OPTIMAL PITCH MAP GENERATION FOR SCANNING PITCH DESIGN IN SELECTIVE SAMPLING

The reverse engineering process represents one of the best known methodologies for creating three-dimensional (3D) virtual models starting from physical ones. Even if in the last few years its usage has significantly increased, the remarkable involvement of the operator has until now represented a significant constraint for its growth. Having regard to the fact that this process, and in particular its first step (that is the acquisition phase), strongly depends on the operator's ability and expertise, this paper aims at proposing a strategy for automatically supporting an "optimal" acquisition phase. Moreover, the acquisition phase represents the only moment in which there is a direct contact between the virtual model and the physical model. For this reason, designing an "optimal" acquisition phase will provide as output an efficient set of morphological data, which will turn out to be extremely useful for the following reverse engineering passages (pre-processing, segmentation, fitting, …). This scenario drives the researcher to use a selectivesampling plan, whose grid dimensions are correlated with the complexity of the local surface region analyzed, instead of a constant one. As a consequence, this work proposes a complete operative strategy which, starting from a first raw preliminary acquisition, will provide a new selectivesampling plan during the acquisition phase, in order to allow a deeper and more efficient new scansion. The proposed solution does not require the creation of any intermediate model and relies exclusively on the analysis of the metrological performances of the 3D scanner device and of the morphological behaviour of the surface acquired

PITCH FUNCTION COMPARISON METHODOLOGY FOR SUPPORTING A SMART 3D SCANNER SELECTION

When working with 3Dscanner devices, one of the most critical problems is usually the low quality of the point cloud provided by the scanning device. This problem mainly consists of the following two aspects. The first one is surely the choice of the strategy used to acquire the object shape. Most of the times, the selected strategy is based on selective sampling. This choice proved to be valid, especially when working with Free-Form surfaces: by using a selective sampling strategy is in fact possible to limit point density increase to those regions showing high morphological complexity. The second aspect is the difficulty of identifying which 3Dscanner device is the one that better fulfils the specific application needs, which vary depending on the specific scenario in which the costumer/user works (resolution, accuracy, …). As far as this last issue is concerned, the presence of many different acquisition technologies and devices on the market is a source of confusion for the users, who sometimes choose the wrong solution instead of finding the most efficient one. Hence, in order to support the potential users in their selection, this paper aims to propose a solution able to integrate the morphological analysis of the object acquired with the costumer needs (resolution, accuracy, …) and with the 3Dscanner performances in order to help users to identify the optimal solutio

Computational evaluation of cochlear implant surgery outcomes accounting for uncertainty and parameter variability

Cochlear implantation (CI) is a complex surgical procedure that restores hearing in patients with severe deafness. The successful outcome of the implanted device relies on a group of factors, some of them unpredictable or difficult to control. Uncertainties on the electrode array position and the electrical properties of the bone make it difficult to accurately compute the current propagation delivered by the implant and the resulting neural activation. In this context, we use uncertainty quantification methods to explore how these uncertainties propagate through all the stages of CI computational simulations. To this end, we employ an automatic framework, encompassing from the finite element generation of CI models to the assessment of the neural response induced by the implant stimulation. To estimate the confidence intervals of the simulated neural response, we propose two approaches. First, we encode the variability of the cochlear morphology among the population through a statistical shape model. This allows us to generate a population of virtual patients using Monte Carlo sampling and to assign to each of them a set of parameter values according to a statistical distribution. The framework is implemented and parallelized in a High Throughput Computing environment that enables to maximize the available computing resources. Secondly, we perform a patient-specific study to evaluate the computed neural response to seek the optimal post-implantation stimulus levels. Considering a single cochlear morphology, the uncertainty in tissue electrical resistivity and surgical insertion parameters is propagated using the Probabilistic Collocation method, which reduces the number of samples to evaluate. Results show that bone resistivity has the highest influence on CI outcomes. In conjunction with the variability of the cochlear length, worst outcomes are obtained for small cochleae with high resistivity values. However, the effect of the surgical insertion length on the CI outcomes could not be clearly observed, since its impact may be concealed by the other considered parameters. Whereas the Monte Carlo approach implies a high computational cost, Probabilistic Collocation presents a suitable trade-off between precision and computational time. Results suggest that the proposed framework has a great potential to help in both surgical planning decisions and in the audiological setting process

