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Grid services will form the base for future computational Grids. Web Services, have been extended to build Grid services. Grid Services are dened in the Open Grid Service Architecture (OGSA). The Globus Alliance has released a Web Service Resource Framework, which is still under development and which is still missing vital parts. One of them is a Concept that allows Grid-Service Requests to securely traverse Firewalls, and its realization. This Thesis aims at the development and realization of a detailed Concept for an Application Level Gateway for Grid services, based on an existing rough concept. This approach should enable a strict division between a local network and the Internet. The internet is considered as a untrusted site and the local network is considered as a trusted site. Grid resources are placed in the internet as well as in the local network. This means that the possibility to communicate through a Firewall is essential. Some further protocols like Grid Resource Allocation and Management (GRAM) and the Grid File Transfer Protocol (GridFTP) must be able to traverse the network borders securely as well, while no further actions must be taken from the user side. The German Federal Oce for Information Security (BSI) proposes a Firewall - Application Level Gateway (ALG) - Firewall solution to the German Aerospace Center (DLR) where this Thesis is written, as a principle approach. In this approach, the local network is divided from the Internet with two rewalls. Between those rewalls is a demilitarized zone (DMZ), where computers may be placed, which can be accessed from the Internet and from the local network. An ALG which is placed in this DMZ should represent the local Grid nodes to the Internet and it should act as a client to the local nodes. All Grid service requests must be directed to the ALG instead of the protected Grid nodes. The ALG then checks and validates the requests on the application level (OSI layer 7). Requests that pose no security threat and fulll certain criteria will then be forwarded to the local Grid nodes. The responses from the local Grid nodes are checked and validated by the ALG as well.
Data management is a challenge in both scientific and technical environments. Therefore researchers have developed a special interest in this field. Modern approaches (i.e. Subversion, CVS) already offer authoring and versioning in distributed systems. However this might be insufficient in a vast number of scenarios, where not only the data resulting from a process, but also data which describes the process that generated those results is crucial.
The introduction of gestures as a supplementary input modality has become of increasing interest to human computer interaction design, especially for 3D computer environments. This thesis describes the concepts and development of a gesture recognition system based on the machine learning technique of Hidden Markov Models. Well-known from the field of speech recognition, this statistical method is employed in this thesis to represent and recognize predefined gestures. Within this work, gestures are defined as symbols, such as simple geometric shapes or Roman letters. They are extracted from a stream of three-dimensional optical tracking data which is resampled, reduced to 2D and quantized to be used as input to discrete Hidden Markov Models. A set of prerecorded training data is used to learn the parameters of the models and recognition is achieved by evaluating the trained models. The devised system was used to augment an existing virtual reality prototype application which serves as a demonstration and development platform for the VRGeo consortium. The consortium's goal is to investigate and utilize the benefits of virtual reality technology for the oil and gas industry. An isolated test of the system with seven gestures showed accuracies of up to 98.57% and the review from experts in the fields of virtual reality and geophysics was predominantly positive.
Today publications are digitally available which enables researchers to search the text and often also the content of tables. On the contrary, images cannot be searched which is not a problem for most fields, but in chemistry most of the information are contained in images, especially structure diagrams. Next to the "normal" chemical structures, which represent exactly one molecule, there also exist generic structures, so called Markush structures. These contain variable parts and additional textual information which enable them to represent several molecules at once. This can vary between just a few and up to thousands or even millions. This ability lead to a spread of Markush structures in patents, because it enables patents to protect entire families of molecules at once. Next to the prevention of an enumeration of all structures it also has the advantage that, if a Markush structure is used in a patent, it is much harder to determine whether a specific structure is protected by it or not. To solve the question about the protection of a structure, it is necessary to search the patents. Appropriate databases for this task already do exist, but are filled manually. An automatic processing does not yet exist. In this project a Markush structure reconstruction prototype is developed which is able to reconstruct bitmaps including Markush structures (meaning a depiction of the structure and a text part describing the generic parts) into a digital format and save them in the newly developed context-free grammar based file format extSMILES. This format is searchable due to its context-free grammar based design. To be able to develop a Markush structure reconstruction prototype, an in depth analysis of the concept of Markush structures and their requirements for a reconstruction process was performed. Thereby it is stated, that the common connection table concept of the existing file formats is not able to store Markush structures. Especially challenging are conditions for most of the formats. Thus, a context-free grammar based file format is developed, which extends the SMILES format. This extSMILES called format assures the searchability of the results by its context-free grammar based concept, and is able to store all information contained in Markush structures. In addition it is generic, extendable and easily understandable. The developed prototype for the Markush structure reconstruction uses extSMILES as output format and is based on the chemical structure recognition tool chemoCR and the Unstructured Information Management Architecture UIMA. For chemoCR modules are developed which enable it to recognize and assemble Markush structures as well as to return the reconstruction result in extSMILES. For UIMA on the other hand, a pipeline is developed, which is able to analyse and translate the input text files to extSMILES. The results of both tools then are combined and presented in chemoCR. An evaluation of the prototype is performed on a representative set of twelve structures of interest and low image quality which contain all typical Markush elements. Trivial structures containing only one R-group are not evaluated. Due to the challenging nature of the images, no Markush structure could be correctly reconstructed. But by regarding the assumption, that R-group definitions which are described by natural language are excluded from the task, and under the condition that the core structure reconstruction is improved, the rate of success can be increased to 58.4%.
