Computing the persistent homology of range images with alpha shapes

Laser scanning is becoming an increasingly popular method for measuring 3D objects in industrial design. Laser scanners produce a cloud of 3D points. For CAD software to be able to use such data, however, this point cloud needs to be turned into a vector format. A popular way to do this is to triangulate the assumed surface of the point cloud using alpha shapes. Alpha shapes start from the convex hull of the point cloud and gradually refine it towards the true surface of the object. Often it is nontrivial to decide when to stop this refinement. One criterion for this is to do so when the homology of the object stops changing. This is known as the persistent homology of the object. The goal of this thesis is to develop a way to compute the homology of a given point cloud when processed with alpha shapes, and to infer from it when the persistent homology has been achieved. Practically, the computation of such a characteristic of the target might be applied to power line tower span analysis.

Perspectivas tecnológicas: educação superior na Ibero-américa 2012-2017

La sèrie d'informes Horizon és el resultat més tangible del Projecte Horizon del New Media Consortium, un esforç de recerca qualitativa iniciat el 2002, que identifica i descriu les tecnologies emergents amb més potencial d'impacte en l'ensenyament, l'aprenentatge, la recerca i la expressió creativa en l'àmbit educatiu global. Aquest volum, Perspectivas tecnológicas: educação superior na Ibero-américa 2012-2017, és la segona edició del Projecte Horizon Iberoamèrica i se centra en la investigació en els països de la regió Iberoamericana (incloent-hi tota Llatinoamèrica, Espanya i Portugal) i en l'àmbit de l'educació superior. Ha estat produït pel NMC i l'eLearn Center de la Universitat Oberta de Catalunya.

Alternative Techniques of Neural Signal Processing in Neuroengineering

Neural signal processing is a discipline within neuroengineering. This interdisciplinary approach combines principles from machine learning, signal processing theory, and computational neuroscience applied to problems in basic and clinical neuroscience. The ultimate goal of neuroengineering is a technological revolution, where machines would interact in real time with the brain. Machines and brains could interface, enabling normal function in cases of injury or disease, brain monitoring, and/or medical rehabilitation of brain disorders. Much current research in neuroengineering is focused on understanding the coding and processing of information in the sensory and motor systems, quantifying how this processing is altered in the pathological state, and how it can be manipulated through interactions with artificial devices including brain–computer interfaces and neuroprosthetics.

Neural mechanisms underlying adaptive actions after slips

An increase in cognitive control has been systematically observed in responses produced immediately after the commission of an error. Such responses show a delay in reaction time (post-error slowing) and an increase in accuracy. To characterize the neurophysiological mechanism involved in the adaptation of cognitive control, we examined oscillatory electrical brain activity by electroencephalogram and its corresponding neural network by event-related functional magnetic resonance imaging in three experiments. We identified a new oscillatory thetabeta component related to the degree of post-error slowing in the correct responses following an erroneous trial. Additionally, we found that the activity of the right dorsolateral prefrontal cortex, the right inferior frontal cortex, and the right superior frontal cortex was correlated with the degree of caution shown in the trial following the commission of an error. Given the overlap between this brain network and the regions activated by the need to inhibit motor responses in a stop-signal manipulation, we conclude that the increase in cognitive control observed after the commission of an error is implemented through the participation of an inhibitory mechanism.

The Connectivity of Musical Aptitude and Foreign Language Learning Skills: Neural and Behavioural Evidence

Given the structural and acoustical similarities between speech and music, and possible overlapping cerebral structures in speech and music processing, a possible relationship between musical aptitude and linguistic abilities, especially in terms of second language pronunciation skills, was investigated. Moreover, the laterality effect of the mother tongue was examined with both adults and children by means of dichotic listening scores. Finally, two event-related potential studies sought to reveal whether children with advanced second language pronunciation skills and higher general musical aptitude differed from children with less-advanced pronunciation skills and less musical aptitude in accuracy when preattentively processing mistuned triads and music / speech sound durations. The results showed a significant relationship between musical aptitude, English language pronunciation skills, chord discrimination ability, and sound-change-evoked brain activation in response to musical stimuli (durational differences and triad contrasts). Regular music practice may also have a modulatory effect on the brain’s linguistic organization and cause altered hemispheric functioning in those who have regularly practised music for years. Based on the present results, it is proposed that language skills, both in production and discrimination, are interconnected with perceptual musical skills.

