Electricity can be considered as the backbone of the modern society. It has become anessential component of the day to day life of all human beings in the world. As a result, thedemand of electricity is growing at a higher rate each year. To fulfill this demand a largenumber of fossil fuels are being consumed all around the world.Sri Lanka, as a developing country in the Asian region, is currently facing this issueof having difficulties in meeting the electricity demand of the country’s population. As acountry having a lower economic growth rate, it is an essential choice to reduce the usage offossil fuel, since it is not only reducing the wastage of money for exportations of fossil fuelfrom other countries but also helps to reduce the harmful gas emissions. Compared to theEuropean countries, the involvement of Sri Lanka in utilizing renewable energy resources forpower generation is observed to be very less. Hence this project aims on analyzing andproviding an overview of the possibility in fulfilling the country’s energy demand bycompletely renewable resources.Three renewable energy components have been taken in to account for this study. Thepower output from solar, wind and hydropower resources have been considered and theanalysis has been carried out with MATLAB software. It is an obvious fact that the electricitydemand varies within the day due to the different lifestyles of people. Hence hourly demandis the most important fact to be considered during a demand-supply analysis. With the dataobtained from Ceylon electricity board, Ministry of power and Renewable Energy, theanalysis has been done for all three resources assuming the wind and solar power plants arebeing constructed at the perfect locations after a careful study of the wind maps and solarirradiation data throughout a year.The Analysis has been carried out with solar power alone and then with wind powerand then with the combined solar and wind power. The results have shown that it is notpossible to fulfill the hourly demand solely from solar and wind power since the solarradiation and the wind flow is not constant throughout the day in each location. Hence forcertain hours the power production is excess and for certain hours power production is lessthan the load requirement. Then Hydro power has been fitted in to place to compensate theshortage in the load. Calculations have been carried out considering only the current hydropower plants which are on operation.Regarding Hydro power plants, the analysis was mainly based on the assumption thatpumps are being used to handle the excess electricity produced by solar and wind plants.Hence the final outcome has been considered based on two main facts which are the currentavailable storage span of all hydro plants as a whole and the required pump power and thehydro turbine power to handle the excess power production from solar and wind.The results have shown that it is possible to meet the total hourly energy demand ofthe whole population of the country by completely renewable resources under certainconditions. The combination and sometimes correlation of solar and wind during the day hasthe capacity to meet the different hourly demands within the day for certain days. However tomeet the hourly demand, hydro power has to be included to balance the shortage. Consideringthe two facts mentioned earlier it has been proven that the current storage span and pumppower is sufficient to balance the excess electricity produced from certain value of installedPV power and certain number of turbines in a wind plant, however for certain value ofinstalled PV power and certain number of turbines ,the pump power requirement and storagelevel to handle the excess power is slightly more than the current power production fromhydro. Hence it is possible to meet the hourly load if more hydro power plants could beconstructed for those cases.Since this is a positive and bright outcome, the next step required is the attention ofthe authorities to execute the construction and implementation of projects related to solar,wind and Hydro plants and thereby opening a new era in the technology and the economy ofSri Lanka.

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Impressive results can be achieved when stacking deep neural networks hierarchies together. Several machine learning papers claim state-of-the-art results when evaluating their models with different accuracy metrics. However, these models come at a cost, which is rarely taken into consideration. This thesis aims to shed light on the resource consumption of machine learning algorithms, and therefore, five efficiency metrics are proposed. These should be used for evaluating machine learning models, taking accuracy, model size, and time and energy consumption for both training and inference into account. These metrics are intended to allow for a fairer evaluation of machine learning models, not only looking at accuracy. This thesis presents an example of how these metrics can be used by applying them to both text and image classification tasks using the algorithms SVM, MLP, and CNN.

Heart arrhytmias are rhythm disorders, which affects the heart and can lead to stroke, hospitalization, and a lower quality of life. Arrhythmias are often detected and diagnosed by using an electrocardiogram (ECG) and in many cases, the early detection of an arrhythmia can greatly increase the chance of a better recovery. The goal of this project is to develop a prototype of a portable ECG-system which can record and classify ECG-signals in real-time so that arrhythmias can be detected as early as possible. The prototype is made up of 1) a wireless ECG measurement unit consisting of a micro-controller, electrodes, an analog front-end, and a Bluetooth module, to capture ECG-signals from a person and transmit them via Bluetooth to a smartphone; and 2) an application implemented on a smartphone that is used to record/receive, visualize and classify ECG-signals. The classification is performed using a machine learning technique - a modular convolutional neural network, which is developed and trained by using a data set of ECG-signal features that is based on R-peaks. The prototype has been developed and tested. The test results have shown that the prototype is fully functional in terms of ECG-signal acquisition, transmission and classification. The results of evaluating the classifier show a weighted average recall of 98%, and a weighted average precision of 98%. The smart phone application used to record and classify the ECG-signals is shown to have a small footprint on the energy consumption of the smartphone, allowing for lengthy recordings. The performance of the classifier can be further improved through the use of a larger and more balanced data set.

The main goal of this project is to evaluate and demonstrate the feasibility of a new framework to be used to design and control external simulators at SAAB AB. The project explore the possibility of developing a web-based framework in a secure environment, based on Docker technology and modern application frameworks. In order to reach the main goal, a qualitative interview is conducted, which is used to design the new framework. An evaluation of the feasibility of the new framework is performed. For that a proof of concept has been developed, together with a measure of performance and the return of investment of the new framework. With the proof of concept together with the performance evaluation, it is proved that a new web-based framework is suitable to design and control external simulators. Furthermore, the return of investment is estimated to be 76.73% per year, which suggests that the benefits would succeed the investment cost of the new framework, if used for more than 16 months.

With the increasing trend of using cloud computing resources, the efficient utilization of these resources becomes more and more important. Working with data stream processing is a paradigm gaining in popularity, with tools such as Apache Spark Streaming or Kafka widely available, and companies are shifting towards real-time monitoring of data such as sensor networks, financial data or anomaly detection. However, it is difficult for users to efficiently make use of cloud computing resources and studies show that a lot of energy and compute hardware is wasted. We propose an approach to optimizing resource usage in cloud computing environments designed for data stream processing frameworks, based on bin packing algorithms. Test results show that the resource usage is substantially improved as a result, with future improvements suggested to further increase this. The solution was implemented as an extension of the HarmonicIO data stream processing framework and evaluated through simulated workloads.

Implementing an indoor navigation system requires alternative techniques to the GPS. One solution is vision-based localization which takes advantage of visual landmarks and a camera to read the environment and determine positioning. Three computer vision algorithms used for pose estimation are tested and evaluated in this project in order to determine their viability in an indoor navigation system. Two algorithms, SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features), take advantage of the natural features in an image, whereas the third algorithm, ArUco, uses a manufactured marker. The evaluation displayed certain advantages for all solutions, however with the goal of using it for a navigation system ArUco was the superior solution as it performed well for key criteria, mainly computational performance and range of detection. An indoor navigation system for Android devices was developed using ArUco marker tracking for positioning and augmented reality for pro- jecting the route. The application was able to successfully fulfill its goal of providing route guidance to a specific target location.

På grund av begärd sekretess innehåller följande rapport enbart projektets bakomliggande problemformulering samt de slutsatser som framtogs.

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