3D Surface Modeling From Point Clouds

The goal of this thesis is to implement software for creating 3D models from point clouds. Point clouds are acquired with stereo cameras, monocular systems or laser scanners. The created 3D models are triangular models or NURBS (Non-Uniform Rational B-Splines) models. Triangular models are constructed from selected areas from the point clouds and resulted triangular models are translated into a set of quads. The quads are further translated into an estimated grid structure and used for NURBS surface approximation. Finally, we have a set of NURBS surfaces which represent the whole model. The problem wasn’t so easy to solve. The selected triangular surface reconstruction algorithm did not deal well with noise in point clouds. To handle this problem, a clustering method is introduced for simplificating the model and removing noise. As we had better results with the smaller point clouds produced by clustering, we used points in clusters to better estimate the grids for NURBS models. The overall results were good when the point cloud did not have much noise. The point clouds with small amount of error had good results as the triangular model was solid. NURBS surface reconstruction performed well on solid models.

Clouds, Op. 7, No. 4

Soitinnus: Ork.

Stellar population synthesis: the effect of improved Asymptotic Giant Branch models and of interstellar dust on mass-to-light–color relations

Teoreettisen populaatiosynteesin avulla voidaan mallintaa tähtijoukkojen ja galaksien fotometrisiä ominaisuuksia yhdistämällä yksittäisten tähtien tuottama säteily, joka saadaan teoreettisista tähtien kehitysmalleista. Valitsemalla sopiva massajakauma syntyville tähdille voidaan muodostaa yksinkertainen tähtipopulaatio, joka koostuu saman ikäisistä ja kemialliselta koostumukseltaan yhtenäisistä tähdistä. Monimutkaisempia tähtipopulaatioita voidaan muodostaa konvoloimalla yksinkertaisten tähtipopulaatioiden luminositeetti jonkin valitun tähtienmuodostushistorian kanssa sekä yhdistämällä näin muodostettuja populaatioita. Tässä työssä tarkastellaan asymptoottisen jättiläishaaran (AGB) tähtien uusien, tarkentuneiden evoluutiomallien vaikutusta populaatiosynteesin tuloksiin niin yksinkertaisten tähtipopulaatioiden kuin galaksien mallinnukseen soveltuvien monimutkaisempien tähtipopulaatioiden kohdalla. Työn päätarkoitus on tuottaa uudistuneisiin malleihin perustuvat populaation massa-luminositeetti -suhteen ja värin väliset relaatiot (MLC-relaatiot). MLC-relaatioita voidaan käyttää populaation massan määrittämiseen sen fotometristen ominaisuuksien (väri, luminositeetti) perusteella. Lisäksi tutkitaan tähtienvälisen pölyn vaikutusta yksinkertaisen spiraaligalaksimallin MLC-relaatioihin. Työssä käytetyt tähtien kehitysmallit perustuvat julkaisuun Marigo et al. (Astronomy & Astrophysics 482, 2008). Havaitaan, että AGB-tähtien vaikutus populaation integroituun luminositeettiin on pieni näkyvillä aallonpituuksilla, mutta merkittävä lähi-infrapuna-alueella. Vaikutus MLC-relaatioihin on vastaavasti merkittävä tarkkailtaessa luminositeettia lähi-infrapunassa sekä käytettäessä värejä, joissa yhdistetään optisia ja lähi-infrapunan kaistoja. Todetaan, että MLC-relaatioiden käyttö lähi-infrapunassa edellyttää tarkentuneen AGB-vaiheen sisällyttämistä populaatiosynteesin malleihin. Tähtienvälisen pölyn vaikutus MLC-relaatioihin todetaan riippuvan käytetystä kaistasta ja väristä, mutta vaikutuksen havaitaan olevan suurin optisen ja lähi-infrapunan väriyhdistelmillä.

SaaS and Public Cloud from Custom Application Provider’s Perspective

The main objective of this master’s thesis is to provide a comprehensive view to cloud computing and SaaS, and analyze how well CADM, a unit of Capgemini Finland Ltd., would fit to the cloud-based SaaS business. Another objective for this thesis is to investigate how public clouds would fit for CADM as a delivery model, if they would provide SaaS applications to their customers. This master’s thesis is executed by investigating characteristics of cloud computing and SaaS especially from application provider point of view. This is done by exploring what kinds of researches and analysis there have been done regarding these two phenomena during past few years. Then CADM’s current business model and operations are analyzed from SaaS’s and public cloud’s perspective. This analyzing part is conducted by using SWOT analysis which is widely used analytical tool when observing company’s strategic position and when figuring out possibilities how to improve company’s operations. The conducted analysis and observations reveals that CADM should pursue SaaS business as it could provide remarkable advantages and strengthen their position in current markets. However, pure SaaS model would not be the optimal solution for CADM because they do not have own product which could be transformed to SaaS model, and they lack of Infrastructure Management ability. Also public cloud would not be the most suitable delivery model for them if providing SaaS services. The main observation of this thesis is that CADM should adopt the SaaS model via Capgemini Immediate offering.

