The proposed cell monitoring unit for sensing voltage, current, and temperature in a 12-cell 18650 lithium-ion battery module aims to be low-power, serving as the core of an energy-efficient battery management system and facilitating battery management functions with cell data. Notable features include a switchable voltage divider, a single op-amp differential amplifier and level shifter, and a high-precision composite amplifier. The proposed circuit is implemented on a printed circuit board. Measurement results show that the highest power dissipation under continuous operation is from the current sensing circuit at 6.03 mW under a 4 A string current, followed by the voltage sensing at 2.52 mW for the top cell and the temperature sensing at 34.9 μW. The measured power figures include the power dissipation from the battery cells in addition to the cell monitoring unit. The maximum output error is 68 mV for cell voltages up to 44.4 V, 36 mA for current up to 4 A, and 0.37 ◦C for temperature up to 73 ◦C. / M.S. / Battery management systems are required in modern rechargeable battery-operated devices to help ensure that the batteries operate within the manufacturer-specified operating range. Otherwise, damage to the batteries or to the device may occur. Battery modules are comprised of smaller energy cells to achieve the specified energy capacity and power output. At the core of a battery management system is a battery cell monitoring unit that interfaces the management system with the battery module by providing data about each of the battery cells, including voltage, current, and temperature. To help minimize the power dissipation of battery-powered devices and prolong the battery life, the power consumed by the battery management system should be small. This project aims to detail the design and results of a low-power cell monitoring unit as the core component of energy-efficient battery management systems. The proposed circuit is designed for a 12-cell lithium-ion battery module and implemented on a printed circuit board. The maximum measured power dissipation under continuous operation is 6.03 mW for the current sensing circuit, followed by the voltage sensing circuit at 2.52 mW and the temperature sensing circuit at 34.9 μW.

Šiuolaikiniame pasaulyje vis daugiau žmonių naudoja išmaniuosius telefonus, kurie perima vis daugiau su kompiuterio atliekamo darbo (el. pašto tikrinimas, apsipirkimas internetu ir t.t.). Šiuose įrenginiuose vis daugiau funkcijų galima atlikti balsu (atidaryti programėles ir kt.), tačiau kol kas tik anglų ir keletu kitų kalbų. Todėl šiame darbe bus nagrinėjamas lietuvių kalbos atpažinimo uždavinys iOS platformai (viena iš pagrindinių išmaniųjų telefonų ir planšetinių kompiuterių platformų), kuri yra naudojama mobiliuose Apple įrenginiuose. Šiame darbe nagrinėjamas CMU Sphinx ir Julius bibliotekų panaudojimas iOS įrenginiuose atpažįstant lietuvių kalbą. Tyrimui buvo sukurtas LSR karkasas paslepiantis CMU Sphinx ir Julius bibliotekų realizacijos ypatumus po Objective-C kalbos sąsaja. Tyrimui buvo naudojamas skaičių nuo 0 iki 9 garsynas ir analizuota, koks atpažinimo tikslumas ir greitaveika yra su tokiu nedideliu 10 žodžių žodynu atpažįstant pavienius skaičius. / Nowadays more and more people use smartphones which replaces more word done with personal computer (e-mail checking, e-shopping, etc.). In these devices more and more functions could be done with voice (open apps and other), but still only in english and some other languages. Therefore, in out work we will investigate Lithuanian speech recognition task in iOS (one of the major smartphones and tablets platforms), which runs in Apple's mobile devices. In this work we investigate CMU Sphinx and Julius libraries use in iOS devices for Lithuanian speech recognition. For this task LSR framework was created which encapsulated CMU Sphinx and Julius realisation nuances under Objective-C interfaces. Experiments were performed with numbers from 0 to 9 corpus and recognition accuracy and speed were investigated.

