This thesis investigates reducing carbon emissions in code generation using large language models (LLMs) by comparing function-level and line-level code completions across models of different sizes (1.5B and 9B parameters). The study utilises the BigCodeBench dataset, comprising 1,140 Python programming problems, to evaluate the energy consumption, test accuracy, and time efficiency of code completions. The models, 4-bit quantised and run on a CPU, performed 30 function-level completions and 30 line-level completions for each line, which were tested for correctness. Results indicate that, while line-level completions require slightly more energy per token, they are more efficient overall in terms of total energy consumption and token usage. The smaller model with line-level completions showed significant reductions in carbon emissions, achieving an average tenfold reduction compared to the large model with function-level completions. With the large model, line-level completions achieved a $4.5\times$ reduction in carbon emissions compared to function-level completions. Line-level completions were more token-efficient, wasting less than 1\% of energy, compared to 20\% for function-level completions. From a sustainability perspective, line-level completions offer a practical strategy to reduce the environmental impact of code generation tasks while maintaining strong performance. The study suggests that optimising completion strategies could help balance energy consumption, test accuracy, and time efficiency. Future research could explore a broader range of model sizes, fine-tuning models specifically for line-level completions, a performance decrease in solution length, and alternative validation metrics to assess code generation performance.

The rapid rise in the popularity of large language models has highlighted the need for extensive datasets, especially for training on code. However, this growth has also raised important questions about the legal implications of using code in large language model training, particularly regarding the potential infringement of code licenses. At the same time, the availability of clean datasets for evaluating these models is becoming increasingly limited, due to a high risk of contamination which restricts the capacity for reliable research. On top of that, this requires researchers to repeatedly perform data curation steps in order to evaluate their models on downstream tasks, based on previously unseen data. This process is not only time- and resource-intensive but also introduces potential inconsistencies across studies, which can impact their reproducibility.
We address these challenges through a comprehensive licensing analysis and by developing robust datasets to support accurate and reproducible large language model evaluations. We compiled a list of 53 large language models trained on file-level code and analyzed their datasets, discovering pervasive license inconsistencies despite careful selection based on repository licenses. Our analysis, covering 514M code files, reveals 38M exact duplicates of strong copyleft code, and 171M file-leading comments, 16M of which are under copyleft licenses and another 11M discouraging unauthorized copying. To further understand the depth of non-permissive code in public training datasets, we developed StackLessV2, a strong copyleft Java dataset decontaminated against The Stack V2 to facilitate accurate model evaluations. Our results revealed that non-permissive code is also present at the near-duplication level, although, this represents a gray area in terms of legal interpretation, where the boundary between acceptable reuse and license violation is still unclear, emphasizing the need for further legal clarification. Finally, we extend on this and introduce The Heap, a large multilingual copyleft dataset covering 57 programming languages, specifically deduplicated to avoid contamination from existing open training datasets. The Heap offers a solution for conducting fair, reproducible evaluations of large language models without the significant overhead of the data curation process.

This thesis investigates the occurrence and mitigation of Sequential Social Dilemmas (SSDs) in Local Energy Communities (LECs) managed through Multi-agent Reinforcement Learning (MARL). LECs have great potential as pivotal elements in the green energy transition, yet the inherent conflict between individual incentives and community-wide objectives creates SSD scenarios that challenge learning processes. To address these issues, we propose an agent-centric approach and develop a custom MARL environment where agents interact via a communal battery system and a local trading mechanism.

We systematically investigate the impact of resource constraints and social interactions on the agents' learning. In non-cooperative settings, limited resources impede policy optimization, while the introduction of a shared battery reveals SSD dynamics driven by both greed and fear factors. Our experiments show that rescaling the training data leads agents to adopt more cooperative behaviors, and that reward function modifications incentivizing community-friendly battery use cause a significant increase in social welfare. These mitigation techniques are further validated in a realistic LEC environment with multiple, heterogeneous households engaging in trading and storage actions.

The contributions of this thesis are threefold: (1) the proposal of a new agent-centric MARL environment for LECs, (2) the demonstration of SSDs impacting MARL performance in these decentralized energy systems, and (3) the introduction of concrete strategies for aligning individual and community incentives.

