Dieselmoottorin analogisen kierrosnopeusmittauksen digitalisointi

Työssä suunnitellaan kierrosnopeusmittauksen toteutus vanhaan Ford -teollisuusdieseliin käyttäen moottorin alkuperäistä kierrosnopeusanturia. Anturi kunnostetaan vaihtamalla vanha, palanut käämi uuteen digitaaliseen järjestelmään soveltuvaan käämiin. Sen toimin-ta halutulla kierrosnopeusalueella varmistetaan mittauksin ja tämän perusteella suunnitel-laan kytkentä sen liittämiseksi kierrosnopeuden laskevaan mikrokontrolleriin. Kytkennän toimivuutta testataan simuloimalla ennen prototyypin rakentamista. Erilaisia vaihtoehtoja analogisen näytön toteuttamiseksi tutkitaan ja niistä valitaan yksi ve-nekäyttöön soveltuva, joka toteutetaan järjestelmän näyttöratkaisuksi. Järjestelmälle suunnitellaan piirilevy, jolle prototyyppi kasataan. Mikrokontrollerille koodataan C -ohjelmointikielellä ohjelma, joka laskee dieselmoottorin kierrosnopeuden anturipulssien perusteella ja ohjaa näyttöä.

Costs and Benefits of a Shared Digital Long-Term Preservation System

This paper describes the cost-benefit analysis of digital long-term preservation (LTP) that was carried out in the context of the Finnish National Digital Library Project (NDL) in 2010. The analysis was based on the assumption that as many as 200 archives, libraries, and museums will share an LTP system. The term ‘system’ shall be understood as encompassing not only information technology, but also human resources, organizational structures, policies and funding mechanisms. The cost analysis shows that an LTP system will incur, over the first 12 years, cumulative costs of €42 million, i.e. an average of €3.5 million per annum. Human resources and investments in information technology are the major cost factors. After the initial stages, the analysis predicts annual costs of circa €4 million. The analysis compared scenarios with and without a shared LTP system. The results indicate that a shared system will have remarkable benefits. At the development and implementation stages, a shared system shows an advantage of €30 million against the alternative scenario consisting of five independent LTP solutions. During the later stages, the advantage is estimated at €10 million per annum. The cumulative cost benefit over the first 12 years would amount to circa €100 million.

Bag-of-Features Approach to Unsupervised Visual Object Categorisation

The large and growing number of digital images is making manual image search laborious. Only a fraction of the images contain metadata that can be used to search for a particular type of image. Thus, the main research question of this thesis is whether it is possible to learn visual object categories directly from images. Computers process images as long lists of pixels that do not have a clear connection to high-level semantics which could be used in the image search. There are various methods introduced in the literature to extract low-level image features and also approaches to connect these low-level features with high-level semantics. One of these approaches is called Bag-of-Features which is studied in the thesis. In the Bag-of-Features approach, the images are described using a visual codebook. The codebook is built from the descriptions of the image patches using clustering. The images are described by matching descriptions of image patches with the visual codebook and computing the number of matches for each code. In this thesis, unsupervised visual object categorisation using the Bag-of-Features approach is studied. The goal is to find groups of similar images, e.g., images that contain an object from the same category. The standard Bag-of-Features approach is improved by using spatial information and visual saliency. It was found that the performance of the visual object categorisation can be improved by using spatial information of local features to verify the matches. However, this process is computationally heavy, and thus, the number of images must be limited in the spatial matching, for example, by using the Bag-of-Features method as in this study. Different approaches for saliency detection are studied and a new method based on the Hessian-Affine local feature detector is proposed. The new method achieves comparable results with current state-of-the-art. The visual object categorisation performance was improved by using foreground segmentation based on saliency information, especially when the background could be considered as clutter.