Extension and evaluation of JXTA protocols for supporting reliable P2P distributed computing

Peer-reviewed

Efficient Peer Selection in P2P JXTA-Based Platforms

Peer-reviewed

Visual SLAM for underwater vehicles using video velocity log and natural landmarks

A visual SLAM system has been implemented and optimised for real-time deployment on an AUV equipped with calibrated stereo cameras. The system incorporates a novel approach to landmark description in which landmarks are local sub maps that consist of a cloud of 3D points and their associated SIFT/SURF descriptors. Landmarks are also sparsely distributed which simplifies and accelerates data association and map updates. In addition to landmark-based localisation the system utilises visual odometry to estimate the pose of the vehicle in 6 degrees of freedom by identifying temporal matches between consecutive local sub maps and computing the motion. Both the extended Kalman filter and unscented Kalman filter have been considered for filtering the observations. The output of the filter is also smoothed using the Rauch-Tung-Striebel (RTS) method to obtain a better alignment of the sequence of local sub maps and to deliver a large-scale 3D acquisition of the surveyed area. Synthetic experiments have been performed using a simulation environment in which ray tracing is used to generate synthetic images for the stereo system

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.

Splat-based surface reconstruction from defect-laden point sets

We introduce a method for surface reconstruction from point sets that is able to cope with noise and outliers. First, a splat-based representation is computed from the point set. A robust local 3D RANSAC-based procedure is used to filter the point set for outliers, then a local jet surface - a low-degree surface approximation - is fitted to the inliers. Second, we extract the reconstructed surface in the form of a surface triangle mesh through Delaunay refinement. The Delaunay refinement meshing approach requires computing intersections between line segment queries and the surface to be meshed. In the present case, intersection queries are solved from the set of splats through a 1D RANSAC procedure

Improved Privacy-Preserving P2P Multimedia Distribution Based on Recombined Fingerprints

Peer-reviewed

Assessing functional relations in single-case designs: Quantitative proposals in the context of the evidence-based movement

In the context of the evidence-based practices movement, the emphasis on computing effect sizes and combining them via meta-analysis does not preclude the demonstration of functional relations. For the latter aim, we propose to augment the visual analysis to add consistency to the decisions made on the existence of a functional relation without losing sight of the need for a methodological evaluation of what stimuli and reinforcement or punishment are used to control the behavior. Four options for quantification are reviewed, illustrated, and tested with simulated data. These quantifications include comparing the projected baseline with the actual treatment measurements, on the basis of either parametric or nonparametric statistics. The simulated data used to test the quantifications include nine data patterns in terms of the presence and type of effect and comprising ABAB and multiple baseline designs. Although none of the techniques is completely flawless in terms of detecting a functional relation only when it is present but not when it is absent, an option based on projecting split-middle trend and considering data variability as in exploratory data analysis proves to be the best performer for most data patterns. We suggest that the information on whether a functional relation has been demonstrated should be included in meta-analyses. It is also possible to use as a weight the inverse of the data variability measure used in the quantification for assessing the functional relation. We offer an easy to use code for open-source software for implementing some of the quantifications.

A Method for Sky-Condition Classification from Ground-Based Solar Radiation Measurements

Identification of clouds from satellite images is now a routine task. Observation of clouds from the ground, however, is still needed to acquire a complete description of cloud conditions. Among the standard meteorologicalvariables, solar radiation is the most affected by cloud cover. In this note, a method for using global and diffuse solar radiation data to classify sky conditions into several classes is suggested. A classical maximum-likelihood method is applied for clustering data. The method is applied to a series of four years of solar radiation data and human cloud observations at a site in Catalonia, Spain. With these data, the accuracy of the solar radiation method as compared with human observations is 45% when nine classes of sky conditions are to be distinguished, and it grows significantly to almost 60% when samples are classified in only five different classes. Most errors are explained by limitations in the database; therefore, further work is under way with a more suitable database

A reliable method of quantification of trace copper in beverages with and without alcohol by spectrophotometry after cloud point extraction

A new cloud point extraction (CPE) method was developed for the separation and preconcentration of copper (II) prior to spectrophotometric analysis. For this purpose, 1-(2,4-dimethylphenyl) azonapthalen-2-ol (Sudan II) was used as a chelating agent and the solution pH was adjusted to 10.0 with borate buffer. Polyethylene glycol tert-octylphenyl ether (Triton X-114) was used as an extracting agent in the presence of sodium dodecylsulphate (SDS). After phase separation, based on the cloud point of the mixture, the surfactant-rich phase was diluted with acetone, and the enriched analyte was spectrophotometrically determined at 537 nm. The variables affecting CPE efficiency were optimized. The calibration curve was linear within the range 0.285-20 µg L-1 with a detection limit of 0.085 µg L-1. The method was successfully applied to the quantification of copper in different beverage samples.