Ohjelmistotuotannon menetelmien käyttö sulautetuissa järjestelmissä

Sulautettujen järjestelmien tekemisessä käytettävät metodit ovat moninaiset. Tämä johtuu siitä, että sulautettuja järjestelmiä on tuhansia erilaisia, sekä laitteiston ja ohjelmiston rakentamisen eroavaisuuksista. Sovellukset vaihtelevat kännyköistä aina avaruusluotaimiin. Näihin projekteihin on sovellettu metodeita joita ei ole alun perin suunniteltu laitteiston ja ohjelmiston yhteissuunnitteluun ja toteuttamiseen. Ohjelmistotuotannon menetelmistä oikean valinta nimenomaan tietylle sulautetulle järjestelmälle on haasteellista. Viimeisimpinä ovat tulleet erilaiset ketterät menetelmät ja niitäkin on olemassa useita erilaisia. Ketteriä ja perinteisempiä ohjelmistotuotannon menetelmiä esitellään tässä kandidaatin työssä. Tässä työssä on tarkoituksena selvittää mitkä olisivat parhaiten soveltuvia sulautetun järjestelmän projektille.

Video transcoding in a distributed cloud computing environment

Video transcoding refers to the process of converting a digital video from one format into another format. It is a compute-intensive operation. Therefore, transcoding of a large number of simultaneous video streams requires a large amount of computing resources. Moreover, to handle di erent load conditions in a cost-e cient manner, the video transcoding service should be dynamically scalable. Infrastructure as a Service Clouds currently offer computing resources, such as virtual machines, under the pay-per-use business model. Thus the IaaS Clouds can be leveraged to provide a coste cient, dynamically scalable video transcoding service. To use computing resources e ciently in a cloud computing environment, cost-e cient virtual machine provisioning is required to avoid overutilization and under-utilization of virtual machines. This thesis presents proactive virtual machine resource allocation and de-allocation algorithms for video transcoding in cloud computing. Since users' requests for videos may change at di erent times, a check is required to see if the current computing resources are adequate for the video requests. Therefore, the work on admission control is also provided. In addition to admission control, temporal resolution reduction is used to avoid jitters in a video. Furthermore, in a cloud computing environment such as Amazon EC2, the computing resources are more expensive as compared with the storage resources. Therefore, to avoid repetition of transcoding operations, a transcoded video needs to be stored for a certain time. To store all videos for the same amount of time is also not cost-e cient because popular transcoded videos have high access rate while unpopular transcoded videos are rarely accessed. This thesis provides a cost-e cient computation and storage trade-o strategy, which stores videos in the video repository as long as it is cost-e cient to store them. This thesis also proposes video segmentation strategies for bit rate reduction and spatial resolution reduction video transcoding. The evaluation of proposed strategies is performed using a message passing interface based video transcoder, which uses a coarse-grain parallel processing approach where video is segmented at group of pictures level.

Cost-efficient virtual machine management : provisioning, admission control, and consolidation

One of the main challenges in Software Engineering is to cope with the transition from an industry based on software as a product to software as a service. The field of Software Engineering should provide the necessary methods and tools to develop and deploy new cost-efficient and scalable digital services. In this thesis, we focus on deployment platforms to ensure cost-efficient scalability of multi-tier web applications and on-demand video transcoding service for different types of load conditions. Infrastructure as a Service (IaaS) clouds provide Virtual Machines (VMs) under the pay-per-use business model. Dynamically provisioning VMs on demand allows service providers to cope with fluctuations on the number of service users. However, VM provisioning must be done carefully, because over-provisioning results in an increased operational cost, while underprovisioning leads to a subpar service. Therefore, our main focus in this thesis is on cost-efficient VM provisioning for multi-tier web applications and on-demand video transcoding. Moreover, to prevent provisioned VMs from becoming overloaded, we augment VM provisioning with an admission control mechanism. Similarly, to ensure efficient use of provisioned VMs, web applications on the under-utilized VMs are consolidated periodically. Thus, the main problem that we address is cost-efficient VM provisioning augmented with server consolidation and admission control on the provisioned VMs. We seek solutions for two types of applications: multi-tier web applications that follow the request-response paradigm and on-demand video transcoding that is based on video streams with soft realtime constraints. Our first contribution is a cost-efficient VM provisioning approach for multi-tier web applications. The proposed approach comprises two subapproaches: a reactive VM provisioning approach called ARVUE and a hybrid reactive-proactive VM provisioning approach called Cost-efficient Resource Allocation for Multiple web applications with Proactive scaling. Our second contribution is a prediction-based VM provisioning approach for on-demand video transcoding in the cloud. Moreover, to prevent virtualized servers from becoming overloaded, the proposed VM provisioning approaches are augmented with admission control approaches. Therefore, our third contribution is a session-based admission control approach for multi-tier web applications called adaptive Admission Control for Virtualized Application Servers. Similarly, the fourth contribution in this thesis is a stream-based admission control and scheduling approach for on-demand video transcoding called Stream-Based Admission Control and Scheduling. Our fifth contribution is a computation and storage trade-o strategy for cost-efficient video transcoding in cloud computing. Finally, the sixth and the last contribution is a web application consolidation approach, which uses Ant Colony System to minimize the under-utilization of the virtualized application servers.

