In this thesis, our focus is on low-complexity, high-performance detection algorithms for multi-antenna communication receivers. A key contribution in this thesis is the demonstration that efficient algorithms from metaheuristics and machine learning can be gainfully adapted for signal detection in multi- antenna communication receivers. We first investigate a popular metaheuristic known as the reactive tabu search (RTS), a combinatorial optimization technique, to decode the transmitted signals in large-dimensional communication systems. A basic version of the RTS algorithm is shown to achieve near-optimal performance for 4-QAM in large dimensions. We then propose a method to obtain a lower bound on the BER performance of the optimal detector. This lower bound is tight at moderate to high SNRs and is useful in situations where the performance of optimal detector is needed for comparison, but cannot be obtained due to very high computational complexity. To improve the performance of the basic RTS algorithm for higher-order modulations, we propose variants of the basic RTS algorithm using layering and multiple explorations. These variants are shown to achieve near-optimal performance in higher-order QAM as well. Next, we propose a new receiver called linear regression of minimum mean square error (MMSE) residual receiver (referred to as LRR receiver). The proposed LRR receiver improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel) to find the linear regression parameters. The LRR receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs well. Finally, we propose a receiver that uses a committee of linear receivers, whose parameters are estimated from training data using a variant of the AdaBoost algorithm, a celebrated supervised classification algorithm in ma- chine learning. We call our receiver boosted MMSE (B-MMSE) receiver. We demonstrate that the performance and complexity of the proposed B-MMSE receiver are quite attractive for multi-antenna communication receivers.

Machine learning algorithms have been used for price prediction within several application areas. Examples include real estate, the stock market, tourist accommodation, electricity, art, cryptocurrencies, and fine wine. Common approaches in studies are to evaluate the accuracy of predictions and compare different algorithms, such as Linear Regression or Neural Networks. There is a thriving global second-hand market for vinyl records, but the research of price prediction within the area is very limited. The purpose of this project was to expand on existing knowledge within price prediction in general to evaluate some aspects of price prediction of vinyl records. That included investigating the possible level of accuracy and comparing the efficiency of algorithms. A dataset of 37000 samples of vinyl records was created with data from the Discogs website, and multiple machine learning algorithms were utilized in a controlled experiment. Among the conclusions drawn from the results was that the Random Forest algorithm generally generated the strongest results, that results can vary substantially between different artists or genres, and that a large part of the predictions had a good accuracy level, but that a relatively small amount of large errors had a considerable effect on the general results.

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.

In forensic autopsies, accurately estimating the postmortem interval (PMI) is crucial. Traditional methods, relying on physical parameters and police data, often lack precision, particularly after approximately two days have passed since the person's death. New methods are increasingly focusing on analyzing postmortem metabolomics in biological systems, acting as a 'fingerprint' of ongoing processes influenced by internal and external molecules. By carefully analyzing these metabolomic profiles, which span a diverse range of information from events preceding death to postmortem changes, there is potential to provide more accurate estimates of the PMI. The limitation of available real human data has hindered comprehensive investigation until recently. Large-scale metabolomic data collected by the National Board of Forensic Medicine (RMV, Rättsmedicinalverket) presents a unique opportunity for predictive analysis in forensic science, enabling innovative approaches for improving  PMI estimation. However, the metabolomic data appears to be large, complex, and potentially nonlinear, making it difficult to interpret. This underscores the importance of effectively employing machine learning algorithms to manage metabolomic data for the purpose of PMI predictions, the primary focus of this project.  In this study, a dataset consisting of 4,866 human samples and 2,304 metabolites from the RMV was utilized to train a model capable of predicting the PMI. Random Forest (RF) and Artificial Neural Network (ANN) models were then employed for PMI prediction. Furthermore, feature selection and incorporating sex and age into the model were explored to improve the neural network's performance.  This master's thesis shows that ANN consistently outperforms RF in PMI estimation, achieving an R2 of 0.68 and an MAE of 1.51 days compared to RF's R2 of 0.43 and MAE of 2.0 days across the entire PMI-interval. Additionally, feature selection indicates that only 35% of total metabolites are necessary for comparable results with maintained predictive accuracy. Furthermore, Principal Component Analysis (PCA) reveals that these informative metabolites are primarily located within a specific cluster on the first and second principal components (PC), suggesting a need for further research into the biological context of these metabolites.  In conclusion, the dataset has proven valuable for predicting PMI. This indicates significant potential for employing machine learning models in PMI estimation, thereby assisting forensic pathologists in determining the time of death. Notably, the model shows promise in surpassing current methods and filling crucial gaps in the field, representing an important step towards achieving accurate PMI estimations in forensic practice. This project suggests that machine learning will play a central role in assisting with determining time since death in the future.

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