This thesis focuses on addressing the limitations of existing human motion prediction models by extending the prediction horizon to very long-term forecasts. The objective is to develop a model that achieves one of the best stable prediction horizons in the field, providing accurate predictions without significant error increase over time. Through the utilization of quantization based models our research successfully achieves the desired objective with the proposed aligned version of Mean Per Joint Position Error. The first of the two proposed models, an attention-based Vector Quantized Variational AutoEncoder, demonstrates good performance in predicting beyond conventional time boundaries, maintaining low error rates as the prediction horizon extends. While slight discrepancies in joint positions are observed, the model effectively captures the underlying patterns and dynamics of human motion, which remains highly applicable in real-world scenarios. Furthermore, our investigation into a correlation-based Vector Quantized Variational AutoEncoder, as an alternative to attention-based one, highlights the challenges in capturing complex relationships and meaningful patterns within the data. The correlation-based VQ-VAE’s tendency to predict flat outputs emphasizes the need for further exploration and innovative approaches to improve its performance. Overall, this thesis contributes to the field of human motion prediction by extending the prediction horizon and providing insights into model performance and limitations. The developed model introduces a novel option to consider when contemplating long-term prediction applications across various domains and sets the foundation for future research to enhance performance in long-term scenarios. / Denna avhandling fokuserar på att hantera begränsningarna i befintliga modeller för förutsägelse av mänskliga rörelser genom att utöka förutsägelsehorisonten till mycket långsiktiga prognoser. Målet är att utveckla en modell som uppnår en av de bästa stabila prognoshorisonterna inom området, vilket ger korrekta prognoser utan betydande felökning över tiden. Genom att använda kvantiseringsbaserade modeller uppnår vår forskning framgångsrikt det önskade målet med den föreslagna anpassade versionen av Mean Per Joint Position Error. Den första av de två föreslagna modellerna, en uppmärksamhetsbaserad Vector Quantized Variational AutoEncoder, visar goda resultat när det gäller att förutsäga bortom konventionella tidsgränser och bibehåller låga felfrekvenser när förutsägelsehorisonten förlängs. Även om små avvikelser i ledpositioner observeras, fångar modellen effektivt de underliggande mönstren och dynamiken i mänsklig rörelse, vilket förblir mycket tillämpligt i verkliga scenarier. Vår undersökning av en korrelationsbaserad Vector Quantized Variational AutoEncoder, som ett alternativ till en uppmärksamhetsbaserad sådan, belyser dessutom utmaningarna med att fånga komplexa relationer och meningsfulla mönster i data. Den korrelationsbaserade VQ-VAE:s tendens att förutsäga platta utdata understryker behovet av ytterligare utforskning och innovativa metoder för att förbättra dess prestanda. Sammantaget bidrar denna avhandling till området för förutsägelse av mänskliga rörelser genom att utöka förutsägelsehorisonten och ge insikter om modellens prestanda och begränsningar. Den utvecklade modellen introducerar ett nytt alternativ att ta hänsyn till när man överväger långsiktiga prediktionstillämpningar inom olika områden och lägger grunden för framtida forskning för att förbättra prestanda i långsiktiga scenarier.

Unreinforced masonry walls subjected to blast loading are vulnerable to collapse and fragmentation. The objective of this thesis is to conduct experimental and analytical research for developing a blast retrofit methodology that utilizes polyurea. A total of four unreinforced masonry walls were constructed and tested under various shock tube induced blast pressures at the University of Ottawa Shock Tube Testing Facility. Two of the retrofitted walls had surface-sprayed polyurea. The results indicate that the use of polyurea effectively controlled fragmentation while significantly increased the load capacity and stiffness of masonry walls. Polyurea proved to be an excellent retrofit material for dissipating blast induced energy by providing ductility to the system and changing the failure mode from brittle to ductile. Single degree of freedom (SDOF) dynamic analyses were conducted as part of the analytical investigation. The results show that the analytical model provides reasonably accurate predictions of the specimen response.

Unreinforced masonry walls subjected to blast loading are vulnerable to collapse and fragmentation. The objective of this thesis is to conduct experimental and analytical research for developing a blast retrofit methodology that utilizes polyurea. A total of four unreinforced masonry walls were constructed and tested under various shock tube induced blast pressures at the University of Ottawa Shock Tube Testing Facility. Two of the retrofitted walls had surface-sprayed polyurea. The results indicate that the use of polyurea effectively controlled fragmentation while significantly increased the load capacity and stiffness of masonry walls. Polyurea proved to be an excellent retrofit material for dissipating blast induced energy by providing ductility to the system and changing the failure mode from brittle to ductile. Single degree of freedom (SDOF) dynamic analyses were conducted as part of the analytical investigation. The results show that the analytical model provides reasonably accurate predictions of the specimen response.

