In this paper we report on an experiment conducted in an attempt to improve further the learning environment in a basic campus course on Object-Oriented Programming and Design given to first-year engineering students studying Computer Science and Engineering. This course has for years had the same traditional set-up that is common also in other engineering schools around the world including lectures, mandatory programming assignments, and a final written exam. What we did was to substitute the lectures for in-class sessions based on a variant of the teaching method known as 'flipping the classroom' combined with certain elements of Peer Discussion and Just-in-time teaching. To make all this work, we introduced a web-based MOOC tool into the course. To be able to investigate the quantitative effects of our experiment, we had an experiment class consisting of 70 students taking the course in this new way and a control class of 57 students taking the course in the traditional way. On the final written exam, which was identical for the two classes and marked the same way, 81% of the students in the experiment class passed compared to 60% in the control class. Moreover, the share of students passing with good grades was 58% in the experiment class compared to 32% in the control class. So, not only did the share of students passing the course increase by a third, but also the share of students passing with good grades almost doubled

In general terms, sintering describes the bonding of particles into a more coherent structure, where joins form between packed particles, usually as a result of heating. Characterization of sintering is an important topic in the fields of metallurgy, steel, iron ore pellets, ceramics, and snow for understanding material properties and material strength. Characterization using image analysis has been applied in a number of these fields but is either semi-automatic, requiring human interaction in the analysis, or based on statistical sampling and stereology to characterize the sample. This paper presents a novel fully automatic image analysis algorithm to analyze and determine the degree of sintering based on analysis of the particle joins and structure. Quantitative image analysis of the sintering degree is demonstrated for samples of iron ore pellets but could be readily applied to other packed particle materials. Microscope images of polished cross-sections of iron ore pellets have been imaged in their entirety and automated analysis of hundreds of images has been performed. Joins between particles have been identified based on morphological image processing and features have been calculated based on the geometric properties and curvature of these joins. The features have been analyzed and determined to hold discriminative power by displaying properties consistent with sintering theory and results from traditional pellet diameter measurements on the heated samples, and a statistical evaluation using the Welch t-test.

We demonstrate how known convolution techniques for uncertain data can be used to set the shapes of structuring elements in adaptive mathematical morphology, enabling robust morphological processing of partially occluded or otherwise incomplete data. Results are presented for filtering of both gray-scale images containing missing data and 3D profile data where information is missing due to occlusion effects. The latter demonstrates the intended use of the method: enhancement of crack signatures in a surface inspection system for casted steel.The presented method is able to disregard unreliable data in a systematic and robust way, enabling adaptive morphological processing of the available information while avoiding any false edges or other unwanted features introduced by the values of faulty pixels.

Lifelogging techniques help individuals to log their life and retrieve important events, memories and experiences. Structuring lifelogs is a major challenge in lifelogging systems since the system should present the logs in a concise and meaningful way to the user. In this paper the authors present an approach for structuring lifelogs as places and activities based on location data. The structured lifelogs are achieved using a combination of density-based clustering algorithms and convex hull construction to identify the places of interest. The periods of time where the user lingers at the same place are then identified as possible activities. In addition to structuring lifelogs the authors present an application in which images are associated to the structuring results and presented to the user for reviewing. The system is evaluated through a user study consisting of 12 users, who used the system for 1 day and then answered a survey. The proposed approach in this paper allows automatic inference of information about significant places and activities, which generates structured image-annotated logs of everyday life.

A novel method for automated inspection of small corner cracks in casted steel is presented, using a photometric stereo setup consisting of two light sources of different colors in conjunction with a line-scan camera. The resulting image is separated into two different reflection patterns which are used to cancel shadow effects and estimate the surface gradient. Statistical methods are used to first segment the image and then provide an estimated crack probability for each segmented region. Results show that true cracks are successfully assigned a high crack probability, while only a minor proportion of other regions cause similar probability values. About 80% of the cracks present in the segmented regions are given a crack probability higher than 70%, while the corresponding number for other non-crack regions is only 5%. The segmented regions contain over 70% of the manually identified crack pixels. We thereby provide proof-of-concept for the presented method.

During recent years the interest in Engineering Studies has declined at universities both in the United States and in many Western European countries including Sweden. In addition, among those students that do enroll, an increasing number drop-out. This paper presents an attempt to mitigate these worrying problems in the form of a new kind of intro- ductory course for first-year engineering students studying on a 5-year long Master of Science program in Computer Science and Engineering. The course is novel in that it takes a holistic approach to motivate and prepare students for their further studies. Core subjects and useful tools are mixed together into an intense 10-week course with 12 separate course modules on different topics, often running in parallel. The course has a total of 21 individual examinations to take, tasks to carry out, and deadlines to meet. The examinations and tasks are chosen among those common in our School of Engineering. Evaluations show that, although demanding for the students, the course works well and fulfills its goals.

Lifelogging techniques help individuals to log their life and retrieve important events, memories and experiences. Structuring lifelogs is a major challenge in lifelogging systems since the system should present the logs in a concise and meaningful way to the user. In this article the authors present a novel approach for structuring lifelogs as places and activities based on location data. The structured lifelogs are achieved using a combination of density-based clustering algorithms and convex hull construction to identify the places of interest. The periods of time where the user lingers at the same place are then identified as possible activities. In addition to structuring lifelogs the authors present an application in which images are associated to the structuring results and presented to the user for reviewing. The proposed approach allows automatic inference of information about significant places and activities, which generates structured image-annotated logs of everyday life.