Use of neural networks in robot positioning of large flexible redundant manipulators

Deflection compensation of flexible boom structures in robot positioning is usually done using tables containing the magnitude of the deflection with inverse kinematics solutions of a rigid structure. The number of table values increases greatly if the working area of the boom is large and the required positioning accuracy is high. The inverse kinematics problems are very nonlinear, and if the structure is redundant, in some cases it cannot be solved in a closed form. If the structural flexibility of the manipulator arms is taken into account, the problem is almost impossible to solve using analytical methods. Neural networks offer a possibility to approximate any linear or nonlinear function. This study presents four different methods of using neural networks in the static deflection compensation and inverse kinematics solution of a flexible hydraulically driven manipulator. The training information required for training neural networks is obtained by employing a simulation model that includes elasticity characteristics. The functionality of the presented methods is tested based on the simulated and measured results of positioning accuracy. The simulated positioning accuracy is tested in 25 separate coordinate points. For each point, the positioning is tested with five different mass loads. The mean positioning error of a manipulator decreased from 31.9 mm to 4.1 mm in the test points. This accuracy enables the use of flexible manipulators in the positioning of larger objects. The measured positioning accuracy is tested in 9 separate points using three different mass loads. The mean positioning error decreased from 10.6 mm to 4.7 mm and the maximum error from 27.5 mm to 11.0 mm.

Infancia, Adolescencia y Tecnologías de la Información y la Comunicación (TICs) en Perspectiva Psicosocial = Childhood, Adolescence and Information Communication Technologies (ICTs) in Psychosocial Perspective

Sobre el impacto que las TIC pueden tener en las relaciones interpersonales, ya sean en la familia y/o con los amigos/as, desde una perspectiva psicosocial

Readout electronics for low dark count pixel detectors based on geiger mode avalanche photodiodes fabricated in conventional CMOS technologies for future linear colliders

The high sensitivity and excellent timing accuracy of Geiger mode avalanche photodiodes makes them ideal sensors as pixel detectors for particle tracking in high energy physics experiments to be performed in future linear colliders. Nevertheless, it is well known that these sensors suffer from dark counts and afterpulsing noise, which induce false hits (indistinguishable from event detection) as well as an increase of the necessary area of the readout system. In this work, we present a comparison between APDs fabricated in a high voltage 0.35 µm and a high integration 0.13 µm commercially available CMOS technologies that has been performed to determine which of them best fits the particle collider requirements. In addition, a readout circuit that allows low noise operation is introduced. Experimental characterization of the proposed pixel is also presented in this work.

Low dark count geiger mode avalanche photodiodes fabricated in conventional CMOS technologies

Avalanche photodiodes operated in the Geiger mode present very high intrinsic gain and fast time response, which make the sensor an ideal option for those applications in which detectors with high sensitivity and velocity are required. Moreover, they are compatible with conventional CMOS technologies, allowing sensor and front-end electronics integration within the pixel cell. Despite these excellent qualities, the photodiode suffers from high intrinsic noise, which degrades the performance of the detector and increases the memory area to store the total amount of information generated. In this work, a new front-end circuit that allows low reverse bias overvoltage sensor operation to reduce the noise in Geiger mode avalanche photodiode pixel detectors is presented. The proposed front-end circuit also enables to operate the sensor in the gated acquisition mode to further reduce the noise. Experimental characterization of the fabricated pixel with the conventional HV-AMS 0.35µm technology is also presented in this article.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Description of epidemics of coffee rust with neural networks

The objective of this paper was to evaluate the potential of neural networks (NN) as an alternative method to the basic epidemiological approach to describe epidemics of coffee rust. The NN was developed from the intensities of coffee (Coffea arabica) rust along with the climatic variables collected in Lavras-MG between 13 February 1998 and 20 April 2001. The NN was built with climatic variables that were either selected in a stepwise regression analysis or by the Braincel® system, software for NN building. Fifty-nine networks and 26 regression models were tested. The best models were selected based on small values of the mean square deviation (MSD) and of the mean prediction error (MPE). For the regression models, the highest coefficients of determination (R²) were used. The best model developed with neural networks had an MSD of 4.36 and an MPE of 2.43%. This model used the variables of minimum temperature, production, relative humidity of the air, and irradiance 30 days before the evaluation of disease. The best regression model was developed from 29 selected climatic variables in the network. The summary statistics for this model were: MPE=6.58%, MSE=4.36, and R²=0.80. The elaborated neural networks from a time series also were evaluated to describe the epidemic. The incidence of coffee rust at four previous fortnights resulted in a model with MPE=4.72% and an MSD=3.95.

Platform as a Service - new opportunities for software development companies

Today cloud computing is the next stage in development information-oriented society in field of information technologies. Great attention is paid to cloud computing in general, but the lack of scientific consideration to components brings to the problem, that not all aspects are well examined. This thesis is an attempt to consider Platform as a Service (a technology of providing development environment through the Internet) from divergent angles. Technical characteristics, costs, time, estimation of effectiveness, risks, strategies that can be applied, migration process, advantages and disadvantages and the future of the approach are examined to get the overall picture of cloud platforms. During the work literature study was used to examine Platform as a Service, characteristics of existent cloud platforms were explored and a model of a typical software development company was developed to create a scenario of migration to cloud technologies. The research showed that besides all virtues in reducing costs and time, cloud platforms have some significant obstacles in adoption. Privacy, security and insufficient legislation impede the concept to be widespread.