Micro/Nano Manufacturing

Micro manufacturing involves dealing with the fabrication of structures in the size range of 0.1 to 1000 µm. The scope of nano manufacturing extends the size range of manufactured features to even smaller length scales—below 100 nm. A strict borderline between micro and nano manufacturing can hardly be drawn, such that both domains are treated as complementary and mutually beneficial within a closely interconnected scientific community. Both micro and nano manufacturing can be considered as important enablers for high-end products. This Special Issue of Applied Sciences is dedicated to recent advances in research and development within the field of micro and nano manufacturing. The included papers report recent findings and advances in manufacturing technologies for producing products with micro and nano scale features and structures as well as applications underpinned by the advances in these technologies

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

Characterization and improvement of copper / glass adhesion

The development of glass substrates for use as an alternative to printed circuit boards (PCBs) attracts significant industrial attention, because of the potential for low cost but high performance interconnects and optical connection. Electroless plating is currently used to deposit conductive tracks on glass substrates and the quality of copper / glass adhesion is a key functional issue. Without adequate adhesive strength the copper plating will prematurely fail. Existing studies have covered the relationship between surface roughness and adhesion performance, but few of them have considered the detail of surface topography in any depth. This research is specifically considering the mechanical contribution of the glass surface texture to the copper / glass adhesive bond, and attempting to isolate new ISO 25178 areal surface texture parameters that can describe these surfaces. Excimer laser machining has been developed and used to create a range of micro pattern structured surfaces on CMG glass substrates. Excimer mask dimensions and laser operation parameters have been varied and optimized according to surface topography and adhesion performance of the samples. Non-contact surface measurement equipment (Zygo NewView 5000 coherence scanning interferometry) has been utilized to measure and parameterize (ISO 25178) the surface texture of the glass substrates before electroless copper metallization. Copper adhesion quality has been tested using quantitative scratch testing techniques, providing an identification of the critical load of failure for different plated substrates. This research is establishing the statistical quality of correlation between the critical load values and the associated areal parameters. In this thesis, the optimal laser processing parameter settings for CMG glass substrate machining and the topographic images of structured surfaces for achieving strong copper / glass plating adhesion are identified. The experimental relationships between critical load and areal surface parameters, as well as the discussions of a theoretical approach are presented. It is more significant to consider Sq, Sdq, Sdr, Sxp, Vv, Vmc and Vvc to describe glass substrate surface topography and the recommended data value ranges for each parameter have been identified to predict copper / plating adhesion performance

Pitch discrimination in optimal and suboptimal acoustic environments : electroencephalographic, magnetoencephalographic, and behavioral evidence

