Non-parametric statistical background modeling for efficient foreground region detection

Most methods for foreground region detection in videos are challenged by the presence of quasi-stationary backgrounds—flickering monitors, waving tree branches, moving water surfaces or rain. Additional difficulties are caused by camera shake or by the presence of moving objects in every image. The contribution of this paper is to propose a scene-independent and non-parametric modeling technique which covers most of the above scenarios. First, an adaptive statistical method, called adaptive kernel density estimation (AKDE), is proposed as a base-line system that addresses the scene dependence issue. After investigating its performance we introduce a novel general statistical technique, called recursive modeling (RM). The RM overcomes the weaknesses of the AKDE in modeling slow changes in the background. The performance of the RM is evaluated asymptotically and compared with the base-line system (AKDE). A wide range of quantitative and qualitative experiments is performed to compare the proposed RM with the base-line system and existing algorithms. Finally, a comparison of various background modeling systems is presented as well as a discussion on the suitability of each technique for different scenarios.     

Thermal assisted direct bonding between structured glasses for lab-on-chip technology

A thermal assisted direct bonding (TADB) technique between structured glasses is proposed for micro-fluidic device fabrication. The bonded glass pairs were characterized by optical microscopy, scanning electron microscopy (SEM), apparent shear strength tests and Vickers hardness measurements across the bonded interface. The optimisation of TADB parameters on flat glasses and on structured glasses was analysed. This technique is user-friendly and low cost, and can be considered for mass production of glass-based micro-fluidic devices.     

Educational recommender systems and their application in lifelong learning

The almost unlimited access to educational information plethora came with a drawback: finding meaningful material is not a straightforward task anymore. Recommender algorithms can be used to make smart decisions in complex information systems and help the users decide upon useful materials; therefore, they become a promising area in academia and industry. The current paper presents a survey on educational recommender systems (RS): a set of analysis criteria are exposed and the technological specifications and challenges of each analysed system are provided, in the context of the main trends in the development of RS. Also, an ontology-based educational recommendation mechanism is proposed and its application to lifelong learning is highlighted, proving that RS can successfully support new learning paradigms.     

Lidar-based mapping of flood control levees in South Louisiana

Flood protection in south Louisiana is largely dependent on earthen levees, and in the aftermath of Hurricane Katrina the state’s levee system has received intense scrutiny. Accurate elevation data along the levees are critical to local levee district managers responsible for monitoring and maintaining the extensive system of non-federal levees in coastal Louisiana. In 2012, high resolution airborne lidar data were acquired over levees in Lafourche Parish, Louisiana, and a mobile terrestrial lidar survey was conducted for selected levee segments using a terrestrial lidar scanner mounted on a truck. The mobile terrestrial lidar data were collected to test the feasibility of using this relatively new technology to map flood control levees and to compare the accuracy of the terrestrial and airborne lidar. Metrics assessing levee geometry derived from the two lidar surveys are also presented as an efficient, comprehensive method to quantify levee height and stability. The vertical root mean square error values of the terrestrial lidar and airborne lidar digital-derived digital terrain models were 0.038 m and 0.055 m, respectively. The comparison of levee metrics derived from the airborne and terrestrial lidar-based digital terrain models showed that both types of lidar yielded similar results, indicating that either or both surveying techniques could be used to monitor geomorphic change over time. Because airborne lidar is costly, many parts of the USA and other countries have never been mapped with airborne lidar, and repeat surveys are often not available for change detection studies. Terrestrial lidar provides a practical option for conducting repeat surveys of levees and other terrain features that cover a relatively small area, such as eroding cliffs or stream banks, and dunes.     

Hyper-arc consistency of polynomial constraints over finite domains using the modified Bernstein form

This paper describes an algorithm to enforce hyper-arc consistency of polynomial constraints defined over finite domains. First, the paper describes the language of so called polynomial constraints over finite domains, and it introduces a canonical form for such constraints. Then, the canonical form is used to transform the problem of testing the satisfiability of a constraint in a box into the problem of studying the sign of a related polynomial function in the same box, a problem which is effectively solved by using the modified Bernstein form of polynomials. The modified Bernstein form of polynomials is briefly discussed, and the proposed hyper-arc consistency algorithm is finally detailed. The proposed algorithm is a subdivision procedure which, starting from an initial approximation of the domains of variables, removes values from domains to enforce hyper-arc consistency.     

Working conditions in the engine department – A qualitative study among engine room personnel on board Swedish merchant ships

The specific problems associated with the work on board within the merchant fleet are well known and have over the years been a topic of discussion. The work conditions in the engine room (ER) are demanding due to, e.g. the thermal climate, noise and awkward working postures. The work in the engine control room (ECR) has over recent years undergone major changes, mainly due to the introduction of computers on board. In order to capture the impact these changes had implied, and also to investigate how the work situation has developed, a total of 20 engine officers and engine ratings were interviewed. The interviews were semi-structured and Grounded Theory was used for the data analysis. The aim of the present study was to describe how the engine crew perceive their work situation and working environment on board. Further, the aim was to identify areas for improvements which the engine crew consider especially important for a safe and effective work environment. The result of the study shows that the design of the ECR and ER is crucial for how different tasks are performed. Design which does not support operational procedures and how tasks are performed risk inducing inappropriate behaviour as the crew members’ are compelled to find alternative ways to perform their tasks in order to get the job done. These types of behaviour can induce an increased risk of exposure to hazardous substances and the engine crew members becoming injured.     

Optimal combination forecasts for hierarchical time series

In many applications, there are multiple time series that are hierarchically organized and can be aggregated at several different levels in groups based on products, geography or some other features. We call these “hierarchical time series”. They are commonly forecast using either a “bottom-up” or a “top-down” method.In this paper we propose a new approach to hierarchical forecasting which provides optimal forecasts that are better than forecasts produced by either a top-down or a bottom-up approach. Our method is based on independently forecasting all series at all levels of the hierarchy and then using a regression model to optimally combine and reconcile these forecasts. The resulting revised forecasts add up appropriately across the hierarchy, are unbiased and have minimum variance amongst all combination forecasts under some simple assumptions.We show in a simulation study that our method performs well compared to the top-down approach and the bottom-up method. We demonstrate our proposed method by forecasting Australian tourism demand where the data are disaggregated by purpose of travel and geographical region.     

On the number of Dejean words over alphabets of 5, 6, 7, 8, 9 and 10 letters

We give lower bounds on the growth rate of Dejean words, i.e. minimally repetitive words, over a k-letter alphabet, for 5≤k≤10. Put together with the known upper bounds, we estimate these growth rates with the precision of 0.005. As a consequence, we establish the exponential growth of the number of Dejean words over a k-letter alphabet, for 5≤k≤10.     

Scheduling loops with partial loop-carried dependencies

This paper deals with task scheduling, where each task is one particular iteration of a DO loop with partial loop-carried dependencies. Independent iterations of such loops can be scheduled in an order different from the one of classical serial execution, so as to increase program performance.The approach that we present is based both on the use of a directive added to the High Performance Fortran (HPF2) language, which specifies the dependencies between iterations, and on inspector/executor support, implemented in the CoLUMBO library, which builds the task graph and schedules tasks associated with iterations. We validate our approach by showing results achieved on an IBM SP2 for a sparse Cholesky factorization algorithm applied to real problems.