Bayesian classification of forest ecological type from satellite data

The main objective of this study was todo a statistical analysis of ecological type from optical satellite data, using Tipping's sparse Bayesian algorithm. This thesis uses "the Relevence Vector Machine" algorithm in ecological classification betweenforestland and wetland. Further this bi-classification technique was used to do classification of many other different species of trees and produces hierarchical classification of entire subclasses given as a target class. Also, we carried out an attempt to use airborne image of same forest area. Combining it with image analysis, using different image processing operation, we tried to extract good features and later used them to perform classification of forestland and wetland.

Visual measurement of laser-arc hybrid welding

Tässä työssä raportoidaan hybridihitsauksesta otettujen suurnopeuskuvasarjojen automaattisen analyysijärjestelmän kehittäminen.Järjestelmän tarkoitus oli tuottaa tietoa, joka avustaisi analysoijaa arvioimaan kuvatun hitsausprosessin laatua. Tutkimus keskittyi valokaaren taajuuden säännöllisyyden ja lisäainepisaroiden lentosuuntien mittaamiseen. Valokaaria havaittiin kuvasarjoista sumean c-means-klusterointimenetelmän avullaja perättäisten valokaarien välistä aikaväliä käytettiin valokaaren taajuuden säännöllisyyden mittarina. Pisaroita paikannettiin menetelmällä, jossa yhdistyi pääkomponenttianalyysi ja tukivektoriluokitin. Kalman-suodinta käytettiin tuottamaan arvioita pisaroiden lentosuunnista ja nopeuksista. Lentosuunnanmääritysmenetelmä luokitteli pisarat niiden arvioitujen lentosuuntien perusteella. Järjestelmän kehittämiseen käytettävissä olleet kuvasarjat poikkesivat merkittävästi toisistaan kuvanlaadun ja pisaroiden ulkomuodon osalta, johtuen eroista kuvaus- ja hitsausprosesseissa. Analyysijärjestelmä kehitettiin toimimaan pienellä osajoukolla kuvasarjoja, joissa oli tietynlainen kuvaus- ja hitsausprosessi ja joiden kuvanlaatu ja pisaroiden ulkomuoto olivat samankaltaisia, mutta järjestelmää testattiin myös osajoukon ulkopuolisilla kuvasarjoilla. Testitulokset osoittivat, että lentosuunnanmääritystarkkuus oli kohtuullisen suuri osajoukonsisällä ja pieni muissa kuvasarjoissa. Valokaaren taajuuden säännöllisyyden määritys oli tarkka useammassa kuvasarjassa.

Improving globally distributed software development and support processes - A workflow view

We propose a new approach and related indicators for globally distributed software support and development based on a 3-year process improvement project in a globally distributed engineering company. The company develops, delivers and supports a complex software system with tailored hardware components and unique end-customer installations. By applying the domain knowledge from operations management on lead time reduction and its multiple benefits to process performance, the workflows of globally distributed software development and multitier support processes were measured and monitored throughout the company. The results show that the global end-to-end process visibility and centrally managed reporting at all levels of the organization catalyzed a change process toward significantly better performance. Due to the new performance indicators based on lead times and their variation with fixed control procedures, the case company was able to report faster bug-fixing cycle times, improved response times and generally better customer satisfaction in its global operations. In all, lead times to implement new features and to respond to customer issues and requests were reduced by 50%.

Using a blog to create and support a Community of Inquiry in Secondary Education

Understanding how blogs can support collaborative learning is a vital concern for researchers and teachers. This paper explores how blogs may be used to support Secondary Education students’ collaborative interaction and how such an interaction process can promote the creation of a Community of Inquiry to enhance critical thinking and meaningful learning. We designed, implemented and evaluated a science case-based project in which fifteen secondary students participated. Students worked in the science blogging project during 4 months. We asked students to be collaboratively engaged in purposeful critical discourse and reflection in their blogs in order to solve collectively science challenges and construct meaning about topics related to Astronomy and Space Sciences. Through student comments posted in the blog, our findings showed that the blog environment afforded the construction of a Community of Inquiry and therefore the creation of an effective online collaborative learning community. In student blog comments, the three presences for collaborative learning took place: cognitive, social, and teaching presence. Moreover, our research found a positive correlation among the three presences –cognitive, social and teaching– of the Community of Inquiry model with the level of learning obtained by the students. We discuss a series of issues that instructors should consider when blogs are incorporated into teaching and learning. We claim that embedded scaffolds to help students to argue and reason their comments in the blog are required to foster blog-supported collaborative learning.

A multi-contrast MRI study of microstructural brain damage in patients with mild cognitive impairment.

