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The ability to track moving people is a key aspect of autonomous robot systems in real-world environments. Whilst for many tasks knowing the approximate positions of people may be sufficient, the ability to identify unique people is needed to accurately count people in the real world. To accomplish the people counting task, a robust system for people detection, tracking and identification is needed.
The work being described in this paper is the result of a cooperation project between the Institute of Visual Computing at the Bonn-Rhein-Sieg University of Applied Sciences, Germany and the Laboratory of Biomedical Engineering at the Federal University of Uberlândia, Brazil. The aim of the project is the development of a virtual environment based training simulator which enables for better and faster learning the control of upper limb prostheses. The focus of the paper is the description of the technical setup since learning tutorials still need to be developed as well as a comprehensive evaluation still needs to be carried out.
This article describes an approach to rapidly prototype the parameters of a Java application run on the IBM J9 Virtual Machine in order to improve its performance. It works by analyzing VM output and searching for behavioral patterns. These patterns are matched against a list of known patterns for which rules exist that specify how to adapt the VM to a given application. Adapting the application is done by adding parameters and changing existing ones. The process is fully automated and carried out by a toolkit. The toolkit iteratively cycles through multiple possible parameter sets, benchmarks them and proposes the best alternative to the user. The user can, without any prior knowledge about the Java application or the VM improve the performance of the deployed application and quickly cycle through a multitude of different settings to benchmark them. When tested with the representative benchmarks, improvements of up to 150% were achieved.
A cost-efficient alternative to outside-in tracking systems for pointing interaction with large displays is to equip the pointing device with a camera, whose images are matched to display content. This work presents the Dynamic Marker Camera Tracking (DMCT) framework for display-based camera tracking. It accounts for typical display characteristics and uses dynamic on-screen markers overlaid to the display content that follow the camera. An example marker implementation and a tracking recovery method are presented. DMCT can measure pointing locations with sub-millimeter precision in large tracking volumes and computes 6-DoF camera poses for 3D interaction. 60 Hz update rate and 24 ms latency were achieved. DMCT's main limitation is the visible marker interfering with display content. In pointing effciency, the prototype is comparable to an OptiTrack system.
Software repository data, for example in issue tracking systems, include natural language text and technical information, which includes anything from log files via code snippets to stack traces. However, data mining is often only interested in one of the two types e.g. in natural language text when looking at text mining. Regardless of which type is being investigated, any techniques used have to deal with noise caused by fragments of the other type i.e. methods interested in natural language have to deal with technical fragments and vice versa. This paper proposes an approach to classify unstructured data, e.g. development documents, into natural language text and technical information using a mixture of text heuristics and agglomerative hierarchical clustering. The approach was evaluated using 225 manually annotated text passages from developer emails and issue tracker data. Using white space tokenization as a basis, the overall precision of the approach is 0.84 and the recall is 0.85.
This work describes extensions to the well-known Distributed Coordination Function (DCF) model to account for IEEE802.11n point-to-point links. The developed extensions cover adaptions to the throughput and delay estimation for this type of link as well peculiarities of hardware and implementations within the Linux Kernel. Instead of using simulations, the approach was extensively verified on real-world deployments at various link distances. Additionally, trials were conducted to optimize the CWmin values and the number of retries to maximize throughput and minimize delay. The results of this work can be used to estimate the properties of long-distance 802.11 links beforehand, allowing the network to be planned more accurately.
Realism and plausibility of computer controlled entities in entertainment software have been enhanced by adding both static personalities and dynamic emotions. Here a generic model is introduced that allows findings from real-life personality studies to be transferred to a computational model. Adaptive behavior patterns are enabled by introducing dynamic event-based emotions. The advantages of this model have been validated using a four-way crossroad in a traffic simulation. Driving agents using the introduced model enhanced by dynamics were compared to agents based on static personality profiles and simple rule-based behavior. The results show that adding a dynamic factor to agents improves perceivable plausibility and realism.
Robots, which are able to carry out their tasks robustly in real world environments, are not only desirable but necessary if we want them to be more welcome for a wider audience. But very often they may fail to execute their actions successfully because of insufficient information about behaviour of objects used in the actions.