An emerging technology is the hybridization of wheel loaders. Since wheel loaders commonly operate in repetitive cycles it should be possible to use this information to develop an efficient energy management strategy that decreases fuel consumption. The purpose of this thesis is to evaluate if and how this can be done in a real-time online application. The strategy that is developed is based on pattern recognition and Equivalent Consumption Minimization Strategy (ECMS), which together is called Adaptive ECMS (A-ECMS). Pattern recognition uses information about the repetitive cycles and predicts the operating cycle, which can be done with Neural Network or Rule-Based methods. The prediction is then used in ECMS to compute the optimal power distribution of fuel and battery power. For a robust system it is important with stability implementations in ECMS to protect the machine, which can be done by adjusting the cost function that is minimized. The result from these implementations in a quasistatic simulation environment is an improvement in fuel consumption by 7.59 % compared to not utilizing the battery at all.

A high-precision mixed-signal mismatch measurement technique for metal-oxide metal (MoM) capacitors as well as the design of a 13-GHz 5-bit 360-degree phase shifter are presented. This thesis presents a high-precision, mixed-signal mismatch measurement technique for metal-oxide–metal capacitors. The proposed technique incorporates a switched-capacitor op amp within the measurement circuit to significantly improve the measurement precision while relaxing the resolution requirement on the backend analog-to-digital converter (ADC). The proposed technique is also robust against multiple types of errors. A detailed analysis is presented to quantify the sensitivity improvement of the proposed technique over the conventional one. In addition, this thesis proposes a multiplexing technique to measure a large number of capacitors in a single chip and a new layout to improve matching. A prototype fabricated in 180 nm CMOS technology demonstrates the ability to sense capacitor mismatch standard deviation as low as 0.045% with excellent repeatability, all without the need of a high-resolution ADC. The 13-GHz 5-bit 360-degree phase shifter consists of 2 stages. The first stage utilizes a delay line for 4-bit 180-degree phase shift. A second stage provides 1-bit 180-degree phase shift. The phase shifter includes gain tuning so as to allow a gain variation of less than 1 dB. The design has been fabricated in 180 nm CMOS technology and measurement results show a complete 360◦ phase shift with an average step size of 10.7◦ at 13-GHz. After calibration the phase shifter presented an output gain S21 of 0.5 dB with a gain variation of less than 1 dB across all codes at 13-GHz. The remaining s-parameter testing showed a S22 and S11 below -11 dB and a S12 below -49 dB at 13 GHz.

This thesis investigates how pre-trained contextualized language models can be adapted for multi-label text classification of Swedish news articles. Various classifiers are built on pre-trained BERT and ELECTRA models, exploring global and local classifier approaches. Furthermore, the effects of domain specialization, using additional metadata features and model compression are investigated. Several hundred thousand news articles are gathered to create unlabeled and labeled datasets for pre-training and fine-tuning, respectively. The findings show that a local classifier approach is superior to a global classifier approach and that BERT outperforms ELECTRA significantly. Notably, a baseline classifier built on SVMs yields competitive performance. The effect of further in-domain pre-training varies; ELECTRA’s performance improves while BERT’s is largely unaffected. It is found that utilizing metadata features in combination with text representations improves performance. Both BERT and ELECTRA exhibit robustness to quantization and pruning, allowing model sizes to be cut in half without any performance loss.