OBJECTIVES: The aim of this study was to investigate pathological mechanisms underlying brain tissue alterations in mild cognitive impairment (MCI) using multi-contrast 3 T magnetic resonance imaging (MRI). METHODS: Forty-two MCI patients and 77 healthy controls (HC) underwent T1/T2* relaxometry as well as Magnetization Transfer (MT) MRI. Between-groups comparisons in MRI metrics were performed using permutation-based tests. Using MRI data, a generalized linear model (GLM) was computed to predict clinical performance and a support-vector machine (SVM) classification was used to classify MCI and HC subjects. RESULTS: Multi-parametric MRI data showed microstructural brain alterations in MCI patients vs HC that might be interpreted as: (i) a broad loss of myelin/cellular proteins and tissue microstructure in the hippocampus (p ≤ 0.01) and global white matter (p < 0.05); and (ii) iron accumulation in the pallidus nucleus (p ≤ 0.05). MRI metrics accurately predicted memory and executive performances in patients (p ≤ 0.005). SVM classification reached an accuracy of 75% to separate MCI and HC, and performed best using both volumes and T1/T2*/MT metrics. CONCLUSION: Multi-contrast MRI appears to be a promising approach to infer pathophysiological mechanisms leading to brain tissue alterations in MCI. Likewise, parametric MRI data provide powerful correlates of cognitive deficits and improve automatic disease classification based on morphometric features.

Reducción del vector de características de reconocimiento facial

In this present work, we are proposing a characteristics reduction system for a facial biometric identification system, using transformed domains such as discrete cosine transformed (DCT) and discrete wavelets transformed (DWT) as parameterization; and Support Vector Machines (SVM) and Neural Network (NN) as classifiers. The size reduction has been done with Principal Component Analysis (PCA) and with Independent Component Analysis (ICA). This system presents a similar success results for both DWT-SVM system and DWT-PCA-SVM system, about 98%. The computational load is improved on training mode due to the decreasing of input’s size and less complexity of the classifier.

Performance Evaluation and Calibration of Machine Vision Measurement

This thesis presents the calibration and comparison of two systems, a machine vision system that uses 3 channel RGB images and a line scanning spectral system. Calibration. is the process of checking and adjusting the accuracy of a measuring instrument by comparing it with standards. For the RGB system self-calibrating methods for finding various parameters of the imaging device were developed. Color calibration was done and the colors produced by the system were compared to the known colors values of the target. Software drivers for the Sony Robot were also developed and a mechanical part to connect a camera to the robot was also designed. For the line scanning spectral system, methods for the calibrating the alignment of the system and the measurement of the dimensions of the line scanned by the system were developed. Color calibration of the spectral system is also presented.

Determinação de constituintes químicos em madeira de eucalipto por Pi-CG/EM e calibração multivariada: comparação entre redes neurais artificiais e máquinas de vetor suporte

Multivariate models were developed using Artificial Neural Network (ANN) and Least Square - Support Vector Machines (LS-SVM) for estimating lignin siringyl/guaiacyl ratio and the contents of cellulose, hemicelluloses and lignin in eucalyptus wood by pyrolysis associated to gaseous chromatography and mass spectrometry (Py-GC/MS). The results obtained by two calibration methods were in agreement with those of reference methods. However a comparison indicated that the LS-SVM model presented better predictive capacity for the cellulose and lignin contents, while the ANN model presented was more adequate for estimating the hemicelluloses content and lignin siringyl/guaiacyl ratio.

Aplicação de técnicas multivariadas e inteligência artificial na análise de espectros de infravermelho para determinação de matéria orgânica em amostras de solos

In this paper studies based on Multilayer Perception Artificial Neural Network and Least Square Support Vector Machine (LS-SVM) techniques are applied to determine of the concentration of Soil Organic Matter (SOM). Performances of the techniques are compared. SOM concentrations and spectral data from Mid-Infrared are used as input parameters for both techniques. Multivariate regressions were performed for a set of 1117 spectra of soil samples, with concentrations ranging from 2 to 400 g kg-1. The LS-SVM resulted in a Root Mean Square Error of Prediction of 3.26 g kg-1 that is comparable to the deviation of the Walkley-Black method (2.80 g kg-1).

Ostotapahtuman ennustemallin kehittäminen ajoneuvomarkkinoinnin tueksi

Perinteisesti ajoneuvojen markkinointikampanjoissa kohderyhmät muodostetaan yksinkertaisella kriteeristöllä koskien henkilön tai hänen ajoneuvonsa ominaisuuksia. Ennustavan analytiikan avulla voidaan tuottaa kohderyhmänmuodostukseen teknisesti kompleksisia mutta kuitenkin helppokäyttöisiä menetelmiä. Tässä työssä on sovellettu luokittelu- ja regressiomenetelmiä uuden auton ostajien joukkoon. Tämän työn menetelmiksi on rajattu tukivektorikone sekä Coxin regressiomalli. Coxin regression avulla on tutkittu elinaika-analyysien soveltuvuutta ostotapahtuman tapahtumahetken mallintamiseen. Luokittelu tukivektorikonetta käyttäen onnistuu tehtävässään noin 72% tapauksissa. Tukivektoriregressiolla mallinnetun hankintahetken virheen keskiarvo on noin neljä kuukautta. Työn tulosten perusteella myös elinaika-analyysin käyttö ostotapahtuman tapahtumahetken mallintamiseen on menetelmänä käyttökelpoinen.

