A Software Platform for Teaching Programming with Grading Systems

Красимир Манев, Милослав Средков, Петър Армянов - Състезателните системи (СС) са незаменимо средство за организация на състезания по програмиране. Напоследък СС се използват и в обучението по програмиране. В статията е предложена платформа, която да интегрира възможностите на СС, създадени или използвани от авторите. Целта е изграждането на проста и ефективна среда за обучение по програмиране, подпомагаща учебния процес. Специфицирани са основните елементи на платформата, като резултат от предходно изследване, и една нейна възможна архитектура.

Building OWL Ontology of Unique Bulgarian Bells Using Protégé Platform

We describe an ontological representation of data in an archive containing detailed description of church bells. As an object of cultural heritage the bell has general properties such as geometric dimensions, weight, sound of each of the bells, the pitch of the tone as well as acoustical diagrams obtained using contemporary equipment. We use Protégé platform in order to define basic ontological objects and relations between them.

How to Elaborate a Demonstrator in Inspiring Science Education Platform

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016

High resolution demodulation platform for large capacity hybrid WDM/FDM microstructures sensing system assisted by tunable FP filter

In this paper we proposed a demodulation scheme based on tunable FP filter for the WDM/FDM sensing system of the microstructure mentioned in the previous work. Simulation is done to prove the feasibility of demodulating the microstructure with the tunable FP filter. The experiments result showed high consistence with the simulation. And with the help of the high speed FPGA module and a high resolution AD/DA card, the system has achieved a very high resolution, up to 2.5 pm, and wavelength ranges 1520nm to 1590 nm.

Progress in the development of mesoporous solid acid and base catalysts for converting carbohydrates into platform chemicals

This chapter provides a general overview of recent studies on catalytic conversion of fructose, glucose, and cellulose to platform chemicals over porous solid acid and base catalysts, including zeolites, ion-exchange resins, heteropoly acids, as well as structured carbon, silica, and metal oxide materials. Attention is focused on the dehydration of glucose and fructose to HMF, isomerization of glucose to fructose, hydrolysis of cellulose to sugar, and glycosidation of cellulose to alkyl glucosides. The correlation of porous structure, surface properties, and the strength or types of acid or base with the catalyst activity in these reactions is discussed in detail in this chapter.

eBEST platform a KKV-klaszterek operatív és együttműködési folyamatainak támogatására (eBEST platform for supporting operational and cooperative processes of SME's clusters)

Az Európai Bizottság jelentése szerint a magyar kis- és középvállalkozások helyzete 2005 óta stagnál. Bár ezek a vállalkozások adják a magyar vállalkozások 99%-át, mégis a közbeszerzési, valamint a növekvő piacokhoz való hozzáférés terén számos akadállyal kerülnek szembe. Az eBEST projekten (Empowering Business Ecosystems of Small Service Enterprises to Face the Economic Crisis) belül kialakított platform olyan funkcionalitással bír, ami mindamellett, hogy lehetővé teszi a vállalkozások szervezett csoportokba, azaz ökoszisztémákba rendeződését, hozzá tud járulni a fogyasztói igények kielégítése érdekében létrejövő ellátási lánc, illetve egyedi folyamatok mentén fellépő információszerzési, kommunikációs vagy együttműködési akadályok lebontásához. ____ It is widely recognised that the most important factor for increasing the productivity of small companies is a deep adoption of computer-based applications and services. The FP7 SME eBEST project proposed a new operational environment specifically conceived for net worked small companies, supported by an advanced suite of ICT services, the eBEST platform. The paper aims at presenting the projects achievements that are validated by a number of company clusters of different EU countries and industry sectors. The general objectives of the eBEST project are attracting customers to work with the clustered companies, facilitating companies to collaborate with each other, and enabling associations to foster the devised innovation.

Platform Competition with Intra-Group Externalities

In this article we analyze asymmetric two-sided markets. Two types of agents are assumed to interact with each other and we assume that agents of one type derive utility from inter-group interactions, while the other type of agents benefit from intra-group rather than from inter-group interactions as it is assumed in the standard symmetric two-sided markets model. First, we consider a monopoly platform, then we analyze competing platforms, both with single-homing and multi-homing abilities.

Maria W. Stewart: America's first Black feminist

Maria W. Stewart was the first American-born and the first African-American woman known to address a mixed audience, from 1831 to 1833, and publish her essays and speeches. The purpose of this thesis is to examine Maria W. Stewart's acts of defiance--as the first public representation of Black Feminism: demanding that white America end slavery and grant rights to black men and women, re-appropriating the hegemonic, patriarchal codes which have significant social power by exposing their inconsistencies and deconstructing their ideologies, voicing the truth about the status of African-American women in early nineteenth-century America, and challenging Black women to become entrepreneurs and (as she did) acquire an education, establish schools, and take an active role in their community. ^

An integrated functional mapping and source localization platform for brain research