[pt] Os sistemas de múltiplas entradas e múltiplas saídas (MIMO) empregam um número crescente de antenas, o que leva a relevantes consumo de energia e custo de hardware dos front-ends correspondentes. Nesse contexto, o uso de conversores analógico-digitais (ADCs) de baixa resolução é promovido como uma solução promissora para este problema. Neste estudo consideramos um receptor MIMO de baixa resolução que implica que os sinais recebidos são processados simultaneamente pelos 1-bit ADCs e pela rede comparadora. Os sinais de entrada da rede comparadora podem vir de antenas diferentes, de modo que a extensão da rede comparadora pode ser interpretada como canais virtuais com saídas binárias. Com base nesses receptores MIMO de baixa resolução, desenvolvemos um estimador de canal e detector lineares de baixa resolução baseados no critério de mínimo erro médio quadrático (LRA-LMMSE) de acordo com o teorema de Bussgang. Duas redes de comparação são propostas, nomeadas, redes total e parcialmente conectadas. Também desenvolvemos uma rede parcialmente conectada baseada em busca gananciosa que usa muito menos comparadores para obter um desempenho bem próximo ao da rede totalmente conectada. Os resultados numéricos mostram que adicionar canais virtuais pode ser melhor do que adicionar canais físicos extras que correspondem a antenas de recepção adicionais em termos de taxa de erro de bit (BER). Além disso, ao empregar o estimador de canal proposto e seu erro de estimativa correspondente, construímos um limite inferior na taxa de soma ergódica para o receptor LRA-MMSE. Os resultados de simulação mostram que os sistemas com a proposta sistemas MIMO auxiliados por rede com quantização de 1-bit no receptor superam o convencional sistema MIMO de 1-bit em termos de desempenho de BER e erro quadrático médio (MSE). Além disso, as simulações numéricas confirmam uma vantagem significativa em termos de taxa de soma para o sistema proposto. / [en] Multiple-input multiple-output (MIMO) systems employs an increasing number of antennas, which leads to relevant energy consumption and hardware cost of the corresponding front ends. In this context, the use of lowresolution analog to digital converters (ADCs) is promoted as a promising solution to this problem. In this study we consider a low-resolution MIMO receiver which implies that the received signals simultaneously are processed by the 1-bit ADCs and the comparator network. The input signals for the comparator network can come from different antennas, such that the comparator network extension can be interpreted as virtual channels with binary outputs. Based on such low-resolution MIMO receivers, we develop low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator and detector according to the Bussgang theorem. Two comparator networks are proposed, namely, fully and partially connected networks. We also devise a greedy search-based partially connected network that can use much less comparators to approach the performance of the fully connected network. Numerical results shows that adding virtual channels can be better than adding extra physical channels which corresponds to additional receive antennas in terms of bit error rate (BER). Furthermore, by employing the proposed channel estimator and its corresponding estimation error, we build up a lower bound on the ergodic sum rate for the LRA-LMMSE receiver. Simulation results show that the systems with the proposed network-aided MIMO systems with 1-bit quantization at the receiver outperforms the conventional 1-bit MIMO system in terms of BER and mean-square error (MSE) performances. Moreover, numerical simulations confirm a significant advantage in terms of sum rate for the proposed system.

The video on demand streaming is raising drastically in popularity, bringing new challenges to the video coding field. There is a need for new video coding techniques that improve performance and reduce the bitrates. One of the most promising areas of research is perceptual video coding where attributes of the human visual system are considered to minimize visual redundancy. The visual attention only makes it possible for humans to focus on a smaller region at the time, which is led by different cues, and with deep neural networks it has become possible to create high-accuracy models of this. The purpose of this study is therefore to investigate how adaptive quantization (AQ) based on a deep visual attention model can be used to improve the subjective video quality for low bitrates. A deep visual attention model was integrated into the encoder x265 to control how the bits are distributed on frame level by adaptively setting the quantization parameter. The effect on the subjective video quality was evaluated through A/B testing where the solution was compared to one of the standard methods for AQ in x265. The results show that the ROI-based AQ was perceived to be of better quality in one out of ten cases. The results can partly be explained by certain methodological choices, but also highlights a need for more research on how to make use of visual attention modeling in more complex real-world streaming scenarios to make streaming content more accessible and reduce bitrates. / "Video on demand"-streamingen ökar kraftigt i popularitet vilket skapar nya utmaningar inom video kodning. Det finns ett behov av nya videokodningstekniker som ökar prestanda och reducerar bithastigheten. Ett av de mest lovade forskningsområdena är perceptuell videokodning där man tar hänsyn till synens egenskaper för att minimera visuell redundans. Det visuella fokuset gör att människan bara kan fokusera på ett mindre områden åt gången, lett av olika typer av signaler, och med hjälp av djupa neurala nätverk har det blivit möjligt att skapa välpresterande modeller av det. Syftet med denna studie är därför att undersöka hur adaptiv kvantisering baserat på en djupinlärningsmodell av visuellt fokus kan användas för att förbättra den subjektiva videokvaliteten för låga bithastigheter. En djup modell av visuellt fokus var integrerad i videokodaren x265 för att kontrollera hur bitarna ditribueras på bildnivå genom att adaptivt sätta kvantiseringsparametern. Den subjektiva videokvaliteten utvärderades genom A/B tester där lösningen jämfördes med en standardmetod för adaptiv kvantisering i x265. Resultaten visar att den visuellt fokus-baserade adaptiva kvantiseringen upplevdes ge bättre kvalitet i ett av tio fall. Detta resultat kan delvis förklaras av vissa metodval, men visar också på ett behov för mer forskning på hur modeller för visuellt fokus kan användas i mer komplexa och verkliga streamingscenarion för att kunna göra innehållet mer tillgängligt och reducera bithastigheten.