Bayesian Networks (BNs) are widely utilized across various industrial sectors to optimize processes, with an emerging focus on the collaboration across multiple parties. While most realistic scenarios require handling a mixture of categorical and continuous data simultaneously, the current state-of-the-art only supports collaborative inference on purely discrete models. The Junction Tree enables efficient and accurate inference on hybrid models but has not been implemented for confidential scenarios yet. To address this gap, we introduce Hybrid CCJT, an innovative framework for confidential multiparty inference in hybrid domains, offering: (i) a method to construct a collaborative, strongly-rooted junction tree for efficient and secure inference, (ii) a confidential-
preserving inference protocol for Hybrid BNs, (iii) an optimized message-passing scheme that
improves communication efficiency even in the purely discrete domain. Our extensive evaluation
show that Hybrid CCJT improves the predictive accuracy of continuous target variables by an average of 32% in Mean Squared Error and reduce the communication cost up to 86-fold, against the best state-of-the-art baseline.

Recommender systems are widely used in modern lives and contribute to many industries. Therefore, methods to evaluate and improve them are important. Nowadays, much research has been done to improve the system aspects such as algorithms. However, user experiences are not only affected by the systems but heavily rely on the context when using the systems. Therefore, the research on user aspects to understand their experiences is as important. This study contributes an approach that uses collaborative reflection to find insights into users' experiences with recommender systems. Using this approach, this study presents the influences of context on user experiences with recommender systems. This study investigates the importance of situational and personal contexts like mood, time, and location in shaping user satisfaction with recommendations. The research adopts a method based on collaborative reflection, where participants engage in tasks using their YouTube watch history, paired with another individual for real-time discussion. By analyzing contextual influences and the values users wish to achieve, the study identifies key patterns in user behavior and insights into personal preferences. This research not only contributes to the evaluation of recommender systems but also highlights the need for systems to align with both the goals of users and broader societal values. The usability of the proposed method was tested to be successful in crowdsourcing, yielding practical implications for future evaluation and improvements of recommender systems.

In the field of public transportation, environmentally friendly and convenient transportation modes are the future trends. The ride-hailing services is an important component of them. However, current ride-hailing systems, particularly the matching systems, still have issues related to low system efficiency and bad user experience. Although existing ride-hailing rider-driver matching system can allocate travel demands and drivers to a certain extent, they still have deficiencies in certain scenarios. For example, they cannot ensure effective rider-driver matching during peak hours, or they cannot find a good balance between pick-up distance and matching rate. As Reinforcement learning (RL) has been proven in many studies to be applicable and effective in solving complex and dynamic optimization problems. This study aims to explore how Reinforcement Learning (RL) can be adapted to the ride-hailing matching system to optimize system efficiency and user experience through a dynamic matching radius policy. The research objective of this study is to simulate an actual ride-hailing system and use RL to train a policy. This policy can output an optimized dynamic matching radius in real-time based on real-time rider-driver demand-supply relationship, hence achieving a higher matching rate, a shorter average pick-up distance, and a higher driver utilization rate of the ride-hailing system.
Adapting Reinforcement Learning (RL) to optimize the ride-hailing system's matching radius has several difficulties and challenges due to the uncertainties in the real-world rider-hailing market. Traditional approaches are normally static, solving the matching problem at specific times through mathematical models. However, these methods often perform inconsistently when dealing with fluctuating ride-hailing supply-demand relationships, particularly during peak hours. On the other hand, the dynamics and complexity of the ride-hailing market and the ride-hailing environment also make it difficult to model the ride-hailing system. The ride-hailing market is easily affected by many variables, such as weather conditions and local traffic conditions. When quantitatively optimizing the matching radius of the ride-hailing matching system, it is critical to reasonably control irrelevant variables. To address these challenges, this study models the ride-hailing matching problem as a Markov Decision Process (MDP). Based on the defined MDP, a ride-hailing matching simulator is developed. Some assumptions and simplifications are also made to ensure high realism while reasonably controlling irrelevant variables and uncertainties. Multi-replay-buffer Deep Deterministic Policy Gradient (MDDPG) algorithm is then applied to handle the optimization problem of the ride-hailing matching radius. Through the interactions between the MDDPG agent and the developed simulator, feedback rewards are received for the agent to improve the policy. The proposed method is then validated in a case study showcasing the application of the developed simulator and the RL algorithm in a real-world scenario in Austin, Texas. The case study includes an analysis of the current ride-hailing market in Austin, how to apply the simulator based on it, the implementation details of the RL algorithm, and the resulting performance improvements. The results of the case study show that the actions obtained from the proposed method outperform all the baselines in multiple scenarios, highlighting the benefits of using Reinforcement Learning to improve ride-hailing efficiency and user experience.
To conclude, the optimization method proposed in this study applies an advanced Reinforcement Learning approach to the ride-hailing system, successfully improving overall efficiency and user experience. The results of this research demonstrate the potential of Reinforcement Learning in optimizing ride-hailing matching systems, offering a promising direction for further exploration. This study lays a solid foundation for future research to build upon, encouraging the development of more optimization methods with RL technologies that can enhance the effectiveness and adaptability of ride-hailing system in increasingly complex and dynamic environments.

