The Politics of the City Image: The Resurrection of the Kul-Sharif Mosque in the Kazan Kremlin (1995-2005)

This article studies the resurrection of the Kul-Sharif Mosque in the Kazan Kremlin in the context of the political transformations that Russia and the Republic of Tatarstan went through in the 1990s. The original structure was destroyed in 1552 when Ivan the Terrible conquered the capital of then Kazan Khanate. In 1990 the Republic of Tatarstan declared its sovereign status. The mosque was cited as a symbol of the heroic resistance to the Russian invaders, restoration of the Tatarstani statehood and cultural revival of the Tatar nation. The mosque became an essential part of the new image of Kazan, the capital city of Tatarstan. The article explores the mechanisms that brought the project into being and attempts to conceptualize the links between the image of the city and political and economic forces of the society.     

Intelligent sensor system for discrimination of material type using neural networks

Neural network based classification of material type even with the variation in the sensor parameter is investigated in this paper. The sensor is developed by means of a lightweight plunger probe and an optical mouse sensor. An experimental prototype was developed which involves bouncing or hopping of the plunger based impact probe freely on the plain surface of an object under test. The experiment is conducted to obtain the bouncing signals for plain surface of an objects kept at different distances from the probe. During the bouncing of the probe, time varying signals are generated from optical mouse that are recorded in data files on PC. Some dominant unique features are then extracted using signal processing tools to optimize neural network based classifier. The time and features of bouncing signal are related to the material type, and each material has a unique set of such properties. It is found that the sensor system is intelligent due to its ability to classify the material type even with the variation in the sensor parameter (distance between the sensor probe and plain objects). The classifiers are developed using two neural networks configurations, namely a well-known Multi-layer Perceptron Neural Networks (MLP NN), and Radial Basis Function Neural Networks (RBF NN). MLP NN and RBF NN models are designed to maximize accuracy under the constraints of minimum network dimension.The optimal parameters of MLP NN and RBF NN models based on various performance measures that include percentage classification accuracy (PCLA) on the testing data, and area under Receiver Operating Characteristics (ROC), and are determined. For the sensor data set, the PCLA of both the classifiers are found reasonable consistently in respect of rigorous testing using different data partitions. The areas under the ROC curves are close to unity. Performances of the two classifiers have been compared. It has been found that the RBF NN is more robust to noise, and epochs required for training are very less as compared to that for MLP NN.     

Costs and benefits of behavioral specialization

In this work, we study behavioral specialization in a swarm of autonomous robots. In the studied swarm, robots have to carry out tasks of different types that appear stochastically in time and space in a given environment. We consider a setting in which a robot working repeatedly on tasks of the same type improves its performance on them due to learning. Robots can exploit learning by adapting their task selection behavior, that is, by selecting with higher probability tasks of the type on which they have improved their performance. This adaptation of behavior is called behavioral specialization. We employ a simple task allocation strategy that allows a swarm of robots to behaviorally specialize. We study the influence of different environmental parameters on the performance of the swarm and show that the swarm can exploit learning successfully. However, there is a trade-off between the benefits and the costs of specialization. We study this trade-off in multiple experiments using different swarm sizes. Our experimental results indicate that spatiality has a major influence on the costs and benefits of specialization.     

Web video classification with visual and contextual semantics

On the social Web, the amount of video content either originated from wireless devices or previously received from media servers has increased enormously in the recent years. The astounding growth of Web videos has stimulated researchers to propose new strategies to organize them into their respective categories. Because of complex ontology and large variation in content and quality of Web videos, it is difficult to get sufficient, precisely labeled training data, which causes hindrance in automatic video classification. In this paper, we propose a novel content‐ and context‐based Web video classification framework by rendering external support through category discriminative terms (CDTs) and semantic relatedness measure (SRM). Mainly, a three‐step framework is proposed. Firstly, content‐based video classification is proposed, where twofold use of high‐level concept detectors is leveraged to classify Web videos. Initially, category classifiers induced from VIREO‐374 detectors are trained to classify Web videos, and then concept detectors with high confidence for each video are mapped to CDT through SRM‐assisted semantic content fusion function to further boost the category classifiers, which intuitively provide a more robust measure for Web video classification. Secondly, a context‐based video classification is proposed, where twofold use of contextual information is also harnessed. Initially, cosine similarity and then semantic similarity are measured between text features of each video and CDT through vector space model (VSM)‐ and SRM‐assisted semantic context fusion function, respectively. Finally, classification results from content and context are fused to compensate for the shortcomings of each other, which enhance the video classification performance. Experiments on large‐scale video dataset validate the effectiveness of the proposed solution.     

Solid Propellant Burning Rate From Strand Burner Pressure Measurement

Strand burner pressure–time data are analyzed to determine if the propellant burning rate can be extracted. This approach is based on strand burner pressure–time history that is related to the temperature change due to exothermic reaction heating of chamber gases and gas addition to the chamber by propellant combustion products. In support of this method, chemical equilibrium calculations were made to project product composition, internal energy, and other needed properties. A mathematical model was formulated and solved numerically and the calculated burning rates were compared with the experimental wire‐break time results provided simultaneously and with the propellant manufacturer's results, when available. The comparisons reveal that the approach has merit and that more accurate pressure determination coupled with additional thermochemical information and strand burner gas temperature measurements has the potential to make this approach a viable technique and one that can be applied in conjunction with other burning rate measurements. The proposed method is similar to a well‐developed technique which is commonly applied to ballistic powders but with adjustments for the differences in geometry, pressure, and time of event.     