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.

This paper explores the noise shaping and noise producing qualities of Delta-Sigma Modulators (DSM) and Pulse-Width Modulators (PWM). DSM has long been dominant in the Delta Sigma Analog-to-Digital Converter (DSADC) as a noise-shaped quantizer and time discretizer, while PWM, with a similar self oscillating structure, has seen use in Class D Power Amplifiers, performing a similar function. It has been shown that the PWM in Class D Amplifiers outperforms the DSM [1], but could this advantage be used in DSADC use-cases? LTSpice simulation and printed circuit board implementation and test are used to present data on four variations of these modulators: The DSM, PWM, the out-of-loop discretized PWM (OOLDP), and the cascaded modulator. A generic form of an Nth order loop filter is presented, where three orders of this generic topology are analyzed in simulation for each modulator, and two orders are used in physical testing.

В данной работе представлена разработка схемы управления зеркальными антеннами 600 метрового радиотелескопа на основе цифровой обработки сигналов. Был произведен анализ диаграммы направленности радиотелескопа при цифровом методе формирования, а также оценено влияние фазовых ошибок на диаграмму направленности радиотелескопа, возникающих в процессе оцифровке аналогового сигнала на несущей частоте. Было произведено математическое моделирование и оценка влияния параметров цифровой элементной базы на характеристики диаграммы направленности радиотелескопа, предложено оборудование для построения диаграммообразующей схемы радиотелескопа. / This work contains the development of a control scheme for mirror antennas of a 600-meter radio telescope based on digital signal processing. An analysis was made of the radiation pattern of the radio telescope under the digital method of formation. The influence of phase errors on the radiation pattern of the radio telescope, which arise in the process of digitizing an analog signal at a carrier frequency, was estimated. Mathematical modeling and estimation of the effect of the parameters of the digital element base on the characteristics of the radiation pattern of the radio telescope were made, equipment for constructing a radio telescope was proposed.

