<p>This paper is based on the XAP system (eXtended Application Provisioning) and serves for the modeling of application family. The importance of modeling application family is increasing rapidly. To improve a mechanism to express the structure, properties of concepts, features and implementations within an application family becomes necessary and important. Feature tree is a well accepted means for the product line. We can use and improve it to suit our requirements in the following way</p><p>In the degree project, we create a tool to model application family with reusability, commonality and variability. The hierarchy, feature properties and dependencies are graphically represented.</p>

Zugl.: Kiel, Univ., Diss., 2009

Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 119-125). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 119-125). Also available on the Internet.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: theorem proving; modular verification; software verification; feature-oriented programming; feature interaction. Includes bibliographical references (p. 131-136).

This thesis describes the application of a novel decision support process for machined part estimating in small and medium-sized engineering companies. Many SMEs tend to adopt manual estimating techniques, however this dependence on human expertise represents a risk to such organizations. Better information management in estimating can improve process performance and contribute to increased competitiveness. The research which is the subject of this thesis investigated whether a systems approach to machined part estimating would extend the capacity of an SME to manage knowledge more effectively. The research explored the workplace learning context, the provision of learning opportunities and the management of organizational knowledge; before determining that an intelligent information system offered the most beneficial solution to the situation-of-interest. The case study company produce low-volume, make-to-order, medium and large sized machined steel forgings; utilising conventional machine tool equipment. The application of the decision support system enabled novice estimators to produce viable cost estimates; reducing the risk from reliance on human expertise inherent in manual estimating. The hybrid feature-based costing / case-based reasoning estimating technique, which is the core of the novel METALmpe cost model, proved exceptionally well suited to the SME environment. Estimates produced using METALmpe were consistently more accurate than those of the human expert; with a level of accuracy that exceeds the initial research aim, i.e. a tolerance of -5% / +10%. Significantly, implementation of METALmpe (hardware, software and support for 5 users), can be provided at a cost which is within the typical information technology budget of many SMEs. With demands on organizations to process and disseminate ever increasing volumes of information, METALmpe can improve an SME’s information management capabilities and contribute to competitive advantage through strengthening strategic assets and core competencies.

“Paradisia” is a feature screenplay that is set in Iran during the opening days of the 1980-1988 war with Iraq. It follows the story of a young couple in a war-torn border town, who, accompanied reluctantly by a random stranger, set off to bury the girl’s deceased father before they leave the city. This document is a report on the process of the development of the script, from the conception of its original idea, to the formulation of its plot, characters and structure, based on my sources of research and inspiration. It offers a brief account of the events of the first days of war, and compares and contrasts it to what I have chosen to portray in the script. It also lays out the major plot and character flaws of the original draft of the story, as were pointed out by my supervisor and readers, and demonstrates how I have attempted to address each one of them in order to improve the composition of my characters, the organization of the plot, and the consistency of the story’s structure. / text

In this thesis, I explore region detection and consider its impact on image matching for exemplar-based object recognition. Detecting regions is important to provide semantically meaningful spatial cues in images. Matching establishes similarity between visual entities, which is crucial for recognition. My thesis starts by detecting regions in both local and object level. Then, I leverage geometric cues of the detected regions to improve image matching for the ultimate goal of object recognition. More specifically, my thesis considers four key questions: 1) how can we extract distinctively-shaped local regions that also ensure repeatability for robust matching? 2) how can object-level shape inform bottom-up image segmentation? 3) how should the spatial layout imposed by segmented regions influence image matching for exemplar-based recognition? and 4) how can we exploit regions to improve the accuracy and speed of dense image matching? I propose novel algorithms to tackle these issues, addressing region-based visual perception from low-level local region extraction, to mid-level object segmentation, to high-level region-based matching and recognition. First, I propose a Boundary Preserving Local Region (BPLR) detector to extract local shapes. My approach defines a novel spanning-tree based image representation whose structure reflects shape cues combined from multiple segmentations, which in turn provide multiple initial hypotheses of the object boundaries. Unlike traditional local region detectors that rely on local cues like color and texture, BPLRs explicitly exploit the segmentation that encodes global object shape. Thus, they respect object boundaries more robustly and reduce noisy regions that straddle object boundaries. The resulting detector yields a dense set of local regions that are both distinctive in shape as well as repeatable for robust matching. Second, building on the strength of the BPLR regions, I develop an approach for object-level segmentation. The key insight of the approach is that objects shapes are (at least partially) shared among different object categories--for example, among different animals, among different vehicles, or even among seemingly different objects. This shape sharing phenomenon allows us to use partial shape matching via BPLR-detected regions to predict global object shape of possibly unfamiliar objects in new images. Unlike existing top-down methods, my approach requires no category-specific knowledge on the object to be segmented. In addition, because it relies on exemplar-based matching to generate shape hypotheses, my approach overcomes the viewpoint sensitivity of existing methods by allowing shape exemplars to span arbitrary poses and classes. For the ultimate goal of region-based recognition, not only is it important to detect good regions, but we must also be able to match them reliably. A matching establishes similarity between visual entities (images, objects or scenes), which is fundamental for visual recognition. Thus, in the third major component of this thesis, I explore how to leverage geometric cues of the segmented regions for accurate image matching. To this end, I propose a segmentation-guided local feature matching strategy, in which segmentation suggests spatial layout among the matched local features within each region. To encode such spatial structures, I devise a string representation whose 1D nature enables efficient computation to enforce geometric constraints. The method is applied for exemplar-based object classification to demonstrate the impact of my segmentation-driven matching approach. Finally, building on the idea of regions for geometric regularization in image matching, I consider how a hierarchy of nested image regions can be used to constrain dense image feature matches at multiple scales simultaneously. Moving beyond individual regions, the last part of my thesis studies how to exploit regions' inherent hierarchical structure to improve the image matching. To this end, I propose a deformable spatial pyramid graphical model for image matching. The proposed model considers multiple spatial extents at once--from an entire image to grid cells to every single pixel. The proposed pyramid model strikes a balance between robust regularization by larger spatial supports on the one hand and accurate localization by finer regions on the other. Further, the pyramid model is suitable for fast coarse-to-fine hierarchical optimization. I apply the method to pixel label transfer tasks for semantic image segmentation, improving upon the state-of-the-art in both accuracy and speed. Throughout, I provide extensive evaluations on challenging benchmark datasets, validating the effectiveness of my approach. In contrast to traditional texture-based object recognition, my region-based approach enables to use strong geometric cues such as shape and spatial layout that advance the state-of-the-art of object recognition. Also, I show that regions' inherent hierarchical structure allows fast image matching for scalable recognition. The outcome realizes the promising potential of region-based visual perception. In addition, all my codes for local shape detector, object segmentation, and image matching are publicly available, which I hope will serve as useful new additions for vision researchers' toolbox. / text