Machine Learning (ML) algorithms have the potential to reproduce biases that already exist in society, a fact that leads to scholarly work trying to quantify algorithmic discrimination through fairness metrics. Although there are now a plethora of metrics, some of them are even contradictory, making fairness become a problem of knowing which measurement to choose over another. Consequently, scholars began considering that fairness should be discussed by placing algorithms in their social contexts. Since (1) these social aspects are related to structures of discrimination and (2) feminism aims to criticise discrimination against the marginalised, I introduce the possibility of analysing the social context of ML algorithms through a feminist lens. By doing this, I highlight social and political aspects that are equally important to consider for a faithful discussion on fairness: corporate lobbying, the lack of diverse hiring which leads to fairness discussions that do not consider the experiences of marginalised groups and, lastly, the broader context that an algorithm is used in. Moreover, I emphasise how feminist ethics of care constitute an essential framework for a conversation about actually implementable fairness solutions, since it shows the need to listen to both the marginalised community and to the developers who might want to build fairer ML but currently cannot. Having built a bridge between the hegemony and the feminist camp, I highlight how Northpointe’s (now Equivant’s) Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) algorithm can be considered biased against black people. Through this, I illustrate how feminist considerations bring clarity to fairness debates by helping choose a fairness metric or by claiming that an algorithm is unfair by nature and should be abolished. To follow, I use the same feminist critiques to draw attention to the possible weak points of current sociotechnical solutions. For instance, the EU AI Act risks being too susceptible to company lobbying, leading to not strict enough regulations. Furthermore, the AI committee should ensure that they hire a diverse group of people in order to develop regulations that positively consider all marginalised groups. Lastly, I highlight how ethics education is essential for creating a new generation of responsible engineers. Considering this, I emphasise the urgency of making ethics courses at TU Delft (and not only) more interdisciplinary by interacting more with critique points coming from the social sciences. This will open up possibilities for more research tackling fairness from a multitude of perspectives.

Central banks communicate their monetary policy plans to the public through meeting minutes or transcripts. These communications can have immense effects on markets and are often the subjects of studies in the financial literature. The recent advancements in Natural Language Processing have prompted researchers to analyze these communications using Transformer-based Large Language Model (LLM) classifiers. The use of LLMs in finance and other high-stakes domains calls for a high level of trustworthiness and explainability of those models. We focus on Counterfactual Explanations, a form of Explainable AI that explains a model's classification by proposing an alternative to the original input. We use three types of CE generators for LLM classifiers on a recent dataset consisting of sentences taken from FOMC communications to assess the usability of their explanations. We perform three experiments comparing different types of generators, one using a selection of quantitative metrics and two involving human evaluators, including central bank employees. Our findings suggest that non-expert and expert evaluators prefer counterfactual methods that apply minimal changes to the texts; however, the methods we analyze might not handle the domain-specific vocabulary well enough to generate plausible explanations for our task. We discuss shortcomings in the choice of evaluation metrics in the literature on text CE generators and propose refined definitions of the fluency and plausibility qualitative metrics.