In the upcoming era of communication systems, there is an anticipated shift towards using lower-grade hardware components to optimize size, cost, and power consumption. This shift is particularly beneficial for multiple-input multiple-output (MIMO) systems and internet-of-things devices, which require numerous components and extended battery lifes. However, using lower-grade components introduces impairments, including various non-linear and time-varying distortions affecting communication signals. Traditionally, these distortions have been treated as additional noise due to the lack of a rigorous theory. This thesis explores new perspective on how distortion structure can be exploited to optimize communication performance. We investigate the problem of distortion-aware beamforming in various scenarios.  In the first part of this thesis, we focus on systems with limited fronthaul capacity. We propose an optimized linear precoding for advanced antenna systems (AAS) operating at a 5G base station (BS) within the constraints of a limited fronthaul capacity, modeled by a quantizer. The proposed novel precoding minimizes the mean-squared error (MSE) at the receiver side using a sphere decoding (SD) approach.  After analyzing MSE minimization, a new linear precoding design is proposed to maximize the sum rate of the same system in the second part of this thesis. The latter problem is solved by a novel iterative algorithm inspired by the classical weighted minimum mean square error (WMMSE) approach. Additionally, a heuristic quantization-aware precoding method with lower computational complexity is presented, showing that it outperforms the quantization-unaware baseline. This baseline is an optimized infinite-resolution precoding which is then quantized. This study reveals that it is possible to double the sum rate at high SNR by selecting weights and precoding matrices that are quantization-aware.  In the third part and final part of this thesis, we focus on the signaling problem in mobile millimeter-wave (mmWave) communication. The challenge of mmWave systems is the rapid fading variations and extensive pilot signaling. We explore the frequency of updating the combining matrix in a wideband mmWave point-to-point MIMO under user equipment (UE) mobility. The concept of beam coherence time is introduced to quantify the frequency at which the UE must update its downlink receive combining matrix. The study demonstrates that the beam coherence time can be even hundreds of times larger than the channel coherence time of small-scale fading. Simulations validate that the proposed lower bound on this defined concept guarantees no more than 50 \% loss of received signal gain (SG). / I den kommande eran av kommunikationssystem finns det en förväntad förändringmot att använda hårdvarukomponenter av lägre kvalitet för att optimera storlek, kostnad och strömförbrukning. Denna förändring är särskilt fördelaktig för MIMO-system(multiple-input multiple-output) och internet-of-things-enheter, som kräver många komponenter och förlängd batteritid. Användning av komponenter av lägre kvalitet medfördock försämringar, inklusive olika icke-linjära och tidsvarierande förvrängningar sompåverkar kommunikationssignaler. Traditionellt har dessa förvrängningar behandlatssom extra brus på grund av avsaknaden av en rigorös teori. Denna avhandling utforskarett nytt perspektiv på hur distorsionsstruktur kan utnyttjas för att optimera kommunikationsprestanda. Vi undersöker problemet med distorsionsmedveten strålformning iolika scenarier. I den första delen av detta examensarbete fokuserar vi på system med begränsadfronthaulkapacitet. Vi föreslår en optimerad linjär förkodning för avancerade antennsystem (AAS) som arbetar vid en 5G-basstation (BS) inom begränsningarna av en begränsad fronthaulkapacitet, modellerad av en kvantiserare. Den föreslagna nya förkodningen minimerar medelkvadratfelet (MSE) på mottagarsidan med användning av ensfäravkodningsmetod (SD). Efter att ha analyserat MSE-minimering, föreslås en ny linjär förkodningsdesignför att maximera summahastigheten för samma system i den andra delen av dennaavhandling. Det senare problemet löses av en ny iterativ algoritm inspirerad av denklassiska vägda minsta medelkvadratfel (WMMSE)-metoden. Dessutom presenterasen heuristisk kvantiseringsmedveten förkodningsmetod med lägre beräkningskomplexitet, som visar att den överträffar den kvantiseringsomedvetna baslinjen. Denna baslinje är en optimerad förkodning med oändlig upplösning som sedan kvantiseras. Dennastudie avslöjar att det är möjligt att fördubbla summahastigheten vid hög SNR genomatt välja vikter och förkodningsmatriser som är kvantiseringsmedvetna. I den tredje delen och sista delen av denna avhandling fokuserar vi på signaleringsproblemet i mobil millimetervågskommunikation (mmWave). Utmaningen medmmWave-system är de snabba blekningsvariationerna och omfattande pilotsignalering.Vi utforskar frekvensen av att uppdatera den kombinerande matrisen i en bredbandsmmWave punkt-till-punkt MIMO under användarutrustning (UE) mobilitet. Konceptet med strålkoherenstid introduceras för att kvantifiera frekvensen vid vilken UE:nmåste uppdatera sin nedlänksmottagningskombinationsmatris. Studien visar att strålkoherenstiden kan vara till och med hundratals gånger större än kanalkoherenstiden försmåskalig fädning. Simuleringar bekräftar att den föreslagna nedre gränsen för dettadefinierade koncept inte garanterar mer än 50 % förlust av mottagen signalförstärkning(SG) / <p>QC 20240219</p>