Nowadays machine learning is becoming more popular in prediction andclassification tasks for many fields. In banks, telemarketing area is usingthis approach by gathering information from phone calls made to clientsover the past campaigns. The true fact is that sometimes phone calls areannoying and time consuming for both parts, the marketing department andthe client. This is why this project is intended to prove that feature selectioncould improve machine learning models.  A Portuguese bank gathered data regarding phone calls and clientsstatistics information like their actual jobs, salaries and employment statusto determine the probabilities if a person would buy the offered productand/or service. C4.5 decision tree (J48) and multilayer perceptron (MLP)are the machine learning models to be used for the experiments. For featureselection correlation-based feature selection (Cfs), Chi-squared attributeselection and RELIEF attribute selection algorithms will be used. WEKAframework will provide the tools to test and implement the experimentscarried out in this research.  The results were very close over the two data mining models with aslight improvement by C4.5 over the correct classifications and MLP onROC curve rate. With these results it was confirmed that feature selectionimproves classification and/or prediction results.

The poaching of rhinoceros has increased dramatically the last few years andthe park rangers are often helpless against the militarised poachers. LinköpingUniversity is running several projects with the goal to aid the park rangers intheir work.This master thesis was produced at CybAero AB, which builds Remotely PilotedAircraft System (RPAS). With their helicopters, high end cameras with a rangesufficient to cover the whole area can be flown over the parks.The aim of this thesis is to investigate different methods to automatically findrhinos and humans, using airborne cameras. The system uses two cameras, onecolour camera and one thermal camera. The latter is used to find interestingobjects which are then extracted in the colour image. The object is then classifiedas either rhino, human or other. Several methods for classification have beenevaluated.The results show that classifying solely on the thermal image gives nearly as highaccuracy as classifying only in combination with the colour image. This enablesthe system to be used in dusk and dawn or in bad light conditions. This is animportant factor since most poaching occurs at dusk or dawn. As a conclusion asystem capable of running on low performance hardware and placeable on boardthe aircraft is presented. / Tjuvjakten av noshörningar har ökat drastiskt de senaste åren och parkvakternastår ofta handfallna mot militariserade tjuvjägare. Linköpings Universitet arbetarpå flera projekt som på olika sätt ska vara ett stöd för parkvakterna i deras arbete.Examensarbetet genomfördes på CybAero AB som jobbar med att bygga fjärrstyrdahelikoptrar, så kallade RPAS (Remotely Piloted Aircraft System). Med derassystem kan man bära högkvalitativa kameror och ha stor räckvidd så hela parkenkan övervakas.Det här examensarbetet syftar på att undersöka olika metoder för att från luftburnakameror kunna ge information om vad som pågår i parken. System bygger påatt man har två kameror, en vanlig färgkamera och en värmekamera. Värmekamerananvänds för att hitta intressanta objekt som sedan plockas ut ur färgbilden.Objektet klassificeras sedan som antingen noshörningar, människor eller annat.Flertalet metoder har utvärderas utefter deras förmåga att klassificera objektenkorrekt.Det visade sig att man kan få väldigt bra resultat när man klassificerar endastpå värmebilden vilket ger systemet möjlighet att operera även när det är skymningeller mörkt ute. Det är en väldigt viktig del då de flesta djuren skjuts vidantingen gryning eller skymning. Som slutsats i rapporten presenteras ett förslagpå system som kan köras på lågpresterande hårdvara för att kunna köras direkt iluften.