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.

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.

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 non-epileptic 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).

Implementação em FPGA de módulo de efeito de reverberação

Reverberation is caused by the reflection of the sound in adjacent surfaces close to the sound source during its propagation to the listener. The impulsive response of an environment represents its reverberation characteristics. Being dependent on the environment, reverberation takes to the listener characteristics of the space where the sound is originated and its absence does not commonly sounds like “natural”. When recording sounds, it is not always possible to have the desirable characteristics of reverberation of an environment, therefore methods for artificial reverberation have been developed, always seeking a more efficient implementations and more faithful to the real environments. This work presents an implementation in FPGAs (Field Programmable Gate Arrays ) of a classic digital reverberation audio structure, based on a proposal of Manfred Schroeder, using sets of all-pass and comb filters. The developed system exploits the use of reconfigurable hardware as a platform development and implementation of digital audio effects, focusing on the modularity and reuse characteristics

Análise geoambiental da plataforma continental Rasa Equatorial, por meio de sensoriamento remoto e dados sedimentológicos: parrachos de Rio do Fogo-RN, Brasil

In the shallow continental shelf in Northeastern Rio Grande do Norte - Brazil, important underwater geomorphological features can be found 6km from the coastline. They are coral reefs, locally known as “parrachos”. The present study aims to characterize and analyze the geomorphological feature as well as the ones of the benthic surface, and the distribution of biogenic sediments found in parrachos at Rio do Fogo and associated shallow platforms, by using remote sensing products and in situ data collections. This was made possible due to sedimentological, bathymetric and geomorphological maps elaborated from composite bands of images from the satellite sensors ETM+/Landsat-7, OLI/Landsat-8, MS/GeoEye and PAN/WordView-1, and analysis of bottom sediments samples. These maps were analyzed, integrally interpreted and validated in fieldwork, thus permitting the generation of a new geomorphological zoning of the shallow shelf in study and a geoenvironmental map of the Parrachos in Rio do Fogo. The images used were subject to Digital Image Processing techniques. All obtained data and information were stored in a Geographic Information System (GIS) and can become available to the scientific community. This shallow platform has a carbonate bottom composed mostly by algae. Collected and analyzed sediment samples can be classified as biogenic carbonatic sands, as they are composed 75% by calcareous algae, according to the found samples. The most abundant classes are green algae, red algae, nonbiogenic sediments (mineral grains), ancient algae and molluscs. At the parrachos the following was mapped: Barreta Channel, intertidal reefs, submerged reefs, the spur and grooves, the pools, the sandy bank, the bank of algae, sea grass, submerged roads and Rio do Fogo Channel. This work presents new information about geomorphology and evolution in the study area, and will be guiding future decision making in the handling and environmental management of the region

Análise de usabilidade de livro digital na EaD: aplicação da avaliação heurística proposta por Pechansky

Nowadays the Distance Learning (DL) is in its fifth generation and New Information and Communication Technologies (NICT) mediate it. In this technological context it is then called Distance e-Learning (DeL). DeL has also in the e-books a potential interactive tool to the collaborative learning. So, this study was accomplished with the purpose of analyzing the e-books developed by SEDIS – Secretaria de Educação a Distância (Department of Distance Learning). This study was performed in two phases: Firstly it was done an exploratory study to check out the reading and the handling of digital e-learning material available to the student-users. These e-books are obtainable on SEDIS Moodle platform. Secondly, it was performed some analysis of an e-book sample by means of the heuristic evaluation. This research aims both to identify potential problems and give some suggestions to solve them. At last it is presented a final report with diagnosis and suggestions for a more applicable design in order to optimize the e-book by means of the distance e-learning usability guidelines

Otimização do sistema de supervisão e controle de uma planta de tratamento de resíduos utilizando plasma

A major and growing problems faced by modern society is the high production of waste and related effects they produce, such as environmental degradation and pollution of various ecosystems, with direct effects on quality of life. The thermal treatment technologies have been widely used in the treatment of these wastes and thermal plasma is gaining importance in processing blanketing. This work is focused on developing an optimized system of supervision and control applied to a processing plant and petrochemical waste effluents using thermal plasma. The system is basically composed of a inductive plasma torch reactors washing system / exhaust gases and RF power used to generate plasma. The process of supervision and control of the plant is of paramount importance in the development of the ultimate goal. For this reason, various subsidies were created in the search for greater efficiency in the process, generating events, graphics / distribution and storage of data for each subsystem of the plant, process execution, control and 3D visualization of each subsystem of the plant between others. A communication platform between the virtual 3D plant architecture and a real control structure (hardware) was created. The goal is to use the concepts of mixed reality and develop strategies for different types of controls that allow manipulating 3D plant without restrictions and schedules, optimize the actual process. Studies have shown that one of the best ways to implement the control of generation inductively coupled plasma techniques is to use intelligent control, both for their efficiency in the results is low for its implementation, without requiring a specific model. The control strategy using Fuzzy Logic (Fuzzy-PI) was developed and implemented, and the results showed satisfactory condition on response time and viability

Cloud Stratus: uma plataforma de middleware para desenvolvimento de aplicações em nuvem

Cloud Computing is a paradigm that enables the access, in a simple and pervasive way, through the network, to shared and configurable computing resources. Such resources can be offered on demand to users in a pay-per-use model. With the advance of this paradigm, a single service offered by a cloud platform might not be enough to meet all the requirements of clients. Ergo, it is needed to compose services provided by different cloud platforms. However, current cloud platforms are not implemented using common standards, each one has its own APIs and development tools, which is a barrier for composing different services. In this context, the Cloud Integrator, a service-oriented middleware platform, provides an environment to facilitate the development and execution of multi-cloud applications. The applications are compositions of services, from different cloud platforms and, represented by abstract workflows. However, Cloud Integrator has some limitations, such as: (i) applications are locally executed; (ii) users cannot specify the application in terms of its inputs and outputs, and; (iii) experienced users cannot directly determine the concrete Web services that will perform the workflow. In order to deal with such limitations, this work proposes Cloud Stratus, a middleware platform that extends Cloud Integrator and offers different ways to specify an application: as an abstract workflow or a complete/partial execution flow. The platform enables the application deployment in cloud virtual machines, so that several users can access it through the Internet. It also supports the access and management of virtual machines in different cloud platforms and provides services monitoring mechanisms and assessment of QoS parameters. Cloud Stratus was validated through a case study that consists of an application that uses different services provided by different cloud platforms. Cloud Stratus was also evaluated through computing experiments that analyze the performance of its processes.