Part I: Battery Management Converter System

Energy storage devices such as rechargeable batteries play a significant role in domestic, transportation, and industrial applications. The development of new battery technologies featuring high power and energy densities along with the strict safety, reliability, and lifetime requirements of the applications have brought the need for Battery Management Systems (BMSs). The first part of this thesis, is focused on a configuration called Battery Management Converter System (BMCS) that provides potential advantages over the conventional configurations such as lower cost and power loss and improved reliability and waveform quality. The balancing algorithm and grid connection control of such configuration for grid connected energy storage application has also been studied and developed in this part.

Part II: Multilevel Converter Topology and Control

Multilevel converters are the state-of-the-art power conversion systems for Medium Voltage (MV) high power applications. As MV drives for fans, pumps, mills, compressors, conveyors etc., multilevel converters have found their use in numerous industries such as mining, marine, metals, water, oil and gas, pulp and paper to name a few. As grid-connected inverters, multilevel converters are used to integrate renewable energy sources and energy storage systems into the grid and regulate the power flow and quality. In the second part of this thesis, the focus will be on the five-level converter topologies with a common dc-link as this feature suits them for many applications involving bidirectional flow of energy. New hybrid multilevel converter topologies along with control and modulation techniques are investigated and developed in this part. Improved trade off between cost and reliability and decreased converter loss are the advantages of the developed topologies and modulation techniques.

In this thesis, application of imaging in assessment of zebrafish (zf) cardiology is discussed. Medical imaging seeks to expose internal structures, as well as to diagnose and treat disease. Medical imaging also establishes a database of normal anatomy and physiology to make it possible to identify abnormalities. Considering that the embryonic zebrafish is transparent, bright field microscopic videos could reveal heart mechanism and could be useful for quantification of it, although microscopic imaging can be useful for adult zebrafish as well. Different imaging methods used in other works is discussed first. Later, the cardiovascular parameters that can be measured using imaging are defined. We then compare different digital image processing and deep learning algorithms that have been employed to process or segment images from zebrafish. At the end of the chapter challenges mutant type are investigated.

Deep learning-based models have revolutionized biomedical image and video segmentation, enabling precise and automated analysis of complex structures. This advancement is particularly critical in the study of zebrafish cardiovascular videos, where accurate segmentation of the heart is essential for understanding cardiac function and development. In this work, we focus on the Zebrafish Automated Cardiac Analysis Framework (ZACAF), utilizing the U-net architecture to achieve high-accuracy segmentation of the zebrafish heart from video frames to calculate important cardiovascular parameters.After multiple collaborations with researchers studying different phenotypes in zf using ZACAF, to enhance the generalizability of our model across varying datasets and conditions, we employed transfer learning techniques, leveraging pre-trained models to adapt to new data efficiently. Additionally, we incorporated Test-Time Augmentation (TTA) to further improve model robustness and accuracy by applying various transformations to the input data during inference. This approach proposed a systematic solution for adopting ZACAF in broader genetic studies using deep learning algorithms. Recognizing the importance of temporal dynamics in video data, we extended our work to integrate temporal features into the segmentation model. By analyzing changes between consecutive frames, we aim to capture the heartbeat dynamics more effectively, providing a comprehensive tool for cardiac analysis in zebrafish embryos. Our approach not only advances the field of biomedical video segmentation but also contributes to the broader understanding of cardiac function in developmental biology.

Background

Methods

Results

Conclusion