Insight into cooperation processes for traffic scenarios: modelling with naturalistic decision making

With the introduction of automatic vehicle guidance (AV), mixed traffic scenarios between automatically and manually guided vehicles are to be expected, at least during a transitions phase. To ensure the safety of motor vehicle transportation, it will be essential to develop a cooperative relationship between human drivers and AV. Research in this area is currently being done to gain insight into the manner of human drivers’ decisions to transfer the same behaviours to automatic vehicle guidance. A lot of research is being done to prepare for the introduction of AV, but there is still a lack of information on how individual road users make decisions in cooperative decisions. Currently, there is no study that has tried to understand the decision-making process with the help of an online-survey. For that reason, a questionnaire study on cooperative traffic situations (N?=?281) was carried out, which was analysed with the Natural Decision Making approach. By means of the NDM approach and the use of the recognition module, links between planned action and the expected action between road users were identified. Furthermore, it was possible to categorize individual communication signals into offensive or defensive signals and thus make predictions about the intention of the driver. These findings can be used to derive design recommendations for automatic vehicle guidance in cooperative situations.     

Photorealistic rendering of mixed reality scenes

Photo‐realistic rendering of virtual objects into real scenes is one of the most important research problems in computer graphics. Methods for capture and rendering of mixed reality scenes are driven by a large number of applications, ranging from augmented reality to visual effects and product visualization. Recent developments in computer graphics, computer vision, and imaging technology have enabled a wide range of new mixed reality techniques including methods for advanced image based lighting, capturing spatially varying lighting conditions, and algorithms for seamlessly rendering virtual objects directly into photographs without explicit measurements of the scene lighting. This report gives an overview of the state‐of‐the‐art in this field, and presents a categorization and comparison of current methods. Our in‐depth survey provides a tool for understanding the advantages and disadvantages of each method, and gives an overview of which technique is best suited to a specific problem.     

Monte Carlo implementation of financial simulation on Cell/B.E. multi-core processor

The processor evolution has reached a critical moment in time where it will soon be impossible to increase the frequency much further. Processor designers such as Motorola, Intel and IBM have all realised that the only way to improve the FLOP/Watt ratio is to develop multi-core devices. One of the most current examples of multi-core processors is the new Sony/Toshiba/IBM Cell/B.E. multi-core processor. For the suitability to run in parallel, Monte Carlo methods are often considered embarrassingly parallel. This paper describes how a common Monte Carlo based financial simulation can be calculated in parallel using the Cell/B.E. multi-core processor. The measured performance with the achieved multi-core speed-up is also presented. With the recent availability of this increasingly available technology, financial simulations can now be performed in a fraction of the time it used to. This can also be achieved with a limited power and volume budget using commercially available technology. The main challenge with multi-core devices is clearly the programmability. The work presented here describes how this challenge could be dealt with.A basic MPI library has been developed to handle the partitioning and communication of data. The thread creation follows a POSIX thread creation model. MPI together with POSIX make the application portable in between various multi-processor systems and multi-core devices. The conclusions made indicate that a function offload MPI implementation on the Cell/B.E. multi-core processor can efficiently be used to speed-up the Monte Carlo solution of financial simulations. The conclusions made herein are also applicable to other situations where an algorithm can be easily parallelized.     

Computation of incompressible thermal flows using Hermite finite elements

Using Hermit basis functions with the finite element method offers a remarkably simple way to compute non-isothermal buoyancy-driven incompressible flow. The Hermite bases we use simplify the governing equation and strongly enforce the continuity equation. For this problem, we use a fourth-order C¹ stream function defined on rectangles here, but other higher and lower-order Hermite elements on rectangles and triangles can easily be derived or modified from elements found in the plate-bending literature. Hermite elements are also used for the temperature and pressure. We conclude with results from application of the method to the square thermal cavity at moderate to high Rayleigh numbers.     

Accuracy of identification of low or high risk lifting during standardised lifting situations

The aim was to classify lifting activities into low and high risk categories (according to The Danish Working Environment Authority guidelines) based on surface electromyography (sEMG) and trunk inclination (tri-axial accelerometer) measurements. Lifting tasks with different weights, horizontal distance and technique were performed. The lifting tasks were characterised by a feature vector composed of either the 90th, 95th or 99th percentile of sEMG activity level and trunk inclinations during the task. Linear Discriminant Analysis and a subject-specific threshold scheme were applied and lifting tasks were classified with an accuracy of 65.1–65.5%. When lifts were classified based on the subject-specific threshold scheme from low and upper back accelerometers, the accuracy reached 52.1–58.1% and 72.7–78.1%, respectively. In conclusion, the use of subject-specific thresholds from sEMG from upper trapezius and erector spinae as well as inclination of the upper trunk enabled us to identify low and high risk lifts with an acceptable accuracy.