[pt] As redes da Internet das Coisas (IdC) incluem dispositivos inteligentes que contêm muitos sensores que permitem interagir com o mundo físico, coletando e processando dados de streaming em tempo real. O consumo total de energia e o custo desses sensores afetam o consumo de energia e o custo dos dispositivos IdC. O tipo de sensor determina a precisão da interface analógica e a resolução dos conversores analógico-digital (ADCs). A resolução dos ADCs tem um compromisso entre a precisão de inferência e o consumo de energia, uma vez que o consumo de energia dos ADCs depende do número de bits usados para representar amostras digitais. Nesta tese, apresentamos um esquema de aprendizado distribuído com eficiência energética usando sinais quantizados para redes da IdC. Em particular, desenvolvemos algoritmos de gradiente estocástico com reconhecimento de quantização distribuído (DQA-LMS) e de mínimos quadrados recursivos com reconhecimento de quantização distribuído (DQA-RLS) que podem aprender parâmetros de maneira eficiente em energia usando sinais quantizados com poucos bits, exigindo um baixo custo computacional. Além disso, desenvolvemos uma estratégia de compensação de viés para melhorar ainda mais o desempenho dos algoritmos propostos. Uma análise estatística dos algoritmos propostos juntamente com uma avaliação da complexidade computacional das técnicas propostas e existentes é realizada. Os resultados numéricos avaliam os algoritmos com reconhecimento de quantização distribuída em relação às técnicas existentes para uma tarefa de estimação de parâmetros em que os dispositivos IdC operam em um modo ponto a ponto. Também apresentamos um esquema de aprendizado federativo com eficiência energética usando sinais quantizados para redes de IdC. Desenvolvemos o algoritmo federated averaging LMS (QA-FedAvg-LMS) com reconhecimento de quantização para redes IdC estruturadas por configuração de aprendizado federativo em que os dispositivos IdC trocam suas estimativas com um servidor. Uma estratégia de compensação de viés para QA-FedAvg-LMS é proposta junto com sua análise estatística e a avaliação de desempenho em relação às técnicas existentes com resultados numéricos. / [en] Internet of Things (IoT) networks include smart devices that contain many sensors that allow them to interact with the physical world, collecting and processing streaming data in real time. The total energy-consumption and cost of these sensors affect the energy-consumption and the cost of IoT devices. The type of sensor determines the accuracy of the analog interface and the resolution of the analog-to-digital converters (ADCs). The ADC resolution requirement has a trade-off between sensing performance and energy consumption since the energy consumption of ADCs strongly depends on the number of bits used to represent digital samples. In this thesis, we present an energy-efficient distributed learning framework using coarsely quantized signals for IoT networks. In particular, we develop a distributed quantization-aware least-mean square (DQA-LMS) and a distributed quantization-aware recursive least-squares (DQA-RLS) algorithms that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. Moreover, we develop a bias compensation strategy to further improve the performance of the proposed algorithms. We then carry out a statistical analysis of the proposed algorithms along with a computational complexity evaluation of the proposed and existing techniques. Numerical results assess the distributed quantization-aware algorithms against existing techniques for distributed parameter estimation where IoT devices operate in a peer-to-peer mode. We also introduce an energy-efficient federated learning framework using coarsely quantized signals for IoT networks, where IoT devices exchange their estimates with a server. We then develop the quantization-aware federated averaging LMS (QA-FedAvg-LMS) algorithm to perform parameter estimation at the clients and servers. Furthermore, we devise a bias compensation strategy for QA-FedAvg-LMS, carry out its statistical analysis, and assess its performance against existing techniques with numerical results.