Tyypin 1 diabeteksen matemaattisista ennustamismenetelmistä

Tutkielmassa käsitellään matemaattisia ennustamismenetelmiä, jotka soveltuvat tyypin 1 diabeteksen ennustamiseen. Aluksi esitellään menetelmiä, jotka soveltuvat puuttuvia havaintoja sisältävien aineistojen paikkaamiseen. Paikattua aineistoa on mahdollista analysoida useilla tavallisilla tilastollisilla menetelmillä, jotka sopivat täydellisiin aineistoihin. Seuraavaksi pyritään mallintamaan aineistoa semiparametrisilla komponenttimalleilla (eng. mixture model), jolloin mallin muotoa ei ole tiukasti etukäteen rajoitettu. Sen jälkeen sovelletaan kolmea luokittelevaa ennustajaa: logistista regressiomallia, eteenpäinsyöttävää yhden piilotason neuroverkkoa ja SVM-menetelmää (eng. support vector machine). Esiteltäviä menetelmiä on sovellettu todelliseen aineistoon, joka on kerätty Turun yliopistossa käynnissä olevassa tutkimusprojektissa. Projektin tavoitteena on oppia ennustamaan ja ehkäisemään tyypin 1 diabetesta (Type 1 diabetes prediction and prevention project, lyh. DIPP-projekti). Erityisesti projektissa on pyritty löytämään uusia tuntemattomia taudinaiheuttajia. Tässä tutkielmassa paneudutaan sen sijaan kerätyn havaintoaineiston matemaattisiin analysointimenetelmiin. Parhaat ennusteet saatiin perinteisellä logistisella regressiomallilla. Tutkielmassa kuitenkin todetaan, että tulevaisuudessa on mahdollista löytää parempia ennustajia parantamalla muita edellä mainittuja menetelmiä. Erityisesti SVM-menetelmä ansaitsisi lisähuomiota, sillä tässä tutkielmassa sitä sovellettiin vain kaikkein yksinkertaisimmassa muodossa.

Using aster multispectral imagery for mapping woody invasive species in pico da vara natural reserve (Azores Islands, Portugal)

This paper aims to assess the effectiveness of ASTER imagery to support the mapping of Pittosporum undulatum, an invasive woody species, in Pico da Vara Natural Reserve (S. Miguel Island, Archipelago of the Azores, Portugal). This assessment was done by applying K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Maximum Likelihood (MLC) pixel-based supervised classifications to 4 different geographic and remote sensing datasets constituted by the Visible, Near-Infrared (VNIR) and Short Wave Infrared (SWIR) of the ASTER sensor and by digital cartography associated to orography (altitude and "distance to water streams") of which the spatial distribution of Pittosporum undulatum directly depends. Overall, most performed classifications showed a strong agreement and high accuracy. At targeted species level, the two higher classification accuracies were obtained when applying MLC and KNN to the VNIR bands coupled with auxiliary geographic information use. Results improved significantly by including ecology and occurrence information of species (altitude and distance to water streams) in the classification scheme. These results show that the use of ASTER sensor VNIR spectral bands, when coupled to relevant ancillary GIS data, can constitute an effective and low cost approach for the evaluation and continuous assessment of Pittosporum undulatum woodland propagation and distribution within Protected Areas of the Azores Islands.

Using fraction images derived from modis data for coffee crop mapping

Coffee production was closely linked to the economic development of Brazil and, even today, coffee is an important product of the national agriculture. The State of Minas Gerais currently accounts for 52% of the whole coffee area in Brazil. Remote sensing data can provide information for monitoring and mapping of coffee crops, faster and cheaper than conventional methods. In this context, the objective of this study was to assess the effectiveness of coffee crop mapping in Monte Santo de Minas municipality, Minas Gerais State, Brazil, from fraction images derived from MODIS data, in both dry and rainy seasons. The Spectral Linear Mixing Model was used to derive fraction images of soil, coffee, and water/shade. These fraction images served as input data for the supervised automatic classification using the SVM - Support Vector Machine approach. The best results concerning Overall Accuracy and Kappa Index were obtained in the classification of the dry season, with 67% and 0.41, respectively.

A feature-based object-oriented expert system to model and support product design

In this paper a computer program to model and support product design is presented. The product is represented through a hierarchical structure that allows the user to navigate across the product’s components, and it aims at facilitating each step of the detail design process. A graphical interface was also developed, which shows visually to the user the contents of the product structure. Features are used as building blocks for the parts that compose the product, and object-oriented methodology was used as a means to implement the product structure. Finally, an expert system was also implemented, whose knowledge base rules help the user design a product that meets design and manufacturing requirements.

Support vector machines in spectral data classification

Työssä käydään läpi tukivektorikoneiden teoreettista pohjaa sekä tutkitaan eri parametrien vaikutusta spektridatan luokitteluun.