Practitioner Summary: This study contributes to the development of a method enabling the automatic detection of high risk lifting tasks, i.e. exposure to high biomechanical loads, based on individual sEMG and kinematics from an entire working day. These methods may be more cost-effective and may complement observations commonly used by practitioners.     

Thermally Induced Flexo‐Type Effects in Nanopatterned Multiferroic Layers

The difficulty to generate and control large strain gradients in materials hinders the investigation and application of flexoelectricity and flexomagnetism. This work demonstrates that thermal expansion can be used to induce very large non‐uniform strains at the nanoscale, resulting in giant strain gradients at moderate temperatures. This is demonstrated in a nanopatterned multiferroic hybrid layer consisting of a regular array of ferromagnetic metallic nanocylinders embedded in a ferroelectric polymer matrix. The thermally‐induced strain gradients can fully depolarize the ferroelectric component, and modify the magnetization of the ferromagnetic component via flexoelectric and flexomagnetic effects, respectively. Finite‐element analysis provides a quantitative view on thermal expansion‐induced strains and strain gradients supporting the experimental findings. This work shows that nanoscale thermal strain engineering provides an additional degree of freedom to control electrical polarization and magnetization, which paves the way for the design and operation of novel functional devices and nanostructures.     

Single Sensor Compressive Light Field Video Camera

This paper presents a novel compressed sensing (CS) algorithm and camera design for light field video capture using a single sensor consumer camera module. Unlike microlens light field cameras which sacrifice spatial resolution to obtain angular information, our CS approach is designed for capturing light field videos with high angular, spatial, and temporal resolution. The compressive measurements required by CS are obtained using a random color-coded mask placed between the sensor and aperture planes. The convolution of the incoming light rays from different angles with the mask results in a single image on the sensor; hence, achieving a significant reduction on the required bandwidth for capturing light field videos. We propose to change the random pattern on the spectral mask between each consecutive frame in a video sequence and extracting spatio-angular-spectral-temporal 6D patches. Our CS reconstruction algorithm for light field videos recovers each frame while taking into account the neighboring frames to achieve significantly higher reconstruction quality with reduced temporal incoherencies, as compared with previous methods. Moreover, a thorough analysis of various sensing models for compressive light field video acquisition is conducted to highlight the advantages of our method. The results show a clear advantage of our method for monochrome sensors, as well as sensors with color filter arrays.     

Data-Mining-Cup 2007

Zusammenfassung  Die Aufgabe des Data-Mining-Cups 2007 war die Entwicklung einer vollautomatischen Methode zur effektiven Steuerung des Einsatzes von Rabattcoupons. Vorverarbeitung und Parameter wurden durch eine Kreuzvalidierung optimiert. Experimente zeigen, dass ein einzelner Klassifikator in der Regel nicht genügt, die Aufgabenstellung mit ausreichender Güte zu l?sen. Vielmehr müssen mehrere, m?glichst verschiedene, Klassifikatoren dazu herangezogen werden. Die eingereichten L?sungen entstanden durch Kombination von bis zu 2.000 Klassifikatoren und haben im Wettbewerb sehr gut abgeschnitten: Sechs von neun Abgaben lagen unter den ersten zehn Pl?tzen, die übrigen drei L?sungen sind immer noch unter den ersten 20 von insgesamt 230 Pl?tzen.     

Cloud computing adoption: an empirical study of customer preferences among start-up companies

Cloud computing represents a paradigm shift to utmost scalable and flexible IT services. However, research related to preferences of certain customers concerning cloud services is scarce. Especially start-up companies with their limited capacities to implement and operate IT infrastructure and their great demand for scalable and affordable IT resources are predestined as customers of cloud based services. In this study, we apply a multi-method approach to investigate customer preferences among start-up companies. Based on a literature review and a market analysis of cloud service models, we propose a set of cloud provider characteristics. These properties were examined among 108 start-up companies and analyzed in three steps using factor analysis to define customer preferences, cluster analysis to identify customer segments and discriminant analysis to validate the identified clusters. The results show that start-ups can be basically divided in five clusters each with certain requirements on cloud provider characteristics.