Analyzing and Computing the Sustainability Gains of Building Automation

Smart home implementation in residential buildings promises to optimize energy usage and save significant amount of energy simply due to a better understanding of user's energy usage profile. Apart from the energy optimisation prospects of this technology, it also aims to guarantee occupants significant amount of comfort and remote control over home appliances both at home locations and at remote places. However, smart home investment just like any other kind of investment requires an adequate measurement and justification of the economic gains it could proffer before its realization. These economic gains could differ for different occupants due to their inherent behaviours and tendencies. Thus it is pertinent to investigate the various behaviours and tendencies of occupants in different domain of interests and to measure the value of the energy savings accrued by smart home implementations in these domains of interest in order to justify such economic gains. This thesis investigates two domains of interests (the rented apartment and owned apartment) for primarily two behavioural tendencies (Finland and Germany) obtained from observation and corroborated by conducted interviews to measure the payback time and Return on Investment (ROI) of their smart home implementations. Also, similar measures are obtained for identified Australian use case. The research finding reveals that building automation for the Finnish behavioural tendencies seems to proffers a better ROI and payback time for smart home implementations.

Power Consumption Measurement and Scenario Based Energy Model in Wearable Computing Applications

The power is still today an issue in wearable computing applications. The aim of the present paper is to raise awareness of the power consumption of wearable computing devices in specific scenarios to be able in the future to design energy efficient wireless sensors for context recognition in wearable computing applications. The approach is based on a hardware study. The objective of this paper is to analyze and compare the total power consumption of three representative wearable computing devices in realistic scenarios such as Display, Speaker, Camera and microphone, Transfer by Wi-Fi, Monitoring outdoor physical activity and Pedometer. A scenario based energy model is also developed. The Samsung Galaxy Nexus I9250 smartphone, the Vuzix M100 Smart Glasses and the SimValley Smartwatch AW-420.RX are the three devices representative of their form factors. The power consumption is measured using PowerTutor, an android energy profiler application with logging option and using unknown parameters so it is adjusted with the USB meter. The result shows that the screen size is the main parameter influencing the power consumption. The power consumption for an identical scenario varies depending on the wearable devices meaning that others components, parameters or processes might impact on the power consumption and further study is needed to explain these variations. This paper also shows that different inputs (touchscreen is more efficient than buttons controls) and outputs (speaker sensor is more efficient than display sensor) impact the energy consumption in different way. This paper gives recommendations to reduce the energy consumption in healthcare wearable computing application using the energy model.

Embedded Service Bus for Internet of Things Interoperability

Internet of Things (IoT) technologies are developing rapidly, and therefore there exist several standards of interconnection protocols and platforms. The existence of heterogeneous protocols and platforms has become a critical challenge for IoT system developers. To mitigate this challenge, few alliances and organizations have taken the initiative to build a framework that helps to integrate application silos. Some of these frameworks focus only on a specific domain like home automation. However, the resource constraints in the large proportion of connected devices make it difficult to build an interoperable system using such frameworks. Therefore, a general purpose, lightweight interoperability framework that can be used for a range of devices is required. To tackle the heterogeneous nature, this work introduces an embedded, distributed and lightweight service bus, Lightweight IoT Service bus Architecture (LISA), which fits inside the network stack of a small real-time operating system for constrained nodes. LISA provides a uniform application programming interface for an IoT system on a range of devices with variable resource constraints. It hides platform and protocol variations underneath it, thus facilitating interoperability in IoT implementations. LISA is inspired by the Network on Terminal Architecture, a service centric open architecture by Nokia Research Center. Unlike many other interoperability frameworks, LISA is designed specifically for resource constrained nodes and it provides essential features of a service bus for easy service oriented architecture implementation. The presented architecture utilizes an intermediate computing layer, a Fog layer, between the small nodes and the cloud, thereby facilitating the federation of constrained nodes into subnetworks. As a result of a modular and distributed design, the part of LISA running in the Fog layer handles the heavy lifting to assist the lightweight portion of LISA inside the resource constrained nodes. Furthermore, LISA introduces a new networking paradigm, Node Centric Networking, to route messages across protocol boundaries to facilitate interoperability. This thesis presents a concept implementation of the architecture and creates a foundation for future extension towards a comprehensive interoperability framework for IoT.