This project aims to harness the potential of music-making and robotic interaction to enhance creative expression and cognitive function among individuals with cognitive impairment and dementia. With the aging population, there is a growing demand for innovative interventions that support cognitive health and active engagement. Music therapy has demonstrated effectiveness in stimulating cognitive function and emotional expression in individuals with dementia. Creative expression through music serves as a unique outlet, fostering cognitive functions and emotional well-being. This study explores the synergy between music therapy and Socially Assistive Robots (SARs) to develop a more immersive therapeutic intervention. It represents an exploratory investigation into an end-to-end robotic intervention, proposing various interaction elements and examining their functionalities. Each element is designed to foster engagement, enhance perceptions of collaboration, and promote feelings of creativity.
In this thesis, we propose an end-to-end interactive music-making experience designed for use with the Pepper robot, an SAR. The system features a user-friendly interface with eight color-coded boxes, each corresponding to a musical note. Users simply tap the boxes to create melodies. The Pepper robot acts as a guide, assisting users in interacting with the interface. It additionally implements an engagement tracking system by monitoring user interaction through the screen taps on the interface and provides real-time feedback and encouragement. If a period of inactivity is detected, Pepper gently nudges the user to re-engage. Furthermore, the robot functions as a collaborative musical partner, providing rhythmic accompaniment if the user desires. The system also records user-created music and provides playback functionality, allowing users to revisit their compositions.
Methodologically, the study involves an end-to-end system comprising an intelligent music-making interface and an interactive robot providing real-time feedback and rhythmic accompaniment. Insights from the exploratory study highlight the benefits of real-time feedback in enhancing engagement, particularly among participants with musical backgrounds. However, rhythmic accompaniment shows mixed results in fostering collaboration, indicating a preference among participants for emotional connection in collaborative settings. Since this is an exploratory study, the empirical study focuses on healthy older adults, a population with an increased risk of cognitive decline. This is because individuals with dementia are a vulnerable group. Music interventions have shown promise in improving cognitive function and engagement in individuals with dementia. Therefore, this study informs the design of future interventions for people with early-stage dementia.\\
Key findings underscore the potential of real-time feedback and interaction in promoting engagement in the activity. The intelligent music interface also shows potential to support creative exploration, albeit with improvements needed for advanced musical participants. Participants appreciate the playback feature, enhancing their sense of creative ownership and motivation. Despite promising outcomes, the study acknowledges limitations in sample size and participant demographics, primarily recruiting from music-engaged older adults rather than the target demographic of individuals with cognitive impairments.
Future research directions include expanding participant diversity, refining robot interaction capabilities, and addressing technical challenges to improve system usability and accessibility. Integrating findings from ongoing research on music and memory could further enhance personalized interventions. Ultimately, this study lays the groundwork for future developments in robotic interventions that promote well-being through music therapy for individuals with cognitive impairments.

UTURN aims to maximize the matching rate on its freight transport platform by efficiently connecting shippers with suitable carriers. To support this matching process, UTURN required a solution that was additive rather than restrictive on the platform. To achieve this, our research leverages recommendation systems and predictive models to help shipments find appropriate carriers in time while avoiding sending unnecessary email recommendations. We addressed three main questions: how to maximize matching probability through recommendations, how to balance the recommendation frequency to prevent spam, and how to ensure the solution adapts to market changes. We developed a recommendation system that ranks carriers based on their historical platform data using personalized k-nearest neighbour models and a custom similarity function. Tested in a controlled experiment, this system resulted in a 2.8% increase in the average matching rate, with improvements up to 3.4% in established regions and a peak of 6.2% in the Netherlands. However, the one-size-fits-all approach proved insufficient due to varying carrier capacities. To address the issue of deciding if and when to recommend, we introduced the Shipment Pulse Monitor (SPM), a decision tree-based predictive model that triggers recommendations when a shipment will likely be cancelled. To represent the trade-off between sending out recommendations to maximize matching probability and avoiding spam, we defined custom metrics for our problem. Our findings suggest that future work should focus on tailoring recommendations to specific carrier groups and combining hard cut-off points in data to allow a narrower focus for predictive models. Our work provides a comprehensive framework for improving matching rates on the UTURN platform with adaptive, targeted solutions.

While most large language models (LLMs) are powerful, they are primarily designed for general purposes. Consequently, many enterprises and institutions have now focused on developing domain-specific models. In the realm of education, an expert LLM can significantly enhance students' ability to find information more effectively and reach their learning goals. Nevertheless, the training of such expert models in education remains largely unexplored. This study explores this research gap by developing a framework to transform semi-structured educational web data into structured datasets and perform instruction tuning on foundation models. Additionally, we conduct a comprehensive performance analysis to determine how various training factors affect model performance.

