Michelson interferometer with porch swing bearing for portable FTIR spectrometer

The interferometer for low resolution portable Fourier Transform middle infrared spectrometer was developed and studied experimentally. The final aim was a concept for a commercial prototype. Because of the portability, the interferometer should be compact sized and insensitive to the external temperature variations and mechanical vibrations. To minimise the size and manufacturing costs, Michelson interferometer based on plane mirrors and porch swing bearing was selected and no dynamic alignment system was applied. The driving motor was a linear voice coil actuator to avoid mechanical contact of the moving parts. The driving capability for low mirror driving velocities required by the photoacoustic detectors was studied. In total, four versions of such an interferometer were built and experimentally studied. The thermal stability during the external temperature variations and the alignment stability over the mirror travel were measured using the modulation depth of the wide diameter laser beam. Method for estimating the mirror tilt angle from the modulation depth was developed to take account the effect from the non-uniform intensity distribution of the laser beam. The spectrometer stability was finally studied also using the infrared radiation. The latest interferometer was assembled for the middle infrared spectrometer with spectral range from 750 cm−1 to 4500 cm−1. The interferometer size was (197 × 95 × 79) mm3 with the beam diameter of 25 mm. The alignment stability as the change of the tilt angle over the mirror travel of 3 mm was 5 μrad, which decreases the modulation depth only about 0.7 percent in infrared at 3000 cm−1. During the temperature raise, the modulation depth at 3000 cm−1 changed about 1 . . . 2 percentage units per Celsius over short term and even less than 0.2 percentage units per Celsius over the total temperature raise of 30 °C. The unapodised spectral resolution was 4 cm−1 limited by the aperture size. The best achieved signal to noise ratio was about 38 000:1 with commercially available DLaTGS detector. Although the vibration sensitivity requires still improving, the interferometer performed, as a whole, very well and could be further developed to conform all the requirements of the portable and stable spectrometer.

Local Features in Image and Video Processing - Object Class Matching and Video Shot Detection

The usage of digital content, such as video clips and images, has increased dramatically during the last decade. Local image features have been applied increasingly in various image and video retrieval applications. This thesis evaluates local features and applies them to image and video processing tasks. The results of the study show that 1) the performance of different local feature detector and descriptor methods vary significantly in object class matching, 2) local features can be applied in image alignment with superior results against the state-of-the-art, 3) the local feature based shot boundary detection method produces promising results, and 4) the local feature based hierarchical video summarization method shows promising new new research direction. In conclusion, this thesis presents the local features as a powerful tool in many applications and the imminent future work should concentrate on improving the quality of the local features.

Aligning Regional Repository Networks

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Large Scale Repository Auditing to ISO 16363

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.