the RGB-D images for pose estimation. The system is based on the probabilistic object representation framework by Detry et al., which utilizes Nonparametric Belief Propagation for pose inference. Experiments are performed on household objects to evaluate the performance and robustness of the system.

Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology

More discussion is required on how and which types of biomass should be used to achieve a significant reduction in the carbon load released into the atmosphere in the short term. The energy sector is one of the largest greenhouse gas (GHG) emitters and thus its role in climate change mitigation is important. Replacing fossil fuels with biomass has been a simple way to reduce carbon emissions because the carbon bonded to biomass is considered as carbon neutral. With this in mind, this thesis has the following objectives: (1) to study the significance of the different GHG emission sources related to energy production from peat and biomass, (2) to explore opportunities to develop more climate friendly biomass energy options and (3) to discuss the importance of biogenic emissions of biomass systems. The discussion on biogenic carbon and other GHG emissions comprises four case studies of which two consider peat utilization, one forest biomass and one cultivated biomasses. Various different biomass types (peat, pine logs and forest residues, palm oil, rapeseed oil and jatropha oil) are used as examples to demonstrate the importance of biogenic carbon to life cycle GHG emissions. The biogenic carbon emissions of biomass are defined as the difference in the carbon stock between the utilization and the non-utilization scenarios of biomass. Forestry-drained peatlands were studied by using the high emission values of the peatland types in question to discuss the emission reduction potential of the peatlands. The results are presented in terms of global warming potential (GWP) values. Based on the results, the climate impact of the peat production can be reduced by selecting high-emission-level peatlands for peat production. The comparison of the two different types of forest biomass in integrated ethanol production in pulp mill shows that the type of forest biomass impacts the biogenic carbon emissions of biofuel production. The assessment of cultivated biomasses demonstrates that several selections made in the production chain significantly affect the GHG emissions of biofuels. The emissions caused by biofuel can exceed the emissions from fossil-based fuels in the short term if biomass is in part consumed in the process itself and does not end up in the final product. Including biogenic carbon and other land use carbon emissions into the carbon footprint calculations of biofuel reveals the importance of the time frame and of the efficiency of biomass carbon content utilization. As regards the climate impact of biomass energy use, the net impact on carbon stocks (in organic matter of soils and biomass), compared to the impact of the replaced energy source, is the key issue. Promoting renewable biomass regardless of biogenic GHG emissions can increase GHG emissions in the short term and also possibly in the long term.

Threat modeling a factory environment using Microsoft Security Development Lifecycle methodology

The number of security violations is increasing and a security breach could have irreversible impacts to business. There are several ways to improve organization security, but some of them may be difficult to comprehend. This thesis demystifies threat modeling as part of secure system development. Threat modeling enables developers to reveal previously undetected security issues from computer systems. It offers a structured approach for organizations to find and address threats against vulnerabilities. When implemented correctly threat modeling will reduce the amount of defects and malicious attempts against the target environment. In this thesis Microsoft Security Development Lifecycle (SDL) is introduced as an effective methodology for reducing defects in the target system. SDL is traditionally meant to be used in software development, principles can be however partially adapted to IT-infrastructure development. Microsoft threat modeling methodology is an important part of SDL and it is utilized in this thesis to find threats from the Acme Corporation’s factory environment. Acme Corporation is used as a pseudonym for a company providing high-technology consumer electronics. Target for threat modeling is the IT-infrastructure of factory’s manufacturing execution system. Microsoft threat modeling methodology utilizes STRIDE –mnemonic and data flow diagrams to find threats. Threat modeling in this thesis returned results that were important for the organization. Acme Corporation now has more comprehensive understanding concerning IT-infrastructure of the manufacturing execution system. On top of vulnerability related results threat modeling provided coherent views of the target system. Subject matter experts from different areas can now agree upon functions and dependencies of the target system. Threat modeling was recognized as a useful activity for improving security.