We first employed a systematic and cost-effective approach involving web data extraction, data cleaning, validation, task design based on student surveys, and automated instruction instance generation using LLMs. Human evaluations confirmed the quality, especially the relevance and accuracy of these datasets.

This study then investigates the impact of various training techniques on domain-specific educational large language models (LLMs) performance. Our experiments reveal that further pre-training enhances model performance, especially with domain-specific terminology, although the performance gains decrease as the dataset size increases. Furthermore, multi-task training also improves model relevance, accuracy, and clarity, but less correlated tasks and datasets can present challenges. These challenges include increased complexity and potential degradation in performance due to the model having to switch between diverse tasks. Lastly, this study conducts a comparative analysis of different models and it highlights trade-offs between computational resources and performance.

The findings demonstrate that a structured approach to dataset generation and strategic training can effectively develop domain-specific LLMs in education. This research benefits the development of educational LLMs and provides a foundation for future researchers to build more specialized models in various domains.

Large language models have achieved breakthroughs in many natural language processing tasks. One of their main appeals is the ability to tackle problems that lack sufficient training data to create a dedicated solution. Manga translation is one such task, a still budding and underdeveloped field, that at the same time deals with a problem even more complex than standard translation. One of its biggest challenges is successfully incorporating the visual modality in translation, to resolve ambiguities. Recently, a new type of LLMs -- multimodal LLMs -- has emerged and showed potential in understanding visual symbolism in narrative pieces like memes and comics. In this work, we investigate whether these models could be useful in the field of manga translation, ultimately helping manga authors and publishers make their works available to wider audiences. Specifically, we evaluate a number of methods based on GPT-4-Turbo for text only translation, image informed translation and volume-level translation. We perform both automatic and human evaluation. Moreover, we contribute new evaluation data -- the first parallel Japanese-Polish manga translation benchmark. Our findings show that our proposed methods are able to achieve state of the art results for English, and set a new standard for Polish. We conclude that while this is not a sufficient replacement for a professional human translator, it could help speed up the translation process or be used as a learning aid.

AI is becoming significantly more impactful in society, especially with regard to decision-making. Algorithmic fairness is the field wherein the fairness of an AI algorithm is defined, subsequently evaluated, and ideally improved. This paper uses a fairness decision tree to critique certain notions of algorithmic fairness through a postcolonial lens by applying Gayatri Spivak's theory of the subaltern alongside other postcolonial principles. A definition and criteria for a subaltern population in AI are provided, depicting that AI and algorithmic fairness rely on subaltern marginalization, silence, and faux inclusion. A theoretical case analysis is then conducted to illustrate how demographic parity, even in cases where it is the best fairness metric, does not include the subaltern. Algorithmic fairness often defines fairness through neoliberalism, assigning a ``cost'' to ethical considerations, wherein morality is second to profits and utility. Furthermore, a large proportion of the ``justice'' conducted through AI is surface level and may actually cause more harm in the long run. A proposal is made to seriously consider not using any AI in socially relevant, complex situations.

This study investigates the effectiveness of Large Language Models (LLMs) in identifying and classifying subjective arguments within deliberative discourse. Using data from a Participatory Value Evaluation (PVE) conducted in the Netherlands, this research introduces an annotation strategy for identifying arguments and extracting their premises. Then, the Llama 2 model is used to test three different prompting approaches: zero-shot, one-shot and few-shot. The performance is evaluated using the cosine similarity metric and later enhanced by introducing chain-of-thought prompting. The results show that zero-shot prompting unexpectedly outperforms one-shot and few-shot prompting, due to the LLM overfitting to the examples provided. Chain-of-thought prompting is shown to improve the argument identification task. The subjectivity of the annotation task is reflected by the low averaged pairwise F1 score between annotators, and the considerable variance in the number of data items marked by each annotator as not being arguments. The subjectivity of the task is further highlighted by a pairwise chain-of-thought prompting analysis, which shows that annotators with more similar annotations received more similar LLM responses.

In order to tackle topics such as climate change together with the population, public discourse should be scaled up. This discourse should be mediated as it makes it more likely that people understand each other and change their point of view. To help the mediator with this task, emotion detection can greatly help. Positive emotions can improve communications, while negative emotions cause people to be irrational and irritated. However, since emotions are highly subjective, it can make both predictions and evaluation more difficult.