Computational models for and from biology : simple gene assembly and reaction systems

The advancement of science and technology makes it clear that no single perspective is any longer sufficient to describe the true nature of any phenomenon. That is why the interdisciplinary research is gaining more attention overtime. An excellent example of this type of research is natural computing which stands on the borderline between biology and computer science. The contribution of research done in natural computing is twofold: on one hand, it sheds light into how nature works and how it processes information and, on the other hand, it provides some guidelines on how to design bio-inspired technologies. The first direction in this thesis focuses on a nature-inspired process called gene assembly in ciliates. The second one studies reaction systems, as a modeling framework with its rationale built upon the biochemical interactions happening within a cell. The process of gene assembly in ciliates has attracted a lot of attention as a research topic in the past 15 years. Two main modelling frameworks have been initially proposed in the end of 1990s to capture ciliates’ gene assembly process, namely the intermolecular model and the intramolecular model. They were followed by other model proposals such as templatebased assembly and DNA rearrangement pathways recombination models. In this thesis we are interested in a variation of the intramolecular model called simple gene assembly model, which focuses on the simplest possible folds in the assembly process. We propose a new framework called directed overlap-inclusion (DOI) graphs to overcome the limitations that previously introduced models faced in capturing all the combinatorial details of the simple gene assembly process. We investigate a number of combinatorial properties of these graphs, including a necessary property in terms of forbidden induced subgraphs. We also introduce DOI graph-based rewriting rules that capture all the operations of the simple gene assembly model and prove that they are equivalent to the string-based formalization of the model. Reaction systems (RS) is another nature-inspired modeling framework that is studied in this thesis. Reaction systems’ rationale is based upon two main regulation mechanisms, facilitation and inhibition, which control the interactions between biochemical reactions. Reaction systems is a complementary modeling framework to traditional quantitative frameworks, focusing on explicit cause-effect relationships between reactions. The explicit formulation of facilitation and inhibition mechanisms behind reactions, as well as the focus on interactions between reactions (rather than dynamics of concentrations) makes their applicability potentially wide and useful beyond biological case studies. In this thesis, we construct a reaction system model corresponding to the heat shock response mechanism based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We also introduce for RS various concepts inspired by biology, e.g., mass conservation, steady state, periodicity, etc., to do model checking of the reaction systems based models. We prove that the complexity of the decision problems related to these properties varies from P to NP- and coNP-complete to PSPACE-complete. We further focus on the mass conservation relation in an RS and introduce the conservation dependency graph to capture the relation between the species and also propose an algorithm to list the conserved sets of a given reaction system.

Analysis of PWR-PACTEL Small Break LOCA experiment using TRACE

Currently, the power generation is one of the most significant life aspects for the whole man-kind. Barely one can imagine our life without electricity and thermal energy. Thus, different technologies for producing those types of energy need to be used. Each of those technologies will always have their own advantages and disadvantages. Nevertheless, every technology must satisfy such requirements as efficiency, ecology safety and reliability. In the matter of the power generation with nuclear energy utilization these requirements needs to be highly main-tained, especially since accidents on nuclear power plants may cause very long term deadly consequences. In order to prevent possible disasters related to the accident on a nuclear power plant strong and powerful algorithms were invented in last decades. Such algorithms are able to manage calculations of different physical processes and phenomena of real facilities. How-ever, the results acquired by the computing must be verified with experimental data.

