AssetCollector: a system for managing collections of cultural assets

In this paper, AssetCollector is presented, which is a system for managing collections of cultural assets. AssetCollector covers the needs of collection curators towards defining, populating and searching a collection in a flexible way, while supporting them in generating reports based on the collection’s assets and reusing them in order to build web sites and CD-ROMs. In order to support the above functionality, the system provides the content structuring subsystem, the content input subsystem, the search subsystem and the report subsystem. The use of the subsystems is straightforward and requires no technical skills from the curators. AssetCollector has been successfully applied for organizing various collections of cultural assets in Greece, such as archaeological sites, museums and published books. In the future, an evaluation procedure is planned in order to further refine the use of the system according to the targeted users’ needs. Furthermore, more import and export facilities will be provided, which will make the system compliant with widely accepted standards.     

Performance analysis of IEEE 802.11 ad hoc networks in the presence of exposed terminals

The paper evaluates the performance effects of exposed terminals in IEEE 802.11 ad hoc networks in finite load conditions. It derives analytical models for the estimation of channel utilization and media access delay for IEEE 802.11 ad hoc networks in finite load conditions with and without exposed terminals. The simulation results show that the analytical estimated channel utilization and media access delay metrics are fairly accurate.     

Exergoeconomic analysis and evaluation of energy-conversion plants—I. A new general methodology

A new approach to the combination of exergetic and economic analysis (exergoeconomic analysis) for the investigation of energy-conversion processes is presented. This approach allows the monetary evaluation of costs caused by irreversibilities (exergy losses), as well as comparisons between these costs and the investment and operating costs for each component of a plant. The present analysis permits identification and evaluation of inefficiencies in an energy-conversion plant and of opportunities for improvement.     

Sequentially optimal repeated coalition formation under uncertainty

Coalition formation is a central problem in multiagent systems research, but most models assume common knowledge of agent types. In practice, however, agents are often unsure of the types or capabilities of their potential partners, but gain information about these capabilities through repeated interaction. In this paper, we propose a novel Bayesian, model-based reinforcement learning framework for this problem, assuming that coalitions are formed (and tasks undertaken) repeatedly. Our model allows agents to refine their beliefs about the types of others as they interact within a coalition. The model also allows agents to make explicit tradeoffs between exploration (forming “new” coalitions to learn more about the types of new potential partners) and exploitation (relying on partners about which more is known), using value of information to define optimal exploration policies. Our framework effectively integrates decision making during repeated coalition formation under type uncertainty with Bayesian reinforcement learning techniques. Specifically, we present several learning algorithms to approximate the optimal Bayesian solution to the repeated coalition formation and type-learning problem, providing tractable means to ensure good sequential performance. We evaluate our algorithms in a variety of settings, showing that one method in particular exhibits consistently good performance in practice. We also demonstrate the ability of our model to facilitate knowledge transfer across different dynamic tasks.     

Online learning and fusion of orientation appearance models for robust rigid object tracking

We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking. Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depth cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields. We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models (AAMs). To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles which does not require off-line training, and can be efficiently implemented online. The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work. This kernel enables us to cope with gross measurement errors, missing data as well as other typical problems such as illumination changes and occlusions. By combining the proposed models with a particle filter, the proposed framework was used for performing 2D plus 3D rigid object tracking, achieving robust performance in very difficult tracking scenarios including extreme pose variations.     

Enzymatic degradation of thermoresponsive poly(N‐isopropylacrylamide) grafted to carboxymethylcellulose copolymers

The enzymatic degradation of poly(N‐isopropyl acrylamide) (PNIPAM) grafted to carboxymethylcellulose (CMC) copolymers with a cellulasic preparation (Trichoderma viride) was studied. The enzymatic activity of the cellulasic preparation against CMC and the grafted copolymers was determined by the Petterson–Porath method, while their reduced viscosity variation in the presence of the same preparation was also followed. It has been shown that the enzymatic degradation behavior depends on the copolymer composition and the reaction temperature. Reducing sugars analysis showed that the experimental values for the grafted copolymers were higher than the calculated ones. At 50°C, the enzymatic reaction is completed in about 20 min for the copolymers, whereas for CMC it takes more than 40 min. It can be concluded that their enzymatic degradation is facilitated by the presence of the PNIPAM grafts. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 87: 1383–1386, 2003     

The contribution of proteomics towards deciphering the enigma of Coxiella burnetii

Coxiella burnetii, the causative agent of Q fever, is an intracellular bacterium and a potential weapon for bioterrorism. The widespread throughout the world, zoonosis is manifested clinically as a self-limited febrile illness, as pneumonia (acute Q fever) or as a chronic illness with endocarditis being its major complication. The recent Netherlands Q fever outbreak has driven the bacterium from a relatively cryptic, underappreciated, “niche” microorganism on the sideline of bacteriology, to one of possibly great impact on public health. Advances in the study of this microorganism proceeded slowly, primarily due to the, until recently, obligatory intracellular nature of the pathogen that in its virulent phase I must be manipulated under biosafety level-3 conditions. Proteomic studies, in particular, have generated a vast amount of information concerning several aspects of the bacterium such as virulence factors, detection/diagnostic and immunogenic biomarkers, inter-/intraspecies variation, resistance to antibiotics, and secreted effector proteins with significant clinical impact. The phenomenon observed following the genomics era, that of generation and accumulation of huge amount of data that ultimately end up unexploited on several databases, begins to emerge in the proteomics field as well. This review will focus on the advances in the field of C. burnetii proteomics through MS, attempting in parallel to utilize some of the proteomics findings by suggesting future directions for the improvement of Q fever diagnosis and therapy.     

Interactive mixed reality white cane simulation for the training of the blind and the visually impaired

This paper presents a mixed reality tool developed for the training of the visually impaired based on haptic and auditory feedback. The proposed approach focuses on the development of a highly interactive and extensible Haptic Mixed Reality training system that allows visually impaired to navigate into real size Virtual Reality environments. The system is based on the use of the CyberGrasp™ haptic device. An efficient collision detection algorithm based on superquadrics is also integrated into the system so as to allow real time collision detection in complex environments. A set of evaluation tests is designed in order to identify the importance of haptic, auditory and multimodal feedback and to compare the MR cane against the existing Virtual Reality cane simulation system.