Visual signatures in video visualization

Video visualization is a computation process that extracts meaningful information from original video data sets and conveys the extracted information to users in appropriate visual representations. This paper presents a broad treatment of the subject, following a typical research pipeline involving concept formulation, system development, a path-finding user study, and a field trial with real application data. In particular, we have conducted a fundamental study on the visualization of motion events in videos. We have, for the first time, deployed flow visualization techniques in video visualization. We have compared the effectiveness of different abstract visual representations of videos. We have conducted a user study to examine whether users are able to learn to recognize visual signatures of motions, and to assist in the evaluation of different visualization techniques. We have applied our understanding and the developed techniques to a set of application video clips. Our study has demonstrated that video visualization is both technically feasible and cost-effective. It has provided the first set of evidence confirming that ordinary users can be accustomed to the visual features depicted in video visualizations, and can learn to recognize visual signatures of a variety of motion events.     

Electrokinetic microslit experiments to analyse the charge formation at solid/liquid interfaces

Electrokinetic effects play an important role in microfluidics and nanofluidics. Although the related phenomena are often utilized to control fluid flow and sample transport in lab-on-a-chip devices, their dependency on the surface charges on the channel walls often remain enigmatic. This is mainly due to the lack of adequate experimental methods to analyse the electrical charging of solid/liquid interfaces of interest. To address this need, an experimental set-up—designated as microslit electrokinetic set-up (MES)—has been recently developed and applied for the investigation of charge formation processes at planar solid/liquid interfaces. The device permits to perform streaming potential and streaming current measurements across a rectangular streaming channel formed by two parallel sample carriers (20×10×3 mm³) at variable distance allowing for the determination of the surface conductivity. Utilizing the MES, charge characteristics can be determined for a wide variety of materials prepared as thin films on top of planar glass substrates. Streaming potential and streaming current data permit to investigate the mechanisms of charge formation while surface conductivity data provide information about mobile charge carriers located in different zones at the interface. The applicability of this advanced experimental approach is demonstrated with examples obtained for surfaces with different levels of complexity:

1. Preferential ion adsorption onto unpolar fluoropolymer (Teflon^® AF) films was characterized in simple electrolyte solutions; the results were quantitatively evaluated with respect to interfacial ion concentrations.
2. Interrelation of charge density and conformation of grafted poly(L-glutamic acid layers) were unravelled from the determination of pH-depended variations of surface conductivity and layer thickness.
3. The impact of spatial confinements of surface functional groups on their acid–base behaviour was studied with self-assembled monomolecular films of alkanethiols chemisorbed on gold.
4. Charging of and ion mobility within poly(acrylic acid) (PAA) brushes prepared by a Langmuir–Blodgett technique were analysed at varied pH and ionic strength.
5. Interfacial modes of adsorbed proteins were distinguished at two polymer surfaces with varied hydrophobicity/charge density.

     

Robustness of radial basis functions

Neural networks are intended to be used in future nanoelectronic technology since these architectures seem to be robust to malfunctioning elements and noise in its inputs and parameters. In this work, the robustness of radial basis function networks is analyzed in order to operate in noisy and unreliable environment. Furthermore, upper bounds on the mean square error under noise contaminated parameters and inputs are determined if the network parameters are constrained. To achieve robuster neural network architectures fundamental methods are introduced to identify sensitive parameters and neurons.     

Vanadia doped tungsten-titania SCR catalysts as functional materials for exhaust gas sensor applications

Urea-SCR systems (selective catalytic reduction) are required to meet future NO_x emission standards of heavy-duty and light-duty vehicles. It is a key factor to control the SCR systems and to monitor the catalysts’ functionalities to achieve low emissions. The novel idea of this study is to apply commercially available SCR catalyst materials based on vanadia-doped tungsten-titania as gas sensing films for impedimetric thick-film exhaust gas sensor devices. The dependence of the impedance on the surrounding gas atmosphere, especially on the concentrations of NH₃ and NO₂, is investigated, as well as cross interferences from other components of the exhaust. The sensors provide a good NH₃ sensitivity at 500 °C. The sensor behavior is explained in light of the literature combining the fields of catalysts and semiconducting gas sensors.     