The premise of this dissertation is to create a highly integrated platform that combines the most current recording technologies for brain research through the development of new algorithms for three-dimensional (3D) functional mapping and 3D source localization. The recording modalities that were integrated include: Electroencephalography (EEG), Optical Topographic Maps (OTM), Magnetic Resonance Imaging (MRI), and Diffusion Tensor Imaging (DTI). This work can be divided into two parts: The first part involves the integration of OTM with MRI, where the topographic maps are mapped to both the skull and cortical surface of the brain. This integration process is made possible through the development of new algorithms that determine the probes location on the MRI head model and warping the 2D topographic maps onto the 3D MRI head/brain model. Dynamic changes of the brain activation can be visualized on the MRI head model through a graphical user interface. The second part of this research involves augmenting a fiber tracking system, by adding the ability to integrate the source localization results generated by commercial software named Curry. This task involved registering the EEG electrodes and the dipole results to the MRI data. Such Integration will allow the visualization of fiber tracts, along with the source of the EEG, in a 3D transparent brain structure. The research findings of this dissertation were tested and validated through the participation of patients from Miami Children Hospital (MCH). Such an integrated platform presented to the medical professionals in the form of a user-friendly graphical interface is viewed as a major contribution of this dissertation. It should be emphasized that there are two main aspects to this research endeavor: (1) if a dipole could be situated in time at its different positions, its trajectory may reveal additional information on the extent and nature of the brain malfunction; (2) situating such a dipole trajectory with respect to the fiber tracks could ensure the preservation of these fiber tracks (axons) during surgical interventions, preserving as a consequence these parts of the brain that are responsible for information transmission.

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).

System-on-a-Chip (SOC) based environmental monitoring platform for the detection of hazardous materials

The purpose of this research is design considerations for environmental monitoring platforms for the detection of hazardous materials using System-on-a-Chip (SoC) design. Design considerations focus on improving key areas such as: (1) sampling methodology; (2) context awareness; and (3) sensor placement. These design considerations for environmental monitoring platforms using wireless sensor networks (WSN) is applied to the detection of methylmercury (MeHg) and environmental parameters affecting its formation (methylation) and deformation (demethylation). ^ The sampling methodology investigates a proof-of-concept for the monitoring of MeHg using three primary components: (1) chemical derivatization; (2) preconcentration using the purge-and-trap (P&T) method; and (3) sensing using Quartz Crystal Microbalance (QCM) sensors. This study focuses on the measurement of inorganic mercury (Hg) (e.g., Hg2+) and applies lessons learned to organic Hg (e.g., MeHg) detection. ^ Context awareness of a WSN and sampling strategies is enhanced by using spatial analysis techniques, namely geostatistical analysis (i.e., classical variography and ordinary point kriging), to help predict the phenomena of interest in unmonitored locations (i.e., locations without sensors). This aids in making more informed decisions on control of the WSN (e.g., communications strategy, power management, resource allocation, sampling rate and strategy, etc.). This methodology improves the precision of controllability by adding potentially significant information of unmonitored locations.^ There are two types of sensors that are investigated in this study for near-optimal placement in a WSN: (1) environmental (e.g., humidity, moisture, temperature, etc.) and (2) visual (e.g., camera) sensors. The near-optimal placement of environmental sensors is found utilizing a strategy which minimizes the variance of spatial analysis based on randomly chosen points representing the sensor locations. Spatial analysis is employed using geostatistical analysis and optimization occurs with Monte Carlo analysis. Visual sensor placement is accomplished for omnidirectional cameras operating in a WSN using an optimal placement metric (OPM) which is calculated for each grid point based on line-of-site (LOS) in a defined number of directions where known obstacles are taken into consideration. Optimal areas of camera placement are determined based on areas generating the largest OPMs. Statistical analysis is examined by using Monte Carlo analysis with varying number of obstacles and cameras in a defined space. ^

The Engaged University: Providing a Platform for Research That Transforms Society

Despite a growing recognition that the solutions to current environmental problems will be developed through collaborations between scientists and stakeholders, substantial challenges stifle such cooperation and slow the transfer of knowledge. Challenges occur at several levels, including individual, disciplinary, and institutional. All of these have implications for scholars working at academic and research institutions. Fortunately, creative ideas and tested models exist that provide opportunities for conversation and serious consideration about how such institutions can facilitate the dialogue between scientists and society

Gesture Recognition Using Neural Networks Based on HW/SW Cosimulation Platform

Hardware/software (HW/SW) cosimulation integrates software simulation and hardware simulation simultaneously. Usually, HW/SW co-simulation platform is used to ease debugging and verification for very large-scale integration (VLSI) design. To accelerate the computation of the gesture recognition technique, an HW/SW implementation using field programmable gate array (FPGA) technology is presented in this paper. The major contributions of this work are: (1) a novel design of memory controller in the Verilog Hardware Description Language (Verilog HDL) to reduce memory consumption and load on the processor. (2) The testing part of the neural network algorithm is being hardwired to improve the speed and performance. The American Sign Language gesture recognition is chosen to verify the performance of the approach. Several experiments were carried out on four databases of the gestures (alphabet signs A to Z). (3) The major benefit of this design is that it takes only few milliseconds to recognize the hand gesture which makes it computationally more efficient.