Development of Calculation Procedures for Wastewater Treatment Technologies for a Training System

Efficient designs and operations of water and wastewater treatment systems are largely based on mathematical calculations. This even applies to training in the treatment systems. Therefore, it is necessary that calculation procedures are developed and computerised a priori for such applications to ensure effectiveness. This work was aimed at developing calculation procedures for gas stripping, depth filtration, ion exchange, chemical precipitation, and ozonation wastewater treatment technologies to include them in ED-WAVE, a portable computer based tool used in design, operations and training in wastewater treatment. The work involved a comprehensive online and offline study of research work and literature, and application of practical case studies to generate ED-WAVE compatible representations of the treatment technologies which were then uploaded into the tool.

Supermatrix-hankkeen vahvuudet, heikkoudet, mahdollisuudet ja uhat

Supermatrix on Finnet-liitto ry:n jäsenyritysten hanke, joka tuo nopeiden valokuituyhteyksien avulla erilaisia sähköisiä palveluita kuluttajien koteihin ja yritysten toimipisteisiin. Näitä palveluita ovat mm. virtualisoidut henkilö-kohtaiset tietokoneet, tiedon tallennuspalvelut ja multimediasisältö, kuten teräväpiirtotelevisio. Tässä diplomityössä arvioidaan Supermatrixin palveluiden toteutumis-mahdollisuuksia sekä teknologian, että markkinoiden kannalta. Työn tarkoituksena oli myös laskea virtualisoitujen tietokoneiden avulla tuotetun työpöytäpalvelun tuotantokustannus. Kustannuslaskentaa varten testattiin useiden valmistajien teknologioita, ja parhaimmista vaihtoehdoista laskettiin tuotantokustannukset. Tulevia Supermatrix-operaattoreita varten kehitettiin työkalu, jolla työpöytäpalvelun vaatimien investointien kannattavuus saadaan nopeasti selville. Tuotantohintojen laskelmat osoittavat, että työpöytäpalvelua on taloudelli-sesti mahdollista tuottaa luomaan lisäarvoa nopeille laajakaistayhteyksille. Yrityksissä työpöytien virtualisointi on jo tämän päivän teknologiaa, mutta kuluttaja-asiakkaiden innostuminen palvelusta vaatii uuden markkinan luomisen. Uhkana on, että kuluttajien ostokäyttäytymisen muuttaminen ei onnistu, koska Supermatrix ei tuota fyysisen tuotteen ostamisen tarjoamaa mielihyvää.

Verbal learning in mild cognitive impairment and alzheimer's disease : behavioural and neural approaches

The main focus of the present thesis was at verbal episodic memory processes that are particularly vulnerable to preclinical and clinical Alzheimer’s disease (AD). Here these processes were studied by a word learning paradigm, cutting across the domains of memory and language learning studies. Moreover, the differentiation between normal aging, mild cognitive impairment (MCI) and AD was studied by the cognitive screening test CERAD. In study I, the aim was to examine how patients with amnestic MCI differ from healthy controls in the different CERAD subtests. Also, the sensitivity and specificity of the CERAD screening test to MCI and AD was examined, as previous studies on the sensitivity and specificity of the CERAD have not included MCI patients. The results indicated that MCI is characterized by an encoding deficit, as shown by the overall worse performance on the CERAD Wordlist learning test compared with controls. As a screening test, CERAD was not very sensitive to MCI. In study II, verbal learning and forgetting in amnestic MCI, AD and healthy elderly controls was investigated with an experimental word learning paradigm, where names of 40 unfamiliar objects (mainly archaic tools) were trained with or without semantic support. The object names were trained during a 4-day long period and a follow-up was conducted one week, 4 weeks and 8 weeks after the training period. Manipulation of semantic support was included in the paradigm because it was hypothesized that semantic support might have some beneficial effects in the present learning task especially for the MCI group, as semantic memory is quite well preserved in MCI in contrast to episodic memory. We found that word learning was significantly impaired in MCI and AD patients, whereas forgetting patterns were similar across groups. Semantic support showed a beneficial effect on object name retrieval in the MCI group 8 weeks after training, indicating that the MCI patients’ preserved semantic memory abilities compensated for their impaired episodic memory. The MCI group performed equally well as the controls in the tasks tapping incidental learning and recognition memory, whereas the AD group showed impairment. Both the MCI and the AD group benefited less from phonological cueing than the controls. Our findings indicate that acquisition is compromised in both MCI and AD, whereas long13 term retention is not affected to the same extent. Incidental learning and recognition memory seem to be well preserved in MCI. In studies III and IV, the neural correlates of naming newly learned objects were examined in healthy elderly subjects and in amnestic MCI patients by means of positron emission tomography (PET) right after the training period. The naming of newly learned objects by healthy elderly subjects recruited a left-lateralized network, including frontotemporal regions and the cerebellum, which was more extensive than the one related to the naming of familiar objects (study III). Semantic support showed no effects on the PET results for the healthy subjects. The observed activation increases may reflect lexicalsemantic and lexical-phonological retrieval, as well as more general associative memory mechanisms. In study IV, compared to the controls, the MCI patients showed increased anterior cingulate activation when naming newly learned objects that had been learned without semantic support. This suggests a recruitment of additional executive and attentional resources in the MCI group.