Pitch discrimination is a fundamental property of the human auditory system. Our understanding of pitch-discrimination mechanisms is important from both theoretical and clinical perspectives. The discrimination of spectrally complex sounds is crucial in the processing of music and speech. Current methods of cognitive neuroscience can track the brain processes underlying sound processing either with precise temporal (EEG and MEG) or spatial resolution (PET and fMRI). A combination of different techniques is therefore required in contemporary auditory research. One of the problems in comparing the EEG/MEG and fMRI methods, however, is the fMRI acoustic noise. In the present thesis, EEG and MEG in combination with behavioral techniques were used, first, to define the ERP correlates of automatic pitch discrimination across a wide frequency range in adults and neonates and, second, they were used to determine the effect of recorded acoustic fMRI noise on those adult ERP and ERF correlates during passive and active pitch discrimination. Pure tones and complex 3-harmonic sounds served as stimuli in the oddball and matching-to-sample paradigms. The results suggest that pitch discrimination in adults, as reflected by MMN latency, is most accurate in the 1000-2000 Hz frequency range, and that pitch discrimination is facilitated further by adding harmonics to the fundamental frequency. Newborn infants are able to discriminate a 20% frequency change in the 250-4000 Hz frequency range, whereas the discrimination of a 5% frequency change was unconfirmed. Furthermore, the effect of the fMRI gradient noise on the automatic processing of pitch change was more prominent for tones with frequencies exceeding 500 Hz, overlapping with the spectral maximum of the noise. When the fundamental frequency of the tones was lower than the spectral maximum of the noise, fMRI noise had no effect on MMN and P3a, whereas the noise delayed and suppressed N1 and exogenous N2. Noise also suppressed the N1 amplitude in a matching-to-sample working memory task. However, the task-related difference observed in the N1 component, suggesting a functional dissociation between the processing of spatial and non-spatial auditory information, was partially preserved in the noise condition. Noise hampered feature coding mechanisms more than it hampered the mechanisms of change detection, involuntary attention, and the segregation of the spatial and non-spatial domains of working-memory. The data presented in the thesis can be used to develop clinical ERP-based frequency-discrimination protocols and combined EEG and fMRI experimental paradigms.Kyky erottaa korkeat ja matalat äänet toisistaan on yksi aivojen perustoiminnoista. Ilman sitä emme voisi ymmärtää puhetta tai nauttia musiikista. Jotkut potilaat ja hyvin pienet lapset eivät pysty itse kertomaan, kuulevatko he eron vai eivät, mutta heidän aivovasteensa voivat paljastaa sen. Sävelkorkeuden erotteluun liittyvistä aivotoiminnoista ei kuitenkaan tiedetä tarpeeksi edes terveillä aikuisilla. Siksi tarvitaan lisää tämän aihepiirin tutkimusta, jossa käytetään nykyaikaisia aivotutkimusmenetelmiä, kuten tapahtumasidonnaisia herätevasteita (engl. event-related potential, ERP) ja toiminnallista magneettikuvausta (engl. functional magnetic resonance imaging, fMRI). ERP-menetelmä paljastaa, milloin aivot erottavat sävelkorkeuseron, kun taas fMRI paljastaa, mitkä aivoalueet ovat aktivoituneet tässä toiminnossa. Yhdistämällä nämä kaksi menetelmää voidaan saada kokonaisvaltaisempi kuva sävelkorkeuden erotteluun liittyvistä aivotoiminnoista. fMRI-menetelmään liittyy kuitenkin eräs ongelma, nimittäin fMRI-laitteen synnyttämä kova melu, joka voi vaikeuttaa kuuloon liittyvää tutkimusta. Tässä väitöskirjassa tutkitaan, kuinka sävelkorkeuden erottelu voidaan todeta aikuisten ja vastasyntyneiden vauvojen aivoissa ja kuinka fMRI-laitteen melu vaikuttaa kuuloärsykkeiden synnyttämiin ERP-vasteisiin. Tutkimuksen tulokset osoittavat, että aikuisen aivot voivat erottaa niinkin pieniä kuin 2,5 %:n taajuuseroja, mutta erottelu tapahtuu nopeammin n. 1000-2000 Hz:n taajuudella kuin matalammilla tai korkeammilla taajuuksilla. Vastasyntyneen vauvan aivot erottelivat vain yli 20 %:n taajuusmuutoksia. Kun taustalla soitettiin fMRI-laitteen melua, se vaimensi aivovasteita 500-2000 Hz:n äänille enemmän kuin muille äänille. Melu ei kuitenkaan vaikuttanut alle 500 Hz:n äänten synnyttämiin aivovasteisiin. Riippumatta siitä, esitettiinkö taustalla melua vai ei, äänilähteen paikan muutoksen synnyttämä ERP-vaste oli suurempi kuin äänenkorkeuden muutoksen synnyttämä vaste. Tämä väitöskirjatutkimus on osoittanut, että sävelkorkeuden erottelua voidaan tutkia tehokkaasti ERP-menetelmällä sekä aikuisilla että vauvoilla. Tulosten mukaan ERP- ja fMRI-menetelmien yhdistämistä voidaan tehostaa ottamalla kokeiden suunnittelussa huomioon fMRI-laitteen melun vaikutukset ERP-vasteisiin. Tutkimuksen aineistoa voidaan hyödyntää monimutkaisten sävelkorkeuden erottelua mittaavien kokeiden suunnittelussa mm. potilailla ja lapsilla