Context-aware platform for mobile data management

Interaction design is a major issue for mobile information systems in terms of not only the choice of input/output channels and presentation of information, but also the application of context-awareness. To support experimentation with these factors, we have developed platforms to support the rapid prototyping of multi-channel, multi-modal, context-aware applications. The Java-based platform presented here is based on an integration of a cross-media link server and an object-oriented framework for advanced content publishing, along with a Client Controller and Context Engine. We also describe how this platform was used to develop a mobile tourist information system for an international arts festival where interaction was based on a combination of interactive paper and speech output. Moira C. Norrie is a Professor at ETH Zurich where she is head of the Institute for Information Systems and leads the Global Information Systems research group. Her research interests include object-oriented models and systems for data management, web engineering, mobile and personal information systems and interactive paper as a medium for integrating printed and digital information. Beat Signer is a Post-Doctoral researcher in the Global Information Systems research group at ETH Zurich. He received a Ph.D. from ETH Zurich in 2005 for his work investigating fundamental concepts for interactive paper and cross-media information management. His research interests include interactive paper, cross-media information management, object-oriented technologies and software engineering. Michael Grossniklaus is a research assistant in the Global Information Systems research group at ETH Zurich. He received a Diploma (M.Sc.) in Computer Science from ETH Zurich in 2001 and is currently completing his Ph.D. His main research interest is empowering information systems for context-aware data management and delivery in the domain of web engineering and mobile computing. Rudi Belotti was a research assistant in the Global Information Systems research group at ETH Zurich from 2004–2006. He received a Diploma (M.Sc.) in Computer Science from ETH Zurich in 2004. In his research, he developed a general model and engine for the management of context information in mobile information systems. He is currently working for an e-business services company in Ticino, Switzerland. Corsin Decurtins is a research assistant in the Global Information Systems research group at ETH Zurich. He received a Diploma (M.Sc.) in Computer Science from ETH Zurich in 2002. His research focusses on model-based approaches and infrastructure for ubiquitous and mobile information environments. In addition to his Ph.D. Corsin also works part-time as a senior software engineer at the software company Netcetera. Nadir Weibel is a research assistant in the Global Information Systems research group at ETH Zurich. He received a Diploma (M.Sc.) in Computer Science from ETH Zurich in 2003 and is currently working on his Ph.D. His research is in the area of interactive paper, particularly on the authoring and publishing infrastructure for interactive documents as well as issues of human computer interaction and mobile environments.     

Multiparametric investigation of competitive and noncompetitive sorption characteristics of SMP fractions (carbohydrate and protein) on activated carbon

Sorption characteristics of soluble microbial products (SMPs) as carbohydrate and protein on activated carbon were investigated. Batch experiments were conducted to evaluate the sorption kinetics and the equilibrium conditions. The parameters studied included initial SMP concentration (50-200mg/L), activated carbon dosage (0.25-50 g/L), contact time (0.02-4h), particle size of activated carbon used (5-75 μm, 75-850 μm, and 850-1000 μm), and presence of one or both SMP fractions. The equilibrium sorption of carbohydrate and protein were significantly affected by the presence of the second SMP fraction in the solutions. Adsorption isotherms were expressed by the Langmuir and Freundlich models. The adsorption rates under noncompetitive and competitive conditions were analyzed with kinetics-based Lagergren pseudo-first order and pseudo-second order models; and diffusion-based external diffusion and Weber-Morris intraparticle models. Both SMP fractions were removed effectively, however, sorption of protein was significantly better than that of carbohydrate in all cases. The relatively significant effect of particle size on sorption of protein indicates that protein is most likely adsorbed as a single layer on the carbon surface. For the carbohydrate, the increase in particle size did not decrease the sorption significantly indicating that carbohydrate may be adsorbed in multiple layers or may diffuse into the porous matrix more effectively.     

Effect of species and grinding disc distance on the surface roughness parameters of medium density fiberboard

Surface quality of medium density fiberboard (MDF), as a very important criterion for further finishing and utilization, was evaluated using a wide range of measuring parameters (instruments, filters, filtering cut-off length, measuring length, measuring resolution, roughness parameters). However, these various approaches make any data comparison rather difficult. Furthermore, filtering the measured data with a simple Gaussian filter has proved unreliable for wood surfaces. A new, more robust approach by using a Robust Gaussian Regression filter together with a previously tested set of measuring and evaluating parameters was used in this paper in order to quantify the surface roughness of MDF manufactured from different species (beech, poplar, birch, Scots pine, mixture 50 % Scots pine and 50 % beech) and using three different grinding disc distances (0.06, 0.15 and 0.6 mm). If manufacturing parameters are kept constant, the processing roughness of MDF is similar, but variable species anatomy and fuzziness effects will give differentiation in total roughness. The results indicated that fibers made of a mixture of wood chips from Scots pine and beech led to the smoothest MDF surface, followed closely by poplar, birch, and Scots pine. The roughest surface was measured for the panels made of beech fibers. Investigating the influence of the grinding disc distance, the roughest MDF surface was obtained when a grinding gap of 0.6 mm was used. However, surface roughness was found to be quite similar applying a grinding disc distance of 0.06 mm slightly lower for 0.15 mm. This finding indicates that a too fine grinding gap does not improve the MDF surface roughness.