The interest in implementing energy-efficient digital circuits using micro and nanoelectromechanical resonator technology has increased significantly over the last decade given their lower energy consumption in comparison to complementary metal oxide-semiconductor circuits. In this thesis, multiple circuit designs based on micro and nanoelectromechanical beam resonators are presented. These circuits include a nano resonator-based flash style analog-to-digital converter, a 4-bit digital-to-analog converter, and a micro-resonator-based 7:3 counter, all among the key building blocks of a microcomputing system. Simulations and experimental results were obtained for all circuits. In general, the proposed circuits based on nanoelectromechanical resonators show up to 90% reduction in energy consumption compared to their complementary metal-oxide-semiconductor counterparts in MHz operation speeds, fulfilling requirements for many applications such as Internet of Things and biomedical devices.

This thesis presents a new power circuit configuration to be used in shunt active power filters. A new control algorithm based on the linear voltage control suitable for the proposed circuit is introduced. The system is analysed both in time and frequency domains. The practical implementation of the system proves its suitability for the proposed task. The switching frequency of the proposed circuit is much lower than that in other active filters. The switching losses are then considerably reduced, in addition to the fact that the switching devices can withstand larger values of currents being switched on and off at lower frequencies which is an advantage to this circuit. The component sizes (capacitors and inductors) in the proposed circuit are also much smaller than those in other filter configurations. In addition, the thesis presents a new method for categorising the active filter systems proposed in the surveyed literature. The survey includes a comparison of these techniques showing their respective merits and drawbacks. The thesis also includes an implementation of a reference current generator that is suitable for single-phase applications without the need for excessive computations. The technique involves a modified Fourier analysis, which is suitable for active filtering applications.

ABSTRACT Federal regulations have recognized that arc flash hazards are a critical source of potential injury. As a consequence, in order to work on some electrical equipment, the energy source must be completely shut-down. However, power distribution systems in mission critical facilities such as hospitals and data centers must sometimes remain energized while being maintained. In recent years the Arc Flash Hazard Analysis has emerged as a power system tool that informs the qualified technician of the incident energy at the equipment to be maintained and recommends the proper protective equipment to wear. Due to codes, standards and historically acceptable design methods, the Arc Flash Hazard is often higher and more dangerous than necessary. This dissertation presents detailed methodology and proposes alternative strategies to be implemented at the design stage of 600 volt facility power distribution systems which will decrease the Arc Flash Hazard Exposure when compared to widely used code acceptable design strategies. Software models have been developed for different locations throughout a power system. These software model simulations will analyze the Arc Flash Hazard in a system designed with typical mainstream code acceptable methods. The model will be changed to show implementation of arc flash mitigation techniques at the system design level. The computer simulations after the mitigation techniques will show significant lowering of the Arc Flash Hazard Exposure.

This work deals with a proposal and construction of a DC/DC converter for a control of a DC motor Heinzmann. The DC/DC converter will be used in an electric drive for a bicycle. The proposal of the DC/DC converter is designed for such engine power to avoid fully any human force to drive - pedaling. The work includes informations about the used motor, progress of proposal and dimensioning of the converter - power circuit and control electronics.

Accurate estimation of voltage drop (IR drop), in Application-Specific Integrated Circuits (ASICs) is a critical challenge, which impacts their performance and power consumption. As technology advances and die sizes shrink, predicting IR drop fast and accurate becomes increasingly challenging. This thesis focuses on exploring the application of Machine Learning (ML) algorithms, including Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN) and Graph Neural Network (GNN), to address this problem. Traditional methods of estimating IR drop using commercial tools are time consuming, especially for complex designs with millions of transistors. To overcome that, ML algorithms are investigated for their ability to provide fast and accurate IR drop estimation. This thesis utilizes electrical, timing and physical features of the ASIC design as input to train the ML models. The scalability of the selected features allows for their effective application across various ASIC designs with very few adjustments. Experimental results demonstrate the advantages of ML models over commercial tools, offering significant improvements in prediction speed. Notably, GNNs, such as Graph Convolutional Network (GCN) models showed promising performance with low prediction errors in voltage drop estimation. The incorporation of graph-structures models opens new fields of research for accurate IR drop prediction. The conclusions drawn emphasize the effectiveness of ML algorithms in accurately estimating IR drop, thereby optimizing ASIC design efficiency. The application of ML models enables faster predictions and noticeably reducing calculation time. This contributes to enhancing energy efficiency and minimizing environmental impact through optimised power circuits. Future work can focus on exploring the scalability of the models by training on a smaller portion of the circuit and extrapolating predictions to the entire design seems promising for more efficient and accurate IR drop estimation in complex ASIC designs. These advantages present new opportunities in the field and extend the capabilities of ML algorithms in the task of IR drop prediction. / Noggrann uppskattning av spänningsfallet (IR-fall), i ASIC är en kritisk utmaning som påverkar deras prestanda och strömförbrukning. När tekniken går framåt och formstorlekarna krymper, blir det allt svårare att förutsäga IR-fall snabbt och exakt. Denna avhandling fokuserar på att utforska tillämpningen av ML-algoritmer, inklusive XGBoost, CNN och GNN, för att lösa detta problem. Traditionella metoder för att uppskatta IR-fall med kommersiella verktyg är tidskrävande, särskilt för komplexa konstruktioner med miljontals transistorer. För att övervinna det undersöks ML-algoritmer för deras förmåga att ge snabb och exakt IR-falluppskattning. Denna avhandling använder elektriska, timing och fysiska egenskaper hos ASIC-designen som input för att träna ML-modellerna. Skalbarheten hos de valda funktionerna möjliggör deras effektiva tillämpning över olika ASIC-designer med mycket få justeringar. Experimentella resultat visar fördelarna med ML-modeller jämfört med kommersiella verktyg, och erbjuder betydande förbättringar i förutsägelsehastighet. Noterbart är att GNNs, såsom GCN-modeller, visade lovande prestanda med låga prediktionsfel vid uppskattning av spänningsfall. Införandet av grafstrukturmodeller öppnar nya forskningsfält för exakt IRfallförutsägelse. De slutsatser som dras betonar effektiviteten hos MLalgoritmer för att noggrant uppskatta IR-fall, och därigenom optimera ASICdesigneffektiviteten. Tillämpningen av ML-modeller möjliggör snabbare förutsägelser och märkbart minskad beräkningstid. Detta bidrar till att förbättra energieffektiviteten och minimera miljöpåverkan genom optimerade kraftkretsar. Framtida arbete kan fokusera på att utforska skalbarheten hos modellerna genom att träna på en mindre del av kretsen och att extrapolera förutsägelser till hela designen verkar lovande för mer effektiv och exakt IR-falluppskattning i komplexa ASIC-designer. Dessa fördelar ger nya möjligheter inom området och utökar kapaciteten hos ML-algoritmer i uppgiften att förutsäga IR-fall.