The software communications architecture (SCA), which has been adopted as an SDR (software defined radio) Forum standard, provides a framework that successfully exploits common design patterns of distributed, real-time, and object-oriented embedded systems software. We have fully implemented the SCA v2.2 in C++. During this implementation process, we have encountered the lack of a suitable design pattern for releasing the SCA applications. Unfortunately, design patterns for releasing objects have been neither extensively addressed nor well investigated as opposed to creational design patterns. This is largely due to the fact that such releasing design patterns are highly dependent on programming languages. In this paper, we investigate three viable design patterns for releasing the SCA applications in C++ and discuss their pros and cons. In addition, we select the most portable and thus most reusable pattern, which we name Vulture design pattern, among those alternatives and detail our specific implementation.

Predictive displays have proven their suitability to compensate time delays in visual feedback which impair the performance of teleoperation applications. Most current implementations employ simple computer graphics for the prediction of the visual feedback given to the operator who is controlling the remote manipulation. Furthermore, they require a well-known remote environment. The aim of the presented work is to improve the efficacy of the predictive display by increasing the degree of immersion and by enhancing the system's flexibility. Potential applications include maintenance and repair of machines located in remote plant- or office-type environments, which are typically non-static and a priori only partially known. Communication media are high- and medium-bandwidth LANs and WANs inducing round trip delays varying from 0.2 to 2 seconds. Additionally, a sufficient bandwidth cannot be assured during the entire remote operation. The paper describes a predictive display system which achieves photo-realism based on autonomously explored and updated scene models. Model acquisition is performed with a binocular CCD camera system. Photo-realism is obtained by using computer graphics techniques, especially the mapping of original, real textures. Besides the concepts, an early stage of implementation and first experimental results in scene reconstruction and photo-realistic scene prediction are presented.

The presented approach integrates proven techniques and original approaches to one robust and fast 3D model-based recognition system. A speedy recognition is achieved by operating on a stream of filtered 3D sensor features, reconstructed for navigation tasks of the robot, instead of using a separate sensor data processing. Furthermore, simple, inexpensive recognition strategies are applied. Robustness is obtained by integrating complementary recognition strategies: four indexing techniques and two (2D and 3D) matching methods for verification, are completed by a hypothesis promotion, based on feedback of information to the sensor system. All strategies differ in their requirements, reliability, selectivity, and temporal constraints. Hypotheses are integrated using fusion, ruling out, and aging techniques. The approach is evaluated experimentally with varying calibration errors, scene complexity, and sensing conditions.