Robots integrated into a social environment with humans need the ability to locate persons in their surrounding area. This is also the case for the WelcomeBot which is developed at the Fraunhofer Institute IAIS. In the future, the robot should follow persons in the buildings and guide them to certain areas. Therefore, it needs the capability to detect and track a person in the environment. In this master thesis, an approach for fast and reliable tracking of a person via a mobile robotic platform is presented. Based on the investigation of different methods and sensors, a laser scanner and a camera are selected as the primary two sensors.
Nowadays perception is still an up-to-date scienti fic issue on a mobile robot system. This thesis introduces an approach on how to recognize objects, namely numbers, using a digital camera on a Volksbot robot. The robot used in this thesis has been specifi cally designed for the SICK robot day. The development of the vision algorithm was done in two stages: the region of interest detection and the actual number recognition. Diff erent algorithms had been tested and evaluated and the Canny edge detector with contour finding has been proven to be the best choice for the region of interest detection and the Tesseract OCR engine was the best decision for number recognition. To integrate the vision component on an existing robot system, ROS was used. This thesis also discusses the integration of the EPOS motor controller into ROS.
This report presents an approach on a quadrotor dynamics stabilization based on ICP SLAM. Because the quadrotor lacks sensory information to detect its horizontal drift an additional sensor as Hokuyo-UTM has been used to perform on-line ICP-based SLAM. The obtained position estimates were used in control loops to maintain desired position and orientation of the vehicle. Such attitude parameters as height, yaw and position in space were controlled based on the laser data. As a result the quadrotor demonstrated two significant for autonomous navigation capabilities: performance of on-line SLAMon a flying vehicle and maintaining desired position in 3D space. Visual approach on optical flow based on Pyramid Lucas-Kanade algorithm has been touched and tested in different environmental conditions though hasn't been implemented in the control loop. Also the performance of the Hokuyo laser scanner and the related to it ICP SLAM algorithm have been tested in different environmental conditions indoors, outdoors and in presence of smoke. Results are presented and discussed. The requirement of performing on-line SLAM algorithm and to carry quite heavy equipment for it forced to seek a solution to increase the payload of the quadrotor with its computational power. A new hardware and distributed software architectures are therefore presented in the report.
In the eld of accessing and visualization mobile sensors and their recorded data, di erent approaches were realized. The OGC1 Sensor observation Service supplies a standard to access these information, stored on servers. To be able to access these servers, an interface must be developed and implemented. The result should be a con gurable development framework for web-based GIS clients supporting the OGC sensor observation services. In particular the framework should allow continuous position updates of mobile sensors. Visualization features like charts, bounding boxes of sensors and data series should be included.
The task of this thesis is to develop an OGC-compliant Sensor Observation Service (SOS) { a component of the SWE { for GPS related sensor data in this context. It should, in contrast to existing implementations, support full mobility of the sensors and be con gurable with respect to adding di erent kinds of sensors. In particular, mobile phones should be considered as sensors, which transmit their data to the SOS server through the transactional SOS interface.
This master thesis describes a supervised approach to the detection and the identification of humans in TV-style video sequences. In still images and video sequences, humans appear in different poses and views, fully visible and partly occluded, with varying distances to the camera, at different places, under different illumination conditions, etc. This diversity in appearance makes the task of human detection and identification to a particularly challenging problem. A possible solution of this problem is interesting for a wide range of applications such as video surveillance and content-based image and video processing. In order to detect humans in views ranging from full to close-up view and in the presence of clutter and occlusion, they are modeled by an assembly of several upper body parts. For each body part, a detector is trained based on a Support Vector Machine and on densely sampled, SIFT-like feature points in a detection window. For a more robust human detection, localized body parts are assembled using a learned model for geometric relations based on Gaussians. For a flexible human identification, the outward appearance of humans is captured and learned using the Bag-of-Features approach and non-linear Support Vector Machines. Probabilistic votes for each body part are combined to improve classification results. The combined votes yield an identification accuracy of about 80% in our experiments on episodes of the TV series "Buffy the Vampire Slayer". The Bag-of-Features approach has been used in previous work mainly for object classification tasks. Our results show that this approach can also be applied to the identification of humans in video sequences. Despite the difficulty of the given problem, the overall results are good and encourage future work in this direction.