Adopting the Vaadin Java framework to rebuild VTT’s Web Interfaces and Database Applications

With the growth in new technologies, using online tools have become an everyday lifestyle. It has a greater impact on researchers as the data obtained from various experiments needs to be analyzed and knowledge of programming has become mandatory even for pure biologists. Hence, VTT came up with a new tool, R Executables (REX) which is a web application designed to provide a graphical interface for biological data functions like Image analysis, Gene expression data analysis, plotting, disease and control studies etc., which employs R functions to provide results. REX provides a user interactive application for the biologists to directly enter the values and run the required analysis with a single click. The program processes the given data in the background and prints results rapidly. Due to growth of data and load on server, the interface has gained problems concerning time consumption, poor GUI, data storage issues, security, minimal user interactive experience and crashes with large amount of data. This thesis handles the methods by which these problems were resolved and made REX a better application for the future. The old REX was developed using Python Django and now, a new programming language, Vaadin has been implemented. Vaadin is a Java framework for developing web applications and the programming language is extremely similar to Java with new rich components. Vaadin provides better security, better speed, good and interactive interface. In this thesis, subset functionalities of REX was selected which includes IST bulk plotting and image segmentation and implemented those using Vaadin. A code of 662 lines was programmed by me which included Vaadin as the front-end handler while R language was used for back-end data retrieval, computing and plotting. The application is optimized to allow further functionalities to be migrated with ease from old REX. Future development is focused on including Hight throughput screening functions along with gene expression database handling

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.

Healthcare Services In CloudComputing

The Thesis title” Healthcare services in cloud computing” discusses the healthcare services available in the new converging technology called cloud computing. This computing technology had craved its path in the desirable market field healthcare. Healthcare is an extensive and a massive mission of maintenance and providing a complete treatment to the person suffering from ailments. In the olden days well equipped healthcare surveillance is not accessible to all communities of people due to several reasons like, geographical locations, equipment cost, and infrastructure, and skilled medical practitioners, now due to the advancement of the medicine in cloud technology has reached some of its barriers making it more viable to all the people (communities) with all the robust technologies and techniques. This study will give an overview of the healthcare transformation of different approaches of cloud computing over information technology and its strategic usage. Further enhancing better healthcare to ensure scalable, compatible functions supporting the well-being, this study also considers the techniques of cloud computing and its application, advancement in healthcare.

Modelling the colour difference by artificial neural network

This thesis work studies the modelling of the colour difference using artificial neural network. Multilayer percepton (MLP) network is proposed to model CIEDE2000 colour difference formula. MLP is applied to classify colour points in CIE xy chromaticity diagram. In this context, the evaluation was performed using Munsell colour data and MacAdam colour discrimination ellipses. Moreover, in CIE xy chromaticity diagram just noticeable differences (JND) of MacAdam ellipses centres are computed by CIEDE2000, to compare JND of CIEDE2000 and MacAdam ellipses. CIEDE2000 changes the orientation of blue areas in CIE xy chromaticity diagram toward neutral areas, but on the whole it does not totally agree with the MacAdam ellipses. The proposed MLP for both modelling CIEDE2000 and classifying colour points showed good accuracy and achieved acceptable results.

Controlling full penetration in MAG welding by the application of infrared thermography and neural network

In this study, an infrared thermography based sensor was studied with regard to usability and the accuracy of sensor data as a weld penetration signal in gas metal arc welding. The object of the study was to evaluate a specific sensor type which measures thermography from solidified weld surface. The purpose of the study was to provide expert data for developing a sensor system in adaptive metal active gas (MAG) welding. Welding experiments with considered process variables and recorded thermal profiles were saved to a database for further analysis. To perform the analysis within a reasonable amount of experiments, the process parameter variables were gradually altered by at least 10 %. Later, the effects of process variables on weld penetration and thermography itself were considered. SFS-EN ISO 5817 standard (2014) was applied for classifying the quality of the experiments. As a final step, a neural network was taught based on the experiments. The experiments show that the studied thermography sensor and the neural network can be used for controlling full penetration though they have minor limitations, which are presented in results and discussion. The results are consistent with previous studies and experiments found in the literature.