RADAR Roadmap: The application of EPrints for the continued development of RADAR at The Glasgow School of Art

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Toward a-state-of-the-art IR copyright management system: a needs assessment

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Fedora 4 as a Shared Linked Data repository for the Art Institute of Chicago Collections

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Waste Management in Commercial Activity: State-of-the-Art and the Potential Innovation in the Urban Areas of Hanoi, Vietnam

As the rapid development of the society as well as the lifestyle, the generation of commercial waste is getting more complicated to control. The situation of packaging waste and food waste – the main fractions of commercial waste in different countries in Europe and Asia is analyzed in order to evaluate and suggest necessary improvements for the existing waste management system in the city of Hanoi, Vietnam. From all waste generation sources of the city, a total amount of approximately 4000 tons of mixed waste is transported to the composting facility and the disposal site, which emits a huge amount of 1,6Mt of GHG emission to the environment. Recycling activity is taking place spontaneously by the informal pickers, leads to the difficulty in managing the whole system and uncertainty of the overall data. With a relative calculation, resulting in only approximately 0,17Mt CO2 equivalent emission, incinerator is suggested to be the solution of the problem with overloaded landfill and raising energy demand within the inhabitants.

Segmentation of overlapping convex objects

This thesis presents a framework for segmentation of clustered overlapping convex objects. The proposed approach is based on a three-step framework in which the tasks of seed point extraction, contour evidence extraction, and contour estimation are addressed. The state-of-art techniques for each step were studied and evaluated using synthetic and real microscopic image data. According to obtained evaluation results, a method combining the best performers in each step was presented. In the proposed method, Fast Radial Symmetry transform, edge-to-marker association algorithm and ellipse fitting are employed for seed point extraction, contour evidence extraction and contour estimation respectively. Using synthetic and real image data, the proposed method was evaluated and compared with two competing methods and the results showed a promising improvement over the competing methods, with high segmentation and size distribution estimation accuracy.

Casus vulneris cum fractura olecrani et ruptura ancyloseos verae complicati; quem venia exper. fac. med. praeside Joh. Agap. Törngren, m. d. chir. [et] art. obst. professore p. [et] o. Pro gradu medico publicae submittit censurae Andreas Jacobus Hjertman, stip. publ. Obstrob. [!] In audit. med. die XXVI Octob. MDCCCXVI, h. a. & p. m. s

Dedikaatio: Carolus Mannerheim.

Catalogus librorum & instrumentorum chirurgicorum b. m. viri Josephi Pippingsköld, m. d. profess. chirurg. [et] art. obstetr. ad Imp. Acad. Aboëns. nec non imp. coll. med. assessoris, sub hasta d. Oct. XXX & sqq. a. MDCCCXVI

Julkaisuvuosi nimekkeestä.

Toward a Reliable Classifier For Arrhythmia Diagnosis

Illnesses related to the heart are one of the major reasons for death all over the world causing many people to lose their lives in last decades. The good news is that many of those sicknesses are preventable if they are spotted in early stages. On the other hand, the number of the doctors are much lower than the number of patients. This will makes the auto diagnosing of diseases even more and more essential for humans today. Furthermore, when it comes to the diagnosing methods and algorithms, the current state of the art is lacking a comprehensive study on the comparison between different diagnosis solutions. Not having a single valid diagnosing solution has increased the confusion among scholars and made it harder for them to take further steps. This master thesis will address the issue of reliable diagnosing algorithm. We investigate ECG signals and the relation between different diseases and the heart’s electrical activity. Also, we will discuss the necessary steps needed for auto diagnosing the heart diseases including the literatures discussing the topic. The main goal of this master thesis is to find a single reliable diagnosing algorithm and quest for the best classifier to date for heart related sicknesses. Five most suited and most well-known classifiers, such as KNN, CART, MLP, Adaboost and SVM, have been investigated. To have a fair comparison, the ex-periment condition is kept the same for all classification methods. The UCI repository arrhythmia dataset will be used and the data will not be preprocessed. The experiment results indicates that AdaBoost noticeably classifies different diseases with a considera-bly better accuracy.

Power and Performance Management Strategies on NoC-Based Multicore Platforms, Using DVFS, Adaptive Algorithm and Task Parallelism

This work presents synopsis of efficient strategies used in power managements for achieving the most economical power and energy consumption in multicore systems, FPGA and NoC Platforms. In this work, a practical approach was taken, in an effort to validate the significance of the proposed Adaptive Power Management Algorithm (APMA), proposed for system developed, for this thesis project. This system comprise arithmetic and logic unit, up and down counters, adder, state machine and multiplexer. The essence of carrying this project firstly, is to develop a system that will be used for this power management project. Secondly, to perform area and power synopsis of the system on these various scalable technology platforms, UMC 90nm nanotechnology 1.2v, UMC 90nm nanotechnology 1.32v and UMC 0.18 μmNanotechnology 1.80v, in order to examine the difference in area and power consumption of the system on the platforms. Thirdly, to explore various strategies that can be used to reducing system’s power consumption and to propose an adaptive power management algorithm that can be used to reduce the power consumption of the system. The strategies introduced in this work comprise Dynamic Voltage Frequency Scaling (DVFS) and task parallelism. After the system development, it was run on FPGA board, basically NoC Platforms and on these various technology platforms UMC 90nm nanotechnology1.2v, UMC 90nm nanotechnology 1.32v and UMC180 nm nanotechnology 1.80v, the system synthesis was successfully accomplished, the simulated result analysis shows that the system meets all functional requirements, the power consumption and the area utilization were recorded and analyzed in chapter 7 of this work. This work extensively reviewed various strategies for managing power consumption which were quantitative research works by many researchers and companies, it's a mixture of study analysis and experimented lab works, it condensed and presents the whole basic concepts of power management strategy from quality technical papers.