Still, Large Language Models (LLMs) could be used to detect these subjective emotions using different prompting strategies and labels. The experiment included zero-, one-, fewshot and Chain of Thought (CoT) strategies. The precision was better for the one- and fewshot method compared to zeroshot. The CoT methods also showed an increase in precision, but a decrease in recall. The different labels were hard majority labels, soft labels and hard per annotator labels. In conclusion, providing examples improved the performance of the LLM. The CoT strategies were more precise, but gave a worse general prediction. The hard majority labels allow for more general predictions, where per annotator hard labels capture the perspective of different annotators. Soft labels reflect the subjective nature of the labels by providing probabilities instead of binary classification.

The experiment was done on a small data sample, so it is recommended to try the strategies on a larger data sample. Looking into appropriate evaluations for subjective predictions is also recommended in order to reflect the actual performance better.

Public deliberations play a crucial role in democratic systems. However, the unstructured nature of deliberations leads to challenges for moderators to analyze the large volume of data produced. This paper aims to solve this challenge by automatically identifying subjective topics behind public discourse by leveraging Large Language Models (LLMs). The study is structured around two core objectives: Identifying Gold Labels and Exploring Subjective Human Labels. The results highlight that fine-tuning the LLaMa-2 model with QLoRa outperforms other methods for Identifying Gold Labels, while the Few-Shot Chain of Thoughts method, enhanced with EmotionPrompt, is particularly effective in capturing subjective variations in human annotations. However, the study also underscores significant limitations, such as the dependency on large, high-quality annotated datasets and the tendency of models to produce hallucinations. These findings highlight the potential of LLMs to identify subjective topics behind public discourse, while also emphasizing the need for further research to address these challenges.

This study evaluates the performance of different sentiment analysis methods in the context of public deliberation, focusing on hard-, soft-, and subjective-label scenarios to answer the research question: ``can a Large Language Model detect subjective sentiment of statements within the context of public deliberation?''. If the answer to this question is affirmative, that is a strong indicator that, with the help of longitudinal studies, sentiment analysis with large language models (LLMs) may be implemented to scale public deliberations by providing support for moderators in such discussions. To answer this question, four prompting methods were tested: zero-shot, few-shot, chain-of-thought (CoT) zero-shot, and CoT few-shot using a Frisian dataset of 50 statements annotated by 5 annotators. The findings indicate that the CoT few-shot method significantly outperforms other methods in all scenarios, that soft-labels outperform their hard equivalent, that the underlying data must be balanced for high performing models, and that capturing the perspective of a specific annotator requires further research. Our study suggests that LLMs may perform best under the supervision, or with the collaboration of a human, due to the multi-faced nature of sentiment.

Web Vulnerability Assessment and Penetration Testing (Web VAPT) is an important cybersecurity practice that thoroughly examines web applications to uncover possible vulnerabilities. These vulnerabilities represent potential security gaps that could severely compromise the web applications' integrity and functionality if exploited by malicious entities.
One of the attacks employed in the Web VAPT process is the Directory Brute-Forcing Attack. This attack aims to identify hidden directories and files not adequately secured in a web application that contain sensitive information or critical functionalities. The attack methodology involves sending many requests of possible directories or files to the target web application, where brute-force generation of requests is performed using a wordlist. Due to its brute-force nature, this attack methodology often results in enormous quantities of requests sent for a small amount of successful discoveries.
With AI's quick progress and diffusion, the paradigm of Offensive AI emerges, where AI-based technologies are employed in traditional cyber attacks to make them more sophisticated and effective.
This research explores whether AI can enhance the standard directory enumeration process. We propose two novel attack methodologies for performing directory brute-forcing attacks that leverage probability and Language Models (LM).
Our experiments - conducted on a testbed consisting of around 1 million URLs from various domains of web applications (academic institutions, hospitals, government agencies, and business corporations) - demonstrate the superiority of our approaches over the standard brute-force attacks.
In particular, the LM-based attack results in an average discoveries increase of 969%, and the probabilistic attack is more efficient at sending successful requests in the early stages of attacks in more than 94% of cases.

Non-invasive head-worn sensors are an upcoming field of interest. Most commercial sensors and sensors presented in research papers are limited in their capabilities and require frequent user interaction. In this Thesis, we research the possibilities of overcoming some of these limitations and extending the capabilities of these sensors.