Model complexity reduction of chemical reaction networks using mixed-integer quadratic programming

The model complexity reduction problem of large chemical reaction networks under isobaric and isothermal conditions is considered. With a given detailed kinetic mechanism and measured data of the key species over a finite time horizon, the complexity reduction is formulated in the form of a mixed-integer quadratic optimization problem where the objective function is derived from the parametric sensitivity matrix. The proposed method sequentially eliminates reactions from the mechanism and simultaneously tunes the remaining parameters until the pre-specified tolerance limit in the species concentration space is reached. The computational efficiency and numerical stability of the optimization are improved by a pre-reduction step followed by suitable scaling and initial conditioning of the Hessian involved. The proposed complexity reduction method is illustrated using three well-known case studies taken from reaction kinetics literature.     

Deforestation in the Miombo woodlands: a pixel-based semi-automated change detection method

Most methods of change detection require a considerable amount of effort and expertise. The procedures of change detection are visual-, classification-, object- or vector-based. The target of this research was to develop an automated and generally unsupervised combination of methods to quantify deforestation on a per pixel basis. The study area was the Gutu district in Zimbabwe. In the first step, Landsat Thematic Mapper (TM) scenes were spectrally unmixed by the Spectral Mixture Analysis (SMA). The calculation of the necessary endmembers was performed by means of the N-FINDR algorithm. After the unmixing process, the data were analysed with change vector analysis (CVA) utilizing spherical statistics. Thereafter, a combination of constraints, including a Bayesian threshold and spherical angles, was applied to identify deforestation. The combination of these methods provided an accurate idea of the state of deforestation and enabled attribution to ‘fire-induced’ and ‘non fire-induced’ classes.     

Monitoring post-tsunami vegetation recovery in Phang-Nga province,Thailand, based on IKONOS imagery and field investigations – a contribution to the analysis of tsunami vulnerability of coastal ecosystems

A major tsunami in December 2004 devastated the coastal ecosystems along the Andaman Sea coast of Thailand. Since intact coastal ecosystems provide many important services for local communities at the Andaman Sea, it is crucial to investigate to what extent (in terms of percentage area and speed) the affected ecosystems were capable of recovering after the tsunami. Field measurements and multi-date IKONOS imagery were used to estimate the recovery and succession patterns of coastal vegetation types in the Phang-Nga province of Thailand, three years after the tsunami. Thus, this study contributes to a holistic understanding of the ecological vulnerability of the coastal area to tsunamis. A zone-based change detection approach is applied by comparing two change detection techniques: the first method involves the calculation of a recovery rate based on multi-temporal TNDVI (transformed normalized difference vegetation index) images (TNDVI approach), whereas the second approach is a combined approach of the change vector analysis (CVA). Although these two methods provide different types of information (quantitative for the TNDVI approach, qualitative for the CVA), they are comparable in terms of results and accuracies. The results reveal that recovery processes vary based on the type of the ecosystem and, furthermore, are strongly influenced by human activities. Grasslands, coconut plantations and the mixed vegetation cover could recover faster than the mangroves and casuarina forests. Among the forest ecosystems, recovery rates of casuarina forests were higher than for mangroves, but the recovery area was smaller. This study also discusses the potential and some limitations and inaccuracies of applying high-resolution optical imagery for assessing vegetation recovery at a local scale.     

Online-Literaturdatenbanken im Bereich der Wirtschaftsinformatik: Bereitstellung wissenschaftlicher Literatur und Analyse von Interaktionen der Wissensteilung

Knowledge sharing is fundamental for innovations in the scientific community. The paper examines how the interaction of knowledge sharing is supported by online services. With respect to business information systems, a survey of online literature databases is given, providing an aggregation of various publication sources, a direct access to publications, and which are available to the public. Furthermore, special analysis services are presented which describe competitive interactions of knowledge sharing via rankings on the one hand, and cooperative interactions of knowledge sharing via co-authorship evaluation on the other.     

Nanosized titania derived from a novel sol-gel process for ammonia gas sensor applications

Highly redispersible anatase nanoparticles were prepared by a novel sol-gel based hydrothermal process for gas sensing applications. Thin titania films composed of nanoparticles were deposited on Au interdigital electrodes by dip-coating, annealed and tested in a gas test bench at 350 °C. The anatase films showed a very high sensitivity towards ammonia and no cross interference by CO₂, O₂ and C₃H₈. To classify the sensor as an ammonia gas sensor, a comparison with other sensor designs from literature has been performed.