Ligandless cloud point extraction of trace amounts of palladium and rhodium in road dust samples using Span 80 prior to their determination by flame atomic absorption spectrometry

In this study, a procedure is developed for cloud point extraction of Pd(II) and Rh(III) ions in aqueous solution using Span 80 (non-ionic surfactant) prior to their determination by flame atomic absorption spectroscopy. This method is based on the extraction of Pd(II) and Rh(III) ions at a pH of 10 using Span 80 with no chelating agent. We investigated the effect of various parameters on the recovery of the analyte ions, including pH, equilibration temperature and time, concentration of Span 80, and ionic strength. Under the best experimental conditions, the limits of detection based on 3Sb for Pd(II) and Rh(III) ions were 1.3 and 1.2 ng mL-1, respectively. Seven replicate determinations of a mixture of 0.5 µg mL-1 palladium and rhodium ions gave a mean absorbance of 0.058 and 0.053 with relative standard deviations of 1.8 and 1.6%, respectively. The developed method was successfully applied to the extraction and determination of the palladium and rhodium ions in road dust and standard samples and satisfactory results were obtained.

Pilvisovelluskehitys Google App Engine -pilvisovellusalustalla

Tämä Diplomityö keskittyy tutkimaan pilvisovelluskehitystä Google App Engine – pilvisovellusalustalle perustuen kuusi –vaiheiseen vesiputousmalliin sekä tutkimaan Google App Engine -pilvisovellusalustan tarjoamia mahdollisuuksia ja rajoituksia sovelluskehityksen muodossa. Tutkimuksen perusteella kuusi –vaiheinen vesiputousmalli soveltuu pilvisovelluskehitykseen,mikäli vaatimusmäärittely on tarkka jo sovelluskehityksen alkuvaiheessa. Tutkimuksen tuloksena syntyi vaatimusmäärittely MikkoMail –pilvisovellukselle. Vaatimusmäärittelyn pohjalta luotiin MikkoMail –pilvisovellus Google App Engine –pilvisovellusalustalle. Google App Engine –pilvisovellusalusta tukee vain Python- ja Java –ohjelmointikieliä eikä sisällä lainkaan ulkoista tietokantapalveluiden tukea. Tästä syystä Google App Engine -pilvisovellusalusta soveltuu pieniin, keskisuuriin ja pilottiprojektinomaisiin sovelluskehitysprojekteihin.

When to Utilize Software as a Service

Cloud computing enables on-demand network access to shared resources (e.g., computation, networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort. Cloud computing refers to both the applications delivered as services over the Internet and the hardware and system software in the data centers. Software as a service (SaaS) is part of cloud computing. It is one of the cloud service models. SaaS is software deployed as a hosted service and accessed over the Internet. In SaaS, the consumer uses the provider‘s applications running in the cloud. SaaS separates the possession and ownership of software from its use. The applications can be accessed from any device through a thin client interface. A typical SaaS application is used with a web browser based on monthly pricing. In this thesis, the characteristics of cloud computing and SaaS are presented. Also, a few implementation platforms for SaaS are discussed. Then, four different SaaS implementation cases and one transformation case are deliberated. The pros and cons of SaaS are studied. This is done based on literature references and analysis of the SaaS implementations and the transformation case. The analysis is done both from the customer‘s and service provider‘s point of view. In addition, the pros and cons of on-premises software are listed. The purpose of this thesis is to find when SaaS should be utilized and when it is better to choose a traditional on-premises software. The qualities of SaaS bring many benefits both for the customer as well as the provider. A customer should utilize SaaS when it provides cost savings, ease, and scalability over on-premises software. SaaS is reasonable when the customer does not need tailoring, but he only needs a simple, general-purpose service, and the application supports customer‘s core business. A provider should utilize SaaS when it offers cost savings, scalability, faster development, and wider customer base over on-premises software. It is wise to choose SaaS when the application is cheap, aimed at mass market, needs frequent updating, needs high performance computing, needs storing large amounts of data, or there is some other direct value from the cloud infrastructure.

