The Future of the Next-Generation Internet and Possible Applications into Education and Culture Heritage

There are several initiatives such as: US Ignite, Software Defined Networking (SDN), OpenFlow, Global Environment for Network Innovation (GENI), WiMAX and Internet 2 dealing with the future of the internet. The goal of the paper is to understand the goals, intricacies, and nuances of some of these techniques and show some of the possibilities of next-generation high-speed networking and their applications into education and culture heritage.

Mobile Applications for Cultural and Historical Places

A review of free applications of smartphones working under the operation system of Android is made in the paper. The applications present users information about historical and cultural places of interest at travelling. There are three main groups of applications subject of discussion in the paper – world, national and regional. Their abilities, positive and negative characteristics are compares and described. A conclusion can be made that there is a necessity of new application that presents tourists detailed information about the Old capital of Bulgaria.

Codifferentiable Mappings with Applications to Vector Optimality

AMS subject classification: Primary 49J52; secondary: 26A27, 90C48, 47N10.

Branching Stochastic Processes: Regulation, Regeneration, Estimation, Applications

2000 Mathematics Subject Classification: 60J80.

Riccati Representation for Elements in H^(-1) and Its Applications

2002 Mathematics Subject Classification: 35L05, 34L15, 35D05, 35Q53

Trends in Digital Cultural Heritage Management and Applications

We present some recent trends in the field of digital cultural heritage management and applications including digital cultural data curation, interoperability, open linked data publishing, crowd sourcing, visualization, platforms for digital cultural heritage, and applications. We present some examples from research and development projects of MUSIC/TUC in those areas.

Modular Digital Watermarking for Image Verification and Secure Data Storage in Web Applications

Our modular approach to data hiding is an innovative concept in the data hiding research field. It enables the creation of modular digital watermarking methods that have extendable features and are designed for use in web applications. The methods consist of two types of modules – a basic module and an application-specific module. The basic module mainly provides features which are connected with the specific image format. As JPEG is a preferred image format on the Internet, we have put a focus on the achievement of a robust and error-free embedding and retrieval of the embedded data in JPEG images. The application-specific modules are adaptable to user requirements in the concrete web application. The experimental results of the modular data watermarking are very promising. They indicate excellent image quality, satisfactory size of the embedded data and perfect robustness against JPEG transformations with prespecified compression ratios. ACM Computing Classification System (1998): C.2.0.

An extension of Lorentz's almost convergence and applications in Banach spaces

2000 Mathematics Subject Classification: Primary 40C99, 46B99.

Dispersion Phenomena in Dunkl-Schrödinger Equation and Applications

2000 Mathematics Subject Classification: 35Q55,42B10.

Local Energy Decay in Even Dimensions for the Wave Equation with a Time-Periodic Non-Trapping Metric and Applications to Strichartz Estimates

2000 Mathematics Subject Classification: 35B40, 35L15.

Stabilization of troponin I for applications in clinical chemistry and forensic medicine. Cardiac troponin I: A time of death marker

Cardiac troponin I (cTnI) is one of the most useful serum marker test for the determination of myocardial infarction (MI). The first commercial assay of cTnI was released for medical use in the United States and Europe in 1995. It is useful in determining if the source of chest pains, whose etiology may be unknown, is cardiac related. Cardiac TnI is released into the bloodstream following myocardial necrosis (cardiac cell death) as a result of an infarct (heart attack). In this research project the utility of cardiac troponin I as a potential marker for the determination of time of death is investigated. The approach of this research is not to investigate cTnI degradation in serum/plasma, but to investigate the proteolytic breakdown of this protein in heart tissue postmortem. If our hypothesis is correct, cTnI might show a distinctive temporal degradation profile after death. This temporal profile may have potential as a time of death marker in forensic medicine. The field of time of death markers has lagged behind the great advances in technology since the late 1850's. Today medical examiners are using rudimentary time of death markers that offer limited reliability in the medico-legal arena. Cardiac TnI must be stabilized in order to avoid further degradation by proteases in the extraction process. Chemically derivatized magnetic microparticles were covalently linked to anti-cTnI monoclonal antibodies. A charge capture approach was also used to eliminate the antibody from the magnetic microparticles given the negative charge on the microparticles. The magnetic microparticles were used to extract cTnI from heart tissue homogenate for further bio-analysis. Cardiac TnI was eluted from the beads with a buffer and analyzed. This technique exploits banding pattern on sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) followed by a western blot transfer to polyvinylidene fluoride (PVDF) paper for probing with anti-cTnI monoclonal antibodies. Bovine hearts were used as a model to establish the relationship of time of death and concentration/band-pattern given its homology to human cardiac TnI. The final concept feasibility was tested with human heart samples from cadavers with known time of death. ^

A research platform for artificial neural networks with applications in pediatric epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and nonepileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that (1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and (2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Scheduling medical application workloads on virtualized computing systems

This dissertation presents and evaluates a methodology for scheduling medical application workloads in virtualized computing environments. Such environments are being widely adopted by providers of "cloud computing" services. In the context of provisioning resources for medical applications, such environments allow users to deploy applications on distributed computing resources while keeping their data secure. Furthermore, higher level services that further abstract the infrastructure-related issues can be built on top of such infrastructures. For example, a medical imaging service can allow medical professionals to process their data in the cloud, easing them from the burden of having to deploy and manage these resources themselves. In this work, we focus on issues related to scheduling scientific workloads on virtualized environments. We build upon the knowledge base of traditional parallel job scheduling to address the specific case of medical applications while harnessing the benefits afforded by virtualization technology. To this end, we provide the following contributions: (1) An in-depth analysis of the execution characteristics of the target applications when run in virtualized environments. (2) A performance prediction methodology applicable to the target environment. (3) A scheduling algorithm that harnesses application knowledge and virtualization-related benefits to provide strong scheduling performance and quality of service guarantees. In the process of addressing these pertinent issues for our target user base (i.e. medical professionals and researchers), we provide insight that benefits a large community of scientific application users in industry and academia. Our execution time prediction and scheduling methodologies are implemented and evaluated on a real system running popular scientific applications. We find that we are able to predict the execution time of a number of these applications with an average error of 15%. Our scheduling methodology, which is tested with medical image processing workloads, is compared to that of two baseline scheduling solutions and we find that it outperforms them in terms of both the number of jobs processed and resource utilization by 20–30%, without violating any deadlines. We conclude that our solution is a viable approach to supporting the computational needs of medical users, even if the cloud computing paradigm is not widely adopted in its current form.