BotCloud Detection System

Leveraging cloud services, companies and organizations can significantly improve their efficiency, as well as building novel business opportunities. Cloud computing offers various advantages to companies while having some risks for them too. Advantages offered by service providers are mostly about efficiency and reliability while risks of cloud computing are mostly about security problems. Problems with security of the cloud still demand significant attention in order to tackle the potential problems. Security problems in the cloud as security problems in any area of computing, can not be fully tackled. However creating novel and new solutions can be used by service providers to mitigate the potential threats to a large extent. Looking at the security problem from a very high perspective, there are two focus directions. Security problems that threaten service user’s security and privacy are at one side. On the other hand, security problems that threaten service provider’s security and privacy are on the other side. Both kinds of threats should mostly be detected and mitigated by service providers. Looking a bit closer to the problem, mitigating security problems that target providers can protect both service provider and the user. However, the focus of research community mostly is to provide solutions to protect cloud users. A significant research effort has been put in protecting cloud tenants against external attacks. However, attacks that are originated from elastic, on-demand and legitimate cloud resources should still be considered seriously. The cloud-based botnet or botcloud is one of the prevalent cases of cloud resource misuses. Unfortunately, some of the cloud’s essential characteristics enable criminals to form reliable and low cost botclouds in a short time. In this paper, we present a system that helps to detect distributed infected Virtual Machines (VMs) acting as elements of botclouds. Based on a set of botnet related system level symptoms, our system groups VMs. Grouping VMs helps to separate infected VMs from others and narrows down the target group under inspection. Our system takes advantages of Virtual Machine Introspection (VMI) and data mining techniques.

Use of airborne laser scanner data in demanding forest conditions

Most of the applications of airborne laser scanner data to forestry require that the point cloud be normalized, i.e., each point represents height from the ground instead of elevation. To normalize the point cloud, a digital terrain model (DTM), which is derived from the ground returns in the point cloud, is employed. Unfortunately, extracting accurate DTMs from airborne laser scanner data is a challenging task, especially in tropical forests where the canopy is normally very thick (partially closed), leading to a situation in which only a limited number of laser pulses reach the ground. Therefore, robust algorithms for extracting accurate DTMs in low-ground-point-densitysituations are needed in order to realize the full potential of airborne laser scanner data to forestry. The objective of this thesis is to develop algorithms for processing airborne laser scanner data in order to: (1) extract DTMs in demanding forest conditions (complex terrain and low number of ground points) for applications in forestry; (2) estimate canopy base height (CBH) for forest fire behavior modeling; and (3) assess the robustness of LiDAR-based high-resolution biomass estimation models against different field plot designs. Here, the aim is to find out if field plot data gathered by professional foresters can be combined with field plot data gathered by professionally trained community foresters and used in LiDAR-based high-resolution biomass estimation modeling without affecting prediction performance. The question of interest in this case is whether or not the local forest communities can achieve the level technical proficiency required for accurate forest monitoring. The algorithms for extracting DTMs from LiDAR point clouds presented in this thesis address the challenges of extracting DTMs in low-ground-point situations and in complex terrain while the algorithm for CBH estimation addresses the challenge of variations in the distribution of points in the LiDAR point cloud caused by things like variations in tree species and season of data acquisition. These algorithms are adaptive (with respect to point cloud characteristics) and exhibit a high degree of tolerance to variations in the density and distribution of points in the LiDAR point cloud. Results of comparison with existing DTM extraction algorithms showed that DTM extraction algorithms proposed in this thesis performed better with respect to accuracy of estimating tree heights from airborne laser scanner data. On the other hand, the proposed DTM extraction algorithms, being mostly based on trend surface interpolation, can not retain small artifacts in the terrain (e.g., bumps, small hills and depressions). Therefore, the DTMs generated by these algorithms are only suitable for forestry applications where the primary objective is to estimate tree heights from normalized airborne laser scanner data. On the other hand, the algorithm for estimating CBH proposed in this thesis is based on the idea of moving voxel in which gaps (openings in the canopy) which act as fuel breaks are located and their height is estimated. Test results showed a slight improvement in CBH estimation accuracy over existing CBH estimation methods which are based on height percentiles in the airborne laser scanner data. However, being based on the idea of moving voxel, this algorithm has one main advantage over existing CBH estimation methods in the context of forest fire modeling: it has great potential in providing information about vertical fuel continuity. This information can be used to create vertical fuel continuity maps which can provide more realistic information on the risk of crown fires compared to CBH.