Memristive Computing

Memristive computing refers to the utilization of the memristor, the fourth fundamental passive circuit element, in computational tasks. The existence of the memristor was theoretically predicted in 1971 by Leon O. Chua, but experimentally validated only in 2008 by HP Labs. A memristor is essentially a nonvolatile nanoscale programmable resistor — indeed, memory resistor — whose resistance, or memristance to be precise, is changed by applying a voltage across, or current through, the device. Memristive computing is a new area of research, and many of its fundamental questions still remain open. For example, it is yet unclear which applications would benefit the most from the inherent nonlinear dynamics of memristors. In any case, these dynamics should be exploited to allow memristors to perform computation in a natural way instead of attempting to emulate existing technologies such as CMOS logic. Examples of such methods of computation presented in this thesis are memristive stateful logic operations, memristive multiplication based on the translinear principle, and the exploitation of nonlinear dynamics to construct chaotic memristive circuits. This thesis considers memristive computing at various levels of abstraction. The first part of the thesis analyses the physical properties and the current-voltage behaviour of a single device. The middle part presents memristor programming methods, and describes microcircuits for logic and analog operations. The final chapters discuss memristive computing in largescale applications. In particular, cellular neural networks, and associative memory architectures are proposed as applications that significantly benefit from memristive implementation. The work presents several new results on memristor modeling and programming, memristive logic, analog arithmetic operations on memristors, and applications of memristors. The main conclusion of this thesis is that memristive computing will be advantageous in large-scale, highly parallel mixed-mode processing architectures. This can be justified by the following two arguments. First, since processing can be performed directly within memristive memory architectures, the required circuitry, processing time, and possibly also power consumption can be reduced compared to a conventional CMOS implementation. Second, intrachip communication can be naturally implemented by a memristive crossbar structure.

Cloud service purchase behavior

This study aims to open up some typical models of organizational buying behavior. As the cloud computing and cloud services seem to be the today´s hype, the study seeks to further facilitate the understanding of organizational buying behavior regarding cloud services by interviewing a decision maker of this field in the purchaser´s side and also for comparison a cloud service provider´s representative from the vendor´s side.

On Energy Efficient Computing Platforms

In accordance with the Moore's law, the increasing number of on-chip integrated transistors has enabled modern computing platforms with not only higher processing power but also more affordable prices. As a result, these platforms, including portable devices, work stations and data centres, are becoming an inevitable part of the human society. However, with the demand for portability and raising cost of power, energy efficiency has emerged to be a major concern for modern computing platforms. As the complexity of on-chip systems increases, Network-on-Chip (NoC) has been proved as an efficient communication architecture which can further improve system performances and scalability while reducing the design cost. Therefore, in this thesis, we study and propose energy optimization approaches based on NoC architecture, with special focuses on the following aspects. As the architectural trend of future computing platforms, 3D systems have many bene ts including higher integration density, smaller footprint, heterogeneous integration, etc. Moreover, 3D technology can signi cantly improve the network communication and effectively avoid long wirings, and therefore, provide higher system performance and energy efficiency. With the dynamic nature of on-chip communication in large scale NoC based systems, run-time system optimization is of crucial importance in order to achieve higher system reliability and essentially energy efficiency. In this thesis, we propose an agent based system design approach where agents are on-chip components which monitor and control system parameters such as supply voltage, operating frequency, etc. With this approach, we have analysed the implementation alternatives for dynamic voltage and frequency scaling and power gating techniques at different granularity, which reduce both dynamic and leakage energy consumption. Topologies, being one of the key factors for NoCs, are also explored for energy saving purpose. A Honeycomb NoC architecture is proposed in this thesis with turn-model based deadlock-free routing algorithms. Our analysis and simulation based evaluation show that Honeycomb NoCs outperform their Mesh based counterparts in terms of network cost, system performance as well as energy efficiency.