Unreinforced masonry walls subjected to blast loading are vulnerable to collapse and fragmentation. The objective of this thesis is to conduct experimental and analytical research for developing a blast retrofit methodology that utilizes polyurea. A total of four unreinforced masonry walls were constructed and tested under various shock tube induced blast pressures at the University of Ottawa Shock Tube Testing Facility. Two of the retrofitted walls had surface-sprayed polyurea. The results indicate that the use of polyurea effectively controlled fragmentation while significantly increased the load capacity and stiffness of masonry walls. Polyurea proved to be an excellent retrofit material for dissipating blast induced energy by providing ductility to the system and changing the failure mode from brittle to ductile. Single degree of freedom (SDOF) dynamic analyses were conducted as part of the analytical investigation. The results show that the analytical model provides reasonably accurate predictions of the specimen response.

Social robots are becoming more and more common in our everyday lives. In the field of conversational robotics, the development goes towards socially engaging robots with humanlike conversation. This project looked into one of the technical aspects when recognizing speech, videlicet speech activity detection (SAD). The presented solution uses a convolutional neural network (CNN) based system to detect speech in a forward azimuth area. The project used a dataset from FestVox, called CMU Artic and was complimented by adding recorded noises. A library called Pyroomacoustics were used to simulate a real world setup to create a robust system. A simplified version was built, this model only detected speech activity and a accuracy of 95%was reached. The finished model resulted in an accuracy of 93%.It was compared with similar project, a voice activity detection(VAD) algorithm WebRTC with beamforming, as no previous published solutions to our project was found. Our solution proved to be higher in accuracy in both cases, compared to the accuracy WebRTC achieved on our dataset. / Sociala robotar blir vanligare och vanligare i våra vardagliga liv. Inom konversationsrobotik går utvecklingen mot socialt engagerande robotar som kan ha mänskliga konversationer. Detta projekt tittar på en av de tekniska aspekterna vid taligenkänning, nämligen talaktivitets detektion. Den presenterade lösningen använder ett convolutional neuralt nätverks(CNN) baserat system för att detektera tal i ett framåtriktat azimut område. Projektet använde sig av ett dataset från FestVox, kallat CMU Artic och kompletterades genom att lägga till ett antal inspelade störningsljud. Ett bibliotek som heter Pyroomacoustics användes för att simulera en verklig miljö för att skapa ett robust system. En förenklad modell konstruerades som endast detekterade talaktivitet och en noggrannhet på 95% uppnåddes. Den färdiga maskinen resulterade i en noggrannhet på 93%. Det jämfördes med liknande projekt, en röstaktivitetsdetekterings (VAD) algoritm WebRTC med strålformning, eftersom inga tidigare publicerade lösningar för vårt projekt hittades. Det visade sig att våra lösningar hade högre noggrannhet än den WebRTC uppnådde på vårt dataset. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

The focus of this thesis is on facial biometrics; specifically in the problems of face detection and face recognition. Despite intensive research over the last 20 years, the technology is not foolproof, which is why we do not see use of face recognition systems in critical sectors such as banking. In this thesis, we focus on three sub-problems in these two areas of research. Firstly, we propose methods to improve the speed-accuracy trade-off of the state-of-the-art face detector. Secondly, we consider a problem that is often ignored in the literature: to decrease the training time of the detectors. We propose two techniques to this end. Thirdly, we present a detailed large-scale study on self-updating face recognition systems in an attempt to answer if continuously changing facial appearance can be learnt automatically. / L'objectiu d'aquesta tesi és sobre biometria facial, específicament en els problemes de detecció de rostres i reconeixement facial. Malgrat la intensa recerca durant els últims 20 anys, la tecnologia no és infalible, de manera que no veiem l'ús dels sistemes de reconeixement de rostres en sectors crítics com la banca. En aquesta tesi, ens centrem en tres sub-problemes en aquestes dues àrees de recerca. En primer lloc, es proposa mètodes per millorar l'equilibri entre la precisió i la velocitat del detector de cares d'última generació. En segon lloc, considerem un problema que sovint s'ignora en la literatura: disminuir el temps de formació dels detectors. Es proposen dues tècniques per a aquest fi. En tercer lloc, es presenta un estudi detallat a gran escala sobre l'auto-actualització dels sistemes de reconeixement facial en un intent de respondre si el canvi constant de l'aparença facial es pot aprendre de forma automàtica.